esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free +
## 2 Method
### Heuristic Optimization Algorithm  
Since optimizing a community quality function has been proven an NP-complete problem [13; 17], heuristic algorithms are generally adopted to obtain the best available solutions. Early methods, such as those in [2; 13], usually have heavy demands on computational resources, while more recently, a number of faster algorithms have been proposed [27; 43; 47; 48; 49]. Among them, the Louvain algorithm proposed by [PERSON] et al. [47] is widely accepted due to its prominent efficiency and high accuracy. The label \"Louvain\" comes from the authors' affiliation (UCLouvain); alternatively, it is also called a \"BGLL\" algorithm, by the authors' initials. Originally, this algorithm was designed as a greedy algorithm to optimize the standard modularity function \(Q\) proposed by [PERSON] [48]; similar algorithms have also been adopted to optimize the Potts Hamiltonian by some dynamical methods [35; 36].  
We employ the Louvain algorithm to optimize our community fitness function (Formula (3)) in this paper. Here, we briefly describe its steps; for more details, please refer to [47].  
1. Initialize communities. At the very beginning, each node of the network is designated to an individual community. A network consisting of \(N\) nodes is then divided into \(N\) communities of size \(1\).
2. Optimize communities of the lowest level. Sequentially consider each node of the network and scan its neighboring communities (i.e., communities sharing at least one edge with the node in focus). Calculate the potential gains of \(F_{\alpha}^{\beta}\) if the node in focus was moved out of its original community and put into each of the neighboring communities. Place the node in focus into the community that leads to a maximum value of \(F_{\alpha}^{\beta}\).
3. Iterate until convergence. Repeat step 2 until a maximum value of Formula (3) is reached where no more moves of any node may further increase this value. During this process, the sequence of node orders is randomized every time a new round of iteration is started.
4. Merge communities to build a higher community level. Consider each community obtained at the convergence of step 3 as a fixed module; hereafter, all its members (nodes) must be moved together. Repeat the above steps 2 and 3 by taking each fixed module as a node. During this process, connected modules gradually condense into communities of higher levels, until a maximum value of Formula (3) is reached.
5. Iterate until convergence at the highest level. Repeat step 4 and detect communities of all levels, until the highest level is detected where no further merging of any communities can increase \(F_{\alpha}^{\beta}\).
6. Output communities. Communities of all levels detected by the above steps 1 to 5 form a hierarchical structure; each level can be independently outputted. Customarily, only the output of the highest level is adopted since it has a maximum value of \(F_{\alpha}^{\beta}\) among all levels.
# Fault Diagnosis for a Class of Robotic Systems with Application to 2-DOF Helicopter
## 3 Fault Isolation Based on Hamiltonian Structure  
The main idea of this work is to present a general approach to fault detection and isolation based on a Hamiltonian system representation. The results are based on the observation that only additive actuator and sensor faults are being considered.
# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
## 5 EE-LoRa Algorithm
### Optimization Problem for SF Selection  
#### 5.1.1 Problem Formulation  
At this step, we suppose that all nodes use the maximum allowed TP (14 dBm as per the regional regulation). We formulate the optimization problem (\(\mathcal{P}\)) of SF selection in order to maximize the network's energy efficiency (\(Eff\)), defined as the ratio of the network utility function (\(\mathcal{R}\)) to the total consumed energy (\(\mathcal{E}\)) during period \(T\)[27]. This is a multi-objective problem in which the optimal node distribution for each SF is determined and their ratios are computed.  
The objective function can be expressed as follows:  
\[(\mathcal{P}):\max_{p(s)}Eff=\max_{p(s)}\frac{\mathcal{R}}{\mathcal{E}} \tag{16}\]  
subject to  
\[\sum_{s=S_{min}}^{S_{max}}p(s)\leq 1 \tag{17}\]  
\[\sum_{i=s}^{S_{max}}p(i)\geq\frac{N(s)}{N_{t}},\forall s\in\Omega \tag{18}\]  
Constraint (17) ensures that the sum of ratios does not exceed 1. Meanwhile, constraints (18) limit the number of nodes selecting SF \(s\) and higher to the maximum number \(N(s)\), since a node using SF \(s\) cannot select an SF lower than \(s\). This fractional problem is non-convex, so it cannot be solved directly. We adopt the Dinkelbach method, which transforms (\(\mathcal{P}\)) into a parameterized convex programming problem as follows [28]:  
\[(\hat{\mathcal{P}}):F(\eta)=\max_{p(s)}\left(\mathcal{R}-\eta\mathcal{E} \right)=\min_{p(s)}\left(-(\mathcal{R}-\eta\mathcal{E})\right) \tag{19}\]  
subject to:  
\[\sum_{s=S_{min}}^{S_{max}}p(s)\leq 1 \tag{20}\]\[\sum_{i=s}^{S_{max}}p(i)\geq\frac{N(s)}{N_{t}},\forall s\in\Omega \tag{21}\]  
where \(\eta\) is a non-negative parameter.
## 3 Results and Analysis
### Scent Characteristics and Delivery Methods  
As already mentioned, nearly all studies included some form of pre-test for evaluation of the perceptual properties of the target scents, principally their pleasantness, which was most often assessed via rating scales, and in one case using consumer biometrics (specifically, facial expression analysis [21]). In this regard, it should be noted that all studies used positively or neutrally valanced scents (Table 2). No studies reported using unpleasant scents. This is not surprising and likely due to constraints placed by the store managers who would otherwise be unwilling to participate in the study. A few studies purposefully selected perceptually different, but equally valenced scents in order to investigate whether qualities other than valence may affect customer behavior ([26; 39]). Other perceptual odor characteristics considered during pre-tests were familiarity, intensity, complexity [26; 28], as well as conceptual associations such \"cool/warm\" [27] or \"masculine/feminine\" [37] (Table 2) to assess congruency with specific products.  
Methodological reporting pertaining to the actual scents and methods of delivery is quite poor. Most papers simply mentioned the commercial or lay name for the scent (e.g., \"rose\", \"coffee\", \"sunflower\", see Table 2), but their chemical constituent(s) was rarely reported in the literature. Additionally, while several studies reported whether consumers were consciously aware and/or attended to any ambient scents, the specific intensity of the scent in the retail environment was generally not reported or only qualitatively described with terms such as \"mild\", \"unobstrusive\", \"not aggressive\", etc. No study explicitly considered measuring the concentration of the scent, the airflow conditions, or any other aspects of air quality that may have affected the concentration of the scented particles in the store environments. Likewise, many studies do not mention the device used for diffusing scents but just refer to a generic \"dispenser\" or \"diffuser\"; only three studies [21; 26; 43] mentioned the specific models and/or technical characteristics of the devices (Table 2). Finally, while the store area is usually reported, the number and the positioning of thescent unit(s) was rarely given; thus, in many cases, it was not clear whether the scent was perceived throughout the entire store or only close to the target products.
# Factory Logistics Improvement: A Case Study Analysis of Companies in Northern Thailand, 2022-2024
## 4 Results and Case Study
### Logistics Administrative Management
#### 4.3.3 Outsourcing  
The strategic adoption of logistics outsourcing has emerged as a pivotal approach for enhancing operational efficiency among Thai manufacturers. A comprehensive multi-company case study, encompassing eight firms, provided compelling evidence of the benefits derived from leveraging Logistics Service Providers (LSPs) across various logistical activities [40].  
The study's findings revealed multifaceted advantages of outsourcing, particularly in cost reduction and service improvement. Notably, firms that engaged in service volume commitment agreements with LSPs were able to secure significant discounts, optimizing their cost structures through economies of scale. A striking outcome was observed in transportation logistics, where the transition from in-house fleets to LSP-managed transportation services yielded substantial benefits. This shift resulted in total cost reductions ranging from 10% to 20%, alongside lead time reductions of 15% to 25% in certain cases, underscoring the efficiency gains achievable through specialized logistics management.  
Furthermore, the integration of e-fulfillment services provided by LSPs led to marked improvements in service levels, with increases exceeding 15% in some instances, highlighting the potential for enhanced customer satisfaction through outsourcing. Beyond operational efficiency, the adoption of outsourcing strategies addressed a critical challenge facing the Thai manufacturing sector: the shortage of skilled labor [41; 42]. By delegating complex logistics operations to specialized providers, manufacturers could mitigate the impacts of labor scarcity while simultaneously accessing a pool of experts with advanced technological capabilities. This multifaceted approach to outsourcing not only streamlined operations, but also positioned Thai manufacturers to better navigate the complexities of global supply chains, enhance their competitive stance, and adapt more flexibly to market fluctuations. The study's findings suggest that the strategic utilization of LSPs represents a significant opportunity for Thai manufacturers to optimize their logistics performance, reduce operational costs, and address systemic challenges in the labor market, thereby fostering long-term sustainability and growth in an increasingly competitive global landscape.
# Post-Earthquake Damage Inspection of Wood-Frame Buildings by a Polarimetric GB-SAR System
## 5 Conclusions  
In this paper, we present a polarimetric GB-SAR system for non-destructive imaging and inspection of wooden structures damaged by earthquakes. SAR processing has been applied to improve the lateral resolution of the reconstructed radar image. The results from our laboratory experiments demonstrate that the high-frequency radar waves can penetrate the wooden walls. Deformations of the wooden structures, as well as the concealed metal nails (about 3 mm in diameter and less than 2 cm in length) and bolts inside the walls can be clearly imaged. The subtle crack (less than 1 mm in aperture) on the wooden members cannot be identified due to its weak reflection and limited resolution. The shape and orientation of the wooden members have shown a great sensitivity to radar polarization. It is concluded that radar polarimetry can provide much richer information on the condition of the concealed building structures than the conventional single-polarization subsurface penetrating radar. The rather complex subsurface scattering mechanisms inside a wood wall will be subject to further investigation to fully understand the information.  
This work is partly supported by National Natural Science Foundation of China (41504111). The research results have been achieved by \"Development of Non-Destructive Inspection Method for Constructions by Electromagnetic Wave\", the Commissioned Research of National Institute of Information and Communications Technology (NICT), Japan.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the laboratory experiments; [PERSON] and [PERSON] processed and analyzed the datasets, [PERSON] drafted the manuscript, which was revised and approved by all the authors.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Recyclability of Ion-Exchange Resins  
The regeneration and reuse of saturated adsorbents are crucial for practical application. The effect of reusing times on adsorption performance of ion-exchange resin was investigated in repeated adsorption and desorption experiments. As illustrated in Figure 9, the adsorption capacity of ion-exchange resins slightly decreased after three times of reuse. Nevertheless, the adsorption ratio of [Bmim][BF\({}_{4}\)] could remain over 90% after reusing for three times. This implies that the Amberlite IR 120 Na can be used as a stable and reusable adsorbent for the adsorption of ILs.  
Figure 8: Effect of ionic strength on the adsorption of ionic liquid ([Bmim][BF\({}_{4}\)]) onto ion-exchange resin (Amberlite IR 120 Na).  
Figure 9: Effect of reusing times on the adsorption of ionic liquid ([Bmim][BF\({}_{4}\)]) onto ion-exchange resin (Amberlite IR 120 Na).  
The loss of adsorption capacity is probably due to the fact that some binding sites might lose activity during the adsorption-desorption cycles, which could not allow further adsorption of the solutes [47]. Moreover, the adsorption sites of resins may be incompletely regenerated. These sites are still occupied with [Bmim] cations after regeneration and thus lose the adsorption ability in the next adsorption process. Remaining of ILs on the regenerated resin was evidenced by the FTIR results (Figure 10). The pure [Bmim][BF4] is characterized by peaks at 1150-1200 cm\({}^{-1}\) (in-plane C-N of the imidazole ring), 1550-1600 cm\({}^{-1}\) (in-plane C-C of the imidazole ring), 2800-3000 cm\({}^{-1}\) (the C-H stretching vibrations of methyl and methylene groups), and 3100-3200 cm\({}^{-1}\) (the ring C-H stretching vibrations) [49]. The spectrum of ion-exchange resin showed a broad band located at 3414 cm\({}^{-1}\), which may be associated with the O-H stretching vibration of interlayer water molecules and the H-bonded OH groups [50]. The peak at 1572 cm\({}^{-1}\) was not observed for fresh resin while there was a weak transmittance in the spectrum of regenerated resin, indicating the presence of residual [Bmim] cations on the resin after regeneration.
## 2 Methodology
### Integrated Methodology for the Elaboration of Geospatial Exposure Databases
#### 2.3.6 Roof Covering Material  
The roof covering material is estimated by unsupervised classification of a fusion product of multispectral imagery and high-resolution orthophotos. For this purpose, free high spectral resolution sources are preferred, as ASTER imagery [56]. An alternative is Sentinel-2 satellite images [55], which are freely available worldwide. Thus, the fused image is characterized by both a high spectral resolution (from the multi or hyperspectral image) and a spatial resolution (from the orthoimage).  
In the case of San Jose, a fused image of the 1:1000 orthoimage and an airborne hyperspectral MASTER image [59] of the city of San Jose was classified. MASTER imagery, a proxy of ASTER, was shared freely by request to the CENAT. They were obtained in 2006 in the frame of the CARTA Mission, during the development of the ASTER program [66]. The preprocessing, fusion, and classification processes were performed in SNAP 8.0 and ENVI 4.8 software. The fusion algorithm was based on the Gram-Schmidt transform [67]. The classification algorithm used was the k-means algorithm [68]. As classes were not defined previously, an unsupervised algorithm was selected in this case. The classification was carried out using the building footprints as a mask for the fused image, resulting in 5 spectral classes or clusters that were ultimately identified (see Section 3) with 3 informational classes or roof covering material types.
## 4 Results
### Experiment Setup
#### 4.2.2 Classification Performance for Cardiovascular Events  
The CE classification model was tested on the 30% of the dataset that constituted 11 real patient records. The model had an accuracy of 82% on the test set; see the confusion matrix in Table A2 in Appendix A.  
Table 7 includes all the performance metrics of our CE models. Model (1) synthetic + real data outperformed models (2) real data and (3) no transfer learning in almost all metrics, demonstrating its effectiveness. For (without CE), (1) achieved a balanced precision and recall (0.83 each) with an F1-score of 0.83, compared to (2)'s 0.80 precision, 0.67 recall, and 0.73 F1-score. Model (3) performed the worst, with a precision of 0.56 and an F1-score of 0.67. Similarly, for (diagnosed with CE), (1) showed a higher precision (0.80 vs. 0.67 for (2) and 0.50 for (3)) and recall (0.80 vs. 0.80 for (2) and 0.20 for (3)), leading to a better F1-score (0.80 vs. 0.73 and 0.29, respectively). Model (1) also had a higher specificity (0.80 vs. 0.67 and 0.50) and AUROC (0.82 vs. 0.73 and 0.52), indicating better overall discrimination and reliability.  
These results highlight the benefit of combining synthetic data with real data and the importance of transfer learning for improved predictive performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Trained on** & **Classes** & **Precision** & **Recall** & **F1-Score** & **Specificity** & **AUROC** \\ \hline \multirow{2}{*}{(1) Synthetic + Real data} & (Without AF) & 0.71 & 0.83 & 0.77 & \multirow{2}{*}{0.83} & \multirow{2}{*}{0.77} \\  & (Diagnosed with AF) & 0.83 & 0.71 & 0.77 & \\ \hline \multirow{2}{*}{(2) Real data} & (Without AF) & 0.62 & 0.83 & 0.71 & \multirow{2}{*}{0.80} & \multirow{2}{*}{0.70} \\  & (Diagnosed with AF) & 0.80 & 0.57 & 0.67 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification report of model performance on atrial fibrillation (AF) datasets: (1) Synthetic + Real data: Deep learning model trained on synthetic + real data. (2) Real data: Deep learning model trained on real data.
## 2 Materials and Methods
### Preparation of PAB Degradation  
PAB samples were subjected to hydrolytic degradation at 37 \({}^{\circ}\)C for 15 days in two different media: a phosphate-buffered saline (PBS) solution with pH = 7.4 mimicking the internal conditions of humans and an NaOH solution with pH = 12 for an accelerated PAB degradation rate. The degradation media were renewed every 24 h. Five samples were prepared for those tests. All experiments were carried out in an incubator (37 \({}^{\circ}\)C /98.6 \({}^{\circ}\)F), and the degradation period was 15 days. Each sample was weighed with an electronic scale (KFS-C series, Kaifeng Inc. China) to obtain its initial mass (\(w_{1}\)).
## 3 Model Description
### Model Geometry and Mesh  
The Coupled Eulerian Lagrangian approach was utilized to accomplish the strength of both the Eulerian and Lagrangian methods. The workpiece was modeled as a Eulerian domain, while the Lagrangian formulation was used to consider the welding tool. In order to diminish the computational time (cost), the tool dimensions were the same as the experimental tool but were featureless. However, the featureless tool produces lower equivalent plastic strain and material flow than that produced in experiments using a threaded tool. In addition, the Eulerian domain size used was four times the tool shoulder diameter to realize the accuracy and computational cost [35; 36; 37; 32]. The Eulerian domain size was \(60\times 60\times 5.5\) mm\({}^{3}\). As previously stated, the experimental workpiece thickness was 3.8 mm, while the physical workpiece and Eulerian thickness were 4 and 5.5 mm, respectively. The difference between the physical workpiece and the Eulerian domain thicknesses was left unfilled (void) for visualization of flash formation during the FSW process.  
Figure 2: The operator split for the Eulerian formulation.  
The rigid tool was meshed as a four-noded thermally coupled tetrahedron (C3D4T), with a 0.55 mm mesh size and total elements of 36,010, as shown in Figure 3a. The Eulerian domain was sectioned into three parts. The center part, which may contain the processed zone, SZ and thermo-mechanical affected zone (TMAZ), has a fine mesh size, and the side parts have a coarse mesh. An eight-noded thermally coupled linear Eulerian brick (EC3D8 RT) was used to mesh the Eulerian domain with 105,600 total number of elements, as is shown in Figure 3b.
## 2 Materials and Methods
### Algorithm
#### 2.1.2 Artifact Detection  
In Figure 1c, the basic flow chart for the detection of artifacts within the signal \(x(t)\) is illustrated. These four steps are similar to the movement detection, as is the output vector \(\zeta_{x}(t)\), which contains \"1\" at time points found to be an artifact and \"0\" otherwise.  
**Step 1:**_MSD\({}_{x}(t)\)_ of the signal \(x(t)\) is calculated as described in Step 1 of Section 2.1.1 Movement detection.  
**Step 2:** The threshold \(T_{x}(t)\) for the artifact detection is obtained automatically using the moving Zack triangle algorithm, as in Step 2 of Section 2.1.1 Movement detection.  
**Step 3:** All values of the signal _MSD\({}_{x}(t)\)_ greater than \(T_{x}(t)\) are marked as \"1\", as in Step 3 of Section 2.1.1 Movement detection:  
\[\xi_{x}(t)=\begin{cases}1\text{ if }MSD_{x}(t)>T_{x}(t)\\ 0\text{ if }MSD_{x}(t)\leq T_{x}(t)\end{cases} \tag{4}\]  
**Step 4:** Finally the information from the accelerometer \(\zeta_{acc}(t)\) is used to verify that the artifact is caused by a movement. For each artifact, the corresponding movement containing vector \(\zeta_{acc}(t)\) is checked. If this contains no movement (thus only \"0\") for the time points of interest, \(\zeta_{x}(t)\) will be set to \"0\" for the corresponding time points. Mathematically, this corresponds to a logic AND operation:  
\[\xi_{x}(t)=\ \xi_{acc}(t)\ \wedge\ \xi_{x}(t) \tag{5}\]  
Furthermore, the algorithm rejects all artifacts shorter than \(L_{min}\). This was done for the same reason as in Step 4 of Section 2.1.1 Movement detection.
# Simulation-Based Analysis on Operational Control of Batch Processors in Wafer Fabrication
## 4 Simulation Experiments and Results
### Simulation Model Description
To obtain meaningful steady-state results from the simulation runs, statistics on the initial transient period (the first three months) of each run were excluded from the analysis. After the warm-up period, statistical data is collected through simulation runs for nine months (270 days, 6480 h). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Station** & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 \\ \hline
**Number of** & & & & & & & & & & & & & & \\
**Reentrant** & 5 & 5 & 5 & 5 & 10 & 3 & 5 & 2 & 5 & 2 & 3 & 3 \\
**Visits** & & & & & & & & & & & & & & \\ \hline
**MPT 1/lot (h)** & 1.800 & 0.300 & 0.540 & 0.250 & 0.258 & 0.380 & 0.300 & 0.680 & 0.330 & 0.570 & 0.620 & 0.523 \\ \hline
**MTBF 2 (h)** & 38.0 & 38.0 & 38.0 & 38.0 & 38.0 & 38.0 & 38.0 & 38.0 & 38.0 & 38.0 & 38.0 & 38.0 \\ \hline
**MTSince this paper focuses on the batch processors, commonly used control rules are applied for lot release decisions and dispatching decisions for DP stations. For lot release rules, two input control rules, an open-loop CONTIME, and a closed-loop CONWIP are applied. The CONTIME rule is selected because the production rate is anticipated with certainty, while CONWIP is selected because it is an easy-to-use closed-loop control rule widely used in industries. The inter-arrival time of the CONTIME rule is one hour, leading to a 95% utilization of the bottleneck station as in Reference [31]. (See Table 1 for the utilization for every workstation.) The number of lots maintained in the fab for the closed-loop CONWIP rule is 70, which is chosen through preliminary experiments so that the average throughput rate is roughly the same as the average throughput rate of the CONTIME lot release cases. For dispatching in the discrete processing stations, the FCFS rule is used because it is easy to apply and widely used in industries.