esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Experimental Methodology
#### 2.4.1 Mode 1: Charging  
In all the charging test, the RPW-HEX was initially completely discharged by setting the PCM temperature to 12 \(\pm\) 0.5 \({}^{\circ}\)C. This value was selected because it is the most common temperature of the return water of refrigeration systems in buildings. Charging tests were started by switching on the condensing unit after preparing the initial experimental conditions. Several experiments were performed at different values of the cooling power (%) of the condensing unit, as shown in Table 4. Each experiment was considered complete when the condensing unit automatically switched off because the evaporation temperature reached the set-point value (-10 \({}^{\circ}\)C).  
Additionally, -18 \({}^{\circ}\)C was also used as evaporation temperature set-point of the condensing unit at 15% cooling power to investigate the effect of this parameter on the RPW-HEX charging process. The value of -10 \({}^{\circ}\)C was chosen as it is the value recommended by the manufacturer's datasheet, and for which the condensing unit was optimized, while the value of -18 \({}^{\circ}\)C temperature was selected taking into account that lower than that would lead to freezing of the HTF inside the RPW-HEX, which could irreversibly damage it. The condensing unit stopped whenever the set-point temperature was achieved.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Initial PCM Temperature [\({}^{\circ}\)C]** & **Compressor Power [\%]** & **Evaporation Set-Point [\({}^{\circ}\)C]** \\ \hline \hline \multirow{4}{*}{12} & 15 & \multirow{4}{*}{–10} \\  & 30 & \\  & 45 & \\  & 60 & \\  & 75 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Set of experiments in Mode 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **At T = \(-\)4 \({}^{\circ}\)C** & **At T = 12 \({}^{\circ}\)C** & **Units** \\ \hline Density & 1053.5 & 1047.4 & kg\(\cdot\)m\({}^{-3}\) \\ Viscosity & 4.8 & 2.8 & mPa\(\cdot\)s \\ Specific heat & 3579 & 3624 & J\(\cdot\)kg\({}^{-1}\cdot\)K\({}^{-1}\) \\ Conductivity & 0.418 & 0.437 & W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\) \\ Freezing Temperature & \(-\)18 & & \({}^{\circ}\)C \\ Boiling temperature & 127 & & \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 3: HTF (water-glycol) properties at different temperatures.
## 1 Introduction  
AISI 52100 steel is used in various industrial fields due to its excellent machinability, dimensional stability, mechanical properties, and wear resistance, and is widely used as a bearing material [1, 2]. However, friction contact under high load pressure conditions and adhesion damages on the surface produce defects, such as white etching cracks, white structure flaking, or decomposition of primary cementie when severe wear occurs [3, 4]. When such damage occurs, the life of the bearing parts is greatly reduced, which in turn can cause other parts of the machine to degrade, resulting in a total system failure [5, 6].  
In order to overcome these issues, the development of metal matrix composites in which high hardness ceramic particles are added to a steel matrix to achieve excellent wear resistance have received attention [7]. Metal matrix composites containing ceramic particles have low density and excellent specific strength and specific stiffness, so their application is increasing in automobile and machine parts [8]. Ceramic reinforcements such as boride, nitride, and carbide have excellent wear resistance. As a result, they are widely used as a reinforcing material in metal matrix composites [9]. Among these reinforcements, titanium carbide (TiC) particles are useful because of their low density, thermal stability, high hardness, and high resistance to oxidation and wear [10].  
Recent metal matrix composite manufacturing methods include powder metallurgy, selective laser melting, and injection molding. When metal matrix composites are manufactured by powder metallurgy, internal defects, such as residual pores and inclusions, may exist, and interfacial defects are generated due to weak wettability between the matrix and the reinforcement [11; 12; 13]. Selective laser melting, on the other hand, generally incurs residual stresses due to very high temperature gradients and extreme cooling rates, which can degrade mechanical performance [14; 15]. Other work has found that the mechanical and wear properties of ceramic particle-reinforced steel composites made using powder injection molding could degrade the density and mechanical behavior of the composite [16]. In order to overcome the disadvantages of these methods, a liquid phase-based liquid pressing infiltration method was developed, which has an excellent wettability of ceramic reinforcement and is known to exhibit effective load transfer due to excellent bonding strength [17].  
In this study, a TiC/ AISI 1020 composite was prepared by the liquid pressing infiltration method, and the microstructure, hardness, and wear resistance were studied. The wear behavior and mechanism of the AISI 52100 steel and the TiC/ AISI 1020 composite were investigated via pin-on-disc tests.
## 4 Existing Jamming Attack Detection Methods and Their Analysis
### Method Suggested by [PERSON] et al. [3]  
[PERSON] _et al._[3] have proposed two algorithms for detecting a jamming attack. The first algorithm is based on threshold values of three detection parameters: Bad Packet Ratio (BPR), Packet Delivery Ratio (PDR), and Energy Consumption Amount (ECA). If all three parameters are below the thresholds, or if only the PDR exceeds the threshold, then it is concluded that there is no jamming; otherwise, there is jamming. The second algorithm is an improvement over the first one where the neighboring nodes' conditions, ascertained through queries to be raised and replies there-to to be received within the threshold time periods, are also taken into account to enhance the jamming detection rate. The results of the simulations are very encouraging, thus establishing the effectiveness of the algorithms. However, the suggested models suffer from fixing of too many thresholds and processing at the node levels, which have their own problems, as discussed earlier. In addition, the PDR, measured at the transmitter-end, as in the instant case, is not suitable for the resource-constrained WSN because it imposes the avoidable burden of acknowledgements.
# AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops
## 3 AgriEnt: Architecture  
AgriEnt is a knowledge-based decision support system that helps farmers to control and manage crop insect pests. It is widely recognized that for managing and analyzing information for the decision-making process, a decision support system must consist of at least three main elements: (1) a user interface, (2) a database, and (3) a model processing module [33]. From this perspective, AgriEnt relies on a layered functional architecture comprising four layers (see Figure 1): (1) the data layer, (2) the semantic layer, (3) the web services layer, and (4) the presentation layer. Each layer, in turn, consists of several components with well-defined functions and responsibilities thus enabling the platform to be scalable and easy to maintain.  
Figure 1 depicts the general architecture of AgriEnt whose general operation is as follows: farmers provide through a Web application a set of symptoms they gathered by observing the crops they are in charge of. Based on the symptoms provided, the rule-based inference engine provides a diagnosis about the insect pest affecting the crop and provides recommendations with the appropriate treatments  
Figure 1: General architecture of AgriEnt.  
to deal with it. Furthermore, AgriEnt extends this information with a set of documents about the control and management of that insect pest aiming to allow users to increase their knowledge about this phenomenon. The communication between the Web application and the semantic layer is performed through the web services layer, which provides a REST-based (Representational State Transfer) API (Application Programming Interface) that is in charge of processing all incoming requests. The layers and components of AgriEnt are thoroughly described below.
# Parametric Analysis on Landing Gear Strut Friction of Light Aircraft for Touchdown Performance
## 3 Analysis of Strut Friction Effect on Touchdown Performance Using RSM  
### Energy Absorption Analysis  
In this subsection, the percentage of strut friction energy absorption of the strut total energy absorption during the landing process is calculated based on the numerical model of the landing gear established in Section 2. Two performance criteria are considered in this work to express the effect of strut friction on energy absorption: energy quantity \(J\) and energy absorption efficiency \(\eta\). The two performance criteria are defined as  
\[J=\int_{0}^{s_{max}}F_{i}ds\,\ \eta=\frac{\int_{0}^{s_{max}}F_{i}ds}{S_{max}F_{ \max}}\ \, \tag{15}\]  
where \(F_{i}\) denotes the elemental force in shock strut. \(S_{max}\) and \(F_{i}\)max are the maximum shock absorber stroke and elemental force during the landing process.  
The landing process can be divided into six phases, of which the impact energy dissipation mainly occurs in the third phase, namely, the first compression of the shock strut [23]. The strut friction, gas spring, and oil damping corporately dissipate the impact energy during the landing process. This work considers the energy absorption in the third phase as the judging criteria of energy absorption capability, with the results shown in Figure 8 and Table 4.  
Figure 8: Energy absorption quantity trend of elemental forces: (**a**) LC-1; (**b**) LC-2.  
Figure 7: The ground vertical force curve with shock absorber stroke: (**a**) LC-1; (**b**) LC-2.  
As shown in Figure 8, the energy absorption of strut friction has a considerable proportion of total impact energy in two landing conditions. Moreover, the oil damping absorbs more energy in LC-2 as the larger vertical descending velocity.  
Table 4 lists the results of energy absorption of elemental forces. The results show that the proportions of energy absorbed by friction are more than 35% in two landing conditions: 38.15% and 35.76%. Moreover, the proportion of energy absorbed by friction decreases by the increase of the landing impact energy. The shock-strut efficiency in LC-2 is larger than the efficiency in LC-1 as the friction efficiency enlarges as the larger landing impact energy.  
According to the results in Figure 8 and Table 4, the strut friction has a considerable influence on aircraft and landing gear touchdown performance when the landing impact energy is small, which is common in light aircraft. The higher friction efficiency can lead to the higher shock-strut efficiency, which illustrates that the touchdown performance of aircraft can be improved by designing the parameters involving the strut friction.
## 1 Introduction
The choice of base classifiers, the decision-making process, the number of classifiers used for the construction of an ensemble, the accuracy of individual models, the diversity among the individual models in an ensemble, and the number of classifiers used are some main factors to be carefully studied in an ensemble-based method [18]. Parameter hyper-tuning [19] in the selected model is the process of ascertaining some particular parameters to optimize the performance of learning algorithms on a specific set. Parameter tuning has proven its vital role in improving the accuracy and overall model performance both for ensemble and non-ensemble classifiers [20; 21]. Everyclassifier has its own set of parameters and needs to tune following the different tuning steps by performing an exhaustive grid search. There are two types of model parameters in every machine-learning algorithm, conventional parameters and hyper-parameters. Conventional parameters are optimized during the training phase of the underlying model, whereas the users, depending upon the problem dataset before the training phase of the model, set hyper-parameter values. In the more complex structure, i.e., with the increasing overlapping samples, the needs for the optimal set of parameters become more in order to maximize the visibility of the minority class samples [22]. The key contribution of the underlying paper is to focus on analyzing the learning performance of ensemble and non-ensemble classifiers over multi-class imbalanced and overlapped data. A detailed investigation of ensemble and non-ensemble approaches was presented to provide deep insight into the nature of multi-class learning strategies. An exhaustive experiment on the hyper-tuning of six state-of-the-art ensemble and non-ensemble classifiers, to efficiently address the multi-class imbalance datasets issue and comprehensively compare and improve their performance, was performed using four different evaluation metrics for accuracy, namely the overall accuracy (ACC), geometric mean (G-mean) [23], F-measure [12], and the area under curve (AUC) metrics [24]. As a third contribution, an algorithm was designed to synthetically generate overlapping samples in the existing dataset by 20% of the existing samples to make it more complex and overlap to highlight the impact of parameter tuning. The comparison of ensemble and non-ensemble classifiers was carried out on 15 publicly available multi-class imbalanced and overlapped datasets. The underlying research article is comprised of the following sections. The literature survey is covered in Section 2. The background of ensemble and non-ensemble-based methods are covered in Sections 3 and 4. In Section 5, we discussed the hyper-tuning of the parameters of the used classifiers in Section 6, and Section 7 discusses the algorithm to synthetically generate the overlapping samples. Section 8 highlights the experimental setup, dataset, and evaluation methods, Section 9 covers the results and discussion. We compare the ensemble and non-ensemble approaches in Section 10 and the article is concluded in Section 11.
## 4 Performance Evaluation in Edge Scenarios  
In this section, the performance evaluation of four different SGX implementations in edge scenarios are conducted, including the SGX SDK-based approach, two LibOS-based approaches (Gramine and Occlum), and the SGX implementation in a virtual environment. CPU-intensive instructions, I/O-intensive instructions, network programming, common system calls and performance under realistic workloads are the five main aspects of testing, covering most edge intelligence scenarios. Through qualitative analysis and quantitative evaluation, we will analyze the performance degradation of SGX in resource-constrained edge scenarios.
## 4 Discussion
### Clock Generator  
Since in wireless sensors the power consumption is one of the key parameters, in our designs, we put a special attention to this parameter. Figure 9 shows the supply current waveform over time. The current pulses are generated only during switching the clock to a next phase. In the worst case scenario, when a new \"1\" impulse is generated by the NOR gate, the value of the current peak equals ca. 175 \(\mu\)A, while an average value during the overall switching period lasting 4 ns does not exceed 50 \(\mu\)A in this case. For the supply voltage of 1.2 V, it means about 240 fJ of energy consumed in this time, in the worst case. In most cases, the consumed energy does not exceed 60-70 fJ per one clock phase.  
Another important parameter is the available circuit speed. In the measurements, due to the limitations of the available measurement equipment, the clock was tested up to 20 MHz. In simulations, the achievable speeds were even 150-200 MHz. This, however, requires confirmation by measurements on more advanced measuring equipment.  
We compared the results obtained during the tests of the realized clock generator with similar state-of-the art solutions. A comparison is shown in Table 1. To enable a more straightforward comparison, we defined a Figure-of-Merit (FOM), which is the achievable data rate over the power dissipation. Thus, the higher is the value of the FOM, the better is the result. The obtained data rate is relatively small, for example, in the comparison with [32] circuit, however proportionally smaller is also the power dissipation. Thus, the obtained FOM is slightly better than in [32] and substantially better than in other presented cases. It is mostly due to to the substitution of DFFs with dynamic memory cells realized on parasitic capacitances of NOT gates.
## 2 Detection Algorithms
### Learned Thresholded Subspace Projection (LTSP)
#### 2.6.3 Adaptive Thresholding and Robustness  
LTSP differs from classical SVD-based methods by employing an adaptive rank thresholding strategy. Specifically, we monitor the energy ratio \(\sigma_{1}/\text{median}(\sigma)\) to ensure that the retained mode meaningfully separates signal from noise. This adaptivity allows LTSP to generalize across varying noise levels and avoids reliance on rigid model parameters.
## 3 Dynamic Model for Low Aspect Ratio Keyholes or High Welding Speeds
### Generalized Piston Model for High Welding Speeds  
A possible way to solve this problem is to first consider that the KH front is stationary in the frame of the laser beam. In fact, in this laser beam frame, the displacement of this KH front results of the vectorial addition of the horizontal welding speed V\({}_{\mathrm{w}}\) and a \"drilling\" speed V\({}_{\mathrm{d}}\) that is directed along the normal to the local surface. This drilling velocity V\({}_{\mathrm{d}}\) results from the recoil pressure due to the local evaporation process that pushes this surface inside the material by expelling the liquid metal along the sides of this surface, which is then ejected rearwards toward the melt pool. This process is similar to the well-known \"piston model\" initially described by [PERSON] and [PERSON] [23] for the drilling process, but applied here on an inclined surface. Now, as this KH front is stationary in the laser beam frame, this means that the resulting normal speed of this surface is zero. As a result, the stationarity of the KH front in the laser beam frame is fulfilled when [29]:  
\[\mathrm{V_{d}}=\mathrm{V_{w}}\,\cos(\alpha), \tag{9}\]  
Therefore, in order to compute the inclination angle \(\alpha\), it is this Equation (9) that must be added to the set of equations describing the process of \"drilling,\" determining the drilling velocity V\({}_{\mathrm{d}}\). The equations used for the physical model describing the \"piston model\" allowing the determination of V\({}_{\mathrm{d}}\) are described in detail in Appendix A.  
The piston model initially used for describing the drilling process involved a circular disk whose diameter is equal to the spot diameter that is moving at a speed V\({}_{\mathrm{d}}\) inside the material [23]. But in the present case of dynamic welding, high speed video visualizations of the KH front show that its surface is rather close to a half-cone shape, which is inclined by the high welding speed. This half-cone shape of the KH can be understood because we are dealing with KHs with rather low aspect ratios, unlike the usual high aspect ratios KHs characteristic of macro-welding regimes that would rather have a cylindrical shape.  
The scheme of the KH front that has been used for the characterizations of the resulting aspect ratios for these conditions is therefore shown in Figure 3. It shows all the geometrical parameters used for the application of this generalized piston model. The KH front inclination \(\alpha\) is defined in the symmetry plane of the half-cone shape. We have also considered that the thickness of the melt layer flowing around this shape decreases linearly along its depth, and this liquid is ejected rearwards with a mean velocity V\({}_{\mathrm{m}}\) by this process. The three equations for mass, momentum and energy conservation, and those describing the recoil pressure, the melt thickness and the Fresnel surface absorptivity, are detailed in Appendix A; only the main results of this model are discussed in the following Section 3.3.
## 5 Azimuth-Variant Bistatic SAR Imaging Algorithm
After range compression with the phase term  
\[\Phi_{2}=\exp\left\{j\pi\frac{k_{eq}}{1+C_{s}}f_{r}^{2}\right\}\cdot\exp\left\{ j\pi f_{r}\frac{\lambda^{2}R_{\text{bref}}f_{a}^{2}}{c_{0}v_{eq}^{2}}\right\} \tag{31}\]  
a range inverse FFT (IFFT) is further applied. To correct the effects of the azimuth-variant Doppler chirp rate, phase filtering is further applied before azimuth IFFT  
\[\Phi_{3}=\exp\left\{j\frac{\pi\lambda^{3}R_{b0}^{4}f_{a}^{4}}{3L_{az}v_{eq}^ {6}T_{\text{image}}^{4}}\right\} \tag{32}\]  
Next, the Doppler chirp rate is corrected by  
\[\Phi_{4}=\exp\left\{j\pi\left[\frac{2v_{eq}^{2}}{\lambda L_{az}}-\frac{v_{eq }^{2}}{\lambda R_{b0}}\right]\tau^{2}+j\pi\left[\frac{\left(\frac{2v_{eq}^{2} }{\lambda L_{az}}-\frac{v_{eq}^{2}}{\lambda R_{b0}}\right)2R_{b0}}{T_{\text{ image}}^{2}L_{az}}\right]\tau^{4}\right\} \tag{33}\]  
We then apply the Fourier transform in the azimuth direction and multiply a phase compensation term  
\[\Phi_{5}= \exp\left[-j\pi\frac{2v_{eq}^{2}}{\lambda L_{az}}f_{a}^{2} \right]\cdot\exp\left[\frac{4\pi k_{eq}C_{s}(1+C_{s})(R_{b0}-R_{\text{bref}}) ^{2}}{c_{0}}\right] \tag{34}\] \[\times\exp\left\{-j\pi\left[\frac{1}{\left(\frac{2v_{eq}^{2}}{ \lambda L_{az}}\right)^{4}+\left(\frac{v_{eq}^{2}}{\lambda R_{b0}}\right)^{4} }-\frac{1}{48v_{eq}^{6}T_{image}^{2}}\right]f_{a}^{4}\right\}\]  
Finally, the focused BiSAR image is obtained by an azimuth IFFT. The detailed processing steps are illustrated to Figure 7.  
To evaluate the performance of the derived imaging algorithm, four example BiSAR data for five point targets are simulated using the parameters listed in Table 1 and the pulsed repeated frequency (PRF) and pulse duration listed in Table 2. A distance separation of five times of the range/azimuth resolution is assumed in the simulation examples. Additionally, the instantaneous squint angles of the transmitter and receiver at their zero-Doppler frequencies are supposed to be \(\zeta_{t0}(\tau=0)=0\) and \(\zeta_{r0}(\tau=0)=0\). After being processed by the proposed image formation algorithm, the results are given in \[k_{eq}=\frac{1}{\frac{1}{k_{r}}-\frac{\lambda R_{tot}}{c_{0}^{2}}\cdot\frac{( \lambda f_{a}/v_{eq})^{2}}{[\sqrt{1-(\lambda f_{a}/v_{eq})^{2}}]^{3}}} \tag{29}\]0 Note that there is a small image shift between Cases A and C, but it is difficult to be observed with the unaided eye because they have only a small difference in the receiver's velocity. As the reference range used in the imaging algorithm is chosen to be the central target range, it can be noticed that