esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Wavelength Path Loss Model for Indoor VLC with Mobile Human Blockage
## 3 Multi-Wavelength Path Loss Analysis and Modeling under Free Space Propagation
### Multi-Wavelength Path Loss Model of Free Space Propagation in Single-LED Scenario  
Based on the VLC channel model in Section 2, the multi-wavelength path loss of the free space propagation in the single-LED scenario is obtained, as shown in Figure 4. Firstly, we investigate the effect of transceiver distance on path loss. It can be observed that the path loss increase along with the increasing transceiver distance. For instance, the path loss is 54.42 dB at \(d\) = 3 m and increases to 61.45 dB at \(d\) = 4.60 m. This is mainly because the channel direct current gain for the LoS component is significantly affected by the square of propagation distance with a nonlinear inverse correlation. Therefore, as the receiver moves away from the transmitter, a large path loss occurs. This positive relationship between the transceiver distance and the path loss coincides with the conclusion obtained from the measurements in [29].  
Meanwhile, we investigate the effect of signal wavelength on path loss. It is found that the path loss increase with the increase in signal wavelength. The extra-wide wavelength range of visible light signal from 380 nm to 780 nm can cause a difference of 1.74 dB on the path loss at \(d\) = 4.60 m. This can be attributed to the NLoS component of the visible light channel. During reflection, the increase of the light wavelength usually leads to a nonlinear increase in the reflection coefficients of the plaster materials. Therefore, the path loss at a smaller light wavelength suffers more severe decay than that at a larger light wavelength.  
Furthermore, we use the ABG model to fit our simulated results. The simulated path loss and the ABG model are plotted in Figure 4. The derived parameters are \(\alpha=3.953\), \(\beta=35.840\), \(\gamma=-0.007\), \(\sigma=0.262\). \(\sigma\) has a small value, which shows that the fitted ABG model is in good agreement with the simulation data.
# Plant-Derived Phytobiotics as Emerging Alternatives to Antibiotics Against Foodborne Pathogens
### Polysaccharides  
Polysaccharides play essential roles in plant physiology, encompassing both structural and functional aspects. As major components of the plant cell wall--such as cellulose, hemicelluloses, and pectins--they ensure cellular integrity and mechanical rigidity. Additionally, they are involved in energy storage (e.g., as starch), contribute to defense mechanisms against pathogens, and participate in processes such as growth, morphogenesis, and responses to environmental stress [92; 93; 94; 95; 96]. Moreover, plant polysaccharides exhibit antimicrobial properties through various mechanisms, including the disruption of bacterial cell membranes, which increases membrane permeability and leads to cell death. They can also inhibit pathogen adhesion to host cells, thus preventing infection, and block nutrient transport across bacterial membranes, effectively starving the bacteria. The antimicrobial activity of these polysaccharides is influenced by factors such as their molecular weight, monosaccharide composition, degree of branching, and chemical modifications, with different extraction methods potentially altering their structure and biological activity. Significant antimicrobial activity has been demonstrated, for instance, for polysaccharides from _Plantago asatica_ L. [97; 98].  
Based on a structural perspective, plant polysaccharides are classified into several basic types. Cellulose, which is a polymer of glucose linked by \(\beta\)(1\(\rightarrow\)4) glycosidic bonds plays a key structural role, forming fibrils that give rigidity to cell walls [95; 99]. Starch, made up of glucose linked by \(\alpha\)(1\(\rightarrow\)4) glycosidic bonds, and also \(\alpha\)-1,6-glycosidic bonds, is the main form of energy storage in plant cells [95; 100]. Hemicelluloses, which include xyloglucans and xylans, are also part of the cell wall, where they interact with cellulose andpectins. Pectins, in turn, are polysaccharides of the cell matrix, which affect the elasticity and permeability of cell walls and participate in cell growth and maturation processes [99].  
In summary, phytobiotics are a heterogeneous group of natural compounds, differing primarily in chemical structure and the resulting variations in biological activity. The majority of phytobiotic classes exhibit antimicrobial properties, thus establishing them as promising agents for controlling pathogens in food. Considering the high structural and functional diversity, individual photobiotics have different mechanisms of action against microorganisms.
# Antioxidant and Antibacterial Activity of Extracts from Selected Plant Material
## 2 Materials and Methods  
### Materials and Reagents  
The dried herb of _Cistus incanus_ L. and _Asarum europaeum_ L., rhizome of _Geum urbanum_ L., root of _Angelica archangelica_ L., leaves of white mulberry (_Morus alba_ L.), lemon balm (_Melisa officinalis_ L.), red raspberry (_Rubus ideaeus_ L.) and _Betula pendula_ Roth. bought from an herbal shop in Warsaw, Poland, were used as the plant material. Folin-Ciocalteu's phenol reagent, 2,2-diphenyl-1-picrylhydrazyl (DPPPH'), gallic acid, 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox), 2,2'-azinobis-(3-ethylbenzothazoline-6-sulfonic acid) (ABTS), and 2,4,6-tri(2-pyridyl)-s-triazine (TPTZ) were bought from Sigma-Aldrich (Poznan, Poland). High performance liquid chromatography (HPLC) standards were purchased from Sigma Life Science (Merck, Darmstadt, Germany) and ChromaDex(r) (Irvine, CA, USA), respectively. Other chemicals and solvents were of analytical grade and were used as received without further purification. They were obtained from Avantor Performance Materials (Gliwice, Poland).  
The microorganisms used in this study were obtained from the collection of the Department of Pharmaceutical Microbiology, Medical University of Warsaw (Warsaw, Poland). They belonged to Gram-positive bacteria: _Staphylococcus aureus_ ATCC 6538P, _Staphylococcus aureus_ ATCC 25923, _Staphylococcus epidermidis_ ATCC 12228, _Enterococcus faecalis_ ATCC 29212, _Enterococcus_ ATCC 6057, _Bacillus subtilis_ ATCC 6633, _Geobacillus steathrethermophilus_ ATCC 7953 and Gram-negative bacteria: _Escherichia coli_ ATCC 25922, _Klebsiella pneumoniae_ ATCC 13883, _Proteus vulgaris_ ATCC 13315, _Proteus mirabilis_ ATCC 12453, _Listeria monocytogenes_ 1043S, _Serreatia marcescens_ ATCC 13880, _Enterobacter cloace_ DSM 6234, _Pseudomonas aeruginosa_ ATCC 27853, _Stentrophomonas malrophilia_ ATCC 12714, _Bordetella bronchiseptica_ ATCC 4617, _Acinetobacter baumanni_ ATCC 19606.
## 4 Experiments
### Ablation Experiments and Result Analysis
#### 4.4.7. DPEM + Transformer  
The DPEM and Transformer adopt a serial architecture design in the network, significantly enhancing the model's adaptability to multi-scale targets and complex backgrounds through a closed-loop collaborative mechanism of \"local difference enhancement-global context integration\". The ablation experiment data in Table 1 show that when both modules are removed simultaneously, the F1 and IoU metrics of the three datasets all exhibit significant degradation. The F1-scores decrease by 2.48%, 3.07%, and 6.44% on the LEVIR-CD, WHU-CD, and CLCD datasets, respectively, and the corresponding decreases in IoU metrics reach 2.99%, 4.35%, and 5.79%. This performance loss significantly exceeds the effect of removing either module alone, revealing that the synergistic effect of the two has a non-linear superposition characteristic. The visualization results in Column f of Figure 6 show that the model missing both the DPEM and Transformer has significant red false detection spots in the LEVIR-CD scene; a large area of green missed detection regions in the WHU-CD dataset; and in the CLCD scene, the farmland boundaries have large-area defects and the red change regions are sporadically distributed, reflecting a decrease in pseudo-change suppression ability.  
This \"local-global\" collaborative modeling mechanism not only improves the feature distinguishability between change regions and backgrounds through difference enhancement but also enhances the semantic consistency of cross-temporal features via context integration. This forms multi-level suppression of pseudo-change interferences and multi-dimensional enhancement of real change signals. The experimental results not only verify the necessity of the DPEM + Transformer in the change detection network proposed in this paper but also provide a new solution path for simultaneously addressing scale diversity and background interference issues in complex scenarios.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 2 Motivations and Contributions  
The increasing demand for wireless services and the limited availability of radio spectrum in B5G networks have led to severe congestion and interference, significantly degrading system performance and QoS. Among the many challenges facing B5G deployment, effective IM remains one of the most critical, particularly in the contexts of D2D communication, HetNets, ICI, and AI-based frameworks. This paper aims to provide a comprehensive and structured review of IM strategies that can enhance the performance, scalability, and reliability of B5G networks.  
While 5G networks introduced key advancements such as URLLC, eMBB, and mMTC, they still face challenges in UD deployments, RT AI integration, and coordination across heterogeneous infrastructures. These limitations highlight the need for B5G systems that offer greater scalability, intelligence, and support for emerging NTN paradigms.  
The main contributions of this review are summarized as follows:  
1. This paper presents a structured review of IM challenges in B5G networks, focusing on D2D communication, HetNets, ICI, and AI-based framework approaches, and introduces a unified taxonomy and comparative analysis of techniques.
2. It critically evaluates existing and emerging IMI techniques by examining their methodologies, advantages, limitations, and applicability as reported in recent literature, supported by summary tables and visual illustrations.
3. The paper examines 3 GPP-standardized IM schemes by reviewing practical techniques proposed for interference control (ICO) in next-generation wireless systems.
4. It identifies open research challenges and future directions, including spectrum allocation, POC, and LB, as well as federated learning (FL), cross-domain IM, and explainable AI, to support the development of more adaptive and intelligent IM strategies for B5G networks.
* A quantitative comparative analysis is introduced, highlighting the focus of existing IM literature on key optimization objectives (e.g., spectral efficiency (SE), power consumption) and revealing underexplored areas such as outage minimization and energy efficiency (EE), especially in AI-based frameworks.
## 2 Traditional A* Algorithm with Improvements
### Improved A* Algorithm  
#### 2.3.1 Optimization of the Evaluation Function  
To address the challenge of expanding the search space due to substantial discrepancies between the estimated cost \(h(n)\) from the heuristic function and the true value, an obstacle weight coefficient is incorporated into the estimated cost \(h(n)\) within the cost function \(f(n)\). The exact expression of this coefficient is outlined in Equation (3).  
\[f(n)=g(n)+e^{\frac{p}{k}}\times h(n) \tag{3}\]  
Here, \(k\) represents the total count of grids within the rectangular region bounded by the starting node and the target point while \(p\) denotes the quantity of obstacle grids within the rectangular area delimited by the current node and the target point.  
The provided formula illustrates that the weight coefficient applied to the estimated cost \(h(n)\) diminishes progressively as the AGV moves from the starting point toward its target. This coefficient's value is contingent upon the count of obstacles encountered. When the obstacle count is high, the path tends to become convoluted, leading to an elevation in the weight coefficient. Consequently, the estimated cost aligns more closely with the actual value. Conversely, with a lower obstacle count, the path tends to be smoother, resulting in a reduction in the weight coefficient. This adjustment facilitates the estimated cost's proximity to the real value in such scenarios.
## 5 Simulation and Performance Evaluation
### Comparison of RSC with Fan-Shaped Clustering
Figure 5: Comparison of RSC and FSC: (**a**) the number of live nodes and (**b**) the total residual energy. The results of RSC and FSC in standard deviation showed that the size of a data set may vary according to the time and range. In a certain interval period between two grouping practices, the rate of packet collection reduces because of the nonexistence of CHs. If the variance measure is high, the deviation of data is also high, which leads to inadequate analysis. This is identified in FSC owing to the size of the central area. Data are transmitted from the outer layer to the inner layer until they reach the CH which is located in the central area. Frequent data collection leads to repeated reclustering,  
Figure 6: Comparison of the package collection rate: (**a**) mean; (**b**) median; (**c**) mode; (**d**) maximum; and (**e**) standard deviation. which decreases the packet collection rate. In the FSC, the initial packet received started decreasing gradually from the starting round and reached 0 at 3500 rounds, whereas in RSC, the initial packet received was 1.00, and it started to decrease gradually at 3400 rounds and reached 0. 85 at about 3500 rounds. Figure 6e shows that the proposed system maintained its stability and achieved an improved packet collection rate compared with the existing system. This work tries to reveal that compared to the existing application-oriented algorithms [30; 31; 39; 40; 41; 42; 43; 44], RSC performs better in the event-based and time-critical applications. The scenarios and the number of rounds used for evaluating the performance of RSC was inherited from fan-shaped clustering and the only difference is both the algorithms were implemented and evaluated for different statistical measures [35; 36; 37; 38; 39; 40; 41]. Since RSC and FSC were evaluated with same set of scenarios, the improvement in percentage may be addressed stochastically. But the improvement varies according to the parameter selected for comparison. Here, the number of nodes alive are 16% more in RSC and from the energy conservation point of view, RSC produces a 34% improvement, whereas, RSC produces 45% improvement in statistical measures mean and median. All the above results are obtained when compared with FSC. Hence, the results were not addressed stochastically. In future, this might be addressed with various quadrants.
## 4 The Cooperative Optimization Algorithm
### Methodology
Finally, the SMC efficiently stores and manages information on all candidate solutions that are relevant for more than one of the above components and, particularly, also the location scenario evaluations provided by the users so far, as well as the solutions \(X_{\text{OC}}\) returned by the OC. The whole process is repeated until some termination criterion is reached, e.g., the discrepancy of user feedback and the results of the EC is small enough or a maximum number of user interactions has been reached. In the end, COA returns a solution with the highest surrogate objective value of all of the so far generated solutions. Figure 3 and Algorithm 1 give a summary of the whole COA procedure and of the main tasks of each of the components of COA. Figure 3: Components of COA and their interaction. The framework consists of the feedback component (FC), the evaluation component (EC), the optimization component (OC), and the solution management component (SMC). Users interact with the framework via the FC. In the next subsections, we describe each component's functionality in more detail. ```
Input:an instance of the GSPDP Output: best solution \(\hat{X}^{*}\subseteq V\) found
1 Feedback Component: for\(u\in U\)do
2 obtain use cases \(C_{u}\) with associated demands \(D_{u,c}\) and service point requirements \(R_{u,c}\), \(\forall c\in C_{u}\), from user \(u\);
3 endfor
4 whileno termination criterion satisfieddo
5 Feedback Component: for each scenario generation strategy \(\mathcal{S}_{r}\)do
6\(R^{\prime}\leftarrow\) random sample of \(R\);
7 for\(r\in R^{\prime}\)do
8\(S_{r}\leftarrow\) generate set of location scenarios according to strategy \(\mathcal{S}_{r}\);
9 present scenario to the corresponding user;
10 update SMC with ratings obtained from \(\mathcal{S}_{r}\);
11 endfor
12
13 endfor
14
15 Evaluation Component: train surrogate model \(\hat{w}_{\Theta}\), yielding updated surrogate obj. function \(\hat{f}_{\Theta}\);
16 re-evaluate all solutions stored in the SMC with new \(\hat{f}_{\Theta}\);
17
18 Optimization Component: \(X_{\text{OC}}\leftarrow\) generate optimal solution w.r.t. the EC's \(\hat{f}_{\Theta}\);
19 update SMC with \(X_{\text{OC}}\);
20 endwhile
21 return overall best found solution \(\hat{X}^{*}\);
```  
**Algorithm 1**Basic Framework
# Evaluation of the Urban Weather Generator on the City of Toulouse (France)
## 4 Discussion  
As mentioned in previous sections, UWG enables simulations of urban air temperatures from a set of input parameters associated with atmospheric, urban surface occupations, and rural climatic conditions. In Section 3, we have evaluated the influence of the rural station, the sensitivity of UWG with respect to its main input parameters, and its ability to retrieve reliable air temperatures. All measured data used for comparison purposes are issued from the Carnes weather station, located in a densely built environment in the center of Toulouse, France. As already observed in [58], UWG performs better in densely urbanized areas. Before discussing the results presented in Section 3, it is important to recall that the observations and conclusions made in this study have to be interpreted considering that we only modified a limited number of parameters for the initialization of UWG. A finer tuning of the parameters, for example in the building description files or the code itself, would probably lead to different results. The idea was to use UWG as a basic user, modifying only accessible parameters with available input data such as in operational use.
# Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods
## 4 Application
### Component Selection Validation  
Given the simple features of the empty pockets in the component box, and the slow and uncomfortable procedure for validation due to the hands of the user interfering with the visuals of the components, the internal hand-tracking capabilities of the HoloLens 2 were used to solve the issue for component selection. The HoloLens 2 tracks the hand movements of the user and estimates the position of hand and finger joints. The application tracks index finger positions using MRTK, a Microsoft-driven project providing components and features for cross-platform MR application development in Unity. Figure 9 demonstrates the tracking of the index finger for component selection.  
To validate which component has been selected, the estimated index finger position is compared to the position of component GameObjects in the component box, each of which has a bounding box. According to how people are expected to place the index finger when picking up a component, a counter computes the number of times the index finger intersects the bounding box. To proceed with validation, the app estimates that the selected component is the one with the highest count. If then the \"Component Selection Manager\" validates the correct component, it tells the \"Master Step Manager\" to go to the placement sub-step; if an incorrect component selection is detected, graphics and audio inform the user of the incorrect selection.
# Query Rewriting for Incremental Continuous Query Evaluation in HIFUN
## 1 Introduction  
Data emanating from high-speed streams are prevalent everywhere in today's data eco-system. Examples of data streams include IoT data [1], data series [2], network traffic data [3], financial tickers [4], health care transactions [5, 6], the Linked Open Cloud [7, 8] and so on. In order to extract knowledge and find useful patterns [9, 10], these data need to be rapidly analyzed and processed. However, this is a challenge, as new data batches arrive continuously at high speed, and efficient data processing algorithms are needed.  
The research community has already provided open-source and commercial distributed batch processing systems such as Hadoop [11], MapReduce [12] and Oracle Cloud Infrastructure [13], that allow query processing over static and historical datasets, enabling scalable parallel analytics. However, even with those technologies, processing and analyzing large volumes of data is not efficient enough. This is true especially in scenarios that need rapid responses to change over continuous (big) data streams [14]. Consequently, stream processing has gained significant attention. Several streaming engines, including Spark Streaming [15], Spark Structured Streaming [16], Storm [17], Flink [18] and Google Data Flow [19], have been developed to that purpose.  
Current research mainly focuses data stream management systems processing continuous queries over traditional data streams [20], or massive amounts of spatio-temporal data that are increasingly being collected and stored. Most of the existing methodologies have been devised to store all the incoming data on distributed storage [21], and others [22, 23] use complex indexing techniques to increase the performance from the data access point of view. However, such approaches are untenable in real world problems, and continuous query processing techniques have been developed to overcome this limitation by utilizingthe storage space to produce results in an incremental fashion rather than aiming to process all the available data [24; 25].  
Continuous query processing is a major challenge in a streaming context. A continuous query is a query which is evaluated automatically and periodically over a dataset that changes over time [26], and allows the user to retrieve new results from a dataset without the need to issue the same query repeatedly. The results of continuous queries are usually fed to dashboards, in large enterprises, to provide support in the decision-making process [27]. In addition, besides their ever increasing volume, datasets change frequently, and as such, results to continuous queries have to be updated at short intervals.