esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Generation  
#### 2.5.1 Water Table Position (WT)  
The study area for the water table position model consisted of 20 ha surrounding the eddy covariance tower (Figure 1b-d). The trees, boardwalks, and equipment sheds were masked out of all the SASI HSI flight lines following [31]. The narrowband index (NDWI\({}_{1240}\)) [14] index was calculated from the reflectance images as (Equation (1)):  
\[\mathrm{NDWI}_{1240}=\frac{\rho 884.5-\ \rho 1238.6}{\rho 884.5+\ \rho 1238.6} \tag{1}\]  
where \(\rho 884.5\) and \(\rho 1238.6\) are the SASI reflectance for the band centers closest to the original formulation of the index (i.e., 860 nm and 1240 nm). The average NDWI\({}_{1240}\) values from the 20 ha study area in the 18 flight lines were related to WT through ordinary least squares (OLS) regression.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Date** & **SZA** & **SAA** \\ \hline \multicolumn{3}{c}{**Spring**} \\ \hline
20 April 2016 & 35.8 & 154.0 \\
11 May 2016 & 30.0 & 151.6 \\
24 May 2016 \({}^{*}\) & 26.6 & 154.0 \\ \hline \multicolumn{3}{c}{**Summer**} \\ \hline
23 June 2016 & 24.7 & 149.2 \\
20 July 2016 & 28.7 & 145.2 \\
19 August 2016 \({}^{*}\) & 35.9 & 152.2 \\ \hline \multicolumn{3}{c}{**Fall**} \\ \hline
21 September 2016 & 46.3 & 164.5 \\
17 October 2016 & 53.6 & 168.2 \\
7 November 2016 & 62.9 & 167.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Date of acquisition of the Sentinel-2A scenes used in the study (Tile ID: 18 TVR). All scenes were acquired in descending orbits 54 or 97 with an inclination of 98.62\({}^{\circ}\). The units for solar zenith angle (SZA) and solar azimuth angle (SAA) are degrees. \({}^{*}\) Denotes scenes where high-altitude cirrus contamination following conversion to surface reflectance was found.
# A Review on the Performance of Linear and Mixed Integer Two-Stage Stochastic Programming Software
## 5 Scenario Decomposition Methods
### Dual Decomposition (DD) Method
#### 5.1.1 Cutting-Plane Method  
The cutting-plane algorithm solves the Lagrangian problem iteratively by implementing the outer approximation on (18) and solving the Lagrangian subproblems (17b) to improve the formulation of the relaxed dual function (\(RZ_{LD}\)) in Equation (19a). The outer approximation is given by the Lagrangian master problem (LMP):  
\[(LMP)\quad RZ_{LD}=\underset{\lambda_{\omega},\phi_{\omega}}{ \text{max}}\sum_{\omega\in\Omega}\phi_{\omega} \tag{19a}\] \[\text{s.t.}\;\;\;\phi_{\omega}\leq\bar{D}_{\omega}^{k}(\lambda_{ \omega}^{k})+(H_{\omega}x_{\omega}^{k})^{T}(\lambda_{\omega}-\lambda_{\omega}^ {k})\;\;\;\forall k\in K,\omega\in\Omega \tag{19b}\]  
where parameters for iteration \(k\) and scenario \(\omega\), \(x_{\omega}^{k}\) and \(\bar{D}_{\omega}^{k}(\lambda_{\omega}^{k})\), represent the previous solution of subproblem (17b), and parameter \(\lambda_{\omega}^{k}\) represents the vector of the previously considered dual multipliers. The dual search is outlined in Algorithm 2. It should be noted that (19) can be unbounded, especially in the first few iterations. An approach to avoid the unboundedness is to add bounds for the \(\lambda_{\omega}\) variables. An alternative way is to use the bundle method [48] instead of Algorithm 2.  
```
1 Set \(k\longleftarrow 0\), \(z_{LB}\longleftarrow-\infty\) and \(\lambda^{0}\longleftarrow 0\)
2 repeat
3 SOLVE (17b) to obtain \((x_{\omega}^{k},y_{\omega}^{k})\) and \(D_{\omega}(\lambda_{\omega}^{k})\) for given \(\lambda_{\omega}^{k}\) for each \(\omega\in\Omega\)
4 set \(z_{LB}\longleftarrow\max\{z_{LB},D(\lambda^{k})\}\)
5 ADD new optimality cut (19b) from \(x_{\omega}^{k}\) and \(D_{\omega}(\lambda_{\omega}^{k})\)
6 SOLVE (19) to obtain \(\lambda^{k+1}\) and \(RZ_{LD}\)
7 set \(k\longleftarrow k+1\)
8 until\(|D(\lambda^{k})-RZ_{LD}|/|D(\lambda^{k})+1e-10|<\epsilon\); return\(x_{\omega}^{k},\lambda^{k},D(\lambda^{k})\)
```  
**Algorithm 2**Cutting-plane dual search  
Cutting-plane methods present similar drawbacks as the BD algorithm, such as slow convergence and strong oscillations of the dual variables. This can be explained by the fact that BD is a special case of the cutting-plane method from the view of nonsmooth optimization. Various alternatives have been proposed to accelerate this technique, including the bundle method and the volume algorithm [47]. Additional strategies consider the suboptimal solution of the master problem, using an interior-point method (IPM) in combination with Benders-like cuts to tighten the Lagrangian subproblems (17b) and exclude infeasible first-stage solutions (see [29; 49]). Other methodologies such as cross decomposition, exchange information with BD to compute additional cuts derived from feasible first-stage candidates [50].
# Validation of Regional-Scale Remote Sensing Products in China: From Site to Network
## 5 A National Validation Network and an Operational Validation System
### An Operational RSP Validation System
Subsequently, the evaluation subsystem provides three methods in validating: direct validation, indirect validation and inter-comparison, according to the land cover heterogeneity. Finally, an evaluation report can provide validation results, particularly the data information, validation method, and accuracy of the RSP being validated. The above three parts of LAPVAS are built using various essential service modules, such as data-read service modules, data-insert service modules, data-associated service modules and  
Figure 5: The structure and workflow of the validation system of the LAPVAS. evaluation-analysis service modules, packaged by a standard web-service description language. Users can flexibly choose these service modules and combine them through the user interface to form a user-defined programme according to validation requirements. Technically, a framework called a service-oriented architecture (SOA) is adopted in LAPVAS. One promising feature of this SOA is the ability to modularise LAPVAS using low cohesion and loose coupling. In this sense, the modularised system can be managed in an operational way, which places less importance on the sequence of service modules in LAPVAS.
## 5 Experiment Analysis
### Experimental Results Analysis
#### 5.2.1 Win Rate Analysis  
In the win-rate-based analysis of this section, we record the win rates during the multi-agent training process. The win rate is calculated as shown in Equation (5). The experimental results demonstrate that DE-COMA improves the low convergence of the COMA algorithm and outperforms other excellent algorithms in specific environments. The experiment was conducted for a total of 1e6 time steps with an evaluation generation of 5000. The specific analysis is as follows: as shown in Figure 10a, in the 2s_vs_1 sc environment, the convergence curve of the improved algorithm proposed in this paper does not exhibit significant fluctuations as seen in the original algorithm. This improvement resolves the issue of large fluctuations in the win rate observed in COMA, maintaining efficient performance in the later stages. As indicated in Table 3, for the 2s1 sc scenario, DE-COMA shows a 21.06% increase in win rate compared to COMA. In this environment, we conducted a Friedman test on the recorded win rate data. The test results and rankings are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Name** & **Variable** & **DE-COMA** & **COMA** & **VND** & **QMIX** & **QTRAN** & **MAVEN** \\ \hline Training epochs & n\_timestpes & \(1\times 10^{6}\) & \(1\times 10^{6}\) & \(1\times 10^{6}\) & \(1\times 10^{6}\) & \(1\times 10^{6}\) & \(1\times 10^{6}\) \\ Evaluation generations & evaluate\_epoch & 5000 & 5000 & 5000 & 5000 & 5000 & 5000 \\ Replay buffer size & batch\_size & 32 & 32 & 32 & 32 & 32 & 32 \\ Greedy rate & epsilon & 0.5 & 0.5 & 1 & 1 & 1 & 1 \\ Population size & NP & 10 & - & - & - & - & - \\ Evolution generations & rounds & 5 & - & - & - & - & - \\ Mutation rate & factor & 0.7 & - & - & - & - & - \\ Crossover rate & CR & 0.7 & - & - & - & - & - \\ Decay rate & min\_epsilon & 0.05 & 0.02 & 0.05 & 0.05 & 0.05 & 0.05 \\ Discount rate & gamma & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental algorithm parameter table. Figure 10: Training convergence plots of win rates for six algorithms on StarCraft II 2s_vs_1 sc (a), 2s3z (b), 3m (c), 8m (d).
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 4 Measurements and Results  
To validate the functionality of the self-powered temperature sensor, a prototype was fabricated in a 0.18 \(\upmu\)m CMOS standard technology provided by Silterra. The microphotograph of the chip that contains the proposed self-powered sensor is shown in Figure 25. The yellow box highlights a silicon area consumption of 1065 \(\upmu\)m \(\times\) 560 \(\upmu\)m. The measurements and characterizations of the prototype were performed for the LF section, HF section, and the whole system.
## 4 Experiments and Results
### Results of Autoencoder-Based Denoising
#### 4.4.1 The Proposed Autoencoder Architecture  
The structure of our CDAE consists of 24 layers: an input layer, eleven 2D layers for the _encoder_ part, and twelve 2D layers for _decoder_ part. The 2D _encoder_ convolutional layers consists of a _rectified linear units (ReLU)_ activation function layer, a _dropout_ layer, and a 2D _max-pooling_ layer. The _convolutional_ layer \(C\) is used to learn the weights and biases of the spectrogram parameters. The _ReLU_ layer computes the \(max(0,x)\) element-wise activation function thresholding at zero. The _dropout_ layer is utilized for better generalization and to avoid overfitting. _Maxpooling_ is performed for downsampling the convolutional layer \(C\) to lower the spatial dimensions.  
The input data of the _encoder_ consist of a quantized spectrogram image with a size of (128, 128). The first hidden layer \(C1\) is a convolutional layer consists of 16 feature maps. Each feature map is connected to a kernel where the size of each kernel is (3, 3). The kernel is a small matrix that is used for feature detection. A _ReLU_ activation function layer is used for the _decoder_ convolutional layers, _upsampling_ is performed, and a _dropout_ layer is included to avoid overfitting. Overall the model has 74,304 parameters.
# Mechanical and Fatigue Behavior of Cellular Structure
## 3 Result and Discussion
### Fatigue Factor of Safety (Soderberg Approach)  
The fatigue factor of safety is one of the important factors used for the evaluation of the femoral stem's durability [23]. Taking into consideration the other factors that need to be included in the porous femoral stem design such as stress shielding, micromotion for enhancing the implant's stability through improving bone tissue ingrowth in order to avoid premature implant failures. Stresses encountered within the stem during the daily activities shall be computed and compared with the yield strength of the used porous structure [39; 40]. Most of the previous studies were predicting the fatigue limit using FEA for solid stems and under concentrated compression load, and less studies were found for porous stem [24; 25]/No literature was discovered on computing the fatigue life for FG porous stems in the radial direction using different physiological loads. Hence, in this study, the factor of safety was calculated for each design using Soderberg approach. This approach is efficient in terms of estimating the stem fatigue limit under high cyclic loads [24] and this approach provide the worst-case scenario among the other approaches (namely, Goodman and Gerber [24]).  
The maximum and the minimum stress were identified based on increments 1 and 10 of the FEA (ABAQUS) results, i.e., the cyclic stress ratio of R = 0.1 for calculating the minimum and maximum stresses as shown in Equations (3)-(6). The factors of safety (\(N_{f}\)) were calculated and the results for all designs with the three walking speeds are shown in Figure 9. The designs that have a factor of safety \(>\) 1 are considered safe under cyclic fatigue loading. None of the fifteen designs were found to be safe under a walking speed of 5 km/h. only Design 8 (D8) is safe to be used up to a walking speed of 3 km/h and the von Mises stress is less the associated yield strength at 1 km/h and 3 km/h. Designs D1, D2, D3, D5, D6, D8, D10, D11, and D15 are safe under walking speeds of 1 km/h all of these designs also had von Mises stresses less than their associated yield strength at 1 km/h walking speed. Most of the THA patients are normally have a maximum normal walking speed of 1.54 km/h and this level is less for the elderly patients [41; 42].  
Figure 8: Stress ratio between the relevant stem layer yield strength \((Y_{sl})\) and the maximum stress value of that layer (\(\sigma_{Lm}\)); (**a**) at 1 km/h walking speed, (**b**) at 3 km/h walking speed, and (**c**) at 5 km/h walking speed.  
The proposed femoral stem designs can be improved to enhance the fatigue factor of safety by adding an outer dense shell to the porous stem structure with different thicknesses [3] which will be part of our future study.  
Figure 9: Soderberg factors of safety obtained for the designed stems under the three loading conditions. (0.0 values shown for D12 and 14 denotes that no results obtained as the FEA simulation does not converge due to the high volumetric porosity at the outer layer of the stem).
# Mobility-Multihoming Duality
## 7 Discussion
### Transport Layer Congestion Control  
As discussed in Section 2.12 and noted in Table 1, transport layer protocols rely on the multihoming provided at the network layer for multipath transport. However, there is an important function that needs to be considered for packet transmission: congestion control.  
Congestion control is considered to be an end-to-end issue and so is typically implemented in transport layer protocols, such as TCP and QUIC. So, transport protocols control packet transmission, yet some level of transmission control is also required at the network level when multihoming is used.  
Our results show that multihoming at the network layer with ILNP can be compatible with TCP congestion control. Note that in our experiments, the Linux default TCP variant was used--TCP CUBIC [52]. Figures 9(a) and 15 show that the overall end-to-end throughput for TCP over ILNP and MP-TCP are equivalent for the same scenario. However, if we consider Figures 11(a) and 15(a), we see that the transmission characteristics are very different. This is due to MP-TCP using transmission control and scheduling at the transport layer and our experiments using DRR at the network layer. In our experiments, we found a smoother distribution of traffic across paths using TCP over ILNP compared to MP-TCP.  
Packet transmission scheduling for MP-TCP has been a key design consideration from its inception, and it remains a research issue. This original design study [61] considered that network layer transmission control using an Equal Cost Multipath (ECMP) [62] was compatible and usable with MP-TCP. A comprehensive study of approaches shows that round-robin scheduling at the network layer can also be beneficial for MP-TCP under some circumstances [63]. However, overall, at the time of writing, there is no single packet scheduling mechanism that is considered as a general approach for use with congestion control for multipath traffic.  
Overall, the use of mutihoming and multipath transmission adds a new dimension when considering the design of congestion control algorithms. There is already interaction between the network layer and the transport layer in order to obtain the benefits of Explicit Congestion Notification (ECN) in QUIC and TCP [64; 65; 66]. There are also numerous TCP congestion control algorithms with different characteristics for different scenarios [67]. So, we can in the future expect the definition of a number of new packet scheduling and congestion control algorithms for multihomed systems using multipath transport protocols.
# A Real-Time Numerical Decoupling Method for Multi-DoF Magnetic Levitation Rotary Table
###### Abstract  
Magnetic levitation technology shows promise for realizing multiple degrees of free precision motion for modern manufacturing, as the bearing and guiding parts are not used. However, motion decoupling in a magnetically levitated (maglev) system is difficult because it is hard to derive accurate magnetic force and a torque model considering the translation and rotation in all axes. In this work, a magnetic levitation rotary table that has the potential to realize unlimited rotation around the vertical axis and a relatively long stroke in the horizontal plane is proposed and analyzed, and the corresponding real-time numerical decoupling method is presented. The numerical magnetic force and torque model solves the current to magnetic force and torque transformation matrix, and the matrix is used to allocate the exact current in each coil phase to produce the required motion in the magnetically levitated (maglev) system. Next, utilizing a high-level synthesis tool and hardware description language, the proposed motion-decoupling module is implemented on a field programmable gate array (FPGA). To realize real-time computation, a pipelined program architecture and finite-state machine with a strict timing sequence are employed for maximum data throughput. In the last decoupling module of the maglev system, the delay for each sampling point is less than 200 us. To illustrate and evaluate real-time solutions, they are presented via the DAC adapter on the oscilloscope and stored in the SD card. The error ratios of the force and torque results solved by the numerical wrench model were less than 5% and 10% using the solutions from the boundary element method (BEM) program package Radia(tm) as a benchmark.  
magnetic levitation rotary table; numerical magnetic force and torque model; multiple DoF motion; motion decoupling; FPGA-based real-time computation Article
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
## 3 Methodology
### Jamming-Aware Routing Protocol (JRP)
#### 3.2.3 Routing Maintenance  
Due to the presence of the jammer, and particularly considering its mobility, the jamming value of a sensor node may change over time, leading to a possible failure of the node's successor node due to jamming. To maintain connectivity in the WSN, it is necessary to design a reasonable JRP maintenance mechanism, for which we propose two approaches.  
The first is local maintenance, in which a sensor node recalculates its target value and broadcasts a target value update when it perceives a change in its interference intensity that exceeds a threshold. Sensor nodes receiving the broadcast will recompare their own target values and select the node with the highest target value as the successor.  
The second approach is global maintenance, in which, after the WSN operates for a period of time, the base station initiates a depth broadcast to update the depth values of the sensor nodes. This is necessary because the presence of a jammer and the local maintenance mechanism can disrupt the propagation of messages, leading to an increase in the number of hops required for sensor nodes to deliver messages to the base station. The  
Figure 3: Path discovery example.  
global maintenance is similar to the depth broadcast in the initialization phase, but some sensor nodes may not receive the depth broadcast due to the jammer, which can affect the global update of the depth value.
## 1 Introduction
In summary, semiconductor photocatalysts contribute to the degradation of dyes in industrial water pollution by generating highly reactive species that can break down organic pollutants, including dyes, into simpler, less harmful compounds. This technology is considered favorable for industrial wastewater treatment due to its environmentally friendly method, low cost, and lack of secondary pollution. However, the efficiency of photocatalysts depends on operational parameters, and it is important to explore the nature of the sample to be degraded to optimize the technology's effectiveness [31]. [PERSON] and [PERSON] (2018) proposed a method for detecting thermal pollution using remote sensing and satellite imagery, specifically utilizing Landsat 8 with OLI and TIRS sensors. The results indicate an increase in temperature in the western channel. This research provides valuable insights into monitoring and addressing environmental concerns related to power plant operations [32]. [PERSON] and [PERSON] (2017) worked on the threat that microplastics pose to both marine ecosystems and human health. According to their study, common sources of microplastic pollution in the ocean include wastewater, rivers, and wind currents, as well as ship-generated litter and careless handling of plastic fishing gear. Additionally, microplastics and nanoplastics can enter the water channels via household and industrial -drainage systems as domestic effluents, such as cosmetic products, toothpastes, hand cleansers, and cleaning products. Additionally, microplastic pollution can have negative impacts on marine ecosystems, including reduced biodiversity and ecosystem function. specifically mentions that corals can ingest microplastics, which can affect their energy sources and food supply [33]. Overall, the literature on waste dumping in the sea demonstrates the need for comprehensive management strategies that take into account the types and sources of waste, the impacts on the environment and human health, and the socio-economic factors that drive the problem [34; 35]. However, further research is needed to address the gaps in our understanding of the problem and to develop innovative solutions that can protect the marine environment and support sustainable development [36].
## 6 Simulation Validation
### Experimental Results  
#### 6.2.1 Case Study 1  
Figure 12 presents the results of case study 1. The case study is set with a road adhesion coefficient \(\mu\) of 0.6, a semi-trailer load of 40 t, and a vehicle speed of 100 km/h. This figure compares the method in this paper with the Sigmoid function optimization method. In terms of path tracking, the trajectory of the method in this paper is better; in terms of LTR, the proposed method achieves a maximum LTR of 0.73249, significantly lower than the 1.0 threshold observed in the comparison method, indicating a lower rollover risk; the average steering angle, average yaw rate, and average lateral acceleration are also smaller than those of the comparison method, indicating that the method in this paper performs better in driving comfort and lateral dynamic stability. The comparison of the data from case study 1 can be found in Table 5.