esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods
## 4 Performance Analysis  
This section offers a set of numerical results with the objective to give the reader a better understanding of the performance of the considered approaches. The performance is analyzed from both computational complexity and localization accuracy point of view. Moreover, the results for average execution time of the algorithms are also included.
# Analysis of Water Quality Improvement Effect of Controlled River by Multi-Functional Weir Operation
## 1 Introduction
Extensive and intensive experiments are needed to evaluate the effect of improving water quality and reducing algae according to pulsed flow. It is difficult to evaluate the effect of pulsed flow with short-term measurement results. The reason is that the water quality concentration and algae density of rivers are the results of complex interactions between physical, chemical, and biological factors such as weather, water quantity, and water quality. Therefore, it is difficult to distinguish the effect of pulsed flow alone through experiments. The most efficient method is to use a model that can derive appropriate alternatives through analysis of various scenarios [10]. The purpose of this study is to analyze the water quality improvement effect through the pulsed flow of a multi-functional weir by modeling the hydraulic flow and water quality phenomenon using a three-dimensional EPDC model. The water quality improvement effect of pulsed flow was analyzed for the section of the Nakdong River from the Gangjeong-Goryeong Weir to the Dalseong Weir, which has become a controlled river due to the construction of multi-functional weirs. Through the analysis, changes in the concentration of variables determining the water quality were expressed as an increase or decrease rates (%). In addition, the increase and decrease periods of the concentrations due to pulsed flow were ascertained in order to arrive at measures for water quality improvement. It is intended to be used as the basic data for the multi-functional weir operation plan for water quality improvement.
# ROS-Based Human Detection and Tracking from a Wireless Controlled Mobile Robot Using Kinect
## 3 System Design  
Our design aims to achieve two main goals: Human detection and wireless remote control of a wheeled mobile platform. Human detection is achieved through the integration of 3D point cloud processes for human detection, tracking, and motion analysis, in one framework. Wireless remote control of the mobile platform is achieved by using an HTTP protocol.  
Figure 1 shows the architecture of the proposed system design. We use a Kinect device to capture 640 \(\times\) 480 24-bit full-color and 320 \(\times\) 240 16-bit depth images, which are used as the input of the proposed system. A laptop is used as a major image processing unit in this system to receive the captured visual sensing data through USB 2.0 port, which provides a high frame rate up to 30 frames per second (fps) for transmitting both full-color and depth images. The function of human detection and tracking is implemented in the laptop to detect the live motion of the humans in front of the mobile platform. This function helps the proposed system for using in real-time applications. The output result of live human detection and tracking can be observed on the display device connected to the laptop.  
As for the mobile platform, the Nodemcu esp8266 WI-FI module plays a major role. This module is integrated with integrated circuit (IC) and WI-FI shield on it. The IP address of the WI-FI module must be browsed in the cell phone, and then we will get the page with a control interface. There are four motors used in the mobile platform. Each motor receives the driving signal from the motor driver, which is connected to the WI-FI module to help the user in controlling the mobile platform wirelessly. Note that the WI-FI module of the mobile platform should connect to the same network with the cell phone.
# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 3 The Pilot Study--ML-Powered Natural Terrain Landslide Susceptibility Analysis
### The Study Area  
The study area comprises the natural terrain areas of the Lantau Island, as well as those of the adjacent outlying islands as indicated in Figure 4. It covers about one-fifth of the natural hills of Hong Kong (about 130 km\({}^{2}\) out of 660 km\({}^{2}\)). Over 30% of the study area is steeper than 30\({}^{\circ}\), with the elevation varying from sea level to 930 m above sea level. The study area is mainly underlain by volcanic and intrusive rock, with a small area of sedimentary rock. According to the ENTLI, there were over 6100 recent natural terrain landslides recorded within the study area. The study area experienced severely intense rainfalls in 1993 and 2008, with the 24-h maximum rolling rainfall measurements of over 500 mm and 600 mm, respectively. The rainstorm on 7 June 2008 alone resulted in about 2500 natural terrain landslides. Given the high variabilities in the terrain-related and rainfall data available within the pilot study area, as well as its rich history of past landslides, it is considered as an appropriate study area providing plentiful data for the pilot study.  
Figure 3: Workflow of the Machine Learning-based Landslide Susceptibility Pilot Study.
# Feasibility of a Patient-Specific Bolus Using the Life-Casting Method for Radiation Therapy
## 2 Materials and Methods
### Treatment Planning  
Treatment plans were used to assess the impact of the patient-specific boluses on dose distribution. We developed 3D conformal radiation therapy (3D-CRT) and volumetric modulated arc therapy (VMAT) treatment plans for each bolus. All treatment plans were created for the in-house phantom using Eclipse Treatment Planning System version 16 (Varian Medical System, Palo Alto, CA, USA). The anisotropic analytical algorithm and the photon optimizer algorithm were used for all plans. For 3D-CRT, two treatment plans were formulated for the in-house phantom using a SuperFlab bolus or patient-specific bolus. In all 3D-CRT plans, parallel-opposed tangential fields were employed using a 6 MV photon beam. The maximum dose rate was set at 600 monitor units per minute (MU/min), and a 10\({}^{\circ}\) dynamic wedge filter was used. To assess the clinical feasibility of patient-specific boluses using the LC method in complex radiotherapy scenarios, two VMAT plans were generated, one with a SuperFlab bolus, and the other with a patient-specific bolus. A 6 MV flattening filter-free photon beam was used, with rotating gantry angles of 300\({}^{\circ}\) to 179\({}^{\circ}\) (four arcs). The maximum administered dose was 600 MU/min. The prescription dose for the PTV in all plans was 50 Gy in 25 fractions.
## 4 Discussion
### Comparison of GHI Studies  
A study reported in [54] reported an RMSE of 73.8 W\(\cdot\)m\({}^{-2}\) and an MB of \(-\)3.7 W\(\cdot\)m\({}^{-2}\), indicating commendable accuracy but one with a slight bias towards underestimation. Conversely, [54] exhibited a higher RMSE, of 85.3 W\(\cdot\)m\({}^{-2}\), and a significant bias (MB = 20.8 W\(\cdot\)m\({}^{-2}\)), yet maintained a strong correlation with ground measurements (R\({}^{2}\) = 0.91). Our study surpassed both predecessors, boasting an RMSE of 65.0 W\(\cdot\)m\({}^{-2}\), a minimal bias (MB = \(-\)5.1 W\(\cdot\)m\({}^{-2}\)), and a similar R\({}^{2}\) value of 0.94. These results signify advancements in GHI estimation techniques, highlighting the efficacy of our methodological approach in achieving heightened precision and reliability.  
In the TAT location, located in Japan, over the period from 2010 to 2014, the variability in MB highlights the different challenges described in the study of [55]. ERA5 shows a negative MB of \(-\)1.2 kW\(\cdot\)m\({}^{-2}\), which may indicate underestimation of actual solar radiation levels. Both ERA-Interim and MERRA2 report positive biases (11.9 kW\(\cdot\)m\({}^{-2}\) and 19.1 kW\(\cdot\)m\({}^{-2}\), respectively), suggesting that these models tend to overestimate solar radiation in this location. Our study indicates an MB of \(-\)5.1 kW\(\cdot\)m\({}^{-2}\), further reinforcing the trend of underestimation observed in some datasets.
# A Review and Experimental Evaluation on Split Learning
## 2 Taxonomy and Review of SL Paradigms
### Model Split-Only Approaches  
A typical paradigm of this type is Vanilla Split Learning (VSL) [13], as shown in Figure 1, which partitions the model into two submodels, namely the server-side model \(\theta^{s}\) and the client-side model \(\theta^{c}\). As such, the complete model is represented as \(\theta=\theta^{s}\circ\theta^{c}\). Assuming that there are \(K\) clients, the local dataset for the \(k\)-th client (\(k\in[1,\dots,K]\)) is denoted as \(\mathcal{D}_{k}\), and, thus, the total dataset is \(\mathcal{D}=\cup_{k=1}^{K}\mathcal{D}_{k}\). The client model \(\theta^{c}\) is shared among all clients. When the \(k\)-th client training is performed, it uses \(\theta^{c}\) and samples \((X_{k},y_{k})\) from its local dataset \(\mathcal{D}_{k}\) to compute the model output \(\mathbf{z}_{k}=\theta^{c}(X_{k})\), which is called the smashed data [16, 46, 86], to transmit to the server. The server, in turn, uses \(\mathbf{z}_{k}\) to compute the loss \(\mathcal{L}_{k}\), update the server model, and send the backward propagated gradients (\(\mathbf{g}_{k}=\bigtriangledown\mathcal{L}_{k}\)) back to the client for updating \(\theta^{c}\). Once all samples in \(\mathcal{D}_{k}\) have been used, the weights of \(\theta^{c}\) are transmitted to the next client for training. Since each client must wait for the previous client to complete a training round before interacting with the server, it is important to note that, in the vanilla paradigm, the clients are trained synchronously. Based on how the next client acquires the global client model weights, the vanilla paradigm can be further divided into two paradigms:  
* Centralized: The client uploads the model weights to the server or a trusted third party. The next client then downloads the model weights and proceeds to train the model, as illustrated in Figure 1a. Figure 1: Illustration of the two VSL paradigms: (**a**) VSL (centralized) and (**b**) VSL (peer-to-peer).
## 4 Conclusions  
The development of AEM for AEMFC and AEMEC applications is still in its infancy stage. Conventional membrane fabrication methods such as solution casting, covalent crosslinking, filler inclusion and pore-filling have been used to produce AEMs. Compared to the conventional methods, the radiation-induced grafting technique has the advantages of simplicity, cost saving, energy efficiency and being green. In addition, this advanced membrane-processing technology is highly customizable, where the irradiation parameters such as radiation sources, rate, dose and environment can be adjusted to control the initiation and propagation velocity of the grafting process at the desired modification depth. Radiation-induced grafting enables the control of hydrophilic and hydrophobic domains to ensure good micro/nanophase separation in the AEM to facilitate high ionic conduction. Radiation grafting spacers onto base polymers also improves the chemical stability properties of AEMs. With the booming of the hydrogen economy and increasing demand of low-cost green hydrogen production, both AEMFC and AEMEC have gained substantial attention because of their potential to reduce the cost of power and hydrogen production, respectively. Although the durability of AEMs has remarkably improved over the years, the durability of AEMs remains the main challenge and requires further improvement to achieve commercial readiness.  
Conceptualization, K.L.L. and W.Y.W.; writing--original draft preparation, K.L.L., C.Y.W., S.S. and N.A.F.O.; writing--review and editing, K.L.L. and W.Y.W.; supervision, K.L.L., W.Y.W. and K.S.L.; project administration, K.L.L., funding acquisition, K.L., K.S.L. and H.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Malaysia Ministry of Higher Education (FRGS/1/2018/STG01/UKM/02/15), Universiti Kebangsaan Malaysia (GUP-2018-041 and PP-SELFUEL-2020), International Atomic Energy Agency (R23130), the Ministry of Science and Technology of Taiwan (MOST-108-3116-F-005-002) and SATU Joint Research Scheme (NCHU6).  
Not applicable.  
Acknowledgments.K.L. [PERSON] also acknowledges the MEXT Nuclear Researcher Exchange fellowship (IR-A-6) to undertake training in radiation technology at Takasaki Advanced Radiation Research Institute, Quantum and Radiological Science and Technology (QST), Japan.
# Stream: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection
## 4 Evaluation
### Interpolation Performance Comparison
It is mainly used to evaluate the quality of image or video compression algorithms, and, the higher the PSNR value, the lower the distortion between the original and compressed signal. The higher the value, the higher the quality. * SSIM measures the structural similarity between images and evaluates the similarity between the original image and the image being compared. It then quantifies image quality by considering brightness, contrast, and structural information. A high SSIM value indicates that the two images are structurally similar and of high quality, with values closer to 1 generally indicating higher quality. * FSIM measures similarity by considering the features of the image and is based on features and structural information in the frequency domain. FSIM is used to measure the information loss in an image and evaluate its similarity to the original image, with higher values meaning higher quality and closer to the original image. * SRE represents the ratio of a signal to the error that occurs in the process of reconstructing that signal. It is primarily used to evaluate how accurately a system reconstructs a signal through comparison between the original signal and the reconstructed signal. A higher SRE value means lower reconstruction error and higher accuracy. According to the table results, our proposed methodology shows superior performance compared to Kalman filter for all metrics. The results from our proposed methodology have RMSE values close to 0 for all labels, which means that the images are numerically close to the original data. Moreover, they have an SSIM value close to 1, which means that they are structurally similar to the original data.
## 4 Conducting the Cyber-Attack Scenario and Evaluating the Results
### Attack Platform
Among the various values contained in these response packets, we wanted to change in transit only the ones corresponding to CC_PumpSpeedCmd (to its normal value of 100, regardless of the actual value being transmitted by ANS). As Ettercap filtering was not designed with the Modbus protocol in mind, it was necessary to design a script that took into account: (a) Ettercap's filter syntax and offset addressing rules; (b) the specifics of the HMI implementation; and (c) ScadaBR's execution routines. Ettercap filter offsets (pointed to by DATA.data + OFFSET = \"value\") start at the beginning of the DATA section of the Ethernet frame, which coincides with the beginning of the Modbus TCP packet section for the Modbus TCP protocol. On the other hand, for the set of variables chosen to be monitored by the HMI, ScadaBR performed 3 sequential requests whose responses had fixed length and thus could be used as identifiers (Length: 251 [ 00 fb ] Registers 8-131; Length: 243 [ 00 f3 ] Registers 132-251; Length: 113 [ 00 43 ] Registers 280-311). We also knew that we must change only the contiguous registers located at 224 and 225, to the value 0X42C80000 = 100 DEC. With these considerations taken into account, it was possible to write the appropriate filter script, shown in Figure 18 and capable of changing only the desired packets and registers. Figure 17: Ettercap ARP poisoning (ANS and ScadaBR). It is understood that different configurations in the HMI would require adaptations to the presented script. Once again, we assume that the Insider has in-depth knowledge of the control architecture. It should be noted that in a real situation, we would also have several SlaveIDs for different PLCs, which in ANS correspond to internal control modules, all gathered under a single SlaveID.
# Wideband Channel Characterization for 6G Networks in Industrial Environments
## 5 Post-Processing and Parameters Extractions
### Path Loss Models  
Path loss characterization using empirical models with parameters adjusted according to the measurement scenario is a useful approach in planning engineering. Several models have been proposed in the literature to describe the dependency of the median path loss on the frequency and distance in typical indoor scenarios. In this study, the most commonly used models were considered: the log-distance CI model and the FI model. The CI path loss model can be expressed as [58]:  
\[PL^{CI}(f,d)[\mathrm{dB}]=PL(f,d_{0})+10n\mathrm{log}_{10}\bigg{(}\frac{d}{d_ {0}}\bigg{)}+X_{\sigma}, \tag{6}\]  
where \(PL(f,d)\) is the path loss at different frequencies with various Tx-Rx separation distances (\(d\) in meters), \(PL(f,d_{0})\) is the path loss at close-in distance \(d_{0}\) of 1 m or less in dB, \(n\) denotes the distance dependency of path loss, which is the PLE, and \(X_{\sigma}\) is a zero-mean Gaussian distributed random variable with a standard deviation of \(\sigma\) dB (shadowing effects). The FI path loss model is defined as [58]:  
\[PL^{FI}(d)[\mathrm{dB}]=\alpha+10\beta\mathrm{log}_{10}(d)+X_{\sigma}{}^{FI}, \tag{7}\]  
where \(\alpha\) and \(\beta\) are the floating-intercept in dB and the slope of the line, respectively. The shadow fading is represented by zero mean Gaussian random variable \(X_{\sigma}{}^{FI}\) dB with a standard deviation of \(\sigma\) dB.
## 3 Comparison of the Statistical Features between the Gaussian Model and SMART 1 Records  
In this section, existing approaches for describing the spatial variation of ground motions are briefly reviewed first. Then, a conditional method based on the Cholesky decomposition and the eigenvalue decomposition is developed to investigate the statistical features of the periodograms' amplitude variation. Normalized periodogram loop variance (NPLV) is defined to measure the periodograms' amplitude variation. The NPLV of the Gaussian model and the actual records are studied and compared.
## 5 Computational Complexity  
The computational complexity of a DL model can be estimated by calculating the number of floating point operations (FLOPs) required for each layer [51; 52; 53]. In the given network, the computational cost of the 1D-CNN layer with ReLU activation function is \(O(2\times\text{input channels}\times\text{kernel size}\times\frac{N}{2}\times F )=O(576N)\). The subsequent BN layer normalizes the feature maps, with a complexity of \(O(2\times\frac{N}{2}\times F)\) FLOPs. Moving on to the Bi-LSTM layer, the FLOP count is provided by \(O(8\times N\times Q\times(64+Q))\). The global max-pooling performs a simple pooling operation with a computational cost of \(O(\frac{N}{2}\times 2Q)\). Finally, the output dense layer with sigmoid activation function incurs a FLOP count of \(O((2\times Q\times m)+m)\). Summing these operations provides the total FLOPs for the HDL model as \(O(197376N+1028)\), providing a complete picture of the computational demands. This measure is crucial for model optimization and deployment, particularly in environments with limited resources.
# Design and Research of Thermoelectric Generator Simulation System for Boiler Flue Gas Waste Heat
## 4 Experimental Results and Analysis
### Experiment of Parallel Connection of Thermoelectric Plate
#### 4.3.2 Influence of Load Resistance on Output Power  
The variation curve of output power with load resistance for the thermoelectric plate in series and parallel mode is shown in Figure 19. The relationship between the output power and the load resistance is parabolic, and the position of the maximum power point is different in different combination modes.  
From the previous analysis, theoretically when the load resistance is equal to the equivalent internal resistance, the output power reaches the maximum value. The actual experiment findings suggest that the TEG system yields the maximum power when the load resistance is marginally greater than the internal resistance, with the ratio between the  
Figure 19: Diagram of Output power and load.  
Figure 17: Diagram of volt-ammetry characteristics.  
Figure 18: Diagram of voltage and power.  
load resistance and internal resistance falling within the range of 1-1.15. Therefore, for the TEG system, it is necessary to consider not only the influence generated by electrical energy, but also the influence generated by thermal energy on the TEG. When TEG is loaded, there will be Peltier effect and Thomson effect, and the larger the temperature difference, the more obvious the effect. At the same time, the higher the temperature, the internal resistance of TEG module will increase.
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 3 Problem Description  
The operational flow of TDVRPD-VRZ-NFZ is as follows: Each vehicle carries a drone and departs from the depot to deliver services to customers. If a vehicle passes through a vehicle-restricted zone during a restricted time period, it must detour. In this case, the customers within the vehicle-restricted zone are served by the drone. If a drone passes through a no-fly zone during a restricted time period, it must detour, and the customers within the no-fly zone are served by the vehicle. If a vehicle-restricted zone or no-fly zone is within a non-restricted time period, the customers in that area are normally served through vehicle-drone collaborative delivery.  
As shown in Figure 1, vehicle 1 departs from the depot, serves customer 1, and then proceeds to customer 2. At customer 2, the vehicle launches the drone to service the customer. Since the path between customer 2 and customer 4 crosses through no-fly zone 1 during a restricted time period, the drone detours along the edge of no-fly zone 1. Vehicle 2 serves customer 6 within vehicle-restricted zone 1 during a non-restricted time period before continuing with the rest of the deliveries. Vehicle 3 delivers to customer 15, launches the drone at this point, and then detours around vehicle-restricted zone 2 during the restricted time period to reach customer 17. After the drone serves customer 16, located within the restricted time period of vehicle-restricted zone 2, it is retrieved by the vehicle at customer 17. The vehicle and drone continue sequentially with all deliveries, before returning to the distribution center.