esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion  
### Optimization Results  
Table 6 shows the test results based on the optimal values (D1, D2) predicted by the two regression models. Both NN and NPR predicted temperatures above 35 \({}^{\circ}\)C from TC1 to TC4, but in the actual experimental results, there was no result exceeding 35 \({}^{\circ}\)C in the case of the NN model. However, in the NPR result, it can be judged that the temperature of 35 \({}^{\circ}\)C or higher was measured in the actual test from TC1 to TC3, and it is consistent with the constraints condition of this optimization.  
The relative errors of the two regression models were compared. The average relative error (predict vs. exp.) occurred about 1.5% in the NN models TC1 to TC4, but the relative error in the NPR model was 0.6%. In Figure 7, the goodness-of-fit results showed that the NPR model had a higher fit, which was also confirmed by the experimental results.  
In the case of TC4 temperature, which is the objective of this optimization, the NPR model showed better results in this optimization because a higher temperature was measured in the NPR model (34.99 \({}^{\circ}\)C) compared to the NN model (34.57 \({}^{\circ}\)C).
## 3 Results and Discussion
### Effect of DES-Lignin on Membrane Performance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Membrane**} & **PES** & **Lignin Content** & **Conact Angle** & \multicolumn{3}{c}{**Pure water Flux**} & \multirow{2}{*}{**Permeability**} & **Leakage** & **Rejection (\%)** \\  & **(\%)** & **(\%)** & & **(\(\downarrow\)) bar** & **2 bar** & **(\(\downarrow\)/m’h bar)** & **(mg/L)** & **(PEG 35 kDa)** \\ \hline \#1 & 20 & - & \(51.9\pm 0.4\) & \(67.7\pm 1.8\) & \(137.9\pm 0.7\) & \(210.0\pm 0.9\) & 74.9 & 0.9 & 85.7 \\  & & & \(75.9\pm 1.7\) & \(151.1\pm 0.5\) & \(233.3\pm 0.8\) & 0.6 & 82.8 \\ \hline \#2 & 20 & 0.25 & \(486.6\pm 0.3\) & \(85.9\pm 2.0\) & \(71.2\pm 1.7\) & \(254.8\pm 1.4\) & & 0.5 & 88.7 \\  & & & \(78.8\pm 1.9\) & \(154.5\pm 1.6\) & \(235.8\pm 0.9\) & & 0.4 & 84.9 \\ \hline \#3 & 20 & 0.5 & \(94.2\pm 1.7\) & \(189.9\pm 0.7\) & \(286.0\pm 0.7\) & 91.5 & 0.4 & 75.7 \\  & & & & \(86.4\pm 0.9\) & \(181.2\pm 0.8\) & \(260.8\pm 0.7\) & & 0.4 & 83.6 \\ \hline \#4 & 20 & 1 & \(45.6\pm 1.2\) & \(105.6\pm 1.3\) & \(206.0\pm 1.0\) & \(309.4\pm 0.9\) & 96.0 & 0.6 & 86.6 \\  & & & \(104.6\pm 1.4\) & \(183.7\pm 0.4\) & \(288.5\pm 0.5\) & & 0.8 & 81.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The effect of DES-lignin on the bulk porosity, DPPH and ABTS antioxidant activity of fabricated membranes.
## 3. Results
### Nutritional Characteristics
#### 3.5.2 Fatty Acids Profile  
Table 10 displays the content and composition of fatty acids found in the lipids of CSPSP. The total fatty acid content per 100 g of CSPSP amounted to 4804.2 mg, equivalent to 89.3% of the lipid content. Concerning the composition of fatty acids in CSPSP, polyunsaturated fatty acids were the most prominent (2210.9 mg, 46.3%), followed by saturated fatty acids (1697.9 mg, 35.3%), with monounsaturated fatty acids (895.4 mg, 18.5%) having the lowest representation. The primary fatty acids present per 100 g of CSPSP were 16:0 (604.8 mg, 12.5%), 18:1n-9 (843.4 mg, 17.5%), 20:5n-3 (457.7 mg, 9.9%), and 22:6n-3 (1001.0 mg, 20.8%).  
Additionally, 22 fatty acids (C12:0 to C22:6n-3) were identified in raw squid, whereas CSPSP contained 29 fatty acids (C6:0 to C22:6n-3). Among these, CSPSP exhibited a high content of fatty acids, with C22:6n-3 (docosahexaenoicacid, 1001.0 mg/100 g), C18:1n-9 (oleic acid, 843.4 mg/100 g), and C16:0 (palmitoleic acid, 604.8 mg/100 g) ranking highest. Notably, the polyunsaturated fatty acid (PUFA) content in CSPSP was 46.3% (2210.9 mg/100 g), surpassing that of raw squid by 244.4%. Additionally, the \(\omega\)-3 fatty acid content in CSPSP exceeded that in raw squid by 169.3%.
## 4 Experimental Results and Analysis
### The Experiment of Attention Mechanism  
To demonstrate the superiority of the attention mechanism, the ECA, I-SKNet, and ResNet networks --without any attention mechanism -- were compared and trained separately. The corresponding accuracy results are presented in Table 4. The accuracy reported is the average accuracy across 12 transfer learning tasks.
# Improved Safety Analysis Integration in a Systems Engineering Approach
## 5 Improved System Behavior Based on DFT Recommendation
### Step 3: Automatic Generation of DFT from SysML Diagrams and Redundancy Profile
#### 5.1.2 Redundancy Terminology  
In the world of engineering, redundancy is known as the duplication of components or functions of a system to enhance its availability and/or reliability [31]. In general, there are three types of active redundancy strategies: active, standby, and mixed redundancy. The active redundancy is used when all redundant components are continuously powered together from time zero and the load is shared. Standby redundancy is used when redundant components do not start operating until the primary component fails. Finally, mixed redundancy is the combination between active and standby redundancy. It is used to improve the reliability of the system [33].
# Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover
## 4 Proposed Model
### Matching Game  
A matching games framework [25] is a suitable tool which can solve a self-organization BSs and UEs association approach as defined by Equation (7).  
Definition 1: The UEs and BSs matching game is characterized by two arrangements of players: UEs \(N\) and BSs \(M\), and the preference relations of every UE (\(\succ_{n}\)) and BS (\(\succ_{m}\)); these allow UEs \(n\in N\) and BSs \(m\in M\) to give preference over one another, to rank the BSs in \(M\) and UEs in \(N\), respectively.  
The function \(\mu\) will be the result of the UE and BS matching game, which assigns each UE \(n\in N\) to a BS \(m=\mu(n)\), \(m\in M\). The preference relation \(\succ\) assigns a binary relation between UE in N and BS in M. Consequently, for any UE k, the preference relation \(\succ_{n}\) is determined over a set of BSs \(M\). Similarly, for any BS m, the preference relation \(\succ_{m}\) is determined over a set of UEs \(N\). Let \(\mu,\mu^{{}^{\prime}}\in N\times M\) two matchings, with \((m,n)\in\mu\) and \((i,n),(m,j)\in\mu^{{}^{\prime}}\). The preference relations \(\succ_{n}\) for any UE n and \(\succ_{m}\) for any BS m are respectively described over the sets of BSs \(M\) and UEs \(N\) thereby, for any two BSs \(m,i\in M^{2},m\
eq i\), and two UEs \(n,j\in N^{2},n\
eq j\), they are respectively:  
\[\begin{split}(m,\mu)\succ_{n}(i,\mu^{{}^{\prime}})& \Longleftrightarrow U_{mn}(r^{(\cdot)}_{mn},\mu)>U_{in}(r^{(\cdot)}_{in}, \mu^{{}^{\prime}}),\\ (n,\mu)\succ_{m}(j,\mu^{{}^{\prime}})& \Longleftrightarrow U_{mn}(r^{(\cdot)}_{mn},\mu)>U_{mj}(r^{(\cdot)}_{mj},\mu^{ {}^{\prime}}).\end{split} \tag{8}\]  
The current matching as defined in Equation (8) proves that the preferences of each UE (BS) will be affected by the set of BSs \(M\) (UEs \(N\)).  
Remark 1: The proposed matching game has externalities, where the preference values of UE depend not only on the actual number of UEs served by the BS that they are matched with, but also on the other UEs asking for a matching, at the same time with the same BS.  
In other words, for a UE and BS association \((m,n)\in\mu\), the interference generated from other upcoming associations \((i,j)\in\mu,(m,n)\
eq(i,j)\) considerably affects its uplink and downlink received SINRs in Equation (2), Equation (3), and long-term rates in Equation (4). Moreover, externalities are used to depict the dynamic effects of these outer consequences on the execution of each UE and BS association. To manage the externalities, we incorporate a suitable iterative step in Algorithm 1 based on dismissing-matching.
## 5 The MRLG Routing Protocol  
In this section, we introduce the Mobile-sink Routing for Large Grids (MRLG) algorithm. Although the greatest performance benefits of this routing protocol are more noticeable in large WSNs following a grid topology, it is able to operate in WSNs of any size or topology.  
MRLG belongs to the reverse-tree family of protocols, similarly to the CTP protocol [33]. Although both protocols are of the distance-vector type and share a somewhat similar route creation process based on sink announcements, they differ significantly in terms of route costs and, more important, routing maintenance.  
Both MRLG and CTP rely on periodic sink broadcasts to announce its presence to neighbor sensors. With CTP, the sink announces its presence with a moderate/large periodicity (typically every 30 seconds), thus being more appropriate for static sinks. With MRLG, the sink announces its presence quite more often, usually every second, to improve responsiveness to mobility. In particular, the procedure followed by MRLG is the following: sensor nodes receiving the sink messages will detect its presence and check whether they were near the sink before. If not, they will update their routing table and notify other sensor nodes about the topology change by propagating the new hop count towards the sink. To avoid too much load, topology update messages will not be propagated throughout the network whenever a node verifies that the uphill node's topology remains the same. Thus, on average, only about half the sensors in the WSN will actually propagate topology update messages, contrarily to CTP where all nodes are involved.
# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 6 Discussion
### Limitations and Future Work  
While our system demonstrates good performance and significant application potential, certain limitations remain. For instance, our method is still limited by inaccurate semantic masks. Future work will focus on selecting a CNN tailored for this task and optimizing the data association process to reduce the impact of inaccurate semantic masks. Furthermore, the system is not designed to handle dynamic objects, as it primarily focuses on navigation map construction for static environments. Although dynamic feature point removal and path planning for dynamic objects represent two distinct research areas, incorporating these capabilities into our system would enhance its performance in real-world applications; thus, we will prioritize research in these areas. Additionally, since the mapping system and motor control of our AGV are self-developed, the navigation system is relatively straightforward to debug within our framework. However, external users may face challenges in utilizing our program. To address this, we plan to develop a Robot Operating System (ROS) version of the system, which will provide a more versatile framework, facilitating easier integration and deployment.
## 4 Evaluation
### Experiment Setup  
#### 4.1.1 Testbed and Resources  
The FL simulation was conducted using two client nodes over three training rounds with the CIFAR-10 dataset [42]. Each client node operated independently, processing data locally and updating model parameters without transmitting sensitive information. This decentralized approach ensures data privacy and security by eliminating the need for centralized data storage, reducing the risk of cyberattacks. The experiments utilized a Tesla T4 GPU, featuring a 1.59 GHz clock rate, 40 cores, and 16 GB of memory with a maximum bandwidth of 300 GB/s. The GPU's computational capabilities include 8.1 TFLOPS for FP32 and 65 TFLOPS for FP16 operations, providing strong performance for machine learning  
Figure 4: Workflow of the proposed framework for blockchain-assisted FL with digital twins in IIoT, depicting preprocessing stages, FL, blockchain validation, and XAI integration for interpretability.  
tasks. These resources and configurations supported efficient and privacy-preserving FL simulations.  
The experimental setup aligns with the proposed blockchain-assisted FL framework's objectives by simulating a distributed IIoT environment. Using two client nodes and three training rounds ensures a balance between computational efficiency and model convergence while preserving data privacy. The CIFAR-10 dataset [42] emulates the heterogeneous data distributions typical in IIoT scenarios. The Tesla T4 GPU, with its high memory bandwidth (300 GB/s) and optimized FP32/FP16 performance, supports efficient and scalable FL simulations. This configuration demonstrates the framework's feasibility for privacy-preserving and resource-efficient IIoT applications.
## 5 Numerical Results
### Spectral Separation Analysis Using the Spectral Separation Coefficient  
The self-SSCs of the already broadcast CDMA waveforms in the upper L-band range from \(-61.9\) to \(-73.1\) dB/Hz, assuming a \(51.150\) MHz two-sided reception bandwidth \(B\). The SSCs between the civilian/OS signals range from \(-67.9\) to \(-68.3\) dB/Hz. The SSCs between the authorized signals range from \(-72.4\) to \(-88.2\) dB/Hz. Table 2 reports the expected SSCs for the currently present CDMA waveforms and the E1D signal candidates with the respective frequency offsets. The signal candidates E1D-a/b/c/g would interfere the most with the BPSK-R(10) waveform as their spectra have a significant overlap. In terms of the SSC, an introduction of the E1D-d/e/f would cause less interference. Among those, the CBOC-modulated E1D-f candidate performs the poorest. Among all investigated candidates, the E1D-e variant would cause the least interference with the already present CDMA waveforms.
## 1 Introduction  
Recent advances in remote sensing are providing numerous opportunities in almost every branch of environmental research. The civil fields of application for multi-spectral remote sensing instruments in Earth observation are diverse, spanning from the monitoring of forests, oceans or urban areas over agricultural applications to the exploration of natural resources, _etc._ A major prerequisite for a sufficient analysis of Earth observation data is the provision of information that is free from external influences and disturbances [1,2]. One possible cause of data gaps is cloud cover. Cloud cover is recognized as a source of significant loss of information and data quality by many scientific studies [3, 4, 5, 6, 7]. The existence of clouds hinders the extraction of meaningful information because they are a considerable source of uncertainty with regard to the application of any algorithm aiming at the retrieval of land surface parameters. In order to overcome the problem of cloud cover in Earth observation data, the reconstruction of areas beneath clouds can be regarded as a fundamental research topic, especially concerning the visible and infrared regions of the electromagnetic spectrum. With the growing availability of multi-spectral, hyperspectral and microwave satellite sensors, opportunities for the analysis of complementary data sources arise. Therefore the task of reconstruction of cloud-obscured pixels in multi-spectral images may be regarded as an issue of synergy between these complementary sources.
# CETD: Counterfactual Explanations by Considering Temporal Dependencies in Sequential Recommendation
## 4 Experiments
### Results  
#### 4.2.1 Fidelity  
Table 1 presents a comprehensive summary of the best results obtained by all models on the two datasets. The information contained in Table 1 allows for several observations.  
On both datasets, our counterfactual explanation model CETD generates explanations for most of the recommended items, whereas the association explanation approach can only offer explanations for a considerably smaller number of recommendations. This is due to the number of input-output pairs being too small to match the global rules with individual interactions and recommendations, thus greatly constraining the flexibility of the association rule model. In contrast, CETD has the capability of generating numerous counterfactual histories, enabling effective causal rule learning, and allowing the extraction of counterfactual explanations beyond the constraints of the original limited data. Meanwhile, our model CETD outperforms the baseline CR-VAE; CETD can capture both long-term preferences and short-term behavior for a user's real history when creating counterfactual histories, which can generate more counterfactual sequences with temporal dynamics. Furthermore, these counterfactual histories can better help the causal rule learning model learn causal dependencies. Although the improvement in our model CETD over CR-VAE is not remarkable, CETD will still boost the fidelity of the explanations due to the huge number of items and users in the real world.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Datasets**} & \multicolumn{4}{c}{**MovieLens100k**} & \multicolumn{4}{c}{**Amazon**} \\ \hline
**Models** & **FPMC** & **GRU4 Rec** & **NARM** & **Caser** & **FPMC** & **GRU4 Rec** & **NARM** & **Caser** \\ \hline AR-conf [40] & 0.3160 & 0.1453 & 0.4581 & 0.1569 & 0.2932 & 0.1449 & 0.4066 & 0.2024 \\ AR-sup [40] & 0.2959 & 0.1410 & 0.4305 & 0.1569 & 0.2949 & 0.1449 & 0.4031 & 0.1885 \\ AR-lift [40] & 0.2959 & 0.1410 & 0.4305 & 0.1569 & 0.2949 & 0.1449 & 0.4031 & 0.1885 \\ \hline CR-VAE [9] & 0.9650 & 0.9852 & 0.9714 & 0.9703 & 0.9511 & 0.9721 & 0.9791 & 0.9599 \\
**CETD** & **0.9873** & **0.9968** & **0.9947** & **0.9915** & **0.9762** & **0.9906** & **0.9918** & **0.9831** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of model fidelity. CR-VAE and our model CETD are tested under \(k=1\) (the number of candidate counterfactual explanations). The bold scores in each column represent the best results, while the underlined scores indicate the best results of the baseline.
# Voting-Based Contour-Aware Framework for Medical Image Segmentation
## 1 Introduction
CL has shown that it can avoid bad local minima and improve model performance and generalization in a variety of tasks, such as classification [35; 36; 37], detection [38], and medical image segmentation [39; 40; 41]. The core of most CL strategies is scheduling the training samples. Ranking functions were designed to improve the performance of the femur fracture classification using prior clinical knowledge and the uncertainty scores of model predictions [35]. An attention-guided curriculum learning framework [36] grouped the data using the radiologist-assessed disease severity and fed those subsets into the CNN in a severe to mild order. In this task, it was easier for a CNN to learn severe samples, which have obvious symptoms, while moderate and mild samples without obvious symptoms were more likely to be ambiguous for a CNN. Furthermore, high-confidence samples and heatmaps were utilized to guide model learning in the next iteration. Uncertainty could also be used to measure the difficulty of the samples. In [37], the small loss samples were deemed clean samples and first fed into the network for learning as simple samples. The authors proposed an online uncertainty sample mining strategy to solve the noisy labels in the skin lesion classification task, which selected the high uncertainty samples and stopped these data from updating the model. A cardiac MR motion artifact detection framework [38] employed a data augmentation based on k-space artifact creation and an easy-to-hard batching strategy based on increasing synthetic artifact severity. A CASED framework [40] proposed an adaptive sampling strategy that favors the predicted false samples for the current model. A sample selection stage [41] was proposed to filter the clean samples by measuring the uncertainty of model predictions. Medical images mainly have texture and shape features, and a meaningful learning order can be beneficial to improve the performance of networks. Considering those contexts, we aimed to study whether the shape features and a meaningful learning order could improve the performance of models for the automatic segmentation of medical images. During the inference, we only need to feed the images into three networks and then average their predictions, so it is convenient to implement this strategy. In this study, our contributions are summarized as follows:  
1. We proposed a novel two-stage framework for medical image segmentation. 2. We developed the contour-aware CNN and employed the voting strategy in our joint optimization framework. 3. We validated our framework using two public datasets, abdomen MRI (CHAOS Challenge) and hippocampus MRI (the Medical Segmentation Decathlon), and could achieve comparable performance compared with other related methods.
## 4 Problem Formulation
### Quality of Service (QoS) Constraints
#### 4.1.1 Primary User Interference  
Interference of PU transmission occurs as a result of interruption from an SU. Interference of PU may occur in case of mis-detection from an SU or in case of collision i.e., when a PU finds an SU on its channel. Thus, PU interference should be a function of both \(p_{m}\) and \(p_{col}\). Interference is defined as the ratio of average PU interference time to the average ON time \(\mu_{on}\) and is given by  
\[I_{PU}=\frac{L_{S}}{R}\left[\frac{p_{m}\left(p_{on}+p_{off}e^{-\frac{L_{S}}{R_{ plan}}}\right)+p_{col}p_{on}}{p_{m}+p_{col}}\right]\frac{1}{\mu_{on}}. \tag{12}\]
## 6 Conclusions  
In this study, we proposed a phenology-GPP method to classify crops for the estimation of source intensity in farmland NSP sensitive areas, thereby solving the problem of differences in NSP intensity distributions caused by different crop types, multiple cropping, and crop growth. Sentinel-2 high-resolution remote sensing images were used to extract the temporal and spatial features of crops. First, the decision tree classification method was used to identify the cultivated land in the Yuecheng Reservoir catchment area. Then, the distributions of multiple crops, winter single crops, and summer single crops were identified using the second difference method combined with month constraints. The overall accuracy of crop mapping is 85% and the Kappa coefficient is 0.8. Because crop growth is not considered in traditional models, MODIS-GPP data were used to simulate crop growth and modify the parameters of the existing model. Finally, the spatial distribution of NSP intensity in the Yuecheng Reservoir catchment area was calculated. Compared with the traditional model, the improved model highlights the differences in the spatial distributions of TN and TP, and it also provides a source intensity estimation result that is closer to actual values because it ensures the precise simulation of farmland status. This provides a reliable decision-making basis for controlling NSP and managing farmland, and it is conducive to more targeted and efficient management and mitigation strategies.  
Conceptualization, M.L., T.W. and S.W.; Data curation, M.L.; Formal analysis, M.L.; Funding acquisition, T.W. and S.W.; Investigation, T.W.; Methodology, M.L.; Project administration, M.L.; Resources, T.W.; Software, M.L.; Supervision, T.W., S.W., S.S. and Y.Z.; Validation, M.L.; Visualization, M.L.; Writing--original draft, M.L.; Writing--review and editing, M.L., T.W. and S.W. All authors have read and agreed to the published version of the manuscript.  
Research grants from the Specially-Appointed Professor program of Jiangsu province and National Natural Science Foundation of China: 42141007; Inner Mongolia Autonomous Region Science and Technology Achievement Transformation Special Fund Project: 2021 CG0045.  
The authors declare no conflict of interest.