esa-sceva/satcom-chunk-collection huggingface dataset:
# Label-Setting Algorithm for Multi-Destination \(K\) Simple Shortest Paths Problem and Application
## 4 Computational Experiments  
Computational experiments were conducted to evaluate the computational performance and solution quality of the label-setting algorithm and heuristic, as well as to determine the suitable data structures for organizing the node-level and network-level temporary labels.  
The algorithm's generality is demonstrated by its consistent effectiveness across diverse road networks from various geographical locations, encompassing different sizes and \(k\) values. The experiments employed five real-world road network datasets: Anaheim (416 nodes, 9145 links) [49], Barcelona (1020 nodes, 2522 links) [49], Rome (3353 nodes, 8870 links) [50], Hessen (4660 nodes, 6674 links) [49], and Austin (7388 nodes, 18,956 links) [49]. Link costs were randomly assigned between 1 and 10 for each network using a uniform distribution. Other non-negative link cost functions, such as the Bureau of Public Roads function, can also be used.  
The heuristic's performance was compared against three algorithms from the literature, namely, [PERSON]'s algorithm (\(\gamma\)) [5], a node classification-based (NC) algorithm by [11], and a sidetrack-based (SB) algorithm using dynamic updates of shortest path trees by [51].  
The following metrics were used to evaluate the performance of the algorithms. The computational performance is measured with the average total running time, and the solution quality or accuracy is measured with the average percentage deviation of the \(k\)th path cost.  
* Average total running time: This metric represents the average time, measured in CPU time, taken to find the required paths. This excludes the time spent reading input data and writing output data.
* Average percentage deviation of \(k\)th path cost: This metric measures the average percentage increase in the \(k\)th simple shortest path cost obtained by the heuristic algorithm (\(c^{*}\)) from the exact \(k\)th simple shortest path cost (\(c\)). It is calculated as \(100\times\left(\frac{c^{*}-c}{c}\right)\%\), expressing the difference as a percentage of the exact cost.  
For each network, the results at different levels of \(k\) correspond to the averages obtained from multiple OD pairs.
# An Effectively Finite-Tailed Updating for Multiple Object Tracking in Crowd Scenes
## 3 Problem Formulation and the Approach
### Revisiting FairMOT
#### 3.1.5 Updating the Appearance Model  
If only motion models are used for matching in crowded scenes, the matching process will result in the misidentification of the object, as there will be many other people in the vicinity of the object and no appearance features are used to distinguish it. Thus, we introduce the appearance model, which will be made more accurate by combining it and the motion model for matching. We take \(f_{1}\) as the appearance template obtained in the previous frame, \(f_{2}\) as the Re-ID feature obtained in the current frame, and \(f\) as the updated appearance template of the current frame. \(\lambda\) is a hyperparameter. Specifically, all of the FairMOT, DeepSort, and CenterTrack algorithms set the value of \(\lambda\) to 0.9. The formula for updating the appearance model is as follows:  
\[f=\lambda f_{1}+(1-\lambda)f_{2} \tag{6}\]
# The Design and FPGA-Based Implementation of a Stream Cipher Based on a Secure Chaotic Generator
## 3 Hardware Implementation of the Proposed SCbSC and Evaluation of Its Performance
### SPCNG Resilience against Statistical Attacks
#### 3.2.2 Histogram and Chi-Square Tests  
An important key property of a secure pseudo-chaotic number generator is that the sequences generated should have a uniform distribution. The histogram of a sequence X (n) produced is given in Figure 8, the uniformity of which is observed visually.  
The visual uniformity result should be confirmed by the chi-square test formulated as follows:  
\[\chi_{ex}^{2}=\sum_{i=0}^{N_{c}-1}\frac{\left(O_{i}-E_{i}\right)^{2}}{E_{i}} \tag{23}\]  
Figure 8: Histogram.  
Figure 7: (a) Mapping of a sequence X(n) of 3,125,000 samples, generated by the proposed SPCNG and the mapping of 1000 samples taken randomly from X(n) in (b).  
where:  
* \(N_{c}=1000\): number of classes.
* \(O_{i}\): number of calculated samples in the ith class \(E_{i}\).
* \(E_{i}=N_{s}/N_{c}\): expected number of samples of a uniform distribution.
* Ns: the number of samples produced--here, Ns = 3,125,000  
After that step, we obtain: \(\chi^{2}_{ex}=909.46<\chi^{2}_{th}(N_{c}-1;\alpha)=1073.64\) (for \(N_{c}=1000\) and \(\alpha=0.05\)). The experimental value of the chi-square test is less than the theoretical one, asserting the histogram's uniformity. This test was performed on 100 different sequences using 100 different secret keys, and all sequences were uniform.
## 3 Results and Discussion
### Effect of Modification on Adsorption  
The purpose of chemical modification of WDA carbon was to improve its sorption properties, because the modification of the carbon surface changes their structural and surface properties in a controlled manner [28]. Activated carbons due to a highly disordered crystal structure and large specific surface area can be easily chemically modified, among others as a result of the action of inorganic acids.  
Figure 1: Effect of pH on the adsorption of chromium (VI).  
The tested carbon was treated with hydrochloric acid (HCI) and nitric acid (HNO\({}_{3}\)). The specific surfaces of the tested carbons were determined by the BET method for N\({}_{2}\) adsorption at 76 K in liquid nitrogen (analyzer SORPTY 1750). The specific surface area of WDA was 1020 m\({}^{2}\)/g, while for WD(HCI) it decreased (3%) to 990 m\({}^{2}\)/g. The oxidizing nitric acid modification caused a significant reduction of the area to 740 m\({}^{2}\)/g (30%).  
The use of hydrochloric acid increased the adsorption capacity by approximately 5%, while the use of hot nitric acid (V) reduced this capacity by approximately 13% (Figure 2).  
The positive effect of the hydrochloric acid (as a modifier) on the adsorptive properties of carbon was caused by its effect on inorganic admixtures contained in WD--extra carbon. The use of hydrochloric acid caused the ash removal of commercial carbon, decreased specific surface and thus increased the volume of mesopores in the carbon [28].  
The effect of nitric acid did not result in increased adsorption capacity, most probably due to its strong oxidizing properties. Partial oxidation of carbon by such a strong oxidant may have contributed to the decrease in the specific surface area, through the destruction of the pore structure. In addition, treatment with such an oxidizer changes the chemical nature of the carbon surface [24]. Modification of the surface of activated carbons by oxidation increases the surface concentration of chemisorbed oxygen, thus increasing the polarity and hydrophilicity of the surface. This suggests the presence of oxidized carbon of various functional groups, e.g., phenolic, carboxylic, aldehyde on the surface. This may change the surface charge of carbon grains from positive to negative and weaken the affinity of chromate ions to the adsorbent [22; 28].  
Due to the adverse effect of carbon modification with nitric acid, WDA and modified WD (HCI) were used for further tests.
## 4 Results and Discussion
### Latency  
Compute latency is the time delay between the input and output of a computation. It is a crucial factor for the performance and reliability of embedded biomedical devices [47]. Embedded biomedical devices typically have limited resources, such as memory, power, and bandwidth, and need to process large amounts of data from sensors or other sources. Therefore, minimizing compute latency is essential for ensuring the accuracy, efficiency, and safety of these devices.  
The VCS simulation waveform of the computation process corresponding to this processor is shown in Figure 14. The _data_en_ signal indicates that the SPI interface have received a new ECG signal. The _ecg_data_, _diff_square_data_, _trifilter_data_, and _pre_hilbert_result_ signals, respectively, represent the signals in each process of the first stage. The _r_peak_flag_ signal indicates the R-peak detection signal. The non-zero width of the _cmp_cnt_ signal represents the feature extraction process. The non-zero width of the _loop_cnt_ signal represents the SVM classification computation process. As shown in the figure below, the main delay is the phase delay of the filter, and the delay ratio of feature extraction and classification is very small; it is only 8.16 ms under the 1 Mz operating frequency.
## 3 Results and Discussion  
### Experimental Results  
A total of 16 model pile shaft friction tests were performed under axial compressive load in this study. Four different model piles were used and embedded into loose to very dense sand. The results were obtained in terms of load-displacement curves, as shown in Figure 10. From the load-displacement curves of the model piles, it is evident that as the relative density of sand increases, so does the shaft friction resistance of model piles. Among the model piles, it can be seen that the timber pile has the highest ultimate shaft resistance while the steel one has the lowest; this indicates that the surface roughness has a great effect on the shaft resistance of the pile. Moreover, it is clear from the load-displacement curves that the model piles' maximum shaft friction resistance is mobilized when the displacement reaches around 5-10% of the diameter of the pile. Many researchers indicated that the shaft friction resistance of the full-scale piles is mobilized when the pile displacement is in the range of 0.5-1% of the pile diameter [7; 21; 67]. Research performed on model pile load tests indicates that The displacement needed to mobilize the ultimate shaft friction resistance is between 5 and 10% of the pile [37; 68]. In this study, the ultimate shaft friction resistance of the model piles was selected based on the criterion that the displacement of the pile is 5% of the pile diameter. The variation in the ultimate shaft resistance of model piles versus relative density is illustrated in Figure 11.  
Figure 10: Load–displacement curves of model piles in different relative densities.
## 1 Introduction
However, in recent years, AC material synthesis has increasingly been carried out using low-cost and available alternative raw materials, primarily agricultural produce waste (including but not limited to fruit seeds, nut shells, rice, corn straw, etc.) and solid waste (sludge, food waste, garden waste, etc.) [18; 19; 20; 21]. The production of AC materials from such waste and other by-products is also driven by the economic feasibility of large-scale production. Therefore, biomass waste is suitable for the production of biocarbon, which has a well-developed porous structure, a surface containing various functional groups and mineral components due to the removal of moisture and volatile substances from biomass by heat treatment [22]. However, the conventional methods for obtaining AC materials do not allow one to synthesise an electrode material of the required quality. Therefore, methods for synthesising composites based on carbon materials have become widely used [23; 24; 25; 26]. Chemical activation of raw materials for the synthesis of activated carbon (AC) materials is an important method to controllable change in the properties of the resulting AC. One of the methods commonly used for this purpose is the application of metal chlorides,  
Figure 1: Structure of supercapacitor with AC materials [12]. most frequently ZnCl\({}_{2}\) and FeCl\({}_{3}\), as an activating agent. Activated carbon modified with iron oxides is widely used to solve environmental problems. The main area of application for such carbon composites is the treatment of wastewater and natural water [27; 28; 29]. But Refs. [30; 31; 32; 33] present the electrochemical properties of carbon obtained from biowastes activated with ferric chloride. Firstly, there is practically no impedance half-circle, which testifies good conductivity of the material and low internal resistance of the cell, and secondly, cyclic voltammograms testify to the electrochemical stability of such carbon in aqueous electrolytes in the cell charging/discharging process. However, the optimal modification methods have not been fully established, because, for instance, the specific capacity of the carbon in the article [30] in an acidic electrolyte is only 57 F/g, while the carbon of [33] already has 252.2 F/g in 0.5 M aqueous Na\({}_{2}\)SO\({}_{4}\) solution, although the synthesis methods are very similar. The difference between the carbon tested in [30] and in [33] was solely a difference in raw materials. Thus, in [30], it was the waste from processing coffee beans, while in [33], it was sunflower. In order to optimize the technology for synthesising AC from different types of raw materials for supercapacitors, studies were conducted on carbon materials from beet pulp and corn stalks.
## 3 Results and Discussion  
### The Chemical Composition of Raw Materials  
The basic indicator of the storability of cereal grains-based products, which influences the risk of the growth of undesirable microorganisms (including moulds), is moisture content. It is generally accepted that moisture content below approximately 15% (wet basis) is safe, due to the minimal metabolic activity that occurs at these levels [25,26]. The analysis of the tested waste bread showed the higher moisture content, particularly in the wheat bread. This indicates that the long-term storage of this raw material, especially in rooms with elevated humidity, may potentially facilitate the development of undesirable microorganisms. The waste bread used in this study was stored in an environment with a low humidity level (approximately 40%), and no indications of microbiological contamination, such as mould growth, were observed.  
One of the most important factors to be considered in the evaluation of raw materials for ethanol production is the sugar content. Cereal grain-based products, such as bread, are rich in starch [27]. The tested waste wheat bread contained starch content of 71.70 \(\pm\)1.71 g/100 g dry mass, which was consistent with the data reported by [PERSON] et al. [28]. In turn, the starch content of the tested wheat-rye bread was higher (77.16 \(\pm\)4.98 g/100 g dry mass) than that of the wheat bread, due in part to the lower moisture content (see Table 1).  
During technological processes, fermentable carbohydrates are produced as a result of the enzymatic degradation of starch [29]. The obtained results show the presence of minimal quantities of reducing sugars in both tested types of bread, with values ranging from 2.02 \(\pm\) 0.31 g/100 g dry mass in wheat-rye bread to 3.03 \(\pm\) 0.20 g/100 g dry mass in wheat bread.  
The protein content of tested bread waste ranges from 7.08 \(\pm\) 0.01 g/100 g dry mass in wheat bread to 8.18 \(\pm\) 0.05 g/100 g dry mass in wheat-rye bread. Following the hydrolysis of proteins, the resulting peptides and amino acids facilitate yeast growth and fermentation [30].  
Cereal grains used for flour production contain various amounts of non-starch polysaccharides (NSPs), which are predominantly composed of arabinoxylans (pentosans), \(\beta\)-glucans and cellulose [31]. Arabinoxylan is defined as a type of hemicellulose that is found in the cell walls of cereal endosperm. It consists of a linear backbone of xylose residues with arabines units attached [32]. The quantity of arabinoxylans (AXs) is less in wheat (6-8%) than in rye (8.9%) [33], which has been confirmed by the lower concentration of pentose sugars in wheat bread in comparison to wheat-rye bread (Table 1).  
In the tested wheat and wheat-rye bread, the presence of succinic acid, lactic acid, acetic acid and glycerol was shown. These compounds are produced by lactic acid bacteria and yeasts of the _Saccharomyces_ genus, which are present in bakery sourdough. The amount of fermentation by-products depends on the type of flour, the active microorganisms present in the sourdough and the fermentation temperature [34].
## 1 Introduction
Regarding the economic analysis, focusing the PCM application on buildings has been widely discussed by several authors. According to [PERSON] [39], the global application of PCM as a passive constructive solution for buildings has a significant potential of energy-saving; however, this requires high investment cost in PCM materials. [PERSON] et al. [40] performed an economic and energy analysis using the Life Cycle Cost (LCC) based on the payback period for PCM wallboards related to the cooling season. From their findings, for an expected reduction in electric rates, PCM application achieves a minimum payback of 9 years in moderate climates regarding the natural cold energy use. On the other hand, [PERSON] et al. [41] investigated the use of PCM materials for both heating and cooling reduction  
Figure 1: Global indicators evolution [1]. TPES—Total Primary Energy Supply. by optimal melting points, showing that their incorporation has a great impact on energy-savings. However, PCM materials are not cost effective, leading to huge paybacks periods over 42 years. The present paper presents several optimised strategies for constructive solutions containing PCM in cold-formed steel-framed buildings. Reduction in the overheating rate (total number of hours when indoor temperature is above 26 \({}^{\circ}\)C, given as a percentage in one year) was improved either by changing the position of the PCM and by adding a solution containing PCM with different melting points. This paper focuses on the development of an integrated multiscale modelling approach interfacing two linked domains: a combination of different temperature melting values of PCM into the same constructive solution; different possibilities of the PCM position into the same construction solutions. The findings are considered a novel contribution in respect to the multiscale optimised design approach to the definition of solutions containing the incorporation of PCM for overheating rate reduction.
## 1 Introduction
Latency-Focused Queue Scheduling: To improve real-time performance and load balance, we devise a composite delay model that leverages trajectory insights to refine packet transmission timing. This mechanism mitigates congestion, reduces end-to-end latency, and provides an effective queue management solution for high-load, multi-hop scenarios in UAV swarms;
4. Comprehensive Simulation Validation: Through extensive ns-3 simulations, we assess Traj-Q-GPSR across diverse scenarios, varying node densities, UAV speeds, and CBR loads. Comparative evaluations reveal substantial enhancements in packet delivery ratio, end-to-end delay, routing efficiency, and throughput, demonstrating the robustness and effectiveness of our approach in dynamic UAV swarm settings.
## 5 Conclusions  
To conclude, understanding the impact of rain erosion on wind turbine leading edges is important nowadays for the wind industry. The FE model developed in this paper successfully simulates the impact of rain droplets, highlighting the critical role of various parameters. The friction coefficient (not considering a frictionless idealisation) does not have a significant impact on the S33 stress through the thickness of the blade, even though the friction coefficient alters the von Mises stress evolution and might be the reason for a decreasing von Mises behaviour during the droplet diameter analysis. However, the impact velocity significantly alters the von Mises and S33 stresses experienced on the LE, emphasising the importance of reducing the velocity during extreme environmental conditions. The accuracy of the contact type between layers may be improved by using a merged technique over the tie contact. Varying the droplet diameter leads to an increase in the S33 stress until it stabilises for diameters larger than 5 mm. While the S33 shows exponential growth, other factors such as S11, S22, S12, and S23 limit the von Mises stress increase beyond droplet diameters of 2.5 mm. The von Mises stress reaches its peak at 2.5 mm droplet diameters, then decreases linearly as the diameter increases further. The empirical fitting equations align with the numerical results with 97% accuracy, for diameters ranging from 0.5 mm to 5 mm. The von Mises and S33 stresses from FE analysis for small diameters and those above 5 mm correlate with results from the empirical equations. Future research can further analyse the relationship between von Mises stress and droplet size, which does not truly represent the real-life scenario.  
Q.L.O.: Conceptualization, Methodology, Formal Analysis, Investigation, Validation, Writing--Original Draft. Q.M.A.: Conceptualization, Methodology, Formal Analysis, Investigation, Validation, Writing and Reviewing. F.S.: Conceptualization, Methodology, Visualization, Investigation, Writing, Reviewing and Editing. L.M.J.: Conceptualization, Methodology, Validation, Writing, Reviewing and Editing. T.M.Y.: Conceptualization, Supervision, Funding Acquisition, Writing, Reviewing and Editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Sustainable Energy Authority of Ireland grant number 21/RDD/671.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors would like to acknowledge the Sustainable Energy Authority of Ireland (SEAI) for funding the SPOTBlade (Strategies for erosion and fouling Protection of Offshore Turbine Blades) project under its Research Development and Demonstration (RD&D) program (Award No. 21/RDD/671). The authors would also like to thank the International Energy Agency (IEA) Wind TCP Task 46 (Erosion of Wind Turbine Blades) for creating an environment for research collaboration.  
The authors declare no conflict of interest.
## 4 Data Analysis and Discussion
### Text Mining Analysis (Clustering)  
In this paper, we resort to a text mining model to understand the ANN research changes over the sample period (2007-2020). Particularly, we have used the k-means algorithm to classify keywords into groups (also known as clusters). K-means is a well-known algorithm belonging to the set of partitioning clustering techniques. As it belongs to unsupervised processes, there are no clusters. They are formed according to keyword similarity. Clustering algorithms are used in a broad variety of applications to split a data set into K clusters. To do so, the user should initialize a number K as the expected number of clusters. The algorithm must repeat many times to adjust the centroids of clusters since they are randomly chosen. By doing this, it can only achieve locally optimal clustering results. As a tool for text mining, we rely on Python 2.7 as a popular language adopted for text processing. In particular, we rely on pandas, numpy, and scikit-learn Python packages to implement the k-means algorithm for clustering. We also used NLTK (the Natural Language Toolkit package) for data preprocessing so as to convert the text to lower-case and remove numbers and punctuation.  
We used k-means to group the publications' keywords into clusters in a dataset file and dealt with each one as one document. We obtain a file with 10,661 documents. Thereafter, we divided the obtained dataset into three different parts based on their year of publishing (variable). Then, we import the dataset into Python. The first, second, and third parts contained data from 2007 to 2011, from 2012 to 2015, and 2016 to 2019, respectively. This separation is important so as to analyze the ANN research trends for each part.