esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement Technique for Group Search Optimization Using Experimental Design Method
## 3 Proposed Method
### GSO Parameter Optimization by UD Method  
The first step of the GSO algorithm is setting the percentage of ranger \(X_{r}\), the initial angle \(\varphi^{0}\), the maximum pursuit angle of producer \(\theta_{\max}\), the maximum rotation angle of producer \(\alpha_{\max}\), and the maximum pursuit distance of producer \(l_{\max}\). Since these five parameters have the most important roles in GSO performance, an efficient and accurate search for the best parameter combinations is critical. Conventional parameter optimization methods have major limitations, i.e., the trial-and-error experimental method is not systematic, and the full-factorial experimental method has a heavy calculation burden. Therefore, this paper selects a systematic and efficient method, the UD method, to search for the best parameter combination in terms of enhancing the adaptive diversity of the GSO algorithm. The recommended settings are as follows [8]:  
* The percentage of ranger \(X_{r}\) is [0%, 100%];
* The initial angle \(\varphi^{0}\) is [\(\pi/8\), \(\pi\)];
* The maximum pursuit angle of producer \(\theta_{\max}\) is [\(\pi/2a^{2}\), 4\(\pi/a^{2}\)];
* The maximum rotation angle of producer \(\alpha_{\max}\) is [\(\theta_{\max}/8\), \(\theta_{\max}\)];
* The maximum pursuit distance of producer \(l_{\max}\) is [\(l_{\max}/8\), 4\(l_{\max}\)].  
The range of each parameter is then used to establish the uniform layout required to search for the best parameter combination for the GSO algorithm. Figure 3 is a flowchart of optimal parameters for the GSO algorithm when the uniform layout is selected. The detailed explanation is as follows. First, the ranges of the parameters are divided into levels according to the recommended parameter settings. Based on the number of parameters and the usage table, \(U_{10}(10^{5})\) uniform layout can be obtained, as shown in Table 6. The levels were put into \(U_{10}(10^{5})\) uniform layout, and Table 7 can be obtained. Next, the parameter combination from Table 7 was taken into GSO orderly to set the parameters and GSO was performed to evaluate the benchmark functions until all experimental combinations of the \(U_{10}(10^{5})\) uniform layout are run at least once. Finally, the best combination is obtained. The best combination can make producers and rangers update to a better position and scrounges can follow them.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **1** & **2** & **3** & **5** & **7** \\ \hline
1 & 1 & 2 & 3 & 5 & 7 \\
2 & 2 & 4 & 6 & 10 & 3 \\
3 & 3 & 6 & 9 & 4 & 10 \\
4 & 4 & 8 & 1 & 9 & 6 \\
5 & 5 & 10 & 4 & 3 & 2 \\
6 & 6 & 1 & 7 & 8 & 9 \\
7 & 7 & 3 & 10 & 2 & 5 \\
8 & 8 & 5 & 2 & 7 & 1 \\
9 & 9 & 7 & 5 & 1 & 8 \\
10 & 10 & 9 & 8 & 6 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: A \(U_{10}(10^{5})\) uniform layout.  
Figure 3: Flowchart of parameter optimization procedure for the GSO algorithm when using the uniform layout.
## 2 Related Work
### Feature Selection for Intelligent Data Analysis in Intrusion Detection Systems
#### 2.3.2 Filter-Based Feature Selection  
In [30], an intrusion-detection model (IDM) was proposed for intrusion detection. A hybrid IDM was proposed by combining a host-based intrusion detection system and a network-based intrusion detection system. Using multiple attributes from the collected data degrades performance; PCA and gain ratio are used for critical feature selection. In the detection model, SVM and NB classifiers are used as data-mining algorithms.  
In a PCA-based IDM proposed in [30], 3 host features were added to 41 features from KDD Cup 99 data and 17 features were selected, from a set of all 44 features, as the most critical features. The most critical features included 14 KDD features and 3 host features. It can be observed through detection experiments with SVM and NB IDM models that using only subsets of features achieves better detection performance than using the full set of features.  
Like [30], there are studies on anomaly detection using PCA-based feature-selection methods. In [31], feature selection and dimensionality reduction methods are proposed by applying an eigen connection approach based on PCA for anomaly intrusion detection. An attack classification model using k-nearest neighbors and a decision tree are applied to classify the KDD' cup 99 datasets. In [31], it was observed that using four principal factors was similar to or better than using the full feature set. In [32], a correlation-based feature-selection method was proposed as one of the filter-based feature-selection methods based on minimal redundancy-maximal-relevance feature selection. In addition, there are studies on feature-selection methods based on scores (e.g., information gain [33], mutual information [34], etc.) in the field of intrusion detection.  
The filter method has a lower level of performance improvement than the wrapper method due to the loss of information in the dataset. However, it has the advantage of being simple to apply and relatively fast in calculating feature selection. In this study, for comparison with the proposed method, a PCA-based filter method feature selection was applied to an intrusion detection model and tested.  
Figure 2: Type of feature selection method.
## 5 Results and Discussion
### Economic Analysis
#### 5.2.2 Cost Evaluation for Scenarios II and IV
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Equipment** & **Scenario I** & **Scenario III** & **Units** & **Variation** \\ \hline \multirow{4}{*}{A} & Electric heater & 1.56 & 0.78 & \(10^{6}\) USD & 100\% \\  & Electrical transformer & 0.45 & 0.45 & \(10^{6}\) USD & 0\% \\  & Heat exchanger & - & 1.72 & \(10^{6}\) USD & - \\  & Compressor & - & 0.53 & \(10^{6}\) USD & - \\ \hline B = 15\% of A & Installation of equipment & 0.30 & 0.52 & \(10^{6}\) USD & \(-73.3\%\) \\ C = 5\% of (A + B) & Engineering and procurement & 0.12 & 0.20 & \(10^{6}\) USD & \(-66.6\%\) \\ D = 10\% of (A + B + C) & Contingencies & 0.24 & 0.42 & \(10^{6}\) USD & \(-75.0\%\) \\ \hline E = A + B + C + D & Total construction cost & 2.67 & 4.58 & \(10^{6}\) USD & \(-71.5\%\) \\ \hline \end{tabular}
\end{table} 
Table 15: Total costs for Scenarios II and IV. Figure 8: LCOE and LCOS Comparison for Scenarios II and IV. The LCOS results for 2016-2021 are higher than those reported in the literature [79; 80]. This is primarily due to the oversizing of components such as the electric heater, which is not optimized for minimizing overall costs. Consequently, other elements dependent on the heater's size, such as the compressor and heat exchanger, are also oversized. It is worth noting that, in real-world conditions, curtailment electricity is often available at very low or even negative prices during periods of grid congestion. Therefore, the economic feasibility of the proposed configurations--particularly in terms of LCOS--could be significantly enhanced under such market conditions. Complete LCOS results are displayed in Figure 8.
# Memory-Non-Linearity Trade-Off in Distance-Based Delay Networks
## 4 Results
### Unoptimized Networks
performance on the NARMA-30 task (again, the same set for each evaluation). Because the input for measuring the IPC is distributed uniformly between \(-1\) and \(1\), when generating the network activity and NARMA-30 outputs, we rescaled the input to the range of \(0\), \(0.5\) required for the NARMA tasks. In Figure 3, we show the corresponding NARMA-30 NRMSE. Note that these are the exact same network initializations as in Figure 2. Again, we see that DDNs achieve a lower NRMSE, confirming findings in earlier work. More interestingly, we observe a (negative) correlation between areas of high task overlap in Figure 2 and areas of low task error in Figure 3. This correlation is particularly pronounced for reservoirs with a larger spectral radius. Moreover, in Figure 4, we see that these two metrics (task performance and overlap) are strongly correlated, with a higher task overlap being associated with a lower task NRMSE. Note that the ESN initializations are more clustered than the DDNs, whereas DDNs are more spread out, but reach a much lower NRMSE. Although we observe this strong correlation for both network types, we see that DDNs especially produce some instances that seem to behave as outliers.
# Software-Defined Small Cell-Linked Vehicular Networks: Architecture and Evaluation
## 4 Implementation of the V2x Data Dissemination over diSRsVN and Its Results
### Results Analysis in Comparison with Existing Works
If a traffic query request is generated by the nearest vehicle nodes, the SDN controller configures new path routing rules; otherwise, the distributed small cell RSU controller holds ideal route information for the source vehicles grouped in their respective cluster region, which is under the control of small cell RSU. At the vehicle density of 80, the proposed architecture has an E2E delay of 24.27 ms, which is lower than 30.64 ms in HetSDVN using a heterogeneous wireless interface, 25.27 ms in HDSV. HetSDVN, using a heterogeneous wireless interface, considers calculating the duration of the route before it is selected and injected into the local controller, which would increase the processing latency. The proposed scheme calls for the use of the assigned small cell RSU chosen based on the topology (classified in the cluster). As a result, the topology discovery quickens the location of small cell RSU.
## 5 The Technologies
### Mixed-Reality Solution  
VR and AR are two key solutions for building digitised manufacturing environments in the transition to the Industry 4 paradigm [141; 142]. VR is a computerised virtual context for people to immersively interact with real-world objects [132; 143]. On the other hand, AR is a technology for enabling the enrichment of physical objects by embedding digital information that can be visible to the users via various smart mobile devices [14; 131].  
The employment of AR and VR is called a mixed-reality solution, which paves the way to offer a new paradigm of learning and training methods in Industry 4 facilities [131; 144; 145]. With the employment of AR and VR technologies, new collaborative processes can be achieved through the entire product lifecycle [66; 147; 66]. For example, AR is mainly integrated in production processes to highlight the system's statistical information, and in assembly and asset management processes to provide dynamic and step-by-step instructions of operation [14]. On the other hand, VR can significantly enhance the training performance in a data-driven context [148; 149].
# Casting Process Improvement by the Application of Artificial Intelligence
## 3 Literature Review
### Improvement of the Pressure Die-Casting Process
The developed ANN models successfully established a correlation between process parameters--such as the pressure duration, squeeze pressure, and pouring and die temperatures--and important quality characteristics of the casting part, such as the surface roughness and tensile strength. They also used three population-based search and optimization methods: the genetic algorithm (GA), particle swarm optimization (PSO), and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD) to optimize multiple outputs simultaneously [39; 40]. The same group of authors--[PERSON] et al.--conducted a similar study by following other output sizes, such as the density, hardness and secondary dendrite arm spacing. They used differenttypes of neural networks such as the genetic algorithm neural network (GA-NN), back-propagation neural network (BPNN), and recurrent neural network (RNN) to establish a correlation between the mentioned outputs and inputs, which were represented by process parameters such as the squeeze pressure, pressure duration, and die and pouring temperatures. All of the developed models were able to make effective predictions. The obtained results will help the foundry personnel to automate and precisely control the squeeze casting process [41]. [PERSON] and [PERSON] optimized the squeeze casting process using an artificial neural network. Their study investigated the influence of the squeeze casting process parameters on AA6061/Al2O3/SiC/Gr hybrid metal matrix composite. The inputs to the neural network were the process parameters--the squeeze pressure, melt temperature, die temperature and pressure holding time--and the outputs were the mechanical characteristics: the hardness and tensile strength. The neural network has shown great potential in the prediction of mechanical properties (hardness and tensile strength). The neural network showed great potential in the prediction of hardness and tensile strength, such that its success rate was 95% [42]. [PERSON] et al. presented a smart-factory platform for the die casting industry. The mentioned platform is based on three core points: (1) cost-effective product-tracking technology, (2) an advanced process data acquisition system, and (3) a fault detection module based on an artificial neural network. The developed fault detection module based on an artificial neural network showed 96.9 % accuracy for untrained data [43]. Table 2 presents a brief overview of the analyzed studies of pressure die-casting process improvement.
# Physics-Informed Neural Networks for Modal Wave Field Predictions in 3D Room Acoustics
## 5 Results
### Adaptive Refinement of Training Set
Overall, the improvement was 60% with an improvement of 53.3% after the first refinement stage. Figure 7 summarizes the adaptive refinement procedure and the added data points to the training set. Notably, more training points were added in the central areas of the plot compared to the case of \(s\to\infty\). Interestingly, in Figure 6(b), the pressure results of the PINN are in good agreement with the FEM. Since the relative errors were significant for the cases of \(s<1\) m [5] (error of nearly 100%), we expect improvements by using the slightly damped material model for the reference FEM case to obtain non-resonant and hence a bounded solution (see Table 1). For this benchmark, the case \(s=0.2\) m is discussed, featuring an error of nearly 4.52%. We expect further improvement to the results from the adaptive refinement procedure. In the adaptive refinement step, the error was reduced to 0.76%, being an acceptable amount of error compared to the FEM results (Table 1). Due to the adaptive refinement, the point distribution showed a remarkably uniform behavior, and the loss is significant in a large area (see Figure 8). Furthermore, a strong cluster of points close to the source location at \(x=y=z=0.5\) m was visible and expected. Overall, the FEM result was captured by the neural network solution, which is illustrated in Figure 8. The findings suggest that the network's capabilities to solve the forward problem in 3D room acoustics is feasible. In summary to H3, adaptive refinement improves the  
Figure 6: The added points based on adaptive refinement for \(s/\text{m}\to\infty\) are highlighted in (**a**) in red, whereas the initial data points are the tiny black points. (**b**) shows the results of the acoustic pressure on the line \(y=z=0.5\) m in comparison to the analytic reference solution. PINN prediction's accuracy while the overall training time increases significantly with the number of adaptive refinement iteration cycles.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### Genetic Algorithm
#### 3.2.3 LEACH-GA  
LEACH-GA is an improved version of LEACH based on a GA [45]. In addition to the set-up and the steady-state of LEACH, LEACH-GA has a preparation phase where nodes initially perform the selection of the CH and determine whether or not each node should be a candidate CH (CCH). During the preparation phase, information like node status, IDs, and the location are sent until the BS. LEACH-GA is also a centralized protocol since the BS receives messages sent from all nodes and performs GA operations in order to determine the optimal arrangement which shall minimize the energy consumption in each round. The proposed algorithm determines the optimal threshold probability for the formation of a cluster. Results have shown that LEACH-GA improves the network lifetime than LEACH by increasing the number of nodes alive per round.  
Figure 4: Presentation of the Genetic Algorithm model.
## 2 Materials and Methods
### Wireless Sensor Network
#### 2.1.1 Hardware and Software  
The WSN is composed of a single master node, many sensor nodes, and their respective sensors. The master node is a TTGO board that contains an ESP32 microcontroller, an organic light emitting diode (OLED) display connected using the inter-integrated circuit (I2C) protocol, very useful for debugging applications as well as showing important information about the system status on screen, and a LoRa antenna that is interfaced through the serial peripheral interface (SPI) protocol. It is powered by a connected battery power bank, ensuring continuous operation for up to five consecutive days.  
The sensor node utilizes a TTGO board, similar to the master node, but requires additional electronic components due to the specific requirements of the sensor used to measure the considered soil parameters. The sensor has an RS-485 interface and requires a 12-volt (V) power supply, while the power bank only provides 5 V of direct current (DC). To address this, a MAX-485 module is employed to convert the serial signals to the RS-485 protocol. Furthermore, an XL6009 DC-DC step-up module converter is employed to boost the 5 V power from the source to the required 12 V for the sensor. Additionally, a 5 V to 3.3 V level shifter is used to reduce the voltage of the signals, ensuring the protection of the ESP32 microcontroller [14]. For a visual representation of the interconnected components, see Figure 2.  
The selection of the JXBS-3001 sensor for this application was based on its comprehensive capabilities in measuring temperature, humidity, pH, electrical conductivity, nitrogen, phosphorus, and potassium (NPK) values from the soil, all within a single device [38]. The manufacturer states that the device has a precision of \(\pm\)0.5 \({}^{\circ}\)C for temperature, \(\pm\)3% for humidity inside a 0-53% range and \(\pm\)5% for 53-100%, \(\pm\)0.3 for pH, and \(\pm\)2% full scale for NPK (from 0 to 1999 mg/kg range, which is approximately 40 mg/kg). Precision for electric conductivity was not provided.  
To obtain measurements using a microcontroller, a simplified serial request-answer communication protocol is established, and it is also defined by the manufacturer [38]. The microcontroller sends a single message containing specific information: the sensor's address code, function code (typically for reading or writing), start register address, data length, and two cyclic redundancy check (CRC) bytes. Upon receiving the request, the sensor responds with a packet containing its own address, the sent function code, the number of data bytes, the requested data, and two CRC bytes. This process allows efficient data exchange between the microcontroller and the sensor, facilitating reliable measurements without relying on external software and complex connections.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 2 Background
### Wearable Medical Devices  
The key features of WMDs for physiological monitoring include portability, which requires battery power. Additionally, the smaller form factor of WMDs limits the battery size, necessitating lower power consumption from various processes. The most common processes performed by WMDs are shown in Figure 1. Vital signs are captured using sensors which transduce physiological activity into corresponding electrical signals. These analog signals undergo signal conditioning and filtering before being converted into digital signals through the analog-to-digital conversion process. Afterward, the data are segmented, reshaped, and formatted via preprocessing for further analytical processing. Various algorithms, including AI models, are used for analysis. Based on the results, feedback is provided. Additionally, the raw data or final results are stored locally in the WMD or transmitted to another device for further storage or detailed analysis.  
All of the aforementioned processes consume power, which can be categorized mainly into _data acquisition, data processing and analysis_, and _data transmission_[3; 20]. Data acquisition involves collecting data from various sensors, where the continuous operation of these sensors consumes significant power. The collected data are then processed and analyzed to extract meaningful information, which involves complex computations and can be highly power-intensive, especially if the device needs to provide real-time feedback. The data are often transmitted to other devices, such as a smartphone or a cloud, for further analysis or storage. This transmission consumes a considerable amount of power. Each of these processes is crucial for the functionality of wearable medical devices, but they also contribute to the overall power consumption, necessitating efficient power optimization strategies in WMDs.  
On the other hand, the increasing demand for secure, real-time, comprehensive physiological monitoring has led to the incorporation of multimodal sensors and AI techniques, along with a shift toward edge computing. These factors are collectively increasing the demand for computational resources, making FPGAs, known for their high computational power and energy efficiency, an interesting choice to address this challenge.  
Notice, however, that the terms energy and power are often used interchangeably in the literature to describe power consumption. Additionally, various phrases are found in the literature describing the reduction in power consumption, such as _power optimization, energy optimization, low-power techniques (LPTs)_, and _energy optimization strategies_. In this work, we have most frequently used the phrases _energy optimization strategies_ and _LPTs_, describing the same concept.
# Analysis of Hydrological Characteristics of Blue Nile Basin, Nishe Watershed
## 2 Materials and Methods
### Data Collection and Analysis
#### 2.5.4 Weather Data  
The daily weather data required for the model are temperature (maximum and minimum), rainfall, solar radiation, wind speed, and relative humidity to simulate hydrological processes. The selected meteorological station was dependent on the presence of the data and representative of the total study area. Similarly, the selection of neighboring stations was based on horizontal distance (geographic distance) and correlation coefficient between stations. The necessary weather data were obtained for five stations (Alibo, Finchaa, Homi, Nashe, and Shambu) within and around the watershed. It is necessary to dedicate careful screening and quality checks for all data before use. The missing values were filled by Xlstat of selected stations before using for analysis. Additionally, the consistency of the data was also checked by a double mass curve and found to be consistent.  
The statistical data required to generate representative daily climatic data for the watershed are contained in the weather generator input file [31]. The weather generator parameters were assessed for rainfall parameters and temperature. To cross-check the parameters calculated the SWAT weather database was also used. Accurate rainfall data are critical for accurate representation of temporal and spatial uncertainties of simulated watershed-scale hydrology and water quality from models. In the numerical simulation precipitation is a critical input of the hydrological responses in a watershed. A precise reproduction of the spatiotemporal variability of precipitation is crucial to accurately simulate hydrological processes. The SWAT model distributes the meteorological data to the sub-basins using the data from a single station or cell data that are closest to the centroid of each sub-basin [32].