esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation of the Proposed Analytical Model  
Sun [29] employed the ABAQUS finite element software to construct a numerical model for assessing the impact of foundation pit dewatering on pre-existing tunnels. The dimensions of the numerical model were 240 m in length, 200 m in width, and 60 m in height. The foundation pit was situated at the model center, measuring 40 m by 45 m. Dewatering wells were positioned 2 m from the outer edge of the foundation pit. The model assumed that the water level remained constant at 100 m away from the foundation pit, unaffected by dewatering. A nearby existing tunnel was characterized by a burial depth of \(h_{2}\) = 21 m, an outer diameter of \(D\) = 6 m, a tunnel wall thickness of 0.5 m, and an elastic modulus of \(E\) = 19 GPa. Field geological exploration and laboratory soil test results [29] indicate that the tunnel passed through a loess layer, featuring an elastic modulus of \(E_{\text{s}}\) = 33.1 MPa, Poisson's ratio of \(v_{\text{s}}\) = 0.32, a soil density above the water level of 1650 kg/m\({}^{3}\), a soil bulk density below the water level of 650 kg/m\({}^{3}\), a soil permeability coefficient of \(k_{\text{t}}\) = 0.8 m/d, and an initial water level burial depth of \(h_{0}\) = 3 m. Given that the target water level was set to be 2 m below the bottom of the foundation pit, which was 22 m in depth, the water level drop was determined as \(s_{\text{w}}\) = 21 m.  
Figure 7 illustrates a comparison of tunnel settlements, with one set calculated using the method introduced in this paper and the other neglecting lateral soil effects. The figure presents settlement curves for tunnels spaced at 13 m and 25 m from the dewatering well. It is evident that the results obtained using the method proposed in this paper closely align with those derived from the numerical model analysis. In contrast, disregarding the influence of lateral soil actions results in notable discrepancies in the calculations. This discrepancy arises because both the method outlined in this paper and the three-dimensional numerical model take into account the constraining effect of lateral soil on tunnel settlement. Neglecting this lateral soil effect is inconsistent with real-world conditions, leading to significant disparities in the calculation outcomes. By comparing our proposed method with the three-dimensional numerical model, we have substantiated the accuracy and validity of our approach.
## 3 Results and Discussions
### LET Difference between Neutrons and Protons
To be noted, compared pair with high C\({}_{\rm v}\) may contribute more to the EI calculation, so that the LETs larger than 6 MeV-cm\({}^{2}\)/mg will dominate our conclusion about beam equivalency. Another comparison is with the work from [PERSON] et al. [22]; our results and theirs agree well for LETs lower than 15 MeV-cm\({}^{2}\)/mg. For higher LETs, no comparison can be made because the work of [PERSON] et al. has a 5 \(\upmu\)m Pb/Au layer which is not present in this study. The EI evaluation of the LET spectra is shown in Table 1, which shows that the 200 MeV proton has a LET spectrum with the best equivalence compared to the LANSCE neutron. The EI decreases with increasing proton energy, reaching the minimum at 200 MeV and increased at 230 MeV, with the exception for the 150 MeV proton, which may be questionable owing to the presence of a switch point for the hadron interaction around 150 MeV [14] in Geant4. The results shown in Table 1 are consistent with Figure 5 where 63 MeV proton gives higher differential fluence than the neutron, and 200 MeV proton gives lower differential fluence than the neutron in LETs \(<\) 10 MeV-cm\({}^{2}\)/mg. Of course, it is also possible that the best proton to replace the LANSCE neutron is with an energy between 105 and 200 MeV. Alternatively, a mix of different proton energies could also provide a better equivalence. All these possibilities are explored later. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **LANSCE** & **63 MeV** & **105 MeV** & **150 MeV** & **200 MeV** & **230 MeV** \\  & **Neutron** & **Proton** & **Proton** & **Proton** & **Proton** & **Proton** \\ \hline EI & 0 & 0.290 & 0.274 & 0.296 & 0.250 * & 0.285 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation index (EI) for LET in layer structure without SiGe. Figure 5: LET spectra in a structure without SiGe from 10, 30, 50, 63, and 200 MeV protons and LANSCE neutron.
# Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels +
## 3 Method  
The signal model in (1) has three sets of unknowns, namely DOAs \(\theta\), frequencies \(f\), and complex amplitudes \(c\). Each physical source may emit multiple frequencies as part of an FH pattern, and it may be observed over multiple DOAs for multipath channels. The goal of the process presented in this section is to determine physical sources, and for each physical source to determine an activity pattern and a FH pattern. Two main ideas are that even though a physical source may be observed over many DOAs, the individual DOA activity patterns of a physical source are \"similar\". DOA information is paired with FH information to associate FH patterns with physical sources. In the following, an approach is proposed that uses the received signal matrix \(\mathbf{Y}\) to estimate FH and activity patterns over the course of the observation interval \(T\). This approach includes an FH estimation stage, a DOA estimation stage, hidden state filtering to refine DOA estimates, and a pairing stage that combines information from the previous stages to associated FH patterns with physical sources. Figure 4 elucidates the relations between the signal processing tasks developed in this section. Each of the processing tasks is detailed next.  
Figure 3: Hidden Markov model (HMM) for a source.
## 4 Experimental Results and Analysis
### Ductility Capacity  
Ductility is the ability of a material to undergo substantial deformation beyond its elastic limit without experiencing failure. This study investigated the flexural behavior of RC beams retrofitted with modular steel plates, aiming to assess their deformation capabilities in terms of ductility. The displacement ductility ratio (\(\mu\)) is determined by dividing the displacement at maximum strength (\(\delta_{u}\)) by the displacement at the yield point (\(\delta_{y}\)), as shown in Equation (1). The yield point for this study is the intersection of the load-displacement curve and a line parallel to the initial stiffness, as outlined in a previous study [24].  
\[\mu=\frac{\delta_{u}}{\delta_{y}} \tag{1}\]  
The displacement ductility ratios for each test specimen, calculated using Equation (1), are presented in Table 3. The results indicate that incorporation of modular steel plates can enhance the ductility of RC beams by approximately a factor of two. This improvement is attributed to the steel plate strategically positioned on the tension zone of the RC beam, which mitigates crack propagation under bending loads. The specimens, labeled MSP-NC and MSP-L, which lack interconnected steel plates, exhibited comparable ductility irrespective of their modular dimensions. These specimens achieved a ductility index close to 2.9, nearly twice that of the control RC beam (CB). Conversely, the MSP-NC specimen with bolted steel plate connections achieved a ductility index of 4.6, almost three times greater than that of the CB. This value is approximately 1.6 times greater than that of specimens without interconnected steel plates, underscoring the notion that the enhancement attributed to the modularized steel plate is critically influenced by the integration efficacy of the plates.  
Figure 10: Load–displacement curves.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Name** & \(\mathbf{P_{y}}\) **(kN)** & \(\mathbf{P_{u}}\) **(kN)** & \(\mathbf{\delta_{y}}\) **(mm)** & \(\mathbf{\delta_{u}}\) **(mm)** & \(\mathbf{\mu}\) \\ \hline
1 & CB & 199.40 & 204.40 & 26.87 & 40.39 & 1.50 \\ \hline
2 & MSP-NC & 232.75 & 247.65 & 27.70 & 82.33 & 2.97 \\ \hline
3 & MSP-C & 290.45 & 311.95 & 25.38 & 116.27 & 4.58 \\ \hline
4 & MSP-L & 201.83 & 228.24 & 27.47 & 78.65 & 2.86 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Displacement ductility ratio.
## 4 Results and Discussion
### Sensor Configurations and Data Segmentation  
The evaluation utilized two sensor configurations--two-sensor (accelerometer and gyroscope) and four-sensor (which includes a linear accelerometer and gravity sensor)--inconjunction with two segmentation strategies: non-overlapping and 50% overlapping. The implementation of overlapping segmentation proved advantageous by preserving transitional information between activities, which is crucial for accurately capturing the dynamics of human motion. This strategy significantly enhanced model performance, leading to notable improvements in accuracy across all models, demonstrating the effectiveness of this approach in real-time activity recognition within ITS.
## 5 Conclusions  
In this article, we have designed a novel FHEOKG-SGC technique for the encryption and key generation process in the IoT environment. The presented FHEOKG-SGC technique accomplishes secure communication in the IoT network via group communication. To achieve this, the presented FHEOKG-SGC technique follows different processes such as FHE-based encryption, SCA-based optimal key generation, and PTA-based route identification in the IoT network. In addition, the FHEOKG-SGC technique employs a trust model to improve the secure communication process and key management center, which is used for the optimal handling of the keys. The simulation validation of the FHEOKG-SGC system was tested using a series of experiments, and the outcomes were studied under several measures. An extensive comparative study highlighted the improvement of the FHEOKG-SGC algorithm over other recent approaches, with a minimum encryption time of 2107 ms, decryption time of 1558 ms, and encrypted file size of 1531 kb. In the future, lightweight cryptographic algorithms can improve the performance of the FHEOKG-SGC technique.  
Conceptualization, methodology, software, validation, writing--original draft preparation: A.A. and R.A.; formal analysis, investigation, writing--review and editing, supervision, project administration: F.A. and S.B.A. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research, Jazan University, for supporting this research work through the Research Units Support Program, Support Number: RUP2-01.  
Figure 4: Delay outcome of the FHEOKG-SGC approach under varying counts of packets sent.  
**Institutional Review Board Statement:** Not applicable. This article does not contain any studies with human participants performed by any of the authors.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing is not applicable to this article as no datasets were generated during the current study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
### Limitations and Suggestions for Future Study  
Due to the cost and time-intensive nature of the experiments described in this study, we did not consider vegetation cover, the effect of slope, and soil material variability. Slope shape and surface roughness design was a relatively simple representation that minimized dimension effects; however, this is common for any mini-physical model experiment [22]. In our study, any dimension effect arises mainly from the underestimated rainfall impact and the \"boundary effect.\" A covering of PTFE (friction coefficient is 0.04) was installed on both sides of the flume in order to reduce the \"boundary effect.\" In the future, the impact of the slope effect and coupled rainfall, slope, and material effects on soil erosion could be our next research direction.
## 3 Temperature Field Model
### FEM Calculation  
The dimension parameters of the actuator are shown in Figure 1b and the thickness of the glue layer (Epoxy resin), which is used for adhering the piezoelectric ceramics (PZT-8) and actuator body (Ferronickel), and conducting film (Polyimide) is set as 0.01 mm. The material parameters of the actuator are shown in Table 1. The heat sources shown in Equation (4) determined that the analyzed model should be 3-D and include all the main structural parts. For solving the transient temperature field, the full method in COMSOL software is selected and the calculation time is set to 3 min with a time step of 1 min. The mesh of the actuator is shown in Figure 3.  
Figure 3: The mesh of the piezoelectric micro actuator.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multicolumn{3}{c}{**Young Modulus (c10\({}^{\text{10}}\) N/m\({}^{\text{2}}\))**} & \multicolumn{3}{c}{**Poisson Ratio (I)**} & \multicolumn{3}{c}{**Density (kg/m\({}^{\text{3}}\))**} & \multicolumn{3}{c}{**Heat Conductivity (W/(m-k))**} & \multicolumn{1}{c}{**Heat Capacity (J/(kg-k))**} & \multicolumn{1}{c}{**Damping Ratio (I)**} \\ \hline Ferronickel & \multicolumn{3}{c}{12.3} & \multicolumn{3}{c}{0.27} & 8100 & 13 & 450 & 0.003 \\ \hline \multirow{4}{*}{PZT-8} & \multirow{4}{*}{\(\left(\begin{array}{cccccccc}12.06&5.35&5.15&0&0&0&0\\ 5.35&12.06&5.15&0&0&0\\ 5.15&5.15&10.45&0&0&0\\ 0&0&0&3.13&0&0\\ 0&0&0&0&0&3.46\\ \hline\end{array}\right)\)} & \multirow{4}{*}{0.31} & \multirow{4}{*}{7600} & \multirow{4}{*}{8.7} & \multirow{4}{*}{935} & \multirow{4}{*}{0.01 [32]} \\  & & & & & & & & & & \\ \hline Polyimide & \multicolumn{3}{c}{0.3} & \multicolumn{3}{c}{0.25} & 1300 & 0.15 & 1100 & 0.01 [33] \\ \hline Epoxy resin & \multicolumn{3}{c}{0.1} & \multicolumn{3}{c}{0.38} & 980 & 0.2 & 1500 & 0.1 [34] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters of the piezoelectric micro actuator.
## 4 Experimental Evaluations  
This section presents the experimental evaluations of the system, comparing the vibration in the beam under different testing cases. This study proposes using different actuator vibration amplitudes, actuation frequencies, and signal durations as a means to control the vibration in the beam. The system's vibrotactile performance was experimentally measured in two studies: the single-actuator variable magnitude study and the dual-actuator pulse input study. These two experiments were collectively used to characterize the system and develop a means to control the vibrotactile feedback throughout the beam. In the future, this knowledge could be applied and extended to a large touchscreen plate with multiple actuators.
New measurement techniques, including EC-based method and model inversions based on EC, Geographic Information System (GIS) or Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance products, are the most common methods in current research to investigate the dynamics of site-level and regional-level ELUE [23; 24]. EC measurement determines carbon fluxes by the covariance between the vertical wind velocity pulsation and the concentration of CO\({}_{2}\) in the air mass [25]. The measurement provides a time-continuous and spatially integrated data set simultaneously with the advantage of minimal disturbance to the surrounding vegetation [26]. The open-access availability of fluxes and tower-based supplementary meteorology data provide important support to reveal the spatiotemporal variations and controlling variables of ELUE. Cultivated croplands covers about 12% of the ice-free land surface on the earth, provides a great deal of the food and fiber for humans and has great potential for greenhouse gas mitigation by Grow-Harvest-Grow management of the global ecosystem [26; 27; 28]. Summer maize, winter wheat, paddy rice and soybean are the most widely produced grains for humans and livestock globally [24]. However, the complex interactions among the environmental and biophysical factors on ELUE have seldom been systematically analyzed in the agroecosystems. In this study, we used EC fluxes and meteorological data collected from 11 EC towers with 4 different coverages (maize, winter wheat, paddy rice and soybean) globally to analyze ELUE. The objectives of the paper are (1) to reveal the dynamics of ELUE diurnally and daily, (2) to explore the mechanisms of how environmental and physiological variables affect ELUE dynamics and (3) to quantify the relative effects of the control factors on ELUE through the path analysis. This study is important for predicting the process of carbon storage and grain production of croplands in the context of climate change.
# Dynamic Graph Representation Learning for Passenger Behavior Prediction
## 4 Methodology
### Dynamic Representation Learning  
The passenger representations and station representations processed by the projection function have the same dimensions. This reflects the historical boarding and alighting behaviors of passengers, as well as their state (whether they are on or off the train) during the current interaction, over time. Additionally, we incorporate the inherent attributes of the station and the passenger flow changes over time. After obtaining the serialized representations, our goal is to use these representations to track changes in the boarding and alighting states of passengers.  
**Temporal Information Aggregation.** To achieve this goal, we use an MLP block composed of stacked feedforward neural networks (FFN) and activation functions. Considering the issue of potential overfitting due to the small number of boarding and alighting events for a single passenger, we use dropout to address this problem. The processed representations \(\mathbf{Z}_{*}^{t}\) are then passed through the stacked MLP modules. The entire process is as follows:  
\[\mathbf{r}^{l}\sim\text{Bernoulli}(p), \tag{4}\]  
\[\text{Dropout}(\mathbf{h}^{l})=\mathbf{\hat{h}}^{l}=\mathbf{h}^{l}\odot\mathbf{ r}^{l}, \tag{5}\]  
\[\text{FFN}(\mathbf{Z}_{*}^{t},\mathbf{W},\mathbf{b})=\text{Dropout}(\text{ ReLU}(\mathbf{Z}_{*}^{t}\mathbf{W}+\mathbf{b})), \tag{6}\]  
\[\mathbf{H}_{*}^{t}=\text{FFN}(\mathbf{Z}_{*}^{t},\mathbf{W},\mathbf{b}). \tag{7}\]  
where \(\mathbf{W}\in\mathbb{R}^{d_{E}\times d_{E}}\), \(\mathbf{b}\in\mathbb{R}^{d_{E}}\). After processing by the MLP model, the input features \(\mathbf{Z}_{u}^{t,0}\), \(\mathbf{Z}_{s}^{t,0}\) are transformed into output features \(\mathbf{H}_{u}^{t}\) and \(\mathbf{H}_{s}^{t}\). This transformed feature can be used for subsequent downstream prediction tasks.  
**Node Embedding Representation** The representations of passenger nodes and station nodes at time \(t\) can be denoted as \(\mathbf{h}_{p}^{t}\) and \(\mathbf{h}_{s}^{t}\). These are obtained by averaging the representations \(\mathbf{H}_{s}^{t}\) and then passing them through a linear output layer. The specific process is as follows:  
\[\mathbf{h}_{u}^{t}=\Bigg{(}\frac{1}{N}\sum_{k=1}^{N}\mathbf{H}_{u}^{t}[k,:] \Bigg{)}\mathbf{W}_{out}+\mathbf{b}_{out}, \tag{8}\]  
\[\mathbf{h}_{s}^{t}=\Bigg{(}\frac{1}{N}\sum_{k=1}^{N}\mathbf{H}_{s}^{t}[k,:] \Bigg{)}\mathbf{W}_{out}+\mathbf{b}_{out}. \tag{9}\]where \(\mathbf{W}_{out}\in\mathbb{R}^{d_{k}\times d_{out}}\) and \(\mathbf{b}_{out}\in\mathbb{R}^{d_{out}}\) are trainable weights, and \(\mathbb{R}^{d_{out}}\) represents the output embedding dimension.
# Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure
## 2 Materials and Methods
### Infrastructure and Backend  
Wearable devices should work regardless of the infrastructure available and use its energy wisely in any condition. We consider two different scenarios: a completely online infrastructure when both beacon and streaming messages can be received by stationary IP-connected gateways and a portable offline infrastructure when there are no stationary gateways in range and just a shepherd with a gateway that can receive beacons. The system infrastructure and communications flow are shown in Figure 6.  
In the first case scenario, we use a commercial gateway that connects to a TSP and send messages to a LoRaWAN Server. Then, we use MQTT integrations to deliver the data to a self-hosted database, and we provide a user dashboard where data can be visualized. It is the most straightforward way to deploy a LoRaWAN network. We use an additional LoRa gateway to route the FSK traffic that cannot be decoded by commercial gateways directly to our database.  
For the second scenario, we developed a gateway using an embedded system (Raspberry Pi) with headless Linux, where we install a custom LoRaWAN server that allows for offline logging and storage of payloads. GNSS payloads are decoded and sent to a web application that runs an offline maps viewer. It consists of a Leaflet map with OSM tiles and Flask server. Users can check the last reports received on interactive browser window in their mobile or tablet devices. A WiFi AP is deployed by the gateway to provide TCP/IP connection with the user mobile device.  
Both gateways use an 8-channel concentrator card that demodulates all channels at the same time and provides the encrypted payload to the host system. LoRaWAN payload is end-to-end encrypted with AES cryptographic algorithms [42], based on the AppKey parameter, known to node and server. In the online scenario the payload is sent to the server to decode it, but in the offline scenario the host system must use the key to decrypt the payload on the fly. The gateway also run a full featured LoRaWAN server for management of devices, database integration and input/output pipelines.  
Figure 6: System infrastructure and communication flow.