esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Consequently, researchers have introduced a two-dimensional water-filling (2D-WF) power allocation algorithm that functions in both the frequency and spatial domains, effectively enhancing the rate achieved by MIMO-OFDM VLC systems in low-pass VLC channels. Simulation results show that the proposed 2D-WF power allocation outperforms all other schemes regarding the achievable bit rate [28]. In a different study, researchers proposed various power allocation schemes for multi-input and multi-output OFDM transmission to reduce the bit error rate and evaluated the other solutions. Simulation results indicate a substantial improvement in the BER performance for QPSK and QAM modulation compared to the equal power allocation scheme [29]. Communication channel conditions can change in real time due to various factors, including smoke, fog, temperature fluctuations, and pressure differences. Additionally, resource limitations require that transmitter power be allocated to communication channels in a manner that maximizes data transmission rates. However, analytical power allocation methods can be unsuitable for real-time applications due to the extensive calculations and processing delays they involve. Therefore, it is more effective to employ faster methods, such as power allocation, using neural networks. In this research, power allocation is performed using analytical methods and neural networks implemented in an embedded system for various types of wireless optical communication channels, including those affected by fog, Rician fading, Rayleigh fading, and models based on the Gamma-Gamma distribution. Next, the graph showing the changes in the bit error rate and data transmission rate regarding the signal-to-noise ratio and transmitter power is presented for all cases. A neural model is trained using data from the analytical power allocation method. The trained model is implemented on the Jetson Nano to control power in the transmitter of the wireless optical communication system. Power allocation in OFDM optical wireless communication using embedded systems and analytical methods is shown in Figure 1.  
In this system, the inputs are the bit error rate and the data transmission rate, while the output is the optimal transmitter power for the wireless optical communication channel. This model allocates higher transmitter power to channels experiencing elevated bit error rates and lower data transmission rates. Low transmitter power values are allocated to channels with a low BER and high data rates to optimize power consumption and reduce interference. The results indicate that the proposed embedded power allocation optimizes transmitter power consumption, enhances the transmission rate, and reduces interference in optical wireless communication systems.
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 7 Landslides
### Reviewed Uses Cases for Landslide EW
The network consists of two types of LoRa nodes (Weather nodes and Ground nodes [98]) connected to a gateway, which communicates to an Edge AI cluster linked to a Cloud solution for storing and further processing using additional computing power. Compared to a Cloud-only approach, the developed solution allowed to reduce latency, bandwidth consumption, and amount of data transferred, even though the whole architecture had not been tested with a real event yet. A further improvement [99] was to introduce Federate Learning to the system, which is a distributed learning for Edge/Fog nodes which allows to continuously improve learning at this level and with different node resources. Another Edge solution is the one by [PERSON] et al. [96], who proposed an architecture called Reliable LEWS. The goal of their architecture is to maintain the system working even if the connection to the Cloud server is interrupted by implementing Edge nodes that are able to run the ML model created and trained in the Cloud. While the Edge nodes in this solution are unable to train the ML models by themselves, they can still provide predictions when the connection to the Cloud is gone by using the previously stored trained model and the data from the sensor nodes, which communicate with the Edge nodes using Wi-Fi. The study by [PERSON] et al. [97] developed a 5G Landslide EW system in China that monitors rainfall, ground fissure, and surface deformations in real time. Their solution uses two complementary modes for data transmission: (i) mesh mode, which enhances fault tolerance and reliability, and (ii) linear mode, which instead allows a more effective long range communication. The system also improves data reliability by using a dual communication mode with the BeiDou satellite module and GPRS for data transmission. The data from GNSS stations (used to track surface displacement), ground monitoring stations, and weather stations is continuously transmitted to a central server. They also developed a web interface that allows to visualize and monitor the data in real time. There are four levels for warnings which are chosen based on the tangent angle of the slope deformation.
## 4 Experiments and Results
### Lithium Polymer Battery Dataset  
We also evaluated our proposed method and the counterpart methods on the lithium polymer battery dataset with an in-house CBCT system. In the same manner as the experiment on the SPARE dataset, we evaluated the proposed method and counterpart methods.  
However, in the experiments on the lithium polymer battery dataset, we trained the neural networks on 200 projection images of \(1248\times 448\) in the proposed method (\(DPS\)) and other deep learning-based methods (\(N2V\) and \(N2S\)) and tested them on the same dataset which was used in the training phase. For each denoising method, the 3D images reconstructed with 200 denoised projection images were compared to the 3D image  
Figure 4: Two visual comparisons of various denoising methods from SPARE challenge dataset: (first and second rows) coarse architecture and (third and fourth rows) fine architecture. All images are in the axial view. Red boxes in the images indicate the expanded regions.  
However, the proposed method applied denoising on the reconstructed image domain, and the artifacts did not appear. Thus, the proposed method exhibited promising performance both quantitatively and visually. Figure 5 depicts the visual comparison of various denoising methods. The image quality was comparable to the target image. The distinction between anodes was especially better in the proposed method than the counterpart methods.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 3 Results
### Data Inputs
#### 3.5.4 Appliance and Technology Data  
Data illustrating the characteristics of the technologies analyzed in each study were another major category of datasets seen throughout this review [3; 6; 7; 10; 12; 14; 15; 21; 28; 31; 32; 34; 35; 36; 37; 39; 44; 54; 59; 60; 64; 65; 69]. This includes characteristics of household appliances, EVs, commercial loads, and industrial units. Characteristics include general load profiles for certain technologies, storage capacities, or cycle characteristics for various residential appliances.  
EVs are an increasingly important source of energy demand across various sectors, this requires special consideration in these models. Studies included datasets specific to EVs, aside from general characteristics of how they operate. Specifically, some included mobility data [40; 50; 55; 56; 64], EV charging data [39; 50; 55], and EV adoption data [21; 56]. These datasets provided information on how many EVs are present per region and how they are used; factors that can affect the overall demand on the grid.
## 3 SHINe Core Building Blocks
### SHINe Software Architecture  
The driving idea during the development of SHINe was to create a tool providing the building blocks to easily set up a SpaceFibre or SpaceWire network infrastructure, while the definition of the applications connected to this network is left up to the user. With this goal in mind, the main effort has been spent simplifying the Application-to-Network interface, hence most of the protocol-related metrics are observable and automatically recorded (packet latency, link usage, Flow Control Token (FCT) credit, generated packet size, received packet size). The main two core building blocks provided by SHINe are the SpaceFibre Endpoint and the SpaceWire Endpoint. They offer a C++ interface compliant with the service interface described in the two standards, completely hiding the protocols details to the application. Internally, both the SpaceFibre Endpoint and the SpaceWire Endpoint modules are composed by several nested submodules (Port and Codec), each one reducing the abstraction level down to the actual C++ implementation of the standard specifications. However, the application can treat the Endpoints as black boxes and it is not necessary for the user to know the details of the SpaceFibre and the SpaceWire standards.  
As shown in Figure 1, the user-defined Application must extend a specific C++ interface in order to be connected to the Endpoints. In such a way, it is possible for the Endpoint to call \"callback\" functions of the application without knowing their actual implementation. For example, the Endpoint may notify through a callback that new data is ready to be read or that the transmission buffer has a free slot to send new data.
## 3 Device Structure and Simulation Results
### Integrated ODL Simulation Results
Figure 8b summarizes the maximal group delay, power transmission, and wavelength by tuning the two heaters together at the push-pull mode. As shown in Figure 8b, the tuning of the input power of Heater 2, from 0 to 8 mW, causes the maximal group delay to vary from 17.81 to 150.81 ps with a similar power transmission change from 97.57% to 81.17% while the wavelength remains constant. Figure 6: Simulation of the group delay, power transmission, and wavelength at the resonant peak versus the input power of (**a**) Heater 2, and (**b**) Heater 1 when the other heater is fixed at 0 mW. Figures 9 and 10 show that the group delay can be tuned over one free spectral range (FSR) by using <30 mW of the Heater 1 power when the input power of Heater 2 is 0 to 8 mW for different group delays. Due to the periodic delay-time characteristics of the ring resonance, Figures 9 and 10 indicate that the proposed tunable delay lines can be tuned to track the wavelength of the input light sources over a wide wavelength range and provide the needed group delay. Figure 8: (**a**) Group delay as a function of the wavelength for [(H2, H1) = (0, 5), (2, 4), (4, 3), (6, 2), (8, 1)] mW, where H1 and H2 indicate the power to Heater 1 and 2, respectively. (**b**) Peak group delay, power transmission, and wavelength as a function of the input power of Heater 2. Figure 7: Contour plot of (**a**) wavelength and (**b**) maximum group delay as a function of input power of heaters.
# Enhancing Unmanned Marine Vehicle Security: A Periodic Watermark-Based Detection of Replay Attacks
## 2 Problem Formulation and Preliminaries  
The considered system Structure is presented in Figure 1, which is comprised of UMVs, a Kalman filter, and an LQG controller for setpoint optimization. The following analysis will explore the Takagi-Sugeno (T-S) fuzzy modeling for UMVs, the replay attacks, and the applications of Kalman filters and LQG controllers.
# Effect of Various Proportions of Rice Husk Powder on Swelling Soil from New Cairo City, Egypt
###### Abstract
It is available as an organic fertilizer or as animal feed. It can also be utilized to make paper, compressed wood, concert, and is made of light walls that are resistant to high temperatures. It can also be utilized to increase soil fertility and power generation [16; 17; 18; 19]. [PERSON] et al. [20] This paper presents the results of a laboratory study on the feasibility of using waste, lignin, and rice husk in the form of powder (RHP) and ash (RHA) to improve the strength and consistency of an expanding soil. Soil mixed with RHP ranged from 5% to 20%. The LL decreased from 148% to 132.2%, PI from 119.28% to 99.28%, swelling percent decreased from 11% to 8.25%, while the PL increased from 24.72% to 32.94%. The ideal value of RHP was 20%. [PERSON] [21] utilized RHA and fly ash to stabilize the expansive soil. After it was set up, the California Bearing Ratio (CBR) improved 47% by adding RHA from 0 to 12%. In contrast, by adding 0 to 25% fly ash, stress and strain increased from 106% to 50%, respectively. In conclusion, in this study, the optimal value of fly ash and RHA is 25% and 12%, respectively. [PERSON] and [PERSON] [22] compared the influence of RHA and lime on the geotechnical characteristics of the lateritic soil. Soil mixed with RHA ranged from 4% to 24% and lime from 2% to 12%. The plasticity index (PI) was decreased from 27.2% to 15.7%. The CBR value of RHA and lime was 77% and 59.4%, respectively. [PERSON] et al. [23] utilized RHA-lime to treat expansive soils. They added the RHA/lime 4:1 by weight for soil stabilization. The S and the SP decreased from 25.8% to 4.6%, 386 kPa to 32.1 kPa, respectively, by adding 20% RHA/lime. The compression index decreased from 0.244 to 0.067. Cohesion and the internal friction angle increased from 51.5 kPa to 189.3 kPa, 7\({}^{\circ}\) to 31.7\({}^{\circ}\) respectively. Recycled materials have reduced landfill space and opened a new portal for sustainable building practices in engineering applications. The primary goal of this work is to assess the improvement of the geotechnical characteristics of swelling soil by adding RHP in different percentages from 5% to 25% and testing the physicochemical characteristics of this mixture to reduce the cost of construction and the number of hazards. Furthermore, it provides a cost-effective solution for pollution prevention by converting harmful agricultural waste substances into useful products.
## 4 Active Systems
### Hot Water Production and Swimming Pools Heating
#### 4.2.2 Primary Energy Consumption in Existing Operation  
Diesel oil is exclusively consumed in Pancretan Stadium for hot water production. The annual consumed amount of 16,500 L corresponds to 128,550 kWh of final thermal energy, if we take into account the specific heat capacity of diesel oil equal to 10.25 kWh/L [53] and the efficiencies of the existing diesel oil burner and the heating distribution network equal to 80% and 95%, respectively, according to the issued energy performance audit for the existing heating production and distribution system.  
The total final thermal energy required for hot water production has been previously calculated at 261,015 kWh. The remaining thermal energy is supplementary produced by 12 electrical resistances of 12 kW each. These electrical resistances are evenly installed in four thermal storage water tanks (three resistances, 36 kW installed in each tank), of 5000 L volume capacity each, allocated in the four corners of the main Stadium. The same tanks are also supplied with the thermal energy produced by the diesel burner, before being distributed in the hot water network. The existing electricity consumption on the electrical resistances for hot water production is simply calculated by introducing the heating network distribution efficiency and by assuming the electricity to thermal energy conversion efficiency at 100%: \((261,015-128,550)\) kWh/0.95 = 139,436 kWh.  
Two air-to-air heat pumps are currently employed in the Stadium, with 25.8 kW and 16.8 kW electrical power input, which exclusively undertake the task of swimming pools heating. The imposed annual electricity consumption is calculated with the relationship (4), where \(\dot{\mathrm{O}}_{\mathrm{h}}\) is the swimming pools heating load (Figure 13a) and COP the involved air-to-air heat pumps coefficient of performance, retrieved from Figure 7a. The electrical power consumption from the existing air-to-air heat pumps for the swimming pools heating is presented in Figure 13b. By integrating the latter annual time series, the corresponding annual electricity consumption is calculated at 52,198 kWh.
## 5 Application and Simulation  
In this section, we describe the simulation of the information transmission process for a real wireless body area network. We chose  
* \(h_{1}^{r}=35+5*rand(1,1)\) as the temperature, within the range of [35; 40] (\({}^{\circ}\)C);
* \(h_{1}^{i}=70+130*rand(1,1)\) to stand for the high blood pressure value, within the range of [70; 200] (mmHg);
* \(h_{2}^{r}=40+60*rand(1,1)\) to stand for the low blood pressure value, within the range of [40; 100] (mmHg);
* \(h_{2}^{i}=60+40*rand(1,1)\) as the heart rate, within the range of [60; 100] (beats per minute, bpm); and
* \(l_{3}=3.9+6.1*rand(1,1)\) as blood sugar, within the range of [3.9; 10] (mmol/L).  
We obtained the CMPS error, as shown in Figure 8. The sender system collected data every minute, such that system errors were larger at the beginning of every minute, but quickly converged to zero. This indicates that CMPS takes place with complex scaling factors \(h_{1},h_{2},h_{3}\). In reality, the physiological condition of the human body does not suddenly change and, so, the CMPS controller is able to maintain synchronization in one sampling period. The information transmission process is depicted in Figures 9-13. The transmitted signal \(s(t)\) completely covered the information signal, while information signals \(h(t)\) were recovered with high precision. The above simulations demonstrate that the experimental results were in accordance with our theoretical analysis. The proposed communication system can quickly transmit information with high security.  
Figure 10: High blood pressure signal transmission process.  
Figure 9: Temperature information transmission process.  
Figure 11: Low blood pressure signal transmission process.  
Figure 12: Heart rate signal transmission process.
## 4 Proposed Methodology  
In this paper, we propose a system that can detect Bengali sign characters and numbers and can thus generate meaningful sentences from the detected sign characters. The overall architecture of the proposed system is illustrated in Figure 4. The system is divided into two segments: Sign-Character Detection (SCD) and Sentence Generation (SG). In the first phase, the input images or frames of video are passed to the detection model. Here, the YoloV4-Tiny detection model is quantized and hyperparameter-tuned to localize hand signs and predict characters of the corresponding signs. In the second phase, the encoder-decoder LSTM model is utilized to generate meaningful sentences from the detected sign characters.
###### Abstract
However, many of these previous works relied on synthetic datasets that did not accurately represent real-life water conditions. Moreover, their models were trained on limited datasets, which might hinder their applicability in real-world scenarios. To address these limitations, this research explores multiple ML models and ensemble methods, training them on a dataset collected from various surface areas in Bangladesh, as well as one other dataset. This proposed approach aims to provide a more comprehensive and robust solution for measuring surface water quality. This work aims to predict water quality using ML models that utilize historical data to make accurate classifications. The ML approach has become increasingly popular in recent times due to its cost-effectiveness and time-saving benefits compared to traditional methods. In addition, this methodology determines the quality level of a new dataset using WQI [17]. The main contributions of the research include the following:  
* An autonomous IoT-based water quality classification system is designed using an ensemble ML model, Terraform, Amazon Web Services (AWS), Telegraf, InfluxDB, and Grafana. These beneficial technologies enhance the system through effective infrastructure management, adaptable data handling, and comprehensive monitoring, along with visualization capabilities. * This study presents an enhanced machine learning model that ensembles three cutting-edge models (RF, XGB, and HGB). Before the model integration, each model's hyperparameters were carefully tuned, as they significantly influenced the final results. * To verify the proposed model's efficiency on regional water samples other than those from China, a novel dataset is introduced in this study, collected from the BWDB. The proposed model gained decent accuracy on the newly introduced data too. This study can assist with the BWDB's management, or that of any water monitoring board, of predicting water quality and improving water resource planning. The application of the IoT and ML to water quality monitoring could revolutionize the management of our water resources. These technologies provide real-time data and predictive insights, ensuring safe and usable water supplies for future generations.
# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model
## 3 Methods
### Generation of Training and Validation Data  
The size of SAR images is significantly large, and the typical size of a single image is several gigabytes. Therefore, a large amount of memory is required to learn an entire image during the training process. Due to insufficient memory of our graphics card, all 30 SAR images were divided into patches with a size of \(320\times 320\). Each patch image was overlapped by 20% to adjacent patch images to prevent ship objects from being cut, which would have resulted in them not being used for training and validation processes. As a result, we generated a total of 1160 patch images and divided them into training and test data in an 8:2 ratio. In other words, the numbers of images for the training process and the test process were 928 and 232, respectively. Furthermore, the entire training images were divided into a 3:1 ratio for cross-validation. In other words, the number of images for the training and validation dataset were 696 and 232, respectively.  
In each patch image, the ground truth was generated using Labellmg [36], and visual analysis and Google Earth software were used to identify the ship on the image.
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 3 Research Methodology
### Preprocessing  
As shown in Figure 1, the proposed system architecture of our study includes many techniques. This figure presents the overall architecture of the malicious URL detection framework using deep learning techniques, specifically the BERT model and the proposed Char2B model. The figure begins with dataset preparation, where both legitimate and malicious URLs are collected and merged into a single dataset. The preprocessing stageincludes several critical steps such as data imbalance handling, cleaning, normalization, tokenization, and dataset validation to ensure data quality and consistency. Following preprocessing, the dataset is split into training and testing sets to facilitate model learning and performance evaluation.