esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantic Multigranularity Feature Learning for
## 4 Experiments
### Results on AID  
In the experiments, we compared SMGFL-Net with the same methods in Section 4.6. The results are shown in Table 6. The first two columns of the table display the evaluated method and its category, and the last three columns show the accuracy of the model that is trained under three training ratio settings--namely, 0.1, 0.2 and 0.3, which specify the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Method** & **Ratio = 0.1** & **Ratio = 0.2** & **Ratio = 0.5** \\ \hline \multirow{2}{*}{Global Methods} & VGG16 [3] & 86.1\% & 90.0\% & 93.4\% \\  & Resnet50 [4] & 90.9\% & 93.6\% & 95.8\% \\  & ResNeXt50 [41] & 91.8\% & 94.0\% & 96.1\% \\ \hline Local Methods & Bilinear-CNN [16] & - & - & 91.2\% \\ \hline \multirow{3}{*}{FGVC Methods} & NTS-Net [9] & 84.5\% & 89.6\% & 93.2\% \\  & DCL [17] & - & - & 94.3\% \\  & Progressive-MG [22] & 90.0\% & 94.1\% & 95.9\% \\ \hline \multirow{3}{*}{RS Methods} & InceptionV3-CapsNet [34] & 89.3\% & 93.7\% & - \\  & VGG16-MSCP [35] & 85.5\% & 89.1\% & - \\  & MGML-FENet [36] & 91.3\% & **94.5**\% & - \\ \hline Ours & SMGFL-Net & **91.9**\% & 93.7\% & 96.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative results on NWPU-RESISC45.  
training set ratio over the entire dataset. The observations are summarized as follows: The proposed SMGFL-Net shows the best performance compared to all of the benchmarks. Specially, SMGFL-Net achieves the best accuracies of 93.3%, 96.2%, and 97.2% with the training ratios of 0.1, 0.2, and 0.3, respectively. Although MGML-FENet achieves the best accuracy with a training ratio of 0.2 on NWPU-RESISC45, SMGFL-Net outperforms it on AID. For the global methods, ResNeXt50 achieves the best accuracy compared with all of the other global methods in this experiment. It is worth noting that, as the backbone network of SMGFL-Net, ResNet50 achieves 89.8%, 91.3%, and 93.7% with training ratios of 0.1, 0.2, and 0.3. Compared with ResNet50, SMGFL-Net achieves 93.3%, 96.2%, and 97.2% with training ratios of 0.1, 0.2, and 0.3. This result demonstrates the effectiveness of the proposed framework. For the local methods, Bilinear-CNN achieves relatively better accuracy compared with the performance on NWPU-RESISC45. For the FGVC methods, Progressive-MG and MGML-FENet also achieve good performance. This shows that multigranularity information extraction is very important in both remote sensing datasets. On the other hand, NTS-Net achieves better accuracy on AID than on NWPU-RESISC45.
## 4 Experimental Setup  
This study examines how the shape of the modulation signal and the sampling frequency influence a G3-PLC transmission when the spreading factor is fixed. Building on the setup in [29], the communication link uses two Microchip PLC360 modems: one as the transmitter, and the other as the receiver joined by a 42 m single phase cable that forms a straightforward point-to-point channel. The signal's bandwidth adheres to the G3-PLC specification, spanning 35 to 91 kHz (CENLEC A Standard), with a center (intermediate) frequency of 63 kHz. The G3-PLC settings are as follows: the modulation is DBPSK, and the use of DQPSK and D8 PSK shows similar results to DBPSK. The total number of frames to send is 3000, the frame size is 65 byte, and the sub-carrier frequency is 1500 kHz.  
Within the same test bed, a DC buck converter serves as the source of electromagnetic interference (EMI). Although such converters typically incorporate filters to suppress EMI, the filter is intentionally omitted here to explore how basic first-order oscillatory current mode impacts G3-PLC performance. Coupling between the converter and the communication circuit is introduced through a capacitance that mimics the parasitic capacitive link described in [29]. The capacitor value is 10 nF. The settings of the spread spectrum modulation are shown in Table 1.
# Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor
## 2 Context-Based Lossless Compression for Sensor Signals  
In this section, we first introduce the basic logic of sequence prediction for digital signals. Then, the general framework of context-based lossless compression algorithms, on which the proposed method of this paper is based, is presented. For mathematical notations, we denote the vectors and matrices by bold lower- and upper-case letters, respectively, e.g., **x** and **W**. Functions are denoted by upper-case letters in calligraphic font, e.g., \(\mathcal{F}\).
## 3 Results of Data Training and Discussion
### Predictive Factors Analysis
#### 3.3.1 Non-Significant Variables  
Several variables, such as \"Age\", \"Training Hours\", \"Previous Injuries\", and others, show no significant association as their \(p\)-values are greater than 0.05. These results indicate that these variables may not be determining factors in the outcome.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Estimate** & **Standard Error** & **tStat** & \(p\)**Value** \\ \hline (Intercept) & \(-1.6796\) & \(1.6205\) & \(-1.0365\) & \(0.29998\) \\ Age & \(-0.0014065\) & \(0.041393\) & \(-0.03398\) & \(0.97289\) \\ Gender & \(0.7634\) & \(0.37307\) & \(2.0463\) & \(0.040728\) \\ Training Hours & \(0.17989\) & \(0.17137\) & \(1.0497\) & \(0.29385\) \\ Training Days & \(0.28663\) & \(0.15229\) & \(1.8821\) & \(0.05982\) \\ Previous Injuries & \(0.33075\) & \(0.57787\) & \(0.57235\) & \(0.56708\) \\ Corrective Treatment & \(0.35246\) & \(0.55783\) & \(0.63185\) & \(0.52748\) \\ Sport with Injury & \(-1.0194\) & \(0.47542\) & \(-2.1442\) & \(0.03202\) \\ Preventive Treatment & \(-0.2304\) & \(0.37792\) & \(-0.60966\) & \(0.54209\) \\ Stress & \(0.019871\) & \(0.007484\) & \(2.6552\) & \(0.007927\) \\ Kinesiophobia & \(0.58079\) & \(0.16835\) & \(3.4498\) & \(0.00056105\) \\ Fatigue & \(0.04619\) & \(0.12905\) & \(0.35792\) & \(0.7204\) \\ Previous Warmup & \(-0.37725\) & \(0.22096\) & \(-1.7073\) & \(0.087765\) \\ Average Hydration & \(0.3888\) & \(0.21644\) & \(1.7963\) & \(0.072445\) \\ Event Day Hydration & \(-0.14376\) & \(0.090605\) & \(-1.5867\) & \(0.11259\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Factors affecting sports injuries.
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 3 Implementation
### Selection of Parameters Tested  
Our experimental methodology relied on several carefully chosen parameters, which we outline below along with their selection criteria:  
* **Iterations:** A range of experimental round sizes were implemented in the environment. These were chosen to validate the results of the tests, and the final outputs predominantly made use of '500,000' key examples--with testing taking place over a period of around 24 h of operation. This iteration count was specified in the input file of the test script.
* **Security Levels:** A range of algorithms were chosen that covered the NIST security levels from 1 to 5, where a broad equivalence of the different algorithms under test was ensured to permit easy comparison.
* **Algorithms:** Three categories of algorithms were included in the testing, all were from the OpenSSL3.5 library as compiled for the Raspberry Pi device under test. The full set can be seen in Table 1.
* Elliptic Curve. Several commonly used ECC algorithms were chosen to enumerate a range of security levels. This was used to baseline the method versus other key generation categories.
* RSA. Another classic algorithm used for key generation. RSA is a more mature solution and still commonly in use. Over the years, the key sizes used have ramped upwards to maintain security as computing power has risen. Several key sizes for RSA were selected in the analysis.
* ML-KEM Post-Quantum Cryptography Technique. This is the currently available key generation technique standardised by the NIST as FIPS 203, and implemented in the April 2025 OpenSSL3.5 library release.
* The ML-DSA algorithm was also included. While this is not explicitly used as a key exchange mechanism, it does perform key generation at equivalent security levels using Lattice techniques, and was therefore a useful yardstick to ensure the broad validity of the results of the ML-KEM method while awaiting the standardisation of HQC and its implementation in OpenSSL for evaluation.
* **Polling Interval:** This is a parameter which was chosen as part of the experimental setup. It is the frequency at which data were recovered by the Windows collector machine from the TC66C meter, and did not impact on the key generation process. The data used for the energy calculations were a cumulative value from the TC66C, so the period chosen here only defined start and stop time granularity and the update period on the screen for the user.
# Secure PBFT Consensus-Based Lightweight Blockchain for Healthcare Application
## 3 Proposed Model
### Computation of Node Trust Value Using Eigen Trust Model  
A reputation-based model trust management system to reduce the number of malicious nodes in the blockchain network, Eigen Trust is used to find the trust value of individual nodes. It considers effectively by computing a unique global trust value (GTp) for each node in the system by considering the transaction history between the nodes. In a blockchain system, to compute the global trust of individual nodes, we are considering the node transaction relationship among various nodes. Every node can show either direct transaction or indirect transaction relationships as shown in Figure 3. Therefore, based on the transaction relationship we have found three distinct types of trust measures, which are direct trust value, indirect trust value, and global trust value.  
1. Direct trust value: If there is a direct transaction between Node and Nodeq, for example in Figure 3 Node1 to Node2, the direct trust value for Nodep is calculated by considering the satisfactory and unsatisfactory transaction history between \(p\) and \(q\) as \(\mathbb{S}pq=\text{Satisfactory}(p,q)-\text{Unsatisfactory}(p,q)\). After certain transactions, we can normalize it as  
\[T_{pq}=\frac{\max(S_{pq},0)}{\sum_{q}\max(S_{pq},0)},\text{ where }q\text{ is all nodes} \tag{2}\]  
2. Indirect trust value: When there is no direct transaction between Nodep and Nodeq, we need to compute indirect trust based on transitive trust, considering direct trust and the transaction history represented in Equation (3)  
\[T_{pq}=\sum_{r}T_{pr}T_{rq},\text{ where }r\text{ is all intermediate nodes} \tag{3}\]  
3. Global trust value: Each Nodep in the blockchain network has unique global trust value GTi based on the current transaction history. The global trust value of Nodep is  
Figure 2: PBFT consensus operation.  
computed using the local trust value of all nodes in the network to Node, global trust of the current node, and trust value of current Nodev created during the last cycle s\({}^{-1}\). The initial global trust value of nodes in the network is GT = 1/n where n is the total number of nodes.  
\[GT_{p}^{s}=\sum_{v}T_{vp}GT_{v}^{s-1}=T_{1p}GT_{1}^{s-1}+T_{2p}GT_{2}^{s-1}+T_{ 3p}GT_{3}^{s-1}+\cdots+T_{np}GT_{n}^{s-1} \tag{4}\]
# The Evaluation of Industry 5.0 Concepts: Social Network Analysis Approach
## 2 Literature Review
### Industry 5.0: Resilience Approach  
As a term related to firms and industry, resilience started to gain attention in 2020, during the COVID-19 pandemic [7]. The challenges that firms faced during this period required them to adapt their business models to newly established circumstances and continue doing business as usual, as well as to go back to their original business models when the circumstances changed to their previous state [48].  
Resilience is improved by implementing digital solutions that contribute to different aspects regarding growth and development, as well as quicker responses to problems [49]. In the context of Industry 5.0, it is expected that firms, supply chains, processes, and business models will become resilient on a daily basis, in order to successfully address both the positive and negative impacts of their environment [35; 47]. Resilience can be achieved by using adaptable technologies, which can improve the quality of the final products [2; 50]. These technologies were developed during Industry 4.0, and will continue to be used in Industry 5.0. The most used technologies are the internet of things and artificial intelligence [29]. Additionally, technologies that will increase firms' resilience and are expected to be used more frequently are collaborative robots, as well as digital twins [4; 51].  
Resilience can be established on an industry level by digitalizing the economy and creating a partnering network between firms, which would bring collaboration to its full potential [50]. Digitalizing the economy by implementing circular economy means that business models are becoming sustainable and more valuable, which contributes to the Industry 5.0 sustainability approach as well [30; 52]. Additionally, firms can improve resilience and sustainability by using renewable energy sources that will contribute to the whole industry [50]. These changes are also significant for the human-centric approach, because they impact the customer's decision when buying products, and they can improve customer loyalty [5; 45].  
The parameters used for measuring resilience in this research are: (1) the use of an internet/network connection in real time for automated data exchange; (2) the use of standardized and detailed work instructions; (3) the use of mobile industrial robots; (4) the use of collaborating robots (co-bots); (5) the implementation of activities raising employees' awareness of data security; (6) the use of software specifically; (7) the use of hardware solutions specifically; (8) the use of organizational measures specifically; and (9) product revamping or modernization.
## 5 Results
### Evaluation of Average Accuracy With and Without Context and Emotions  
Further experiments were conducted to evaluate the system's performance when incorporating emotions, preferences, and context. Table 6 highlights the results for item recommendations using the yoga mat dataset. The findings reveal that the highest accuracy, 97%, was achieved when combining context with preferences. When emotions were also included, the accuracy increased to 100%. These results demonstrate that integrating emotions with context and preferences significantly enhances the system's performance compared with relying solely on context and preferences.
## 2 Materials and Methods
### Evaluation Methods  
#### 2.5.1 Generation of Daily Average SIF  
Daily average SIF offers a more reasonable estimate of photosynthetic activity compared to instantaneous SIF, and our approach involves the computation of a daily correction factor. This paper utilizes daily average SIF to generate a global-scale composite SIF product for comparative validation. The calculation of this factor is dependent on various parameters such as overpass time and solar zenith angle (SZA), among others [28].  
The conversion from instantaneous SIF (SIF(t\({}_{\text{m}}\))) to daily SIF (SIF) average is performed using the following equation:  
\[\overline{\text{SIF}}=\text{SIF}(\text{t}_{\text{m}})\cdot\sec(\theta(\text{t }_{\text{m}}))\int_{\text{t}=\text{t}_{\text{m}}-12\text{h}}^{\text{t}=\text{t }_{\text{m}}+12\text{h}}\cos(\theta(\text{t}))\cdot\text{H}(\cos(\theta(\text{ t})))\text{dt} \tag{6}\]  
where \(\theta(\text{t}_{\text{m}})\) represents the measured solar zenith angle at time t\({}_{\text{m}}\), and the integral is computed in 10-minute time increments (dt). The Heaviside step function H is employed to eliminate negative values of \(\cos(\theta(\text{t}_{\text{m}}))\).
## 2 Related Work
### Delivery Service Sharing (DSS)
#### 2.2.1 Vehicle-Oriented DSS  
Each customer \(c\) can be represented by a tuple \((v_{c}^{p},v_{c}^{d},[t_{c}^{c},t_{c}^{l}])\), where \(v_{c}^{p}\) denotes the pick-up location, \(v_{c}^{d}\) denotes the drop-off location, and the time window \([t_{c}^{c},t_{c}^{l}]\) represents its minimal and maximal possible pickup/drop-off times [17]. In the vehicle-based DSS, each vehicle can only serve one customer at a time, and customers are served in sequence. For the special scenario with the fixed pickup/drop-off time, i.e., \(t_{c}^{c}=t_{c}^{l}=t_{j}\), the vehicle-shareability network is constructed in [18]. In the shareability network, each node represents a customer, and there is an directed edge between customers \(c\) and \(c^{\prime}\) if and only if \(c^{\prime}\) can be served immediately after \(c^{\prime}\), i.e., \(t_{c}+t_{cc^{\prime}}\leq t_{c^{\prime}}\), where \(t_{cc^{\prime}}\) denotes the time distance of traveling from \(c\)'s destination to the origin of \(c^{\prime}\) and delivering \(c^{\prime}\) to its destination. Given the constructed shareabilitynetwork, the Hopcroft-Karp algorithm [19] is utilized to maximize the number of customers served with \(n\) vehicles [6]. Considering the flexible pickup windows \([t_{c}^{e},t_{c}^{l}]\), [PERSON] et al. [7] propose an iterative Backbone algorithm, and, at each iteration, the fixed time is chosen randomly within the time window and the desirable edges are remained. The optimal mixed integer programming is used to return the optimal solution within the remained shareability network. Being aware of the soft constraints of minimizing the delay of the fixed time window, a linear programming-based approximation algorithm is proposed in [20], and a heuristic local search strategy with a diversification updating strategy is used to improve the solution quality iteratively [21]. Compared with existing heuristics for vehicle-oriented DSS with flexible time windows, we propose an approximation algorithm which has theoretical performance guarantee on system throughput. Moreover, we also systemically analyze the customer-oriented sharing scenarios, where multiple customers can be served by a vehicle at the same time.
## 2 Results and Discussion
### Application of the HS-SPME/GC-MS Method to Strawberry Fruit Samples
#### 2.2.6 Alcohols and Acids  
Alcohols, together with esters, furanones, and aldehydes, are the compounds that mainly determine strawberry aroma. Alcohols account for as much as 35% of the volatiles, but normally contribute little to strawberry aroma [42]. In this study, 16 alcohols were detected in all the analyzed samples; 1-hexanol, 1-octen-3-ol, 1-heptanol, (_Z_)-3-hexen-1 ol, (_E_)-2-hexen-1-ol, 1-octanol, and phenyl ethyl alcohol were the major ones. A high alcohol content was maintained in the fresh sample (17.10%), but showed a decrease in percentage in all treatments as follows: microwave (8.24%), natural air-dried (5.91%), oven-dried (60 \({}^{\circ}\)C) (3.46%), oven-dried (45 \({}^{\circ}\)C) (2.18%), freeze-dried (1.15%), and frozen (0.09%) samples. The major alcohol in fresh strawberry was 1-hexanol (14.37%) which displayed a drastic reduction in content in natural air-dried (1.14%), freeze-dried (0.35%), and frozen (0.03%) samples; it was not detected in artificial heat-drying methods (oven and microwave).  
The unsaturated alcohol, (_Z_)-3-hexenol, is an important contributor to the green, leafy aroma in strawberry [32]. This compound was detected in fresh strawberry, as well as frozen and freeze-dried samples, while it was undetectable in the other heat-dried samples, and this is consistent with the literature [43].  
With respect to acids, all the current treatments increased the acid content compared to fresh sample (0.12%); oven-drying (45 \({}^{\circ}\)C) (8.79%) showed the highest acid content, and microwave-dried sample demonstrated the lowest value (0.13%). Different aromas are given by volatile organic acids, but they are minor components of strawberry aroma [32]. The level of hexanoic acid (off-flavor), which contributes to an unpleasant aroma impression [33; 43], was increased in all samples, with the highest value in oven-drying (60 \({}^{\circ}\)C) (0.89%).
# Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement
## 2 Methodology
### Applied Algorithms for Predicting Lung Cancer
#### 2.12.9 Multilayer Perceptron  
Artificial neural networks (ANNs) are inspired by how the human brain uses multiple layers. ANNs can learn patterns and relationships in data by training on examples, allowing them to generalize and make predictions for new situations. One of the most widely used types of ANN is multilayer perceptron (MLP), which is a powerful tool for modeling complex relationships. MLPs create nonlinear models, enabling them to predict outputs based on given inputs [40].  
Lung cancer prediction is typically a classification task (e.g., predicting whether a patient has lung cancer or not). Since tabular data are used in this study, MLP is a suitable choice. Since MLPs consist of multiple layers of neurons, the layers typically involved in the lung cancer prediction model are as follows:  
Input Layer The input layer of multilayer perceptron (MLP) represents the features (columns) in the dataset, as our dataset consists of 12 features. If the dataset contains 16 features, such as age, alcohol consumption, chest pain, and chronic disease, the input layer will have 12 neurons, each corresponding to a specific feature.  
Hidden Layer Hidden layers are responsible for learning the underlying patterns and relationships in the data. Typically, the network has 100 hidden layers, as mentioned in Table 3. The sigmoid activation function is often chosen because it is better for binary classification tasks, and it also helps mitigate issues like vanishing gradients, improving the netlet's ability to learn effectively.
###### Abstract  
The base post-grouting technology is widely used in pile foundation engineering to improve its behavior. For super-long piles, due to the large compression of pile shaft, conventional static load tests cannot effectively test the effect of post-routing at the pile base. This study introduced a novel method to test the effect of base post-grouting of three 88 m long piles. A total of three test piles were grouped at the bottom of the piles. The grouting amount of the No. 1 pile was 500 kg, the No. 2 pile was 250 kg, and the No. 3 pile was 0 kg. The transmitted wave method in the pile and dynamic penetration method were used in combination to quantitatively evaluate the effect of base post-grouting on the soil below the pile base. The influencing range of base post-grouting was firstly evaluated by the transmitted wave method in the pile, and the strength of the soil before and after grouting under the same pile base was measured by the dynamic penetration method. The test results showed that: the depth of the pile base reinforcement was 2 m for No. 1 pile, and 1 m for No. 2 pile, which was consistent with the grouting amount; the elastic modulus of the soil below the pile base after base post-grouting was about twice that of the soil before base post-grouting; the strength of the soil below pile base was largely improved after grouting; and the bearing capacity of the base soil layer increased from 35.5% to 41.7% when the grouting amount increased from 250 kg to 500 kg.  
Keywords:base post-grouting; super-long pile; transmitted wave method; dynamic penetration test, field test +
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