esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Bus Synchronization Problem
### Bus Occupation Model
```
Input: Samples, \(T\), \(\overline{\Lambda_{i}}\), \(T2E_{i}\), \(td_{i}\)
1:\(busocup\leftarrow\) zeros\((Samples,T2E_{i})\)\(\triangleright\) A \(T2E_{i}\) minutes simulation per-row
2:for{s \(\gets\) 1;s \(\leq\)\(Samples\);s++} do
3:\(arrivals=\) poisrnd\((\overline{\Lambda_{i}},T2E_{i}-td_{i})\)\(\triangleright\) One Poisson sample per-minute&simulation
4:\(busocup(s,1)=arrivals(1)\)
5:for{t \(\gets\) 2;t \(\leq\)\(td_{i}\);t++} do\(\triangleright\) Ramp-up: arrivals only
6:\(busocup(s,t)=busocup(s,t-1)+arrivals(t)\)
7:for{t \(\gets\)\(td_{i}\);t \(\leq\)\(T2E_{i}-td_{i}\);t++} do\(\triangleright\) Cruise: arrivals and alightments
8:\(busocup(s,t)=busocup(s,t-1)+arrivals(t)-arrivals(t-td_{i})\)
9:for{t \(\leftarrow\)\(T2E_{i}-td_{i}\);t \(\leq\)\(T2E_{i}\);t++} do\(\triangleright\) Ramp-down: alightments only
10:\(busocup(s,t)=busocup(s,t-1)-arrivals(t-td_{i})\)
11:Output:\(arrivals\in\mathbb{N}^{Samples,T2E_{i}}\)
```  
**Algorithm 1** Bus Occupation Simulator  
A sample distribution of passengers on a bus is illustrated in Figure 1. In this figure, the magenta crosses represent outliers in the sample. It must be ensured that the whiskers of the box plot do not exceed the bus capacity, which is 65 passengers in the given case study. Therefore, for each line \(i\), larger values of \(F^{i}\) are explored until an upper whisker reaches the passenger limit. Let \(\overline{F^{i}}\) represent the maximum headway that respects this requirement for line \(i\). Then, for respecting the capacity of the buses, higher outliers--those located above the upper whiskers--were excluded from consideration. Figure 1: Box plot of an example simulation of the bus occupation along a \(T2E_{i}=60\,\mathrm{min}\) end-to-end travel with \(td_{i}=16\,\mathrm{min}\) and \(\overline{\Lambda_{i}}=\frac{9}{8}\,\mathrm{min}^{-1}\), after _Samples_\(=10,000\) simulations.
# Design Criteria for High-Gradient Radio-Frequency Linacs
## 5 Design Considerations for High-Gradient Accelerating Structures
### Vacuum  
During the development of the NLC/JLC project at SLAC, experiments were carried out to study the effect of the vacuum pressure on the rf breakdown rate. It was found that the vacuum level had little effect on the breakdown rate at gas pressures below \(10^{-6}\) torr. Taking into account that modern linacs have a typical vacuum pressure in the range of \(10^{-10}\)-\(10^{-8}\) torr, static vacuum pressure is not a hard-to-achieve parameter in the design of high-gradient structures. However, quick pressure recovery from a spike in the vacuum pressure is very important for the linac's operation, since it determines how fast the linac's pulsing restarts after a vacuum trip. Therefore, sufficient pumping is necessary, especially for high-frequency accelerating structures with small apertures, such as those presented in [66; 116].
# Molecular Identification of Keratinase DogkerA from Deinococcus gobiensis for Feather Degradation
## 3 Results  
### Identification of Keratinase DgoKerA  
The gene _dgoKerA_ (1239 bp, DGo_CA0576), derived from _D. gobiensis_ I-0, encodes a protein comprising 412 amino acids with a calculated mass of 41.17 kDa. Phylogenetic analysis of DgoKerA and other homologous keratinases was constructed based on the amino acid sequence. As shown in Figure 1, The DgoKerA showed significant relatedness to peptidase from genus _Deinococcus_; they were localized at the same branch in the phylogenetic tree, indicating a common evolutionary origin. The protein sequence of DgoKerA displayed similarity to MtaKerA from _M. taivanensis_ WR-220 (AWR86689.1) with 58.68%. DgoKerA had a certain homology with the keratatinases from the _Thermus_, such as aqualysin I (WP_053768483.1) from _Thermus aquaticus_ and peptidase (WP_172955617.1) from _Thermus scotoductus_ with 54.84% and 57.44% identity, respectively. The amino acid sequence of DgoKerA, compared with those of keratinases, were BliKerA from _Bacillus licheniformis_ PWD-1, and MtaKerA from _M. taivanensis_ WR-220, as seen in Figure 2. It was proposed that DgoKerA consists of three parts, including signal peptide (pre), N-terminal pro-peptide (N-pro), and the mature domain with a three highly conserved catalytic triad residues which is formed by Asp171, His203 and Ser358 (black triangle). The results of phylogenetic tree and sequence identity analysis indicated that DgoKerA is a keratinase.
## 1 Introduction
Furthermore, due to the channel interference and limitation of acoustic channel capacity, a single sink node may not be able to receive all of the sensing data. Hence, multiple sink nodes are required to collect the whole sensing data. Due to the limitation of AUVs' energy, sink nodes should be carefully selected. This paper addresses all of these problems mentioned above to solve the underwater data gathering problem. Targeting the reduction of the total energy consumption on acoustic communication and balancing the energy consumption among the networks, we propose HAS\({}^{4}\), a Heuristic Adaptive sink sensor set selection algorithm for underwater AUV-aid data gathering. HAS\({}^{4}\) has both centralized and distributed versions. They both achieve energy efficient by decreasing the possible acoustic communication or reducing the maximum acoustic link length. HAS\({}^{4}\) prolongs the network lifetime by adaptively selecting short lifetime sensors as sink nodes to eliminate the energy consumption on data transmission. The contributions of this paper are as follows:  
1. We use a mix integer linear problem (MILP) to formulate the underwater AUV-aid data gathering problem. In the formulation, we take the energy consumption on acoustic communication, AUV traveling distance, and energy balance into account to prolong the network lifetime and reduce the AUV traveling cost. 2. We propose HAS\({}^{4}\) to solve the MILP (mix integer linear problem). HAS\({}^{4}\) is independent from network topologies, and has parameters for trading off between AUV traveling cost and network lifetime. By providing both centralized and distributed versions, HAS\({}^{4}\) widens the applicable scenarios. 3. We conduct simulations to verify the efficiency of HAS\({}^{4}\). Experiment results show both centralized and distributed HAS\({}^{4}\) prolong the network lifetime with low AUV traveling cost. The remainder of this paper is organized as follows. Section 2 discusses related work. In Section 3, we introduce the problem of AUV-aid underwater data gathering problem. Centralized and distributed algorithms are proposed in Section 4 to solve the problem. Section 5 reports some simulation results. Finally, Section 6 concludes the paper.
## 2 Literature Review
### Document Classification  
Classifying massive numbers of textual documents is an important need in knowledge discovery. There are many studies available in the literature in the field of classification, and studies on this subject have been performed focusing on feature dimension reduction [7], word embedding [8], and the experimental evaluation of classification algorithms [9]. Document classification applications can be categorized into six groups, as follows: information retrieval, information filtering, sentiment analysis, recommender system, knowledge management, and document summarization [7].  
In a study conducted on three different legal document sets using CNN, RNN, GRU and LSTM algorithms, the highest precision value of 92.11% was obtained using the CNN model [10].  
In a study researching classification using word embedding methods, experiments were conducted using pretrained word2 vec, GloVe, fastText methods and trained in-domain word2 vec and Doc2 Vec neural word embeddings. The study demonstrated that using word2 vec and Doc2 Vec neural word embeddings improved distance-based multi-class textual document classification [11].  
Word embedding algorithms can be employed with the help of feedforward neural networks to extract semantic relationships from large numbers of text documents. However, they need huge amounts of textual data to perform well. Studies have been conducted using external semantic sources to improve the semantic relations among the words revealed by these models. In one of these studies, Wikipedia was used as an external semantic resource in order to better be able to work with distributed methods like word2 vec in problems with small amount of labeled data. In another study, to resolve the requirement for vast training sets for the word2 vec word embedding algorithm and to work well in practice, small labeled datasets of semantic features were used, which were reproduced by human experts [12].
# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 4 Conclusions  
The study performed spatiotemporal LULC and NDVI mapping for Gelephu. In addition, the LULC maps for Gelephu Thromde have also been closely evaluated. Both the LULC and NDVI maps provide valuable information for decision-making processes related to land management, urban planning, and environmental conservation.  
The results of the study highlight significant changes in Gelephu's land cover types between 2016 and 2023. The vegetation cover witnessed a decrease of 11.99%, resulting in a decline in percent cover from 81.30% to 69.31%. The built-up area, on the other hand, showed significant growth and increased by 5.65% from 7.39% to 13.04%. In addition, the barren land area saw a significant increase of 7.25%, with the percentage cover increasing from 10.35% to 17.60%. Likewise, the analysis of land cover changes in Gelephu Thromde between 2016 and 2023 shows significant changes in the study area. Key results indicate a significant decrease in vegetation, with a 29.93% decrease in area and a corresponding decrease in percentage coverage from 67.04% to 37.11%. The built-up area and barren land saw momentous growth, increasing by 15.05% and 14.76%, respectively. Significant growth in built-up areas and barren land indicates rapid urbanization over seven years and may pose future environmental and land management challenges.  
The analysis of NDVI change detection data between 2016 and 2023 in the study area reveals significant shifts in vegetation cover. The category of \"No vegetation\" increased by 9.56%, \"Low vegetation\" expanded by 41.24%, \"Healthy vegetation\" grew by 25.95%, and \"Very healthy vegetation\" decreased by 75.11%. These findings highlight the dynamic changes in vegetation cover and the potential ecological implications within the study area.  
Not applicable.  
All data generated or analyzed during this study are included in this published article. The authors would like to thank the Office of the Vice-Chancellor, Royal University of Bhutan (RUB), Bhutan for their generous philanthropic support. The authors declare no conflict of interest.
## 4 Results and Discussion
### Limitations and Future Directions  
This pilot study has several limitations to note. First, the sample size is relatively small, which may affect the generalizability of the results. A larger sample would help to confirm the findings and provide a more comprehensive understanding of the learning methods' effectiveness. Additionally, the sample exhibits a gender bias, which may influence the results and limit their applicability to all gender groups. Future research should aim to include a more balanced and larger sample to address these issues and ensure that findings are representative of diverse populations. Ensuring gender balance and a sufficient sample size will enhance the validity and generalizability of the study's conclusions. Second, further improvements in performance will be possible in the future by expanding and incorporating deep learning methods like convolutional neural networks that are inspired by neural network approaches. It is not feasible to explore this approach here due to a lack of data. Third, this study's data focus on high school students from one American school, limiting the generalizability of the findings. Additional research is needed to explore how neural patterns in different learning settings might unfold in other age groups or schools. Finally, [PERSON] et al.'s study [20] is the non-randomized nature of the data  
Figure 8: Statistical results of two-tailed paired \(t\)-test. * denotes \(p<0.0001\).  
Figure 7: Topographical maps of different learning styles. (**a**) Reading, (**b**) video, (**c**) lecture, (**d**) discussion.  
collection sequence. The fixed sequence of learning methods--reading, video, lecture, and discussion--is used throughout the study, which may introduce a learning effect that could influence the results. The participants are exposed to these methods in a specific order, potentially affecting their engagement and learning outcomes. To enhance the validity of future research, it is recommended that studies be designed with multiple groups, where the sequence of learning methods is varied. This approach will help to determine whether the sequence influences the results and contribute to a more robust understanding of the effectiveness of each learning method. Incorporating such variations in sequence will help establish the credibility of the findings by addressing the potential sequence-related biases.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 1 Introduction
### The Need for Calibration Procedures
Calibrated measurements are traceable to international standards when they relate to a reference quantity (itself traceable to the SI). The _International Vocabulary of Metrology_, VIM [11], provides definitions of terms in the field of measurements. According to the VIM, a calibration is an:  
_operation that, under specified conditions, in a first step, establishes a relation between the quantity values with measurement uncertainties provided by measurement standards and corresponding indications with associated measurement uncertainties and, in a second step, uses this information to establish a relation for obtaining a measurement result from an indication._  
Additionally, the _Guide to the expression of Uncertainty in Measurement_, GUM [12], suggests analytical methodologies for assessing uncertainties based on the law of propagation of uncertainties. In this study we attempt to answer the following research questions:  
1. Can (nacelle-based) wind lidars be calibrated via a generic procedure, independent of the lidar type or design? 2. How to assess lidar measurement uncertainties? The use of profiling lidars in power performance and need for calibration procedures were first introduced. In Section 2, two plausible concepts for field calibration of nacelle lidars--so-called 'black' and 'white box' approaches--are discussed. The principles of the generic methodology are detailed in Section 3. Section 4 shortly introduces the calibration of the beam positioning quantities. Section 5 focuses on the calibration and uncertainty quantification of the main input of the lidar reconstruction algorithms: the line-of-sight velocity. Calibration results are illustrated with the examples of the 5-beam Avent Demonstrator and ZephIR Dual-Mode lidar units (Figure 1) that have been calibrated during campaigns in 2014-2015 at DTU's test site for large wind turbines, Hovsere, Denmark. The propagation of inputs uncertainties to lidar-reconstructed wind characteristics is investigated in Section 6 via the simple case of a two-beam lidar system. Finally, we discuss several aspects of the generic methodology, in particular the prevailing uncertainty sources, the question of repeatability, its limitations and how it could be improved.
## 2 Results and Discussion
### Effect of the APPJ Exposure on the Seed Coat Integrity  
Due to damage affecting the seed coat, an increase in permeability can occur, which may be measured with the conductometer by determination of a degree of leakage of the electrolytes to the external medium [53]. Thus, we undertook this methodology to state whether the APPJ exposure leads to losses in the seed coat integrity, and the conductivity measurements have been performed shortly after CAPP treatment (0 h), 1 h, 2 h, and 24 h post-subjection to the plasma source. The collected results pointed to no statistically significant differences between the seed coat integrity of the seeds subjected to APPJ in contrast to the corresponding controls 24 h after the treatment (Figure 2). The study conducted by [PERSON] et al. on the effect of CAPP exposure on soybean seeds indicated that solely after a 180 s or longer APPJ exposure, the conductivity of the solution in which the seeds were submerged increased [42]. In another study, mung bean seeds treated by CAPP generated in an air-based DBD discharge system showed significant disturbances of the seed coat integrity even after a short 20 s treatment time [54]. In general, most of the literature references report higher conductivity values for the APPJ-treated groups in juxtaposition to the controls. Contrarily, in the current research, in the case of almost all plant species and the examined post-CAPP exposure periods, there were no statistically significant differences between the APPJ-treated seeds and untreated controls in terms of the seed coat integrity. There was only one notable exception of the mung bean seeds, which shortly after CAPP subjection and 1 h post-treatment showed even lower degrees of electrolyte leakage than the corresponding untreated control (Figure 2). The results obtained herein confirm that there is no harmful effect of the application of APPJ on the structure of the seed coat. This parameter is important for the determination of the quality of the planting material. Furthermore, it is associated with the efficacy of the seed germination process, and distortion of the seed coat integrity may lead to the increased susceptibility to penetration by various phytopathogens [55].
## 4 Experiment and Results
### Runtime Analysis  
Figure 6 shows the runtime of the classifiers in seconds. It is seen that the OCNN model performance has the lowest time, of 0.00447 s. The highest compilation time is received from the OANN classifier, of 0.00968 s. The classifier OLSTM performs with the time of 0.00527 s.  
Table 5 contains the false positive rate, false negative rate, negative predictive value, and false discovery rate for the three classifiers.
# Causality Analysis with Different Probabilistic Distributions Using Transfer Entropy
## 2 Mathematical Modeling
### Cauchy Probabilistic Density Function
#### 2.3.1 Laplace Double Exponential Distribution  
The Laplace distribution, sometimes known as the double exponential distribution, is constructed based on the difference between two independent variables with identical exponential distributions. Its probability density function is expressed as  
\[F_{\mu,b}^{Lap}(x)=\frac{1}{2b}e^{-\frac{|x-\mu|}{b}}, \tag{11}\]  
where \(\mu\in\mathbb{R}\) is an _offset_ factor and \(b>0\) is a scale parameter.  
The Laplace distribution represents the tailed function, and includes the weights of the different tails. It should be remembered that its scale factor \(b\) is equivalent to the measure of the sum of the absolute deviations, often denoted as the Integral of Absolute Errors (IAE).
# Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
## 3 Results and Discussion
### HSI Analyses
#### 3.2.3 Classification by HSI

Table 4: Performance indicators for the “polymer type” PLS-DA classification model in calibration (Cal) and cross-validation (CV). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Face Verde** & \\ \hline  & **Class 1** & **Class 2** & **Class 3** \\  & **Plastic** & **Sand** & **Other Material** \\ \hline
**Sensitivity (Cal)** & 1.000 & 0.885 & 0.984 \\
**Specificity (Cal)** & 0.995 & 0.988 & 0.924 \\
**Sensitivity (CV)** & 1.000 & 0.884 & 0.985 \\
**Specificity (CV)** & 0.995 & 0.988 & 0.924 \\ \hline  & **Capo Portiere** & \\  & **Class 1** & **Class 2** & **Class 3** \\  & **Plastic** & **Sand** & **Other material** \\ \hline
**Sensitivity (Cal)** & 1.000 & 0.874 & 0.983 \\
**Specificity (Cal)** & 0.995 & 0.978 & 0.939 \\
**Sensitivity (CV)** & 1.000 & 0.875 & 0.981 \\
**Specificity (CV)** & 0.995 & 0.978 & 0.940 \\ \hline  & **Sabaudia** & **Class 2** & **Class 3** \\  & **Plastic** & **Sand** & **Other material** \\ \hline
**Sensitivity (Cal)** & 0.998 & 0.802 & 0.997 \\
**Specificity (Cal)** & 0.996 & 0.909 & 0.937 \\
**Sensitivity (CV)** & 0.998 & 0.802 & 0.996 \\
**Specificity (CV)** & 0.996 & 0.908 & 0.937 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance indicators for the “sand/plastic/other material” PLS-DA classification model built for each sand type in calibration (Cal) and cross-validation (CV). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Class 1** & **Class 2** \\  & **Plastic** & **Sand** \\ \hline
**Sensitivity (Cal)** & 1.000 & 1.000 \\
**Specificity (Cal)** & 1.000 & 1.000 \\
**Sensitivity (CV)** & 1.000 & 1.000 \\
**Specificity (CV)** & 1.000 & 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance indicators for the “sand/plastic” PLS-DA classification model in calibration (Cal) and cross-validation (CV). Nevertheless, the performance indicators for'sand' are still acceptable, indicating that the model performs well overall, as reflected in the prediction results. Regarding the \"sand/plastic\" PLS-DA classification model, sensitivity and specificity values reached unity for both the plastic and sand classes in calibration and cross-validation (Table 3). As for the classifier aimed at identifying polymer types, the sensitivity and specificity parameters in calibration and cross-validation are equal or very close to unity (Table 4), indicating that only a few pixel misclassifications occurred.
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 3 Proposed Method  
### Quantum Circuit for PRESENT Block Cipher  
In the proposed PRESENT quantum circuit, only qubits for plaintext and key are allocated. Therefore, no additional qubits were used. All operations, including AddRoundkey, Sbox, Permutation, and Keyschedule, were optimized in terms of qubits and quantum gates.
## 5 Materials and Methods
### Data Preprocessing
#### 5.2.1 Data Cleaning  
Timestamp Conversion: Pickup and drop-off timestamps were converted into datetime format for temporal feature extraction.  
Trip Duration Calculation: The trip duration was computed as the difference between pickup and drop-off timestamps in minutes.  
Handling Missing Data: Records with missing values in essential fields (e.g., pickup/drop-off coordinates, trip duration) were either interpolated or removed based on data distribution.  
Outlier Removal: Trips with extreme durations, defined as less than 1 min or more than 120 min, were filtered out to eliminate anomalies.