esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 1 Introduction
[19] evaluated the application of Sentinel-2 derived VIs, such as the NDVI, the wide dynamic range vegetation index (WDRVI), the green-red vegetation index (GRVI), and the green normalized difference vegetation index (GNDVI), for estimating barley production. Regarding almond yield prediction using RS data, two studies have emerged. [PERSON] et al. [20] applied ML models to satellite (Landsat 8) and aerial imagery to forecast almond yield from orchards in California. They achieved a coefficient of determination (\(R^{2}\)) of 0.71 for early and mid-season predictions using stochastic gradient boosting (SGB). [PERSON] et al. [21], also in the context of California, explored the use of deep learning (DL) methods, using unmanned aerial vehicle (UAV) data, and developed a convolutional neural network (CNN). Their model obtained an \(R^{2}\) of 0.96 and a low error of 6.6% for tree-level almond yield estimation, emphasizing the significant potential of DL for precise tree-level yield prediction. Although the abovementioned studies exhibit strong results in forecasting almond yield, they are tailored to California [20, 21] and, as far as we know, there is a notable absence of research specifically dedicated to Portuguese almond yield forecasting. Despite the growing importance of almond cultivation in this country, this absence represents a critical gap in our understanding of the factors that influence almond yields in this region. The current research seeks to fill this research gap by developing a method for predicting almond yields in the TM region of Portugal, using ML regression models, and identifying the key factors that significantly influence almond yields. Furthermore, an improvement on previous studies may be the analysis of RS data from a diverse range of platforms, including freely available medium-resolution data and proprietary higher resolution data. This strategic combination may be used to investigate the effectiveness of medium-resolution RS platforms, compared to their higher resolution counterparts, for predicting almond yields. This information could potentially be used by sector stakeholders to enhance the decision-making process, enabling more informed and strategic choices for optimizing cultivation practices, resource allocation, and overall productivity. Considering the research gaps identified, the purpose of this study is 4-fold: (1) to use state-of-the-art ML regression models to accurately simulate the yield from several orchards in the TM region; (2) to integrate RS data from different platforms at different spatial resolutions, including from both open and proprietary platforms; (3) to identify the key features that significantly influence these predictions; and (4) to discuss potential applications of these findings in the sector.
# Flexural Performance of Removable Deck Slabs with Fixing Device Details
## 6 Conclusions  
A variable removable deck plate was developed to improve product quality and construction efficiency and to evaluate the safety of the product during construction and use. Tensile tests, deflection tests for deck plate construction loads, and bending performance evaluations produced the following conclusions:  
Figure 21: Load–strain relationship: (**a**) bottom bar; (**b**) fixing device.  
Figure 20: Load–displacement relationship.  
1. The anchorage performance test of the fixing device showed that the IR test specimen exhibited 7% to 25% greater ultimate strength compared with the R and RP types, making it the most effective shape. The improvement of the cross-sectional shape enabled structural performance improvement and steel thickness reduction.
2. A deflection test for construction determined that the maximum spacing of the fixing device should be less than 300 mm when considering design safety. Staggered placement at 300 mm intervals is recommended as the deflection during construction can be reduced if staggered arrangement is performed rather than in line arrangement. This suggests a need to secure a sufficient safety factor for detailed devices in the entire deck plate in case of sagging, particularly in places where there is a concern about concentrated loads by casting load and equipment in future design and on-site construction.
3. The strain of the fixing device in the flexural performance test was found to be in an elastic state, while all reinforcing bars yielded before maximum strength were reached and exhibited sufficient deformation. The installation method and spacing of the fixing device, therefore, have little to do with bending performance.
4. The FEA performed in this study was used only as a tool to set the experimental parameters. Although the most effective shape was found through the experimental results, finite element analysis under various conditions, such as thickness, width, and curvature, for shape optimization was not performed. Therefore, future studies should apply finite element analysis to various variables based on the experimental results to identify the optimal height and width of IR-type ribs so that the experimental results can be generalized.  
Original draft preparation and editing, performing tests, and investigations, H.-S.J.; validation and editing, M.Y.; supervision and review writing, C.-S.C. All authors have read and agreed to the published version of the manuscript. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2023-00207763).  
Not applicable.  
Data availability StatementData are contained within the article.  
The authors declare no conflict of interest.
# Challenging Assumptions of Normality in AES s-Box Configurations under Side-Channel Analysis
## 2 Related Works
Following this procedural step ensures that no selection bias is introduced during data collection. Contrary to CPA, TVLA allows a user to test for differences between the two data groups using a number of statistical methods, such as [PERSON]'s \(\chi^{2}\) test [14] or [PERSON]'s \(t\)-test [15]. However, utilizing the [PERSON]'s \(\chi^{2}\) test assumes that each group is independent of each other [16], and the [PERSON]'s \(t\)-test assumes that the data are normally distributed [15]. To ensure that there is high confidence in detecting leakage, [PERSON] et al. [13] chose a confidence level of \(\alpha=0.0001\) with its inversely related value, \(C=4.5\). While [PERSON] et al. uses the variable \(C\), the traditional usage of [PERSON]'s \(t\)-test refers to this value as \(t\), representing the computed \(t\)-score. A \(t\)-value of 4.5 is the associated value with \(\alpha=0.0001\), such that if a computed \(t\)-value, \(\hat{t}\), satisfies \(|\hat{t}|>4.5\), then there is 99.99% confidence that leakage was detected between the two groups. To extend this to using [PERSON]'s \(\chi^{2}\) test, the \(p\)-value of testing the power traces must be less than 0.0001 to have the same confidence level as using [PERSON]'s \(t\)-test. As outlined above, this process can be used to test implementations of AES for possible power-based leakage. One simple method to visualize the output from TVLA is given in Figure 2. It is important to note that the various horizontal lines illustrate a few possible \(\hat{t}\)-values. The overlapping elements are sourced from splitting both groups of power trace data into two equal-sized subsets and then using [PERSON]'s \(t\)-test to obtain a point-wise  
Figure 1: Visualizations for Simple Power Analysis (SPA), Differential Power Analysis (DPA), and Correlation Power Analysis (CPA) that accompany the methodological discussions in Section 2. (a) Sample SPA visualization. (b) Sample DPA visualization. (c) Sample CPA visualization. \(\hat{t}\)-value. The two subsets are then plotted to generate a simplistic visual verification of potential leakage. While they are tangential to this work, some alternative statistical methods have been examined. For example, [PERSON] et al. [4] demonstrate an application of using the Kullback-Leibler test [17], which evaluates for significant divergence between two groups; this acts as a comparison to [PERSON]'s \(t\)-test. The assumptions of this test are that the two data groups have the same range of values which are positive, known as the support. The Kullback-Leibler test does not assume normality of the data, although it does have its own assumptions on the support of the data. This application of statistical testing is with regard to Register-Transfer Level (RTL) leakage exploitation-based attacks [18, 19, 20]. On the contrary, this work primarily focuses on leakage detection, leading to the exploration of alternate statistical methods.
## 3 Results and Discussion
### Comparison between Input-Coupled HMT and Input-Coupled HMT with Lockup Clutch--Mission Test  
Both transmissions were also tested on a real driving cycle. Figure 18a,b show the comparison between the global efficiency and the fuel consumption, respectively.  
Figure 18: Comparison between input-coupled HMT and input-coupled HMT with lockup clutch on a real driving cycle. (**a**) Global efficiency. (**b**) Fuel consumption.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Power Split** & **Power Split Lockup** \\ \hline Global efficiency & 0.63 & 0.91 \\ Fuel consumption & 0.95 g/s & 0.7 g/s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between power split and power split with lockup clutch through the standard UNI-EN 15429-2 cycle.  
The comparison between the global efficiencies of the input-coupled HMT and the input-coupled HMT with lockup clutch proves that the use of the lockup clutch leads to an increase of the global efficiency of the system when the lockup clutch is engaged (Figure 17a). Moreover, the improvement of the efficiency leads to a decrease in the fuel consumption, as shown in Figure 17b. In this case, the control system allows a mechanical gear to be engaged in the range speed between 4 km/h and 6 km/h, in a similar way to an adaptive cruise control. Hence, in this phase, the vehicle follows the mission test, changing the angular speed of the engine and not controlling the pump displacement, like in power split mode. It is necessary to observe that, with respect to the standard cycle and taking into account a real driving cycle (mission test), the fuel consumption reduction becomes less evident due to the dynamics of a real cycle. However, if the projected daily working cycle is taken into consideration, for example, in one year of work, and also considering the high number of streetsweepers operating in an urban center, the reductions in the fuel consumption and emissions could become considerable. In addition, this advantage in terms of efficiency and fuel consumption in achieved through a simple solution from a design point of view, only involving the addition of a mechanical gear. Hence, this fact could justify the adoption of the lockup clutch.
# Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand
## 4 Solution Approach
### Simulated Annealing Chimp Optimization Algorithm with Sine-Cosine Operator  
The traditional CHOA is mainly suitable for solving continuous optimization problems. The VRPDTWDD problem is a combinatorial optimization problem, thus CHOA cannot be directly applied to VRPDTWDD. To solve this problem, we need to design an appropriate discrete strategy. Furthermore, there exists a Hamiltonian loop with a branching structure in the solution of the VRPDTWDD problem, i.e., the same nodes appear in the truck route and the drone route (such as customer 5 in Figure 3). However, this branch route structure cannot be directly compiled and expressed. Therefore, we need to design a new initial solution deconstruction method and encoding method. Based on the principle of the algorithm, other chimpanzees estimate the location of the prey based on the location of the attacker and then update their own location to hunt. To further improve the convergence accuracy of the algorithm, this study introduces a sine-cosine operator to update the attacker's position. Meanwhile, to avoid local optimum, this study also introduces a simulated annealing mechanism. To sum up, we propose an improved chimp optimization algorithm named Simulated Annealing Chimp Optimization Algorithm with Sine-Cosine Operator (SACHOA). It includes the following main key steps:
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 4 High Performance Computing: Fundamentals Concepts and Models
### Cloud Computing vs. HPC
Currently, HPC outperforms CC as the crunching of a large amount of data with structured scientific processing is concerned. Although a large effort has been devoted to improving CC performance, HPC scientific workload computing in cloud-computing environments remains unresolved [179; 180]. Nonetheless, CC platforms as GEE or AWS provide convenient ways of storing, accessing, and analyzing SAR datasets, thus offering computing power, storage services, and software packages. With specific reference to InSAR applications, it is worth noting that the tiling concept underlying GEE is unfortunately incompatible with the processing of complex SAR data [169].
# Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm
## 3 Machine Learning Algorithms
### Artificial Neural Network  
Artificial neural networks (ANN) are methods developed to automatically realize the abilities of the human brain, such as acquiring and discovering new information by learning, without any help [42]. ANNs have advantages, such as not requiring a mathematical definition for the relevant event and being able to make predictions using a limited number of experiments. The layers that make up the ANNs are as follows [43].  
The artificial nerve cell structure is shown in Figure 3. The x\({}_{n}\) signal at the input of the n synapse is multiplied by the synaptic weight w\({}_{nj}\) and connected to the n neuron. The summation function adds the input signals and the corresponding synapses of the neuron via a linear combiner [44]. Threshold, piecewise linear, sigmoid, and Gaussian functionsare used as activation functions in the literature. The relationships in an artificial neural network can be expressed mathematically as follows:  
\[u_{k}=\sum_{j=1}^{m}w_{kj}x_{j} \tag{6}\]  
\[y_{k}=f(u_{k}+b_{k}) \tag{7}\]  
Here, \(u_{k}\) is the linear combiner output, \(w_{k1},\cdots,w_{km}\) are the synaptic weights of neuron k, \(x_{1},\cdots,x_{m}\) are input signals, \(y_{k}\) is the output signal of the neuron, \(f(\ldots)\) is the activation function, and \(b_{k}\) is the bias value. If artificial neurons are represented by nodes and neuron inputs and outputs are connected by directed arrows, ANNs become weighted directed graphs. ANNs can be divided into two classes according to the structure of the graph. The first is feedforward networks without loops, and the second is recurrent networks with loops due to feedback connections. In feed forward ANNs, the information transferred only moves forward, i.e., there is a movement from input to output [45].
## 4 Analysis of Old Hanger Test Detection
### Steel Wire Detection  
#### 4.4.1 Tensile Performance  
A representative sample with a length of 1200 mm was selected from the upper, middle, and lower parts of the hanger to carry out the tensile property test on steel wire. An electronic tensile testing machine stretched the sample until it was broken. The material's mechanical properties were measured, including tensile strength and elongation after fracture. The experiment was conducted at a room temperature of 10-35 \({}^{\circ}\)C. The test results are shown in Figure 15. Established research shows that corrosion weakens steel wire cross-sections, reducing load-bearing ability. Pore corrosion increases surface roughness, decreasing final elongation. See Figure 15 for the surface corrosion of the steel wire in the old hanger. When the corrosion is mild, the steel wire shows apparent necking shrinkage before fracture and a cup-shaped fracture with radial stripes in the middle. The tensile test results of the wire showed that the minimum value of tensile strength was 1705 MPa, the minimum value of the modulus of elasticity was 1.92 \(\times\) 10\({}^{5}\) MPa, and the minimum value of elongation after break was 4% in all three specimens, all of which followed the specification [18], which indicated that the tensile properties of the wire did not significantly deteriorate.
## 3 Results  
### Accuracies of PML-V2 GPP Estimates  
The observations from eight flux stations (Figure 3) were compared with the average GPP estimates within 3 \(\times\) 3 neighboring pixels centered at the geographical locations of the flux stations. As can be seen from Figure 3a, the R\({}^{2}\), RMSE, and MRE values of the GPP estimates on the 8-day scale were calculated to be 0.89, 7.92, and 11.35%, respectively. It can also be seen from Figure 3b that the R\({}^{2}\), RMSE, and MRE values of the GPP estimates on the annual scale were 0.92, 161.93, and 10.37%, respectively. The validation results for both the 8-day and annual scales indicate that the PML_V2 GPP estimates generally performed well in China and are a reliable data resource for exploring the spatiotemporal changes in the GPP across China.
## 3 Simulation Modeling of Dynamics for EMU Gearbox System
### CRH5 gearbox Rigid-Flexible Coupling Dynamic Modeling
#### 3.1.1 Theory of Rigid-Flexible Coupled Dynamics  
Universal joints and gears undergoing elastic deformation need to be interpreted in terms of a relative deformation field, which must satisfy the boundary conditions of the member, and the expression is then as follows:  
\[\begin{cases}u_{f1}=\sum\limits_{k=1}^{\infty}a_{k}f_{k}\\ u_{f2}=\sum\limits_{k=1}^{\infty}b_{k}g_{k}\\ u_{f3}=\sum\limits_{k=1}^{\infty}c_{k}h_{k}\end{cases} \tag{16}\]  
where \(u_{f1}\), \(u_{f2}\), and \(u_{f3}\) denote the deformation components of the flexible body coordinates (\(x_{1}\), \(x_{2}\), and \(x_{3}\)); \(a_{k}\), \(b_{k}\), and \(c_{k}\) are the coefficients with respect to time \(t\), and \(f_{k}\), \(g_{k}\), and \(h_{k}\) are the basis functions of the deformed body.  
The motion constraints of universal joints and gears need to consider elastic deformation. Their motion constraint equations are as follows:  
\[\begin{cases}C(q,t)=0\\ C_{q}q=-C_{t}\\ C_{q}\ddot{q}=-\left[C_{tt}+\left(C_{q}\dot{q}\right)\ddot{q}+2C_{qt}\dot{q} \right]\end{cases} \tag{17}\]  
where \(C\) is the constraint equation; \(q,\dot{q},\ddot{q}\) are the velocity, position, and acceleration vectors of the flexible body; \(C_{q}q\) is the coordinate velocity of the flexible body; and \(C_{q}\ddot{q}\) is the acceleration of its motion.  
The CRH5 gearbox rigid-flexible coupled dynamics system is connected by constraints and force elements, and the dynamics equations of the system are as follows:  
\[M^{i}\ddot{q}^{i}+C_{q^{i}}^{IT}\lambda=Q_{e}^{i}+Q_{V}^{i},\ \ i=1,2,3 \tag{18}\]  
where \(M\) is the inertia matrix of the gearbox; \(\lambda\) is the Lagrange operator; \(Q_{e}\) is the generalized external force vector; \(Q_{V}\) is the generalized inertia force vector related to the velocity quadratic; and \(i\) denotes the flexible body components, i.e., the universal joints, driving gear, and driven gear.
# A Sigfox Energy Consumption Model
## 5 Evaluation
### Use of Energy Harvesting  
While using a battery as the energy source for a sensor device is a common approach, energy harvesting is also a relevant alternative. This section evaluates the minimum feasible transaction period for a Sigfox device powered by an energy harvesting source. The results are obtained based on Equations (1)-(4) and (8)-(18), considering 1-byte and 12-byte payloads, unidirectional and bidirectional transactions, and two energy harvesting system models: a Panasonic AT-7665A film outdoor solar panel, and a Panasonic AM-1815 CA glass indoor solar panel [23; 24]. The former  
Figure 12: Average current consumption of the device, for different transaction types and uplink payload sizes, as a function of \(I_{sleep}\), for FLR = 0.  
Figure 13: Average current consumption of the device, for different transaction types, as a function of \(I_{tx}\), for FLR = 0, and for different uplink payload size of 1 byte.  
typically provides a current of 38.6 mA and a voltage of 3 V, whereas the latter typically gives 47 uA and 3 V.  
Table 4 shows, for each considered transaction type, for an uplink bit rate of 100 bit/s, and for the two considered energy harvesting sources, the energy required to carry out the transaction, and the minimum feasible transaction period. The device is assumed to be in sleep state between transactions. The outdoor solar panel allows using all \(T_{Period}\) values considered in this paper, i.e., it does not limit by itself the minimum feasible \(T_{Period}\). However, the indoor panel supplies a much lower current (three orders of magnitude below the outdoor panel one). This requires accumulating the energy harvested by the indoor panel over relatively large periods of time before allowing completing a transaction, up to \(\sim\)1.5 h or \(\sim\)4 h for unidirectional and bidirectional transactions, respectively.
# Feedback Modulation between Human IN080 Chromatin Remodeling Complex and miR-372 in HCT116 Cells
## 3 Discussion
In conclusion, the experimental results in this paper reveal the mutual regulatory relationship between miR-372 and the INO80 complex and make it clear that miR-372 can directly interact with the 3\({}^{\prime}\)-UTR of multiple components in the complex, thereby post-transcriptionally regulating the expression and stability of the complex, altering the complex's chromatin remodeling activity, which in turn further affects the intracellular biological function of the INO80 complex. As shown in the schematic diagram below (Figure 7), in normal cells, both miR-372 and p53/p21 are suppressed by the INO80 complex by regulating the chromatin structure; when the INO80 complex is silenced, it will lead to instability of the complex and loss of chromatin remodeling enzyme activity; thus, the inhibition of INO80 complex on miR-372 and p53/p21 is released, leading to the up-regulation of miR-372 and p53/p21 expression, further inhibiting the proliferation of HCT116 colon cancer cells. However, most data supporting the above mutual regulation model are limited to HCT116 cells in this study, and whether such a regulatory pattern also exists in other cancer types still needs to be verified. In summary, the results of this paper provide a theoretical basis for elucidating the mechanism of action of the INO80 chromatin complex and provide new ideas for the development of subsequent cancer therapeutics. MiR-372 is also expected to serve as a biomarker for early cancer diagnosis. Figure 7: Working model of miR-372-INO80 complex axis. \(\downarrow\), downregulation; \(\uparrow\), upregulation.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 2 Materials and Methods  
### Continuous Wavelet Transform  
In a rolling-bearing FD task, a fault signal from monitored equipment is easily lost in noise due to vibrations from the equipment and environmental factors; this noisiness increases the difficulty of later feature extraction [27]. The continuous wavelet transform (CWT) provides rich time-frequency information, which more effectively highlights the fault characteristics of the target components [28]. Considering the limited computational resources of edge devices, this study employed the CWT to convert the signals into time-frequency FMs with a resolution of \(128\times 128\times 3\); the FMs were then used as fault samples. The CWT is calculated as shown in Equation (1):  
\[\text{CWT(a,b)}=\frac{1}{\sqrt{\text{a}}}\int_{-\infty}^{\infty}\text{x(t)} \psi^{*}(\frac{\text{t}-\text{b}}{\text{a}})\text{dt} \tag{1}\]  
where \(\text{x(t)}\) is the input signal, \(\psi^{*}(\text{t})\) is the complex conjugate of the scaled and shifted wavelet function \(\psi(\text{t})\), a is the scale factor, and b is the translation factor.