esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Evaluation of Accessibility to Essential Services for Vulnerable Populations  
In this section, we present a detailed accessibility analysis of vulnerable populations by constructing and operating isochrone data based on pedestrian and vehicle movements. Traditional methods of urban environment analysis often set usage areas without considering the actual road networks or terrain. This approach tends to result in low-resolution outputs and a high error rate owing to the oversimplification of the spatial environment.  
To address these limitations, our study employed an advanced methodology that accounts for the three-dimensional spatial environment, including terrain and road network complexities. Additionally, we focused on the primary modes of transportation used by vulnerable populations and their typical travel speeds. This allowed us to generate a more accurate and realistic representation of accessible areas, by incorporating time as a factor rather than solely relying on distance.  
For hospitals and clinics, we established a standard walking distance of 15 min as the threshold for accessibility. We visualized the areas (parcels) within Gangseo-gu that could be reached within a 15 min walking distance from each hospital or clinic. As depicted in Figure 5, of the 42,850 lots in Gangseo-gu, 10,096 lots were identified as inaccessible within a 15 min walking distance to any hospital or clinic. This analysis highlights significant gaps in healthcare accessibility, revealing regions where residents may face challenges in promptly accessing medical services. By identifying these gaps, this study provides crucial insights that can inform urban planning and healthcare service improvements for vulnerable populations.  
Figure 5: Visualization of 15-min walking accessibility to hospitals and clinics in Gangseo-gu.
## 2 Materials and Methods  
### Inclusion and Exclusion Criteria  
Twenty patients (ages 18-30) having contra lateral premolar teeth, scheduled for extraction because of orthodontic reasons were selected. A total of 40 teeth, 2 in each patient were selected. A split mouth design was used, where both MTA materials were applied in the same patient. Sample size was calculated with 95% confidence level and 80% power, according to existing literature [16]. Patients suffering from any systemic disease were excluded. The exclusion criteria were teeth with decay, restorations, trauma, periapical lesions, and periodontal pathologies. If teeth could not be isolated using a rubber dam and if patients were allergic to local anaesthesia, with systemic diseases, or when extraction is contraindicated, were also excluded. Informed consent of each patient was a prerequisite for participation in the study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ProRoot MTA** & **RetroMTA** \\ \hline Tricalcium silicate & Calcium carbonate \\  & Silicon dioxide \\ Dicalcium silicate & Aluminium oxide \\  & Calcium Zirconium complex \\ Tricalcium aluminate & \\ Tetracalcium aluminoferrite & \\ Calcium oxide & \\ Bismuth Oxide & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the experimental MTA cements used.
## 3 Topology of Proposed Method
### Finite-Time Stable Tracking Differentiator  
A tracking differentiator is introduced to improve the system's response by minimizing overshoot and enhancing noise robustness [53]. It acts on input signals and avoids sudden changes by differentiating them. The performance of the ULRRE to track the trajectory has been improved by applying the FTSTD design specified in Equation (11).  
\[\begin{array}{l}\dot{z}_{1}(t)=z_{2}(t)\\ \dot{z}_{2}=R^{2}\;\left(-a_{1}[z_{1}-\upsilon(t)]\frac{1}{2}\;-a_{2}\;[\frac {z_{2}(t)}{R}]\frac{2}{3}\right)\end{array} \tag{11}\]  
\(a_{1}\) and \(a_{2}>0\). In this case, \(z_{2}\;\) is a derivative of \(z_{1}\). The required trajectory is \(z_{1}\). \(R\) controls the rate of the transient profile and is selected as a compromise between noise tolerance and tracking precision.
## 3 Materials and Methods
### Unified Attention Fusion Module  
The integration of multi-level features is crucial in achieving high segmentation accuracy. In remote sensing imagery, area palm trees are typically scattered in distribution. The attention mechanism enhances the model's feature learning for these sparsely distributed area palm trees through global associations, thereby improving the model's segmentation capabilities. Drawing on insights from previous research [33], this study introduces  
Figure 3: (**a**) Architecture of the Conv-block. (**b**) Architecture of the UAFM. (**c**) Structure of the attention module.  
a unified attention fusion module (UAFM) that employs spatial attention to effectively merge feature representations from both the primary and auxiliary encoders.  
As depicted in Figure 3b, the overall framework of the UAFM applies an attention module to obtain a weight \(\alpha\). This weight is then fused with the input features through pixel-wise multiplication and summation. In more concrete terms, \(F_{main}\) represents the input features from the primary encoder, and \(F_{aux}\) denotes the input features from the auxiliary encoder. The attention module calculates a proportion \(\alpha\) for the primary encoder's features and \(1-\alpha\) for the auxiliary encoder's features. These weighted features are summed to yield the final output feature map. The entire process is formulated as in Equations (2) and (3).  
\[\alpha=Attention(F_{main},F_{aux}) \tag{2}\]  
\[F_{out}=F_{main}\cdot\alpha+F_{aux}\cdot(1-\alpha) \tag{3}\]  
The attention module is designed as a plug-and-play component that can be replaced with various attention mechanisms--most commonly spatial [34] or channel attention [35]. In this study, we will address the choice of attention mechanism in the experimental section. For illustration, Figure 4 presents the spatial attention approach. Given the input features \(F_{main}\), \(F_{aux}\epsilon\mathbb{R}^{C\times H\times W}\), the module first computes the mean and maximum values along the channel dimension, yielding four feature maps of size \(\mathbb{R}^{1\times H\times W}\). These four maps are then concatenated to form a new feature map of size \(\mathbb{R}^{4\times H\times W}\). A convolution operation followed by a sigmoid function is applied to this new map, producing the final attention weights \(\alpha\epsilon\mathbb{R}^{1\times H\times W}\). Equations (4) and (5) formally describe this procedure.  
\[F_{cat}=Concat(Max(F_{main}),Mean(F_{main}),Max(F_{aux}),Mean(F_{aux})) \tag{4}\]  
\[\alpha=Sigmoid(Conv(F_{cat})) \tag{5}\]
# A Comprehensive Survey on Real-Time Applications of WSN
## 2 Types and Requirements of WSN
### Underground Wireless Sensor Network  
An underground WSN is a sensor node which is placed under the ground for collecting information on underground conditions. The drawback of WSN is its limited battery power because this is very difficult to recharge or change. The wireless communication's main limitation is the loss of signal at high potential. Therefore, in order to enhance the reliability of WSN, efficient routing algorithms have been carried out for solving the limitation of the WSN's accuracy during the routing process [15].  
[PERSON] et al. (2011) proposed a mathematical framework in order to analyze the dynamic connectivity in the wireless underground sensor network (WUSN). Their aim was to monitor the changes in the environmental parameters such as the soil composition and the random soil moisture [16]. [PERSON] et al. (2016) developed a sensor node named Molenet to handle the wireless underground operation. The proposed system is part of a revitalization project in Cameroon. Their main goal was to keep the network operational for a long time and enhance the communication range [17].  
Figure 2: Classifications of WSN.
## 3 Results
### Characterization of OP
The participation of the different functional groups has been reported in several works [3; 9; 16; 18], where the interaction that exists between functional groups and Cr ions is mentioned. The results obtained at a pH of 2.7 suggest that the Cr adsorption process on the surface of the OP does not depend on electrostatic attraction, but on the presence of functional groups on the surface of the material, and that the higher the concentration of these groups, the better the removal of Cr. Based on all the results obtained, it can be assumed that OP can be used as a low-cost bioadsorbent, given its abundance, renewable nature, and the fact that it requires minimal processing for its use as an adsorbent, in addition to having a similar adsorption capacity to that of those adsorbents reported in the literature. Additionally, the use of OP as a bioadsorbent material would contribute to the protection of the environment by avoiding contamination of the soil, water, and air due to the presence of these easily fermentable residues in sanitary landfills.
# Mesh Stiffness and Dynamic Modeling and Analysis of Modified Straight Bevel Gears
## 3 Finite Element Verification of TVMS  
To validate the accuracy of the meshing stiffness calculations, gear models with varying parameters were established. Three-dimensional solid geometric models were imported into finite element preprocessing software to generate their finite element mesh models. Additionally, according to contact mechanics, it is known that contact pressure is highly concentrated in the vicinity of the contact area, with its intensity decreasing sharply as the distance from the contact point increases. Therefore, stress in the contact area does not depend on the geometric shape and boundary conditions far from the contact region. Considering that this study primarily focuses on stiffness variations during single-tooth contact and to avoid the influence of edge rigid body coupling constraints on the computational results, the analysis was performed by joining all gear teeth.  
Figure 6: Calculation procedure of TVMS for a straight bevel gear pair with CM.
_Appl. Sci._**2015**, \(5\), 1252-1271; doi:10.3390/app5041252  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
Dispersion of Functionalized Silica Micro- and Nanoparticles into Poly(nonamethylene Azelate) by Ultrasonic Micro-Molding  
**[PERSON], [PERSON] and [PERSON] *  
Chemical Engineering Department, Polytechnic University of Catalonia, Av. Diagonal 647,  
Barcelona E-08028, Spain; E-Mails: [EMAIL_ADDRESS] (A.D.);  
[PERSON]] (M.T.C)  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]];  
Tel.: +34-934-016-684; Fax: +34-934-017-150.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 23 September 2015 / Accepted: 10 November 2015 / Published: 17 November 2015_  
**Abstract:** Ultrasound micro-molding technology has proved useful in processing biodegradable polymers with minimum material loss. This makes this technology particularly suitable for the production of biomedical microdevices. The use of silica (SiO\({}_{2}\)) nanoparticles is also interesting because of advantages like low cost and enhancement of final properties. Evaluation of the capacity to create a homogeneous dispersion of particles is crucial. Specifically, this feature was explored taking into account micro- and nano-sized silica particles and a biodegradable polyester derived from 1,9-nonanodiol and azelaic acid as a matrix. Results demonstrated that composites could be obtained with up to 6 wt. % of silica and that no degradation occurred even if particles were functionalized with a compatibilizer like (3-aminopropyl) triethoxysilane. Incorporation of nanoparticles should have a great influence on properties. Specifically, the effect on crystallization was evaluated by calorimetric and optical microscopy analyses. The overall crystallization rate was enhanced upon addition of functionalized silica nanospheres, even at the low percentage of 3 wt. %. This increase was mainly due to the ability of nanoparticles to act as heterogeneous nuclei during crystallization. However, the enhancement of the secondary nucleation process also played a significant role, as demonstrated by [PERSON] and [PERSON] analysis.  
**Keywords:** ultrasound micro-molding technology; functionalized silica nanoparticles; nanocomposites; poly(alkylene dicarboxylate); crystallization kinetics
## 3 Multiagent DRL Design for Proposed Method
### Proximal Policy Optimization  
Proximal policy optimization (PPO) is based on the Monte Carlo policy gradient method, which computes an estimator of the policy gradient (PG) using a stochastic gradient ascent algorithm. An estimated return form of the PG algorithm was reported in a previous article to update the policy parameter \(\theta\) iteratively using the Monte Carlo method. Because the total expected return \(G_{t}\) can be calculated from the real sample trajectory, the policy gradient is updated as  
\[\
abla_{\theta}I(\theta)=\
abla_{\theta}V^{\pi}(s_{0})(\theta)=\mathrm{E}_{ \pi}[Q^{\pi}(s,a)\
abla_{\theta}In\pi_{\theta}(a|s)] \tag{6}\]  
\[\theta\;\leftarrow\;\theta+\alpha\
abla_{\theta}I(\theta) \tag{7}\]  
where \(\alpha\) is the learning rate, which can be tuned manually, and \(J(\theta)\) is the objective function.  
However, the PG algorithm does not guarantee adequate convergence, so PPO secures it in two ways: (1) using the advantage function of Equation (8) instead of the action-value function \(Q^{\pi}(s,a)\) of Equation (6) to reduce the variance of the estimate during parameter update and (2) adopting a certain trust region to improve the stability of training by ensuring a clipped surrogate objective at every iteration by modifying \(J(\theta)\) in Equation (6) to Equations (9) and (10).  
\[Ad^{\pi}(s,a)=Q^{\pi}(s,a)-V^{\pi}(s) \tag{8}\]  
\[J(\theta)=\mathrm{E}_{\pi_{\theta_{old}}}\left[\min\biggl{(}\frac{\pi_{ \theta}(a|s)}{\pi_{\theta_{old}}(a|s)}Ad^{\pi_{\theta_{old}}}(s,a),g\bigl{(} \epsilon,Ad^{\pi_{\theta_{old}}}(s,a)\bigr{)}\biggr{)}\right] \tag{9}\]  
\[Jg\bigl{(}\epsilon,Ad^{\pi_{\theta_{old}}}(s,a)\bigr{)}=\left\{\begin{array}[ ]{cc}(1+\epsilon)Ad^{\pi_{\theta_{old}}}(s,a),&Ad^{\pi_{\theta_{old}}}(s,a) \geq 0\\ (1-\epsilon)Ad^{\pi_{\theta_{old}}}(s,a),&Ad^{\pi_{\theta_{old}}}(s,a)<0\end{array}\right. \tag{10}\]  
where \(\theta_{old}\) is the present parameters in the NN, and the size of the trust region for updates is decided by \(\epsilon\). By clipping operators, the new policy does not influence excessively to avoid being trapped in the local minimum of the objective function geometry. The convergence performance of the PPO algorithm becomes stable and effective when learning multiple agents through these two methods. For the reasons mentioned above, PPO was applied to the method of learning the PV inverter to mitigate the voltage unbalance at the PCC.
## 4 Proposed Framework
### Ontology Design and Deployment
#### 4.2.3. Deployment  
**Ontology distribution:** The central unit ensures that the ontology \(O\) is distributed to all \(m\) edge servers. This guarantees uniform data representation and alignment across the system as follows:  
\[O_{j}\gets O,\quad\forall j\in\{1,2,\ldots,m\} \tag{12}\]  
**Integration at edge servers:** On receiving the ontology, integration at each edge server is performed to preprocess and harmonize IoT data locally. Integration involves a series of basic steps as follows:  
* **Schema mapping:** Maps raw data fields \(f_{i}\) to ontology-compliant attributes \(o_{i}\) as follows: \[M:f_{i}\to o_{i},\quad\forall f_{i}\in A_{j},\;o_{i}\in O\] (13)
* **Data transformation:** In this stage, unrefined data are translated into a uniform format according to the ontology, thus providing assurance of uniformity.
* **Metadata enrichment:** In this stage, contextual information is added to the data, including timestamps, geographical locations, and device IDs.
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 4 The HO-FIM Architecture
### Overview of HO-FIM
#### 4.1.1 Master Controller  
The master controller decides to activate the handover controller only when needed to minimize handovers; a decision is made based on the current status of radio resource usage reported by the radio resource controller, i.e., when a macrocell's load exceeds a certain threshold (activation threshold). The master controller stops the handover controller running if the network load status gets: (1) close enough to an optimum; or (2) lower than a certain point (deactivation threshold). The latter will go through fewer handovers.
## 3 Materials and Methods
### Development  
In 2019 a spatial ability measuring application was developed using the Unity game engine to answer the RQs and Hs. This application is compatible with newer Windows (7+) and Android (7.0+) versions. The latter version was required to create an immersive virtual reality experience: it can be used with the Samsung Gear VR SM-R322 head-mounted display [39], while a Samsung S6 Edge+ smartphone was placed into it [40].  
Two differences exist among the two versions: interaction and level of immersion. Naturally, when the Gear VR is used, users feel that they are \"inside\" the virtual environment, thus the level of immersion is increased. It is not possible to move; only the rotation of their head is possible. Regarding interaction, users have to look at objects and tap the touchpad on the right side of the Gear VR to select objects, while when using the desktop display, objects can be selected by pressing the left mouse button or corresponding keys on the keyboard (1-4 or 1-5 depending on the test types).  
At the moment, there are three types of tests in the application: the MRT, MCT, and PSVT test types, although the Heinrich Spatial Visualization Test [41] is in process of being implemented in it. The MRT, MCT, and PSVT test types are shown in Figure 1.  
Figure 1: The various test types: (a) MRT test with an orthographic camera, 3:1 contrast ratio, no rotation, and shadows turned on; (b) MCT test with a perspective camera, 60\({}^{\circ}\) FoV, 1.5:1 contrast ratio, no rotation, and shadows turned off; (c) PSVT test with an orthographic camera, 7:1 contrast ratio, 15\({}^{\circ}\) camera rotation, and shadows turned on.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 3 Impact of Network Slicing
### CN Slicing
#### 3.1.1 Slice Identity Management  
The network slicing feature was first introduced in Release 15, with the ability of the 5 GC to support multiple network slices and differentiate among them. This differentiation is done using two signalling identifiers: the Single Network Slice Selection Assistance Information (S-NSSAI) and the Network Slice Selection Assistance Information (NSSAI).  
The S-NSSAI identifies a network slice across the UE, RAN and the 5 GC. It is a 32-bit parameter comprised of two fields:  
* A Slice/Service Type (SST): mandatory 8-bit field that refers to the expected network slice behavior in terms of features and supported services. The SST field may have standardized and operator-specific (non-standardized) values. The standardized SST range [10] includes values from 0 to 127, while values 128 to 255 belong to the operator specific range. For now, the following SST values have become normative: SST = 1 (enhanced Mobile Broadband), eMBB), SST = 2 (Ultra Reliable Low Latency Communication, uRLLC), SST = 3 (massive IoT, mIoT), SST = 4 (Vehicle to Everything, V2X) and SST = 5 ( High-Performance Machine-Type Communications, HMTC).
* A Slice Differentiator (SD): optional 24-bit field that allows the operator to differentiate among multiple network slices with the same SST. This differentiation can be in terms of slice features (e.g., mobile vs fixed-wireless access services, charging), customer information (tenancy) and slice priority.  
An NSSAI is a collection of S-NSSAIs sent by the device to assist the network in selecting a particular network slice for this UE. Within the Public Land Mobile Network (PLMN), the NSSAI is managed at the Tracking Area level in the RAN, and at the Registration Area level in the 5 GC. Different types of NSSAIs exist, including Configured NSSAI (NSSAI provisioned in the device), Subscribed NSSAI (NSSAI stored in the UDM), Requested NSSAI (provided by the UE to the serving PLMN during registration) and Allowed NSSAI (provided by the serving PLMN to the UE during registration) [10]. The 5 GC uses the Requested NSSAI for slice selection and validation, and returns the Allowed NSSAI. The Allowed NSSAI indicates the S-NSSAI values that the UE can use in the serving PLMN for the current Registration Area.  
Figure 4 summarizes the use of S-NSSAI and NSSAI artifacts in the 5G network.  
Figure 4: Impact of slicing in 5 GC.
## 3 Results and Discussion
### Bogie Retrofit Influence
Therefore, as half the time, the HFT cars are running on the empty train, and the other half, they carry soybean to the port, the mean gain in wheel life is 40%. This percentage is in accordance with other, similar studies, which found 43% reductions in wheel wear after applying shear pads. The shear pad has lower stiffness, which can benefit the wheel wear behavior [20]. However, it was considered inapplicable to the VLI's side frame, as explained in Section 2.2. Figure 11 shows the results of the wear rate comparison. * (Loaded HFT 1E without retrofit ): y = \(-\)0.091x + 52.91; R\({}^{2}\) = 0.9994;
* (Loaded HFT 3E without retrofit): y = \(-\)0.112x + 55.4; R\({}^{2}\) = 1;
* (Loaded HFT 1E with retrofit): y = \(-\)0.14x + 59.7; R\({}^{2}\) = 1;
* (Loaded HFT 3E with retrofit): y = \(-\)0.112x + 53.2; R\({}^{2}\) = 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Curve (m)**} & **1E W/O Retrofit** & **3E W/O Retrofit** & **1E Retrofit** & **3E Retrofit** \\  & **(N/mm\({}^{2}\))** & **(N/mm\({}^{2}\))** & **(N/mm\({}^{2}\))** & **(N/mm\({}^{2}\))** \\ \hline
300 & 19.9 & 17 & 17.5 & 17 \\ \hline
350 & 16.8 & 14.6 & 14.4 & 14.3 \\ \hline
400 & 14.3 & 12.8 & 11.7 & 12.1 \\ \hline
450 & 11.9 & 10.7 & 8.2 & 10.3 \\ \hline
500 & 9.1 & 8.8 & 0 & 7.8 \\ \hline
550 & 0 & 6.7 & 0 & 0 \\ \hline
600 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Empty HFT results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Curve (m)**} & **1E W/O Retrofit** & **3E W/O Retrofit** & **1E Retrofit** & **3E Retrofit** \\  & **(N/mm\({}^{2}\))** & **(N/mm\({}^{2}\))** & **(N/mm\({}^{2}\))** & **(N/mm\({}^{2}\))** \\ \hline
300 & 25.5 & 21.8 & 17.7 & 19.6 \\ \hline
350 & 21.1 & 16.2 & 10.7 & 14.0 \\ \hline
400 & 16.7 & 0.0 & 0.0 & 0.0 \\ \hline
450 & 11.8 & 0.0 Figure 12 compares the angle of attack in a 300 m radius curve under conditions with the retrofit (red line) and without the retrofit (green line) for a loaded HFT. It is possible to observe that, for this curve, the value of the angle of attack for the bogie with the retrofit is extremely low, culminating in a low lateral slip and, consequently, the wear indicator. The minus sign in the angle value is due to sign convention. This leads to a considerable reduction in the angle of attack for three-piece bogies by 43%. In order to achieve a better behavior, it is necessary to evaluate a different bogie technology, such as the active steering control conception, which could decrease the angle of attack by 92.6%. It should be noted that this gain was applied to the following boundary conditions: ride control bogie, broad gauge (1600 mm), AAR-1B narrow flange wheel profile, Arema 136 RE rail profile, Hopper vehicle, and a track with a mild and sharp curved track.