errors.property_type_double={0} è un numero double.
errors.property_type_date={0} è una data.

errors.storage_file_upload_failure=Caricamento di {0} fallito.
errors.storage_file_not_found=Il file di destinazione non è presente nell'archivio.
errors.storage_file_download_failure=Download di {0} fallito.
errors.storage_access_error=Errore di accesso all'archivio: {0}
errors.storage_no_upload_file=Specificare un file da caricare.

errors.failed_to_find_plugins=No se encontraron plugins disponibles.
errors.failed_to_process_sso_request=No se pudo procesar la solicitud: {0}

errors.property_required={0} es obligatorio.
errors.property_type_integer={0} es un número.
errors.property_type_long={0} es un número.
errors.property_type_float={0} es un número.
errors.property_type_double={0} es un número.
errors.property_type_date={0} es una fecha.

        // Test error message constants
        assertEquals("{errors.front_header}", FessMessages.ERRORS_front_header);
        assertEquals("{errors.front_footer}", FessMessages.ERRORS_front_footer);
        assertEquals("{errors.front_prefix}", FessMessages.ERRORS_front_prefix);
        assertEquals("{errors.front_suffix}", FessMessages.ERRORS_front_suffix);
        assertEquals("{errors.header}", FessMessages.ERRORS_HEADER);

    }

    // Test error handling in doCrawl
    public void test_doCrawl_withErrors() {
        // Clear any previous errors
        try {
            Field errorsField = Crawler.class.getDeclaredField("errors");
            errorsField.setAccessible(true);
            Queue<String> errors = (Queue<String>) errorsField.get(null);
            errors.clear();
        } catch (Exception e) {

errors.plugin_file_is_not_found={0} is not found.
errors.failed_to_install_plugin=Failed to install {0}.
errors.failed_to_find_plugins=Could not find available plugins.
errors.failed_to_process_sso_request=Failed to process a request: {0}

errors.property_required={0} is required.
errors.property_type_integer={0} must be an integer.
errors.property_type_long={0} must be a long.
errors.property_type_float={0} must be a float.

errors.plugin_file_is_not_found={0} is not found.
errors.failed_to_install_plugin=Failed to install {0}.
errors.failed_to_find_plugins=Could not find available plugins.
errors.failed_to_process_sso_request=Failed to process a request: {0}

errors.property_required={0} is required.
errors.property_type_integer={0} must be an integer.
errors.property_type_long={0} must be a long.
errors.property_type_float={0} must be a float.

    /**
     * Adds an error message to the error queue for later processing.
     * This method is thread-safe and can be called from multiple crawler threads.
     *
     * @param msg the error message to add; ignored if null or blank
     */
    public static void addError(final String msg) {
        if (StringUtil.isNotBlank(msg)) {
            errors.offer(msg);
        }
    }

    /**

labels.defaultLabelValue=Etiqueta por defecto
labels.designFileName=Nombre de archivo
labels.incrementalCrawling=Comprobar fecha de última modificación
labels.errorCount=Número de errores
labels.errorLog=Registro de errores
labels.errorName=Nombre del error
labels.expiredTime=Fecha de caducidad
labels.expires=Caduca
labels.failureCountThreshold=Umbral de fallos
labels.fileConfigName=Nombre de configuración de rastreo de archivos

   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}

   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}