esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality of Service Based NOMA Group D2D Communications
## 2 System Model
### Q-NOMA Group D2D Communication  
Since DRs are clustered around GTs and are located in proximity of each other, therefore, the DRs connected to same GT would have very similar channel conditions. Consequently, in the context of D2D communications, ordering DRs according to their channel conditions to apply NOMA at GT may not achieve the desired multiplexing gains and fairness among DRs. Hence, in this work, we propose ordering the DRs of the GT according to QoS requirements, which are determined by their targeted data rates.  
Let there be a total of \(M\) DRs distributed inside coverage of a test GT. The probe DR is assumed to be located at the origin, with a desired test GT at \(x_{0}=(d,0)\) with \(d\
eq 0\). Without loss of generality, we assume that the DRs are ordered as \(R_{1}\leq...\leq R_{M}\), where \(R_{i}\) is the targeted data rate of DR \(i\), \(1\leq i\leq M\). Correspondingly, the power allocation coefficients are sorted as \(\beta_{1}\geq...\geq\beta_{M}\).  
The aforementioned procedure to order DRs of the GT and compute their power allocation coefficients according to users' targeted rates is termed as \"_Q-NOMA_\". When it is applied to D2D communications, we refer to the communication as \"_Q-NOMA group D2D communication_\".  
Consider that the NOMA DR \(m\) is the probe receiver, then, the received signal from the test transmitter at the probe DR is given as:  
\[y_{m}=h_{m}\sum_{i=1}^{M}\sqrt{\beta_{i}P_{\mathrm{GT}}}s_{i}+n_{m}, \tag{1}\]  
where \(P_{\mathrm{GT}}\) is the transmission power of test GT, \(s_{i}\) is the message signal of DR \(i\) and \(n_{m}\) is the additive white Gaussian noise (AWGN) with zero mean and variance \(\sigma^{2}\).
The findings indicate that these freeze-thaw cycles markedly altered the physical characteristics of volcanic tuff, leading to a degradation of its pore structure and mechanical properties. [PERSON] [26] conducted an investigation into the evolution of pore structure in vacuum-saturated volcanic rocks subjected to freeze-thaw cycles. The analysis of variations in the physical properties of the rocks and their corresponding strain paths indicates that the physical failure of the rocks is primarily attributed to the propagation of cracks. Furthermore, the formation of cracks in brittle elastic solids due to freeze-thaw processes is linked to the fatigue failure of the rocks, which arises from the effects of ice crystallization and the accumulation of water pressure. [PERSON] [27] conducted an investigation into the alterations in the microstructure and the degradation responses of the physical and mechanical properties of Himalayan rocks subjected to freeze-thaw cycles. The study revealed microscopic damage and a decline in various physical and mechanical properties of the rock samples, including effective porosity, P-wave velocity, and tensile strength. In conclusion, extensive discoveries have been made in previous research on the deterioration of the mechanical properties of the rock mass that are induced by excavation unloading and freeze-thaw erosion. This study focuses on typical sandstone in the deep underground tunnel engineering of hydropower projects in cold regions and aims to explore the mechanical characteristics and degradation response patterns of rocks under the dual influences of unloading and freeze-thaw conditions, with the objective of providing theoretical insights for excavation support and the prevention of freeze damage in hydropowertunnel projects located in cold climates. The schematic diagram of the combined effects of excavation unloading and freeze-thaw erosion at the tunnel excavation face is shown in Figure 1.  
Figure 1: _Cont._
## 4 Experiments  
In this section, we will present experiments conducted in both highway crack detection and highway crack classification. For highway crack detection, to validate the crack detection performance of the CrackNet network on UAV remote sensing highway images, we conducted comparative experiments with other mainstream crack detection algorithms and ablation experiments on the improvements made to the original model. For highway crack classification, a series of data preprocessing operations were first conducted, including roadbed identification, central yellow line removal, roadbed coordinate acquisition, and environmental noise removal from full-size images. Then, crack classification was performed on the UAV remote sensing highway crack detection results from three roads, totaling 108 km. Finally, the results were compared with manual road inspection results to demonstrate the scientific soundness and feasibility of the method.  
For all the experiments, training and testing was conducted on a server with an Intel(R) Xeon(R) CPU E5-2680 v3 processor, 128 RAM, and a single NVIDIA A40 GPU with 48 GB video memory. The software environment for the experiments in this paper was 18.04.1-Ubuntu OS, CUDA 11.3, PyTorch 1.12.1, and Python 3.9.  
Figure 8: The CrackClassification algorithm flow chart.  
Figure 9: (**a**) The area within the pixel coordinates of the roadbed is divided into equal-sized grids. (**b**) Framing of transverse and longitudinal cracks with rectangular anchor frames.
# Detection and Tracking of Moving Pedestrians with a Small Unmanned Aerial Vehicle
## 3 Target Tracking with IMM Filtering
### Multi-Mode Interaction  
The state vectors and the covariance matrices of the IMM mode filters at the previous frame \(k\)-\(1\) are mixed to generate the initial state vectors and the covariance matrices for each of the IMM mode filter at the current frame \(k\):  
\[\hat{\mathbf{x}}_{0j}^{t}(k-1|k-1)=\sum_{i=1}^{M}\hat{\mathbf{x}}_{i}^{t}(k-1| k-1)\mu_{i\hat{\mathbf{x}}j}^{t}(k-1|k-1),\ \ t=1,\ldots,n(k),\ j=1,\ldots,M, \tag{10}\]  
\[P_{0j}^{t}(k-1|k-  
\[\mu^{t}_{ij}(k-1|k-1)=\frac{p_{ij}\mu^{t}_{i}(k-1)}{\sum\limits_{i=1}^{M}p_{ij} \mu^{t}_{i}(k-1)}, \tag{12}\]  
where \(\hat{\kappa}^{t}_{i}(k-1|k-1)\) and \(P^{t}_{i}(k-1|k-1)\) are, respectively, the state vector estimation and the covariance matrix at the previous frame, \(\mu^{t}_{i}(k-1)\) is the \(i\)-th mode probability of target \(t\), and \(p_{ij}\) is the mode transition probability from mode \(i\) to mode \(j\).
## 3 Experimental Results and Discussion
### Cyclic Cleaning
#### 3.3.3 Sandblasting--RJS Ageing  
To finalise the cleaning tests, a mechanical cleaning alternative was analysed to evaluate its usability. The failure load values are shown in Figure 18, in which the 0 s condition represents the initial condition.  
A clear behavioural pattern can be observed--higher exposure times leading to a larger drop in the maximum shear strength--when compared against the initial condition. However, though the strength did not decrease below the stabilised threshold, this cleaning option would not be the best choice since it removes the adhesive  
Figure 17: Maximum shear strength of RJS specimens immersed in an ultrasound mixture following a cyclic pattern. The red dotted line represents the threshold value for the RJS specimen’s minimum allowed joint strength (1.5 kN).  
joint to protect and improve its strength--after about 30 s, which would hinder its long-term usability.
# Low-Power 8T SRAM Compute-in-Memory Macro for Edge AI Processors
## 1 Introduction
However, this structure also faces limitations, as it suffers from high power consumption and a complex cell configuration, which may hinder its scalability in certain applications [11]. In contrast, our proposed architecture introduces a novel 8T bit-cell that leverages voltage differentials for multiply-and-accumulate (MAC) operations and employs body biasing effects to enhance power efficiency. By addressing the high power consumption challenges posed by complex control methods in prior designs, our architecture minimizes reliance on ADCs, thereby achieving a lower power footprint. Simulation results demonstrate that our architecture achieves significant improvements in energy efficiency and simplicity, positioning it as a suitable SRAM-CIM structure for parallel processing applications, where power savings and streamlined architecture are essential to performance.
## 4 Data Storage and Processing: Automated Data Processing and Storage
### Irrigation Decision Making with Machine Learning Models
#### 4.3.1 Deploying ML Models  
The deployment of ML models in irrigation management systems requires careful consideration of various technologies and techniques to ensure efficient, scalable, and reliable performance in real-world agricultural settings. Key deployment strategies, frameworks, and optimization methods, such as TensorFlow Serving, Apache MXNet Model Server, ONNX Runtime, model compression, hardware acceleration, and distributed training, can be used to transform ML models into operational components of irrigation decision support systems (Table 4). These technologies enable efficient and scalable inference, interoperability between diverse environments, improved resource utilization, and accelerated training of complex models. The dynamic nature of agricultural environments poses challenges for the effective deployment and continuous improvement in ML models. Online learning algorithms, which enable the real-time update of models based on incoming data, are particularly valuable in irrigation management, where conditions can change rapidly due to weather patterns, soil moisture fluctuations, and crop growth stages. For example, [PERSON] and [PERSON] [97] developed a hybrid model combining deep learning, nonlinear system identification, and ensemble tree intelligence algorithms for pan-evaporation estimation, demonstrating significant improvements in prediction accuracy compared to single models by incorporating principles of online learning. Figure 5 illustrates the automated data processing pipeline in the cloud for irrigation management, showcasing the seamless translation of raw sensor data into actionable irrigation insights and control commands. The process begins with the acquisition of diverse data types from IoT sensors and external sources, followed by preprocessing steps to ensure data quality and compatibility with ML models. The clean, processed data are then fed into deployed ML models for predictive modeling tasks, such as yield prediction, anomaly detection, and irrigation scheduling optimization. The generated insights are combined with constraint optimization techniques to create precise irrigation schedules and control parameters, which are translated into actuation commands for IoT-enabled irrigation systems.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### Moderate-Power Thrusters
#### 4.2.2 Gridded Ion Thrusters  
Although Hall thrusters are high on their s-curve, as shown in Figure 8, moderate-power gridded ion thrusters are perhaps slightly more mature, having reached their inflection point near the turn of the century. Gridded ion thrusters at 1-20 kW were developed heavily and flown extensively by the U.S. during the Twentieth Century; for example, the Xenon Ion Propulsion System family of GITs developed by (first Hughes and then) Boeing were flown extensively in the 1990s [77]. These devices  
Figure 10: The HT20k DM1 magnetically-shielded Hall thruster developed by SITAEL, before first firing (**left**) and operating at 400 V, 20 kW (**right**); reproduced from [55] courtesy of Mr. [PERSON], SITAEL.  
similarly have been flown in deep space missions like Dawn [78] and (at slightly lower power levels) Hayabusa [79] and, currently, the BepiColombo mission to Mercury [80]. Further, they have undergone extensive wear testing in ground facilities [81; 82], which has led to improvements in the understanding of the erosion of these thrusters [83]. Figure 11 shows an example of two state-of-the-art gridded ion thrusters.  
The challenges facing GITS at moderate power levels are now largely related to incremental improvements in the testing and implementation of these devices. For instance, facility effects are known to influence GITS as they do Hall thrusters. For example, numerical modeling accompanying life tests of the NASA Evolutionary Xenon Thruster (NEXT) indicated that accelerator grid groove erosion could be reduced by 30% due to redeposition of sputtered beam dump material [85]; this effect, if unaccounted for, could spuriously increase estimates of thruster longevity and therefore pose a significant risk for flight operation. Assessing the lifetime of moderately-powered GITs is another lingering obstacle. Although simulations can effectively reproduce erosion patterns in these devices, the causes of cathode erosion and failure are manifold and still under study [86; 87], and the presence of long-duration failure mechanisms are difficult to explore without strenuous ground testing.  
Alternatives to gridded ion thrusters at moderate power levels that overcome the fundamental space-charge-limited operation of this technology are also being explored. For example, the High-Efficiency Multi-stage Plasma (HEMP) thruster is a cusped-field thruster in development by Thales Electron Devices GmbH since 1996 [88] and has demonstrated over 6600 h of operation in life tests [89]. This technology, although firmly not a type of GIT, is generally considered to occupy a similar performance space. There is also the exploration of extending this concept to low power, where the magnetic shielding of the discharge chamber walls greatly reduces heat loss [90].
## 3 Benefits of an Optimized Enterprise Network System
### Drive Towards Efficiency and Productivity  
The effectiveness and productivity of an organization's numerous departments are greatly enhanced by a well-designed and reliable IT infrastructure [50]. Meeting project deadlines and the productivity of an employee are directly impacted by the seamless transfer of information, which in addition promotes quicker and more effective communications [51]. Apart from enhancing communication speed, an optimized enterprise network will guarantee improved client interactions through enhanced website functionality, online transactions, and interactive customer experiences.
## 2 Distribution System Testbed
### Three-Phase Diagram  
The three-phase diagram of the distribution system testbed was created based on the one-line diagram in Figure 1a. This diagram is shown in Figure 4, and it was created using MATLAB/Simulink (version 2015b). The three-phase diagram has three electrical utilities: the substation grid (utility _A_) of 34.5/12.47 kV, the fossil fuel power plant with a transmission/sub-transmission system (utility _B_) of 69/34.5 kV, and the IB-PV array farm (utility _C_) of 0.575/12.47 kV connected to the electrical grid (utility _A_). Utility \(A\) has two 34.5/12.47 kV transformers connected in parallel and two feeders. Each power line of 12.47 kV is controlled by a breaker, and the loads are fed by the power lines. These power loads have 50 and 100 T fuses [47]. The three-line diagram is based on a previous diagram used at the electrical substation grid testbed [22; 44; 45; 46], and the IB-PV array farm (utility _C_) is based on a modified MATLAB/Simulink model to fit there.  
In Figure 4, the substation and grid (utility _A_) are fed by utility \(B\). Utility \(A\) consists of the substation, power lines, and loads. Utility \(C\) has an IB-PV array farm. The fault resistances for the use case scenarios are implemented into a MATLAB/Simulink three  
Figure 4: Three-phase substation and grid with IB-PV farm.  
Figure 3: (**a**) Center and (**b**) levels of the testbed.  
phase fault block (Figure 4i). It is a programmable phase-to-phase and phase-to-ground fault breaker system that represents the fault (0.001 \(\Omega\)) and ground (0.01 \(\Omega\)) resistances, snubber resistance (1 \(\times\) 10\({}^{-6}\)\(\Omega\)), and infinite snubber capacitance. These values correspond to the default values of the MATLAB/Simulink three-phase fault block. In the electrical faults, the effects of the load current and fault resistance are combined. The fault resistance may be particularly high for ground faults, which represent many of the faults on overhead lines. However, all types of electrical faults are performed in this study. The tests for the electrical faults (3 LG, LLG, SLG, and LL) at the power line end of utility \(A\) are performed with a fault block, which is presented in Figure 4i. The fault block in Figure 4 is set before running the tests for different electrical fault types and is set to start the electrical fault at 20 s with a fault state duration of 0.5 s (30 cycles). The relays/meters record their data using the trigger event signal circuit (Figure 4h) that generates the signals for recording the events. In Figure 4, the analog signals at different locations from the IB-PV array and electrical fault site are simulated and collected from meters and relays using no similar sampling frequencies. In the IB-PV array side, the SEL351 (Figure 4a) and SEL700 GT relay (Figure 4b,c) record events with 32 samples per cycle. In the electrical substation side, the SEL451 (Figure 4d) and SEL421 (Figure 4e) relays record events with 133 samples per cycle. In the load feeder side, the SEL735 meters (Figure 4f,g) record the events with 128 samples per cycle.
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 4 Results and Discussion
### Comparison of Shaker, Cycling, and Walking Tests  
For the sake of comparison, the highest output power achieved by both circuits under three different tests is summarised in Table 4. Across all three tests, maximum output power obtained is 21.05 \(\mu\)W from the walking test with the use of H-Bridge circuit at the highest walking speed. It is also interesting to note that the walking test yielded significantly higher power than the cycling test and shaker test. This is likely to be caused by higher input energy from the large amplitude of walking motion and significant impact energy generated by footstep. Despite the similar frequency, the amplitude of motion in shaker test and cycling test is relatively smaller. In other words, the input energy is lower.  
Overall, it is found that the use of H-Bridge consistently produce higher output power and rectified voltage, in comparison with the FBR circuit, regardless of the source of excitation, the capacitor and resistance in use. It is thus confidently concluded that the H-Bridge circuit is superior to the FBR counterpart in rectifying voltage generated by PD under human motion-induced, low frequency excitation.  
As stated in [5,42], walking, jogging, and cycling are some of the most common human activities in daily life. In this study, the power generated from such activities performed by an average-sized person is presented. Based on the experimental results, it is observed that the power generated with the use of either rectifier circuits and a range of adopted device [33] is still relatively low when compared with conventional artificial power supply. At this stage, the output power from PD can potentially be used for powering small electronic devices, like LEDs, lighters, piezoelectric microphones, quartz watches, and for charging batteries.
# Estimating the Human Error Probability during Lifeboat Drills
## 2 Methodology
Unlike the child nodes, the root nodes (ones without parents) are assigned marginal probabilities. Thecausal probability relationships in BNs can be proposed by experts or updated using the Bayes theorem and new data being collected [27; 28; 29]. The BN-SLIM takes into account the dependencies among the SLIs of the task due to common PSFs and prevents the overestimation or underestimation of the total HEP [27]. In addition, it enables the diagnostic analysis and identification of the PSFs that contribute the most to HEP. It can be used as a proactive measure to prevent lifeboat drill accidents and reduce the likelihood of operator error [25]. According to the original SLIM, the effect of PSFs on HEP is modelled through the variable of the Success Likelihood Index (Equation (1)), which is already estimated by the experts (in the SLIM). Therefore, two functions are needed to estimate HEP value: (a) one to model the relationship between the PSFs and SLIs, and (b) the second to calculate the HEPs using SLIs. Each PSF within the BN has several states representing its ratings. Since, according to Equation (1), PSF ratings and weights determine SLI values, the edges are drawn from PSF nodes to SLI nodes. The number of states of the SLI nodes is equal to the number of possible combinations of states of the PSF rating nodes. For the sake of simplicity, only two scores, \"R1 = 1\" and \"R100 = 100\", are considered as the states of the PSF nodes of the Bayesian network. Therefore, conditions were taken for the worst and best possible influences of PSFs on the selected tasks. In addition, the developed BN was verified by utilising sensitivity, a technique for the verification of the parameters of a BN [30]. Highly sensitive causal nodes will have a significant impact on the targeted node, and acting on those nodes will have the best effect on the target node (in this case, HEP) [31]. Furthermore, the BN-SLIM model was validated using data from real-life accident investigation reports where causal factors were identified, and the BN-SLIM model was updated with real data. The sequence of the methodology utilised in this paper is presented in Figure 1.  
The study involved eight experts, all master mariners (sea captains) with at least ten years of seagoing experience. They have been involved in the inspections and maintenance of lifeboats and have conducted lifeboat drills onboard seagoing ships.
## 3 Proposed Methodology
### Decision Making  
At this stage of our proposal system, the CH monitors the number of duplicate suspicious packets; if there is a confirmation of duplication, the CH node will cut off the connection with the suspicious WSN node, add WSN node information to its blacklist, and send a broadcast message to the all neighbor CHs and the AP informing them about the suspicious WSN node. This process is illustrated in Figure 4.  
Based on Figure 4, each data Packet that Comes (CP) will be scanned; if it is labelled as 1, CH will count it as a suspicious packet and then count the number of times it repeats over a specified period of time. Subsequently, if the _CP.count_ exceeds \(\alpha\), the CH will execute the broadcast command and disconnect the connection, where \(\alpha\) is the maximum number of occurrences of suspicious packets. This process gives an opportunity to reduce the false-negative rate in our proposal by confirming the attack. As for the values of time and \(\alpha\), they are subordinate to the policy of the organization or company, which will determine the number of repeated DoS or DDoS packets in a period of time to be considered malicious attacks or not.