esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Data Collection Mechanism Using Multiple Mobile Sinks in Wireless Sensor Networks
## 6 Conclusions  
This paper considers the data collection issue in a given mobile wireless sensor network where multiple mobile sinks aim to collect data from a given set of static sensors. The proposed CDCA mechanism aims to prolong the network lifetime by visiting as more CPs as possible such that the forwarding cost of each static sensor is reduced. The proposed CDCA mechanism primarily consists of three phases: Network Partition Phase, CP Selection and Path Construction Phase, and Speed Control Phase. In the Network Partition Phase, the CDCA partitions the sensor nodes into \(k\) disjoint subsets. In the Collection Point Selection Phase and Path Construction Phase, CDCA further selects \(k_{i}\) CPs from sensors in \(T_{i}\) and constructed a path \(\pi_{i}\) which passes through the \(k_{i}\) CPs and collects data from higher burden CPs. In the Speed Control Phase, the algorithm coordinates to control the speed of mobile sinks such that the global sink and each local mobile sink can be rendezvous. The performance results show that the proposed CDCA outperforms other mechanisms in terms of rendezvous time, network lifetime, fairness index, standard deviation (SD) of energy consumption, as well as efficiency index.  
Figure 8: The comparison of efficiency index of three algorithms in four scenarios. (**a**) \(k=7\); (**b**) \(k=9\).  
The proposed CDCA reduces the energy consumption of tested WSNs by 18% and 37% in comparison to HiCoDG and BRPA, respectively.  
Further studies for exploring energy recharging are planned. The large WSN is partitioned into smaller areas where each area is assigned a mobile sink. The proposed CDCA can be extended such that it can establish a recharging path for each area while considering the coverage issue. Moving along the path, a mobile recharger can recharge the sensors for maintaining maximal coverage for a given WSN.  
[PERSON] and [PERSON] discussed the problem formulation and the algorithm design; [PERSON] reviewed the related work and designed the algorithm details; [PERSON] and [PERSON] provided technical guidance to support the WSN implementation; [PERSON] and [PERSON] cooperatively finished the implementation of performance experiments and paper writing.  
This research was funded by Key Project of Science and Technology of Anhui [No. 1704e1002217], Natural Science Research Projects of Higher Education in Anhui Province [No. KJ2016B18, No. KJ2016B20 and No. KJ2017A420], Outstanding Youth Support Projects of Anhui Province [No. gxyq2018095 and No. gxyq2017087], and Project Leading Talents in Universities of Anhui Province [No. gxfxZD2016250].  
The authors declare no conflict of interest.
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 1 Introduction
Firstly, we form an affinity matrix (i.e., similarity matrix) of the whole vehicular network by using link connectivity of vehicles. Secondly, a heuristic approach (i.e., Eigen gap) of spectral clustering is applied to find the optimal number of clusters [12]. To select an appropriate CH, our proposed clustering scheme ranks all the clusters and selects a vehicle as a leader based on the maximum Eigen-centrality. Furthermore, the route discovery process in the proposed connectivity-based clustering is performed by the cluster-based evolving graph (CEG)-Dijkstra [3]. The rest of the paper is structured as follows: Section 2 consists of the previous work related to the cluster-based routing. Section 3 presents vehicular connectivity in detail. Section 4 describes the complete procedure of our suggested connectivity-based clustering. Section 5 indicates the route discovery process of the proposed routing scheme. Section 6 presents the simulation results along with a discussion. Finally, Section 7 concludes the paper.
## 1 Introduction
The authors reported that EL metrics, such as Player Load (PL) (269.9 \(\pm\) 83.3 AU) and PL/min (6.54 \(\pm\) 1.29 AU/min), alongside a higher frequency of movements such as jumps, accelerations, and changes of direction performed at low-intensity zones. The IL analysis revealed high HR values with a maximum HR of81.7 \(\pm\) 8.1% and a low (sRPE of 148.9 \(\pm\) 69.7 AU for the actual game duration, implying that high physiological demand did not correspond to relatively low perceived effort [43]. Engaging in regular and organized recreational team sport activities can help seniors meet the current World Health Organization (WHO) recommendations of 150 min of moderate-intensity or 75 min of vigorous-intensity physical activity per week, or an equivalent combination of the two, to achieve significant health-related benefits [46; 47]. To date, numerous studies have attempted to relate TL to PA guidelines by employing disparate methodological approaches [15; 16; 24; 25]. Based on their findings, recreational walking soccer (WS) is classified as moderate-intensity activity, with HRmean at ~80% of HRmax and GPS-measured distance (~2409 m) supporting this classification [15; 16]. Recreational futsal yields energy expenditures aligning with international guidelines, with one session equating to ~634 kcal [24]. Recreational handball showed high HR values (~80% HRmax for 42 min), though accelerometer data indicated that only 27% of the game time was moderate-to-vigorous physical activity (MVPA) [25; 46; 47]. Basketball is an easily accessible team sport, as only a ball and a hoop are required, and can be played in diverse settings, from dedicated courts to improvised open spaces [48]. Its flexibility accommodates various group sizes, from full teams to just two players, making it highly inclusive. Beyond its accessibility, basketball is characterized by intermittent high-intensity activities, and it has been proposed as a well-rounded approach for maintaining physical activity throughout the lifespan. Although physical activity in, and biological adaptations resulting from, team sports such as football, volleyball, and handball have been thoroughly examined across various populations [2; 4; 7; 21; 22], their investigation in basketball remains obscure. While the demands of basketball training sessions and games on smaller courts have been assessed in males and females, no study has quantified TL during a basketball game using PA as a metric and compared it among different age groups. Therefore, understanding the quantification of TL and PA will allow practitioners to consider it as a form of exercise and determine effective training load characteristics. Hence, the aim of the present study was to objectively assess and compare TL and PA among different age groups of master basketball athletes and to determine whether participation in official basketball competitions aligns with PA guidelines. We hypothesized that participation in a basketball game provides an adequate physical activity stimulus and that both TL and PA differ among age groups.
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 3 Underlay D2D Network Based on ProSe Service
### Underlay D2D Topology Models  
ProSe direct D2D communication can be established using one-to-one or one-to-many methods [11]. Users in coverage receive authorization and radio parameters from the PLMN, while out-of-coverage users use their preconfigured data. The one-to-many communication method is based on a multicast method to enable subscribed users having the same Group ID to receive a copy of a message. In D2D communications, link quality is evaluated according to the Channel Quality Indicator (CQI) by attributing a value between 1 and 15 [13]. These values indicate the level of modulation and coding the UE could operate. The value 15 is assigned to the channel having the best quality. In this work, we define some D2D models according to the following topologies, as illustrated in Figure 6:  
* Loosely coupled: In this model, illustrated in Figure 6a, a minimum number of links should be created and maintained within the D2D group using the one-to-one communication method.  
Figure 5: Infrastructure vs. D2D-based communication in cellular networks. (**a**) Underlay network in a cellular network. (**b**) Underlay network in D2D.  
* Fully coupled: In this model, illustrated in Figure 6b, each UE should connect with all neighbors in its range. This approach can be achieved using a multicast one-to-many communication method.
* Tightly coupled: In this approach, illustrated in Figure 6c, starting from the loosely coupled model, new additional selected links can be added between UEs according to one of these strategies: (1) based on the CQI by maintaining only links with a certain minimum CQI level, (2) based on the maximum number of links per user, where connections are made with a neighbor having a lower number of connections instead of the neighbor with the highest CQI, and (3) based on a combination of the two previously mentioned strategies, where the additional links are both limited by their number and also by the minimum level of the CQI.
## 2 Problem Formulation and Theoretical Methods
### Interference Reduction in Settlement Surveillance Sensor  
The interference reduction in the settlement surveillance sensor strongly depends on the original database of the finite element computation for the natural frequencies of the transmission tower structure. The parameters corresponding with the settlement of the transmission tower and the natural frequencies are related to the Kriging surrogate model by implicit expression. By the Kriging surrogate model, the input signals provided by the sensors installed in the transmission tower are filtered, and the interference signals are effectively reduced to identify the settlement. Then, real time safety surveillance of the transmission tower is successfully implemented. The process of interference reduction in the settlement surveillance sensor is shown in Figure 2.  
Due to the satisfied accuracy and feasibility, and the computational cost saving compared with the finite element method, the Kriging surrogate model is chosen for interference reduction in the settlement surveillance sensor. The uncertainty and randomness of the settlement in the transmission tower are propagated as that in the real situation. A number of 1000 stochastic samples of the transmission tower with the settlement are simulated by the finite element model. The records of the transmission tower settlement and the corresponding natural frequencies in the five vibration modes are shown in Figure 3. The results of the natural frequencies and related settlement of transmission tower provide the useful original database for the Kriging surrogate model.  
Figure 3: Records of the transmission tower settlement and the corresponding natural frequencies (**a**) is the records of the transmission tower settlement, and (**b–f**) are the natural frequency records of the first to fifth order vibration modes, respectively.  
Figure 2: Schematic diagram of interference reduction in the settlement surveillance sensor.
Therefore, quantitatively analyzing the spatial distribution patterns and correlation characteristics of multiple annual ESVs at the regional scale is helpful for understanding the spatial pattern characteristics of ecosystems in the study area and revealing the influencing factors and driving mechanisms of ESV changes. This study focused on southern Guangzhou, which is a typical urban fringe area located in the Pearl River Delta (PRD) urban agglomeration of southern China, as well as the geometric heartland of the Guangdong-Hong Kong-Macao Greater Bay Area (GHM). Since the 21 st century, due to rapid urbanization and rapid economic and social development, land use in the study area has undergone tremendous changes, which may significantly affect ecosystem services and functions. Taking Guangdong Province as an example, the ecosystem service equivalent value (ESEV) and its standard value volume (\(D\) value) in Guangdong Province in 2004, 2010 and 2016 were revised. The revised ESEV and \(D\) values were applied to the ESV evaluation in the study area through a series of analysis processes, such as grid analysis, trend analysis and spatial autocorrelation analysis, to conduct a quantitative study on the spatial and temporal characteristics and regularities of ESV and the impact of built-up expansion on the spatial differentiation of ESV in the process of rapid urbanization in a typical urban fringe area, such as the study area. As the field of natural resource management and the ecological environment become increasingly extensive and in-depth, the research content and data types are becoming increasingly rich and diverse, the volume of data for spatial analysis, calculation and management is also increasing, and the demand for data sharing is growing. Traditional stand-alone GIS software cannot support the collection, management, spatial analysis, calculation, sharing and visualization of multisource data. Therefore, we introduce the NRIDB to support the research in this paper, which is designed for natural resource big data management and service. The NRIDB is a server-side + web-side GIS that integrates technologies such as Geospatial Database Cluster, Spatio-Temporal Big Data Engine, Spatial Analysis Computing Cluster and Micro-Service Cluster.
## 2 Materials and Methods
### Apparent Resistivity Data Calculation  
Apparent resistivity data were calculated using RES3 DMODx64 v. 3.06 software [27] by simulating 3D electrical tomographies on a simple model of resistive (200 \(\Omega\)m) and conductive (10 \(\Omega\)m) prisms [28], considering a square surface of 120 m \(\times\) 120 m (Figure 2). The top of the prisms is at a depth of 10 m. The heights of the prisms are equal to 10 m, the widths are 20 m, and the length is 80 m for the resistive ones and 40 m for the conductive one. The background resistivity is 50 \(\Omega\)m. A noise of 2% was added to the calculated electric potential, considering an average spontaneous potential equal to 20 mV.  
The spatial distributions of the apparent resistivity measurements are shown in Figure 3. In these, the horizontal location of the data point is placed at the mid-point of the set of electrodes used to make that measurement, whereas the vertical position is the median depth of investigation [29], or pseudo-depth, of the electrode array used. This pseudo-depth value is based on the sensitivity values or Frechet derivative for a homogeneous half-space. This spatial distribution changes considerably for each of the combinations of electrode areal arrangement and array configuration. Observing the electrode arrangement  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Electrode Areal** & **Electrode Array** & **Amount of Data** & **Number of** & **Average** \\
**Arrangement** & **Configuration** & **Amount of Data** & **Current Dipoles** & **Potential** \\  & & & & **Measures per** \\  & & & & **Current Dipole** \\ \hline  & \(\alpha\) & 7255 & 793 & 9 \\ \(A\) & \(\beta\) & 8584 & 524 & 16 \\  & \(\gamma\) & 7326 & 160 & 46 \\ \hline  & \(\alpha\) & 21,240 & 980 & 21 \\ \(B\) & \(\beta\) & 8766 & 480 & 18 \\  & \(\gamma\) & 13,114 & 390 & 69 \\ \hline  & \(\alpha\) & 21,618 & 982 & 22 \\ \(C\) & \(\beta\) & 12,730 & 590 & 21 \\  & \(\gamma\) & 8303 & 293 & 28 \\ \hline \hline \end{tabular}
\end{table}
Table 1: For each of the nine combinations of electrode areal arrangement and array configuration, the amount of data, the number of current dipoles, and the average amount of potential measurements per current dipole are shown.  
Figure 2: Three-dimensional electrical resistivity model characterized by a conductive prism in the center and two lateral resistive prisms, simulated with the RES3 DMODx64 v. 3.06 software [27].  
\(A\), for this \(\alpha\) and \(\gamma\) arrays show a much more regular data distribution versus \(\beta\) array. This trend is observable, albeit with a lesser effect, also for the electrode arrangements \(B\) and \(C\).
## 5 Proposed Models
### Transformer-Enhanced CNN-RNN (TE-CNN-RNN)
#### 5.1.3 Fusion Mechanism  
The fusion mechanism is a critical component of our model, integrating features extracted by the CNN and GRU layers. It utilizes a gating strategy to modulate the flow of information, selectively emphasizing the most relevant features for precise prediction. The gated fusion is computed by concatenating the outputs from the CNN and GRU layers, followed by a sigmoid activation that forms a gate-controlling feature significance.  
\[G=\sigma(\textbf{W}_{g}\cdot[A;B]+\textbf{b}_{g}) \tag{7}\]  
where \(A\) and \(B\) are the flattened outputs from the CNN and GRU layers, respectively, and \(\textbf{W}_{g}\) and \(\textbf{b}_{g}\) are learnable gate parameters. The resultant gated features are then combined element-wise with the concatenated features and further processed through a dense layer, culminating in the final fused output that embodies the spatial-temporal intricacies required for accurate network traffic forecasting.  
\[F=\text{ReLU}(\mathbf{W}_{f}\cdot(G\odot[A;B])+\mathbf{b}_{f}) \tag{8}\]  
where \(\odot\) represents element-wise multiplication.
# EXOLOv3: An Efficient Real-Time Detection Model for Floating Object on River
## 3 Proposed Method
### EYOLOv3: Optimized YOLOv3 Model
#### 3.3.4 Balanced Focal Loss Function  
The YOLOv3 does not make candidate box extraction, so it faces the imbalance between the foreground and background during training. To solve this problem, this paper uses a focal loss function replace the cross entropy loss function, and the binary classification cross entropy loss function is shown in Formula (5):  
\[E(p,y)=\left\{\begin{aligned} -log(p)\ y=1\\ -log(1-p)\ others\end{aligned}\right. \tag{5}\]  
where \(y\in\{\pm 1\}\) indicates whether it is a foreground, and \(p\in[0,1]\) refers to the probability classified as a foreground, that is \(y\) = 1. The \(p_{t}\) is shown in Equation (6):  
\[p_{t}=\left\{\begin{aligned} & p\ y=1\\ & 1-p\ others\end{aligned}\right. \tag{6}\]  
Therefore, the cross entropy function can be defined as shown in Equation (7):  
\[CE(p,y)=CE(p_{t})=-log(p_{t}) \tag{7}\]  
The cross entropy loss function will always produce a loss value, even if the samples can be easily classified and identified. When the loss values are accumulated, the classes with fewer samples will be missed. In order to solve the category imbalance, a weighting factor \(\alpha_{t}\in[0,1]\) is introduced into the cross entropy loss function. The definition of \(\alpha_{t}\) is shown in Equation (8):  
\[\alpha_{t}=\left\{\begin{aligned} &\alpha\ y=1\\ & 1-\alpha\ others\end{aligned}\right. \tag{8}\]  
Therefore, the balanced loss function can be optimized as shown in Equation (9):  
\[CE(p_{t})=-\alpha_{t}log(p_{t}) \tag{9}\]  
This function will be greatly affected by the imbalance of foreground and background samples due to the negative samples dominating the gradient and loss value. In addition, we need to reduce the weight of simple samples in training process, in order to solve hard sample mining. Therefore, we use a weight factor \((1-p_{t})^{\gamma}\), where \(\gamma\) represents an adjustable parameter. We obtained the final focus function as shown in Equation (10):  
\[FL(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}log(p_{t})\ \gamma\geq 0 \tag{10}\]  
It can be concluded that the focal loss function can reduce the weight of simple samples, thus increasing the weight of hard samples. Therefore, the contribution of hard samples can be improved during model training, so as to solve the problem of imbalance between foreground and background samples and improve the detection accuracy of the model.
## 2 Related Work
### Combining OCR and ASM  
Very little literature exists that explains a specific application and workflow in a business environment that uses OCR functionality alone to convert visual data to text data. Research has however been conducted in the implementation of natural language processing techniques to improve the results after a commercial OCR system has run [9]. [PERSON] et al. [9] address the need to run additional pattern matching steps after OCR had been done on a document in order to get more accurate results from the text. Their findings suggest that OCR does well on normal text but performs poorly on characters that are in italics or in small text. This correlates to the finding in this study that OCR also performs poorly when the document quality is low. They have used pattern matching techniques on the OCR output and used a dictionary of predefined, well known words for the domain of their study. Previous research studies also focused on the improvement of data outputs after image digitization and were used as the base for this research study in order to extend and incorporate their findings and recommendations.
## 2 EVs Performing Multiple Functions in Cancer Formation
### EVs in Immunomodulation  
The metastatic cascade may be favored by the ability of EVs to mediate immunosuppression through the transport of inflammatory factors [31]. Importantly, tumor-derived EVs can be transmitting inflammatory factors to recipient cells to regulate their behaviors. For instance, the transfer of tumor-derived EVs into human THP-1 monocytic cells led to the production and secretion of various pro-inflammatory cytokines and tumor necrosis factor (TNF) -\(\alpha\), via TLR2 and TLR4 binding [32]. Moreover, EVs from breast cancer expressing Annexin A2 mediate an increase in the secretion of IL-6 and TNF-\(\alpha\) by M1 macrophage activation [33].  
Tumor-derived EVs can precondition an environment for future metastasis using other mechanisms as well. For example, EVs derived from gastric cancer express transforming growth factor (TGF)-61, which is associated with lymph node metastasis through the induction of regulatory T cell (Treg) formation [34]. Contrarily, it has been observed that TGF-61 in EVs derived from breast cancer cells can suppress T cell proliferation [35]. In addition, tumor-derived EVs appear to be capable of inhibiting the activation of T lymphocytes and induce apoptosis through the expression of the ligand FasL, a member of the tumor necrosis factor (TNF) family, which can promote the apoptosis of lymphocytes [36].  
Cancer-derived EVs can also enhance immune evasion of cancer cells through the suppression of T cell activity and natural killer (NK) cells. For example, it has been identified that tumor-derived EVs can block IL-2-mediated activation of NK cells and their cytotoxic activity [37].  
Conversely, EVs derived from immune cells can be considered attractive tools for fighting against cancer. For example, NK cell-derived EVs can induce the immune response by compromising the spread of solid tumors [38]. However, the role of EVs released by these cells is still poorly understood.