esa-sceva/satcom-chunk-collection huggingface dataset:
# RMFNet: Redetection Multimodal Fusion Network for RGBT Tracking
## 3 Our Method
### The Fusion Strategy
B \(\times\) C \(\times\) W \(\times\) H represents the dimensions of the tensor, where B is the batch size, C is the number of channels, and W \(\times\) H represents the width and height of the feature map. The channel-wise concatenation of two input features is denoted as \([\cdot,\cdot]\), where \(w^{t}\) and \(w^{t}\) are the weights of the two input features. \(X_{f}\), \(X_{r}\), and \(X_{t}\) represent the fused feature and the two original input features, respectively. (2) Late Decision-Level Fusion  
In target tracking, Decision-level multimodal fusion refers to the fusion of tracking results from different modalities according to some rules during the tracking process. This approach can effectively utilize the tracking results from different modalities, thereby improving tracking accuracy and robustness. However, most existing RGBT tracking algorithms [3; 4; 14; 27] are based on the feature-level fusion framework, ignoring the importance of decision-level fusion in object tracking. In this paper, we adopt a late decision fusion strategy, using visible light modality features, thermal infrared modality features, and fused modality features for classification tracking, obtaining three tracking results based on different features. Then, we combine the three results by addition to obtain the final prediction score. The advantage lies in utilizing tracking results from three different features, fully leveraging the modality-specific and shared information for target tracking. This approach also mitigates the impact of two modality images needing to be more strictly aligned, thereby improving the tracker's accuracy, robustness, and stability. The formula for late decision fusion in this paper is presented below:  
\[X=\mathcal{F}_{fc}^{6v}\Big{(}\mathcal{F}_{fc}^{5v}\Big{(}\mathcal{F}_{fc}^{4 v}(X_{v})\Big{)}\Big{)}+\mathcal{F}_{fc}^{6f}\Big{(}\Big{(}\mathcal{F}_{fc}^{5f} \Big{(}\mathcal{F}_{fc}^{4f}\Big{(}\mathcal{X}_{f}\Big{)}\Big{)}\Big{)}+ \mathcal{F}_{fc}^{6f}\Big{(}\Big{(}\mathcal{F}_{fc}^{5f}\Big{(}\mathcal{F}_{ fc}^{4f}(X_{t})\Big{)}\Big{)} \tag{7}\]  
The symbol \(\mathcal{F}fc\) denotes a fully connected layer, while \(Xf\), \(X_{v}\), and \(X_{t}\) represent the fused feature, visible light feature, and thermal infrared feature, respectively. The late fusion process is depicted in Equation (7), in which three fully connected layers are employed to learn feature representations for each feature type. Then, the scores obtained from the three modalities are combined using element-wise addition to produce a two-dimensional feature for background and target binary classification.
# Relay Selection Schemes for FSO Communications over Turbulent Channels
## 3 The Average Bit Error Rate (ABER) Analysis
### G-G Channels  
The CDF of the G-G channel taking into consideration the pointing error effect is substituted by signal to noise ratio threshold and is derived in [18] as  
\[F_{\gamma}(\gamma_{th})=I_{H}=\frac{\xi^{2}}{\Gamma(a)\Gamma(b)}\times C_{2,4} ^{3,1}\Bigg{[}\frac{ab}{\beta P_{M}}\Big{|}_{\xi^{2},a,b,0}^{1,\xi^{2}+1}\Bigg{]} \tag{26}\]  
where \(P_{M}=\sqrt{\frac{\pi}{\gamma_{th}}}\) is the power margin.  
The ABER over G-G channels at the receiving node can be derived by the same method that is derived in [11]  
\[ABER_{H}\approx\frac{1}{2\sqrt{\pi}}{\sum_{i=1}^{S}{\omega_{i}I_{H}}} \tag{27}\]  
Substitute Equation (26) in Equation (27), the ABER over the G-G channels while considering the pointing error effect at the receiving node is derived as  
\[ABER\approx\frac{1}{2\sqrt{\pi}}{\sum_{i=1}^{S}{\omega_{i}\times\Bigg{(}\frac {\xi^{2}}{\Gamma(a)\Gamma(b)}C_{2,4}^{3,1}\Bigg{[}\frac{2 abx_{i}}{\sqrt{\pi} \beta^{2}}\Bigg{|}_{\xi^{2},a,b,0}^{1,\xi^{2}+1}\Bigg{]}}\Bigg{)}} \tag{28}\]  
The ABER over G-G channels for the S-R-D, S-R, and R-D schemes can be derived by directly substituting Equation (26) in Equations (23), (24) and (25), respectively.
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 4 Agriculture Requirements for IoT
The WSN and actuator part consisted of sensor nodes for collecting the soil moisture and weather temperature, an actuator node to control the irrigation process, and a sink node that could pass all farm information to the cloud platform. All sensor, sink, and actuator nodes adopted low-power ZigBee wireless protocol. WSN and 3G network-based agriculture IoT was considered in [146] to monitor the air temperature, air humidity, and soil moisture. The entire system consisted of sensor nodes, actuators sink nodes, server, gateway, and the mobile phone device of the farmer. The collected information by the sensor nodes was sent to the server via gateway and Internet network. The gateway connected the WSN with the Internet to relay the command control among the sensor nodes, the phone of the farmer, the actuators, and the server. However, in this system, the data transmission over the 3G network must ensure real-time communications to support the agricultural WSN. IoT-based agriculture applications changed classical agricultural surveillance approaches by speedily providing quantitative data with significant temporal and spatial resolution. Several IoT platforms have recently been available in the market. For instance, in agricultural applications, SmartFarmNet [147] platforms can be used to provide more than 30 packages for good commercial products, such as Libelium [45], and experimental, such as Motes [148] and Arduino [149] sensor platforms. Other examples of agricultural applications based on IoT are provided in Table 8. The table presents a comparative analysis of several scholars in terms of sensors, actuators, IoT devices, and IoT platforms that can be used to support agricultural applications.
# Advanced Smartphone-Based Identification of Transport Modes: Resilience under GNSS-Based Attacks
## 3 Deceiving the Ticketing System
### Multisensor-Based Transport Mode Identification
#### 3.1.3 System-Under-Attack Check A  
The faulty navigation signal we generated for the journey, recorded in Munich, was introduced at minute 16 and consists of a modified GNSS position and GNSS velocity. The modifications were performed with the aim of replicating a bicycle trajectory. To this end, the GNSS position was moved from the bus lane to the bicycle lane and the GNSS velocity was reduced to 20 km/h. The length of the journey was reduced as well.  
As a result of the faulty navigation signal, the multisensor-based identification of transport modes obtains non-coincident outputs from minute 16 on; thus, the system is considered under attack and the identification of transport modes only based on the acceleration, the magnetic field and the air pressure are taken into account.  
The faulty navigation signal we generated for the journey, recorded in Paris, was introduced at minute 21 and consists of a modified GNSS position and GNSS velocity. The modifications were performed with the aim of shortening the journey. To this end, the GNSS position was modified to leave the station at Montparnasse and walk 500 m in the south direction and the GNSS velocity was reduced to 4 km/h to replicate walking.  
As a result of the faulty navigation signal, the multisensor-based identification of transport modes obtains non-coincident outputs from minute 21 on; thus, the system is considered under attack and the identification of transport modes only based on the acceleration, the magnetic field and the air pressure are taken into account.
## 4 Role of URLLC in Operating IoT
### Base Station Densification and Device-to-Device (D2d) Communications  
In typical automated industry, clusters of sensors and actuators are working in a fixed area. One of the crucial use cases for the 5G URLLC is to support the wireless industrial automation (e.g., Industry 4.0 [44]). With the emerging industrial automation, M2M and D2D communications require URLLC features to deliver short messages from a controller to a cluster of sensors or machines. A reasonable amount of traffic is expected to be handled by WiFi and small-cell-technology based on mmWave frequencies, as shown in Figure 11. The METIS project estimated that dense metropolitan areas might have up to 200 devices per km\({}^{2}\), with an expected data volume generated by each device could be 500 Gbyte/month [45]. Such an immense number of devices could force a drastic change in network infrastructure to avoid congestion and availability of service. With the limited frequency bands, improvement of the spectral efficiency (SE) could be an answer to support massive data.  
The massive MIMO network topology can theatrically support high-density traffic [46]. The theoretical performance and limitations of massive MIMO communications are extensively studied by a number of researchers [46; 47; 48].  
One of the main issues with massive MIMO is to manage data generated with unpredictable behavior [46]. It is essential to highlight that the building block of next-generation networks will be data packets, which show an unpredictable data generation behavior in non-streaming applications (such as social network applications and web browser).  
Figure 11: Illustration of a single user data packet and multiple user data packets relayed by a server/cluster head.  
Figure 10: Basic vehicle-to-vehicle (V2V) road safety.
## 2 Materials and Methods
### Phagocytosis Assay  
The phagocytosis assay was performed according to the previously published literature [7]. Briefly, 2 \(\times\) 10\({}^{4}\) cells/well were seeded into a 96-well culture plate and allowed to adhere for 24 h at 37 \({}^{\circ}\)C. The cells were treated with different dilutions (0, 50, 100, 200, or 400 \(\upmu\)g/mL) of the three algal extracts (SH, UP, and SF) separately for 24 h. The phagocytic properties of different algal extracts were examined using a CytoSelect 96-well phagocytosis assay kit (Cell Biolabs, Inc., San Diego, CA, USA) following the supplier's instructions. Briefly, the cells were incubated with or without the phagocytosis inhibitor cytochalasin D (2 \(\upmu\)M) followed by the addition of _E. coli_ suspension. After 4 h of incubation, the external particles were blocked. The phagocytized particles were examined at 450 nm using an Epoch Microplate Spectrophotometer (Biotek Inc., Winooski, VT, USA).  
\begin{table}
\begin{tabular}{c c c} \hline
**No** & **Gene** & **Primer Sequences (5’-3’)** \\ \hline
1. & IL-1\(\upbeta\) &
\begin{tabular}{c} GGGCCTCAAAGGAAAGATC \\ TACCAGTTGGGGAACTCTC \\ \end{tabular} \\ \hline
2. & TNF-\(\alpha\) &
\begin{tabular}{c} ATGACCAGAAAGCATGATC \\ TACAGGCTTCTCACTGAATT \\ \end{tabular} \\ \hline
3. & IL-6 &
\begin{tabular}{c} AGTTGCCTTTTGGAACTGA \\ CAGAATTGCCATGCACAAC \\ TTCAGAATTCCCTGACAAG \\ TGGTCAAACTCTTGGGTTTC \\ \end{tabular} \\ \hline
5. & COX-2 (Ptgs2) &
\begin{tabular}{c} AGAAGGAAATGGTGCAGA \\ GCTCCGGTCCAGTATTGAG \\ \end{tabular} \\ \hline
6. & IL-12 &
\begin{tabular}{c} GGAAGCACGCGACGAAATA \\ AACTTGAGGAAAGTGG \\ AGAAGCATGCCCAGAAATC \\ CCAAGGTTTTTTCCCTTAGC \\ \end{tabular} \\ \hline
8. & \(\beta\)-actin (Actb) &
\begin{tabular}{c} CATTGCTGACAGGAGG \\ TGCTGAAGGTGGACAGTGG \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Primer sequences of different genes of mouse origin.
# Mesoporous Carbon as Galvanic-Corrosion Activator Accelerates Fe Degradation
###### Abstract  
Iron (Fe) has attracted intensive attention as a bone implant material because of its inherent biodegradability, favorable biocompatibility and mechanical properties. Nevertheless, it degrades too slowly in a physiological environment, which limits its further clinical application. In this work, mesoporous carbon (MC) was introduced into Fe bone implant manufactured via a laser-additive manufacturing process. Particularly, MC possesses a noble standard corrosion potential and excellent electrical conductivity, thus acting as an effective cathode and activating micro-galvanic corrosion in the Fe matrix. More importantly, its high specific surface area enhanced the area ratio between cathode and anode, which further enhanced the galvanic corrosion effect. As a consequence, the corrosion rate was enhanced from 0.09 to 0.24 mm/year based on immersion tests. Besides, Fe/MC composite exhibited good cytocompatibility, as well as excellent mechanical properties. The positive results proved that the Fe/MC composite shows great potential as a bone implant.  
mesoporous carbon; Fe bone implant; degradation behavior; laser additive manufacturing +
Footnote †: journal: Applied Sciences
# Elastoplastic Analysis of Frame Structures Using Radial Point Interpolation Meshless Methods
## 4 Numerical Results
### Elastoplastic Analysis of 2D Frames
#### 4.1.2 Bowstring Bridge  
This example was developed by the authors. The purpose of this specific problem is the elastoplastic analysis of a commonly used structure in the industry. Due to the symmetry of the solid considered, only half of the total bridge will be analyzed. The geometry and dimensions of the bowstring bridge considered are represented in Figure 13(a).  
For the 2D model, the structure was analyzed with a nodal density of 6024 nodes, using the FEM, RPIM, and NNRPIM, combined with the full Newton-Raphson non-linear solution algorithm. Every element present in the structure has a thickness of 100 [mm]. The boundary and loading conditions are represented in Figure 13(b). The mechanical properties of the material, common structural steel, are presented in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mechanical Properties** & **Value** \\ \hline \(E\) [MPa] & 205,000 \\ \(E_{T}\) [MPa] & 6700 \\ \(\
u\) & 0.3 \\ \(\sigma_{Y}\) [MPa] & 230 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mechanical properties of the bowstring bridge.  
Figure 13: Evolution of the effective stress on the plastic connections given by the FEM (**a**), the RPIM (**b**), and the NNRPIM (**c**).  
The vertical displacement of point **A**, represented in Figure 14, for all three numerical methods, is presented in Figure 15.  
As it can be seen in Figure 15, the FEM and NNRPIM obtain almost equal results, in that the load/displacement curves for the two numerical methods are almost equal to each other. As for the RPIM curve, it follows the tendency of the FEM and NNRPIM up to a certain point. It then deviates to a lower level when compared with the other two curves. Nevertheless, all three results are very similar to each other, and good agreement between them exists.  
The effective plastic strain distribution maps for the FEM, RPIM, and NNRPIM, are presented in Figures 16-18.  
Figure 16: Development of the effective plastic strain for the FEM: (**a**) 8.62 \(\times\) 10\({}^{5}\) N/m. (**b**) 1.38 \(\times\) 10\({}^{6}\) N/m. (**c**) 1.72 \(\times\) 10\({}^{6}\) N/m. (**d**) 2.24 \(\times\) 10\({}^{6}\) N/m.
## 3 Proposed Algorithm
### Control Time-Slot Occupation and Collision Recognition Procedures  
Each vehicle searches for the control channel at the initial network entry, analyzes the frame information message received in the control time-slot, and occupies the control time-slot to be used. At this time, the empty control time-slot are randomly selected by the vehicle to prevent collision of control time-slot allocation. If a control time-slot assignment is successfully made, this control time-slot assignment information is normally represented in the FI messages of neighbor vehicles. However, if collision of the control time-slot occurs, the neighbor vehicle does not update its control time-slot allocation information. In this way, each vehicle can judge whether there is a collision of its control time-slot allocation through the FI messages of neighbor vehicles. If a control time-slot allocation collision occurs, 2-hop neighbor vehicles attempt to re-occupy one of the unassigned control time-slots.
## 2 Literature Review
In [16], Redundancy Elimination for Accurate Data Aggregation (READA) is proposed where it eliminates the duplicate data by applying the grouping mechanism for organizing the total network into clusters where each cluster acts as cluster head. It is a delayed process where data loss and accuracy issues may occurs. [PERSON] et al. suggest a structure-free real-time routing protocol for static sensor nodes in WSNs named RAG (Real-Time Data Aggregation) which exploits the temporal and spatial convergence of the data packets [17]. It takes advantage of available data by the waiting policy and aggregates the delayed data packets by the any-casting policy based on real-time data operating for the Medium Access Control (MAC) layer of the network. Some further modifications can be performed on the RAG protocol in order to be fit for mobile WSNs. Moreover, the spatial correlation-based data aggregation and enhancement for the smooth functioning of WSN applications can be implemented further.
## 2 Experimental Program  
### Engineering Background  
#### 2.1.1 Project Profile  
The Hong Kong-Zhuhai-Macau Bridge is an ongoing construction project which consists of a series of bridges and tunnels, which cross the Lingdingyang channel connecting Hong Kong, Macau and Zhuhai, three major cities on the Pearl River Delta in China. The Gongbei Tunnel in the Hong Kong-Zhuhai-Macau Bridge is a key project of Zhuhai connection of this bridge with a length of 2705 m.  
The Gongbei Tunnel is a critical link of the Hong Kong-Zhuhai-Macau Bridge. It is a double deck, three-lane in each direction roadway tunnel with a length of 255 m, including an 88 m transitional curve with a curvature ranging from 886 m to 906 m and a 157 m circular curve with a curvature of 886 m, shown in Figure 1. The excavation cross-sectional area is 18.8 m in width and 20.6 m in height, which represents the largest single tunnel excavation in China. The tunnel is buried within soft sandy and silty clay about 4 to 5 m below the ground surface at the Gongbei customer port. The construction steps include pipe jacking, ground freezing and excavation.  
Figure 2 shows the geological profile of the Gongbei tunnel. The ground surface is roughly at an elevation of 5 m and the top of sound rock is about at an elevation below \(-\)40 m, leaving more than 45 m thickness of soils and decomposed rock. Below the miscellaneous fill (stratum1), the natural soils mostly consist of clay and silt with interlayers of sands, deposited in a marine environment during the geological history. The tunnel is designed to be excavated in the stratum1, miscellaneous fill; stratum3-1, very soft organic clay and silt; stratum3-2, very soft to soft clay and silt; stratum3-3, loose to medium dense coarse sand; stratum4-3, soft to medium stiff sandy clay and silt; stratum5-1, soft sandy clay and silt; stratum5-2, medium dense fine sand; stratum5-3, medium stiff sandy clay and silt; and stratum6-2, medium dense fine sand. The tunnel is underlain by stratum7-1, decomposed rock; stratum8-1, completely weathered bedrock; stratum8-2, heavily weathered bedrock, and sound bedrock, granite. The groundwater table is close to the ground surface with elevation varying from 1.9 m to 3.5 m.  
Figure 1: The alignment of Gongbei tunnel.  
Figure 2: Geotechnical profile of Gongbei Tunnel.  
The soil layer related to in-situ test was stratum(r)-1 and r)-2, relevant soil parameters was shown in Table 1.  
The poor ground conditions, shallow burial depth, large opening area, small alignment curvature, complicated site conditions, and political sensitivity of the construction site make the Gongbei Port tunnel the most challenging engineering task of the Hong Kong-Zhuhai-Macau Bridge project.
# Technique Variables Associated with Fast Bowling Performance: A Systematic-Narrative Review
## 3 Results
### Study Characteristics  
All the studies investigated fast bowling biomechanics associated with BRS. A total of 11 studies included in the review adopted a cross-sectional study design and the other four studies adopted a longitudinal comparative [16], retrospective comparative [17] or cross-sectional comparative [10; 18] approach. All studies were conducted in a laboratory setting. While all studies tested male fast bowlers, the cohort varied across age (16-27 years), playing experience (recreational to elite) and bowling speed (25-40 m/s). Various data collection methodologies were used, namely (1) a combination of motion capture camera system with video cameras (\(n=6\)), (2) only motion capture camera system (\(n=4\)) and (3) only video cameras (\(n=5\)). The bowling protocol required bowlers to deliver anywhere between 1 and 48 balls, and the selection of deliveries to be analysed varied greatly across studies (Table 2). Statistical approaches to determine the relationships between biomechanical variables and fast bowling performance (ball speed, accuracy) also varied across studies (Table 2).
## 2 Theoretical Background  
Generally, there are two kinds of algorithms to solve the reconstruction problem of the CS signal, i.e., the greedy algorithm [57; 58] and the convex optimization algorithm [59; 60; 61]. Among them, the greedy algorithm is generally a heuristic method, which gradually builds the global optimal solution by selecting the local optimal solution every time. The convex optimization algorithm approximates the optimal solution step by step iteratively and has a convergence guarantee. For the problem with sparse structure, stable and accurate results can be obtained. The mathematical model for solving the signal inversion problem of compressed sensing under sparse conditions is defined as follows:  
\[\min_{x\in\mathbb{R}^{n}}\frac{1}{2}\|Ax-y\|_{2}^{2}+\lambda\|\mathbf{\varphi}x\| _{1}, \tag{1}\]  
where \(x\in\mathbb{R}^{n}\) is the signal to be reconstructed, \(A\) is the measurement matrix, \(y\) is the measurement value; \(\lambda\) is the regularization parameter, and \(\mathbf{\varphi}\) is the transformation matrix.
## 3 Analysis and Results of CGAN-IFCM
### Comparison Between CGAN-IFCM and CGAN-FCM  
To demonstrate the effectiveness of IFCM, performance of detection models using CGAN-IFCM and CGAN-FCM are compared and summarized in Table 7. The proposed CGAN-IFCM improves the TNR and TPR by 7.3-9% and 3.1-12%, respectively. Therefore, combining the results in Tables 6 and 7 shows that both CGAN and IFCM help improve the detection accuracy of voice disorder detection models.
## 5 Case Analysis
### Investigations of a Wind Farm in Gansu Province  
#### 5.1.1 Experimental Settings  
Wind power data from a 2-MW standard wind turbine located at a wind farm in Gansu province from 1 July 2014 to 31 August 31 2014 were used as the experimental data. The data was sampled at 10-minute intervals and normalized. The first 7000 points of data were used as the training set, and the next 1500 points were used as the test set. The input parameters of the point-based wind power prediction model were the previous nine wind power values, with the number of required data points determined by repeated trials. The input wind power sequence was decomposed into five modes using VMD, and five modes were reconstructed using PSR to obtain five matrices of \(3\times 5\). The matrices were fed into the first-layer CNN sub-models to obtain the predicted values of the modes. The predicted values of the five modes and the four previous actual values formed a \(5\times 5\) matrix, which was fed into the second-layer CNN to obtain the predicted values of the wind power. Finally, the wind power interval was obtained using PSO.