esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
To overcome this issue, there are various routing protocols, such as the Ad-Hoc on-demand distance vector (AODV), dynamic source routing (DSR), optimized link state routing (OLSR), and geographic routing protocol (GRP) currently being developed [32]. An energy-efficient UAV-routing protocol for a WSN is proposed in [33], where the energy efficiency of the SNs and the dynamic distance with a traveling DS is considered to determine the shortest routing path. This type of optimization may perform better in a densely populated WSN and may not be suitable for a WARM application where the SNs are widely distributed. An UAV-WSN routing protocol is implemented in [34] based on the shortest total path, using multiple UAVs as agents, which is mainly suitable for a clustered WSN. Besides the WSN, there is some off-line or point-to-point data acquisition performed for WARM applications. Many mobile sensor nodes are implemented by being mounted on autonomous vehicles such as floating boats [35], amphibious vehicles [36], UAVs [37], and underwater vehicles [38]. All of these require a considerable amount of electric energy for their mobility. These vehicles may not be capable of traveling the long range required for a WARM application. Moreover, static SNs powered by a solar panel [39], wireless power transmission [40], and network-connected sensors using ZigBee [41] need a large number of BSs for wider coverage. Satellite-based wireless data transmission is used in [42] for remote locations, but it is cost-intensive when using a large number of SNs. Community-based learning and data gathering [43] using smartphone apps [44] are being studied for their cultural acceptance, accessibility, and cellular network coverage requirements.
## 4 Results
### The Influence of Temperature on the Power Generation Capacity of PV Glass Curtain Walls
#### 4.1.2 Influence of Temperature on Power Generation Capacity  
At 11:55 a.m., the surface temperature of the A\({}_{1}\) PV glass curtain wall was 39.7 \({}^{\circ}\)C, and its power generation capacity was 54 W. The surface temperature of the A\({}_{2}\) PV glass curtain wall was 28 \({}^{\circ}\)C, with a power generation capacity of 81 W. The surface temperature of the A\({}_{2}\) PV glass curtain wall was 11.7 \({}^{\circ}\)C lower than that of the A\({}_{1}\), resulting in an increase in power generation capacity by 27 W. At 12:15 p.m., the surface temperature of the A\({}_{1}\) PV glass curtain wall was 44.6 \({}^{\circ}\)C, and its power generation capacity was 155 W. The surface temperature of the A\({}_{2}\) PV glass curtain wall was 31.9 \({}^{\circ}\)C, with a power generation capacity of 405 W. The surface temperature of the A\({}_{2}\) PV glass curtain wall was 12.7 \({}^{\circ}\)C lower than that of the A\({}_{1}\), leading to an increase in power generation capacity by 250 W. At 12:50 p.m., the surface temperatures of both groups of PV glass curtain walls reached the highest values during the test period, with the surface temperature of the A\({}_{1}\) PV glass curtain wall being 50.7 \({}^{\circ}\)C and the surface temperature of the A\({}_{2}\) PV glass curtain wall being 35.6 \({}^{\circ}\)C. At this time, the surface temperature of the A\({}_{2}\) PV glass curtain wall was 15.1 \({}^{\circ}\)C lower than that of the A\({}_{1}\), resulting in an increase in power generation capacity by 99 W (Figure 9).  
At the same time, both A\({}_{1}\) and A\({}_{2}\) PV glass curtain walls received the same light intensity. Due to the lower surface temperature of the A\({}_{2}\) PV glass curtain wall compared to the A\({}_{1}\) PV glass curtain wall, its power generation capacity was significantly improved.  
During the period from 11:55 a.m. to 1:25 p.m., the average surface temperature of the A\({}_{1}\) PV glass curtain wall was 45.7 \({}^{\circ}\)C, with an average power generation capacity of 114 W. The average surface temperature of the A\({}_{2}\) PV glass curtain wall was 32.1 \({}^{\circ}\)C, with an average power generation capacity of 190 W. Compared to the A\({}_{1}\) PV glass curtain wall, the average surface temperature of the A\({}_{2}\) PV glass curtain wall decreased by 13.6 \({}^{\circ}\)C. In contrast, the average power generation capacity increased by 76 W (Figure 10).  
Figure 8: Surface temperature of A\({}_{1}\) and A\({}_{2}\) PV glass curtain wall.  
In summary, under consistent influencing factors, reducing the surface temperature of the photovoltaic glass curtain wall can significantly enhance its power generation capacity.
## 3 System Model
### Hybrid Beamforming Scheme  
BS first needs to digitally pre-process the signal in the baseband to compensate for the disadvantage that the RHS does not have a digital processing ability. Without loss of generality, we assume that the base station encodes the user signal and the artificial noise via vectors \(\mathbf{v}_{1},\mathbf{v}_{2}\), respectively, and then up-converts the signal to the carrier frequency via the RF chain and sends it on to the feeds connected to the RF chain. The number of RF chains is the same as the number of feeds on the RHS. Each RF chain is connected to a feed, and the signal is transmitted to the feed where it is converted by high-frequency current into an electromagnetic wave (which is also called a reference wave) that is transmitted on the RHS. The amplitude of the object wave excited at each radiation element is controlled by an amplitude controller to obtain the beam propagating in the desired direction.
# An Efficient Finite-Difference Stencil with High-Order Temporal Accuracy for Scalar Wave Modeling
## 2 Method  
### Review of the Staggered-Grid FD Scheme with the Cross-Rhombus Stencil  
The 2D constant density scalar wave equation for space \((x,z)\) and time \(t\) can be expressed as follows:  
\[\frac{1}{v^{2}}\frac{\partial^{2}p}{\partial t^{2}}=\frac{\partial^{2}p}{ \partial x^{2}}+\frac{\partial^{2}p}{\partial z^{2}}, \tag{1}\]  
where \(p(x,z,t)\) is the scalar wavefield and \(v\) is the velocity. The Taylor expansion of Equation (1) with respect to time [1] yields  
\[\frac{p^{1}-2p^{0}+p^{-1}}{\Delta t^{2}}\approx v^{2}\bigg{(}\frac{\partial^{ 2}p^{0}}{\partial x^{2}}+\frac{\partial^{2}p^{0}}{\partial z^{2}}\bigg{)}+ \sum_{j=0}^{N}w_{j}\frac{\partial^{2N}p^{0}}{\partial x^{2}\partial z^{2N-2j}}. \tag{2}\]  
Here, \(p^{l}=p(x,z,t+l\Delta t)\) is the discretized scalar wavefield, \(\Delta t\) is the time step, and \(w_{j}\) represents the weights of the spatial mixed derivatives. Then, Equation (2) can be approximated by the FD operator with a cross-rhombus stencil such that  
\[\begin{split}& a_{0}p_{0,0}^{0}+\underbrace{\sum_{m=1}^{M}a_{m} \left(p_{-m,0}^{0}+p_{m,0}^{0}+p_{0,-m}^{0}+p_{0,m}^{0}\right)}_{cross\ stencil}\\ &+\underbrace{\sum_{m=1}^{N-1}\sum_{n=1}^{N-m}b_{m,n}\left(p_{-m,-n}^{0}+p_{m,n}^{0}+p_{-m,n}^{0}+p_{m,-n}^{0}\right)}_{rhombus\ stencil}\\ &\approx r^{-2}\left(p^{1}-2p^{0}+p^{-1}\right),\end{split} \tag{3}\]  
Here, \(p_{m,n}^{l}=p(x+mh,z+nh,t+l\Delta t)\), \(h\) represents the grid spacing and \(r=v\Delta t/h\) is the Courant number, where the parameters \(M\) and \(N\) define the size of the cross- and rhombus stencils, respectively. The cross-rhombus stencil can achieve (2M)th-order spatial and (2N)th-order temporal accuracies when the FD coefficients are derived from the time-space domain dispersion relation [15].
# Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
## 2 Materials and Methods
### Groundwater Vulnerability Mapping through the DRASTIC Model  
The DRASTIC approach has been utilized as a standard method for comparison with the ML techniques used in the current research in order to evaluate the vulnerability of groundwater to contamination. To assess the groundwater vulnerability, the data for different parameters are obtained from different government departments (for reference, see [PERSON] et al. [3]). The input parameters for mapping the groundwater vulnerability through the DRASTIC model comes from the word DRASTIC itself, where every word is an acronym that specifies a certain parameter: depth of water (D), net recharge (R), aquifer media (A), soil media (S), topography (T), vadose zone impact (I), and hydraulic conductivity (C) [61]. Initially, the parameters are allocated the value ratings (r) from 1 to 10, and then assigned weights (w) from 1 to 5 based on the existing literature [3,62]. The DRASTIC model is a collective multiplication of factor weights with their rates (r), as shown in Equation (1) [3,63]:  
\[RASTIC\ Index\ (DI)=D_{r}D_{w}+R_{r}R_{w}+A_{r}A_{w}+S_{r}S_{w}+T_{r}T_{w}+I_{r}I_{w}+C_{ r}C_{w} \tag{1}\]  
where w is the specific parameter weight with the rate (r) in the DRASTIC model, representing w and r for specific parameters. The assigned weight and rating values to each parameter provisional to their comparative importance are given in Table 1.  
Figure 1: Map of the study area, showing the elevation and location of wells in the area.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Range** & **Rating** & **Weight** \\ \hline \multirow{4}{*}{Depth to water table (m)} & \textless{}40–60 & 7 & \\  & 60–80 & 5 & 5 \\ \cline{1-1}  & 80–100 & 3 & \\ \cline{1-1}  & \textgreater{}100 & 1 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Drastic parameters their classes, ratings, and assigned weights.  
Figure 2: Flowchart for the groundwater contamination risk mapping of the study area.
## 4 Trajectory Prediction Model Based on AIS Data and ACDE-SVM
### Results Analysis
#### 4.3.2 Comparison of Prediction Results from Different Optimization Algorithms  
To verify the superiority of the ACDE algorithm for optimization of SVR trajectory prediction model parameters, parameters determined by other optimization methods were used for comparison. Parameters optimized by the standard DE algorithm, PSO (particle swarm optimization) algorithm, grid search algorithm and GA (genetic algorithm) and the default parameters of the libsvm toolbox were used for trajectory prediction simulations. The predicted results were compared and analyzed and the error indicator values are given in Table 5. First, the prediction results of the ACDE-SVR model are compared with the standard DE-SVR model. The ACDE-SVR algorithm converges faster than the DE-SVR algorithm and each error index is smaller than that of the DE-SVR, which indicates that the ACDE-SVR trajectory prediction model has the advantages of faster convergence and higher prediction accuracy. In addition, the ship trajectory prediction models based on the parameters optimized by grid search and default toolbox values require less run time but have low prediction accuracies. Using parameters optimized by GA and PSO algorithms to build the model greatly improves the prediction accuracy but also increases the convergence time. The model based on the parameters optimized by the ACDE algorithm has the highest prediction accuracy, lowest run time and the best overall performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Sample Data**} & \multicolumn{1}{c}{\begin{tabular}{c} **Maximum Absolute Error** \\ _EMAX_/(°) \\ \end{tabular} } & \multicolumn{1}{c}{\
\begin{tabular}{c} **Mean Absolute Error** \\ _MAE_/(°) \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Mean Square Error** \\ _RMSE_/(°) \\ \end{tabular} } \\ \hline \multirow{2}{*}{With de-noising} & Longitude & \(3.475414\times 10^{-5}\) & \(1.216385\times 10^{-5}\) & \(1.562460\times 10^{-5}\) \\  & Latitude & \(3.957327\times 10^{-5}\) & \(1.345339\times 10^{-5}\) & \(1.660051\times 10^{-5}\) \\ \hline \multirow{2}{*}{Without de-noising} & Longitude & \(6.785011\times 10^{-5}\) & \(4.477475\times 10^{-5}\) & \(2.201641\times 10^{-5}\) \\  & Latitude & \(7.387650\times 10^{-5}\) & \(5.524124\times 10^{-5}\) & \(2.932073\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of trajectory sequence prediction errors with and without de-noising. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Penalty Factor _C_** & **Insensitive Factor _\(\epsilon\)_** & **Kernel Function Parameter _g_** \\ \hline Latitude & 20.7043 & 0.016 & 652.1897 \\ Longitude & 81.4909 & 0.0075 & 905.7929 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter optimization results of the ACDE algorithm.
## 4 Methodology for the Performance Analysis of the Physical Layer Proposals
### Standard and Controlled Channel Disturbances  
The overall analysis comprises 38 disturbance input sources selected in [10] as a set of representative channel disturbances to be found in the LV distribution grid. First, the ETSI TS 103 909 v1.1.1 [15] standard defines a real-world noise collection composed of 31 waveforms, which are well defined, repeatable, and supported by the scientific community. This collection aims to represent the most challenging situations for PLC communications. Second, this set of noises is completed with a selection  
Figure 5: Payload spectral evolution using tone map power reallocation with 500 Hz frequency resolution and a total transmission power of 120 dB\(\micro\)V.  
of disturbances generated by DER, which have demonstrated to be critical noise sources for PLC [23; 24]. Therefore, the complete selection of disturbances is composed of:  
* Twenty-five tonal noises (ETSI): Noise sources modeled come in the form of off-line AC switch-mode power converters.
* One periodic impulse noise (ETSI): Noise sources are generated by a triode for alternating current controlled lamp dimmer.
* One random impulse noise (ETSI): Series-wound AC motors are a very common source of this type of noise.
* Four intentional communicator noises (ETSI): Comprised of one waveform of a device complying with the ISO/IEC 14908-3 standard [25] and three waveforms of powerline intercoms (or baby monitors).
* Six DER noises (field measurements): The second source of representative noises was recorded in some field trials carried out close to several DER at facilities of the Centre for the Development of Renewable Energy Sources (CEDER) of the Research Centre for Energy, Environment and Technology (CIEMAT), a Spanish center for research, development and promotion of renewable energies [26]. A compilation of these noise recordings is available in [23; 27]. The six noises are identified as _der04_, _der06_, _der34_, _der36_, _der50_, and _der51_.  
Furthermore, the performance under additive white Gaussian noise is evaluated to serve as a reference whose results can be compared to other kinds of noise.
## 5 Application to a Specific Case Study: ITER Divertor Remote Handling System
### ITER MAMIC Model Definition
At the lower level, each node completes a reactive control loop driven by feedback from sensors. At the higher level, each node formulates and executes plans intended to fulfill task objectives, priorities, and limitations communicated by commands from above. Within each node, deliberative plans are integrated with reactive behaviors. * Communication domain: Different communication protocols need to be configured to establish efficient data handling with the RH devices, RH database, augmented reality, virtual reality, I/O input devices, third-party frameworks, CODAC workstations, and OMS applications. The ITER MAMIC model proposal aims to overcome the deficiencies or constraints of prior architectural models (anthropomorphic, predictive, cognitive, and hexagonal) by implementing the following strategies:  
* Adopting a clear, well-defined separation of concerns (bounded context) so the initial complexity can be managed more effectively. * Minimizing dependencies between modules and optimizing resource allocation, which can mitigate the risk of increased layers and abstractions. * Designing elements to gracefully degrade or adapt based on available capabilities, allowing for progressive enhancement as technology evolves. * Employing a scalable MAMIC model that can dynamically allocate resources based on workload can enhance scalability and reduce costs. * Implementing mechanisms for facilitating user understanding and control over technology interactions.
## 3 Results
### Number Analyzed, Outcomes and Estimation, Ancillary Analyses  
Twenty subjects for each group were included in each analysis. ANOVA test revealed significant differences (\(p\) < 0.0001) for all the clinical measures investigated. For both groups, results demonstrated significantly lower BI (Table 2 and Figure 2), MGI (Table 3 and Figure 3), and PI (Table 4 and Figure 4) scores at T1 (one month after baseline point) compared to those assessed at T0 (\(p\) < 0.05). No significant differences were showed between T1, T2 (three months after T0), and T3 (six months after T0) (\(p\) > 0.05), conversely statistically significant differences were found comparing all these values with respect to those assessed at T0. Additionally, no significant differences were reported between the two groups in each single time considered (\(p\) > 0.05). Although the average values of GI and PI at T1, T2, and T3 were lower in the intervention group compared to those in the control group, no significant differences between the two groups occurred (\(p\) > 0.05).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**BI** & **Mean** & **Standard Deviation** & **Intragroup/Intergroup Differences \({}^{*}\)** \\ \hline Control T0 & 0.56 & 0.27 & A \\ Control T1 & 0.26 & 0.22 & B \\ Control T2 & 0.23 & 0.17 & B \\ Control T3 & 0.30 & 0.24 & B \\ Test T0 & 0.54 & 0.26 & A \\ Test T1 & 0.24 & 0.22 & B \\ Test T2 & 0.25 & 0.20 & B \\ Test T3 & 0.27 & 0.29 & B \\ \hline \hline \end{tabular}  
*: different letters (A or B) show significant intragroup/intergroup differences (\(p\) < 0.05).  
\end{table}
Table 2: Descriptive statistics of BI results.  
Figure 2: Bleeding index (BI). Mean and standard deviation of the BI in the intervention and control group.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline PI & Mean & Standard Deviation & Intragroup/Intergroup Differences * \\ \hline Control T0 & 0.87 & 0.23 & A \\ Control T1 & 0.53 & 0.20 & B \\ Control T2 & 0.55 & 0.17 & B \\ Control T3 & 0.57 & 0.21 & B \\ Test T0 & 0.93 & 0.20 & A \\ Test T1 & 0.49 & 0.21 & B \\ Test T2 & 0.46 & 0.19 & B \\ Test T3 & 0.47 & 0.27 & B \\ \hline \hline \end{tabular}  
*: different letters (A or B) show significant intragroup/intergroup differences (\(p<0.05\)).  
\end{table}
Table 4: Descriptive statistics of PI results.  
Figure 3: Modified gingival index (MGI). Mean and standard deviation of the MGI in the intervention and control group.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**MGI** & **Mean** & **Standard Deviation** & **Intragroup/Intergroup Differences * \\ \hline Control T0 & 1.23 & 0.41 & A \\ Control T1 & 0.77 & 0.36 & B \\ Control T2 & 0.70 & 0.44 & B \\ Control T3 & 0.74 & 0.58 & B \\ Test T0 & 1.18 & 0.39 & A \\ Test T1 & 0.65 & 0.46 & B \\ Test T2 & 0.55 & 0.49 & B \\ Test T3 & 0.59 & 0.51 & B \\ \hline \hline \end{tabular}  
*: different letters (A or B) show significant intragroup/intergroup differences (\(p<0.05\)).  
\end{table}
Table 3: Descriptive statistics of MGI results.
# SiamCAM: A Real-Time Siamese Network for Object Tracking with Compensating Attention Mechanism
## 2 Related Work
### Attention Mechanisms  
Attentional mechanisms were first proposed in the neuroscience neighborhood and later extended to other fields such as image classification, target detection, tracking, and human re-identification. For the object tracking task, RASNet [33] is an end-to-end residual attention twin network framework designed to learn generic feature representations and simultaneously have adaptive discriminators. The algorithm draws on the idea of residual network learning. It decomposes the spatial attention mechanism into prior and residual attention with strong individual adaptation capability.  
[PERSON] [34] adds two attention modules to the framework of MDNet [35] to extract better features, a spatial attention mechanism, and a channel attention mechanism, respectively. It enables the network to extract discriminative and robust features or context through fused spatial channel attention. SA-Siam [36] considers a channel attention module in the semantic branch of their framework to improve the discrimination ability. IMG-Siam [37] considers the channel attention mechanism in the Siamese network to improve the matching model. Therefore, it separates the foreground and background of the object template in the template branch, extracts features and introduces attention separately, and then merges them to enhance the matching model. SiamFRN [38] introduces end-to-end features refine-based object tracking framework to improve the target representation utilizing semantic features. SCSAtt [39] employed channel attention and spatial attention mechanisms together to improve tracking performance with end-to-end learning.  
SATIN [40] is a target tracking framework based on keypoint detection. The algorithm uses the idea of centroid detection borrowed from CenterNet [41], transposing the keypoint detection method to target tracking and introducing a spatial channel attention mechanism to enhance the representational and discriminatory capabilities of the feature extraction network. Similarly, we introduce spatial attention mechanisms to each branch of the Siamese Network separately and add a compensating attention model to branch attention. By introducing the attention mechanism to enhance the representation and discriminative ability of the network.
# Design, Modeling, and Analysis of IEEE Std 80 Earth Grid Design Refinement Methods Using ETAP
## 2 Literature Review
### Increasing the Tolerable Safety Limits of Touch and Step Voltage  
#### 2.2.1 Fault Clearance Time Variation  
Since power systems consist of varying system voltage levels, for each of these voltage levels, the maximum permissible fault clearance time varies as well. The higher the system voltage gets, the quicker the fault needs to be cleared and, as a result, a low maximum fault clearance time is set by the system operators to protect the stability of the power system [9]. The fault clearance time consists of various factors that add together to determine the minimum time for the fault to be cleared from the system, which also depends on the type of relays (high speed or low speed), contacts, and circuit breakers used, including the relay fault detection response time, trip relay/circuit breaker operating time, and arc extinction [9; 10]. The fault clearance time is a critical parameter in both power system analysis and earth grid design as, along with the system load, the transient stability of a fault is mainly controlled by the fault clearance time [11].  
Fault Clearance Time Variation--Acceptable Operating Ranges  
Additionally, since power systems have unique protection philosophies and differ in size in terms of voltage levels (132/11/6.6/0.55 kV), they will have different parameters/settings, such as trip times, that are expected to accurately operate when required to allow for both maximum protection and a sufficient protection coordination time to avoid undesired trips [12]. The general norm for good practice of an earth grid design is to have a maximum fault current duration of 0.5 s as stated in IEEE Std 80-2013 [1]; this was adapted from a study conducted by [PERSON] and [PERSON] [13] that provides evidence that, when the time of exposure to the current is in the region of 0.06 s-0.3 s, the heart will not be susceptible to ventricular fibrillation. In addition, since high ground gradients resulting from fault currents are normally infrequent and the shocks are also infrequent and occur in a very short period, it would therefore not be practical to design against currents that are below the fibrillation threshold; hence, the use of a fault current time of 0.5 s is acceptable as the worst-case scenario [1].