esa-sceva/satcom-chunk-collection huggingface dataset:
# Reflectance Transformation Imaging as a Tool for Computer-Aided Visual Inspection
## 3 Dynamic Perceptual Assessment for RTI-Based Visual Inspection: Methodology
### Design of Experiment
#### 3.2.1 Protocol, Studied Factors, and Modalities  
The parameters retained for this experiment, noted as \(P_{1-4}\) in the following, are detailed below.  
* \(P_{1}\), the reconstruction model (three modalities): As stated before, many models have indeed been developed and applied to RTI acquisitions with a significant effect on the quality of rendering. For this experiment, we chose the Polynomial Texture Mappings (PTM) approach, the Hemispherical Harmonics (HSH) technique, and Discrete Modal Decomposition (DMD).  
Figure 3: (**a**) Custom RTI acquisition system (ImViA Laboratory). (**b**) The associated user interface.  
* \(P_{2}\), the angular density of acquisition, or the number of lighting directions in the acquisition sequence (four modalities): RTI acquisitions were performed with 50, 100, 200, and 400 positions homogeneously distributed in the (\(\theta\), \(\phi\)) angular space (see Figure 4). These values correspond to what is commonly used in existing RTI acquisition systems, where typically, between 50 and 200 lighting directions are used. The value of 400 corresponds to a value that we consider to be maximal in an industrial context, where the inspection time is a major constraint.
* \(P_{3}\), the measurement scale (two modalities): Mechanisms related to visual perception are very sensitive to the scale. We chose here two modalities for this parameter which were associated with the zoom factor (40% and 80%). These two zoom factors correspond to pixel sizes of approximately 5 \(\upmu\)m and 2 \(\upmu\)m, respectively.
* \(P_{4}\), the surface material and roughness (three modalities): For the choice of the preceding parameters (\(P_{1-3}\)) having to be correlated with the type of surface inspected, three industrial surface samples were retained. These samples were described in detail previously in Section 3.1.  
Many other RTI parameters could be influential on the visual perception of surfaces such as, non-exhaustively, the light path during the inspection, the light wavelength of the light source, or other general imaging parameters, such as the resolution, the gamma value, or even the type of sensor. However, we assumed that the proposed methodology could be transposed if necessary for the study of their impact and that the chosen parameters here were particularly relevant. Concerning the light path, a single light path was thus implemented, as presented in Figure 4a using a white line. To carry out this experiment, a dense RTI acquisition was realized on this chosen lightpath (397 acquisitions, linearly distributed) for each surface sample in order to reconstruct the reference video for the psychometric experiment. Thus, this reference video corresponded to the raw data and was not subject to any processing or modeling.
## 4 Results and Discussion
### Effect of Electrode Surface Area  
As discussed in Section 2, EDL capacitors were formed at the interface of the electrode and water. Therefore, the total induced mobile charges should be dependent on the  
Figure 5: Dependence of the ionic current signals on water temperature.  
Figure 6: Effect of the distance between the working electrode and the sensing electrode on ionic current.  
capacitance of the EDL capacitors. To investigate the dependence, the effect of the electrode surface area on the generated ionic current was studied by bringing the gold sheet into contact with different electrode surface areas. Figure 7a shows the experimental results of the ionic current when the working electrode surface area increases (experimental data can be seen in Table A6 of Appendix A). The results showed that the ionic current decreased linearly in magnitude with the increase in working electrode surface area. As the copper working electrode worked as the function of an electric wire, the larger surface area meant more mobile counter ions were attracted to the surface to neutralize the electrons. This result led to a decreased trend in the ionic current with the increase in working electrode surface area. Nevertheless, the ionic current increased linearly in magnitude with the increase in the sensing electrode surface area. The results are shown in Figure 7b (experimental data can be seen in Table A7 of Appendix A). It is clearly evident that more surface charges were attracted to the surface of the sensing electrode, leading to a larger ionic current.
## 4 Implementation and Experiments
### Results and Evaluation
#### 4.2.1 Qualitative Results
G is for Google data and W is for Wikipedia data. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **R1** & **R2** & **R3** & **R4** & **Average** \\ \hline \multirow{2}{*}{Computer science} & 119/142 & 110/142 & 111/142 & 106/142 & 111.5/142 \\  & (83.80\%) & (77.46\%) & (78.17\%) & (74.65\%) & (78.52\%) \\ \hline \multirow{2}{*}{Diabetes} & 127/157 & 132/157 & 124/157 & 134/157 & 128/157 \\  & (77.71\%) & (84.08\%) & (78.98\%) & (85.35\%) & (81.53\%) \\ \hline \multirow{2}{*}{Politics} & 217/251 & 240/251 & 225/251 & 206/251 & 222/251 \\  & (86.45\%) & (95.62\%) & (89.64\%) & (82.07\%) & (88.45\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Harvest Rate values for Wikipedia seed URLs per rater. R1 is for rater 1, R2 is for rater 2, etc. The best results were obtained with the diabetes topic which is a more specific topic than computer science and politics. The average value for the computer science and diabetes topics is closed, whereas, for political topics, there is an important difference for Google and Wikipedia. The computer science and politics topics contain several subtopics, e.g., the root level of the Wikipedia category of computer science contains 18 subcategories, the category of politics contains 38 subcategories, and the category of diabetes contains 10 subcategories. The corpus for each topic was built only with the first level of the Wikipedia category, leaving aside a significant number of Wikipedia pages, e.g., Table 12 shows the number of Wikipedia pages for the first five levels. The average results obtained by computer science and politics are promising since it does not contain the whole Wikipedia pages from their corresponding categories. The diabetes category is a more specialized category, containing specific terms of the topic and, as can be seen in Table 12, the number of Wikipedia pages does not exponentially increase level by level. The average results obtained with the diabetes topic are better than those obtained with the remaining categories. In the particular case of the diabetes topic for Google results, the number of seed URLs crawled was 9 and the total number of web pages analyzed was 957, resulting in 265 accepted web pages. These numbers are lower in comparison with the seed URLs crawled from Wikipedia; however, the average percentage is quite similar, even when the number of accepted web pages are too different.
## Materials and Methods
### Selection of Environmental Covariates
#### 2.3.3 Remote Sensing Covariates  
Landsat 8 remote sensing imagery with a 30 m resolution (11 September 2019) was downloaded from the Geospatial Data Cloud platform [[https://www.gscloud.cn/](https://www.gscloud.cn/)]([https://www.gscloud.cn/](https://www.gscloud.cn/)) (accessed on 11 October 2023). The first three principal components, PCA1, PCA2, and PCA3, were obtained after radiometric calibration and atmospheric correction implemented using ENVI 5.3 (Harris Geospatial Solutions Inc., Broomfield, CO, USA).
# Hydrogel Tissue Expanders for Stomatology. Part II. Poly(styrene-maleic anhydride) Hydrogels
## 3 Results and Discussion
### In Vivo Test  
The histopathologic observation after implantation of polySMA-0.7 hydrogels in mice was performed and evaluated by macrography and microscopy. All of the implants were surrounded with fibrous capsule formed by the growth of the implant; with the time of sample implantation the size of capsule increased (Figure 6). We observed no signs of necrosis in the border between the implant and the connective tissue capsule. There was visible small cellular infiltration around the implants (acute inflammatory response), however we observed no macrophages, foreign body giant cells nor granuloma formation around the implants (Figure 7). The sizes of the implants were measured immediately after their removal and the volume expansion expressed as volume ratio \(V_{t}/V_{0}\) is given in Table 5. The volume ratio increased from 8.7 on day 3 to 12.7 on day 31 showing thus higher initial volume expansion compared with the values determined in vitro in PBS or FBS (see Figures 2 and 3) at corresponding swelling time.  
It can be expected some difference between the in vitro and in vivo swelling caused by different environments and spatial constraints in the body. In this study, the in vivo experiments provided rather incipient guidance and valuable evidence that the polySMA expanders are biocompatible and that they do not induce necrosis. The swelling course of the hydrogel can be further tuned up (slown down) by the choice of polymer composition (partial substitution of hydrolyzing comonomer MA by structurally similar but nonhydrolyzing one), or by the crosslinker type and content (more flexible crosslinker at a higher concentration). Perhaps quite an interesting approach to solve this issue is to use the expanders pre-swollen in vitro in a physiological solution to a desired swelling degree before inserting them in vivo. This simple pre-swelling treatment can help to optimize the gel volume growth window for the healing process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Time (days)**} & \multicolumn{2}{c}{\(V_{t}/V_{0}\)1  } \\ \cline{2-3}  & **Right Leg** & **Left Leg** \\ \hline
3 & \(8.8\pm 0.3\) & \(8.7\pm 0.2\) \\
5 & \(11.1\pm 1.1\) & \(11.2\pm 0.3\) \\
7 & \(11.0\pm 2.2\) & \(10.7\pm 0.2\) \\
18 & \(12.5\pm 0.4\) & \(11.6\pm 1.1\) \\
31 & \(12.6\pm 1.4\) & \(12.7\pm 1.1\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: The volume ratio (\(V_{t}/V_{0}\)) of polySMA-0.7 expanders removed after certain time of the implantation into rat legs.  
Figure 5: Number of viable human fibroblast cells after various time of their growth on polySMA-0.7 hydrogel determined by AlamarBlue assay. Error bars were calculated using the standard deviation (\(n=3\)).
# A Lightweight Barcode Detection Algorithm Based on Deep Learning
## 4 Experiments
### Evaluation Metrics for Model Performance  
Since the object detection model studied in this paper will be applied in real-time barcode detection and localization in practical scenarios, the evaluation criteria of significance include detection accuracy, inference speed, model size, and computational complexity.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Configuration** \\ \hline Programming language & Python \\ Deep learning framework & Pytorch1.8.1 \\ CPU & Intel (R) Xeon Gold 6248R \\ GPU & NVIDIA RTX A6000 \\ CUDA & 11.8 \\ Batch size & 16 \\ Initial learning rate & 0.01 \\ Epoch & 200 \\ SGD momentum & 0.937 \\ Weight decay & 0.0005 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment.  
Figure 7: Barcode dataset. It includes real-world scenarios such as commodity retail, logistics management, and invoice receipt handling.  
The experiments in this paper primarily select recall rate (R) and mAP50:95 as evaluation metrics for model accuracy, with their calculations provided in Formula (6).  
\[R=\frac{TP}{TP+FN},mAP=\frac{\sum_{i}^{C}AP_{i}}{C} \tag{7}\]  
In ablation experiments, we utilize metrics such as localization error, background error, missed GT (ground truth) error, and False Positive error, as defined in TIDE [26], to intuitively reflect the discrepancy between the predicted bounding boxes of the barcode localization model and the ground truth boxes. Under the assumption that a predicted box and a ground truth box share the same category and have an IOU (Intersection over Union) overlap greater than a set threshold t, we select the one with the highest overlap as a True Positive, and the rest are considered False Positives. Consequently, the four aforementioned errors can be defined as follows:  
Localization error (\(\mathbf{E_{Loc}}\)): The prediction box is correctly classified but mislocated.  
Background error (\(\mathbf{E_{Big}}\)): Background is falsely detected as foreground.  
Missed GT error (\(\mathbf{E_{Miss}}\)): All ground truth boxes that are not detected, excluding classification errors and localization errors.  
False Positive error (\(\mathbf{E_{FP}}\)): It can arise from mislocalization, background confusion, duplicate detections, and other reasons.
## 2 Methods
### Signal Processing and Algorithm Development  
#### 2.2.1 Data Processing  
Custom-designed MATLAB algorithms were drafted, tested, and produced to process torque data from randomized trials. The data-processing procedure (Figure 2) summarizes how the data were processed. Initially, an algorithm was made; then, refinements had to be made as partitions in blocks were plotted out of order (Figure 2). Therefore, this procedure details both peak-plotting procedures and the unjumbling of torque plateaus related to the initial randomized stimulation sequence provided.  
Figure 1: Sample partition of stimulation shown in terms of torque generated. Peaks in order as they appear (left to right): ramp peak (Pr), peak 1 (P1), peak 2 (P2), peak 3 (P3), and partial pulse train (Pp).
###### Abstract  
Modeling and simulation of the skeletal muscles are usually solved using the Finite Element method (FEM) which, although accurate, commonly needs a complex mesh and the solution is not processed in real-time. In this work, a meshfree model that simulates skeletal muscles considering their functioning and control based on electrical activity, their structure based on biological tissue, and that computes in real-time, is presented. Meshfree methods were used because they are able to surpass most of the limitations that are present in mesh-based methods. The muscular belly was modelled as a particle-based viscoelastic fluid, which is controlled using the monodomain model and shape matching. The smoothed particle hydrodynamics (SPH) method was used to solve both the fluid dynamics and the electrophysiological model. To analyze the accuracy of the method, a similar model was implemented with FEM. Both FEM and SPH methods provide similar solutions of the models in terms of pressure and displacement, with an error of around 0.09, with up to a 10% difference between them. Through the use of General-purpose computing on graphics processing units (GPGPU), real-time simulations that offer a viable alternative to mesh-based models for interactive biological tissue simulations was achieved.  
musculoskeletal simulation; fluid simulation; GPGPU
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 2 Related Works
The results proved the generalization capability of MLPs. The paper from [PERSON] et al. [40] considers the outputs of the WRF meso-scale model as inputs of an ELM to predict solar irradiation. They proposed the application of the coral reefs optimization algorithm with species (CRO-SP) as a feature selection procedure to minimize the number of input variables. The same author proposed the use of coral reefs optimization (CRO) hybridized with an ELM to predict solar irradiation [41]. A binary-encoded CRO is used as a variable selection model. A comparison was performed considering the performances of the genetic algorithm replacing the CRO, and the SVR, MARS, and MLR as the predictor. The results revealed the viability of the proposed method. The deep belief network was applied to perform the monthly solar forecasting task considering Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data in the work of [PERSON] et al. [42]. The authors also evaluated the use of 15 feature selection algorithms among the filters, wrappers, and bio-inspired approaches. The computational results revealed that the model can overcome MLP, decision trees, RF, and GB. A similar architecture (functional DBN) was proposed together with embedding clustering for daily global solar radiation forecasting by [PERSON] et al. [43]. The model could overcome the stand-alone DBN, functional DBN, and other ML-based models.
## 3 System Model and Symbol Definitions
### RACH Procedure Scenario
#### 3.1.1 RACH Messaging Structure  
Two random access (RA) procedures are supported: a four-step RA type with MSG1 and a two-step RA type with MSGA. Both types of RA procedures support contention-based random access (CBRA) and contention-free random access (CFRA) [35]. The MSG1 of the four-step RA type consists of a preamble on the physical random access channel (PRACH). The MSGA of the two-step RA type includes a preamble on PRACH and a payload on the physical uplink shared channel (PUSCH). Depending on the active messaging type, after the initial message's (MSG1 or MSGA) transmission, the UE monitors for a response from the network within a configured window.  
If the RA procedure with a two-step RA type is not completed after several MSGA transmissions, the UE can switch to CBRA with a four-step RA type [36].
## 3 SAT-Hadoop-Processor
### RS Data Integration and Storage: Hadoop Framework
#### 3.5.2 The Integration of RS Data in the HBase  
HBase is a column-oriented key/value storage system made to run on the upper of the HDFS. The Apache Software Foundation accomplishes its development. HBase became a top-level Apache project in 2010. It is designed to manage significant table operations and request rates (billions of rows and millions of columns) and scale-out parallelly in distributed computing clusters [36]. HBase is recognized for offering robust data consistency on reads and writes, which differentiates it from other NoSQL databases [37]. It uses the architecture of master nodes to handle region servers that distribute and process parts of data tables. HBase is a chunk of a long list of Apache Hadoop frameworks that embrace Hive, Pig, and Zookeeper tools. HBase is typically coded using Java, not SQL. The most common Filesystem used with HBase is HDFS [38]. Nevertheless, you are not limited to HDFS because the Filesystem used by HBase has a pluggable architecture and can replace HDFS with any other supported system. In effect, you could also implement your Filesystem (see Supplementary Material File).
# Secure Video Communication Using Multi-Equation Multi-Key Hybrid Cryptography
## 3 Proposed Method
### Proposed Algorithms
This approach not only enhances data privacy but also mitigates key management challenges. By allowing multiple recipients to encrypt data using their respective public keys, multi-key hybrid cryptography enables scalable and secure communication, ensuring that each recipient can only access data intended for them. Moreover, it provides resistance against common cryptographic attacks due to the use of strong, randomly generated symmetric keys. However, it is imperative to emphasise that the security of this scheme heavily relies on meticulous key management practices and the protection of private keys. Additionally, the video-chunk-level encryption using different equations enhances the robustness of the proposed model. While multi-key hybrid cryptography boosts several strengths, it is not without its challenges. Complex key management, especially when dealing with a multitude of recipients, can introduce overhead and potential vulnerabilities if not handled diligently. Furthermore, achieving data integrity may require additional cryptographic mechanisms, such as digital signatures or hash functions, which can increase computational costs. Nonetheless, with proper implementation, secure key handling, and adherence to best practices, multi-key hybrid cryptography remains a potent tool in the arsenal of cryptographic techniques, addressing various security requirements and ensuring the confidentiality and privacy of sensitive data in modern communication systems. In theory, both the AES and the RSA encryption algorithms possess vulnerabilities that make them susceptible to brute-force attacks. The method under consideration employs a key size of 128 bits, which is relatively shorter compared to other key sizes. Consequently, there exists a potential for the key to be learned over an extended period. The proposed model incorporates a multitude of parameters in order to derive the key, thereby significantly augmenting the complexity involved and subsequently diminishing the potential for a successful brute-force attack. The sharing of basic details is facilitated by using static user attributes, such as the Media Access Control (MAC) address, among other methods.
## 4 Discussion
### Seismotectonic Implications of GNSS-Based Strain Tensor Analysis
In this study, relative displacement vectors across GNSS baselines were used to construct planar strain tensors. Eigenvalue decomposition of these tensors yielded principal strain components and orientations, allowing for the identification of maximum compressive and extensional stress directions. Notably, zones exhibiting elevated maximum shear strain or dilatation corresponded spatially with known active fault segments. This alignment suggests ongoing tectonic loading in structurally vulnerable regions and highlights the value of integrating elastic strain indicators into seismic hazard zonation efforts. Furthermore, the sensitivity of GNSS baselines--capable of detecting sub-centimeter annual movements--enables the monitoring of crustal strain accumulation even in relatively quiescent tectonic settings. When combined with conventional seismic catalogs, these measurements enhance our ability to detect aseismic strain buildup and forecast zones of future seismic activity [63; 64; 65; 66; 67; 68; 69; 70]. Figure 11 conceptually illustrates the core workflow adopted in this study: from the acquisition of GNSS displacement data, through the evaluation of stress and strain tensors, to the identification of potential seismic hazard zones. This process highlights how high-resolution geodetic observations can be systematically transformed into meaningful seismotectonic insights. By linking surface displacements with elastic strain modeling, the figure underscores the value of GNSS-based methods in detecting ongoing crustal deformation and informing earthquake hazard assessment frameworks.