esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Wavelet Weighted Least Squares Support Vector Machine (WWLS-SVM)  
In this study, WWLS-SVM is used as the main surrogate model for addressing the problem of uncertainty in vibration damage detection. Fundamentally, support vector machine (SVM) is a machine learning method based on statistical learning theory (SLT), introduced by [PERSON] and [PERSON] [39]. The LS-SVM is an extended version of standard SVM [40]. In the LS-SVM, a sum-squared error (SSE) cost function has been substituted by [PERSON]'s \(\varepsilon\)-insensitive loss function. In addition, instead of inequalities, the LS-SVM considers equality type constraints as in the classic SVM method [40]. This reformulation simplifies the problem significantly by directly solving a series of linear equations rather than a convex quadratic program (QP).  
In this paper, the weighted version of LS-SVM [41] will be used as a surrogate model. WLS-SVM was introduced as a highly effective machine learning algorithm in large-scale problems by [PERSON] et al. [41]. In fact, the assignment of weights to both the SVM and the least square version of SVM (LS-SVM) results in a more robust and accurate function prediction [42].  
Figure 3: Space for damage and undamaged ESP.  
Figure 2: Scheme for PoDE.  
Wavelets as kernel functions have been introduced and developed in ANNs and SVMs [43; 44]. Wavelet kernel functions have been demonstrated to be superior to other kernel functions in ANN and SVM training. [44]. Accordingly, the kernel function of WLS-SVM is substituted with a specific type of wavelet function proposed by [PERSON] et al. [17]. In fact, the thin plate spline Littlewood-Paley wavelet is used as the kernel function of WLS-SVM. Finally, this WLS-SVM based on wavelet kernel function is called WWLS-SVM. For further detailed information on the mathematical basis of WWLS-SVM algorithm, readers are referred to the original work by the main authors [15], and the work reported in [39].
## 3 Results and Analysis
### Tests Performed in the System  
The evaluation of the proposed solution is performed with detailed circuit simulations executed in Matlab Simulink 2018a. Due to the wide difference between the time responses of the LED current and LED temperature, three kind of simulations were conducted: verification of the ZAD and LED current controllers tracking the LED current reference for different batteries, which requires short simulations of 0.06 s; verification of the ZAD and LED current controllers to battery discharge, which requires simulations of 6 s; verification of the luminous flux regulation at the LED start-up, which requires simulations of 1 s; and verification of the luminous flux regulation for long time frames to account for the temperature effect, which requires to simulate 1 h and 57 min of operation. All simulations were implemented using a fixed-step size solver, where the fundamental sample time is defined as \(1/(\Delta\cdot F_{s})\), with \(\Delta=1000\) and \(F_{s}=20000\) Hz, corresponding to the PWM switching frequency. The solver used was Ode1 (Euler) with solver selection type: fixed-step and periodic sample time constraint: unconstrained.  
To ensure realistic simulation results, all battery-related parameters used in the tests were based on real commercial models. The values for voltage, capacity, and cost were extracted from datasheets and market sources. These parameters were used to configure the Simulink battery blocks appropriately, enabling accurate modeling of discharge behavior and energy availability. In summary, the tests are presented below, and the actual batteries used, along with their specifications, are listed in Table 3.  
* Test 1: LED current control in boost mode with a 12-volt battery.
* Test 2: LED current control in buck-boost mode with a 18-volt battery.
* Test 3: LED current control in buck mode with a 24-volt battery.
* Test 4: LED current control with a discharged 12-volt battery.
* Test 5: luminous flux regulation, testing the LED start-up and long time operation.
# Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM +
## 6 Conclusions  
We studied downlink precoding for MU-MISO channels where the base station uses OFDM and low-resolution DACs. A QCM algorithm was introduced that is based on the MAGIQ algorithm in [39] (see also [19]) and which performs a coordinate-wise optimization in the time-domain. The performance was analyzed by computing the GMI for two auxiliary channel models: one model for pilot-aided channel estimation and a second model for a data-aided channel estimation. Simulations for several downlink channels, including a Winner2 NLOS urban scenario, showed that QCM achieves high information rates and is computationally efficient, flexible, and robust. The performance of QCM was compared to MAGIQ and other precoding algorithms including SQUID and MSM. The QCM and MAGIQ algorithms achieve the highest information rates with the lowest complexity measured by the number of multiplications. For example, Figure 4 shows that \(b=3\) bits of phase modulation operates within 3 dB of LP-ZF. Moreover, BER simulations for a 5G NR LDPC code show that GMI is a good predictor of the coded performance. Finally, for noisy CSI the performance degradation of QCM and SQUID is qualitatively similar to the performance degradation of LP-ZF.  
Investigation, A.S.N., F.S. and G.K.; Software, A.S.N.; Writing--original draft, F.S.; Writing--review & editing, A.S.N. and G.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Deutsche Forschungsgemeinschaft through the grant KR 3517/9-1, and by Nokia Solutions and Networks through the project \"Low Cost Booster Arrays for Massive MIMO Precoding\" in 2017.  
The authors wish to thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for useful discussions.  
The authors declare no conflict of interest.  
Figure 8: Average GMIs for System A and imperfect CSI at \(\text{SNR}=12\) dB.
## 2 Soil-Structure Interface Shear Tests
### Soil Sample and Test Process  
The test soil sample is silty clay and was taken from a subway construction site in Xuzhou, Jiangsu Province, China. Its basic physical properties are listed in Table 2. Based on the construction site condition, the soil specimen was prepared with a moisture content of 20% and a dry density of 1.70 g/cm\({}^{3}\), using which the soil sample is well formed, and hardly any moisture is squeezed out during compression. The sample height of this study is 30 mm to overcome the increase in normal compression caused by the high pressure.  
The structure interface is made of a uniform triangular rough steel plate, ensuring that repeated shearing does not cause wear, thereby maintaining test consistency, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Index** & **DRS-1** \\ \hline specimen size & 61.8 mm diameter \\ shearing mode & shear stress/strain control \\ normal stress range & 0–30 MPa \\ temperature range & \(-\)40–30 \({}^{\circ}\)C \\ shear rate & 0–4 mm/min \\ shear displacement & 0–20 mm \\ acquisition rate & 0.3 mm \\ \hline \end{tabular}
\end{table}
Table 1: DRS-1 technical parameters.  
Figure 1: DRS-1 test system.  
Figure 2: Schematic of the shear test process.  
After preparing all experimental materials, reset and adjust the control and acquisition system for each test, load the structural plate and the specimen into the shear box sequentially. The soil-structure interface shear test process involves four steps, as follows:  
* Consolidation Stage: Apply the normal stress to the specimen.
* Freezing Stage: Set the corresponding freezing temperature (\(-20\)\({}^{\circ}\)C for the sample).
* Thawing Stage: Adjust the circulating cold bath temperature to the target thawing temperature, start the thawing process until the sample reaches the target temperature, and maintain dynamic stability.
* Shearing Stage: During this stage, shear stress and displacement, normal load and displacement, and temperature are automatically collected and saved.  
The mechanical properties of the frozen-thawed soil-structure interface are affected by many factors, such as normal stress (\(\sigma_{n}\)), thawing temperature (\(T_{t}\)), roughness of structural interface (\(R_{si}\)), and shear rate (\(v\)), which are considered in this study. The detailed test arrangement is shown in Table 3.
###### Abstract  
Monitoring athletes' well-being during the competitive season is a common practice in team sports, and countermovement jump (CMJ) is often introduced to monitor neuromuscular fatigue of the players. In this pilot observational study, we aimed to evaluate changes in jump performance in professional volleyball players during their two competitive seasons after introducing different executions of the jump test. Different execution of the flight phase of the jumps was introduced as a differentiating factor. Nine professional volleyball players (age: 23.3 \(\pm\) 1.9 years; volleyball training experience: 10.4 \(\pm\) 1.9 years; body height: 192.3 \(\pm\) 8.3) performed CMJ measurements 1-2 times a week on a contact mat during two competitive seasons, and a total number of 1008 jumps (540 in season 1 and 468 in season 2) was collected. Players' CMJ height significantly decreased (\(p\) < 0.05) in season 2 by approximately 4%, and standard deviation of the data reduced by approximately 30%. Our study confirms that the testing procedures need to be rigorous as different execution of the jump may be associated with changes in CMJ height. Execution of the jump should not be interchangeable using the same measurement device, and the technique introduced in season 2 seems to be more appropriate for this measurement device.  
testing; measurements; contact mat; neuromuscular fatigue; monitoring; high performance; sport +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
In this study, detailed insights into the cone-hemispherical gypsum particle breakage under normal contact at different contact diameter \(d\), cone angle \(\theta\) and sphere diameter \(D\) was obtained. By analyzing particle breakage processes, breakage modes, normal force-displacement curves and the effect of particle shape variables on particle breakage were investigated, and particle breakage discrimination based on particle shape was proposed. The main conclusions are as follows:  
1. After a local crushing at the contact interface, the particle breakages with increasing normal forces turn out to be three modes: peeling, peeling-splitting and splitting. The particle breakage mode transfers from peeling to splitting with the increases in contact diameter \(d\) and cone angle \(\theta\), but a decrease in sphere diameter \(D\);
2. Each breakage mode has a distinct force-displacement curve. The first peak of normal force \(F_{\text{cr}}\) has a positive linear correlation with contact diameter \(d\) and cone angle \(\theta\), but no direct correlation with sphere diameter \(D\). The relationship between the critical normal force \(F_{\text{cr}}\) and sphere diameter \(D\) heavily depends on the breakage mode;
3. The critical contact diameter \(d_{\text{cr}}\) described by cone angle \(\theta\) and sphere diameter \(D\) is proposed to discriminate particle breakage mode. If contact diameter \(d\) is smaller than the lower critical contact diameter \(d_{\text{crl}}\), peeling will occur, and if contact diameter \(d\) is larger than the upper critical contact diameter \(d_{\text{cru}}\), the particle would be split; otherwise, the particle would crush as peeling-splitting.  
Conceptualization, J.Z. and Y.Z.; methodology, Y.Z., J.Z. and Q.J.; formal analysis, Q.J. and Q.H.; data curation, Q.H., L.L. and Y.N.; writing--original draft preparation, Y.Z.; writing--review and editing, L.L. and Y.N.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51479138.  
Not applicable.  
The data presented in this study are available in the insert article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Discussion
### Use of Domain-Specific LLMs  
Most existing RAG systems use generalist foundation models like BERT and GPT-3. Even when these models are augmented with specialized documents, they often cannot fully capture or understand the technical language and industry-specific jargon necessary to address complex problems. However, domain-specific LLMs--such as SciBERT [61] and ChemGPT [62]--are pre-trained on data particularly relevant to fields like law, medicine, and finance. Such specialized training enables them to generate more accurate, relevant, and nuanced content in response to domain-specific queries. By incorporating RAG, these models can access up-to-date, precise information from external databases and knowledge sources, further enhancing the accuracy of their responses. As such, researchers should further investigate RAG-based domain-specific LLMs' application in gaining more insightful literature reviews.
## 3 Q2: What Are the Challenges of the Current UIoT System
### Auditability
#### 3.4.2 Passive Attacks  
The unauthorized attacker attacks the UIoT channel without altering the data. These attacks have silent carriers because they do not carry any signals. The attacker is hidden during a passive attack and can cause node tampering, jamming, message distortion and replaying. Furthermore, the attacker can anticipate the idea of UIoT networks by identifying packet traffic, observing packet exchange nodes and predicting the location of nodes. Passive attacks are also known as privacy-based attacks. The types of passive attacks are mentioned below:  
_Monitoring and eavesdropping:_ It is the most commonly used attack against data privacy in UIoT environment. When the network traffic is at its peak, the attacker can steal important information by tapping the network configuration. This type of attack is categorized under privacy-based attacks.  
_Adversary and camouflage:_ In this case, the invisible attacker injects an adversary node into the UIoT network. In effect, the adversary node can track and modify the information in UIoT networks, such as stealing packets, rerouting packets and altering nodes.  
_Traffic analysis:_ In these attacks, the attacker infuses the UIoT networks by accessing the pattern in the communication channel. Through this, the attacker can listen to the location of each node, the routing path, the behavior, etc.
# The Impact of Blockchain Technology Adoption on Tourism Industry: A Systematic Literature Review
## 3 Results and Discussion
### The Advantage of BC Technology Adoption in the Tourism Industry
#### 3.2.1 Consumers' Attitudes  
[PERSON] et al. [18] carried out a self-administered questionnaire in Hangzhou and revealed that Chinese customers are positively disposed towards the adoption of novel technologies by service providers. The authors found that the possibility of using BC technology for receiving specific services generated their loyalty towards the service-provider companies. Moreover, [PERSON] [19], through quantitative marketing research, studied Romanian customers' satisfaction after using an e-commerce application for tourist-service acquisition, and they concluded that BC application would advance this industry by ensuring disintermediation, operational efficiency, cost reduction, and a comfortable way of making transactions. Additionally, [PERSON] et al. [20] studied the society on Twitter and discovered that there is significant interest in BC application in the tourism sector. The authors exposed several aspects regarding BC adoption. Specifically, they argue that, in the BC network of the tourism sector, there are some participants who have a high impact on the information flow, as well as on the positive and negative attitudes of other \"peers\" towards a specific matter.
## 3 Microalgae Genetic Engineering Methods  
Knowledge of the genome of the microalgae of interest facilitates the genetic engineering process. Initially, microalgal genetic engineering was based on the _Chlamydomonas reinhardtii_, the very first microalgae to have its genome sequenced [57]. This strain has been used as a model organism for developing most of the tools available at the moment. With the evolution of next generation sequencing technologies and the associated affordability, many strains have since been sequenced. Furthermore, transcriptomic analysis has become just as accessible enabling the possibility to find, e.g., inducible promoters, controlling elements, and other powerful tools.  
Various protocols have been developed to insert foreign DNA in to microalgae. The most common ones are biolistic, glass beads, electroporation, and _Agrobacterium_-mediated transformations [58]. These protocols have been adapted mainly from plants' applications due to a similar cell wall characteristics. One of the problems faced after transformation is genetic stability. Most microalgae have tools to recognize foreign DNA, and degrade/silence it, supposedly to protect themselves from viral infections, transposable elements, or transgenes [59; 60; 61]. Insertion of specific introns in foreign genes can circumvent such problems by enhancing expression levels and gene stability [62]. Chloroplast transformation protocols (and the associated higher copy number this provides following homoplasmy) have been developed in order to improve expression of particular genes or produce specific molecules whose precursors are in this organelle. With the development of new genetic engineering tools, an exogenous chloroplast genome has been created in order to deal with complex sequence manipulation [63].  
Directed gene editing is developing in microalgae as well. The CRISPR-Cas9 system enables scar-free gene modification and can be used to better understand the phenotype after knockout of specific genes or to improve the production of specific molecules blocking competitive pathways [64; 65]. It is worthy of mention that, despite recent advances, genetic engineering in microalgae is behind in comparison to bacteria and yeast. This is for a variety of reasons including a relatively recent start of the research field, fewer interested researchers, and doubling times (and therefore selection times) are typically much longer. Furthermore, the focus in microalgae has been on developing commercially relevant industrial platforms in addition to model organisms, in order to exploit market opportunities faster, thereby diluting developmental efforts over a range of organisms.
# Dilated Filters for Edge-Detection Algorithms
## 4 Experimental Results
### Dilation with Different Operators  
In this Section, we analyze if the hypothesis that dilating the filters yields better results than the classical approach is maintained if we change the kernel used. In Figure 14, we present the results for edge-detection operators that are based on first-order discrete kernels and in Figure 15, we present the results of edge-detection operators that are based on second-order discrete kernels. From the previous results, because both metrics showed similar conclusions, we consider in the next part of this paper, only the \(BSDS500\) dataset evaluation.  
For each version of the kernel (classical, extended or dilated) we used the best-suited parameters found in Section 4 for this evaluation. Statistical metrics are presented in Figures 14 and 15 and visual comparison results of the operators are presented in Figures A8-A12 from Appendix B.  
The first aspect worth mentioning for Figure 14 is that for each algorithm, the best results were obtained when using dilated versions of the kernels. Another important aspect is that in most cases, even if we change the kernel we obtain the same trend, which is that dilation obtains better results.  
For the first-order derivative orthogonal gradient operators, Figure 14a, we observe that using the Kirsch operator obtains good results in all cases classical, extended or dilated. In the opposite part, we have the Kayyali operator, where dilating the filters produced a degradation of the resulting edge map.  
In Figure 14b, we present the results for the first-order derivative compass gradient operators, where we observe that for both variants of the [PERSON] compass operator, we obtained similar results if we use the classical or dilated approach. Unfortunately, we can see clear diminution of the \(F1\) metric when dilating using Prewitt compass operator.  
In the case of the [PERSON] operator, Figure 14c, we see that the trend we observed for the [PERSON] operator is maintained for most of the other operators we experimented with. We consider it worth mentioning that we used the dilated \(7\times 7\) Prewitt operator, and we see a significant increase in the overall results. In the case of this algorithm, we actually observed that the pixel difference operator and separated pixel difference operator do not obtain any noticeable results.  
In Figure 14d, we can see that, for the ED algorithm, we obtained the same close results for the dilated \(7\times 7\) and extended \(7\times 7\) for several operators, such as: [PERSON], Kroon, Prewitt and Orhei. Actually, in this case, the first results were where the extended kernels produced the best overall results. On the other hand, we can conclude that the Kayyali operator has no benefit when it is used in this algorithm.  
Figure 14: Results for classic, extended and dilated using different operators for: (**a**) first-order derivative orthogonal gradient operators; (**b**) first-order derivative compass gradient operators; (**c**) Canny operator; (**d**) ED edge operator.