esa-sceva/satcom-chunk-collection huggingface dataset:
# LoRaWAN Networking in Mobile Scenarios Using a WiFi Mesh of UAV Gateways
## 5 Simulation Results
### Study of the Impact of the MP Algorithm  
The MP algorithm represents the last attempt of the system to recover a connection between the aerial mesh and the GNs that went out of range. For this reason, the PRR is used as the main evaluation metric. In order to increase the triggering chances of the MP algorithm, the firefighters mobility has been extended to support the splitting of teams. With this modification, a team can be configured to split in half during the simulation, so that an UAV that is covering alone a team of GNs is forced to create at least one hologram when half of the team eventually goes out of range. Given this modification, it is of particular interest to observe the behavior of the MP algorithm in two situations: a more conventional setting, where team splitting is not active, and in a more challenging setting where teams can split. The end-to-end PRR is collected individually for all the 100 runs of the same scenario and a direct comparison between corresponding runs of different scenarios is performed. The results of the comparison are reported in Table 3, considering three teams and 2500 m of area side and 3000 s of simulation time. The first observation is that the MP algorithm is not always beneficial to the system. In fact, a relevant number of runs shows a PRR that is, on average, even 7% lower  
Figure 6: AE-PRR with VSF (**A**), VSF+CRM (**B**) and VSF+CRM+MP (**C**) (no team splits) in scenario with the same parameters of Section 5.1.  
than the one achieved without the MP algorithm. The loss is more marked in scenarios in which teams do not split. This is caused by the fact that the strategy of recovering the connectivity with lost GNs is not always beneficial to the UAV formation since it does not follow any global optimality criteria. However, the simultaneous activation of the MP and CRM algorithms is beneficial in most of the situations. In fact, the average PRR undergoes a considerable increment in at least 66 % of the runs when teams do not split and 71 % of the runs when teams split. In some cases, the gain that is obtained is more than double of the loss observed in analogous scenarios without MP algorithm. When reverting to the default simulation parameters, the activation of the MP algorithm and of the CRM algorithm produces the results shown in Figure 6C. Even if small, the improvements allow to achieve AE-PRR levels above 0.9 with 12 UAVs and between 0.8 and 0.9 with 8 or 10 UAVs. Similar tests have been run for the VSF+MP configuration with no noticeable advantages.
## Case Study
### Equivalence of Initial Support Parameters  
In order to facilitate the numerical simulation, the design parameters of the tunnel's initial support structure need to be equivalent, as follows:  
(1) The spacing of the grille steel frame is 0.6 m, and the initial support thickness is 0.25 m. Take the initial support of a rectangle with width \(b=1.8\) m and height \(h=0.25\) m as the model. The stiffness of the model is calculated by the equivalent stiffness method.  
The equivalent concrete elastic modulus of the shotcrete supported by the grillage steel frame \(E_{1}^{\prime}\) can be calculated by Equation (20).  
\[E_{1}^{\prime}I_{1}=E_{1}I_{1}+E_{2}{\sum{I_{2}}} \tag{20}\]  
where \(E_{1}\) is the elastic modulus of the concrete; \(I_{1}\) is the moment of concrete inertia for the calculation model; \(E_{2}\) is elastic modulus of the steel bar; \(I_{2}\) is the moment of inertia of the virtual axis of each bar in the grille steel frame, denoted by Equation (21). In the study, \(E_{1}=23\) GPa, \(E_{2}=210\) GPa.  
\[I_{2}=\frac{\pi d^{4}}{64}+Ab_{1}^{2} \tag{21}\]  
where \(d\) is the circle diameter of the section of steel bar; \(A\) is the circle area of the bar section; \(b_{1}=\) the half of the thickness of the initial support structure minus the thickness of the protective layer and a steel bar radius.  
In this illustrative example, \(E_{1}^{\prime}=26.311\) GPa, obtained by Equation (20).  
Figure 5: Collapse of tunnel primary support structure.  
(2) The reinforcement effect of grouting a rock bolt on surrounding rock can be equivalent to improving the cohesion of surrounding rock, which may be obtained by Equation (22).  
\[c_{1}=c_{0}+\eta\frac{\tau_{\rm s}S_{\rm s}}{ab} \tag{22}\]  
where \(c_{0}\), \(c_{1}\) are, respectively, the cohesion of surrounding rock before and after reinforcement; \(\tau_{\rm s}=\frac{c_{\rm th}}{\sqrt{3}}\), \(\sigma_{\rm bs}\) is the yield strength of the rock bolt; \(S_{\rm s}\) is the cross-sectional area of the rock bolt; \(a\) and \(b\) are, respectively, longitudinal spacing and transverse spacing; \(\eta\) is an experience coefficient. In this paper, \(\eta=0.963\); \(\sigma_{\rm s}=335\) MPa; \(a=0.6\) m; \(b=1.2\) m.  
In this illustrative example, \(c_{1}=c_{0}+127.05\) kPa, obtained by Equation (22).
## 2 Experimental Program, Materials, Mix Designs, Mix Preparation, and Test Methods
### Mixing, Dispersion of MWCNT/rGO, Casting, and Curing of Specimens  
The powdered reagent components were first mixed thoroughly and then added to the rigorously blended source materials (and MgO, if needed). The complete binder system was then dry mixed for about 5 min in a shear mixer before two-thirds of the required water was gradually added to the mix while mixing continued for 3-4 min. Then, HRWRA mixed with the remaining amount of water was gradually added for 4-5 min to make a flowable paste. After that, silica sand was added slowly for 3-4 min as per the proportions given in Table 2, then PVA fibers were added slowly for 3-4 min and mixed for an additional one minute to make control and MgO AAECs.  
For AAEC mixes with MWCNT and rGO, two-thirds water and 50% HRWRA were mixed for 1-2 min by hand stirring in the beaker, after which MWCNT/rGO was added and hand stirred for 2-3 min. The beaker was then placed inside the sonicator, a probe was inserted into the beaker, and sonication was performed for 30-40 min. The amount of sonication energy was 50 J/mL-75 J/mL to effectively disperse MWCNT/rGO to the water [35; 38; 69]. The reason behind using this range of sonication energy is the shortening effect in MWCNTs for energy amounts over 75 J/mL [65]. [PERSON] et al. [70] reported that a lower sonication energy with a longer sonication time provides better conditions than a higher sonication energy with a shorter sonication time in terms of preparing MWCNTs functionalized with carboxylic groups. However, the optimal level of sonication energy for dispersion is related to the CNT concentration. Still, when the concentration reaches a certain level, it cannot be well dispersed, requiring different technologies with different degrees of dispersion and a different optimal sonication energy. The mixture of sonicated MWCNT/rGO with water and HRWRA was then slowly added into the already-made dry mix (as explained previously in control mix preparation) for 4-5 min while mixing continued in shear mixer, after which silica sand was gradually added to prepare MWCNT/rGO-incorporated AAECs. Figure 3 shows MWCNTs before, during, and after sonication and the sonication device. The use of the dry-mixing method allowed us to avoid the handling of solution-based corrosive alkaline reagents and used less powder-based reagent (0.35 of total binder content) than the solution-based one [4; 60; 61]. Thus, it demonstrated its enhanced sustainability, user-friendliness, and commercial viability in developing AAECs on a large scale compared to the conventional two-part method.  
The total mixing procedure lasted about 20-25 min. At least 15 cube specimens with dimensions of 50 mm \(\times\) 50 mm \(\times\) 50 mm were prepared for each AAEC mix. The molds/specimens were kept in the curing room at 23 \(\pm\) 3 \({}^{\circ}\)C and 95 \(\pm\) 5% relative humidity (RH). The molds were de-molded after 24 h of casting and were kept in the curing chamber until the day of testing.
# Data Spaces in Manufacturing and Supply Chains: A Review and Insights from European Initiatives
## 2 Materials and Methods
### Content Analysis of Scientific Contributions  
A qualitative content analysis was performed on the selected publications to assess the conceptual development of data spaces in manufacturing and supply chains. Each study was systematically reviewed and categorized according to several analytical dimensions, including the following:  
* **Industrial domain:** to identify which sectors (e.g., automotive, energy, food) are leading or emerging in the adoption of data spaces and to assess cross-sectoral applicability.
* **Operational objectives:** to determine the operational focus of data space use (e.g., cost efficiency, sustainability, resilience).
* **Type of integration:** to distinguish between vertical integration (across supply chain titers) and horizontal integration (among peer organizations), which reflect different collaboration models and data-sharing structures.
* **Integrated technologies:** to capture the technological ecosystems (e.g., IoT, digital twins, AI) that integrate with data space functionality and enable data-driven applications.
* **Types of shared data:** to analyze what categories of data (e.g., production metrics, maintenance logs, environmental indicators) are most commonly exchanged, indicating the functional scope of data spaces.
* **Use of standards and architectures:** to examine adherence to recognized frameworks (e.g., International Data Space (IDS), Gaia-X), which is essential for interoperability, scalability, and compliance with governance models.  
To execute this analysis, the lead author conducted a full-text reading of each publication, annotating passages that matched the predefined dimensions and recording analytical memos to refine category definitions. Periodic discussions with co-authors ensured coding decisions were transparent and consistent, resolving any ambiguities by consensus. This structured yet adaptable procedure yielded a systematic, reproducible classification of the literature, which underpins the thematic synthesis presented in Section 3.2. The goal was to identify dominant application patterns, variations in technical implementation, and recurring gaps in documentation.
## 3 Results and Discussion  
### Soil-Plant Metal Concentration and Its Biomedical Safety  
The bio-concentration of Pb, Zn, Cu, and Cd with correction for dilution factors in soil and _X. strumarium_ plant samples is presented in Figure 2A-D. To validate the concentration level at the spatial domain, we analyzed the samples at 15 abandoned habitats. Pb and Zn concentrations above the permissible level, i.e., Pb = 5 mg/kg and Zn 60 mg/kg for soil, Pb = 8.15 mg/kg, and Zn = 44.19 mg/kg for plants in eight locations. Conversely, Cd andCu concentration were above threshold (SPI \(\geq\) 1) i.e., Cd = 0.1 mg/kg and Cu 10 mg/kg for soil, Cd = 0.31 mg/kg and Cu = 8.39 mg/kg for plants in all the abandoned sites except TBM, GCD, AMS, and CBS sites (Figure 1) indicating metals contamination at a broad spatial area covering parts of the Malakand, Hazara, and Peshawar Divisions in Khyber Pakhtunkhwa (formerly Northwest Frontier Province) of Pakistan (Figure 1). The substantially higher magnitudes of Pb and Cd in plant tissues except those in TFN, TBM, GCD, AMS, and CBS sites revealed that these plants are not an ideal choice for medicinal, phytochemical, or veterinary (forage) purposes, but can efficiently be utilized as phytoremediator. Cu and Zn concentrations were below the threshold in plant tissues in all the abandoned habitats and hence secured if subsequently used for biotechnological aspects. Concerning Pb, alkyl-lead compounds as anti-knock agents in fuel are the primary source of elevated levels in soils [39], or its pollution might be caused by vehicle wheels imbalance weights [40] The high Cd concentrations in soil and _X. strumarium_ might originate from the wear and tear of old tires due to surface friction [15; 41]. Likewise, oil spills, exhaust fumes, corrosion of various vehicle components, wearing of engine parts, and brake lines enhanced surplus Zn quantity along roads [42]. These abandoned habitats may have one or more sources adding heavy metals to the soil and translocating successively into plant tissues.
# A Cooperative Transmission Scheme in Radio Frequency Energy-Harvesting WBANs
## 2 Related Works
In RF energy harvesting-based WBANs, nodes must harvest energy first before transmitting data, making them highly constrained by available energy and having a higher demand for energy efficiency compared with battery-powered WBANs. Furthermore, different nodes exhibit varying energy demands and energy harvesting efficiencies; strategies must be formulated based on the differences in available energy among nodes and the characteristics of network topology when selecting relay nodes to optimize the collaborative transmission efficiency. Most protocols deployed dedicated relay nodes between sensors and the coordinator. A transmission power-chosen scheme which considers the energy harvesting efficiency and usage requirements is proposed in [27]; energy efficiency can be effectively improved in a one-hop star network. However, the proposed scheme does not consider collaborative transmission among nodes. Considering the requirement to maximize the spectral efficiency, transmission optimization methods are designed in [28] considering both single-hop and dual-hop scenarios; dedicated relays are needed in the dual-hop network. To improve the network performances of a network which contains only one source and one destination with multiple relays, a relay selection scheme is proposed in [29]; the proper relay can be chosen based to the status of the relays. A WBAN consisting of n sensors, a destination and a relay node, is considered in [30]; weights of sensors are considered when designing the network protocol. In these networks, dedicated relays need to be deployed to improve network performance, which increases the size and complexity of the network. Table 1 presents a summary of the related works.
# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 5 Data-Driven Analysis  
In this section, we examine the impact of rainfall on the pre-processed mobile signal metrics. Specifically, we explore the variations in signal measurements during dry periods, analyzing both small- and large-scale fluctuations, as detailed in Section 5.1. Following this, Section 5.2 analyzes the impact of rainfall on LTE signal indicators, focusing on standard deviation and frequency.
# Robust Simulation of Cyber-Physical Systems for Environmental Monitoring on Construction Sites
## 6 Results and Discussion
### Sensory Data Fusion  
The experimental data of the robustness of sensor data fusion comes from the TSP values of three monitoring points at seven times (0:01, 0:02,..., -0:07), on 24 November 2020.  
The seven sets of data are processed according to the arithmetic mean and belief function method. Figure 10 shows the fluctuation of the monitoring data after the belief function fusion is reduced, which is also verified by the sample standard deviation. The sample standard deviation of the data fusion of the arithmetic mean is 3.49, while the sample standard deviation of the belief function method fusion is 2.78, which is 20.34% lower than that of the arithmetic mean method. This indicates that the input stability of the monitoring data should be improved.  
In addition, a comparative analysis of the anti-interference performance of the two fusion algorithms is conducted. The first data in the 7 th group of measured data is changed to 47.12, while the other data remain unchanged. The fusion results obtained using the arithmetic mean method vary from 51.45 to 50.78, with a decrease rate of 1.29%. Consequently, the fusion results based on the belief function method vary from 52.16 to 52.50, with a decrease rate reduced to 0.65%. The random experiments using the seven sets of data demonstrate that, when large interfering data exist in a set of data, the belief function can identify the interfering data at a desirable level, which improves the robustness of the output data.  
Figure 10: Data-fusion results obtained in two ways.  
Figure 9: Anomaly recognition effect after adjusting the constant-value detection threshold.
## 3 Results and Discussion
### Antimicrobial Activity of the Films  
The antimicrobial efficacy tests demonstrated that incorporating a lipid-rich extract from _Nannochloropsis_ sp. and bioactive-rich extracts from _Nannochloropsis_ sp. and _Limnospira_ sp. significantly reduced the growth of the Gram-positive bacterium _Listeria monocytogenes_ compared to the control film (film A). In contrast, the inclusion of microalgal extracts did not affect the growth of the Gram-negative bacterium _Salmonella enterica_ serovar Enteritidis, with bacterial proliferation remaining comparable to that of the control film composed of 3% alginate and 1% zein (Figure 6). These results are consistent with those reported by [PERSON] et al. [34] for alginate films combined with either protein-rich, polysaccharide-rich, or bioactive-rich extracts from _Lobosphaera_ sp.  
[PERSON] et al. [38] suggested that the antibacterial efficacy of extracts embedded in alginate films might be reduced due to potential chemical interactions between the hydroxyl groups of the alginate matrix and the phenolic compounds in the extracts. Such interactions may block the active sites of the bioactives, thereby inhibiting their antimicrobial effect, particularly against Gram-negative bacteria. Similarly, [PERSON] et al. [65] observed that Gram-negative bacteria are generally less susceptible to inhibition than Gram-positive bacteria, attributing this to the hydrophilic nature of the lipopolysaccharide-rich outer membrane, which hinders the diffusion of hydrophobic compounds into the cell. These authors further noted that a 70% hydroethanolic extract of spirulina exhibited greater antimicrobial activity than isolated phycocyanin, likely due to the phenolic compounds in spirulina disrupting bacterial cell membranes and inhibiting growth.  
[PERSON] et al. [66] emphasized that antibiotics and antimicrobial compounds are generally less effective against Gram-negative bacteria because their complex, multilayered cell walls act as a barrier to penetration. These collective findings help to explain the lack of inhibitory effect observed in the present study against _S. enterica_ serovar Enteritidis (Gram-negative) in films containing microalgal extracts (Figure 6). Conversely, the observed inhibition of _L. monocytogenes_ may be attributable to the presence of lipids in the extract, such as eicosapentaenoic acid, known for its antimicrobial properties [45].  
Figure 6: Growth curves of _Listeria monocytogenes_ (**i**) and _Salmonella enterica_ serovar Enteritidis (**ii**) in contact with films A (control, blue line) and B (orange line).
# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 4 Experiment
### The Effectiveness of the Scan Interaction Structure  
In order to verify the effectiveness of scan interaction structure (SIS) in alleviating the impact of sparse data and extracting feature interaction information, we design control experiments based on the above experimental preparation and work: select the DCN model specifically for the sparse data environment, and use the original features and the features output through single-layer SIS to predict RT and TP. We perform a total of 10 replicates on each different piece of sparse data. The average of all experimental results is used as the final prediction. The experimental results are shown in Tables 4 and 5.  
From the above results, it can be clearly seen that when the scanning interaction structure is fused, the prediction performance of the DCN model for RT and TP is greatly improved when the data are more sparse. In addition, carefully looking at Figure 6, it can be found that the original DCN model will show higher error with the higher data sparsity, and its rising slope is larger. When the DCN model fuses the scanning interaction structure, although there is still a rising error, the rising slope is obviously stable. The above two phenomena strongly illustrate that the SIS structure has an obvious mitigation effect on alleviating the impact of sparse data. And the SIS structure can significantly improve the prediction performance of the model for QoS values.
###### Abstract  
In amyloid brain PET, after parcellation using the finite element method (FEM)-based algorithm FreeSurfer and voxel-based algorithm PMOD, SUVr examples can be extracted and compared. This study presents the classification SUVr threshold in PET images of F-18 florbetaben (FBB), F-18 flutmetematol (FMM), and F-18 florapronol (FPN) and compares and analyzes the classification performance according to computational algorithm in each brain region. PET images were co-registered after the generated MRI was registered with standard template information. Using MATLAB script, SUVr was calculated using the built-in parcellation number labeled in the brain region. PMOD and FreeSurfer with different algorithms were used to load the PET image, and after registration in MRI, it was normalized to the MRI template. The volume and SUVr of the individual gray matter space region were calculated using an automated anatomical labeling atlas. The SUVr values of eight regions of the frontal cortex (FC), lateral temporal cortex (LTC), mesial temporal cortex (MTC), parietal cortex (PC), occipital cortex (OC), anterior and posterior cingulate cortex (GCA, GCP), and composite were calculated. After calculating the correlation of SUVr using the FreeSurfer and PMOD algorithms and calculating the AUC for amyloid-positive/negative subjects, the classification ability was calculated, and the SVUr threshold was calculated using the Youden index. The correlation coefficients of FreeSurfer and PMOD SUVr calculations of the eight regions of the brain cortex were FBB (0.95), FMM (0.94), and FPN (0.91). The SUVr threshold was SUVr(LTC,min) = 1.264 and SUVr(THA,max) = 1.725 when calculated using FPN-FreeSurfer, and SUUr(MTC,min) = 1.093 and SUVr(MCT,max) = 1.564 when calculated using FPN-PMOD. The AUC comparison showed that there was no statistically significant difference (\(p>0.05\)) in the SUVr classification results using the three radiopharmaceuticals, specifically for the LTC and OC regions in the PMOD analysis, and the LTC and PC regions in the FreeSurfer analysis. The SUVr calculation using PMOD (voxel-based algorithm) has a strong correlation with the calculation using FreeSurfer (FEM-based algorithm); therefore, they complement each other. Quantitative classification analysis with high accuracy is possible using the suggested SUVr threshold. The SUVr classification performance was good in the order of FMM, FBB, and FPN, and showed a good classification performance in the LTC region regardless of the type of radiotracer and analysis algorithm.  
**Keywords:** amyloid PET; Alzheimer's disease; finite element method (FEM)-based algorithm; voxel-based algorithm