esa-sceva/satcom-chunk-collection huggingface dataset:
# Discretly Exploiting Inter-Session Information for Session-Based Recommendation
###### Abstract  
Limited intra-session information has constrained the performance of early Graph Neural Network (GNN)-based session-based recommendation (SBR) models. To address this issue, researchers have increasingly incorporated inter-session information to improve next-item prediction. Although such additional information can provide valuable cues, it may also introduce undesirable interference. However, existing approaches often fail to effectively extract relevant information while filtering out extraneous interference, largely owing to incomplete modeling of inter-session dependencies. Specifically, inter-session connections have been examined merely at the item level, overlooking more intricate distinctions and associations between sessions. Moreover, current methods rely exclusively on similarity as the metric for evaluating inter-session dependencies, overlooking their multidimensional complexity. In this work, we propose a GNN-based SBR model that discreetly exploits inter-session information (DEISI). To overcome the first limitation, we introduce factor-level inter-session dependencies using disentangled representation learning, enabling the capture of finer-grained interactions. To address the second limitation, we design a novel metric named \"stability\" to complement similarity, providing an additional perspective on inter-session relationships. Consequently, DEISI constructs more detailed and reliable inter-session dependencies. Extensive experiments on three benchmark datasets demonstrate the superior performance of DEISI over state-of-the-art models.  
session-based recommendation; inter-session information; disentangled representation learning; contrastive learning +
Footnote †: journal: Applied sciences
# Mobility Prediction of Mobile Wireless Nodes
## 2 Mobility Models
### RSSGM  
The RSSGM model is a relatively new model created to mimic human movement that has a recurrent pattern and self-similarity behavior. This model relies on the Gauss-Markov mobility model to gain temporal dependencies and random characteristics. This addition guides the direction of nodes' travels to predefined places, called centroids, that are regularly visited. Furthermore, the RSSGM model provides self-similarity movement simulating real human mobility [1]. Figure 1c shows the behavior of the RSSGM model.
## 2 Related Work
### Overview of Wireless Radio Link Models
**(a)** Boolean disk model. **(b)** Shadowing model. **(c)** Probabilistic model. [PERSON] _et al._[19] concentrate on estimating the link quality between pairs of sensors. Received signal strength is chosen as the link quality indicator. They use dimensionality reduction technique such as SVM to understand the topology of the network and identify potential bottleneck of the network. [PERSON] _et al._[16] establish a radio model called the Radio Irregular Model (RIM) with empirical data obtained from the Mica2 platform. RIM takes into account both the non-isotropic properties of the propagation media and the heterogeneous properties of devices. With this model, they find solutions to improve the communication performance in the presence of radio irregularity. Most important to our work is the analysis of transitional region in [13]. Different from empirical models which require specific radio and environment conditions, the analytical model in [13] has a general methodology and can be used for a number of different configurations and hardware designs. The incorporation of different parameters in the link model greatly facilitates our analysis of network connectivity in presence of unreliable and asymmetric links. Detailed description of the link model we use can be found in Section 3.3.
## 4 Coverage-Preserving Control Scheduling Scheme
### Fault-Tolerant Backbone
If \(u\) is the direct neighbor of any dominated node, \(u.dom\) should be updated by subtracting the number of neighbor node in dominating set. Otherwise, \(u.dom\) can keep constant. Once the node is \(k\)-dominated or is selected as the dominated node, \(u.dom\) will be set to zero. \(u.SUM\) is the sum of the \(dom\) of all the neighbors around the node \(u\). Algorithm 2 is described as follows:  
```
Input: Graph \(G=(V,E)\), integer \(k\) Output: A two-connected \(k\)-dominating set \(C\)
1. for each node \(\
u\in V(G)\) do
2. color \(\
u\) as white;
3. \(v.dom=k\), \(I=\varnothing\), \(W=V(G)\);
4. broadcast \(HELLO\) message and find all neighbor nodes;
5. restore the neighbor information (ID, State_color) to the local \(neighborLIST\);
6. update \(pathLIST\);
7. end for;
8. for each node \(\
u\in V(G)\) do
9. for each node \(u\in neighborLIST(\
u)\) do
10. if \(u.color\) = white and \(u.SUM>MAX\_ID\) then
11. \(v.dom--\);
12. \(MAX\_ID=id(u)\);
13. color \(u\) as black, and set \(I=I\cup\{u\}\), \(W=W-\{u\}\);
14. end if;
15. end for;
16. end for;
17. while W \(\
eq\varnothing\) do
18. \(C=I\cap W\);
19. for each \(c\in C\) do
20. suppose \(c\) is \(k\)-dominated node from set \(I\);
21. if \(dist(c,MAX\_ID)<r_{c}\) && \(|\)\(neighborLIST(c)\)\(|\geq k\) then
22. pop out the vertex \(c\) from \(I\), and set \(D=\{c\}\);
23. else
24. \(C=C-\{c\}\);
25. end if;
```  
**Algorithm 2**Constructing two-connected \(k\)-dominating sets for fault-tolerant backbone
## 3 The Mesotheliomagenic Activity of CNT: A Remaining Issue  
### Mesothelioma and Particles  
Malignant mesothelioma (MM) is a cancer affecting the mesothelium, a layer of squamous cells covering the serous cavities of the body (pleura, peritoneum, pericardium) and protecting the organs they contain (lungs, peritoneal organs, heart). The most commonly affected tissue is the pleura and peritoneum; this is referred to as pleural and peritoneal malignant mesothelioma [40,41].  
MM is an uncommon cancer and most of the cases are due to asbestos exposure [42]. In 1997, all asbestos fibers were classified as carcinogenic to human by IARC (International Agency for Research on Cancer, monograph 100C) because they are inhalable, poorly soluble and can migrate from the lungs to the pleura or peritoneum, directly or through the lymphatic system [42]. These low-degradable fibers trigger MM and other terminal pathologies such as pulmonary fibrosis and bronchial cancer in animal and human [43].  
MM can occur more than 40 years after exposure. Considering the use of asbestos until the 1980s and the long latency period of the disease, a peak incidence is expected for 2020 [44]. Unfortunately, patients often reach the final stage of the disease when they are detected making the prognosis of MM very poor (12-15 months) [45,46]. Treatment with conventional therapies is not effective. The classical clinical management is chemotherapy based on platinum salts (alkylating agents) and Pemetrexed (anti-metabolite), which prolongs the patient survival by only 15 months. Debates also remain regarding other multimodal approaches as surgery and radiation [47,48].  
Some CNT have been incriminated as being responsible for MM because their physically similarity to asbestos fibers. Several in vivo studies demonstrated that the long and straight multi-walled (MW) CNT-Mitsui-7 are indubitably mesothelioma (see below) [49,50]. In 2014, IARC debated on the carcinogenicity of CNTs (monograph 111) and the consulted experts classified CNT-7 in Group 2b, i.e., as possibly carcinogenic to human [51,52]. All other CNT (single-walled or multi-walled nanotubes) were classified in group 3 (not classifiable as to their carcinogenicity to human) [53].
## 4 Evaluation and Benchmarking
### Edge Data Processing Evaluation
Table 6 depicts the processing rates achieved by each platform in a number of messages processed per second. Evidently, the Tergo nodes, those based on the Raspberry Pi single-board computer, achieved the lowest processing rate among the three platforms considered. Interestingly, the processing rate on average reached 15.36 messages per second, which although low, was still higher than the message generation rate of the experiment. In this sense, it was safe to conclude that the processing power of the Raspberry Pi was enough to handle the load. However, given that the message generation rate used in the real-world deployment was about 20 times lower than the one used in the experiments, we concluded that the processing power provided by this low-power single-board computer was adequate. We were therefore confident that a Raspberry Pi could be used to handle more than 20-30 smart meters. On the other hand, the other two platforms considered, that is the Tergo Zotac and Tethys nodes, had a much larger processing rate and could, therefore, accommodate a much higher number of sensor metering devices.
# Monitoring of Structures and Infrastructures by Low-Cost GNSS Receivers
## 3 The Experience
### Results of Relative Motion
On the other hand, in the horizontal coordinates \(X\) and \(Y\), there now appears a quite clear periodic signal with a daily period and a mean oscillation of about 4 mm; this will be discussed in Section 4.
## 2 Results and Discussion
### Antioxidant Activity  
#### 2.4.1 DPPH\(\bullet\) Scavenging Activity  
DPPH\(\bullet\) with a single electron exhibits deep violet and shows maximal absorbance at 517 nm in ethanolic solution, and the absorbance decreases gradually while the free radicals are scavenged by accepting an electron or hydrogen radical and the color of the solution changes from deep violet to light yellow at the presence of a proton-donating substance [32]. As shown in Figure 5A, MCPE-A, MCPE-B, and MCPE-C showed moderate DPPH\(\bullet\) scavenging activities and there was also a positive correlation between the concentration and the radical-scavenging activity. The half elimination ratio (EC\({}_{50}\)) values of MCPE-A, MCPE-B, and MCPE-C were 3.73, 1.87, and 2.30 mg/mL, respectively, and MCPE-B exhibited the highest radical scavenging ability among three isolated peptides, but its activity was still lower than that of the positive control of ascorbic acid at the same concentration. The EC\({}_{50}\) of MCPE-B was lower than those of peptides from protein hydrolysates of loach (Pro-Ser-Tyr-Val (PSYV): 17.0 mg/mL) [33], blue mussel (Phe-Leu-Asn-Glu-Phe-Lue-His-Val (FLNEFLHV): 4.950 mg/mL) [16], blueinf leather jacket (Trp-Glu-Gly-Pro-Lys (WEGPK): 4.438 mg/mL; Gly-Val-Pro-Leu-Thr (GVPLT): 4.541 mg/mL) [8], grass carp skin (Gly-Phe-Gly-Pro-Leu (GFGPL): 2.249 mg/mL; Val-Gly-Gly-Gly-Arg-Pro (VGGRP): 2.937 mg/mL) [34], salmon pectoral fin (Thr-Thr-Ala-Asn-Ile-Glu-Asp-Arg-Arg (TTANIEDRR): 2.503 mg/mL) [35], and skate cartilages (Phe-Ile-Met-Gly-Pro-Tyr (FIMGPY): 2.60 mg/mL; Gly-Pro-Ala-Gly-Asp-Tyr (GPAGDY): 3.48 mg/mL; Ile-Val-Ala-Gly-Pro-Gln (IVAGPQ): 3.93 mg/mL) [24]. However, the EC\({}_{50}\) of MCPE-B was higher than those of peptides from protein hydrolysates of Chinese leek (Gly-Ser-Gln (GSQ): 0.61 mg/mL) [36], blue mussel (Pro-Ile-Ile-Val-Tyr-Trp-Lys (PIIYVWK): 0.713 mg/mL; Pro-Tyr-Ser-Phe-Lys (PYSFK):  
Figure 4: Mass spectra of Gly-Ala-Glu-Arg-Pro (MCPE-A) (**A**); Gly-Glu-Arg-Glu-Ala-Asn-Val-Met (MCPE-B) (**B**); and Ala-Glu-Val-Gly (MCPE-C) (**C**) from cartilage protein hydrolysate of spotless smoothness.  
1.575 mg/mL) [34], grass carp skin (His-Phe-Gly-Asp-Pro-Phe-His (HFGBPFH): 0.20 mg/mL) [37], and corn gluten meal (Phe-Leu-Pro-Phe (FLPF): 0.789 mg/mL; Leu-Pro-Phe (LPF): 0.777 mg/mL; Leu-Leu-Pro-Phe (LLPF): 1.084 mg/mL) [38]. Therefore, these results indicated that MCPE-B had the strong ability to donate an electron or hydrogen radical for inhibiting the DPPH reaction.
###### Abstract  
Low-Earth-Orbit (LEO) satellite Signal-of-Opportunity (SOP) positioning technology has gradually matured to meet the accuracy requirements for unmanned aerial vehicle (UAV) positioning in daily scenarios. Advancements in miniaturization technology for positioning terminals have also made this technology's application to UAV positioning crucial for UAV development. However, in the increasingly complex electromagnetic environment, there remains a significant risk of degradation in positioning performance for UAVs in LEO satellite SOP positioning due to unintentional or malicious jamming. Furthermore, there is a lack of in-depth research from scholars both domestically and internationally on the anti-jamming capabilities of LEO satellite SOP positioning technology. Due to significant differences in the downlink signal characteristics between LEO satellites and Global Navigation Satellite System (GNSS) signals based on Medium Earth Orbit (MEO) or Geostationary Earth Orbit (GEO) satellites, the anti-jamming research results of traditional satellite navigation systems cannot be directly applied. This study addresses the narrow bandwidth and high signal-to-noise ratio (SNR) characteristics of signals from LEO satellite constellations. We propose a Consecutive Iteration based on Signal Cancellation (SCCI) algorithm, which significantly reduces errors during the model fitting process. Additionally, an adaptive variable convergence factor was designed to simultaneously balance convergence speed and steady-state error during the iteration process. Compared to traditional algorithms, simulation and experimental results demonstrated that the proposed algorithm enhances the effectiveness of jamming threshold settings under narrow bandwidth and high-power conditions. In the context of LEO satellite jamming scenarios, it improves the frequency-domain anti-jamming performance significantly and holds high application value for drone positioning.  
U
## 3 Methodology
### Data Preprocessing  
Data preprocessing is a crucial step that significantly influences the accuracy of ML models utilized for localization. Techniques such as Kalman filtering, which smooths out noise [48], median filtering to eliminate outliers, and data normalization, which standardizes RSSI values to a uniform scale [49], are commonly employed in this context. Additionally, feature selection algorithms are applied to reduce dimensionality and enhance model performance [50]. These approaches help to mitigate the impact of noise in the data, ultimately leading to improved localization accuracy.
###### Abstract  
A stacked multi-layer substrate integrated waveguide (SIW) microstrip patch antenna with broadband operating bandwidth and low cross-polarization radiation is provided. A complete study on the propagating element bandwidth and cross polarization level is presented to demonstrate the importance of the design. The proposed antenna includes three stacked printed circuit board (PCB) layers, including one layer for the radiating 2 \(\times\) 2 rectangular patch elements and two SIW PCB layers for the feeding network. There are two common methods for excitation in cavity-backed patch antennas: probe feeding (PF) and aperture coupling (AC). PF can be used to increase the bandwidth of the antenna. Although this method increases the antenna's bandwidth, it produces a strong cross-polarized field. The AC method can be used to suppress cross-polarized fields in microstrip patch antennas. As microstrip patch antennas are inherently narrowband, the AC method has little effect on their bandwidth. This paper proposes an antenna that is simultaneously fed by AC and PF. As a result of this innovation, the operating bandwidth of the antenna has increased, and cross-polarization has been reduced. Actually, the combination of probe feeding and aperture coupling schemes leads to achieving a broadband operating bandwidth. The arrangement of radiator elements and cavities implements a mirrored excitation technique while maintaining a low cross-polarization level. In both numerical and experimental solutions, a less than \(-\)30 dB cross-polarization level has been achieved for all of the main directions. A fractional impedance bandwidth of 29.8% (10.55-14.25 GHz) for S\({}_{11}<-\)10-dB is measured for the proposed array. Simulated and measured results illustrate good agreement. Having features like low cost, light weight, compactness, broadband, integration capabilities, and low cross-polarization level makes the designed antenna suitable for remote-sensing and satellite applications.  
antenna array; broadband; cross-polarized fields; satellite application; SIW +
Footnote †: journal: Sensors
## 4 Spatiotemporal Feature Extraction Techniques
### Feature Extraction Methods Future Research Directions
#### 4.5.3 Three-Dimensional Convolutional Neural Network  
Three-dimensional convolution, also known as 3D convolution, is a Convolutional Neural Network (CNN) technique used to process 3D data. Unlike standard 2D convolutions, 3D convolutions operate on three dimensions (usually depth, height, and width) to better synchronize temporal and spatial features [79; 80].
# Dual-View Integral Imaging 3D Display Based on Multiplexed Lens-Array Holographic Optical Element
## 2 Structure and Principle
### Fabrication and Reconstruction Principles of the MHOE  
Compared with the conventional holographic optical element (HOE), the MHOE is recorded on the volume hologram, which has the characteristics of angular and spatial multiplexing. HOE has angular selectivity; when the incident beam deviates from the Bragg condition, the diffraction efficiency of the reconstructed beam is reduced. When the incident beam greatly deviates from Bragg condition,  
Figure 1: Schematic diagram of the dual-view 3D display system by using the MHOE.  
the diffracted beam cannot be reconstructed. Therefore, multiple optical elements can be recorded on a single holographic plate by angular and spatial multiplexing.  
The angular and spatial multiplexing method are used in the fabrication process. Two sets of micro-lens array wavefronts are recorded in a single green sensitive photopolymer material with two different angles. The fabrication process is divided into two steps, which are shown in Figure 2. In the first step, the parallel beam I is incident into the micro-lens array at an oblique angle \(\theta_{1}\), and then the spherical wavefront array I is generated. The reference beam I is generated by point source I. Reference beam I interferes with spherical wavefront array I with a pair of angles \(\theta_{2}\) and \(\theta_{3}\), as shown in Figure 2a. In the second step, the parallel beam II is incident into the micro-lens array at an oblique angle \(\theta_{4}\), and then the spherical wavefront array II is generated. The reference beam II is generated by point source II, and reference beam II interferes with spherical wave array II with a pair of angles \(\theta_{5}\) and \(\theta_{6}\), as show in Figure 2b. The parallel beams I and II are symmetric to each other. By strictly controlling the exposure time of the first and second steps in the fabrication process. Finally, the required MHOE is obtained.  
The reconstruction principle of the MHOE is shown in Figure 3. Reference beam I is projected onto the MHOE with a pair of angles \(\theta_{2}\) and \(\theta_{3}\). The incident angles of the reference beam I are consistent with the angles of fabrication, and the Bragg condition is satisfied. So the spherical wavefront array I can be reconstructed. Reference beam II is also projected onto the MHOE with a pair of angles \(\theta_{5}\) and \(\theta_{6}\). The incident angles of the reference beam II are the same as the angles of fabrication, and the Bragg condition is also satisfied. Then, the spherical wavefront II is reconstructed. Therefore, the MHOE has the optical characteristics of two sets of micro-lens arrays in different directions. Reference beams I and II are generated by the point source I and II, which have various directional light components. In the reconstruction of 3D images, a Bragg-mismatch occurs when reference beams are produced by projector I and II. So, the angular variation caused by angular deviation of the reconstructed beam should be analyzed.
## 2 Related Work
On the contrary, in WSNs, hundreds of sensor nodes cover large areas such as an agricultural field or a city and use multi-hop communications. Thus, clustering can be a useful solution for WSNs but for WBANs with typically 6-20 nodes (standard for typical medical network [2]), it can add unwanted overheads. The body sensors require mostly single-hop or two-hop data transmission. Not only node lifetime, but also the rate of energy depletion due to computational and transmission power is important for WBAN as these factors directly correspond to SAR. Existing research works on development of transmission strategies are listed in Table 1 according to time line. The works in [12; 14; 24] primarily focused on analytical development of transmission strategies for intra-BAN data communications. In [17; 18], authors reported the development of network layer protocols which incorporate the adaptive selection of transmission power as a component of the routing process. An energy-efficient fuzzy routing protocol was proposed in [17] which attempted to prolong the lifetime of the network by taking the optimal route to a destination based on energy level, traffic load and link usage. The following steps are found to be executed by the protocol proposed in [17]. At the beginning, nodes access their own battery level. Next, transmission power is adjusted depending on distance and subsequently, routing queries are generated to transfer data. Thereafter, a single neighbor's information is obtained, and routing path is calculated. Finally, a fuzzy inference system is invoked to take the decision about the next hop. Here the node with low energy is avoided to act as the next hop for the data packets to be delivered to the sink. The transmission power is adjusted in the work according to the distance of the receiver node. However, adaptive power control and routing in multi-hop WBANs are considered in [18] to develop a low overhead energy-efficient routing scheme based on Collection Tree Protocol (CTP). However, these protocols attempted to obtain optimal transmission power during network activities after deployment of body-sensor nodes which could impose an additional overhead to the resource-constrained system. Nevertheless, the use of mathematical models in developing transmission strategies have been a common practice over the years. The works reported in [11; 12; 13; 14; 22; 24] are found to exploit MDP [15] to formulate transmission policies whereas in [25] Monte Carlo Simulation is used to design energy-efficient adaptive transmission power control scheme. Network-coded transmission policies are presented in [26] to reduce the number of transmissions in simple multi-hop networks. However, the existing literature mostly uses MDP to design transmission policy as this is found to be the convenient mathematical framework for planning under uncertainty. Hence, Table 1 refers to the existing works formulated with MDP only.
# Edge Federated Optimization for Heterogeneous Data
## 5 Experimental Results
### Performance Evaluation
#### 5.2.1 FedAvg/FedProx with Strategy 1  
The confusion matrices presented in Figure 7 reflect the classification results obtained by applying the FedAvg and FedProx algorithms with initial configurations to two different datasets. Based on the experimental data in this study, we examine the F1 score and confusion matrix for a comprehensive performance evaluation. The F1 score reveals that the classification results are superior when employing the FedProx algorithm compared to FedAvg. Such comparative results indicate the superiority of FedProx over FedAvg in model performance when handling heterogeneous datasets.