esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Computational Overhead Analysis  
In addition to model performance, we also focus on the computational overhead of models. As shown in Table 9, we compare the computational time, including the time for meta-training and meta-testing, of all the methods. The results show that the total time of our method is less than that of four baseline methods, and is just a little longer than the total time of DS-FSL. Compared with DS-FSL, our method is able to significantly improve model performance with only a small increase in computational cost. This proves that our model design is reasonable and effective. Besides, our method shows a short inference time, which makes MultiDS valuable in real-world application scenarios.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **Clinc\(\rightarrow\)Huffpost** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Clinc\(\rightarrow\)Hwu** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Clinc\(\rightarrow\)Liu** \\ \end{tabular} } \\ \cline{2-2} \cline{4-5}  & **10-Way-1-Shot** \\ \hline MultiDS & **29.71** & **67.72** & **65.09** \\ \(-Att\) & 22.86 & 54.40 & 48.45 \\ \(-Att_{da}\) & 27.47 & 64.82 & 61.19 \\ \(-Att_{ds}\) & 29.29 & 67.61 & 64.82 \\ \(-Att_{cs}\) & 29.40 & 67.33 & 64.83 \\ \(-Cal\) & 29.59 & 67.27 & 64.61 \\ \(-Cal_{df}\) & 29.42 & 67.49 & 64.76 \\ \(-Cal_{ds}\) & 29.59 & 67.58 & 64.92 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation studies on Clinc\(\rightarrow\)Huffpost, Clinc\(\rightarrow\)Hwu and Clinc\(\rightarrow\)Liu. The best results are emphasized using bold fonts.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{\(\boldsymbol{N}\)**-Way-5-Shot on Clinc\(\rightarrow\)Liu**} \\ \cline{2-7}  & \(N=\boldsymbol{5}\) & \(N=\boldsymbol{6}\) & \(N=\boldsymbol{7}\) & \(N=\boldsymbol{8}\) & \(N=\boldsymbol{9}\) & \(N=\boldsymbol{10}\) \\ \hline DS-FSL & 88.34 & 86.78 & 85.98 & 85.08 & 84.12 & 82.17 \\ DualAN & 90.08 & 88.64 & 85.95 & 86.07 & 85.16 & 82.90 \\ MultiDS & **90.34** & **89.08** & **87.16** & **86.51** & **85.69** & **84.99** \\ \hline \multirow{2}{*}{Model} & \multicolumn{5}{c}{10-way-\(K\)-shot on Clinc\(\rightarrow\)Liu} \\ \cline{2-7}  & \(K=1\) & \(K=2\) & \(K=3\) & \(K=4\) & \(K=5\) & \(K=6\) \\ \hline DS-FSL & 57.45 & 70.90 & 77.24 & 80.90 & 83.43 & 84.55 \\ DualAN & 63.65 & 74.65 & 79.67 & 82.20 & 82.90 & 81.48 \\ MultiDS & **65.09** & **75.55** & **80.37** & **83.28** & **84.99** & **86.03** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Model stability verification on Clinc\(\rightarrow\)Liu. The best results are emphasized using bold fonts.
## 2 Results and Discussion
### Antioxidant Activity of Scepe-A, Scepe-B and Scepe-C
#### 2.4.3 ABTS\({}^{+}\bullet\) Scavenging Activity  
The ABTS\({}^{+}\bullet\) scavenging assay is a sensitive method to determine the antioxidant capacity of bioactive compounds, in which sodium persulfate converts ABTS to its radical cation with a blue color and an absorption maximum of 734 nm, and the blue ABTS\({}^{+}\bullet\) is converted back to its colorless neutral form when ABTS\({}^{+}\bullet\) is reactive towards an antioxidant [10; 37; 38]. The abilities of SCPE-A, SCPE-B and SCPE-C to scavenge ABTS\({}^{+}\bullet\) in comparison with ascorbic acid were investigated, and dose-related effects were observed at different peptide concentrations ranging from 0 to 5.0 mg/mL (Figure 6C). SCPE-B, with an EC\({}_{50}\) of 0.18 mg/mL, showed the strongest scavenging activity on ABTS\({}^{+}\bullet\) among the protein hydrolysate, fractions, and prepared peptides at all tested concentrations. The EC\({}_{50}\) of SCPE-B was lower than those of WDR (0.34 mg/mL) [19], LDK (0.19 mg/mL) [20], FLNEFLIV (1.548 mg/mL) [29], FPELLI (0.40 mg/mL) and VFAAL (0.38 mg/mL) [4], FLPF (1.497 mg/mL), LPF (1.013 mg/mL), LLPF (1.031 mg/mL) [33], GFGLP (0.328 mg/mL), VGGRP (0.465 mg/mL) [30], WEGPK (5.407 mg/mL), Gly-Pro-Pro (GPP) (2.472 mg/mL), GVPLT (3.124 mg/mL) [6], FIMGPY (1.04 mg/mL), GPAGDY (0.77 mg/mL) and IVAGPQ (1.29 mg/mL) [11] from the protein hydrolysates of scalloped hammerhead muscle, salmon, Chinese cherry seeds, corn gluten meal, grass carp skin, bluefin leather jacket heads and skate cartilage. The present results indicated that SCPE-A, SCPE-B and SCPE-C could strongly donate electrons or hydrogen atoms to inactivate ABTS\({}^{+}\bullet\).
# Synthetized Multilanguage OCR Using CRNN and SVTR Models for Realtime Collaborative Tools
###### Abstract  
**Background**: Remote diagnosis using collaborative tools have led to multilingual joint working sessions in various domains, including comprehensive health care, and resulting in more inclusive health care services. One of the main challenges is providing a real-time solution for shared documents and presentations on display to improve the efficacy of noninvasive, safe, and far-reaching collaborative models. Classic optical character recognition (OCR) solutions fail when there is a mixture of languages or dialects or in case of the participation of different technical levels and skills. Due to the risk of misunderstandings caused by mistranslations or lack of domain knowledge of the interpreters involved, the technological pipeline also needs artificial intelligence (AI)-supported improvements on the OCR side. This study examines the feasibility of machine learning-supported OCR in a multilingual environment. The novelty of our method is that it provides a solution not only for different speaking languages but also for a mixture of technological languages, using artificially created vocabulary and a custom training data generation approach. **Methods**: A novel hybrid language vocabulary creation method is utilized in the OCR training process in combination with convolutional recurrent neural networks (CRNNs) and a single visual model for scene text recognition within the patch-wise image tokenization framework (SVTR). **Data**: In the research, we used a dedicated Python-based data generator built on dedicated collaborative tool-based templates to cover and simulated the real-life variances of remote diagnosis and co-working collaborative sessions with high accuracy. The generated training datasets ranged from 66 k to 8.5 M in size. Twenty-one research results were analyzed. **Instruments**: Training was conducted by using tuned PaddleOCR with CRNN and SVTR modeling and a domain-specific, customized vocabulary. The Weight & Biases (WANDB) machine learning (ML) platform is used for experiment tracking, dataset versioning, and model evaluation. Based on the evaluations, the training dataset was adjusted by using a different language corpus or/and modifications applied to templates. **Results**: The machine learning models recognized the multilanguage/hybrid texts with high accuracy. The highest precision scores achieved are 90.25%, 91.35%, and 93.89%. **Conclusions**: machine learning models for special multianguages, including languages with artificially made vocabulary, perform consistently with high accuracy.  
CRNN; SVTR; CNN; RNN; multilingual OCR; remote diagnosis; real-time translation; collaborative diagnostics; real-time text detection; assessment
## 5 Simulation Characterization
### Simulation Parameters  
Table 1 presents the common parameters for all our experiments. First, we vary network size from 40 to 300 hosts to simulate small to large groups of network users with random walk and bus movement models. We vary node numbers from 120 to 200 hosts for the working day model. Each host is equipped with two mobile interfaces: Bluetooth and Wi-Fi. This configuration accurately depicts people using their mobile devices during their daily routines. Simulated space is defined in two different ways. The first case (1000 m, 1000 m) represents a small to medium city square or campus building where people gather or move for different purposes like social events, meetings, daily routines, or attending conferences. We simulated the amount of 1-h networking usage with this type of zone. The second case (10,000 m, 8000 m) outlines a large movement zone like a whole city or a village. It is suitable to simulate people during an entire working day. They will be moving from home to work/office to entertainment places or shops and back to their homes at the end of the day. Thus, with this kind of space, we set the duration of the simulation scenario to 10 h. To simulate host communication, we use a random uniform generator. It chooses randomly based on a uniform distribution couple (source, destination) and exchange events. So messages will be sent from sources to destinations during the whole scenario duration and covering the entire simulated zone.
## 3 Results and Discussion
### Attributions Analysis to Airborne Sound Insulation
The attribution spectrum of the total density of a facade reveals a peak near 150 Hz and a dip near 1.25 kHz, which is probably due to effects of fundamental and critical frequencies, respectively. This is likely due to coupling between resonant facade components that allows energy to transfer between elements [65]. The latter happens when elements are physically connected, and they have sufficiently close natural or critical frequencies. This amplifies the radiations from components and affects the isolation negatively [17]. Plots in Figure 8 illustrate the contribution of the thickness and density of interior, main and exterior parts of facade walls to prediction of insulation curves. It can be observed that the density and thickness played an important role on the estimations across all frequencies. The same trends for the attribution of facade total density (Figure 7) is noticed for the total density of the exterior part. Moreover, the effects of fundamental and critical frequencies are also remarkable on the attribution of the total density of interior and main parts, but with lower scale. Table 4 summarizes error distributions in the prediction of airborne insulation curves considering three frequency ranges: low (50-200 Hz), middle (250 Hz-1 kHz) and high (1.25-5 kHz) frequency. The (\(RMSE\)) function is employed to calculate the errors. The best accuracy achieved is at middle frequencies, with an error of 3.52 dB. However, error deviations increased at low and high frequencies with \(RMSE\) values of 4.67 and 4.99 dB, respectively. Again, the higher deviations can be presumably described due to presence of resonance and critical frequencies. However, one may consider that the ANN model does not treat uncertainties. In other words, the prediction model does not consider the uncertainties in acoustic measurements which may affect the predictive accuracy. Since the measurements are collected from different laboratories (Lund University and NRC-CNRC), this could present a source of uncertainties. The latter is also reported by a part of a European project that conducted acoustic measurements for lightweight partitions using 19 different European laboratories [69]. The results presented by authors emphasize that the measured acoustic performance of structures could depict some differences from one laboratory to another depending on various factors, such as installation details of the partitions, laboratory design, number of diffuses, etc., [69]. In addition, the human factor can also affect the accuracy of acoustic measurements, depending on the person performing the measurements and her/his knowledge and experience [70]. Those factors are not taken into account while developing the ANN, and they somehow have an influence on the predictive capability of the model.
# Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model
## 1 Introduction
The classification was further increased in 2014 by using the newly developed VGG [11], and GoogleNet [12] model. Understanding the advancement of the CNN model and considering the necessity of monitoring the cracks in concrete structures, including railway sleepers, researchers nowadays are more inclined to utilize CNN models for detecting cracks. [PERSON] et al. proposed a Convolutional Neural Network (CNN) classifier in 2016 for detecting cracks in concrete structures [13]. The main aim of this research was to design a classifier based on patches for detecting cracks in concrete structures. After that, many other CNN models have been designed for solving crack image classification, detection, and segmentation tasks. Among the three types of solutions, crack segmentation has become the most popular research. Crack segmentation provides pixel-wise classification, where each of the pixels are classified as cracks or non-cracks. The predicted image from the CNN-based segmentation model highlights the cracks on the image and provides an idea about the cracks' location and geometric shape. Furthermore, the segmented pictures can be utilized to extract a few key pieces of information (such as crack length, width, and area) that can estimate the severity of cracks in concrete sleepers. Though there are already many developed DL models, and though past research on utilizing CNNs to detect concrete cracks has generated significant results, only a few research studies have been conducted for segmenting cracks on railway sleepers using the CNNs. Despite a little research, there is still room to develop new methods to achieve higher accuracy and contribute to railway sleeper crack detection. So, in this paper, we utilize a CNN to develop a Dense U-net model by modifying the original U-net architecture to detect cracks on the railway sleeper images. Moreover, we design an algorithm for quantifying the cracks. This research study primarily offers the following contributions  
* Collecting railway sleeper images and processing them in the form of a dataset. * Proposing a modified U-net model for the first time to detect cracks on railway sleepers. * Quantifying the cracks of railway sleepers for knowing the severity of the cracks. The rest of this study is structured as follows: The existing crack detection techniques are briefly discussed in Section 2. Section 3 offers a summary of the methodology employed in this study. Section 4 shows as well as discusses the experimental process and assessment outcomes concerning crack detection on railway sleepers. Lastly, Section 5 concludes the paper.
# Overview on Different Types of Solar Cells: An Update
## 2 Organic Solar Cells
### Challenges for Organic Solar Cell Technology  
Silicon is the most widely used material for the commercial photovoltaic market due to its high-power conversion efficiency [20; 21; 22]. Inorganic solar cells are associated with certain challenges such their its high-cost and complicated and energy-intensive fabrication protocols. Organic solar cells are gaining attention due to their simple fabrication protocols. OSC technology has not yet gained much attention commercially because of its low power-conversion efficiency (PCE) and limited lifetime [23; 24]. The heterojunction generated by contact of donor and acceptor material is the basis of OSC. There are a number of challenges in OSC technology such as limited absorption efficiency of donor materials, hindered exciton diffusion, charge dissociation, and transport of charges. Different approaches to overcome these challenges could be improved in exciton diffusion length, crystal structure, and the collection of photogenerated carriers. These improvements can be realized by the application of polymer-based nanocomposites, plasmonics, and tandem architectures. Moreover, hybrid nanocomposites (composed of both organic and inorganic materials) can be a synergistic combination to take advantage of the unique properties of both components [25; 26; 27]. The operation of hybrid solar cells has more similarities with OSC. Hybrid solar cells are composed of n-type inorganic material along with p-type semiconductor polymer and are capable of achieving high PCE.
# The Impact of the 4.0 Paradigm in the Italian Agricultural Sector: A Descriptive Survey
## 2 Literature Review
### Agri 4.0: Benefits and Obstacles
\begin{table}
\begin{tabular}{c c c c} \hline
**TBL Cluster** & **Cluster of Benefit** & **Benefits** & **References** \\ \hline \multirow{6}{*}{Profit} & & Product quality increase & \\ \cline{3-4}  & \multirow{2}{*}{Effectiveness} & Yield increase & [24; 29] \\ \cline{3-4}  & & Soil quality increase & \\ \cline{3-4}  & & Increase in selling price & \\ \cline{3-4}  & & Less water consumption & \\ \cline{3-4}  & \multirow{2}{*}{Efficiency} & Less technical input consumption & \\ \cline{3-4}  & & Less machinery usage & [30; 31] \\ \cline{3-4}  & & Simplification in the cultivation decisions to be & \\ \cline{3-4}  & & made & \\ \cline{3-4}  & & General cost reduction & \\ \hline \multirow{2}{*}{Planet} & \multirow{2}{*}{Environmental benefits} & Air pollution (CO\({}_{2}\), N\({}_{2}\)O,... ) decrease & [23] \\ \cline{3-4}  & & Water pollution decrease & \\ \hline \multirow{6}{*}{People} & & Work safety increase & \\ \cline{3-4}  & & Reduced time spent on bureaucratic tasks & [25; 32; 33] \\ \cline{3-4}  & & Physical labor reduction & \\ \hline \end{tabular}
\end{table} 
Table 2: List of benefits. 1. **Technological challenges:** This cluster refers to technical and technological issues related to the implementation of 4.0 solutions in agriculture [34]. Technical barriers can be limited or without interoperability (the data collected cannot be reused and different solutions do not work together) and lacking connectivity. However, it is also important to mention the limited or absent flexibility in the sense that the provided solution works only under optimal operating conditions (primarily weather) [27]. 2. **Economic challenge:** The implementation of innovative solutions and technologies in every field leads to a significant economic effort by the company that intends to adopt an innovative path. For this reason, it takes into consideration the economic return of the investment made in 4.0 solutions [35]. The challenge in question is the low investment return rate. 3. **Implementation challenges:** The skills needed to properly implement 4.0 solutions, especially in companies in the primary sector, inevitably lead to the challenge of usage difficulty. Subsequently, the challenge connected to the first one is insufficient assistance because many companies can face obstacles that are difficult to overcome without an appropriate implementation assistance path [26]. 4. **Cultural and organizational challenge:** Because of the introduction of digitalization in agriculture, the 4.0 revolution in all economic sectors will require a new set of skills related to the introduction of digital solutions in companies [25]; for this reason, the challenge presented in the survey is the lack of key digital skills in the farm. Out of these clusters, seven different obstacles could be derived. The full list of challenges and references is presented in Table 3.
## 3 Results  
To investigate the effect of SNR thresholds used in the STE method on the automatic evaluation performance and evaluation of bowel motility by the method, experiments were performed in which the SNR thresholds used in the STE method were 0, 0.5, 1 and 2 dB.
# Realistic Texture Mapping of 3D Medical Models Using RGBD Camera for Mixed Reality Applications
## 2 Materials and Methods
### CT Images Processing
#### 2.1.2 Creation of a 3D Pointcloud  
Since the purpose was to create a 3D pointcloud expressed in meters, it was necessary to convert the values \(x,y,\) and \(z\) of each point, into coordinates expressed in meters. Because the SeeFactor CT3 machine (Imaginals S.r.l-Florence-Italy) has an isotropic acquisition volume, the slice thickness is equal to the volume pixel spacing. For this reason, the conversion was performed by multiplying each value by the DICOM standard parameter called slice thickness:  
\[x=X*\frac{ST}{1000}\qquad y=Y*\frac{ST}{1000}\qquad z=Z*\frac{ST}{1000} \tag{1}\]  
where: \(x,\)\(y\) and \(z\) are the 3D values expressed in meters, \(X,\)\(Y\) and \(Z\) are the 3D values expressed in CBCT volume pixels and \(ST\) is the slice thickness value. In particular, a slice thickness value of 0.35031 mm was used. The result obtained at the end of this procedure is shown in Figure 4. In the subsequent sections of the article, the pointcloud defined in this way will be referred to as the DICOM-based pointcloud.  
Figure 3: Application of the [PERSON]’s method for the outer contour detection: the left image shows the original DICOM image while the right one shows the result obtained by applying the [PERSON]’s thresholding method.
# A Novel Method for Spectrometry Based on Imaging Systems
## 4 Conclusions  
In this work, a DoI parameter estimator has been evaluated using a 6 mm thick white painted CRY018 monolithic crystal coupled with a MAPMT. The scintillation material and the optical treatment were chosen to optimize MAPMT spectrometric performances in terms of collection of light, energy resolution, and pulse height uniformity response. Relying on simple spectrometric assessments, the DoI parameter has shown to be highly sensitive to the depth of interaction of photons, allowing to deeply understand the internal interaction processes of light. Moreover, the estimator allows to perform a DoI dependent events selection providing several spectra of the same sample which could be useful in multigamma emission sources measurements. Furthermore, the imaging capabilities of the proposed system offer new possibilities in \(\gamma\)-peaks investigation such as the use of an ROI method to better reject background events that affect countings and peak identification. Hence, the matching of the DoI with ROI enables new methods for an optimized identification of gamma peaks and finally of the revealed unknown radioactive source.  
The novelty of this work lies in the application of the DoI selection in spectrometry enabled by the use of a position-sensitive system. The proposed method is particularly practical since it is simple to implement and does not require complicated hardware solutions or complex computational procedures. Finally, the investigation of new detectors such as the one proposed is important for the characterization of materials, whether they are environmental matrices or nuclear materials. In addition, the development of devices sensitive to low energies will become an important issue in the future, especially for questions related to radionuclides that are difficult to measure. Further analysis should be carried on using a larger crystal (i.e., size of the order of several cm, for practical applications) coupled to a SiPM device, in order to extend the analysis in a wider energy range and provide a proper calibration of \(N/I\) parameter with the crystal thickness.  
R.F. and S.S. have contributed for conceptualization, methodology, software, formal analysis, validation, data curation, visualization, writing--original draft preparation and writing--review and editing. Supervision, R.P. and L.I; project administration, R.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript.  
\begin{tabular}{l l} PMT & Photomultiplier Tube \\ MAPMT & Multi Anode Photomultiplier Tube \\ SiPM & Silicon Photomultiplier \\ ADC & Analog to Digital Converter \\ FPGA & Field Programmable Gate Array \\ DoI & Depth of Interaction \\ SBA & SuperBialkali \\ ROI & Region of Interest \\ ER & Energy Resolution \\ \end{tabular}