esa-sceva/satcom-chunk-collection huggingface dataset:
# YOLO-DFAN: Effective High-Altitude Safety Belt Detection Network
## 2 Related Work
### Object Detection  
As one of the core problems in computer vision [19], object detection has been challenging due to the different appearances, shapes, and poses of various objects and the interference of factors such as illumination and occlusion during imaging. In recent years, the rapid development of deep learning has caused object detection algorithms to gradually shift from traditional algorithms based on manual features to detection algorithms based on deep neural networks [20].  
Deep-learning-based object detection algorithms are primarily divided into two categories: one- and two-stage. The common two-stage object detection algorithms [21], such as the R-CNN series, are predominantly divided into two steps: First, region generation is performed to generate a preselected box (i.e., region proposal) that may contain objects. Then, the classification is performed using CNN samples. Some of the more common one-stage object detection algorithms [22], such as YOLO and SSD, do not need the region proposal stage to generate the class probability and coordinate location values of the object directly. The final detection results can be directly obtained after a single detection.  
Deep learning networks and CNNs are constantly evolving in object detection, which involves the issue of the number of parameters and the computational effort when designing CNNs. Lightweight networks are created, aiming to reduce the number of parameters and the model complexity while maintaining accuracy as much as possible. Common algorithms--such as YOLOv3-tiny, YOLOv4-tiny, and SSD-tiny--are implemented by introducing lightweight feature extraction networks to replace the backbone in the baseline network. The common deep learning detection algorithms have difficulty in extracting the features of high-altitude safety belts because they are objects with a low effective pixel ratio. Therefore, we introduced the attention mechanism to help the detection network extract their features.
## 3 Results
### Relationships among the PK Variables, under Different Absorption Kinetic Conditions
#### 3.2.1 Development of a Population Pharmacokine Model for Donepezil  
The initial step in completing this assignment was to create a PK model that characterizes the kinetics of donepezil. A NLME analysis was performed to the real C-t data, and a population PK model for donepezil was developed. A two-compartment model with first-order absorption and elimination kinetics best characterized the kinetics of donepezil (Table 2). The residual variability was estimated using a combined error model. The mean value of donepezil's first-order absorption rate constant was 0.18 h\({}^{-1}\), the mean apparent clearance was 14,466.77 mL/h, and the mean apparent intercompartmental clearance was 80,120.11 mL/h. Furthermore, the mean apparent volume of distribution of the central compartment was 44,386.59 mL, while it was 905,886.42 mL for the peripheral compartment. In addition, the between-subject variability estimates were reasonable for all parameters, and the highest estimate of the percent relative standard error did not exceed the value of 22.5%.  
The good descriptive and predictive ability of the model is depicted in Figure 4, where the visual prediction check plots (Figure 4a) are shown alongside the individual predicted versus observed concentration values (Figure 4b). The visual prediction check (Figure 4a) demonstrates that, despite the high heterogeneity of the C-t data, the created model's prediction interval contains the experimental concentration data in all circumstances. In Figure 4b, the individual predicted vs. observed concentration values are linearly correlated, and almost every observation is included within the 90% prediction interval (the outliers' proportion was 2.13%). Thus, a robust population PK model was constructed, suitable for the needs of this study.  
Figure 3: Variable importance scores for the feature parameters (AUC, lambda, Tmax) of the actual bioequivalence study. Three random forest models were developed each one referring to Cmax (**a**), Cmax/AUC (**b**), and Cmax/Tmax (**c**).
## Appendix B Formal Description of ONRCRA  
More details on the Label Setting method [17] are given in this Appendix.  
Given a directed graph which has costs associated to nodes and weights associated to arcs, the objective of the algorithm is to find the path with minimum cost from source node to destination node, with the constraint of not exceeding a maximum value of weight \(W\).  
The algorithm uses in each node a set of label, each one identifying a path from source node to that node and consisting of a pair of numbers representing the cost and the weight of the corresponding path.  
If \(I_{i}\) represents the index set of labels on node \(i\), for each label \(k\in I_{i}\) there is a corresponding path \(P_{i}^{k}\) identified by the label \((W_{i}^{k},C_{i}^{k})\). For path \(P_{i}^{k}=(j_{0},j_{1},\ldots,j_{n_{i}^{k}=1})\) weight and cost can be easily calculated by \(W_{i}^{k}=w(P_{i}^{k})=\sum_{m=1}^{n_{i}^{k}-1}w_{j_{m-1}j_{m}}\) and \(C_{i}^{k}=c(P_{i}^{k})=\sum_{m=1}^{n_{i}^{k}-1}c_{j_{m-1}j_{m}}\) where \(w_{j_{m-1}j_{m}}\) and \(c_{j_{m-1}j_{m}}\) are the weight and the cost of the edge \((j_{m-1},j_{m})\) respectively. Starting with a label only on the first node, at each iterations the algorithm extends all the labels extending the corresponding path along all outgoing arcs. If \(\delta^{+}(i)\) is the set of outgoing arcs from node \(i\), considering each arc \((i,j)\in\delta^{+}(i)\), a label \((W_{i}^{k}+w_{ij},C_{i}^{k}+c_{ij})\) is saved on node \(j\) only if \(W_{j}^{k}=W_{i}^{k}+w_{ij}\leq W\), and for each existing label \(l\) on node \(j\) is verified that \(W_{j}^{k}\leq W_{i}^{l}\) and \(C_{j}^{k}\leq C_{i}^{l}\) (we will refer to this last property saying that a label added to a node is _efficient_).  
When all the efficient labels have been generated, the algorithm stops and the solution path is the one idenfied by the label on destination node having minimal cost.  
The pseudo-code of the algorithm is given in Algorithm 1 where \(N\) is the set of nodes of the graph, \(L_{i}\) denotes the set of labels in node \(i\) and \(T_{i}\subseteq I_{i}\) demote the index set of the labels on node \(i\) which have been handled.  
```
1:Step 0: Initialization
2: Set \(L_{s}=\{(0,0)\}\) and \(L_{i}=\emptyset\) for each \(i\in N\setminus\{s\}\).
3: Initialize \(I_{i}\) accordingly for each \(i\in N\).
4: Set \(T_{i}=\emptyset\) for all \(i\in V\setminus\{s\}\).
5:Step 1: Selection of the label to be handled
6:if\(\cup_{i\in N}(I_{i}\setminus T_{i}=\emptyset)\)then
7: STOP: all efficient labels have been generated
8:else choose \(i\in N\) and \(k\in I_{i}\setminus T_{i}\) so that \(W_{i}^{k}\) is minimal.
9:endif
10:Step 2: Handling of label \((W_{i}^{k},C_{i}^{k})\)
11:for all \((i,j)\in\delta^{+}(i)\) with \(W_{i}^{k}+w_{ij}\leq W\)do
12:if\((W_{i}^{k}+w_{ij},C_{i}^{k}+c_{ij})\) is not dominated by \((W_{j}^{l},C_{j}^{l})\) for any \(l\in I_{j}\)then
13: Set \(L_{j}=L_{j}\cup(W_{i}^{k}+w_{ij},C_{i}^{k}+c_{ij})\).
14: Update \(I_{j}\) accordingly.
15: Set \(T_{i}:=T_{i}\cup k\).
16:endif
17:endfor
18:go to Step 1.
```  
**Algorithm 1** The Label Setting Algorithm
## 2 Materials and Methods
### LC/Q-TOF-HRMS Analysis  
The crude extract of the _S. aspera_ aerial part was dissolved in methanol (LC-MS grade, Fischer Scientific, Madrid, Spain) (70% \(\
icefrac{{\gamma}}{{\upsilon}}\)). Standard solutions of rutin trihydrate, catechin, luteolin and luteolin glucoside, were similarly prepared. Reference compounds were all purchased from Extrasynthese (Genay, France). All the tested solutions were prepared on the day of the analysis.  
LC/Q-TOF/HRMS analysis was performed on an HPLC system (Agilent Series 1260-Agilent Technologies, Santa Clara, CA, USA) coupled to a 6530 Q-TOF mass spectrometer (Agilent Technologies, Singapore). The HPLC system consists of a degasser, autosampler, quaternary pump, diode array detector and a thermostatically controlled column oven. Chromatographic separation was performed at 40 \({}^{\circ}\)C on a Zorbax Extend-C18 Rapid Resolution HT 2.1 mm \(\times\) 50 mm, 1.8 \(\upmu\)m reversed-phase column. The solvent system consisted of water LC-MS (solvent A) and acetonitrile LC-MS (solvent B) both acidified with 0.1% formic acid. The elution program was as follows: 5-95% solvent B from 0 to 33 min and maintained at 95% up to 38 min. Experimental conditions were adjusted as in the study of [13]. The extract was analyzed under the positive and negative ionization modes. The parameters set for the Q-TOF mass analysis follow those described in our previous analysis [13]. CID-ms/ms spectra were recorded on the auto MS/MS mode. The mass range was set to 50-1000 and the collision energy was set at 40 V. The results were analyzed using the Agilent MassHunter Workstation software LC-MS Data Acquisition for 6530 series Q-TOF.  
Molecular formulas were automatically generated using the 'Generate formulas' of the Agilent Masshunter Qualitative Analysis. The identification of the compounds was based on the available standard references for rutin trihydrate, luteolin, catechin and luteolin glucoside and data from the available literature [16; 17]. Accurate mass \(\leq\) 5 ppm was used to generate molecular formulas.  
Quantification of Flavonoids by LC/Q-TOF-HRMS  
Identified flavonoids of _S. aspera_ leaves and stems were quantified with the single-point external standard method [18]. Standard solutions of known concentration were analyzed as aforementioned. Peak areas were recorded and the respective response factor was calculated according to the following formula:  
Response factor = (peak area of the standard solution)/(concentration of the standard solution)  
Afterward, the analytes sample of unknown concentration was treated under the same conditions and the peak area was also recorded. Then the amount of each analyte was calculated using the following equation:  
Amount of analyte = (peak area of the analyte)/(response factor)  
Analyte quantification when no standard solution was available, was performed by using the standard solutions of similar molecular structures.
# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
## 5 Real-World Evaluation  
The results presented in this section use a deployment of the iSense hardware platform (v2, Coalescences, Lubeck, Germany). The iSense platform provides an IEEE 802.15.4 compliant radio, a 32-bit RISC controller running at 16 MHz, 96 kbytes of memory, a highly accurate clock and a switchable power regulator. A total of 46 iSense nodes were deployed in an indoor environment. In all experiments the transmission power of the 802.15.4 radio was set at \(-6\) dB to enforce one-hop exchanges in room resolution. Data samples were collected every one second via a USB connectivity attached to each node. In this way, debugging was encoded out-of-band and it did not affect the experiments.  
Due to space limitations, we report the results from the UNIGE experiment here. Similar results hold for the other testbeds.
# A Review of Properties of Nanocellulose, Its Synthesis, and Potential in Biomedical Applications
## 2 Classification and Synthesis of Nanocellulose  
### Cellulose Nanocrystal (CNC)  
Acid hydrolysis using mineral acids such as phosphoric, sulfuric, or hydrochloric acid and methods such as enzymatic procedures are commonly known for isolating CNC [38; 39; 40; 41; 42]. The amorphous region of cellulose fibers is cleaved during hydrolysis, resulting in a rigid,  
Figure 1: Schematic representation of the classification, modifications, printing methods, and biomedical applications of nanocellulose and its derivatives.  
highly crystalline, and rod-like nanostructure. During the acid hydrolysis process, the incorporation of acid leads the charge on the surface of CNC, allowing colloidal dispersion to form quickly [40]. Based on the source of cellulose, the crystalline percentage can vary from 50-90%, and the elastic moduli have been determined to be between \(\sim\)105\(-\)168 GPa [43; 44]. The longest type of CNC can be obtained from tunicates, having a length of 100-300 nm and 5-50 nm in diameter [40]. CNC shows a shear-thinning property and functions like liquid crystals, creating asymmetrical nematic phases at specific concentrations [45]. When CNC is introduced into polymer materials, hydrogen bond networks emerge, allowing for stress transfer, which is highly intriguing in additive manufacturing [46].
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 3 Scenarios for 5G Network and Its Requirements
### Wireless Regional Area Networks (WRAN)  
WRAN is the last setup thought for 5G network. The main objective of this scenario is to provide new application related to remote areas which are lightly populated. The main requirement of this scenario is long-distance communications with cells, which are more significant than 50 km in diameter.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 5 Comparative Analysis of Grounding Performance  
This section presents a comparative analysis of grounding performance in low-voltage (400 V), medium-voltage (10 kV), and high-voltage (35 kV) systems. Key factors such as fault current dissipation, grounding resistance, ground potential rise (GPR), step/touch voltage, and mitigation techniques are analyzed. Table 6 presents a comparison of grounding system configurations.  
Figure 4: Hybrid grounding system.
## 3 Case Studies: Application to Continuous and Lumped-Parameters Systems
### Case Study 1: Coupling of Continuous Substructures
#### 3.1.1 RCFBS Method  
Figure 2 illustrates the assembly system (ABC), consisting of substructures A, B, and C, including their coordinates and DoFs at the connection points. The analysis focuses on the connection coordinates and corresponding DoFs, excluding internal coordinates.  
Due to system symmetry, substructures A and C are combined into a single substructure, AC. The receptance matrix for the combined substructure AC is diagonal, based on the receptance matrices of A and C. No cross-receptance components exist for AC, as there is no direct connection between A and C. Consequently, the problem is reduced to coupling two substructures, as depicted in Figure 3.  
The receptance matrix for the combined subsystem AC is expressed as follows:  
\[\left[\mathrm{H}^{\mathrm{AC}}\right]_{4\times 4}=\begin{bmatrix}\left[ \mathrm{H}^{\mathrm{A}}\right]_{2\times 2}&\left[0\right]_{2\times 2}\\ \left[0\right]_{2\times 2}&\left[\mathrm{H}^{\mathrm{C}}\right]_{2\times 2} \end{bmatrix} \tag{7}\]  
The receptance coupling for the assembly of two substructures, as presented in Equation (1), simplifies in this case by omitting the internal body coordinates, resulting in Equation (8). In this configuration, substructure AC is coupled with substructure B at four connection points, in contrast to the two connection points used in the three-substructure case, to provide the receptance matrices for the assembly system ABC:  
\[\mathrm{H}^{\mathrm{ABC}}_{\mathrm{cc}}=\mathrm{H}^{\mathrm{AC}}_{\mathrm{cc} }-\mathrm{H}^{\mathrm{AC}}_{\mathrm{cc}}\Big{(}\mathrm{H}^{\mathrm{AC}}_{ \mathrm{cc}}+\mathrm{H}^{\mathrm{B}}_{\mathrm{cc}}\Big{)}^{-1}\mathrm{H}^{ \mathrm{AC}}_{\mathrm{cc}} \tag{8}\]
# Fake Base Station Detection and Link Routing Defense +
## 5 Our Scheme
### Link routing vs. handover  
Our proposed link routing is also different from the cellular handover procedure in that it enables the user equipment to redirect the radio connection in the event of a fake base station detection, whereas in a handover, the radio connection redirects during the user mobility. In a handover, the currently serving base station decides to which base station the user equipment connection will be redirected based on the radio signal measurement given by the user equipment. In link routing, the user equipment decides which base station to select, although the fake base station may have the highest radio signal power. The user equipment may utilize a handover procedure to switch connections to a legitimate base station after the detection of a fake base station, but it is not possible. This is because the currently serving base station, i.e., the fake base station, will not transfer its connectivity to a legitimate base station, or else, even though it wants to, the fake base station cannot make this transfer because it does not have any connectivity with the legitimate network.
# Operationalisation of Building Inspections and Repair: Systematisation-Based Approach
## 4 Discussion  
### Most Common Diagnosis Methods to Prepare an Inspection  
Section 3.1 establishes separately the relevant diagnosis methods to be used while inspecting roof claddings, facade elements/claddings, and floorings. In roof claddings, \"D-C3 Infrared thermography\" is helpful in the detection of several defects, with the added advantages of (i) not requiring direct access to the roof, provided the angle of inspection is within limits mentioned in the literature [71] and (ii) also allowing for the inspection of the top area of the facades, where eventual leakages or water runoff (from the roof) can be detected. In facade elements/claddings, \"D-C1 Measurement of the ambient and/or surface temperature and humidity\", \"D-C3 Infrared thermography\", and \"D-F4 Initial surface absorption test (ISAT) and Karsten-tube\" seem to be more useful, considering the high relevance of humidity-related defects (superficial moisture, dirt, biological colonisation). As for floorings, \"D-A4 Tell-tale gauge and gypsum testimonies\", \"D-A5 Crack width ruler and crack-measuring microscope\", and \"D-A6 Mechanical strain gauge\" are the highlighted methods, as mechanical defects have a higher prevalence (cracking, fracture and splintering). Given these results, an essential kit to inspect the building envelope could involve these six diagnosis methods (D-A4, D-A5, D-A6, D-C1, D-C3, D-F4), given that they are useful for elements, and even more in specific building elements/materials.  
Other authors also advise the use of an electronic moisture-reading meter or an electronic hygrometer (included in D-C1) and thermography (D-C3) in long lists of useful equipment for professionals dedicated to building surveys [5; 72; 73]. It should be noted that surprisingly, in the case of [PERSON] and [PERSON] [72], testing equipment for crack assessment is not advised to prepare for the inspection. On the other hand, the Structural Engineering Institute and the American Society of Civil Engineers [5] highlight the importance of testing cracks in the context of air infiltration and long-term testing of movements in the building envelope. [PERSON] [73] also highlights the advantages of properly measuring cracks, as they are a relevant indicator of structural issues. [PERSON] and [PERSON] [63] also include crack-measuring equipment (callipers and gauges) in the set of basic equipment for building inspections, as well as electrical resistance meters for measuring moisture content. These authors consider thermography a specialised test. In 1993, with different equipment costs (higher), that could be justifiable, but not nowadays.
# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 5 Experimental Assessment
### Analysis of Simulation Results
#### 5.2.5 Performance Trend Analysis with the Number of Edge Servers  
The performance of different algorithms is analyzed as the number of ESs increases from 20 to 70, with the results presented in Figures 12 and 13 using bar charts with error bars. Each bar represents the average hop count of an algorithm for a specific number of ESs, and the error bars indicate the standard deviation of 10 independent runs, reflecting the variability in algorithm performance due to different initial conditions.  
Figure 11: Time consumption trend with increasing BTS network density.  
Figure 12: Hop count trend with increasing ES count.  
As seen in Figure 12, the average hop count decreases as the number of ESs grows, eventually stabilizing at 1. This occurs because when the number of ESs matches the number of base stations, each base station hosts an ES, allowing user requests to be processed locally. QSA consistently achieves the lowest average hop count across all ES counts, with smaller error bars compared to other algorithms. This demonstrates that QSA is the best-performing algorithm for minimizing network latency while also exhibiting greater stability under varying initial conditions.  
Figure 13 shows that as the number of ESs increases, computational time decreases. This is because a higher number of ESs reduces the complexity of the solution space, accelerating convergence. Once ESs are fully deployed at all base stations, every feasible solution is optimal, significantly reducing the required search time.
# Measurement of Dielectric Properties of Thin Materials for Radomes Using Waveguide Cavities
## 3 Results
### Measurement Setup
#### 3.2.2 Network Analyzer Setup  
The measurement steps are as follows:  
1. Connect waveguide adapters to the empty cavity with shims on either side;
2. Save resonant peak data for the empty cavity;
3. Disconnect adapters from the cavity;
4. Place foam blocks within the cavity;
5. Reconnect shims and adapters;
6. Save resonant peak data for the cavity filled with foam;
7. Disconnect adapters from the cavity;
8. Remove foam blocks from the cavity;
9. Place the sample within foam blocks;
10. Carefully place foam blocks and the sample within the cavity;
11. Reconnect adapters to the cavity;
12. Save resonant peak data for the cavity filled with foam and the sample material;
13. Repeat steps 9 to 12 for each material sample.  
Figure 6 displays the motions of adding a sample to the cavity before the second waveguide adapter is connected. The first waveguide adapter is connected to the network analyzer, and then the shim is placed on the adapter, with the cavity connected next. The material under test is placed between the two foam blocks, which are then carefully placed within the cavity, avoiding the movement of the sample between the blocks. The second shim is then placed on the other side of the cavity before the second waveguide adapter is connected to the cavity. The final setup for this experiment is shown in Figure 7, where the cavity is connected to the network analyzer.  
Once the waveguide cavity was connected to the network analyzer, the resonant peaks were observed across a large frequency range. To ensure smooth peaks, averaging on the network analyzer was used to allow the waveform to settle. Increasing the averaging on the network analyzer can ensure smoother waveforms. However, this can increase the amount of time needed for this measurement process to wait for the averaging to complete. Through this observation, it was decided that the dominant mode (TE\({}_{101}\)) resonant peak would be used throughout the measurement process for WR650. This was done as it had the cleanest peak, identified at about 1.6 GHz, and thus would provide the most accurate results, as there was less chance of mode degeneracy. A similar process was followed to determine that the TE\({}_{103}\) mode would be the most appropriate to observe for the WR137 cavity, as it is the most isolated resonant peak for this