esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Measurement Quality Evaluation  
Different strategies and error metrics (Table 3) were used to evaluate measurement quality achieved with photogrammetry. As much as possible, they were used simultaneously to assess results in terms of accuracy, precision and reliability [66]. While accuracy and precision were estimated in comparison with reference data (ground truth) supposed of higher quality, reliability represented the consistency between data obtained using different processing parameters determined through DEMs of differences (DoDs).  
Results' accuracy, representing systematic deviations (bias) from the ground truth, and the precision with which they were obtained, were calculated, respectively, as the mean error (ME, Equation (1)) and the standard deviation of error (SDE, Equation (2)) between photogrammetric models and ground targets. RMSE (Equation (3)) is also provided, representing the global precision within results, combining both ME and SDE into a single statistical measure (Equation (4)). Error statistics were calculated along all three dimensions (x, y and z) and were eventually added in quadrature to produce a measure of the 3D error (Equation (5)). For ease of use and clarity of the text, error metrics without a direction specifically mentioned refer to vertical error, which is of immediate interest for measuring morphological change with DEMs.  
At La Palue field site only, DEM vertical accuracy and precision were also derived through comparisons with points surveyed with the bike GNSS. This allowed for independent error evaluation at a dense network of ground truth points. For assessing photogrammetric results, each GNSS survey point was compared with the closest DEM grid point, given a maximum distance for comparison of 0.2 m. The latter was decided considering comparisons with points further away would account more for the change in the local bed topography than measurement error itself.  
Finally, the availability of four repeat DEMs obtained using the same workflow for collecting (Table 1) and processing (Section 2.3) the data enabled assessing photogrammetric replicability at La Palue. The temporal variability in bed elevations was estimated for each DEM cell using both the standard deviation of elevations and the maximum elevation range between repeat surveys. These metrics were used to assess whether a surface cell (and, hence, the underlying terrain) was stable over the survey period. We used a maximum range of 0.035 m to distinguish stable and unstable cells (cells with elevation range above threshold were considered unstable). To ensure a robust statistical characterization, only cells measured across all four repeat surveys were retained. A multitemporal ground truth DEM was formed by averaging repeat elevations over stable cells. Comparing individual surveys with the ground truth provided information on eventual deviations from the \"average topography\" of the stable zones.
# Fast Flow Reconstruction via Robust Invertible \(n\times n\) Convolution
## 3 Background  
### Flow-Based Generative Model  
Let \(\mathbf{x}\) be a high-dimensional vector with unknown true distribution \(\mathbf{x}\sim p_{\mathcal{X}}(\mathbf{x})\), \(x\in\mathcal{X}\), a simple prior probability distribution \(p_{\mathcal{Z}}\) on a latent variable \(z\in\mathcal{Z}\), a bijection \(f:\mathcal{X}\rightarrow\mathcal{Z}\), the change of variable formula defines a model distribution on \(\mathcal{X}\) as shown in Equation (1).  
\[p_{\mathcal{X}}(\mathbf{x})=p_{\mathcal{Z}}(\mathbf{z})\Big{|}\det\Big{(} \frac{\partial f(\mathbf{x})}{\partial\mathbf{x}}\Big{)}\Big{|}, \tag{1}\]  
where \(\frac{\partial f(x)}{\partial\mathbf{x}}\) is the Jacobian of \(f\) at \(\mathbf{x}\). The log-likelihood objective is then equivalent to minimizing:  
\[\begin{split}\mathcal{L}(\mathcal{X})&=-_{\mathbf{ x}\in\mathcal{X}}\log p_{\mathcal{X}}(\mathbf{x})\\ &=-_{\mathbf{x}\in\mathcal{X}}\bigg{[}\log p_{\mathcal{Z}}( \mathbf{z})+\log\Big{|}\det\Big{(}\frac{\partial f(\mathbf{x})}{\partial \mathbf{x}}\Big{)}\Big{|}\bigg{]}.\end{split} \tag{2}\]  
Since the data \(\mathbf{x}\) are discrete data, we add a random uniform noise \(u\in\mathcal{U}(0,a)\), where \(a\) is determined by the discretization level of the data, to make \(\mathbf{x}\) be continuous data. The generative process can be defined as Equation (3).  
\[\begin{split}\mathbf{z}&\sim p_{\mathcal{Z}}(\mathbf{ z})\\ \mathbf{x}&=f^{-1}(\mathbf{z}).\end{split} \tag{3}\]  
The bijection function \(f\) is constructed from a sequence of invertible and tractable Jacobian determinant transformations: \(f=f_{1}\circ f_{2}\circ...\circ f_{K}\) (\(K\) is the number of transformations). Such a sequence of invertible transformations is also called a normalizing flow. Here, Equation (2) can be written as in Equation (4).  
\[\begin{split}\mathcal{L}(\mathcal{X})&=-_{\mathbf{ x}\in\mathcal{X}}\log p_{\mathcal{X}}(\mathbf{x})\\ &=-_{\mathbf{x}\in\mathcal{X}}\bigg{[}\log p_{\mathcal{Z}}( \mathbf{z})+\sum_{k=1}^{K}\log\Big{|}\det\Big{(}\frac{\partial\mathbf{h}_{k}}{ \partial\mathbf{h}_{k-1}}\Big{)}\Big{|}\bigg{]}\end{split} \tag{4}\]  
where \(\mathbf{h}_{k}=f_{1}\circ f_{2}\circ...\circ f_{k}(\mathbf{h}_{0})\) with \(\mathbf{h}_{0}=\mathbf{x}\).
## 4 Discussion
### Advantages of Metre-Scale Pleiades Data in Monitoring Water Quality Parameters  
The advantages of Pleiades data, compared to SPOT-like (20 m) and MODIS-like (250 m), in monitoring water quality parameters in a dynamic water zone, over the whole estuary, and in small or confined areas are discussed below.  
Along representative transects in test site 1 and test site 2 (Figures 12 and 13), both the 2 m and 20 m spatial resolution data show detailed variations of turbidity while the 250 m resolution introduces a loss of information, especially for test site 1. The very high spatial resolution of Pleiades shows much better results than the resampled 250 m product in test site 1, indicating the usefulness of this kind of imagery in maximum turbidity zones and zones with sharp turbidity variations. For test site 2, smaller differences are observed between 2 m, 20 m and 250 m resolution images (Figure 13). The benefit of having metre-scale satellite observations is less evident in the mouth area.  
For the mapping of turbidity in large sections of the GE (Figures 14 and 15), an improvement is also observed using higher resolution satellite data. However, the improvement of spatial resolution from 20 m to 2 m (differences of turbidity \(\leq\) 10%) is not as significant as from 250 m to 2 m (differences \(\leq\) 18%). Therefore, over a large water body, even in a macro-tidal estuary which is a highly dynamic and turbid water zone, the advantage of metre-scale Pleiades satellite data is actually limited.  
At smaller scale, around the Pauillac turbidity station located by the shores of the estuary, coarser spatial resolutions than Pleiades data result in an overestimation (8.27% for the 20 m and 26% for the 250 m resolution) of turbidity based on comparisons with in situ measurements. This is caused by the contamination of pixels due to sharp turbidity variations and mixed pixels in the near shore area. Hence, high-spatial-resolution imagery is required for retrieving the small-scale turbidity variations in such areas (Figure 11). Pleiades data can also be used to detect the turbulent phenomena of the Gironde Estuary such as the strong turbidity variations over test site 1. Human activities or constructions can be observed sharply with 2 m resolution Pleiades images, such as ship and airplane tracks, but also dredging plumes. The water turbidity can also be mapped inside ports (e.g., Figure 11), which certainly can help in the management of sediment transport and deposits in the estuary, thus help the management of the dredging activities conducted daily to prevent the filling of ports and navigation channels. Such detailed information can hardly be provided by other sensors associated to a coarser spatial resolution. In the Appendix A, twelve Pleiades images are shown to illustrate metre-scale and short-term variability of water turbidity in the GE (Figures A1 and A2).
## 3 Results
### Voluntary Blink Detection Efficiency  
Blink detection performance was evaluated, focusing primarily on detecting voluntary blinks. Figure 12 shows a blink in the 4-20 Hz filtered EEG signal.  
The graph's peak corresponding to the blink clearly indicates the moment when the event occurred. Tests were conducted with three subjects to ensure the detection system functioned properly.  
The participants were healthy adults with an average age of 27.33 \(\pm\) 6.85 years. Before beginning the experimentation, informed consent was obtained from each participant. Participants were instructed to perform continuous, voluntary blinks for 10 s, followed by a 10-s period during which they were required to refrain from blinking voluntarily, allowing only involuntary blinks. Subsequently, they were directed to alternate between keeping their eyes open for 3 s and closing them for another 3 s, for 15 s. This sequence, lasting 35 s, was repeated in three cycles, resulting in a total test duration of 105 s.  
Each participant completed three tests, a cumulative duration of 315 s. During the tests, blinks were only to be detected during the voluntary blinking period; any detection during the resting period or the 3-s intervals of closing/opening the eyes was considered an error. Based on this protocol, performance data were calculated and are presented in Table 2.  
In test T1, Subject 2 showed the lowest performance, with a value of 70%, while Subject 1 achieved the highest performance, reaching a value of 97% in Test 2. The overall detection average was 84%, indicating effective recognition of voluntary blinks and fulfilling the requirements for implementation in the serious game.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{2}{c}{**Voluntary Blink Detection Efficiency (\%)**} & \multirow{2}{*}{**Subject Average**} \\  & **T1** & **T2** & **T3** & \\ \hline
1 & 84 & 97 & 86 & 89 \\
2 & 70 & 81 & 95 & 82 \\
3 & 76 & 87 & 78 & 80 \\  & Overall Average of Participant & & & 84 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of voluntary blink detection.  
Figure 12: Visualization of blinking in 4–20 Hz filtered EEG signal.
# Some Aspects of the Modelling of Dried Red Beets Rehydration Process
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Rehydration Index**} & \multicolumn{6}{c}{**Input and Hidden**} & \multicolumn{6}{c}{**Hidden and Output**} \\ \cline{2-9}  & **No.** & \multicolumn{3}{c}{**Weights**} & **Bias** & **Weights** & **Bias** \\ \cline{2-9}  & \(i\) & \(D_{\text{line-bit},i}\) & \(D_{\text{line-bit},i}\) & \(D_{\text{line-bit},i}\) & \(D_{\text{line-bit},i}\) & \(B_{\text{line-bit},i}\) & \(D_{\text{bit-out},1}D_{\text{bit-out},2}\) & \(B_{\text{hidden-out},1}\) & \(B_{\text{hidden-out},2}\) \\ \hline \multirow{4}{*}{RI} & 1 & 1.4223 & 3.4719 & 0.0789 & 0.0483 & \(-3.8950\) & \(-1.4493\) & \multirow{4}{*}{2.6578} \\  & 2 & \(-2.7266\) & 3.8635 & 0.2521 & 0.4678 & 2.7240 & 1.2268 & \\  & 3 & \(-0.6425\) & 0.0878 & \(-0.0362\) & 5.7709 & 5.1401 & \(-3.6946\) & \multirow{4}{*}{2.7519} \\  & 4 & \(-1.0771\) & \(-0.9887\) & \(-1.7848\) & \(-5.7598\) & 2.1956 & \(-1.4348\) & \\ \hline \multirow{4}{*}{WAC} & 1 & 3.5059 & \(-4.0066\) & 0.8629 & \(-4.6100\) & 2.0212 & \(-0.0432\) & \multirow{4}{*}{2.7519} \\  & 2 & \(0.45239\) & \(-3.9539\) & \(-1.7749\) & \(-2.6745\) & \(-1.1051\) & \(-0.8289\) & \\  & 3 & \(0.1079\) & \(-0.7704\) & \(-0.0989\) & 6.0285 & 6.5638 & \(-5.1909\) & \\  & 4 & \(0.6567\) & \(-5.3133\) & \(-0.4883\) & 0.7989 & 4.8056 & 2.5714 & \\ \hline \multirow{4}{*}{RI + WAC} & 1 & 9.8042 & \(-16.1895\) & 0.0718 & 1.7426 & \(-15.6224\) & \(-0.9165\) & \(-0.8905\) & \multirow{4}{*}{\(-5.9015\)} \\  & 2 & \(-0.4290\) & 1.8800 & 0.3195 & \(-17.017\) & \(-18.9434\) & 13.0804 & 14.3147 \\ \cline{1-1}  & 3 & \(0.6545\) & \(-9.7334\) & \(-0.5522\) & \(-4.3675\) & 5.7102 & 5.7079 & 6.1074 & \\ \cline{1-1}  & 4 & \(-13.3228\) & \(6.3649\) & \(-10.1912\) & 9.2477 & \(-8.3975\) & \(-0.3901\) & \(-0.3650\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Weights and biases of ANNs for rehydration indices. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rehydration Indices** & **Refer to** & \(\mathbf{R^{2}}\) & **RMSE** \\ \hline \multirow{2}{*}{RI} & RI & 0.9207 & 0.0584 \\ \hline \multirow{2}{*}{WAC} & WAC & 0.9078 & 0.2285 \\ \hline \multirow{4}{*}{RI + WAC} & RI & 0.9348 & 0.0529 \\  & WAC & 0.9327 & 0.0549 \\ \cline{1-1}  & RI + WAC & 0.9347 & 0.0539 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Results of statistical analyses on the modelling (using ANNs) of rehydration indices. observed dependences is the fact that rehydration is a complicated process connected with the structural (physical and chemical) changes that occur in the vegetal cells and tissues of biological product during predrying treatments, drying and also rehydration [78; 79].
# Evaluating the Efficacy of a Passive Exoskeleton for Enhancing Ergonomics in Manufacturing
###### Abstract  
Manual material handling (MMH) significantly impacts worker health and productivity, often leading to musculoskeletal disorders (MSDs) primarily in the lower back. As a novel assistive technology, exoskeletons may serve as ergonomic tools to mitigate these work-related MSDs. It is essential to examine exoskeletons from the users' perspectives before their widespread implementation in occupational settings. This study investigates the effectiveness of a passive back-support exoskeleton (BExo) in reducing perceived physical exertion and improving ergonomic safety in a manufacturing context. Twenty-two college students were recruited to perform MMH tasks in a controlled lab environment, both with and without the BExo, followed by completing a survey questionnaire on various aspects of the BExo. Using ANOVA, the study analyzed biomechanical exertion across various body parts and tasks. The findings indicate that the BExo substantially alleviated discomfort and physical exertion in the low back, shoulders and knees, thereby enhancing an ergonomic posture and reducing fatigue. These results underscore the potential of passive exoskeletons to boost workers' safety and efficiency, providing valuable insights for future ergonomic strategies in industrial settings.  
manual material handling; manufacturing ergonomics; passive back-support exoskeleton; occupational health and safety; musculoskeletal disorder prevention; user acceptance; ergonomic interventions 2020
# Neuroimaging of Pediatric Cerebellum in Inherited Neurodegenerative Diseases
## 1 Introduction
### Ataxia Telangiectasia  
Ataxia-telangiectasia (AT) is the most common recessive ataxia in children under 5 years of age. It is characterized by prominent progressive ataxia and abnormalities of posture and tone, movement disorders for extrapyramidal dysfunction, and ocolucutaneous telangiectasia appearing between the age of 2 and 8 years. The clinical spectrum of AT is characterized by recurrent infections due to immunodeficiency, skin and endocrine abnormalities, predisposition for malignancies, and radiation sensitivity [34]. AT is caused by mutations in the ataxia-telangiectasia gene (ATM) involved in DNA repair [35]. Neuropathological studies show cerebellar atrophy, mainly cortical, degeneration of spinal posterior columns, especially of the gracile fascicle, and axonal degeneration in the peripheral nerves; deep cerebellar nuclei and inferior olives abnormalities are inconstantly reported [36].  
Radiological imaging is limited to MRI since CT and studies with x-rays can increase the risk of genetic mutation in these patients highly sensitive to DNA damage by ionizing radiation [37; 38]. MR images reveal diffuse cerebellar atrophy, with initial involvement of the superior vermis and lateral cerebellar hemispheres and unusual decreased cortical thickness along cerebellar hemispheres. Supratentorial brain shows no sign of volume loss. At the same time, confluent hyperintensities in T2/FLAIR and multiple focal hypointensities (thought to be deposits of haemosiderin related to capillary telangiectasia) are seen in deep white matter, mainly in adult patients [39]. Hypoplasia of the inferior vermis and a large cisterna magna were also frequent signs [40] (Figure 7).  
Figure 6: Infantile NCL (CLN6). Brain MRI scan of a 6-year-old girl. MR images demonstrate diffuse cerebellar and cerebral atrophy associated with the markedly decreased signal intensity of the thalami. The last image shows hyperintensity in the deep and periventricular white matter.
# Physicochemical Characterization and Antioxidant Activity of Jara Honey Produced in Western Georgia
## 3 Results and Discussion
### Sugars  
Sugar is a crucial component of honey [47], primarily existing in the form of monosaccharides and disaccharides. The total content of monosaccharides (fructose and glucose) is about 75%, and 10-15% consists of disaccharides [48; 49]. The qualitative and quantitative content of sugars in honey is primarily determined by its botanical and geographical origin. However, the amount of sugars can also be slightly influenced by factors such as weather, processing, and storage conditions [11; 50]. One of the important characteristics when evaluating the quality of honey is the ratio of fructose and glucose, as well as glucose and water. When the fructose-to-glucose ratio is less than 1.0, crystallization occurs more quickly, and it decreases when the ratio is greater than 1.0 [51; 52; 53]. When confirming the naturalness of honey, the concentration of sucrose is also important because a higher-than-permissible content serves as an indicator of adulteration [54]. The high-pressure liquid chromatography method was used to identify and quantify the individual components of sugars in Jara honey. Based on standard calibration, the quantitative results for each honey sample are shown in Figure 3. Among the compounds identified (fructose, glucose, sucrose, and maltose), the dominant compounds were fructose and glucose. Based on standard calibration, the quantitative results for each honey sample are shown in Table 3.  
Comparing the obtained results, the concentration of fructose was 44.5-56.4% and that of glucose was 27.18-37.8% (Table 3). In particular, the fructose content of Keda's honey samples ranged from 45.175% to 52.58%, with an average of 48.79%. Glucose concentrations ranged from 37.8% to 34.97%, with an average of 32.67% glucose. In the samples from Shuakhevi and Khulo, the fructose content was higher compared to the honey from Keda,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Moisture (\%)**} & **Dry** & **Free Acidity,** & & **Electrical** \\  & & **S Substances,** & **mEq/kg** & & **(μs/sm)** \\ \hline JH-1 & 17.0 \(\pm\) 0.26 & 83.0 \(\pm\) 1.25 & 22.48 \(\pm\) 34 & 5.18 \(\pm\) 0.08 & 1.185 \(\pm\) 0.018 \\ \hline JH-2 & 1  
at about 51% (average value); the average glucose concentration in the Shuakehvi honey was 31.32%, while it was 32.21% in the Khulo samples. In the samples taken for analysis, considering the average value, fructose was the dominant sugar. (The mass share of fructose in the total sugars (average of 23 indicators) was as follows: in the Keda samples--52.58%; in the Shuakehvi samples--60.37%; in the Khulo samples--61.42%). This was followed by glucose: in the Keda samples--37.06%; in  
Figure 3: Chromatograms of the sugars in the Jara honey samples.
# ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management
## 2 Materials and Methods
### ExtractEO Architecture
#### 2.3.3 ExtractEO SaaS  
EEO has also been deployed as a Software as a Service (SaaS), in order to make it available through a web browser that simplifies the user experience and allows people outside of SERTIT to use it. The home web page is illustrated in the Figure 3.  
The parameters and the number of sensors managed are reduced. Indeed, the SaaS focuses solely on Sentinel-1 and Sentinel-2 automatic download and processing. The SaaS component of EEO was built to be shared with rapid-mapping partners and, eventually, to be sold to customers through a service subscription. Three chains are available: SAR water, optical water, and fire, based on optical imagery. The user just has to fill in parameters, with an example shown below from the Sentinel-2 optical flood detection chain:  
* Area of Interest
* Start date
* End data
* Extraction resolution
* Minimum mapping unit
* Visualization stack (true color, false color, SWIR)
* Visualization resolution  
The user can track the processing status, and can download the event delineation masks output directly from the website.  
The SaaS is a cloud-native solution hosted by the University of Strasbourg's Data Center, where distributed computing has been enabled using open source, state-of-the-art technologies like Docker, Celery, RabbitMQ and PostgreSQL to orchestrate distributed jobs.  
Initially, the SaaS component of EEO was an internal service among the CEMS-RM consortium, in order to provide all partners with the ability to process huge volumes of EO data over widespread disasters. Now that the platform is operational, it has the potential to become an independent fee-paying service. The implementation of other EO sources is not foreseen for the moment, because EEO SaaS is designed to cover large areas with the download function included.
# Adaptive Neural Network for a Stabilizing Shunt Active Power Filter in Distorted Weak Grids
## 2 System Configuration
### DC-Link Voltage Regulation  
For regulating the DC-side voltage of SAPF, \(I_{dc}\) is needed and approximated by using a simple PI controller that helps to minimize the voltage difference between reference DC-side voltage \(v_{dc,ref}\) and \(e_{dc}\) according to the following equations.  
\[I_{dc}=k_{p}e_{v}+k_{i}\int_{0}^{t}e_{v}dt \tag{31}\]  
\[e_{v}=v_{dc,ref}\ -v_{dc} \tag{32}\]  
where \(k_{p}\) and \(k_{i}\) are the constant gains of the PI controller. The bandwidth of the DC-link voltage controller is tuned to be higher than the bandwidth of the PLL to avoid instability due to the interaction between the PLL and DC-link controller. The reference signal \(I_{S,ref}\) can be generated according to the following equation.  
\[I_{S,ref}=\Big{(}\,i_{Lf,q}+i_{Lh}+I_{dc}\Big{)}sin(\omega t+\theta) \tag{33}\]  
Hysteresis current controller is used for generating gate pulses to SAPF. The hysteresis current controller will take the difference between \(I_{S,ref}\) and \(I_{S}\), where \(I_{S}\) is the supply current. Considering the difference between \(I_{S,ref}\) and \(I_{S}\), the hysteresis current controller will switch the IGBTs of the voltage source inverter according to the following equation.  
\[\mathrm{g}=hys(\mathrm{I_{S}}-\mathrm{I_{S,ref}})=\begin{cases}1\ \ \ \mathrm{if}\ \ \ (\mathrm{I_{S}}-\mathrm{I_{S, ref}})>\mathrm{band}\\ 0\ \ \mathrm{if}\ \ \ \ (\mathrm{I_{S}}-\mathrm{I_{S,ref}})<\mathrm{band}\end{cases} \tag{34}\]  
where \(\mathrm{g}\) is the status of the IGBT, \"1\" symbolizes on and \"0\" symbolizes off, and _band_ denotes the hysteresis band. The switching status of each IGBT in a branch is complementary to one another. Hence, when \(\mathrm{g}=0\), \(\overline{\mathrm{g}}=1\), or contrariwise.