esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Research
### Experimental Results and Analysis
#### 4.3.4 Image Segmentation Module Ablation Experiment  
To prove the effectiveness of the different improvement programs on the U-Net network in the 0.02 scale noise environment, four representative images were selected to show the results of the ablation experiments in Figure 14 from left to right: the original image, the U-Net recognition effect combined with the Res2 Block recognition effect, the multi-channel residual attention module recognition effect combining Res2 Block and fusion null convolution with the full-scale connection structure recognition effect, and the proposed model's image segmentation module recognition effect. The objective analysis of the ablation experiment is shown in Table 4.  
Replacing the encoder with the Res2 Block structure (Group 2) led to improved segmentation accuracy, with only a slight increase in the overall parameter count. The use of a dilated convolution-based multi-channel attention module in Group 3, replacing the intermediate convolutional layer, resulted in better segmentation results and fewer trainable parameters compared to the baseline.  
Group 4 replaced the skip connections in the U-Net architecture with a full-scale connectivity structure, which resulted in improved segmentation accuracy at the cost of a marginal increase in parameters. Group 5 integrated all the aforementioned enhancements, achieving the best evaluation metrics among all groups without significantly increasing the parameter count or computational time. This configuration effectively enabled accurate coal-rock image segmentation even under noisy conditions.  
In summary, the proposed improved model significantly enhances the segmentation accuracy of coal-rock images while maintaining a low parameter footprint and high computational efficiency.  
The multidimensional loss function employed in our model is crucial in guiding effective feature learning and enhancing segmentation precision. By jointly optimizing decomposition consistency, reflectance denoising, and luminance fidelity, the network maintains semantic and structural coherence across all stages of the pipeline. This integrated supervision ensures that the enhanced image remains consistent with its physical and visual properties, thereby improving the quality of features provided to the segmentation module. As a result, the segmentation head benefits from inputs with more uniform brightness, reduced noise, and preserved boundary cues, leading to superior pixel-level classification performance. The improvements in the MIoU and MPA scores observed in Table 4 validate the practical significance of this multidimensional optimization strategy.
## 4 Results and Discussion
### Numerical Study on the Performance of Three-Layer Covers with Capillary Barrier
#### 4.4.2 Comparison of the Diversion Length, Lateral Drainage, and Percolation of the Covers
Time-dependent variations in deep percolation across distances revealed interesting insights. Figure 14: Change in diversion length over time with varying infiltration rate. Figure 15: Variation in lateral drainage of the middle layers over time at an infiltration rate of 8.6 mm/day. Unexpectedly, when examining the time-dependent changes in percolation across all models, the typically less permeable clayey sand model showed the poorest performance. Percolation occurred along half of the cover in this area, mainly in the lower elevation region. However, percolation was only observed across a third of the cover in sections with a granular layer. This difference could be attributed to water diversion by the barrier effect and subsequent lateral water drainage through the granular layer after the complete dissipation of the barrier (approximately 12 days). Percolation was observed in all sections featuring granular material in the middle layer only after the capillary barrier effect had completely dissipated. Despite all models with a granular mid-layer performing equally or even better than the clayey sand model, those with medium sand exhibited the best performance. Notably, the quantity of percolating water in all models aligned with the overall system's saturated permeability and remained highly consistent across the range.
# MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model
## 2 Materials and Methods  
In recent years, the continuous enhancement in the spatial resolution of remote sensing images has encapsulated more complex features, significantly raising the bar for image feature extraction. Considering the prowess of the ResNet architecture in deep feature extraction, multiscale processing, and overcoming spectral limitations, this work adopts it as the foundational framework. It integrates a multispectral stacking module and a multi-label classification module, with optimizations conducted from four perspectives: the residual blocks, input and output layers, fully connected layers, and loss functions. Duringthe feature extraction phase, to better learn and represent the intricate features in remote sensing imagery, this paper doubles the number of channels within each residual block and adjusts the threshold to prevent overfitting. To achieve enhanced classification performance, the pixel size of the input layer is modified to 120 \(\times\) 120 to align with the dataset, while the output layer is linked with the fully connected layer, utilizing 19 classes. For an appropriate loss function within multi-label multi-classification tasks, the binary cross-entropy loss function is employed in the TensorFlow design. This choice facilitates easier gradient computation without the need for any encoding transformations of the labels.
## 2 Results and Discussion
### Polyphemols  
Phenolic compounds vary significantly between date species, cultivars, climatic conditions, and ripening stage [12]. Polyphemols are essential bioactive constituents of palm date juice, wine, and vinegar owing to their nutritional and sensory contributions. In palm date juice, polyphenols enhance the functional beverage properties and provide antioxidant and anti-inflammatory benefits [32]. In wine, they impact its color, flavor, astringency, while also protecting cardiovascular and metabolic health [33]. In the process of producing vinegar, especially from fruits and wines, polyphenols are either retained or transformed, thereby augmenting the final product's antioxidant capacity, antimicrobial activity, and health-promoting benefits. The overall shelf life and functional value of these products have improved due to their presence. Studies have reported total phenolic contents ranging from 281.17 mg GAE/L to 641.17 mg GAE/L with significant differences (\(p<0.05\)) noted between homemade and commercial date vinegar [34; 35]. Our samples showed an increase in the total phenolic content from the palm date juice to vinegar, respectively, 138 \(\pm\) 1.95 mg/L in juice, 152 \(\pm\) 1.56 mg/l in the alcoholic derivative, and 181 \(\pm\) 0.78 mg/L in palm date vinegar. Although the literature is not always aligned, generally an increase in phenolic content is observed during fermentation [9; 34; 36]. The increase in total phenolic content is due to a breakdown of cell walls from microbial enzymes releasing bound polyphenols in which solubility and stability is enhanced from the acidification. This phenomenon is still occurring during the submerged fermentation kept under agitation and heating in which polyphenols continue to leach out from solids over time [36; 37]. These polyphenols contribute to the antioxidant properties of date vinegar, which have been associated with potential health benefits such as reducing cholesterol levels and inflammation [21; 32; 33].
## 4 Data Analysis
### SEL and SEFI Correlation  
In Figure 7, the current measurements (blue line) and error information (green dots) are superposed, allowing a time-coherent analysis of the memory behavior under irradiation. In order to support the visualization, a histogram is also presented, providing the average number of errors for a period of 25 ms. Using this information, it is possible to identify a peak of errors (bit upsets) more than 10 times higher than the average during a SEL. This correlation was observed in most SEL events, leading to the conclusion that SEL and error accumulation are associated events. Figure 7 is an example run that was selected from the heavy-ions campaign, but this correlation was also observed in the proton irradiation.  
Figure 6: Histogram of SEL current levels per LET during heavy-ion irradiation. Current levels are the average measured during the SEL period for the runs in standby mode.  
Figure 7: Test run for March C- (1) during heavy-ion irradiation showing the bit upset histogram and the DUT current consumption superposed with synchronized timestamps.  
To further investigate this behavior, we performed an additional analysis, in which errors during and outside SEL events were highlighted to allow further classification. Figure 8 presents an example for this analysis using a heavy-ion test run in dynamic mode. It uses the timestamped bit error results to plot them in a logical bitmap representation. The logical bitmap is a two-dimensional plot of the memory indexing contiguous data addresses following rows, columns, and arrays logical mapping. It is possible to observe that, during SEL events, not only error accumulation is observed, but also larger patterns (column block errors) are formed. This behavior is important to take into consideration since block errors are capable of surpassing mitigation schemes, such as common Error Correcting Codes (ECC). In this context, based on their characteristics and occurrence (95% of test runs), we classified the accumulated column block errors during SELs as SEFI events and the isolated upsets (mainly outside the SELs) as SEU events. Therefore, the SEL not only changes the memory's current behavior but also triggers SEFIs with more potential impact on the execution than most SEUs.
# An Anonymous Offline RFID Grouping-Proof Protocol
## 4 Investigation of [PERSON]'s Protocol  
Literature [19] proposed a grouping-proof protocol based on ECC. The framework of this protocol is shown in Figure 2. Table 1 describes the notations in this protocol.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(P\) & Base point in the elliptic curve group \\ \(k,K\) & The private/public key of reader \\ \((s_{a},S_{a}),(s_{b},S_{b})\) & The private/public key of tag A and tag B \\ \(y,Y\) & The private/public key of verifier \\ \(x(T)\) & The x-coordinate of point \(T\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of notations in [PERSON]’s protocol.  
The protocol has four stages: (1) initialization stage, (2) authentication stage, (3) grouping-proof generation stage, and (4) verification stage. In initialization stage, the server writes the \(\{s_{a},\,K,\,Y\}\) into tag A, the \(\{s_{b},\,K,\,Y\}\) into tag B. The authentication stage is used to authenticate the identity of a reader. It can prevent the reader impersonation attack. In this stage, the reader generates its authentication code \(\{C_{0}=rP,\,C_{1}=rK,\,s=r+kx(C_{1})\}\) and uses it to identify itself to two tags. Then the reader starts the grouping-proof stage:  
1. According to the random number broadcasted by the reader, tag A generates random number \(r_{a}\), calculates \(T_{a,1}\) and sends it to tag B via the reader.
2. Tag B calculates \(T_{b,1},T_{b,2}\) and sends \(T_{b,2}\) to tag A via the reader.
3. Tag A calculates \(T_{a,2}=(r_{a}+x(T_{b,2})s_{a})Y\) and send it to the reader.
4. Finally, the reader passes these data as grouping-proof to the verifier for validation.  
[PERSON]'s protocol uses authentication to solve the impersonation attack, and there are some flaws which need to be pointed out.  
1. The key distribution: in [PERSON]'s protocol, tag A and tag B need to store the reader's public key. If the reader is changed, the new public key needs to be written into all the tags. If the amount of tags is very big, the overhead is too serious.
2. The DoP attack: the reader in [PERSON]'s protocol can not validate the proof and is unable to check the legality of tags. If the reader suffered from DoP attack or some unrelated tags taken part into the proof process, before the proof be sent to the verifier, the failure can not be identified immediately which will reduce the system real-time performance.
3. Communication overhead: the using of authentication stage increases the number of communication times between the tag and the reader, which leads to the additional overhead of communication.  
Figure 2: The [PERSON]’s protocol.
# LDPC-Coded CAP with Spatial Diversity for UVLC Systems over Generalized-Gamma Fading Channel
## 2 CAP-Based UVLC System
### Selection of LDPC Codes  
In this study, we employed a quasicyclic LDPC (QC-LDPC) code based on the IEEE 802.16 standard. Compared to the randomly structured codes, QC-LDPC codes are more advantageous due to lower encoding and decoding complexity, less storage space, more flexibility, and simpler implementation [28]. The output codeword of LDPC code is represented as \(c=(s,p1,p2)\), where \(s\) represents systematic bits, and \(p1\) and \(p2\) are parity-check bits. Detailed techniques for constructing and encoding LDPC can be found in [20; 29]; hence, it is not explained in detail here. The output codewords were mapped to \(M\)-QAM, followed by \(M\)-CAP modulation and transmission through the UVLC channel as described in the section above. At the receiver end, the electrical signal given in Equation (6) was soft-demapped to provide initial bit likelihoods for belief-propagation (BP) algorithm-based LDPC decoder. When the modulated symbols were equiprobable, the log-likelihood ratio (LLR) for the \(i\)th bit \(b_{i}\) was represented as [30]  
\[L(b_{i})=\ln\frac{\sum\
olimits_{b_{i}=0}p(y(t)\big{|}x(t))}{\sum\
olimits_{b _{i}=1}p(y(t)\big{|}x(t))} \tag{16}\]  
where \(p(y(t)\big{|}x(t))\) represents the probability of \(y(t)\) conditioned on \(x(t)\), which is given by:  
\[p(y(t)\big{|}x(t))=\frac{1}{\sigma\sqrt{2\pi N}}\exp\Bigg{(}\frac{\Big{(}y(t)- \eta x(t)Ps\sum\
olimits_{i=1}^{N}\alpha_{i}\Big{)}^{2}}{2N\sigma^{2}}\Bigg{)} \tag{17}\]  
where we used Gaussian approximation in the calculation of symbol reliabilities [13], and assumed that the sum of fading coefficients \(\sum\limits_{i=1}^{N}\alpha_{i}\) was known to the receiver by automatic gain controller due to slow fading.  
LLRs obtained by soft-demapping \(M\)-QAM symbols were fed into the LDPC decoder as a priori inputs. An iterative decoding min-sum algorithm (MSA) [31] based on a soft decision was utilised for LDPC decoding. The MSA is a lower-complexity approximation to the sum-product algorithm (SPA), and can provide better BER performance than the bit-flipping (BF) algorithm based on the hard decision.
## 2 Materials and Methods
### The Process of Selecting Spectral Ranges May Include the following Main Steps  
1. Determination of the survey scale and optical, spectral, and time parameters characterizing the object of study.
2. Definition and selection of spectral ranges.
3. Minimization of the number of working spectral ranges, as well as assessment of practical feasibility on a modern element base, taking into account technical and economic conditions.  
Based on the above, the development of remote monitoring of plants based on multi- and hyperspectral sensors has become an important method for assessing their physiological state, disease damage, and the condition of potato tubers.  
In laboratory conditions, it is advisable to conduct studies of the optical properties and spectral characteristics of potato tubers with the determination of significant differences in the intensity of the reflection spectra of different varieties: 420-470 nm and 860-980 nm with the allocation of clusters of identical images of varieties of the studied biological objects of potato tubers.  
The test results were recorded in an observation log. We repeated the experiments four times when studying the influence of technological parameters of a roller sorting surface on the force impact of the sorting material.  
Using the presented graphical relationships, we can determine a spot of the greatest force impact on the separating product output. The results are helpful in adjusting the constructive parameters of separating surfaces and optimizing the mode and technological parameters. For this purpose, after finding the corresponding spot along the length of the separating surface, we found it necessary to draw a straight line parallel to an axis of ordinates up to the crossing with a chart.
## 2 Materials and Methods
### Analysis Methods  
#### 2.2.1 Estimation of Carbon Use Efficiency  
The carbon use efficiency of vegetation is quantified as the ratio of net primary productivity to gross primary productivity.  
\[CUE=\frac{NPP}{GPP}=\frac{GPP-R_{a}}{GPP}\]  
where:  
_CUE_ represents the carbon use efficiency of vegetation.  
_NPP_ denotes the net primary productivity of vegetation, measured in g C/m\({}^{2}\).  
_GPP_ stands for the gross primary productivity of vegetation, quantified in g C/m\({}^{2}\).  
_Ra_ signifies the vegetation autotrophic respiration, expressed in g C/m\({}^{2}\).
###### Abstract  
Only a specific location can make sensor data useful. The paper presents an simplify belief propagation and variation expectation maximization (SBPVEM) algorithm to achieve node localization by cooperating with another target node while lowering communication costs in a challenging environment where the anchor is sparse. A simplified belief propagation algorithm is proposed as the overall reasoning framework by modeling the cooperative localization problem as a graph model. The high-aggregation sampling and variation expectation-maximization algorithm is applied to sample and fit the complicated distribution. Experiments show that SBPVEM can obtain accurate node localization equal to NBP and SPAWN in a challenging environment while reducing bandwidth requirements. In addition, the SBPVEM has a better expressive ability than PVSPA, for SBPVEM is efficient in challenging environments.  
cooperative localization; belief propagation; expectation maximization; variation inference
# Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles
## 5 Validation
### Technical Details  
#### 5.3.1 Equipment Used for Running the MTRR  
The MTRR, as part of the SWARMs middleware, was deployed in a VMware virtual machine running Ubuntu 14.04 LTS with a Linux kernel version 4.2.0-42-generic. The virtual machine was configured with 2 GB of RAM, 4 cores and 20 GB of hard disk storage (prior tests were carried out with just 1 core). It was run on top of a Windows 10 machine with 16 GB of RAM and 8 i7 cores using VMware Workstation Player 12.  
The MTRR was integrated with the rest of the SWARMs middleware as a Java application, and executed using a Java SE Runtime Environment version 8u101 from Oracle. Also, although the middleware could be run as standalone software, it was decided to run it from the development environment used, which was Eclipse Mars. All the invocations between the middleware components were done through Java method calls.  
The historical database was deployed as a relational database using MySQL Server version 5.5.55, (package version 5.5.55-0 ubuntu0.14.04.1 installed directly from the Ubuntu repositories) and it was deployed in the same virtual machine as the SWARMs middleware (and therefore the MTRR). The required resources to query the SWARMs ontology were deployed as a SPARQL server using Apache Jena Fuseki version 3.4.0, and it was also deployed in the same virtual machine as the SWARMs middleware, with a specific maximum memory allocation of 1200 MB.
## 3 Mathematical Model
### Calculating the \(p\)-Order Gauss-Markov Model Parameters  
#### 3.3.1 Calculating the Order \(p\)  
\(X_{n}\) is a stationary process, and is considered autoregressive of an order \(p\). There exist several methods to estimate the best order \(p\) of an autoregressive model such as the Akaike information criterion (AIC), Akaike Final Prediction Error (FPE), Minimum Description Length (MDL), and the reflection coefficients of a Yule Walker method. We chose to employ AIC method to estimate the best order of the autoregressive model. AIC is used to compare different statistical models as a measure of their relative quality for a given set of data.  
\[AIC\ =\ 2p-\ 2 ln(L) \tag{4}\]  
where \(L\) is the maximum likelihood of the model given the set of data, and reflects the goodness of fit of the model; _i.e._, smaller values indicate worse fit. AIC is based on balancing the goodness of fit of a model with the complexity of the model; _i.e._, the order \(p\). In fact, increasing the order of a model does not necessarily provide a more accurate estimation. The AIC method, while giving preference to models with a better goodness of fit, it simultaneously penalizes models with higher order \(p\), to avoid settling to models with larger \(p\) and less gain in the goodness of fit. In a nutshell, AIC provides a tradeoff between accuracy and complexity.  
For a set of data and a set of autoregressive models with different values of \(p\), the best model is identified as the model that has the minimum AIC value. Figure 5 shows the AIC values for different  
Figure 5: \(AIC\) values for different \(AR\) models, compared for five vehicle trips.  
autoregressive models with different orders, calculated for five different vehicles whose location information were extracted from the TAPASCologne vehicular mobility trace [24]. We can observe that the lowest AIC value for the majority of the vehicles is achieved when the autoregressive model order \(p\) is equal to 4, and that the model achieves an optimal balance between prediction accuracy and model complexity when \(p=2\). However, we will show through simulation experiments that the simplest order, at \(p=1\), is sufficient for prediction. The gain in prediction precision that is achieved by an autoregression model of order 4 is not large enough to justify the complexity cost incurred.