esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Connectivity for Multicast Video Streaming in Cellular Networks
## 2 Multi-Connectivity in MBMS  
Multicast services were first standardized as part of release 9 [32] of the 3 GPP standards as MBMS [33] and later as evolved-MBMS (eMBMS) [34], which is also a part of the Fifth Generation (5G) New Radio (NR) [35] standards. Within MBMS, two modes of multicast operation are defined, namely, Single-Cell Point-To-Multipoint (SC-PTM) and MBSFN. SC-PTM, which refers to the multicast mode where content is multicast to users within a single cell. In the MBSFNs mode of operation, all the base stations within a designated MBSFN area [36] transmit the same content in strict synchronization [33]. MBSFN transmissions necessitate precise synchronization between all base stations in the MBSFN area and extended cyclic prefixes. This is crucial to enhancing service quality for cell-edge receivers, as it enables the combination of signals from various base stations, resulting in improved user experience. However, the extended cyclic prefix reduces system throughput, and the requirement for tight synchronization results in significant control overheads. MC multicast overcomes these limitations with a considerably simpler framework than MBSFN and lower transmission overheads. We discuss this in greater detail in Section 2.2.  
The supporting architecture for MBMS with 5G NR is shown in Figure 1. The network elements that support MBMS services are the Broadcast Multicast Service Centre (BM-SC), the MBMS GateWay (MBMS-GW), and the Multicell/Multicast Coordination Entity (MCE) [33]. The BM-SC serves as an interface between the core network and the multicast/broadcast content providers. It is responsible for transporting MBMS data into the core network, managing group memberships and subscriptions, and charging for MBMS sessions [32]. The MCE is responsible for allocating radio resources to the base stations for MBSFN operations. The MBMS-GW uses IP multicasting to forward the MBMS session data to the base stations. The base stations can then transmit the data to the User Equipments (UEs) via wireless multicast/broadcast.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Explanation** \\ \hline \(M\) & Number of UEs in the system \\ \(C\) & Number of cells/base stations in the system \\ \(N\) & Number of PRBs available for allocation in each cell \\ \(R\) & Rate of transmission of the multicast content \\ \(r_{jk}^{c}[t]\) & Maximum rate that UE \(k\) can decode on PRB \(j\) of cell \(c\) at time \(t\) \\ \(\mathbf{K}^{\star}\) & MC multicast resource allocation problem \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation of the most commonly used variables.  
In the following section, we discuss the features of MBMS that enable the use of the proposed MC multicast operations.
## 3 Results  
### Prevalence and Molecular Serogroups of Listeria monocytogenes Strains  
A total of 122 _Listeria_ spp. isolates were recovered, of which 24 were characterized as _Listeria monocytogenes_ (19.7% of the isolates). More specifically, 5 strains of _Listeria monocytogenes_ were recovered from the environmental samples (4.0%, 95% C.I. = 1.3-9.2%) and 19 strains from the fish samples (11.5%, 95% C.I. = 7.1-17.4%). In particular, four out of five environmental strains were isolated from the cutting boards (21.0%). One strain was recovered from a sample of ice (5.3%). All the strains recovered from fish came from the large fish (25.3%, 95% C.I. = 16-36.7%). No strains were found in the small fish (95% C.I. = 0-4%). Concerning the _Listeria_ spp., 56 strains of _Listeria_ spp. originated from the environmental samples and 42 strains of _Listeria_ spp. were isolated from fish samples, of which 68 were identified as _L. innocua_ (69.4%) and 2 as _L. ivanovii_ (2.0%). Twenty-eight isolates were not typed to the species level. Combined with the _Listeria monocytogenes_-positive samples, _Listeria_ strains were found in 61 of the 165 fish samples (37.0%, 95% C.I. = 29.6-44.8%) and in 61 of the 123 environmental samples (49.6%, 95% C.I. = 40.5-58.8%).  
All _Listeria monocytogenes_ strains belonged to the molecular serotypes IIc and IVb. More specifically, two strains were characterized as molecular serotype IVb, while the remaining strains belonged to molecular serotype IIc. The two isolates characterized as molecular serotype IVb were recovered from samples taken from cutting boards (Table 5). The majority of the IIc isolates were recovered from fish samples (19 isolates; 5 were  
\begin{table}
\begin{tabular}{c c c} \hline
**Gene** & **Primer** & **Product (bp)** \\ \hline _inLA_ & For: ACGAGTAACGGACAAATGC & 800 \\  & Rev: CCCGACAGTGGTGTCATGATT & 800 \\  & For: TGGAGAGTAACCAACC & \\ _inLB_ & Rev: GTTGACTTGCATGGTGTGT & 884 \\  & For: AATTCCCACAGGACACAACC & \\ _inLC_ & Rev: CGGGAATGCAATTTTTCCATA & 517 \\  & For: TGTAAACCCGTTACACAGTT & 238 \\  & Rev: AGCGCGTTGGCAGTCTAATA & \\ _plcA_ & For: CTGCTTGAGCGTTCATGTCTCATC & 1484 \\  & Rev: CATGGGTTTTCACTCTCCTC & 1484 \\ _prfA_ & For: CTGTTGAGGCTTTTTTTGGTGAGACATCG & \\  & Rev: ACGACACCTGACCATTATATACATC & 1060 \\  & For: GCAGTTGCAAGCCTTGAGTGAA & \\  & Rev: GCAACGTATCCCGAGGTATG & 456 \\  & For: ACAAGCTGACACCTGTTGCAG & 131 \\  & Rev: TGACAGCGTGTGTAGTAGCA & 839 \\ _actA_ & For: ACGAGGAACCCGGTGTAG & \\ \hline \end{tabular}
\end{table}
Table 4: Primers for the detection of virulence-associated genes in _Listeria monocytogenes_ isolates.  
from chub mackerel, 7 from hake, and 7 from horse mackerel), whereas 3 IIc isolates were recovered from environmental samples (2 from cutting boards and 1 from ice).
# Deep Spatio-Temporal Graph Attention Network for Street-Level 110 Call Incident Prediction
## 4 Problem Statement  
The aim of this study is to forecast the probability of \(110\) Call incidents for each street over the subsequent hour. The features input into the forecasting model are primarily categorized into spatial features based on the graph structure, temporal features based on the calendar information, and external environmental features based on the weather information. This study adopts different feature representation methods according to the characteristics of these three types of features to obtain embedded vectors, which are ultimately used for discrimination by a binary classifier. In this section, we begin by presenting the representation learning methods applied to the three types of features, followed by a comprehensive description of the overall model architecture.
## 3 Results
### Para Rubber Land Use and Biomass Characteristics  
The land use distribution patterns of rubber tree plantations, sizes, number of plantations and cultivation communities throughout the districts of Songkhla province are presented in Table 4. The result indicates that the proportion of land area devoted to para rubber plantation ranges from 0.1 to 90.2%, with Ranot and Na Mom districts having the lowest and highest proportions, respectively. The plantation sizes range from 20.8 hectares to 77,441.5 hectares, with Sadao and Sathing Phra districts as the highest and lowest sizes, respectively.  
Moreover, the trend revealed a range of 8 to 1149 para rubber plantations spanning across the province. The largest number was within the Hat Yai district while the lowest number of plantations were in the Sathing Phra district. To describe the biophysical features connected with AGB, some biomass-related profiles of para rubber are summarized and presented in Table 5.  
Figure 7: A map showing the distribution of net primary productivity in Songkhla province.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**District** & \begin{tabular}{c} **Number of Rubber** \\ **Planting Communities** \\ \end{tabular} & \begin{tabular}{c} **Number of Rubber** \\ **Plantations per District** \\ \end{tabular} & \
\begin{tabular}{c} **Plantation Size** \\ **(Hectares)** \\ \end{tabular} &
\begin{tabular}{c} **Proportion of Rubber Land** \\ **Use to Total Land Area (\%)** \\ \end{tabular} \\ \hline \hline Ranot & 7 & 11 & 48.1 & 0.1 \\ \hline Sathing Phra & 6 & 8 & 20.8 & 0.2 \\ \hline Singhanakhon & 7 & 17 & 80.3 & 0.4 \\ \hline Krasae Sin & 3 & 28 & 1602.8 & 16.6 \\ \hline Sadao & 9 & 644 & 77,441.5 & 75.3 \\ \hline Hat Yai & 13 & 1149 & 36,313.1 & 42.6 \\ \hline Saba Yoi & 9 & 843 & 52,790.3 & 61.9 \\ \hline Na Thawi & 10 & 700 & 53,199.7 & 85.8 \\ \hline Thepha & 7 & 570 & 43,952.3 & 44.9 \\ \hline Chana & 14 & 975 & 35,657.2 & 70.9 \\ \hline Na Mom & 4 & 221 & 8340.9 & 90.2 \\ \hline Mueang Songkhala & 5 & 197 & 5453.0 & 31.7 \\ \hline Khlong Hoi Khong & 4 & 411 & 18,781.4 & 68.3 \\ \hline Bang Klam & 4 & 318 & 12,819.9 & 86.7 \\ \hline Khuan Niang & 4 & 410 & 10,148.9 & 48.8 \\ \hline Rattaphum & 8 & 1077 & 31,631.9 & 53.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Rubber Tree Plantation sizes and proportion of land use across the districts in Songkhala Province.  
Figure 8: (**a**) Validation of MODIS NPP distribution in Songkhala Province. (**b**) Correlation between MODIS NPP and EVI distribution in Songkhala province. (**c**) Correlation between MODIS NPP and NDVI distribution in Songkhala province. (**d**) Correlation between FLUXNET NPP and EVI distribution in Songkhala province.
## 4 Taillight Detection and Light Signal Identification Based on Image Processing
### Color Space Conversion  
Due to the complex and varying lighting conditions during the day, it is difficult to process rapidly changing color information by simply using the R channel thresholds in the RGB color model to extract the taillight regions. Detecting the taillights with the R-channel threshold results in inaccurate segmentation of the taillight area for red vehicles, as shown in Figure 6.  
Figure 7a is an RGB color space image, and Figure 7b is an HSV color space image after conversion. From the images, the taillight area in the target image in Figure 7b is more obvious.  
The image threshold segmentation is performed on the HSV color space image. Image segmentation is performed using a specific threshold. A pixel in which the gradation value is greater than or equal to a certain threshold is judged to belong to the target object, and a pixel whose gradation value is smaller than the threshold is excluded from the target.  
Then select Sobel for edge detection in the taillight area. The image is denoised as much as possible and the internal holes are narrowed to reconnect the adjacent areas. The image is subjected to a morphological closing operation to eliminate narrow discontinuities and small voids in the taillight region. A morphological opening operation is performed on the image to smooth the image boundary. The detection result is shown in Figure 8.  
Figure 5: The process of taillight detection and light recognition.  
Figure 6: Results of color space conversion. (**a**) Image of vehicle in RGB space; (**b**) The segmentation result of the R channel threshold; (**c**) The segmentation result of the HSV color space.  
Figure 7: Image processing result. (**a**) Image of vehicle in RGB space; (**b**) Image of vehicle in HSV space.
# TrendFlow: A Machine Learning Framework for Research Trend Analysis
## 6 Discussion
### Advantages  
TrendFlow boasts the following notable advantages:  
**Advanced text encoding**. TrendFlow employs cutting-edge text encoders, leveraging the MTEB leaderboard [35] to significantly enhance subsequent clustering processes. By utilizing state-of-the-art encoding methods, the framework can accurately capture and represent textual information, which is crucial for effective clustering and trend identification.  
**High-quality research trend generation**. The KeyBART-adapter model, after being finetuned on the meticulously annotated RTA dataset, demonstrates exceptional performance in keyphrase generation. It surpasses the baseline KeyBART model in terms of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & \begin{tabular}{c} **Relevance of** \\ **Clusters** \\ \end{tabular} & \
\begin{tabular}{c} **Coherence of** \\ **Clusters** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy of** \\ **Research Trends** \\ \end{tabular} \\ \hline Conf1 & 4.66 & 4.33 & **4.33** \\ Conf2 & 4.66 & 4.33 & 4.16 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Human evaluation results for the TrendFlow framework with KeyBART-adapter (Conf1) and KeyBART (Conf2) configurations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **F-score@M (\%)** & **Precision@M (\%)** & **Recall@M (\%)** \\ \hline KeyBART-adapter & **33.25** & 40.33 & 30.07 \\ KeyBART & 32.75 & 32.92 & **33.08** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Keyphrase generation evaluation results for KeyBART-adapter and KeyBART.  
F-score@M and Precision@M, showcasing the framework's ability to generate accurate and pertinent keyphrases, which is essential for pinpointing research trends.  
**Flexibility and adaptability**. TrendFlow has been designed with a modular architecture, facilitating the effortless replacement of its components to accommodate the integration of more robust models as needed. This framework offers support for a wide array of customizable clustering algorithms, enabling users to select the most suitable algorithm for their specific research domains or datasets. This flexibility and adaptability empowers users to generate accurate research trends by leveraging the algorithm that best aligns with their requirements.  
**User-friendly UI experience and illustrative visualization**. Our web application streamlines the configuration of TrendFlow, enabling users to effortlessly query papers across various literature platforms and automatically receive analyzed results. The visualized outcomes are not only accessible but also easy to comprehend, facilitating user understanding and interpretation of research trends.
# A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks
## 3 System Model
### Calculation of Physical Layer Trust  
Energy consumption rate is an important parameter at the physical layer. A malicious node usually sends or receives more packets than a normal node. It will inevitably consume more node energy, so we choose energy consumption as trust metric at this layer. The monitoring node \(\mathrm{i}\) can obtain the energy consumption of its neighboring node \(\mathrm{j}\) during the time period of \(\Delta\mathrm{t}\). The relative deviation of energy consumption of node \(\mathrm{j}\) can be calculated by:  
\[\mathrm{RD}_{\mathrm{EC}}(\mathrm{t})=\frac{\Delta\mathrm{E}_{\mathrm{j}}( \mathrm{t})-\overline{\Delta\mathrm{E}(\mathrm{t})}}{\Delta\mathrm{E}(\mathrm{ t})}, \tag{3}\]in which \(\Delta\mathrm{E_{j}(t)}=\mathrm{E_{j}(t-\Delta t)-E_{j}(t)}\), \(\overline{\Delta\mathrm{E(t)}}=\frac{1}{n}\sum_{i=1}^{n}\Delta\mathrm{E_{i}(t)}\). \(\mathrm{E_{j}(t)}\) indicates the residual energy of node \(\mathrm{j}\) at time \(\mathrm{t}\) and \(\Delta\mathrm{E_{j}(t)}\) represents the energy consumption of node \(\mathrm{j}\) during the time period of \(\Delta\mathrm{t}\). \(\overline{\Delta\mathrm{E(t)}}\) is the average energy consumption level of all neighboring nodes of node \(\mathrm{i}\) during this time period and \(\mathrm{n}\) denotes the number of neighboring nodes of node \(\mathrm{i}\). Node \(\mathrm{i}\) can roughly evaluate the energy consumption of its neighboring nodes during the time period of \(\Delta t\) by monitoring their packet transmission activities. The greater the deviation of energy consumption is, the lower the nodal trustworthiness will be. So we obtain the physical layer trust as:  
\[\mathrm{T_{ij}^{PHY}(t)}=\left\{\begin{array}{c}1-\mathrm{RD_{EC}(t),\;if\; 0<\mathrm{RD_{EC}(t)}<1\\ 0,\;\mathrm{RD_{EC}(t)}\geq 1\\ 1,\;\mathrm{RD_{EC}(t)}\leq 0\end{array}\right., \tag{4}\]  
In Equation (4), if the relative deviation of energy consumption is less than or equal to 0, which means the energy consumption of the monitored node is less than the average energy consumption, the monitored node is considered trustworthy at the physical layer. If \(\mathrm{RD_{EC}}\) is greater than or equal to 1, which means the energy consumption of the monitored node is more than double or double the average energy consumption, the monitored node will be considered untrustworthy at the physical layer.
# Human Activity Classification Based on Angle Variance Analysis Utilizing the Poincare Plot
## 2 Materials and Methods
### Walking and Running Classification  
Angle variance analysis was considered as a medium (walk) to vigorous (run) activity classifier. Table 2 shows the classification between walking and running activities with count and angle variance analysis. The classification results are shown in Table 2 for SD1, SD2, and SDRR, as well as count of X, Y, and Z, in separate and combined form. The ROC curve for walking and running classification with the Actigraph count and Poincare plot-based classification is shown in Figure 12. The mean count of the X, Y, and Z-axis shows accuracy (100%) with sensitivity (100%) and specificity (100%), whereas SD1, SD2, and SDRR of angle show accuracy (100%) with sensitivity (100%) and specificity (100%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Walking–Running Classification**} \\ \hline \hline  & \multicolumn{1}{c}{**Accuracy (\%)**} & \multicolumn{1}{c}{**Sensitivity (\%)**} & \multicolumn{1}{c}{**Specificity (\%)**} \\ \hline \multirow{4}{*}{Actigraph count} & X & 100 & 100 & 100 \\ \cline{2-5}  & Y & 100 & 100 & 100 \\ \cline{2-5}  & Z & 85 & 76 & 100 \\ \cline{2-5}  & X, Y, Z & 100 & 100 & 100 \\ \hline \multirow{4}{*}{Poincare plot parameters} & SD1 & 100 & 100 & 100 \\ \cline{2-5}  & SD2 & 100 & 100 & 100 \\ \cline{2-5}  & SDRR & 100 & 100 & 100 \\ \cline{2-5}  & SD1, SD2, SDRR & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy with count- and angle variance (Poincare plot)-based classification for the MHealth (walk–run) dataset.  
Figure 11: ROC curve for sitting and standing classification. The left panel denotes the ROC curve for the Actigraph count-based sitting and standing classification. The right panel denotes the mean angle-based static and dynamic activities’ classification.
## 6 Simulation Result
### Evaluation Results of Modifying Computational Complexity  
In this subsection, the two metrics in term of computational complexity are discussed, in which the computational complexity denotes the CPU frequency demand for computing a unit bit of data. Figure 5a,b present the successful ratio and the task execution cost of the schemes involving uploading. Figure 5a shows that, when increasing the computational complexity, the task completion rates decrease for all schemes. This is in conformity with our intuition. We can observe that our learning-based scheme performs the best, while the LC scheme is the worst in performance. When the computational complexity is set to 1200 rounds/bit, the task completion rate is only 8% for the LC scheme and 32% for the RF scheme, while the successful rate for the proposed DRL based scheme in this paper is 64%. Compared to the RF scheme and the LC scheme, the task successful rate of our scheme increases by 56% and 32%, respectively. The reasons are as follows: Firstly, computational resources are reasonably allocated by the resource assignment strategy discussed in this paper. Secondly, both the features of flexibility and long-term planning of DRL are fully utilized in our scheme. Moreover, when the computational complexity is 900 rounds/bit, the successful rate of the proposed scheme is up to 83%, which is much higher than other two schemes involving uploading.  
Figure 4: Evaluation under different parameters of training. (**a**) Task successful rate under various learning rates, and (**b**) task successful rate under various attenuation factors.  
Figure 5: Simulation under diverse computational complexity values. (**a**) Task successful rate under various computational complexity, and (**b**) execution cost under various attenuation factors.  
From Figure 5b, we can see that the task execution cost of the LC scheme tends to increase linearly when the values of the computational complexity are modified due to both the increase in task execution time and the increase in energy consumption. The proposed scheme obtains great advantage on the total task execution cost even when the computation complexity is set to a high value. The main reason is that, in the proposed scheme, task offloading can be continuously optimized through multiple iterations to maximize the reward by using the DRL model.
## 7 Conclusions  
The idea, fundamentals, motivation, proposed architecture models (global SSN, node, and network architectures), building requirements, and challenges of SSNs has been overviewed throughout this article. The benefits of SSNs over the traditional WSNs. Subsequently, a novel taxonomy is presented based on the type of used virtualization technique. The taxonomy presents three main categories: HLV, LLV, and hybrid. The category of LLV presents the multitasking OSs, VM, and MW solutions. More efforts are required to virtualize the individual components of sensor nodes, such as MAC and routing layers, as the trend moves towards more powerful IP-SSNs. While the second category is the HLV was divided into two branches, which are network and system levels: the first branch presents both Virtual NW/Over-relay Sensor NW and Cluster-based solutions, and the second branch presents the Federation and Participation solutions. The overlay networks can provide an efficient solution, as they are robust and they can work efficiently without changes in the underlying network. Some solutions exist, like those in Sensomax, SenShare, Melete system, etc., but they are still embryonic in nature and do not consider the requirements of multiple applications concurrently utilizing SSNs. Finally the hybrid category presents the Middleware/Hierarchical Clustering, Middleware Virtual NW/Overlay, and Virtual Machine Dynamic Grouping solutions. Each solution in all of the categories was briefly summarized with its performance evaluation and a comparative analysis with other peer solutions; also, a characteristics summary table for each category is presented. Some of the projects using the technology of SSNs are briefly explained. Additionally, some applications in different life aspects that are based on SSNs are presented. Under the umbrella of virtualization, a promising future is behind of SSNs, where this concept opens the ability of being capable of supporting the expected enormous growth of the IoT sensor-based uplink data traffic generated by customer devices in smart cities, smart farming, industrial applications, healthcare monitoring systems, driverless cars navigation, drones/robotics applications, and a wide diverse of user applications.