esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This work demonstrated that the high-pressure homogenization process, which has been used mainly for mixing hydrophobic and hydrophilic components, could be employed to effectively break down the structure of _S. platensis_. First, this process showed the ability to greatly increase the extraction yield of chlorophyll \(a\) and other bioactive substances using a simple and easily scaled-up method. Additionally, this unique extraction process was found to be very effective in extracting chlorophyll \(a\) from _Spirulina_ at a low temperature, which is beneficial because the major chlorophyll in _Spirulina_ is the extremely heat-sensitive chlorophyll \(a\). Therefore, the extract from this process showed very high antioxidant activity through the additive effects of large amounts of chlorophyll \(a\) and other biologically active components in the extract, which effectively reduced oxidative stress in mouse hippocampal neuronal cells and consequently protected them from neuronal damage due to glutamate. It was also shown for the first time that chlorophyll \(a\) had greater neuroprotective activity and efficacy than other chlorophylls from plant sources. Moreover, the crude extracts from a high-pressure extraction process had greater neuroprotective activity than chlorophyll \(a\) alone in that chlorophyll \(a\) was less efficacious than crude extract containing the same amounts of chlorophyll \(a\), possibly due to the additive effects of other substances extracted by the HP process. In conclusion, these results clearly confirmed that the high neuroprotective effects of the HP extract were caused mainly by the additive efficacy of abundant intact chlorophyll \(a\) and other bioactive components in the extract even at a low temperature. It was also shown that the neuroprotective activity of the extracts was strongly associated with high antioxidant activity. Therefore, the results of this work will be employed to expand the use of a nonthermal extraction process to obtain heat-sensitive components from natural resources.  
[PERSON] carried out all of the experiments and [PERSON] designed all of the experiments and drafted the manuscript. All authors read and approved the final manuscript.  
The authors declare no conflict of interest.
## 4 Fault-Tolerant Controller Design
### Active FTC without Actuator Saturation
If \(V_{3}^{\{[p/(q+h(I_{VS}))]+1\}/2}>\beta/\vartheta_{2}\), the inequality (79) is rewritten in the following form  
\[\dot{V}_{3}\leq-\vartheta_{1}V_{3}-(\vartheta_{2}-\beta/V_{3}^{\{[p/(q+h(I_{VS }))]+1\}/2})V_{3}^{\{[p/(q+h(I_{VS}))]+1\}/2} \tag{82}\]  
Similarly, the convergence time satisfies  
\[T_{5}\leq t_{0}+\frac{1}{\vartheta_{1}(1-\frac{p}{q+h(I_{VS})})}\ln\left\{ \frac{\vartheta_{1}V_{3}(t_{0})^{[1-p/(q+h(I_{VS}))]/2}}{\vartheta_{2}-\beta/ V_{3}^{\{[p/(q+h(I_{VS}))]+1\}/2}}+1\right\} \tag{83}\]  
Therefore, the state trajectory of the faulty closed-loop attitude systems can converge into a neighborhood of the origin in finite time. This completes the proof. \(\square\)  
**Remark 4**. : _In the study by [29], a non-singular terminal sliding model surface was designed as:_  
\[S=\omega+k\text{sig}^{(a/b)}(\sigma),\]  
_where \(k>0\) and \(1<(a/b)<2\). It provides a fast convergence rate at a distance from the origin and becomes slow as it nears the equilibrium. The closer to the origin, the slower the convergence rate. In this paper, a non-singular fast terminal sliding model surface was designed in the form:_  
\[S=\omega+(k_{1}+k_{h,1}(J_{VS}))\sigma+k_{2}\text{sig}(\sigma)^{p/(q+h(I_{VS} ))},\]  
_when the system states are far away from zero, the linear term \(k_{1}\sigma\) offers a fast convergence rate, and when the system states are close to zero, the nonlinear term \(k_{2}\text{sig}(\sigma)^{p/(q+h(I_{VS}))}\) guarantees the finite-time convergence. Therefore, the system states converge quickly to the sliding model surface designed in this study._
## 4 Experiments
### The Overall Comparison
COLA, MB-Cal, and MVSD(LLM) refine prompt engineering strategies to enhance LLM reasoning, resulting in significantly better performance. Among these, MVSD(LLM) excels on SEM16, highlighting its strength in incorporating HSN-augmented knowledge for enhanced stance detection. In contrast, on P-Stance, MVSD(LLM) performs comparably to COLA and MB-Cal. This difference stems from dataset characteristics: SEM16 includes numerous abstract targets, where external knowledge and relational reasoning from HSN significantly enhance stance detection. Conversely, P-Stance centers on political figures, where stance expressions are more explicit, diminishing the impact of graph-based enhancements. A particularly notable case is the performance on target _Atheism_ in SEM16. MVSD(LLM) outperforms direct GPT-3.5 usage by over 30% but falls slightly behind COLA and MB-Cal. This discrepancy arises from prompt design: while MVSD(LLM) employs simple prompts to leverage HSN, COLA and MB-Cal utilize more complex prompts that enable finer-grained target comprehension. The strengths and weaknesses of MVSD(GCN) and MVSD(LLM) vary based on the availability of training data. On SEM16, MVSD(GCN) outperforms MVSD(LLM) overall, benefiting from the ability to fine-tune on labeled training data. Unlike LLM-based methods that encode HSN information via prompt engineering, GCN offers a holistic perspective by explicitly modeling stance relationships within the dataset. This allows MVSD(GCN) to better capture complex indirect stance dependencies, an advantage that is particularly pronounced for abstract targets. On P-Stance, however, the advantage of GCN is limited. Since stance expressions in this dataset tend to be more direct, the need for global relational reasoning is less critical. As a result, MVSD(LLM) surpasses MVSD(GCN)'s performance without any training data, underscoring its strong generalization ability. In summary, the results validate the effectiveness of our proposed models across both supervised and zero-shot stance detection tasks. MVSD(GCN) demonstrates its superiority in leveraging training data to build structured graph-based representations, while MVSD(LLM) showcases the potential of prompt-optimized LLMs for generalizing to novel targets without the need for labeled data. These results underscore the robustness and versatility of our multi-view stance detection framework in real-world applications.
## 3 Research Method
### Development of the Research Hypotheses
#### 3.1.2 Relationship between E-Service Quality and E-Trust  
Several studies have explored the intricate relationship between e-SQ and e-trust, finding that customers who perceive high-quality services tend to have greater confidence in the company. [PERSON] and [PERSON] [42] noted positive correlations between different aspectsof e-SQ and e-trust in online interactions. Similarly, [PERSON] et al. [3] and [PERSON] et al. [4] observed the substantial impact of e-SQ on e-trust in online shopping, suggesting that enhanced e-SQ is linked to increased e-trust among online shoppers. [PERSON] et al. [7] found that e-SQ influences e-trust in a business-to-business (B2B) context. Consequently, the current study hypothesizes the following relationship between e-SQ and e-trust in e-commerce marketplace platforms:  
**Hypothesis 2 (H2).**_E-SQ is positively related to e-trust._
## 2 System and Channel Models
### Channel Model
Additionally, the Fried parameter \(r\) is given by  
\[r=\left[0.42\sec(\theta)k_{1}^{2}\int_{h_{\ell}}^{h_{k}}C_{n}^{2}(h)dh\right]^ {-3/5}, \tag{12}\]  
while \(\Delta_{pe}=\frac{\sigma_{pe}^{2}}{d_{k}}\) denotes the beam-wander-induced pointing errors, with the beam-wander-induced pointing error variance given by  
\[ \begin{split}\sigma_{pe}^{2}&=0.54(h_{k}-h_{\ell})^ {2}\sec(\theta)^{2}\bigg{(}\frac{\lambda_{f}}{2W_{0}}\bigg{)}^{2}\\ &\times\bigg{(}\frac{2W_{0}}{r}\bigg{)}^{5/3}\Bigg{[}1-\left( \frac{C_{r}^{2}W_{0}^{2}/r^{2}}{1+C_{r}^{2}W_{0}^{2}/r^{2}}\right)^{1/6}\Bigg{]},\end{split}  \tag{13}\]  
with \(C_{r}=2\pi\) being the scaling constant. For the RF links, the PDF of the instantaneous received SNR at the G can be expressed as ([29] [Equation (7)])  
\[f_{\gamma_{2}}(\gamma)=\gamma^{-1}\mathcal{S}_{1}G_{2,2}^{2,2}\bigg{(}\frac{m _{1}m_{2}\gamma}{\gamma_{2}}\Big{|}_{m_{1},m_{2}}^{1-a_{2},1-a_{1}}\bigg{)}, \tag{14}\]  
where \(\mathcal{S}_{1}=\frac{1}{\Gamma(m_{1})\Gamma(m_{2})\Gamma(a_{1})\Gamma(a_{2})}\). It is noted that (14) is an experimentally verified composite fading model that accurately describes both small-scale and large-scale fading effects in UAV-to-ground communication scenarios. In particular, coefficients \(m_{1},m_{2}\) describe the severity of the small-scale fading effects, i.e., as \(m_{1},m_{2}\) increase, line-of-sight conditions are approximated. On the other hand, coefficients \(\alpha_{1},\alpha_{2}\) are related to the severity of the shadowing (large-scale fading) effects, i.e., lower values of \(\alpha_{1},\alpha_{2}\) result in lighter shadowing conditions. The corresponding CDF expression is given by ([29] [Equation (12)])  
\[F_{\gamma_{2}}(\gamma)=\mathcal{S}_{1}G_{3,3}^{2,3}\bigg{(}\frac{m_{1}m_{2} \gamma}{\gamma_{2}}\Big{|}_{m_{1},m_{2},0}^{1-a_{2},1-a_{1},1}\bigg{)}. \tag{15}\]
# Locating the Epidemic Source in Complex Networks with Sparse Observers
## 3 Experiments and Analysis
### Parameter Estimation Accuracy  
Considering that PESL algorithm can estimate the initial time while locating the epidemic source, we further study MSE of the initial time with different infection probabilities and reporting probabilities in ERT and BAT. Figure 5 shows the mean square error of the initial time \(t_{0}\) of our algorithm. The lower the mean square error is, the more accurate the estimate will be. It can be seen from the results that the MSE of \(t_{0}\) is positively correlated with the infection probability \(\beta\) and the reporting probability \(\delta\). When the infection probability \(\beta\) or the reporting probability \(\delta\) increases, the estimation accuracy of the initial time will also increase.
# Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer
## 3 Methods
### MSGNet-MLLA-Transformer  
#### 3.2.1 Problem Formulation  
This paper focuses on short-term power load forecasting. As a subtask of multivariate time series forecasting, this problem can be described as follows: Let \(X_{1}^{i}\) represent the state of the \(i\)-th influence sequence of power load at the \(t\)-th time step. Then, \(X^{i}=\big{\{}X_{1}^{i},\ldots,\ X_{T}^{i}\big{\}}\) represents the data of the \(i\)-th influence sequence at all historical moments, and \(T\) is the historical time length. \(X=\big{\{}X^{1},\ldots,X^{n}\big{\}}\) represents all the sequences affected by the power load, and \(n\) is the number of sequences. If the future values of the power load target sequence Y are predicted by using the model through the sliding time window of size \(w\), then \(\hat{Y}_{(t+1:t+l)}=f\Big{(}X_{(t-w+1:t)},Y_{(t-w+1:t)};\ \theta\Big{)}\), where \(\theta\) is the learnable parameter of the model, \(t\) is the observation moment, \(l\) is the prediction step size, \(X_{(t-w+1:t)}\) and \(Y_{(t-w+1:t)}\) are the historical moment influence sequence and target sequence data intercepted by the sliding time window, respectively, and \(f\) is the model examined in this paper.
## 2 Background and Related Work
### Related Work  
#### 2.2.1 E-Commerce Recommendation Systems  
The landscape of e-commerce recommendation systems has evolved significantly, with early models relying predominantly on collaborative filtering (CF) and content-based filtering techniques [15; 16; 17]. These methods, while effective in certain contexts, often struggled with scalability and the cold start problem (i.e., the difficulty in making recommendations for new users or items with little to no historical data) [18]. Recent advancements have seen a shift toward more sophisticated models that leverage user and item metadata to improve recommendation quality [19; 20; 21]. Deep learning techniques, such as neural collaborative filtering [22] and DeepFM [13], have further enhanced the performance of recommendation systems. These deep learning techniques have garnered significant attention in recent years by leveraging the power of deep learning to seamlessly integrate the strengths of collaborative filtering and content-based recommendation models. However, despite these improvements, traditional recommendation systems seldom account for the complex nature of consumer purchasing behavior, particularly in the context of concurrent purchases.
## 2 Materials and Methods  
### Study Area  
The study area is located in southwest China (Figure 1), including Sichuan, Yunnan, Guangxi, Guizhou, and Chongqing (97\({}^{\circ}\)22\({}^{\prime}\)-112\({}^{\circ}\)10\({}^{\prime}\) E, 20\({}^{\circ}\)57\({}^{\prime}\)-34\({}^{\circ}\)26\({}^{\prime}\) N), and the whole study area is about 1,364,000 km\({}^{2}\)[31; 32; 33]. The area is rich in various vegetation types, mainly farmland, grassland, forest, and shrubland (Figure 1a). The average precipitation in the study region during 2000 and 2015 is 50-415 mm (Figure 2a), and the temperature is \(-\)22-20 \({}^{\circ}\)C (Figure 2b). The spatial and temporal distribution of precipitation is extremely uneven, with distinct dry and wet seasons [34]. The area is located on the plateau of low latitude, with a special geographical location and complex terrain (Figure 1b). In spring, southwest China is controlled by subtropical high pressure; the rainfall decreases; and the precipitation variation rate is large, which is easy to produce spring drought [32; 34]. In the region, rivers and valleys are widely spread; the landform is mainly plateau and mountain but also has widely distributed karst landform, valley, hilly and basin landforms, the terrain ups and downs. Under the influence of a special geographical location, a karst landform, climate and geological factors, this region faces ecological problems such as vegetation degradation and soil erosion [15; 33; 34]. The ecosystem is very fragile, which is a frequent drought area.  
Figure 1: (**a**) Geographical location of southwest China; (**b**) vegetation types’ distribution map of the study area, reclassified according to 1:1,000,000 vegetation types in China; the main vegetation types in southwest China are cropland, forestland, grassland, and shrubland; (**c**) elevation map of the study area. The terrain in southwest China is very undulating and different.
# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 7 Results and Discussion
### Results from Applying Isolation Forest  
Figure 15 displays the outcomes of the iForest applied specifically to the noise points identified in the DBSCAN step across three scenarios: _ACD-OCC_ with 17 points, _ACD-ABS_ with 20 points, and _ACD-RSL_ with 8 points. Each subfigure illustrates the latitude and longitude of the reports, overlaid with the iForest's classification results, represented by the outlier scores (color gradient). Blow is a detailed explanation of each scenario:  
* iForest _ACD-OCC_ For the first scenario, _ACD-OCC_ (Top Figure), the iForest refined the resulting 17 noise points from DBSCAN by classifying a subset as outliers (blue,) representing 7 MRs, and the rest as inliers (red) with 10 MRs. The outlier percentage in this step is 41.18%, which demonstrates how the iForest narrows down potential serious anomalies within the initially noisy MRs, helping identify MRs that significantly deviate from the expected behavior.
* iForest _ACD-ABS_ For the second scenario, _ACD-ABS_ (Middle Figure), the iForest refined the 20 noise points from DBSCAN by classifying a subset as consistent inliers (red) with 12 MRs, and some MRs that were classified as anomalies (blue) (8 MRs). The outlier percentage in this step is 40%.
* iForest _ACD-RSL_ The third scenario, _ACD-RSL_ (Bottom Figure), exhibits a moderate balance of inliers (red) equal to 5 MRs and outliers (blue) equal to 3 MRs, which represents a percentage of 37.5% of outliers. The iForest aids in detecting misleading or erroneous MRs in a scenario where there might be conflicting observations about the resolution of an event.  
The iForest results illustrate the efficacy of further examining noise points to distinguish serious anomalies from less critical deviations. This layered approach enhances the RS reliability of misbehavior detection and decision-making regarding the received MRs.  
Figure 14: DBSCAN clustering results for different scenarios: _ACD-OCC_, _ACD-ABS_, _ACD-RSL_.  
Figure 15: Isolation Forest results on DBSCAN noise points.
## 1 Introduction  
According to the International Geosynthetics Society's recommended geosynthetics terminology, mathematical and graphical symbols, and descriptions of geosynthetic functions, geosynthetics are planar, relatively impermeable, synthetic or natural polymeric sheets for use in civil engineering applications and infrastructures [1]. Most conventional geosynthetics can be categorised as geomembranes, geogrids, geotextiles, and geocomposites, as shown Figures 1 and 2. Geocomposites are manufactured by integrating various geosynthetic materials or by merging geosynthetics with non-synthetic materials such as bentonite clay to optimally and cost-effectively address particular field applications [2]. Geocomposite structures and materials include geosynthetic clay liners (GCLs); geotextile geopets, geomembranes, geogrids, and polymeric cores; geomembrane geonets; and three-dimensional polymeric cell structures [3, 4]. In the last few decades, the application of geosynthetic materials to various civil projects and ground treatment has considerably increased owing to the important benefits of geosynthetic materials, has attracted the attention of scientists, and has advanced geosynthetic production technologies and materials. The advantages of using geosynthetic materials include lower repair and maintenance costs, cost-efficiency, design predictability, installation and transportation convenience, quick installation, applicability to a wide range of soils, enhanced performance, extended service life, space savings, good quality control owing to natural homogeneity, increased safety factors, less environmental sensitivity, and compatibility with field conditions [5; 6]. However, these materials must meet certain requirements and be evaluated and tested before their field applications. The features required for the application of geosynthetic materials are summarised in Table 1.  
Figure 1: Biaxial and uniaxial geogrids. Figure 2: Different geocomposite materials. With the recent development and construction of smart cities, the demand for smart multifunctional geosynthetic materials has also drastically increased. These materials can perform their traditional roles such as construction layer structural elements, filtration, or protective barriers and various other functions while also extracting and transmitting information about the system into which they are embedded for structural health monitoring (SHM) [7; 8; 9]. Measuring geosynthetic strain and stress and/or monitoring severe (e.g., seismic) events are among the most important health monitoring objectives of geosynthetic structures during their service life [10]. The importance of instrumentation and health infrastructure monitoring is increasingly recognised to address the challenges and uncertainties of site conditions, construction practice, material properties and behaviour, environmental effects, and loading conditions [3; 11].
# QiOi: Performance Isolation for Hyperledger Fabric
###### Abstract  
This paper investigates the performance interference of blockchain services that run on cloud data centers. As the data centers offer shared computing resources to multiple services, the blockchain services can experience performance interference due to the co-located services. We explore the impact of the interference on Fabric performance and develop a new technique to offer performance isolation for Hyperledger Fabric, the most popular blockchain platform. First, we analyze the characteristics of the different components in Hyperledger Fabric and show that Fabric components have different impacts on the performance of Fabric. Then, we present QiOi, component-level performance isolation technique for Hyperledger Fabric. The key idea of QiOi is to dynamically control the CPU scheduling of Fabric components to cope with the performance interference. We implement QiOi as a user-level daemon and evaluate how QiOi mitigates the performance interference of Fabric. The evaluation results demonstrate that QiOi mitigates performance degradation of Fabric by 22% and improves Fabric latency by 2.5 times without sacrificing the performance of co-located services. In addition, we show that QiOi can support different ordering services and chaincodes with negligible overhead to Fabric performance.  
blockchain; performance interference; performance isolation, cloud computing; private blockchain; Hyperledger Fabric
## 5 Taxonomy of Technology-Enabled Smart City Applications in 6G Networks
### Extended Reality (XR)  
Extended reality (XR) technologies involve AR, Mixed Reality (MR), and VR applications. The current wireless communication technologies are unable to provide an immersive XR experience for users of these applications, such as 3D medical imaging, surgical training, immersive gaming, guided remote repair and maintenance, virtual property tours, e-commerce purchase, hands-on virtual learning, and virtual field trips for students. This is due to the inability of the currently deployed 5G technology to deliver ultra-low latencies and very high data rates [102]. These XR applications are highly demanding in terms of communication and computation due to the incorporation of perceptual needs (human senses, physiology, and cognition). The envisioned 6G networks should ensure the Quality-of-Physical-Experience (QoPE) [103] for these XR applications by providing URLLLC and eMBB services.
## 3 Extraction of Echo Envelope Fluctuation Features
### Standard Deviation of Fluctuation Intensity  
Figure 11 compares the STDs of the theoretical and experimental echo envelope fluctuation intensities with LFM 20-40 kHz and pulse lengths of 1 ms and 3 ms, respectively. Figure 11 shows that the tendency of the experimental and simulated results are fundamentally similar: the STD of the fluctuation intensity is minimum on the beam, and maximum on the keel-line.  
Figure 11: STD of theoretical and tested echo envelope fluctuation intensity with the frequency of LFM 20–40 kHz. **(a)** The pulse length is 1 ms; **(b)** the pulse length is 3 ms.  
Figure 10: **(a) Fluctuation intensity of the CW echo envelope with different pulse lengths; **(b)** fluctuation intensity of the wideband echo envelope with different pulse lengths.  
Figure 12 is the STD results of fluctuation intensity of the scaled benchmark model, varying with azimuth: Figure 12a shows the results of the broadband signal, and Figure 12b shows the results of the narrowband signal. The results of the two kinds of tested signals show that the standard deviation of the fluctuation intensity depends on the carrier frequency; it increases with increasing carrier frequency, and it is more notable for the broadband signal--similar to the fluctuation intensity results. Compared with the fluctuation intensity of the narrowband signal, the narrowband signal's standard deviation of fluctuation intensity changes with carrier frequency, and these changes are relatively weak. The resolution performance of this feature is not notable.