esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area  
The autonomous community of Galicia (northwest Spain) is mostly on Precambrian and Palaeozoic basic-ultrabasic metamorphic and granitoid rocks [20]. Three types of granitic rocks can be found: calc-alkaline syncinematic, peraluminous syncinematic and late-collisional cinematic, and calc-alkaline late-collisional [20]. In the first group, both granodiorite and monzogranites are the most frequent, with high values of K [30]. Peraluminous granitoids--the most abundant--vary widely in composition and texture [31], while a range of monzogranites predominate among late-collisional granites [32]. One of the most extended peraluminous granitoid is the granodiorite of A Coruna. The rock is present in most of the area of the city of A Coruna (about 80%), which has 250,000 inhabitants (Figure 1), with around 90% of the population (around 225,000) living in the area of such rock.  
The main campus of the University of A Corunta (Elvina-A Zapateira) is located on this rock. Such rock shows two facies: early-stage and late-stage; both are very similar in colour (grey), texture (coarse-grained), and chemical and mineralogical composition, with quartz, plagioclase, microcline, and biotite as the main minerals [20]. More muscovite is observed in the late stage. The rock is usually weathered, showing an ochre colour and loss of cohesion. According to the geological map of the city (and campus) of A Corunta, the contact between both facies is located in the Elvina campus area, although at present it is below the buildings and urban structures of the campus and below the sediments in some parts. In this study, we sampled both facies.  
Figure 1: Location of the Elvina-A Zapateira campus of the University of A Corüta (northwest Spain), with a geological map of the study area (modified from [33]). Satellite image from Google Earth. Sampling sites are marked and explained in Table 1.  
The Elvi\(\hat{\text{n}}\)-A Zapateira campus (Figure 1) has a total surface area of 63 ha and 27.3 ha of built area distributed over 20 buildings: 12 for teaching and 8 for sports, research, and administrative use. Most of these buildings were built between 1995 and 2006 and are used for 13-14 h a day. The vast majority are heated by gas oil, except for five that are heated by electric radiators and one with a heat pump [34]. The total average annual consumption for these buildings is approximately 14,300 MWh [34]. The average annual consumption of diesel for heating is 9500 MWh/year, which is equivalent to approximately 725,880 litres of diesel. This fuel generates CO\({}_{2}\) emissions of 260 tons per year on average.
## 4 Discussion
However, there are situations where additional variables are necessary to establish a functional diffusion zone. Despite its simplicity and suitability as the preferred technique for particle preparation, the literature demonstrates that nanoprecipitation is not exempt from evaluations of its critical parameters, as these are related to the diffusion zone and still require further exploration in such studies [44]. Using nanoparticles with Eudragit(r) polymers is a new strategy for the targeted delivery of flavonoids such as luteolin and Qr, which favors their function in target organs such as the colon [45; 46]. Furthermore, it hasbeen observed that they do not cause significant changes in the organs after administration of the NPs [45]. To produce polymeric nanoparticles with a defined S and PDI, it is necessary to modify various experimental variables in the method employed for their preparation. There are several techniques for manufacturing nanoparticles, including evaporation of the emulsion, diffusion of the emulsion, and solvent displacement from preformed polymers. Among these methods, nanoprecipitation (solvent displacement) is a preferred one-step process for producing monodisperse NPs within a size range of approximately 50-300 nm [47]. This method is reproducible, fast, and cost-effective. However, despite its many advantages, there is still a need to fully understand the impact of the implicit variables involved in nanoparticle formation [48]. In addition, the use of surfactants has been reported to help decrease the average particle S in nanoformulations [49], such as Qr-PNPs. For a pharmaceutical drug to be useful, it must possess bioactive properties and exhibit a noncytotoxic profile [50; 51]. Erythrocytes have been used as a model system by several researchers to determine the interaction of drugs with mammalian membranes; the erythrocyte model has been commonly used in the elaboration of toxicity profiles since it provides a direct indication of the toxicity of formulated either injectable or administered by another route such as oral [52]. Hemolysis results from the destruction of the erythrocyte caused by the lysis of the lipid bilayer of the membrane; the lysis of erythrocytes can cause anemia, an increase in plasma hemoglobin that causes nephrotoxicity and vasomotor instability [53]. The four polymers and Qr did not show significant cytotoxicity, and the hemolytic activity was <0.1% (Table 2) in all the treatments; when compared to the positive control, there was a high significance (\(p<0.001\)). Therefore, the hemolytic activity of less than 1% obtained for the four polymers indicates nontoxicity [36] for the red cell membrane [50], which favors subsequent studies with these polymers and flavonoid.
## 4 Methodology
### Evaluation Framework  
#### 4.5.1 Automatic Metrics  
We employ multiple automatic evaluation metrics to capture different aspects of translation quality:  
**BLEU:**: Standard n-gram precision metric measuring lexical overlap:  
\[\text{BLEU}=\text{BP}\times\exp\left(\sum_{n=1}^{4}\frac{1}{4}\log p_{n}\right) \tag{6}\]
**chrF:**: Character-level F-score particularly suitable for morphologically rich languages:  
\[\text{chrF}=\frac{(1+\beta^{2})\times\text{chrP}\times\text{chrR}}{\beta^{2} \times\text{chrP}+\text{chrR}} \tag{7}\]
**BERTScore:**: Semantic similarity using contextual embeddings:  
\[\text{BERTScore}=\frac{1}{|x|}\sum_{x_{i}\in x}\max_{y_{j}\in y}\text{emb}(x_{i })\cdot\text{emb}(y_{j}) \tag{8}\]  
The combination of these metrics provides complementary perspectives on translation quality, with BLEU capturing lexical fidelity, chrF addressing morphological variations, and BERTScore measuring semantic preservation.
## 3 Errors Identification Principle
### Calibration of the Relative Position
Since the aim of the computation of the relative positioning was to obtain the position of each feature point relative to the feature point of the reference revolutionary paraboloid, the \(X\) coordinate \(X_{F0}\) of the feature point of the reference revolutionary paraboloid was equal to 0. Therefore, in the \(X\) direction, the relative position of the feature point of the \(i\)th revolutionary paraboloid can be calculated as follows:  
\[X_{Fi}=k(x_{M0}-x_{F0})+lx_{MiM0}+k(x_{Fi}-x_{Mi}). \tag{5}\]  
Figure 7: (**a**) Spot image of the feature point (by measuring the corresponding plane mirror); and (**b**) spot image of the measurement point on a revolutionary paraboloid. Similarly, in the \(Y\) direction, we have:  
\[Y_{Fi}=k(y_{M0}-y_{F0})+ly_{MiM0}+k(y_{Fi}-y_{Mi}). \tag{6}\]  
In these equations, \(X_{Fi}\) and \(Y_{Fi}\) represent the relative position of the \(i\)th feature point in the object system. Besides this, \(x_{M0}\), \(y_{M0}\), \(x_{Mi}\), \(y_{Mi}\), \(x_{F0}\), \(y_{F0}\), \(x_{Fi}\), and \(y_{Fi}\) are the coordinates of the spot in the image system, and \(lx_{MiM0}\) and \(ly_{MiM0}\) are, respectively, the displacement in the \(X\) direction and the \(Y\) direction measured by laser interferometer or machine tools with higher precision.
# Determining the Optimal Configuration of the Multi-Ring Tree for Bluetooth Multi-Hop Networks
###### Abstract  
In this work, a multi-ring tree algorithm is proposed for Bluetooth low-energy networks with non-uniform distribution of devices. In a dense area, a leader root is elected during the leader election phase and a min-path algorithm is introduced to determine the optimal number of rings for various numbers of discoverable roots. According to the optimal configuration, the leader root connects to its one-hop neighboring roots to form the first-tier ring; each new root connects with one downstream root, and these roots connect together to form the second-tier ring until the desired outermost ring is reached. In sparse areas, each root constructs its own spanning tree subnet, finally creating a multi-ring tree scatternet. To achieve the least route discovery overhead, a multi-hop self-routing protocol is developed to efficiently deliver packets. Computer simulations show that the optimal multi-ring subnet can be determined; the optimal multi-ring tree outperforms conventional dual ring-tree (DRT) and Bluetooth hybrid ring-tree (BlueHRT) in terms of network performance.  
Keywords:Bluetooth networks; scatternet formation; topology configuration; routing protocol +
Footnote †: journal:
## 4 Implementation  
A real scenario of a WSN with IEEE 802.15.4 MAC and physical layers was implemented in NS-2. The scenario TCL file was edited with the values given in Table 2. In an 8-node-based scenario, as shown in Figure 3, a one-to-one (node _ID_ 8 as the source and node _ID_ 0 as the destination) traffic pattern was used. The traffic pattern of 4 source nodes to 1 destination node was used in 50-node (source node _IDs_ 25, 31, 37, and 43), as shown in Figure 4, and 121-node scenarios (source node _ID_s 90, 100, 110, and 120), as shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scenario Parameters** & **Selection/Value** \\ \hline MAC Type & IEEE 802.15.4 \\ Physical Type & IEEE 802.15.4 \\ Initial Battery Power & 3.6 \\  & Shadowing \\  & (Reference Distance =1 m \\ Model of Propagation & Path loss exponent = 2.5 \\  & Shadowing deviation = 4 dB) \\  & 8 nodes = 100 s \\ Simulation Run Time & 50 nodes = 100 s \\  & 121 nodes = 300 s \\ Packet Size & 70 bytes \\ Traffic & Constant Bit Rate (CBR) \\  & 0.7/s \\ Packet Rate & 1.5/s \\  & 2.2/s \\  & 3.6/s \\ Frequency & 2.4 GHz \\ Area & 80 m \(\times\) 80 m \\ Sink Node & 1 \\ Source Nodes & 8 nodes = 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: TCL script configuration.  
Figure 4: Scenario with 50 nodes and 20 malicious nodes.  
Figure 5: Scenario with 121 nodes and 20 malicious nodes.  
Figure 3: Scenario with 8 nodes and 4 malicious nodes.  
The simulation results were generated by running multiple simulations with variations in scenarios and with different routing protocols, which are E-BIOSARP, BIOSARP, SRTLD, and the novel P-BIOSARP.  
The metrics for performance evaluation are \(batt_{cc}\), packet delivery ratio (PDR), and traffic overhead. First, \(batt_{cc}\) is calculated based on the difference between \(batt_{i}\) and \(batt_{cc}\) by Equation (5) [24; 33].  
\[batt_{cc}=batt_{i}-batt_{cc} \tag{5}\]  
The PDR of the data packet is calculated by Equation (6) based on \(TP_{s}\), the total number of packets sent by the source node or nodes, divided by \(TP_{d}\), the total number of data packets received at the destination node [24; 35; 38].  
\[PDR=TP_{d}/TP_{s} \tag{6}\]  
The packet overhead (PO) is estimated by Equation (7), where total data packets \(TP_{d}\) received at the destination node is divided by the total control packets \(TCP\)[24; 35; 38].  
\[PDR=TP_{d}/TCP \tag{7}\]
# Rebar Fabrication Plan to Enhance Production Efficiency for Simultaneous Multiple Projects
## 2 Materials and Methods
### Modeling with DES
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Author** & **Year** & **Contents** \\ \hline \multirow{6}{*}{Management of product and rebar import} & \multirow{2}{*}{[13]} & \multirow{2}{*}{2007} & Presented a simulation-based decision support system to assist contractors in selecting the most economical rebar management system before starting construction. \\ \cline{2-4}  & & & Described how the just-in-time (JIT) concept can help enhance the performance of transportation and material delivery activities in industrialized building system projects. \\ \cline{2-4}  & \multirow{2}{*}{[1]} & \multirow{2}{*}{2020} & Proposed a framework based on takt time (to identify the optimal time for each process) and discrete-event simulation (DES) to integrate building information modeling (BIM) with JIT to realize realistic and optimal planning. Combined planning models with DES and BIM to simulate the dynamic environment to reduce uncertainties. \\ \hline \multirow{6}{*}{Loss rate minimization} & \multirow{2}{*}{[4]} & \multirow{2}{*}{2014} & Developed novel problem-solving techniques, leading to effective cutting plans with a low trim loss and stock usage. \\ \cline{2-4}  & & & Proposed a novel approach for minimizing cutting waste from rebars by exploiting the slight flexibility in selecting the location of lap splices of rebars within reinforced concrete members, as specified by design codes. \\ \hline \multirow{6}{*}{Loss rate minimization} & \multirow{2}{*}{[9]} & \multirow{2}{*}{2020} & Proposed a special-length-priority cutting waste minimization algorithm for rebars. A minimization method based on special and stock lengths was applied. The required rebar quantity was 6.04\% lower than the actual quantity used. \\ \cline{2-4}  & \multirow{2}{*}{[8]} & \multirow{2}{*}{2021} & Optimized the use of available market length rebars to minimize generated waste. Proposed a BIM-based automated framework integrated with mixed-integer linear programming (MILP). The trim waste could be rapidly and efficiently decreased using the BIM–MILP approach. \\ \cline{2-4}  & \multirow{2}{*}{[10]} & \multirow{2}{*}{2021} & The cutting process was managed using optimization models based on three field variables: merging sequential demands, multiple stock lengths, and usable leftovers. The cutting waste in the case study decreased by more than 70\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing studies on rebar fabrication management and loss rate minimization.
# Secure D2D Communication for 5G IoT Network Based on Lightweight Cryptography
## 5 Security Analysis  
In this section, we perform security analysis of the proposed secure D2D communication system. As we mentioned before, secure D2D communication requires authentication, data confidentiality/integrity and anonymity. In addition, considering the resource-constrained nature of a 5G IoT network, it must be implemented lightly and must perform efficiently. We first discuss the proposed D2D communication system based on our security considerations. Moreover, we discuss security against typical threats of D2D communication.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Supflix Tree-Based Alignment Methods
#### 2.1.1 MUMmer Technique
While using MUMmer 3.0, the query time for a whole-genome alignment for comparison between human chromosomes is about 300 min and can reach 600 min depending on the sizes of the chromosomes used for the alignment. With large genomic data, MUMmer 3.0 has limitations and fails to perform whole-genome alignments. In 2018, the new version MUMmer 4.0 was launched, which includes an improved version of MUMmer algorithm and uses a 48-bit suffix array to overcome genome size constraints. In addition, MUMmer 4.0 introduces improved speed and memory usage through parallel processing of input query sequences [20]. With this improvement, a whole-genome alignment of human/chimpanzee genomes can be performed in 2897 min. This operation cannot be performed using MUMmer 3.0. Furthermore, with a theoretical limit on the input size of 141 Tbp, MUMmer 4.0 can perform alignment with input sequences of any biologically realistic length. The complexity of MUMmer 4.0 is \(O(n\lg n)\) for constructing suffix trees and \(O(m+n)\)\(O(m+n)\) for aligning sequences, where \(n\) is the length of the reference genome and \(m\) is the length of the query sequence. Table 1 provides a comprehensive overview of the differences between various versions of MUMmer, highlighting the evolution of the algorithm over time and major improvements such as enhanced suffix arrays in MUMmer 3.1 and optimized suffix tree utilization in MUMmer 4. Whole-genome alignment algorithm classification is based on various approaches to alignment. Table 2 provides a detailed overview of these classification approaches. Figure 3: Cluster detected with MUMmer 2.1. The algorithm detects the adjacent MUMs between Genome A and Genome B. If a gap between these MUMs is lower than a parameter \(k\), the two adjacent MUMs are regrouped in the same cluster.
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 6 Results and Discussion  
### Comparative Analysis with Existing Methods  
While our approach represents a novel integration of mobile audio acquisition with real-time motor control, direct quantitative comparison with existing methods is challenging as most of the literature treats these as separate problems. Our work advances beyond existing solutions such as those by [PERSON] et al. [1] and [PERSON] et al. [19], which focus on stationary or high-resource platforms, by demonstrating concurrent high-quality audio capture and responsive mobility control on resource-constrained hardware. The achieved packet delivery rates (90-95%) and command latencies (<150 ms) represent significant improvements over traditional approaches that sacrifice one capability for the other.  
Quantitative comparison framework: To address the lack of directly comparable systems, we establish performance benchmarks against theoretical limits and component-level comparisons:  
Audio quality benchmarks: Our system achieves SNR values of 31-38 dB across test environments, comparing favorably to theoretical limits of 40-42 dB for eight-bit compressed audio. The 85-95% theoretical efficiency demonstrates our compression algorithm's effectiveness.  
Control system benchmarks: Command latencies of 105-142 ms represent 70-85% of a theoretical minimum (considering Bluetooth protocol overhead), significantly outper  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Environment** & **Packet Delivery Rate** & **Calculated SNR** & **Subjective Quality (1–5)** \\ \hline Quiet room (40 dB) & 95.3\% & 38.7 dB & 4.2 \\ Office environment (65 dB) & 93.8\% & 35.4 dB & 3.9 \\ Busy street (85 dB) & 90.1\% & 31.2 dB & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Audio acquisition performance across environments.  
forming traditional sequential processing approaches that would require 300-500 ms for equivalent functionality.  
Power efficiency comparisons: Our integrated approach achieves 4.5 h operation combining both functions, compared to theoretical separate system power consumption that would limit operation to 2-3 h for equivalent functionality.
## 2 Materials and Methods
### Samples, Vineyards and Harvests Characteristics, and Sampling
#### 2.1.3 Harvest Weather Conditions  
The harvest year weather information (mean precipitation and mean temperature) for a ten-year period (2010-2020) was obtained from the local meteorological station (type: EMA II climatologic station, number 705), from IPMA (Instituto Portugues do Mar e da Atmosfera, IP, [[https://www.ipma.pt/pt/](https://www.ipma.pt/pt/)]([https://www.ipma.pt/pt/](https://www.ipma.pt/pt/)) (accessed on 1 April 2021)), located in the municipality of Anadia (Portugal).  
As can be observed in Figure 2a, the weather conditions of Bairrada Appellation have not changed significantly in the last decade. Looking for the weather conditions of 2010, 2011, and 2012 (Figure 2b), the harvests object of this study, the precipitation amount of this appellation is irregular throughout the year, attaining its maximum in November/December. Although the occurrence of moderate precipitation in spring is common, in 2012, an unusual precipitation amount was observed, contrasting with its also rare dry winter season (Figure 2b). Moreover, the 2010 harvest was considered an equilibrated year, with moderate temperatures during spring and a moderate precipitation amount, which were considered suitable for the maturation process by the winemaker involved in this work. On the other hand, except in July and August, 2011 exhibited an unusual hot spring (only similar to the one observed in 2015), while 2012 exhibited lower temperatures throughout the year (except in March and September), thus being fresher and rainier (Figure 2b), which contributed to accelerating or delaying the grapes' maturation process, respectively (Figure S1).
## 3 Experimental Results
### K-Fold Cross-Validation  
To eliminate the potential influence of specific or low-feature representation datasets, k-fold cross-validation is introduced. The typical method is to separate a portion of the training dataset as the validation dataset to evaluate the training performance of the model. Even if the validation dataset is taken from the training dataset, it does not participate in model training, which can help objectively evaluate the matching performance of the model with data outside the training dataset. Cross-validation, also referred to as circular validation, is frequently used for assessing model performance on a validation dataset. Typically, the k-fold cross-validation process involves the following steps: (1) dividing the training dataset into k equal segments (i.e., k folds); (2) iteratively using each fold as the validation dataset, while the remaining k-1 folds serve as the training dataset, resulting in k times of model training; (3) obtaining results from the k times of model training to evaluate the model; and (4) selecting the optimal model based on the performance of the evaluation index and subsequently retraining the model with the corresponding training dataset to obtain the final model.  
In this study, five-fold cross-validation is used to ensure that each validation fold contains more than 5000 microseismic waveforms. Table 2 shows the results of the five-fold cross-validation of the model. The maximum validation accuracies for the onset  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Value \(\sigma\)**} & \multicolumn{2}{c}{**Onset Time Picking (\%)**} & \multicolumn{2}{c}{**Detection (\%)**} \\ \cline{2-5}  & **Validation** & **Test Accuracy** & **Validation Accuracy** & **Test Accuracy** \\ \hline
0.5 & 98.9 & 97.5 & 97.8 & 96.6 \\ \hline
1.0 & 99.6 & 98.8 & 98.3 & 97.7 \\ \hline
1.5 & 99.0 & 98.2 & 98.4 & 97.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of the proposed PDNetwork under different values of \(\sigma\).  
time picking and detection are 99.6% and 98.4%, respectively, with average validation accuracies of 99.46% and 98.26%. This proves that even when trained on the semisynthetic dataset, the PDNetwork has good performance for the onset time picking and detection of microseismic waveforms.