esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Temporal Distribution of GPP
#### 3.3.3 Future GPP Trend Prediction  
The Hurst exponent of the regional GPP was calculated using Equation (9) and visualized through ArcGIS (Version 10.8), as shown in Figure 7a. Based on the classification conditions in Table 4 combined with Figure 7a, it is evident that most areas with unchanged trends are distributed in the western part, while areas with changing trends are located in the eastern part and some central regions. Referring to Figure 3, it can be observed that regions with changing trends predominantly correspond to areas with low GPP values, whereas regions maintaining their trends are mostly areas with high GPP values.  
The results of the Hurst exponent were overlaid with the GPP slope results and classified according to the conclusions in Table 4. Due to the limited number of classification categories, an area plot was chosen for visualization along the elevation to provide a more intuitive analysis of the results, as shown in Figure 7b. Firstly, overall, the areas with trends opposite to the original pattern exceed those maintaining the original trend. Among the areas with changing trends, regions shifting from a decreasing to an increasing trend  
Figure 7: GPP trend analysis and prediction. **(a)** GPP Hurst (0–0.5: contrary; 0.5: unpredictable; 0.5-1: sustained). **(b)** Future GPP trend prediction.  
are mostly distributed at elevations of 1500 m and below, while regions shifting from an increasing to a decreasing trend are predominantly found above 1500 m, with both categories having roughly equal proportions. Among the areas maintaining their original trends, those below 2000 m are predominantly characterized by sustained decreases, while those above 2000 m are overwhelmingly characterized by sustained increases, which is closely related to forest cover regions.
# VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network
## 3 Methodology
### VLSI Architecture
#### 3.2.1 Dynamic Voting Predictor  
The predictor component in Figure 5 illustrates that the voting predictor is composed of five registers, three subtractors, one fuzzy controller, and one voting predictor. The four registers in the input stage are used to store the input data \(X(n)\), \(X(n-1)\), \(X(n-2)\) and \(X(n-3)\). The three subtractors provide the values for \(PD_{1}\), _diff_1_, and _diff_2_. Both _diff_1_ and _diff_2_ are used as inputs to the fuzzy controller. Their output together with \(PD_{1}\) are used as inputs to the voting predictor. The fifth register \(PD_{2}\) holds the final output value of the adaptive voting predictor. Register \(PD_{2}\) is interconnected to both of the two-stage Huffman and Golomb-Rice encoders and is also used as the pipeline register to improve the performance of the proposed design.
## 4 Discussion and Analysis
### Challenges and Prospects
It should focus on the modularity of the equipment to achieve cost reduction and efficiency. In short, through the shared design, shared factory, and shared installation mode to promote the development of assembly electromechanical, this mode can significantly reduce the operating costs of each family and unified standardized design standards. Reduce the pressure of long-distance transportation of prefabricated components. Flexible mobilization of the installation team will improve the quality of workers and reduce the cost of the installation team problem. However, it is also necessary to consider the market and its situation carefully, according to its actual application scenarios, to make the equipment modular rather than the whole room or system, increasing its flexibility. Finally, the assembly mode is in line with the country's dual-carbon economy needs, and the government should seek more support.
## 2 The Applications in Smart Grid Based on Time Synchronization
### Digital Fault Recorders  
Since their introduction in the 1980s, Digital Fault Recorders (DFRs) have become essential tools for monitoring and analyzing the bulk electric power system [24]. By digitizing oscillographic fault data and recording event sequences, DFRs provide crucial insights into system behavior under fault conditions. These devices capture voltage and current waveforms by replicating primary power system signals when triggered by external events. This capability enables utilities to swiftly analyze and restore system operations, offering valuable information on power system protection. Additionally, DFRs display functional system diagrams with analog voltage and current waveforms, facilitating root cause analysis of fault events [25].  
To ensure accurate fault analysis, several standards mandate specific time-stamping and synchronization requirements for DFRs. For instance, NERC PRC-002-2 [26] specifies that (1) time stamps must have sufficient resolution to capture circuit breaker changes within 4 ms, and (2) all recorded data must be synchronized within \(\pm\)2 ms of Coordinated Universal Time (UTC).
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 7 Evaluations
### Procedure  
The experimentation procedure consisted of several stages:  
The QRNG device generated true random numbers, which were converted into a series of frequency values within a specified range. This sequence determined the order of frequency changes for each communication link in the drone network.  
To establish a secure communication channel, a shared key was exchanged between the drones using a secure key exchange protocol. This key was used for both encrypting the frequency sequence and generating HMACs for integrity verification.  
All drones synchronized their internal clocks to a common preset time, which in practice would correspond to a GPS-based time reference. An agreed-upon start time was set, and all drones commenced frequency hopping simultaneously, adhering to the pre-generated sequence.  
During the simulation, each drone entity transmitted data packets over the network. Here each drone is an instantiation entity of its correspondent class in python. The data, including the frequency sequence and synchronization information, was encrypted using AES. HMACs were generated and attached to each packet to ensure data integrity.  
The experiment's performance was monitored in real-time, where the code in python simulated a drone ring topology, and the QRNG was accessed through a lock mechanism in the internal VPN of the BARCELONA Supercomputing Center. We focused on synchronization accuracy, data integrity, and resistance to potential interception or jamming. The frequency hopping sequence and synchronization data were logged for subsequent analysis.
## 4 Numerical Simulation
### Stability Analysis
#### 4.3.3 Calculation of Safety Coefficient  
Nodal 1622 at the top of the slope is selected as the characteristic point, and the graphs related to the safety coefficient and displacement in the process of discounting are obtained. From Figures 13 and 14, two obvious inflection points can be seen. The appearance of the inflection points indicates that the slope is unstable or close to damage when the plastic stress of the slope increases and the displacement increases suddenly. If the inflection point of displacement is used as the stability index of the slope, then it can be concluded that the safety coefficient of the slope without microbial reinforcement Fs = 1.16, and the safety coefficient of the slope after microbial reinforcement Fs = 1.41, the safety coefficient after reinforcement is increased by 21.5 percent, and the reinforcement effect is obvious. If the nonconvergence of numerical analysis calculation is used as an index to evaluate the stability of slope, then it can be concluded that the safety factor of slope without microbial reinforcement Fs = 1.18, the safety factor of slope after microbial reinforcement Fs = 1.46, and the safety factor after reinforcement is increased by 23.7 percent.  
Figure 12: (**a**) Total displacement cloud after microbial reinforcement. (**b**) X-direction displacement cloud after microbial reinforcement. (**c**) Total displacement vector diagram after microbial reinforcement. (**d**) X-direction displacement vector diagram after microbial reinforcement.  
Figure 13: Relationship between safety factor and lateral displacement before microbial reinforcement.
## 4 Results and Discussion
### Important Bubble Parameters and Profiles for a Wide Range of the Bond Numbers  
The present numerical results for important bubble parameters in a wide range of Bond numbers (\(10^{-40}<B<1000\), corresponding to \(8\times 10^{-6}<Bo<5723\)) are listed in Table 4. The relative tolerance of the ODE solver is set to \(TOL=2.22\times 10^{-16}\). For the range of \(B\) considered here, the present computations satisfy \(|\sin\alpha_{\infty}|<10^{-19}\) and \(\varepsilon_{H}<7\times 10^{-8}\). Thus, the present method computes the boundary condition of the meniscus more accurately and converges the position of the junction point more precisely than the earlier methods. As a result, the present method provides highly precise and robust numerical results for a wide range of bubble sizes. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Image in Figure 8a**} & \multicolumn{2}{c}{\(\theta_{c}\)} \\ \cline{2-3}  & [PERSON] et al. [37] & Present \\ \hline (a) & 25.7 & 26.6 \\ (b) & 38.5 & 39.6 \\ (c) & 52.5 & 54.7 \\ (d) & 83.9 & 84.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The angle of the junction point (\(\theta_{c}\) ) as measured experimentally by [PERSON] et al. [37] and as predicted in the present work. Figure 8: Comparison of the presently computed bubble profiles (red outlines) with the experimental photographs obtained by (**a**) Reprinted with permission from [37]. Copyright 2015 American Chemical Society (from top to bottom \(X_{c}\) = (a) 1, (b) 2.1, (c) 4.2 and (d) 32.5 mm) and (**b**) Reprinted from [36], with the permission of AIP Publishing. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Image in Figure 8b**} & \multicolumn{2}{c}{\(Bo\)} \\ \cline{2-3}  & **[PERSON] et al. [36]** & Present \\ \hline Top Left & 0.004 & 0.004 \\ Top Right & 0.03 & 0.03 \\ Middle & 0.5 & 0.63 \\ Bottom & 2.46 & 3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Bond numbers (\(B_{O}\)) measured by [PERSON] et al. [36] and those of the present computational work.
## 3 Analysis of Construction Monitoring Challenges  
As a newly emerged and efficient construction technique, the large-segment lifting method unavoidably presents various challenges and features compared to traditional construction methods. Therefore, its monitoring and control technology has not yet matured. The complexity of construction and precision requirements of the large cross-sea bridge project pose the following challenges for monitoring work:  
1. Accurate prediction of mechanical behavior of steel box girders [7; 10]  
During the process of assembly and on-site hoisting, steel box girders experience continuous variations in support boundaries, construction loads, and structural systems, leading to changes in both deformation and stress states. Accurately predicting various mechanical behaviors of the steel box girder presents one of the challenges in monitoring steel box girder construction.
2. Temperature impact [11; 20]  
Steel structures exhibit high thermal conductivity and are susceptible to temperature variations, not only in their entirety, but also locally due to solar radiation. Variations in temperature and their gradient patterns may lead to further stress and deformation in the structure, with higher temperature differences having a more significant impact on the stress linearity of the steel box girder. The challenge also lies in controlling temperature fluctuations during the segment assembly and hoisting process while properly assessing the temperature impact and mitigating it in a timely fashion.
3. Pre-set angle of steel box girder [6]  
The construction of steel box girders involves segmental assembly with numerous welds, and the attainment of a smooth and linear beam section requires the pre-setting of the angle at the end section. However, due to the variations in section height and width and the large number of segments, different pre-set angles are required for each section. Therefore, ensuring the appropriate setting of the pre-set angle during construction monitoring poses a significant challenge.
4. Accurate connection control between steel box girder segments [6; 21]  
The large segments of the steel box girder are connected by bolt welding, and the adjustable range of bolt connection is much smaller than that of weld connection (only a 2 mm adjustment). To ensure accurate connection between large segments, precise control of the linearity of the steel box girder is necessary. Accurately predicting and controlling the linearity of the steel box girder will be a monitoring challenge for this project, due to the considerable interference of external factors such as temperature and wind speed on the linearity of non-navigable hole steel box girders.
## 4 Objective Evaluation
### Directivity Index Comparison  
The directivity pattern of the logarithmically-spaced array \((l_{spread}\approx 35,s=0.01\,\mathrm{m})\) is more directive for high frequencies than the one of a linearly-spaced array \((l_{spread}=1,s=0.125\,\mathrm{m})\) having the same total length of \(Length=1\,\mathrm{m}\). Less reverberant sound is recorded by the first type of array than by the latter one. As a measure, we choose the directivity index \(DI\), which is the logarithm of the directivity \(D\)[15]:  
\[D(f_{p}) =\frac{\sum_{m=0}^{M-1}\max_{\phi_{m}}(|b(f_{p},\phi_{m})|^{2})}{ \sum_{m=0}^{M-1}|b(f_{p},\phi_{m})|^{2}} \tag{27}\] \[DI(f_{p}) =10\log_{10}(D(f_{p}))\]  
In fact, Figure 6 shows that the linearly-spaced array has lower \(DI\) values for high frequencies (\(f_{p}>1200\,\mathrm{Hz}\)) than the logarithmically-spaced one. This is caused by aliasing effects, as the aliasing frequency for the linearly-spaced array is \(f_{al}\approx 1460\,\mathrm{Hz}\). There is a big drop of the \(DI\) values (\(DI<7\,\mathrm{dB}\)) for the logarithmically-spaced array for very high frequencies (\(f_{p}>10,500\,\mathrm{Hz}\)), which is also caused by aliasing effects. The lowest values of the \(DI\) for the logarithmically-spaced array are located around the aliasing frequency \(f_{al}(\Delta x=s)\approx 17,000\,\mathrm{Hz}\).
# An Efficient VQ Codebook Search Algorithm Applied to AMR-WB Speech Coding
## 3 BSS-VQ Algorithm for ISF Quantization
### Training Procedure of BSS-VQ  
Following the determination of the dichotomy position for each dimension, a training procedure is performed to build a lookup table in each subspace. The lookup tables give the probability that each codeword serves as the best-matched codeword in each subspace, referred to as the hit probability of a codeword in a subspace for short.  
A training procedure is stated below as Algorithm 1. With more than 1.56 GB of memory, a duration longer than 876 min and a total of 2,630,045 speech frames, a large speech database, covering a diversity of contents and multiple speakers, is employed as the training data in a training procedure.  
```
Step 1. Initial setting: assign each codeword to all the subspaces, and then set the probability that the codeword \(\mathbf{c}_{i}\) corresponds to the best-matched codeword in \(bss_{k}\)\(P_{hit}(\mathbf{c}_{i}|bss_{k})=0\), \(1\leq i\leq CSize\), \(0\leq k<BSize\). Step 2. Referencing Table 3 and through Equations (9) and (10), an input vector can be efficiently assigned to \(bss_{k}\). Step 3. A full search is conducted on all the codewords according to the Euclidean distance, given as: \[dist(\mathbf{x}_{n},\mathbf{c}_{i})=\sum_{j=0}^{Dim-1}(\mathbf{x}_{n}(j)- \mathbf{c}_{i}(j))^{2}\] (11) and an optimal codeword \(\mathbf{c}_{opt}\) satisfies: \[\mathbf{c}_{opt}=\operatorname*{argmin}_{\mathbf{c}_{i}}\{dist(\mathbf{x}_{n},\mathbf{c}_{i})\},\ 1\leq i\leq CSize.\] (12) Step 4. Update the statistics on the optimal codeword, that is, \(P_{hit}(\mathbf{c}_{opt}|bss_{k})\). Step 5. Repeat Steps 2-4, until the training is performed on all the input vectors.
```  
**Algorithm 1**Training procedure of BSS-VQ  
A lookup table is built for each subspace, following the completion of a training procedure. The lookup table gives the hit probability of each codeword in a subspace. For sorting purposes, a quantity \(P_{hit}(m|bss_{k})\), \(1\leq m\leq CSize\), is defined as the \(m\) ranked probability that a codeword hits the best-matched codeword in subspace \(bss_{k}\). Taking \(m=1\) as an instance, \(P_{hit}(m|bss_{k})|_{m=1}=\max_{\mathbf{c}_{i}}\{P_{hit}(\mathbf{c}_{i}|bss_{ k})\}\), namely, the highest hit probability in \(bss_{k}\). As it turns out, the lookup table in each subspace gives the ranked hit probability in descending order and the corresponding codeword.
## 5 Experimental Results
### Case Study of English Benchmark  
In our evaluation of _Expert-B_ we have based the performance ratio on the one introduced in Section 4.2.3. The results may be summarised as follows:  
* When in a comparison with Phoenix [42] the result is greater than 1, it can be concluded that _Expert-B_ outperformed Phoenix for the English benchmark. This observation is further supported by the category scores shown in Table 4 where our _Expert-B_ model improved on the performance of Phoenix with a total score of 51 wins out of 80 categories, compared to Phoenix which achieved 29 wins.
* The regeneration method used to generate the study dataset has provided more detailed answers compared to the original method used by [PERSON] [48]. This suggests that the performance of _Expert-B_ in the English benchmark is even more impressive, as  
Figure 10: Evaluation prompt submitted to ‘GPT-3.5-turbo’ API to obtain score and evaluation description for two answers.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Performance Ratio** & **English** & **Vietnamese** \\ \hline Expert-B vs. Phoenix & 107, 89 & 96, 73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance ratio (%) of Expert-B compared to Phoenix after obtaining score through ‘GPT-3.5-turbo’ and calculated by Equation (9).  
it was able to outperform Phoenix using more detailed answers. In particular, _Expert-B_ dominates Phoenix in the remaining criteria categories, namely writing, knowledge, and generic, with _Expert-B_ taking almost all of the scores in these categories.
* Overall, the results (see Table 4) suggest that _Expert-B_ has a better performance than Phoenix in the English benchmark.
## 3 Response Models  
### Test Results Analysis  
Statistical summaries of the experimental results, presented in Table 4, were obtained in the full factorial design and for the set of the results for eight central points. A good fit for the D-flow, T-funnel and Rc,24h was predicted with the coefficient of variation on the central points (Ci) and all CCD points (all Ci, Fi and CCi). Conversely, for the coefficient of variation obtained for F,24h, when evaluating the central points (4.16%), good fits are not expected. In fact, this coefficient of variation is close to the coefficient of variation obtained for the evaluation of the 50 points of the CCD (6.93%).  
Tables 5-7 report statistical summaries of the experimental results obtained for the Full, Fractionated A, Fractionated B and Small CCDs, respectively, and the results for each plan's set of central points. The coefficients of variations obtained were similar to the Full plan and predicted a suitable fit for the D-flow, t-funnel and Rc,24h. Moreover, the F,24h coefficients of variation for the Fractionated and Small plans evaluated on the central points decreased, and the coefficient of variation evaluated on the CCD increased, which indicates a good fit for the F,24h response model.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **D-Flow** & **T-Funnel** & **E24h** & **Rc,24h** \\  & **[mm]** & **[s]** & **[MPa]** & **[MPa]** \\ \hline \multicolumn{5}{c}{Central points} \\ \hline Min & 339.50 & 13.56 & 11.03 & 59.31 \\ Max & 348.00 & 14.98 & 12.29 & 62.17 \\ Average & 343.75 & 14.29 & 11.67 & 60.52 \\ Std Dev & 3.31 & 0.47 & 0.48 & 1.10 \\ CV (\%) & 0.96\% & 3.31\% & 4.16\% & 1.81\% \\ \hline \multicolumn{5}{c}{Full CCD} \\ \hline Min & 168.00 & 6.91 & 9.43 & 48.92 \\ Max & 398.00 & 114.06 & 12.79 & 78.42 \\ Average & 323.31 & 22.39 & 11.17 & 61.61 \\ Std Dev & 48.97 & 21.63 & 0.77 & 6.24 \\ CV (\%) & 15.15\% & 96.64\% & 6.93\% & 10.14\% \\ \hline \end{tabular}
\end{table}
Table 4: Summary statistics for full CCD and central points.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **Full** & **Fractioned A** & **Fractioned B** & **Small** \\ \hline Factorial point & 2’5 = 32 & 2’(5 - 1) = 16 & 2’(5 - 1) = 16 & 11 \\ Axial points & 10 & 10 & 10 & 10 \\ Central points & 8 & 6 & 6 & 5 \\ \hline Total points & 50 & 32 & 32 & 26 \\ \hline \end{tabular}
\end{table}
Table 3: Central composite design plans under study: Full, Fractioned A, Fractioned B and Small.
# A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks
## 5 Simulation Analysis
### Results
Figure 8: Comparison between GCMR, ST, PIM-SSM and BIER in the simulation platform for the 46k map: (**a**) stretch; (**b**) memory size in bits; (**c**) communication cost in number of messages; (**d**) transmission cost in bits. In summary, we can conclude that the GCMR scheme is very competitive against PIM-SSM and BIER; GCMR obtains the lowest memory space requirements and the best stretch. Nevertheless, GCMR requires around 10 and 40 times more messages than PIM-SSM and BIER to setup the MDT, respectively. BIER presents the lowest communication cost and would probably obtain similar performance in the other metrics with smaller leaf node sets than the one considered in this work. Nonetheless, considering that the internet is continuously growing in the number of ASes, equipment and hosts and we are experiencing a globalisation of the multimedia interests, we do believe that a multicast service should be able to reach as many receivers as possible at the least possible cost.