esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Formulation of HRESs
### Formulation of the WT System  
The mathematical model for the WT's power is calculated using the following equation [31]:  
\[\left\{\begin{array}{ll}P^{wt}(t)=0&v(t)<v^{ci},\\ P^{wt}(t)=0&v(t)>v^{co},\\ P^{wt}(t)=(a.v(t)^{3}-b.P^{wt}_{v})&v^{ci}<v(t)<v^{r},\\ P^{wt}(t)=P^{wt}_{nom}&v^{r}<v(t)<v^{co},\end{array}\right. \tag{3}\]  
where \(P^{wt}\) is the output power generated by the WT at time slot \(t\). The symbol \(v\) depicts the wind speed, and \(P^{wt}_{nom}\) shows the nominal rated power generated by the WT in time slot \(t\). Rated, cut-out, and cut-in wind speeds are represented by symbols \(v^{r}\), \(v^{co}\), and \(v^{ci}\), respectively. \(a\) and \(b\) represent parameters calculated by Equation (4):  
\[\left\{\begin{array}{ll}a=P^{wt}_{nom}/((v^{r})^{3}-(v^{ci})^{3}),\\ b=(v^{ci})^{3}/((v^{r})^{3}-(v^{ci})^{3}).\end{array}\right. \tag{4}\]  
If \(N^{wt}\) number of WTs exists, then the overall produced power \(\xi^{wt}\) at time slot \(t\) is obtained by \(N^{wt}\times P^{wt}(t)\).
# Research on an Intelligent Test Method for Interconnect Resources in an FPGA
## 2 FPGA Interconnect Resource Structure and Fault Model Analysis
### Fault Model Analysis  
Interconnect wires within the FPGAs can be divided into two categories: metal segment lines and programmable interconnect points. Each of these interconnect types can experience different types of faults.  
Metal line segments may experience open/short faults, bridging faults, and stuck-at faults. An open circuit fault may occur if the metal line segments disconnect during the manufacturing process due to deviations in the process. A short-circuit fault can cause interference to the shorted components during the transmission of the signal. Such faults can cause signal transmissions to the short-connected parts during transmission. Bridging failures may occur if the metal segment line is shorted together with the metal interconnect of an adjacent channel due to process deviations. This type of failure can cause coupling problems during the signal transmission. If the metal interconnect wire is short-circuited with adjacent components due to process deviations, this can result in the signal being fixed at a certain level, such as being shorted to VDD or GND, which is manifested as a fixed 0 fault (SA-0) or a fixed type 1 fault (SA-1).  
Figure 3: PIP structure and transmission schematic.  
Figure 2: Schematic diagram of FPGA switch matrix and CLB structure.  
As shown in Figure 3, the minimum unit of the programmable interconnect points consists of an SRAM and an NMOS (N-Metal-Oxide-Semiconductor) transmission gate, forming a switch that can be controlled by the SRAM. When the value stored by the SRAM is one, the switch is closed, and the NMOS and connection points, END and BEG, are on, enabling signal transmission. Conversely, when the value stored by the SRAM is zero, the switch is open, and NMOS gate is disconnected, and the END and BEG connection points are not conducting, thus preventing signal transmission. Since the probability of failure of the NMOS transmission gate is much less than that of the SRAM, we only discuss the failure of the SRAM.  
If the programmed data cannot be written correctly when the SRAM fails, the data stored by the SRAM becomes fixed at 0 or 1. This is known as a stuck-at-0 or stuck-at-1 failure. A stuck-at-0 failure occurs when the programming data cannot be written correctly internally by the SRAM, maintaining a fixed value of 0 that cannot be modified. When this fault occurs, the PIP will normally be open and cannot be programmed to turn on. Conversely, a stuck-at-1 failure occurs when the SRAM cannot write the programming data correctly, resulting in a fixed value of one that cannot be modified. When this fault occurs, the PIP will normally be closed and the PIP will always be on [11; 12; 13].
###### Abstract  
In modern years, network edges have been explored by many applications to lower communication and management costs. They are also integrated with the internet of things (IoT) to achieve network design, in terms of scalability and heterogeneous services for multimedia applications. Many proposed solutions are performing a vital role in the development of robust protocols and reducing the response time for critical networks. However, most of them are not able to support the forwarding processes of high multimedia traffic under dynamic characteristics with constraint bandwidth. Moreover, they increase the rate of data loss in an uncertain environment and compromise network performance by increasing delivery delay. Therefore, this paper presents an optimization model with mobile edges for multimedia sensors using artificial intelligence of things, which aims to maintain the process of real-time data collection with low consumption of resources. Moreover, it improves the unpredictability of network communication with the integration of software-defined networks (SDN) and mobile edges. Firstly, it utilizes the artificial intelligence of things (AIoT), forming the multi-hop network and guaranteed the primary services for constraints network with stable resources management. Secondly, the SDN performs direct association with mobile edges to support the load balancing for multimedia sensors and centralized the management. Finally, multimedia traffic is heading towards applications in an unchanged form and without negotiating using the sharing of subkeys. The experimental results demonstrated its effectiveness for delivery rate by an average of 35%, processing delay by an average of 29%, network overheads by an average of 41%, packet drop ratio by an average of 39%, and packet retransmission by an average of 34% against existing solutions.  
multimedia sensors; optimizing resources; software-defined networks; delay controlled; artificial intelligence of things +
Footnote †: journal: Sensors  
1
Footnote 1: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))
## 1 Introduction
Injection pressures of 300 MPa and an injection hole size of 0.08 mm have shown high efficiency in increasing the speed of turbulence and reducing fuel particle sizes. This is very important in reducing the mixture preparation time and rich mixture formation for the premixed compression ignition (PCI) combustion [28; 29; 30]. Besides, smoke is lower at constant \(\mathrm{NO_{x}}\) emissions with an increase in the injection pressure from 200 to 250 MPa. Alternatively, the increase in the injection pressures significantly reduced fuel consumption at constant \(\mathrm{NO_{x}}\) and smoke emissions. From 250 to 300 MPa, the effect of injection pressure on smoke and fuel consumption is limited at higher \(\mathrm{NO_{x}}\) levels [31]. With a fuel injection pressure of 320 MPa, the duration of combustion is shorter, while the ignition delay changes very little if the injection fuel mass and the fuel injection pressure are the same [32]. When the turbocharger is used, the engine power increases and specific fuel consumption decreases. However, its main disadvantages are the high thermal factor of the engine parts, the increase in the price of a diesel engine, and its maintenance. Additionally, the maximum value of the boost pressure also depends on the engine design [33; 34]. The combination of turbocharging and increasing fuel injection pressure improves engine performance. This is a solution that can be chosen as a combination of two factors. It has been shown that density, temperature and air pressure in the cylinder increase as the fuel injection pressure is low or medium and the turbocharging pressure rises. This significantly affects fuel injection characteristics [35; 36; 37]. The purpose of this study is to find out the effect of the geometric parameters of the combustion chamber and the boost pressure on the combustion characteristics when using an ultra-high fuel injection pressure of 300 MPa. The shape of the piston bowl was determined by the ratio between its diameter and depth of the combustion chamber when the compression ratio of a single-cylinder four-stroke diesel engine is fixed at 15.4. An electro-hydraulic nozzle with eight holes of 0.1 mm diameter each was used. Simulation was performed by AVL Fire software version 2020, which is developed by AVL Ltd. (Graz,Austria). The results of this study are the basis for designing, improving, and optimizing diesel engines to increase engine power and reduce fuel consumption and emissions.
## Abbreviations  
List of abbreviations used in study:  
BW Bandwidth  
CF Carrier Frequency  
CR Code Rate  
CRC Cyclic Redundancy Check  
CSS Chirp Spread Spectrum  
FEC Forward Error Correction  
FLoRa Framework for LoRa  
FSPL Free Space Path Loss  
GSM Global System for Mobile communication  
GPRS Global packet radio service  
GPS Global Positioning System  
IEEE Institute of Electrical and Electronics Engineers  
IoT Internet of Things  
IP Internet Protocol  
LoRa Long Range  
LoRaWAN LoRa Wide Area Network  
MQTT Message Queuing Telemetry Transport  
PCB Printed Circuit Board  
RFID Radio Frequency Identification  
SF Spreading Factor  
SoC System on a Chip  
ToA Time on Air  
SNIR Signal-to-Noise Ratio  
VANET Vehicular Ad Hoc Network  
Wi-Fi Wireless Fidelity  
Wireless Sensor Network
## 4 Results and Discussion
### Analysis with Damaged Structural Components
#### 4.3.1 The Effect of Structural Deterioration  
Strong railway vibrations could produce damaging effects on structural components of the rail [11]. Similarly, the third rail resonant vibrations can easily degrade the third rail and its components such as brackets, insulators, or claws over time [17]. Such vibration-induced damages can cause fatigue failure and greater wear and tear, as well as reducing the stiffness of the third rail and the supporting structures [27]. Mathematically, it can be modeled by reducing the bending stiffness (\(EI\)) for the third rail and the spring stiffness (\(K\)) for the support structures. An effect of structural deterioration on its dynamic performance is illustrated in Figure 6.  
The deterioration on the third rail bending stiffness has a profound effect on the vibrational characteristics of the third rail compared with the deterioration of the supporting structures. It is well known that a flexible beam shows resonance at low frequencies. Besides intense vibration, the aging process and continuous exposure to harsh environmental conditions such as snow, rain, and humidity can expedite the deterioration of the third rail and reduce its structural stiffness. A similar effect can be observed in Figure 6a. The frequencies corresponding to pin-to-pin mode of vibration shifted toward the lower frequency region when the bending stiffness was reduced. However, the pin-to-pin mode of vibration was not affected significantly by the reduced stiffness of supporting structures, as shown in Figure 6b. This is because the pin-to-pin resonance frequency depends on bending stiffness (EI) and the span length of the third rail structure [26]. Nonetheless, the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pin to Pin Mode** & **Analytical Solution (Hz)** & **Numerical Solution (Hz)** & **\% Difference** \\ \hline
1 & 11.94 & 11.74 & 1.7 \\
2 & 46.72 & 44.93 & 4.0 \\
3 & 103.29 & 103.02 & 0.2 \\
4 & 183.19 & 176.83 & 3.4 \\
5 & 286.32 & 269.36 & 6.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Resonance frequencies for the pin-to-pin modes of vibration obtained from both analytical and numerical approach.  
main effect of reducing the stiffness of support structures was observed at approximately 20 Hz. This frequency corresponds to a resonance mode where the entire third rail bounces on the support. This particular mode of resonance is shown in Figure 7. It was observed that, with the deteriorated supporting structures, the overall third rail structure starts resonating at an even lower frequency. Similar resonance modes and characteristics are also reported in railway track dynamics [10].
# Experimental Test and Prediction Model of Soil Thermal Conductivity in Pernafrost Regions
## 3 Testing Scheme
### Distribution of Dry Density and Water Content, and Effect on Thermal Conductivity
#### 4.3.2 Effect of Dry Density and Water Content on Thermal Conductivity  
Preliminary research results show that both dry density and water content are significant influencing factors of soil's thermal conductivity [7; 8]. Generally, it is considered that dry density and  
Figure 8: (**a**) Dry density and (**b**) water content cumulative proportion of three soil clusters.  
Figure 7: Probability distribution of thermal conductivity of three soil clusters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cluster** & \(\tilde{\textit{p}_{d}}\)**(g/cm\({}^{3}\))** & \(\tilde{\textit{w}}\) **(\%)** & \(\tilde{\textit{\lambda}_{\textit{u}}}\)**/W**-**(m\({}^{-1}\cdot\)K\({}^{-1}\))** & \(\tilde{\textit{\lambda}_{\textit{f}}}\)**/W**-**(m\({}^{-1}\cdot\)K\({}^{-1}\))** \\ \hline HWHCS & 1.648 & 28.730 & 1.418 & 1.822 \\ MWHCS & 1.813 & 23.996 & 1.556 & 1.999 \\ LWHCS & 1.936 & 17.464 & 1.822 & 2.258 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average thermal conductivity for three soil clusters.  
water content have positive correlation with soil thermal conductivity. However, the test results of thermal conductivity of three soil clusters show that this conclusion is sometimes invalid. Thermal conductivity distribution of different dry densities and water content are shown in Figures 9 and 10. It shows that the thermal conductivities of three soil clusters discretely distributes at different dry density and water content intervals. In most cases, the thermal conductivity has no obvious correlations with dry density or water content. This is because the combination of soil dry density and water content parameters is diverse under natural environmental conditions, and as a complex multiphase mixture, soil thermal conductivity is influenced by many other factors. Meanwhile, it should be noted that the thermal conductivity shows an approximate increasing trend for certain cases, such as unfrozen LWHCS with increasing dry density, and frozen LWHCS and MWHCS with increasing water content.
# Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things
## 5 Implementation and Results  
In this section, we demonstrate the implementation of the proposed IDS for IoT networks using a Raspberry Pi. We implement the DNN using Keras, an open-source neural-network library written in Python and test using the open Cooja network simulator developed in Contiki operating system [33]. We also use the Texas Instruments sensor tags CC2650 to create the IoT network testbed and evaluate  
Figure 6: Overview of proposed DNN Training.  
our results. We use Keras library because of its light-weight, modularity, and easy extensibility, and create a _Sequential_ Deep-Learning model, constructed as a linear stack of DNN layers. In addition, Keras library is fast and can process large amounts of data easily. It automatically distributes the work over different processing threads with the machine, without the need for providing optimization or distributed processing parameters as in the case of other machine-learning libraries. Hence, Keras enables implementation of the anomaly-based IDS on a low-powered resource-constrained Raspberry Pi, with a raw processing speed of approximately 700 MHz and a volatile memory of 512 megabytes.  
The implementation consists of three stages: input data collection and preprocessing, creation and training of DNN classifier, and testing. Input data collection and preprocessing is used to generate an IoT network-traffic dataset as an input for the anomaly-detection process (ADP). Creation, and training of the DNN classifier are the core sub-processes in the ADP and the detection process in general. Training assigns weights to each classifier node to filter a certain type of input and matures the binary classifier.
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 4 Material and Methods
### Evaluation Metrics
#### 4.3.3 Detection Delay  
Detection delay is another metric related to the time that must be assessed for drift detectors. This metric refers to the time interval between the occurrence of a concept drift and its detection by the detector. Drift detectors with low detection delays enable applications to make decisions about detected changes quickly, allowing them to meet real-time constraints. On the other hand, a high detection delay may result in missed opportunities, false alarms, or incorrect decisions, which can have serious consequences in critical applications. Therefore, it is essential to evaluate the detection delay of data stream drift detectors in addition to their accuracy and detection time performance, to ensure that they can detect concept drifts in a timely and reliable manner. The detection delay depends on various factors, such as the complexity of the data, the frequency and magnitude of the drifts, the performance of the detector and the decision threshold used. The trade-off between the detection delay and accuracy is an important consideration in the selection and optimization of data stream drift detectors and may depend on the specific requirements of the intended application.  
In Figure 1, the calculation of the detection delay is illustrated as the difference between the exact onset point of each drift (true detection) and the point at which it was detected by the algorithm, as described in the work by [PERSON] et al. [32]. Notably, when detection occurs outside of the drift area, it is disregarded and excluded from any calculations related to detection delay metrics.
# Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning +
## 6 Censored Path Loss and Positioning
The OLS fitted models systematically overestimate the link distances related to \(PL^{*}\), and therefore, OLS-MLE includes many more censored BSs than MLE-MLE.
## 2 IGDT-Based Optimization Model for Industrial Parks
### Economic Model of Industrial Parks
E and \(SOC\) represent the ES capacity and state of capacity, respectively. \(\mu_{1}\) represents the 0-1 variable, indicating that ES cannot charge and discharge at the same time. The fifth formula of Equation (5) indicates that the first and last states of ES are equal. The last formula of Equation (5) indicates that that the ES capacity state cannot be less than 0.2 and greater than 1. 2. Power balance constraints \[ \begin{cases}P_{\mathbf{b}}(t)+\sum\limits_{i=1}^{N}P_{i,\mathbf{ex}}(t)=P_{ \mathbf{s}}(t)\\ P_{i,\text{out}}(t)+P_{i,\mathbf{ex}}(t)+P_{i,\mathbf{c}}(t)+P_{i,\text{load}}(t )=P_{i,\mathbf{g}}(t)+P_{i,\mathbf{pv}}(t)+P_{i,\mathbf{d}}(t)+P_{i,\text{in} }(t)\\ P_{i,\mathbf{ex}}(t)=\eta_{\mathbf{tr}}P_{i,\text{out}}(t)-P_{i,\text{in}}(t) /\eta_{\mathbf{\pi}}\end{cases} \] (6) where the first two formulas of (6) denote the power balance of the industrial park and microgrid, respectively. \(P_{i,\text{ex}}(t)\) denotes the exchange power of the microgrid. \(P_{i,\text{in}}(t)\) and \(P_{i,\text{out}}(t)\) are the input and output power of the microgrid, respectively. \(\eta_{\mathbf{tr}}\) is the transformer loss factor. \(P_{i,\text{load}}(t)\) and \(P_{i,\mathbf{pv}}(t)\) are the load demand and PV output, respectively. The economic optimization model proposed for the industrial park focuses solely on the economic aspects without considering the uncertainties associated with PV generation and load demand. This oversight can compromise the security and reliability of the industrial park's operation. Hence, it is crucial to develop a method that effectively addresses PV and load uncertainties while ensuring the economic efficiency and robustness of the optimization model. In order to address this challenge, this paper emphasizes the integration of a method that accounts for PV and load uncertainties within the industrial park optimization model. By incorporating uncertainty handling techniques, the model aims to strike a balance between economic considerations and the robustness of the operation. In the subsequent sections of this paper, the specific methodology to handle PV and load uncertainties, based on IGDT, will be presented. The proposed stochastic optimal scheduling model, which integrates IGDT and envelope constraint modeling, will be simulated and analyzed under various scenarios. The findings of the simulation analysis will demonstrate the effectiveness of the proposed method in coping with PV and load uncertainties within the industrial park setting.
# The Contributions of Climate and Human Activities to Water Use Efficiency in China's Drylands
## 4 Discussion
### Role of Human Activities in the Temporal Variation of WUE  
This study utilized residual analysis and relative contribution rate calculation methods to assess the contributions of climate and human activity to changes in WUE in China's drylands. The findings indicated that human activities contributed to 46.35% of WUE changes, whereas climate change accounted for 53.65%. Human activities had a relatively dominant influence. In terms of spatial distribution, more than half (62.57%) of WUE changes were attributed to both climate change and human activities. The study area showed a concentration of larger anthropogenic contributions in the eastern and northeastern parts of China's drylands, where human activities were stronger, which aligned with the findings of [PERSON] et al.'s study on WUE in Inner Mongolia [21]. Since 1978, China has implemented 13 protection and restoration projects, such as the \"Grain to Green Program\" and the \"Three-North Shetlerbelt Program\" [29], which were proven to be effective in greening China's drylands and partially responsible for the increase in GPP in the study area (Figure 2d). Furthermore, human activities in large areas of the eastern and northeastern parts of the study area, including the Xilingol League in Inner Mongolia, Hebei Province, and eastern Liaoning Province, have contributed to a decrease in WUE (Figure 5b). On the one hand, the expansion of urban development and land use [21] in these areas can lead to a reduction in green space, and the ground hardening caused by urbanization can increase ET and reduce WUE. On the other hand, large-scale afforestation was vital for increasing ET. It has been observed that ecological engineering measures, such as tree planting, can result in lowering of the water table and drier deeper soils [56; 57], and afforestation activities can cause significant changes in local plant communities. Although these plant communities can contribute to the greening of drylands, they can also result in a significant increase in evapotranspiration from the ecosystem.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 8 Performance Analysis
### Scalability and Future Enhancements  
Despite the overhead, the protocol scales linearly with data size and number of clusters. Future optimizations include  
Utilizing batching techniques and ciphertext compression to reduce communication cost;  
Exploring leveled homomorphic encryption schemes (e.g., BFV, CKKS) for improved efficiency;  
Incorporating federated or multi-user settings to extend applicability in cross-domain clustering.  
The performance analysis validates that the proposed protocol is viable for privacy-preserving clustering on medium to moderately large datasets. Though encryption overhead remains a bottleneck, the security-utility trade-off achieved by exact homomorphic operations makes the protocol suitable for privacy-critical applications such as healthcare and finance.