esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Descriptive statistics of the collected field data on soil EC, and soil pH, are presented in Table 3. The values of soil EC across the three experimental fields ranged from 1.23 to 3.27 dS m\({}^{-1}\), with a coefficient of variation (CV) value of 41.4%, which indicates that the soil EC in the experimental field was relatively heterogeneous. The soil pH values, however, varied between 7.97 and 8.66 with a CV value of 1.91, indicating a homogeneous distribution of soil pH across the experimental fields. Based on the standard measures of soil EC and pH scales, the soil of the experimental field was characterized as non-saline and moderately alkaline. The ideal pH level for growing carrots is between 6.0 to 6.8 [57]. Hence, to the soils of the experimental fields were added wood ashes or dolomitic lime to balance the pH. The accumulated GDDs of the tested carrot crops varied between 333 at the seedling stage to 3374 at root maturation (i.e., 120 DAP). The GDD was higher (43%) in season 1 compared to season 2 (Figure 3). Phenological growth stages of carrot plants were assigned based on the GDD as the seedling stage-SS (GDD \(\leq\) 948), vegetative growth stage-VS (GDD = 948 to 1215), root development stage-RD (1216 \(<\) GDD \(>\) 2210), and root maturation-RM (GDD \(>\) 2210) stages. The carrot plant population, enumerated at 10-13 DAP, varied between 54.2 and 64.8 plants m\({}^{-2}\), with a mean value of 58.5 and 61.1 plants m\({}^{-2}\) for the first season (CV% = 18.4) and second season (CV% = 14.9), respectively (Table 4). The mean nitrogen content at harvest time was 2.09 g kg\({}^{-1}\), where the minimum and maximum values were 1.31 g kg\({}^{-1}\) and 2.58 g kg\({}^{-1}\), respectively. The results indicated that the harvested carrot root yield (Y\({}_{\rm A}\)) ranged between 62.0 t ha\({}^{-1}\) (Season-1) and 53.7 t ha\({}^{-1}\) (Season-2), with a mean yield of 55.7 t ha\({}^{-1}\) and a CV of 14%  
Figure 3: Season-wise and field-wise growing degree days (GDD). \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Field**} & \multicolumn{2}{c}{**Season-1**} & \multicolumn{2}{c}{**Season-2**} \\  & **Soil EC** & **Soil pH** & **Soil EC** & **Soil pH** \\ \hline N1 & 1.23 & 7.97 & 1.21 & 7.99 \\ N4 & 2.29 & 8.12 & 2.46 & 8.22 \\ N6 & 3.19 & 8.66 & 3.27 & 8.64 \\ Mean & 2.24 & 8.25 & 2.31 & 8.28 \\ CV\% & 41.40 & 1.91 & 36.90 & 1.88 \\ \hline \end{tabular}
\end{table} 
Table 3: Soil electrical conductivity (EC, dBm\({}^{-1}\)) and pH of experimental fields. The temporal advancement of LCC values measured by the SPAD meter is provided in Figure 4. The mean LCC values of carrot plant leaves increased sharply from 29.4 at a crop age of 30 days after planting (DAP) to 82.1 at 75 DAP, and then decreased to values of 73.3 at 90 DAP and dropped to 60.5 at 120 DAP (i.e., 15 \(\pm\) 3 days before harvest), respectively. The seasonal dynamics of the studied VIs across the growing period of the carrot crop are provided in Figure 4. It was observed that approximately all the tested VIs (except the GLI) showed an increasing trend with carrot crop age, where they reached their peak values at a crop age of 60 DAP.
# Silicon Ribbon-Based Dual-Beam Optical Phased Array with Low Crosstalk and Large FoV
## 4 Summary and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Grating** & \multirow{2}{*}{**Aperture Size**} & **Number of** & **FWHM** & **Steering** \\  & **Type** & & **Elements** & **(\(\theta\)/\(\Phi\))** & **Range (\(\theta\)/\(\Phi\))** \\ \hline This work & Fishbone & 400 \(\mu\)m \(\times\) 37 \(\mu\)m & 32 & 0.24\({}^{\circ}\)/1\({}^{\circ}\) & 30\({}^{\circ}\)/95\({}^{\circ}\) \\ \hline
[14] & Shallow-etch & 2000 \(\mu\)m \(\times\) 665 \(\mu\)m & 512 & 0.15\({}^{\circ}\)/0.08\({}^{\circ}\) & 6\({}^{\circ}\)/70\({}^{\circ}\) \\ \hline
[15] & Shallow-etch & 910 \(\mu\)m \(\times\) 910 \(\mu\)m & 128 & 0.14\({}^{\circ}\)/0.14\({}^{\circ}\) & 17\({}^{\circ}\)/80\({}^{\circ}\) \\ \hline
[16] & Shallow-etch & 2000 \(\mu\)m \(\times\) 2000 \(\mu\)m & 512 & 0.04\({}^{\circ}\)/0.04\({}^{\circ}\) & 15\({}^{\circ}\)/56\({}^{\circ}\) \\ \hline
[17] & Shallow-etch & 576 \(\mu\)m \(\times\) 144 \(\mu\)m & 32 & 0.6\({}^{\circ}\)/1\({}^{\circ}\) & 3.6\({}^{\circ}\)/23\({}^{\circ}\) \\ \hline
[2] & Fishbone & 500 \(\mu\)m \(\times\) 100 \(\mu\)m & 50 & 0.18\({}^{\circ}\)/0.85\({}^{\circ}\) & 24\({}^{\circ}\)/36\({}^{\circ}\) \\ \hline
[5] & Microlens & 750 \(\mu\)m & 9 & NA/NA & 33.7\({}^{\circ}\)/NA \\ \hline \hline \end{tabular}
\end{table} 
Table 2: OPA grating emitter summary and comparison.
# Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle
## 4 Experiments
### Experimental Results
#### 4.3.3 Result Data-Plots: Experiment A  
The following figures, Figures 3 and 4, shows the data plots for the results recorded from the experiments.  
These data plots are very significant and helps to easily understand the effectiveness of plasticity influenced HTL algorithm. Here STL means standard or traditional transfer learning algorithm, and HTL is short for our purposed Hebbian transfer learning algorithm. The red color of the curve represents HTL results, and black colored curve represents the STL results, respectively. While studying the data plots in Figure 3, it is recommended to study dataset from Tables 2 and 3. Figure 3 is also comparable to the corresponding validation accuracies from Table 5.  
The results from data-plots in Figure 3 can be used for quick understanding of the results from experiment A.  
The comparison of learning curves of STL and HTL for 10 different datasets in terms of top-5 accuracy are shown in Figure 3. The \(x\)-axis represents the number of epochs and the \(y\)-axis represents the validation accuracies. In each graph, the black color is for STL and the red color is for HTL. Looking at the graphs, we can observe that the Hebbian transfer learning adapts to target domain a little more slowly but eventually achieves higher validation accuracy in all cases. Again, the data curves of STL and HTL for target dataset (T\({}_{1-10}\)), shows highest improvement in top-5 accuracy for HTL algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **Source** & **Target** & **STL** & **HTL** & **Improvement** \\ \hline \multirow{8}{*}{Top-1 Accuracy} & D\({}_{1}\) & D\({}_{2}\) (Homogeneous) & 73.2\% & 73.2\% & 0.0\% \\  & D\({}_{1}\) & D\({}_{7}\) (Heterogeneous) & 37.8\% & 40.2\% & +2.4\% \\ \cline{1-1}  & D\({}_{3}\) & D\({}_{4}\) (Homogeneous) & 67.4\% & 67.2\% & \(-\)0.2\% \\ \cline{1-1}  & D\({}_{3}\) & D\({}_{7}\) (Heterogeneous) & 42.2\% & 43.6\% & +1.4\% \\ \cline{1-1}  & D\({}_{5}\) & D\({}_{6}\) (Homogeneous) & 72.2\% & 72.8\% & +0.6\% \\ \cline{1-1}  & D\({}_{5}\) & D\({}_{8}\) (Heterogeneous) & 62.4\% & 63.8\% & +1.4\% \\ \cline{1-1} \cline{2-5}  & Average accuracy for Homogeneous case & 70.93\% & 71.06\% & +0.13\% \\ \cline{1-1} \cline{2-5}  & Average accuracy for Heterogeneous case & 47.46\% & 49.20\% & **+1.80\%** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Top-1 validation accuracies of STL and HTL for experiment B.  
Figure 3: _Cont._
# A Survey on Shortest Unique Substring Queries
## 2 Preliminaries
### Data Structures  
The _suffix tree_ data structure of string \(\mathcal{S}[1,n]\) is a compact trie of the \(n\) suffixes of \(\mathcal{S}\) appended with a letter \(\$\in\Sigma\)[27]. This suffix tree consists of \(n\) leaves (one for each suffix of \(\mathcal{S}\)) and at most \(n-1\) internal nodes. The edges are labeled with substrings of \(\mathcal{S}\). We denote the suffix tree of string \(\mathcal{S}\) with \(\mathsf{ST}(s)\). The _Suffix Array_ of string \(\mathcal{S}\) of length \(n\) is denoted by \(\mathsf{SA}\), which is a permutation of \(\{1,\cdots,n\}\), such that \(\mathsf{SA}[i]=j\) if \(\mathcal{S}[j,n]\) is the ith lexicographically smallest suffix of \(\mathcal{S}\). The _Inverse Suffix Array_ of string \(\mathcal{S}\) of length \(n\), is a permutation of \(\{1,\cdots,n\}\), such that \(\mathsf{SA}^{-1}[\mathsf{SA}[i]]=i\). \(\mathsf{SA}\) of \(\mathcal{S}\) can be constructed in linear time and space [28; 29]. The _longest common prefix_ (\(\mathsf{lcp}\)) _array_ of a string \(\mathcal{S}\) of length \(n\) is an integer array of length \(n\) such that \(\mathsf{lcp}[1]=0\) and \(\mathsf{lcp}[i]\) stores the length of the longest common prefix between \(\mathcal{S}[\mathsf{SA}[i-1],n]\) and \(\mathcal{S}[\mathsf{SA}[i],n]\). Given the suffix array of \(\mathcal{S}\), \(\mathsf{lcp}\) array can be constructed in \(\mathcal{O}(n)\) time. Let \(A[1,n]\) be an array of length \(n\). A range minimum query (RMQ) with an input range \([i,j]\) asks to report \(RMQ(i,j)=\arg\min_{k}\{A[k]\mid k\in[i,j]\}\). By maintaining a data structure of size \(2n+o(n)\) bits, any RMQ on \(A\) can be answered in \(O(1)\) time [30] (even without accessing \(A\)).  
Consider \(s\) as a subset of \(\{1,2,\ldots,n\}\). Then, \(s\) can be preprocessed into an \(O(|s|)\) space data structure, such that for any query \(p\), we can return \(\mathsf{pred}(p,s)\) and \(\mathsf{succ}(p,s)\) in \(O(\log\log n)\) time [31], where  
\[\mathsf{pred}(p,s)=\max\ \{i\mid i\leq p\ \text{ and }i\in s\cup\{-\infty\}\}\]  
\[\mathsf{succ}(p,s)=\min\ \{i\mid i\geq p\text{ and }i\in s\cup\{\infty\}\}\]  
The eertree of a string \(\mathcal{S}\), is a pair of two rooted trees \(T_{odd}\) and \(T_{even}\) that represent all distinct palindromic substrings of \(\mathcal{S}\)[32]. \(T_{odd}\) and \(T_{even}\) store the palindromic substrings of odd and even length, respectively. There is a directed edge \((r,a,v)\) from root \(r\) of \(T_{odd}\) if \(v\) represents a single character \(a\in\Sigma\). For any non-root node \(u\) in either \(T_{odd}\) or \(T_{even}\), there is a labeled directed edge \((u,a,v)\) from \(u\) to \(v\) with character label \(a\) if \(aua=v\). There are no two out-going edges from a node with the same label.
# Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation
## 4 System Design  
The combination of large model sizes, long sequence lengths, and batching creates significant memory constraints for efficient training of transformer models. Due to the limited sets of operations available on GPUs, batching is typically performed on CPUs, which can accommodate user-defined functions for batch formation. Especially in the NLP domain, where tokenizations require specific sets of string-manipulating operations, these operations are currently only available on the CPU devices. However, in time series, the batching process also involves window-based operations that can be greatly accelerated by modern GPUs.  
We propose a GPU-based batch formation process for time series data by pre-calculating the pre-batch data size and performing view-based sliding-window techniques on GPU directly prior to creating batches of training data. This technique can avoid re-materializing the redundant data in each window resulting in two-fold benefits, allowing users to increase batch sizes, which in turn reduces the overall training time, and models could also attend to longer history by increasing the size of the look-back window. It is important to note that identifying the most effective look-back window is an active area of research that is not covered in this paper. In cases of resource-constrained developing environment, the zero-copy sliding-window technique would significantly reduce the memory requirement and allow the training process to take place on devices with limited memory space. The novelty of our proposed method lies in two critical aspects. First, leveraging view-based operations directly on GPU-resident tensors to generate zero-copy sliding-window views without duplicating data. Second, introducing a dual-mode operational strategy, \"Optimize\" (batch-wise GPU loading) and \"Optimize in memory\" (full GPU pre-loading), to adapt flexibly based on available GPU resources, while the view-based operation itself is not new, applying it in this GPU-native, view-only manner for memory-efficient batching in time series transformer models represents a previously unexplored approach.  
In the following subsections, we explain our system design by first outlining the two variants of our optimized batching approach in Section 4.1. Then, we elaborate on each of the components required to enable our optimizations on time series datasets during training in Section 4.2.
# Malware Detection Issues, Challenges, and Future Directions: A Survey
## 5 The Taxonomy of Feature Extraction and Representation Methods
### Feature Representation Methods
#### 5.2.4 Image Characterization-Based Vector  
In the gray-scale-based malware detection and classification models, the gray images of malware and benign samples are generated by dividing the PE malware files into 8-bit vectors consisting of hexadecimal values. Those values have been represented in the form of pixels, which range from 0 to 255 to simulate the gradient from the white to the black colors for each pixel in order to represent the malware files as images. The width of the images is predetermined, while the length can be hung based on its size [105]. Therefore, those created images have been utilized in many studies during the representation phase when the generated vectors comprise the numerical data that characterize the created image.  
To generate image characteristics-based vectors and represent the malware files as vectors that describe the created malware images from several aspects, there are various algorithms such as Color Layout Descriptor (CLD), Homogeneous Texture Descriptor (HTD), and GIST for obtaining the vector from the generated images. The vectors which are generated to describe the constructed images of malware and benign files have to contain distinguishable values since those values represent several aspects of the images, such as texture and intensity, which are expressed differently in malware and benign files.  
In their studies [75], they converted each byte in the binary malware file into one pixel of the image that represents the malware file. The authors of [58] integrated the static and dynamic features after visualizing them as two images. Both images of the original binary file were encoded into a single image. Therefore, the image characterization vector method is applied to generate the vector that consists of numerical data that represents the texture and color of the hybrid image. However, it takes more time to generate the images. Furthermore, representing the files as images requires a large amount of storage space. Additionally, visualization-based models depend on the matching process to classify the testing files. Therefore, samples with few variants in the training data lead to no matching and then misclassification for them.
## 3 Discussion  
The main objective of the SEN4 LT project has been to exploit the synergy between S3/OLCI and S3/SLSTR data to improve LST retrievals by proposing new methodologies, but also taking advantage of the heritage of the AATSR LST standard product and the combination of MERIS/AATSR (Synergy Project). Despite several candidate methodologies having been identified, it has been concluded that the split-window method stands out for its simplicity and good performance and it has been selected to be part of the processor. The SW has the input emissivity estimated from a simple approach based on the NDVI (NDVI-THM). This algorithm provided better results in the validation made using simulated and in situ data than the standard AATSR L2 product ([7; 32]). A complete database of in situ measurements, satellite imagery and simulated data was compiled. The SEN4 LT Processor was also developed and implemented in BEAM, which allows the processing of the simulated data and the processing of MERIS/AATSR imagery to derive the new LST products. The results of the project have demonstrated that the LST candidate algorithm developed in the framework of the SEN4 LT project can be applied in an operational processing chain to generate LST products, with potential improvements to the existing AATSR Operational product. A second synergistic approach in the processor allows for the combination of the OLCI and SLSTR instruments on-board Sentinel-3. Although this option must be regarded as experimental and has room for improvements, it allows a full LST retrieval from OLCI/SLSTR surface reflectance simulation datasets. One point under discussion in the scientific community that has not been faced here is the angular dependence of the proposed algorithm from the observation zenith angle. This is a complex matter over land and so far there is not an operational solution. Some solutions like the one in [33] are still to be quantified in absolute and relative terms. Maybe to follow some of the normalisation approaches made over ocean (BRDF) could be the path to follow.  
Figure 7: Monthly LST for July 2003 over the Europe, screen-shot of VISAT.
## 2 Specification and Experiment of Basic WFSM
To comprehensively investigate this influence, experimental trials were meticulously conducted, systematically varying the rotor current (\(I_{dc}\)) across three discrete levels: 2 A, 4 A, and 6 A. Additionally, the rotational speed was varied from 200 rpm to 500 rpm in precise 100 rpm increments. Under no-load conditions, a fundamental observation was the proportional increase in the root mean square (\(V_{rms}\)) of the no-load back electromotive force (EMF) with both the rotor current and the rotational speed. Under lower-rotor-current conditions, the motor core exhibited minimal saturation, resulting in a negligible discrepancy between analytical predictions and measured values. However, as the rotor current was progressively increased, the motor core approached saturation, leading to nonlinearity in the back EMF characteristics. Consequently, this nonlinear behavior contributed to a corresponding increase in the error between the FEM analysis results and the measured values. In the low-speed range, the influence of noise is relatively significant, resulting in larger errors. However, as the motor speed transitions to higher speeds, the errors gradually decrease. This trend suggests that the system becomes more stable and predictable at higher speeds, leading to more accurate predictions and mitigating the impact of noise. During the load testing phase, the electric motor was energized and the average torque was derived by capturing internal torque commands communicated from the servo motor. This methodically involved incrementally adjusting the armature current, varying it from 1.5 A to 9.5 A in 4 A increments, to enable a thorough comparison between experimental findings and FEM analysis outcomes across multiple data points. Despite grappling with challenges such as core saturation and nonlinear behavior, the comprehensive analysis yielded invaluable insights. The deviation was derived by comparing the FEM analysis results of the motor with experimental values, thereby verifying the credibility of the computational results. The error between the FEM analysis results and the experimental data was calculated. For the EMF, the experimental value measured was  
Figure 4: WFMS experiment procedure: (**a**) experimental environment; (**b**) variation in field current with rotation speed; (**c**) no-load EMF experimental waveform (300 rpm, \(I_{dc}\) = 4 A); (**d**) no-load EMF FEM waveform (300 rpm, \(I_{dc}\) = 4 A). 40.55 \(V_{rms}\), while the FEM analysis calculated 39.17 \(V_{rms}\). An error of 3.5% was attributed to noise during the experimental measurement process. The comparison results of the torque average, depicted in Figure 5, reveal an overall deviation of 8.15% between the experimental measurements and the FEM analysis results. This finding underscores the reliability of the analysis and the low discrepancy observed, reaffirming the credibility of the experimental methodology and the analytical framework employed.
## 4 Dynamic Performance of the New Semiactive HEM
### Simulation Validation Using the Finite Element Method  
There is a fluid-structure interaction for the HEM. To compare and verify the theoretical linear analysis scheme of the new semiactive HEM, three finite element models using the parameters in Table 1 are established to analyze the dynamic stiffness spectrum of the HEM. Three models of the inertia track with three geometry parameters are shown in Figure 6.  
The metal inserts are subjected to a series of sine wave excitations, each with an amplitude of 1 mm and a frequency ranging from 0 to 100 Hz. Force-displacement curves at each excitation frequency are calculated for each finite element model with specified inertia track lengths, and force-displacement curves at some typical frequencies are shown in Figure 7. From each hysteresis loop, the dynamic stiffness of the HEM at a certain frequency point can be calculated. The dynamic stiffness spectrum was calculated when the inertia track length \(L_{1}\) is equal to 1.0 \(L_{0}\), 0.5 \(L_{0}\), and 0.2 \(L_{0}\), respectively. The results shown in Figure 8 reveal that when the excitation frequency is less than the fixed frequency point \(P_{2}\), the dynamic stiffness from the simulation model decreases as the length of the inertia track increases; when the excitation frequency is greater than the fixed frequency point \(P_{2}\), the dynamic stiffness from the simulation model increases as the length of the inertia track increases. The fixed point \(P_{1}\) of the linear model and the fixed point \(P_{2}\) of the simulation model are close to each other. The overall trend is consistent with the results of the linear model, but there is still a discrepancy between the peak and valley dynamic stiffnesses.  
Figure 5: Dynamic stiffness spectrum with different control parameters, fixed frequency point \(P_{1}\).  
Figure 6: Geometry and mesh for different inertia tracks.
## 5 Conclusions  
In this article, a theoretical analysis of the near field of an ultra-large-scale metasurface has been performed based on calculations. The vortex beams generated by the large metasurface array converged toward the center and generated fewer beam sidelobes. Gaussian noise was used to simulate the actual noise distribution. The findings demonstrate that as the metasurface size increases, the metasurface utilized for generating vortex waves exhibits enhanced anti-interference capability, enabling a clear distinction of both the amplitude and phase of the electric field on the sampling surface. Conversely, smaller arrays exhibit weaker anti-interference ability. A single-layer broadband \(EE\) unit cell has been proposed to design a vortex beam metasurface. Vortex beams with different sizes were also simulated and analyzed and may find further applications in communication systems.  
Figure 13: Far-field calculation of vortex waves generated by the \(l=-3\) metasurface and \(l=-5\) metasurface at 12 GHz. (**a**) Trimetric view and (**b**) top view of the \(l=-3\) metasurface; (**c**) trimetric view and (**d**) top view of the \(l=-5\) metasurface.  
**Author Contributions:** B.S.: materials and method, conclusions and suggestions, writing--original draft, and writing--review and editing. K.G.: numerical calculation and simulation analysis and writing--original draft. A.Q.: introduction, numerical calculation, writing--original draft, and writing--review. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Self-Funded Project of the Scientific Research of Hebei Department of Education, grant numbers ZC2025077 and ZC2023117; Hebei Key Laboratory of Resource and Environmental Disaster Mechanism and Risk Monitoring, grant number FZ248108; and the State Key Laboratory of Advanced Rail Autonomous Operation Project, grant number RAO2023 ZZ004.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.