esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Wireless bandwidth allocation
#### 2.2.2 Routing Format  
In Section 2, we mentioned that local controllers need to create a RREP packet respectively for each node along the route and unicast it to the corresponding node in order to establish one link. Furthermore, the RREP packets will be updated and transmitted periodically to maintain the link until the end of data transmission. The RREP format consists of fields as shown in Figure 2, where \(Rsu\_ID\) denotes the RSU number. \(Vehicle\_ID\) denotes the vehicle identity of destination. _Package ID_ denotes the RREP packet number. _Last Hop_ field denotes the vehicle identity of the previous hop on the route, and _Next Hop_ denotes vehicle identity of the next hop on the route. _SendTime_ is the sending time of the RREP packet. _DelayTime_ is the delay time when the destination receives this routing packet. _ExpiredTime_ denotes the expiration time of this routing packet. _CMC_ is the specific check code encrypted by the RSU's private key that verifies the correctness of the packet. As can be seen in Figure 2, the length of RREP packet in the proposed algorithm is shorter than the AODV algorithm, and the local controllers only need to send a RREP packet to each vehicle node on the route. Therefore, we believe that our algorithm performs better in packet overhead, compared to AODV.  
Each vehicle stores a simple routing table inside. When receiving the RREP packet, these nodes need to update the information of their own routing table immediately. Note that the link duration should be greater than or equal to the sending cycle of the RREP packet.
# Using the Guided Fireworks Algorithm for Local Backlight Dimming
###### Abstract  
Local backlight dimming is a promising display technology, with good performance in improving the visual quality and reducing the power consumption of device displays. To set optimal backlight luminance, it is important to design high performance local dimming algorithms. In this paper, we focused on improving the quality of the displayed image, and take local backlight dimming as an optimization problem. In order to better evaluate the image quality, we used the structural similarity (SSIM) index as the image quality evaluation method, and built the model for the local dimming problem. To solve this optimization problem, we designed the local dimming algorithm based on the Fireworks Algorithm (FWA), which is a new evolutionary computation (EC) algorithm. To further improve the solution quality, we introduced a guiding strategy into the FWA and proposed an improved algorithm named the Guided Fireworks Algorithm (GFWA). Experimental results showed that the GFWA had a higher performance in local backlight dimming compared with the Look-Up Table (LUT) algorithm, the Improved Shuffled Frog Leaping Algorithm (ISFLA), and the FWA.  
Keywords:local backlight dimming; displayed image quality; structural similarity index; guided fireworks algorithm +
Footnote †: journal: Applied Sciences
# A Method for Adapting Stereo Matching Algorithms to Real Environments
## 3 Materials and Methods  
Pattern-based calibration and bundle adjustment are procedures that are not intended to be executed in real time. Pattern-based calibration is regarded as a part of the process of assembling a stereo camera. When the camera is ready, stereo matching can be used. Bundle adjustment is expected to process a large number of input images; therefore, using it in real time can be a challenging task, but it is possible.
## 2 Theoretical Analysis
### General Solutions for the Pile Responses under Combined Loads  
Considering that the continuity for values of the lateral responses for the active, passive and free segment of the pile shaft emerged equally at their interfaces, the following relationships hold true:  
\[S_{fm_{f}}=S_{p0} \tag{33}\] \[S_{pn_{p}}=S_{a0}\]  
Hence, the transfer-matrix equation for the total pile shaft is derived by combining Equations (15), (24), (32) and (33):  
\[S_{an_{a}}=U_{a}U_{p}U_{f}S_{f0}=US_{f0} \tag{34}\]  
\(U_{a}\) and \(U_{p}\) denote the transfer-matrix coefficient, which is calculated by Equation (32) for piles in the active segment and by Equation (28) for piles in the passive segment. \(U_{f}\) denotes the transfer matrix coefficient of the pile in the free segment as calculated by in Equation (15), \(U\) denotes total transfer coefficient matrix of the pile shaft, which is the product of the transfer-matrix coefficient of each pile slice in the sequence from the pile bottom to the pile top.  
Equation (34) is composed of four equations with eight variables, including the pile deflection, slope, bending moment, and sheer force of the pile bottom and pile head. The boundary conditions of the pile toe and pile head must be specified to solve this equation. Specifically, in the current paper, the boundary conditions can be categorized by two major divisions:  
(1) Constraints in the top of the pile shaft For the case of free constraint, the relative boundary conditions need to be satisfied:  
\[M_{f0}=M_{t};Q_{f0}=Q_{t} \tag{35}\]  
For the case of fixed constraint, the relative boundary conditions need to be satisfied:  
\[y_{f0}=0;\varphi_{f0}=0 \tag{36}\]  
(2) Constraints in bottom of the pile shaft  
For the case of free constraint, the relative boundary conditions need to be satisfied:  
\[M_{an_{a}}=0;Q_{an_{a}}=0 \tag{37}\]  
For the case of fixed constraint, the relative boundary conditions need to be satisfied:  
\[y_{an_{a}}=0;\varphi_{an_{a}}=0 \tag{38}\]  
Substituting one of the pile-top boundary conditions and one of the pile-bottom boundary conditions into Equation (15), the rest of the variables of the pile top could be obtained. Thus, based on the response of the pile top \(S_{f0}\), the solution of the pile in the free segment at any depth is expressed as  
\[S_{fi}=U_{fi}U_{f(i-1)}\cdots U_{f2}U_{f1}S_{f0} \tag{39}\]  
Similarly, the responses of pile in the passive segment and active segment at any depth are expressed as  
\[S_{pi}=U_{pi}U_{p(i-1)}\cdots U_{p2}U_{p1}U_{f}S_{f0} \tag{40}\]  
\[S_{ai}=U_{ai}U_{a(i-1)}\cdots U_{a2}U_{a1}U_{p}U_{f}S_{f0} \tag{41}\]  
The algorithm to calculate \(S_{fi}\), \(S_{pi}\) and \(S_{ai}\) is summarized in Figure 7.  
Figure 7: Algorithm to estimate pile response under combined loads with the transfer matrix approach.
## 3 Results and Discussion
### Total Phenol Content  
The contribution of phenolic compounds to the antioxidant activity of wines is considered crucial even though in some cases it is overestimated [7]. The concentration of different phenolic compounds varies significantly within wines from the same variety according to various factors such as vintage year, soil composition and climatic conditions. Moreover, significant changes may occur in the composition and content of phenolic compounds as a result of the vinicitation, maturation, and bottle storage [2,47].  
The total phenol content of the wine samples prior HRL treatments ranged from 220 to 305 mg/L GAE. HRL treatments provoked a linear positive response to the values of TPC. The increase in TPC is demonstrated graphically in Figure 3. Correlation coefficients (R\({}^{2}\)) were in the range of 0.974 to 0.988 denoting a good correlation between TPC of wine samples and HRL additions. Treatments with 0.8 g HRL/L resulted in a 11.3 to 20.1% increase in TPC values in comparison to the initial values whereas treatment with 1.6 g/L HRL resulted in a mean 37.5% increase in TPC values.  
The increase in total phenol content of the samples was expected since aqueous and hydroalcoholic extracts of _H. rhamnides_ leaves have been found to contain significant quantities of bioactive phenolic compounds such as quercetin-3-O-galactosidase, quercetin-3-O-glucose, kaempfer, and isorhammetin [22]. [PERSON] et al. [18] report that beverages in the form of tea-type infusions of HRL were found rich in flavonoid glycosides, mainly derivatives of quercetin kaempfer and isorhammetin as well elagitannins, namely Casuaricin, Casuarinin, Hippophaenin B, Hippophaenin C, Pedunculagin, and Stachyurin. Values of 40.49 mg gallic acid/g dry leaf for aqueous extracts and 56.28 mg gallic acid/g dry leaf for hydroalcoholic leaf extracts have been previously reported [22]. A detailed description regarding to the phenolic content and the whole chemical profile of HRL can be found in a recent review by [PERSON] et al. [17].  
TPC values prior HRL additions were in the range of previously published data. [PERSON] et al. (2008) [57] report values for Slovakian and Austrian white wines ranging from 210 to 390 mg GAE/L while [PERSON] et al. (2008) [58] report values ranging from 213 to 277 mg GAE/L.  
Figure 2: Ferric Reducing Antioxidant Power vs. increasing HRL treatments in each wine. Error bars correspond to the standard error of the mean.  
Results above imply that the use of HRL in the above concentrations in red wines in this stage would have had a limited contribution comparing to their natural antioxidant properties and total phenolic content [56; 57; 58].  
It was not possible to compare the results for the DPPH, FRAP assays, and total phenol content on the wines after the HRL treatments since relevant bibliographic references were not found available.
# An Empirical Study of the Code Generation of Safety-Critical Software Using LLMs
## 3 Methodologies
### A New Method of Code Generation for Safety-Critical Software
#### 3.2.4 Code Generation Based on Software Requirements through Augmented Prompt  
Based on specific requirements, we can generate code that is closer to complete requirements. However, for safety-critical domains, implemented functional requirements of software are only basic requirements. Aspects such as compliance of software to standards and safety are also crucial requirements that need to be emphasized. Only when the codemeets the industry's regulations it can be applied to practical engineering. Therefore, this raises the third question that this paper aims to investigate:  
**RQ3:**: _How can augmented prompts be provided to enable LLMs to generate code that meets all functional requirements and comply with industry regulations?_  
To validate this question, it is necessary to study the augmented prompts method suitable for safety-critical software. These augmented prompts will be inputted into GPT-4, which will auto-generate the corresponding code. The correctness of the code will then be verified through a manual code review and testing. In this approach, we propose a new augmented prompt method called Prompt-FDC.
## 3 The Proposed Algorithm
### Partitioning Phase
#### 3.1.2 Partitioning Initial Sub-Cluster  
As previously mentioned, the two-dimensional dataset aggregation was used as an example to calculate local densities via kernel density estimation and generate density curves. The resulting curves exhibit distinct changes in density distribution, as illustrated in Figure 2a,b.  
It can be seen that the curve presents multiple regions of high density. These regions record the probability distribution of the data density in this dimension, and the wave peaks represent the probability that the data appearing in this region are the largest such that the density reaches the maximum. These peaks can be used to rationally partition the initial subclusters. The wave crest coordinate matrix coordinate = 1, 2, 3,......, n is recorded. Subsequent work will use these peak coordinates as a basis for partitioning sub-clusters.  
All the peaks are utilized on the above basis, with any one of them marked as a separate category. In particular, because of the spatial partitioning properties of the Kd-Tree, it is possible to quickly locate points within a specific region, which helps to quickly identify which points belong to the same density peak while reducing the running time of the algorithm. In this paper, Kd-Tree is introduced to calculate the distance from all data points to the categories, find the nearest category index corresponding to each data point, and finally assign it to the nearest-neighbor category, as shown Figure 3a,b. Meanwhile, data points that are classified into the same category are marked with the same color.  
Figure 3: (**a**) Segmentation results obtained by utilizing the peaks of the wave in the first dimension. (**b**) Segmentation results obtained by utilizing the peaks of the wave in the second dimension. Different colors represent different clusters.  
Figure 2: (**a**) Density curves in the first dimension generated using kernel density estimation. (**b**) Density curves in the second dimension generated using kernel density estimation. Black points are the coordinates of the labeled density peaks.
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
###### Abstract  
Drill ships and offshore rigs use azimuth thrusters for propulsion, maneuvering and steering, attitude control and dynamic positioning activities. The versatile operating modes and the challenging marine environment create demand for flexible and practical condition monitoring solutions onboard. This study introduces a condition monitoring algorithm using acceleration and shaft speed data to detect anomalies that give information on the defects in the driveline components of the thrusters. Statistical features of vibration are predicted with linear regression models and the residuals are then monitored relative to multivariate normal distributions. The method includes an automated shaft speed selection approach that identifies the normal distributed operational areas from the training data based on the residuals. During monitoring, the squared Mahalanobis distance to the identified distributions is calculated in the defined shaft speed ranges, providing information on the thruster condition. The performance of the method was validated based on data from two operating thrusters and compared with reference classifiers. The results suggest that the method could detect changes in the condition of the thrusters during online monitoring. Moreover, it had high accuracy in the bearing condition related binary classification tests. In conclusion, the algorithm has practical properties that exhibit suitability for online application.  
anomaly detection; azimuth thruster; classification; feature extraction; linear regression; multivariate normal distribution; noisy data; vibration
# Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals
## 3 Elderly Stroke Monitoring System Based on Machine Learning and EEG
### Stroke Prediction Module for the Elderly Based on Machine Learning
(2) In the preprocessing of brain waves, corrections and noise data collected in real time during daily life are deleted. (3) Through FFT, the raw data values of each of the six channels of brain waves are decomposed into frequency components, and frequency information is extracted for the signal. (4) In the real-time stroke disease prediction block, the machine-learning-based learning module of the offline module uses a learned prediction model to make real-time predictions of elderly stroke disease using the incoming EEG data. Finally, the stroke disease prediction results and analysis information are delivered to the medical staff to be used as objective data for clinical treatment and diagnosis.
# Immunomodulatory Properties of Probiotics and Their Derived Bioactive Compounds
## 4 Immunomodulatory Properties of Probiotics  
Probiotics are non-pathogenic distinct microorganisms, which provide health benefits when administered in adequate amounts to the host [10], contributing to the maintenance of gut microbiota homeostasis [61]. They are considered to be one of the most valuable options regarding the restoration of microbial diversity [61] and the treatment of infections caused by antibiotic resistant bacteria or invading viruses [24; 54]. However, the precise molecular mechanisms via which they confer their positive effects have not been completely elucidated [9].  
The effects of probiotic bacteria on immunity are exerted through epithelial colonization with simultaneous induction of mucin secretion [61], production of several bioactive compounds [24], competitive exclusion of pathogens by preventing their adherence on the intestinal epithelial surface [54], and inhibition of pathogens' proliferation through competition for essential nutrients [2; 61; 62]. The immunomodulatory properties of probiotics vary between individuals and are mostly attributed to the release of cytokines and chemokines from immune cells [14], the activation of TLRs [2; 63], or the inhibition of the nuclear factor-xB (NF-xB) pathway [64; 65]. Probiotics are also involved in the regulation of the JAK/STAT and the mitogen-activated protein kinase (MAPK) signaling pathways via the secretion of cytokines and AMPs, thus promoting the mucosal and systemic immune response [64; 66]. It is well established that cellular fragments or surface molecules of probiotics can trigger the phagocytic capacity of macrophages and DCs [67; 68; 69] while contributing to the enhancement of NK cells [70; 71] and CD8+ T cells' cytotoxic activity [72]. The significance of probiotics in inducing the maturation and differentiation of adaptive immune cells through modulation of innate immune cells has been designated [73; 74]. Upon adherence to the IECs, probiotics induce the secretion of cytokines leading to the activation of Tregs, the key mediators in maintaining gut homeostasis [2; 51; 75]. The increased production of the anti-inflammatory cytokine interleukin (IL)-10 by Tregs to the detriment of pro-inflammatory cytokines in the colonic mucosa may suppress inflammatory responses [51] and stimulate immune tolerance to commensal microbes [2; 75]. In regard to the required balance of T cell subtypes for the proper function of gut immunity [44], probiotics promote a shift from Th2 to Th1 cells, in order to restrict allergic reactions and control autoimmune disorders [51; 54]. Furthermore, the association of probiotics with IECs triggers the maturation of DCs and the subsequent induction of Tregs, thus promoting the immunoglobulin class switching by mature B cells, in the Peyer's patches, towards the secretory IgA [76]. The secretory IgA plays a significant role in preventing the interaction of pathogens with epithelial receptors, preserving the integrity of the mucosal barrier, and neutralizing bacterial toxins on the mucous membrane [77,78].
# A Unified Nonlinear Elastic Model for Rock Material
## 5 Experimental Validation of the New Model
### The \(m\) Value Range of the Rock Material  
The range of variation in the \(m\) values for the different rock types was 0-2, as listed in Table 1. Based on the unified nonlinear elastic model, the experimental curves for the different rock types were fitted using the least square algorithm method when the \(m\) values were increased from 0.1 to 2.0 with a gradient of 0.1 and when the \(E_{ni}\) values were not fixed. The scatter plots of the coefficients of correlation R\({}^{2}\) values and the \(m\) values are listed in Figure 11, and the scatter plots of the \(E_{ni}\) and \(m\) values are listed in Figure 12. All the curves in Figure 11a-d are parabolic in their shape; that is, the R\({}^{2}\) values near the extreme point are high and the R\({}^{2}\) values far from the extreme point are low, which conforms to the local optimal feature of the least squares algorithm method. It is clear that the parameters fit to the unified nonlinear elastic model were optimal and unique. The highest points of the different curves in each picture are close, and the two side curves decrease rapidly in Figure 11a-d, indicating that the appropriate \(m\) values are in a small range and the R\({}^{2}\) values decrease rapidly when they are greater than this range.  
The \(m\) values of different rock types were counted under the following three conditions: maximum R\({}^{2}\), an R\({}^{2}\) greater than 0.95 and an R\({}^{2}\) greater than 0.8. The maximum and minimum values of \(m\) were used to describe its range in each condition, and the results are listed in Table 2. The ranges of the \(m\) values were 0.65-1.05, 0.28-1.26 and 0.00-1.52 in the above three conditions for the R\({}^{2}\) values, the differences were small and the upper and lower limits were close. The fitting degree will be poor when the \(m\) values are greater than 1.52, and the unified nonlinear elastic model will not be applicable. The nonlinear elastic deformation characteristics of each rock type were different and are easy to ignore since their differences are small. In previous studies, there was no model or method that could accurately describe these behaviors, whereas the unified nonlinear elastic model can distinguish and accurately describe them.  
Figure 11: The R\({}^{2}\) values with different \(m\) values for different rock types. (**a—**Dolomite; **b—**Granite; **c—**Limestone; **d—**Sandstone).  
Figure 12: The \(E_{ni}\) values with different \(m\) values for different rock types. (**a—**Dolomite; **b—**Granite; **c—**Limestone; **d—**Sandstone).  
In addition, the \(m\) value corresponding to the simple exponential model was 1, and that corresponding to the \(BB\) model was 2, so the former can be used to describe the nonlinear elastic deformation of some rocks, whereas the latter has poor applicability.
# Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN
## 6 Discussion  
Energy management is essential for the Wireless Sensor Network, influencing network security and maximising performance. In this paper, we proposed a heuristic modification of the DEC protocol to decrease and better spread energy consumption in the network. The DEC protocol is a very energy-efficient cluster protocol that outperforms LEACH and SEP protocols because the CH selection is not random but determined by each node's residual energy. The K-means algorithm is proposed to improve the nodes' clusterisation and cluster head selection. It was shown that this algorithm chooses the best location of CH depending on the position of all nodes in the same cluster and checks if each CH has the demanded energy level. The set of rules implemented in the last algorithm stage ensures choosing the nodes of high energy level close to the centre point in a cluster area and close to the base station, as well as rotating the CHs whose energy has been exhausted due to data transmission. The DEC-KM protocol takes advantage of the DEC protocol and K-means clusterisation in CH selection.  
The computer simulation results showed that by applying the DEC-KM, the sum of the distances between new cluster heads and nodes in the network was less by 28% than in the original DEC protocol, and the total energy consumption was reduced by 31%. The most significant improvement of the proposed solution is more compact clusters, resulting in a reduction of long link distances in the network, even from 40% to 64%. Simulations have been carried out for three different networks consisting of 50, 100 and 200 nodes, and the above-mentioned relationships regarding the reduction of link distances and energy consumption in the networks were confirmed. The new approach also significantly extended the network's stability period and the network's lifetime. All performance metrics shown in Table 7 for the DEC-KM protocol are 17.5% to 12% better than for the original DEC protocol, and the network's lifetime is longer by about 500 rounds.  
Comparison with related works [17] indicates that DEC-KM also has a longer stability period than another distance-based DEC protocol (DDEC) without K-means clusterisation. The FSD metric for DEC-KM protocol is 17.5% higher than for DEC, while the FSD for DDEC is 12.7% higher than for the original DEC.  
Figure 13: Dead nodes per round in DEC and DEC-KM.  
All this determines that the proposed algorithm outperforms the old ones.