esa-sceva/satcom-chunk-collection huggingface dataset:
# Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques
## 3 Prediction Model for Dementia Diagnosis
### Data Collection  
The data used in the study were collected from people who visited the dementia center in Gangbuk-Gu, Seoul, from 2008 to 2013 and received a screening test.  
The data collection method is as follows. First, MMSE-KC examines the cognitive decline of the patient. If the resulting diagnosis indicates cognitive decline, CERAD-K would be further conducted. After the precise examination, it is decided whether or not to take a doctor's examination, consult with the doctor, decide whether to be confirmed at the hospital or participate in the program run by the center.  
Two types of data were used in this study. The collected data consists of 14 attributes for Phase 1 data and 31 attributes for Phase 2 data. First, the data used in Phase 1 were gender, age, education, and MMSE-KC scores. MMSE-KC results (normal, cognitive decline) were used as class data for classification. When performing neuropsychiatric treatment, the age, education level, physical condition and basic cognitive ability of the subject should be considered. In addition to MMSE-KC score data, demographic data such as patient's sex, age, and education level were collected. The second data is the data used in Phase 2, which classifies the normal in Phase 1 and adds CERAD-K data to  
Figure 1: The architecture of the proposed model.  
the data, which is not classified as normal. In Phase 2, we used the data of the patients who were confirmed (dementia or dementia high risk) visiting the hospital after the final examination.  
Data from Phase 1 consisted of data from a total of 14,000 patients, 9799 in the normal group and 4201 in the cognitive decline group. The mean age of the patients was 73 years old, 72 years in the normal group, and 74 years in the cognitive impairment group. The MMSE-KC score was 25 points for the normal group and 18 points for the cognitive decline group, which was about 7 points different from the normal group. The overall average was 23 points.  
In Phase 2, the average age of all patients was 76 years, the difference average age gap between MCI and dementia patients was 5 years. When measuring cognitive ability, the level of the patient's education also affected the results, but MCI and dementia did not show much difference. The MMSE-KC score was 17 points out of 30 as a whole, with an average of 20 points for MCI patients and a dementia score of 15, slightly lower than the average. Details are shown in Tables 3 and 4.
# Investigation of a Possible Material-Saving Approach of
## 3 Results
### Evaluation of Deposited Layer  
The deposition performed by the MS technique at the working distance of 4.5 cm, reached 200 \(\upmu\)m of thickness. Without any additional heating and optimization of the parameters, the layer deposited turned out dense and without any visible defects. The low adherence of the coating with the quartz substrate gave us the possibility for a complete delamination of the relatively thick layer of Cu. Thus, it was possible to conduct a cross-section analysis of the first MS deposition, which showed a difference in the layer deposition growth. As can be seen in Figure 3, the structure of the deposition changes from the columnar to nucleation type of growth after 100-120 \(\upmu\)m.  
However, the selection of a closer distance between the source and the SH improved the deposition rate by a factor of 5. The thickness of the deposition achieved during the second magnetron sputtering process (see Figure 4a) was ultra-thick, around 960 \(\upmu\)m-1 mm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **MS** & **DS** \\ \hline Working pressure, Pa & 0.78 & 34 \\ Voltage, V & 584–377 & 690–840 \\ Current, A & 1 & 0.1 \\ Deposition time, min & 390 & 630 \\ Deposition rate, \(\upmu\)m/min & 2.4 \(\pm\) 0.02 & 0.3 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main deposition parameters of sputtering processes (MS and DS) used in the experimental tests and calculated deposition rate.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material Results** & **Magnetron, \%** & **Diode, \%** \\ \hline Usage of the target & 26.9 \(\pm\) 0.003 & 68.7 * \\ On the SH & 38.9 \(\pm\) 0.003 & 21.6 \(\pm\) 0.035 \\ On the recovering shield & 56.3 \(\pm\) 0.005 & 71.6 \(\pm\) 0.113 \\ Losses & 4.8 \(\pm\) 0.008 & 6.8 \(\pm\) 0.148 \\ \hline \hline \end{tabular}  
* Theoretically calculated value.  
\end{table}
Table 2: Summary of the material dissipation results inside the vacuum chamber for both MS and DS processes.  
The SEM micrographs of DS deposition, shown in Figure 4b, let us see a more defined columnar structure through the thickness. The deposition rate in this case was 0.3 \(\upmu\)m/min.
## 2 Model Development and Numerical Computation
### Establishment of S-RM Models in the DEM  
To establish a discrete element model for the stochastic microstructure, the complex morphology of gravel granule clumps must be carefully considered. The system described below was employed to develop the discrete element model for S-RMs. Single spherical granules and particle clumps were used to simulate sand particles, gravel granules, and S-RMs in numerical simulation, as illustrated in Figure 4.  
* The CT-scanned gravel microstructure model was imported into the PFC3d program. Subsequently, leveraging the parameters established in Section 2.2, the PFC particle clump template system was utilized to generate all gravel granule clumps within the microscale mold, as illustrated in Figure 4a.  
Figure 2: Stochastic microstructure model of S-RMs in DEM with different structural characteristics. Different gravel content (GC = 10%, 20%, 30%, 45%, 55%, 70%, 80%).  
* Based on the determined scales of the microscopic pattern, a test piece model comprising simple sand particles of uniform dimensions was established. Notably, the radius of the sand particles should be less than one-fifth of the gravel granule size, as shown in Figure 4b.
* Irregular clumps of gravel granules were arbitrarily generated within the specimen of simple sand particles. Sand particles overlapping with these gravel clumps were eliminated, and the S-RM model was subsequently established. To ensure close contact between gravel clumps and sand particles, and to prevent any particles from entering a floating state, the radius of all sand particles was initially scaled to 1% of their original size after the gravel particle clumps were clustered into the simple sand pattern. Next, sand particles in active contact with the gravel clumps were removed. The radii of the remaining sand particles were then gradually increased back to their original dimensions, with cycle operations performed after each adjustment to ensure continuous compaction and intimate contact between gravel and sand particles. Figure 4c illustrates the final three-dimensional discrete element model of the S-RM.  
The discrete element model of S-RMs with varying gravel content, established using the aforementioned approach, exhibits higher relative density and an optimal particle quantity. This enhances the efficiency of numerical computations and ensures the reliability of calculation results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{\
\begin{tabular}{c} **Gravel** \\ **Content** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Consolidation** \\ **Pressure (kPa)** \\ \end{tabular} } & \multicolumn{3}{c}{**Numbers of Particles**} & \multirow{2}{*}{**Initial Void Ratio \(\epsilon_{0}\)**} & \multicolumn{2}{c}{**Consolidation-**} \\ \cline{5-8}  & & & **Gravel Clumps** & & **Sand Particles** & **Induced Void Ratio \(\epsilon\)** \\ \hline  
& & 0.438 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the three-dimensional model of S-RM.
## 3 Materials and Methods
### Classifier
As shown in Figure 4, a multi-scale attention pooling fusion framework through multi feature fusion and dynamic attention mechanism is described. Compared with traditional pooling, attention pooling is data-dependent and learnable, which can adaptively adjust information aggregation strategies. The weights are dynamically allocated, and the attention weight of each element is calculated based on the input feature sequence itself, rather than based on fixed rules, so as to achieve the function of content-aware aggregation. Specifically, the attention module computes importance weights across different time steps in the traffic sequence, enabling dynamic aggregation of temporal features. Important information will receive more weight, and irrelevant information will be suppressed, avoiding information loss in maximum pooling or blurring in average pooling, and retaining fine-grained features. In this way, attention pooling highlights semantically rich segments of traffic patterns and provides the model with more discriminative input representations than traditional pooling methods. The attention score of each element is calculated using a learnable function, as shown in Formula (8), where each \(e_{i}\) denotes the importance score of the \(i\)-th time step in the input feature sequence \(\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{n}\). The normalized weights \(\alpha_{i}\) enable the model to perform dynamic temporal aggregation by focusing on key segments and down-weighting uninformative or noisy inputs. The aggregated representation \(\mathbf{z}\) is computed as a weighted sum of input features, which preserves fine-grained discriminative cues. The weight matrix \(\mathbf{W}\in\mathbb{R}^{d\times d}\), bias term \(\mathbf{b}\in\mathbb{R}^{d}\), and scoring vector \(\mathbf{v}\in\mathbb{R}^{d}\) are learnable parameters, enabling the model to adaptively optimize the feature weighting scheme based on the content of the input. \[e_{i}=\mathbf{v}^{\top}\tanh(\mathbf{W}\mathbf{x}i+\mathbf{b}),\quad\alpha_{i }=\frac{\exp(e_{i})}{\sum j=1^{n}\exp(e_{j})},\quad\mathbf{z}=\sum_{i=1}^{n} \alpha_{i}\mathbf{x}_{i} \tag{8}\]  
Overall, the coordinated design of these three layers enables EFTransformer to have powerful multi-level feature extraction, adaptive feature weighting, and classification capabilities, and it can cope with complex encrypted traffic detection scenarios. Figure 4: Attention pooling.
## 4 Tracking
### Estimation
#### 4.2.4 Ckf  
The CKF was proposed by [PERSON] and [PERSON] [13]. This algorithm also inherits the Kalman filtering framework, and uses the numerical integration method of the spherical-radial cubature rule to approach the Gaussian integral. Similar to the UKF, the CKF also selects a set of point sets, and passes the selected point set through the nonlinear function, and then approximates the Gaussian integral in the filtering of the nonlinear system through a series of calculations. The CKF also avoids the large error caused by linearization of nonlinear functions by the EKF algorithm. It can handle any nonlinear system, and it does not depend on the specific equation of nonlinear function in the process of filtering. Unlike the UKF, the CKF algorithm used the spherical-radial cubature rule and Bayesian estimation to approximate the integral nonlinear Gaussian filter.  
Regarding the system model shown in Table 18, the third-degree spherical-radial cubature rule selects \(2n\) cubature points as follows:  
\[\begin{split} x^{(i)}&=\bar{x}+\bar{x}^{(i)}\  
mobile phone base stations, and resulting in conclusion that in tested scenarios no particular filter in this family is clearly superior.  
Therefore, although the CKF was developed later than the UKF, we find that it does not have the obvious advantages of UKF. Recently, some developed nonlinear estimation methods have been proposed, but most are based on the fundamental principle of a UKF or CKF. For example, from the numerical-integration viewpoint, a sampling points set was derived by orthogonal transformation of the cubature points in Ref. [88]. By embedding these points into the UKF framework, a modified nonlinear filter, designated a transformed unscented Kalman filter (TUKF), is derived. The TUKF purportedly can address the nonlocal sampling problem inherent in the CKF while maintaining the virtue of numerical stability for high dimensional problems. [PERSON] et al. [89] pointed out that traditional filters providing local estimates of the state, such as the EKF, UKF, or CKF, are based on computationally efficient but approximate integral evaluations, and therefore proposed a general local filter that utilizes stochastic integration methods providing the asymptotically exact integral evaluation with computational complexity similar to the traditional filters.  
From all of these research works, we conclude that the UKF and CKF are the popular estimation methods for the nonlinear systems, because they all have efficient computation speeds and nonlinear processing ability. However, for nonlinear highly complex problems, the performance of both filters still must be improved.
# Analysis of Macroscopic Traffic Network Impacted by Structural Damage to Bridges from Earthquakes
## 2 Literature Review
proposed a method to prioritize seismic reinforcement of bridges within road networks in California and created probabilistic earthquake scenarios to evaluate the effects of seismic reinforcement by measuring traffic delay for various scenarios [11]. [PERSON] proposed a methodology to select disaster evacuation roads after the Great East Japan Earthquake. The disaster response transportation plan was developed by separating emergency transportation routes [12]. [PERSON] et al. developed a scenario-based methodology to integrate trip distribution and an assignment model to measure the performance of post-earthquake transportation systems. The proposed model was applied to the Sioux Falls road network [13]. While structural damages from earthquakes have been investigated in various capacities, there has been a limited examination of the impact on traffic networks and the resilience of bridges after earthquakes [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. Additionally, most impact analyses of traffic networks have been based on the simplified application of traffic capacity. To better analyze traffic networks impacted by structural damage to bridges from earthquakes, various speed restrictions and lane closure scenarios are examined in this study.
# Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles
## 4 Description of the Missions and Tasks Register and Reporter (MTRR)
### Autonomous Underwater Vehicle (AUV) Virtualization at the MTRR  
Virtualization can be defined as the creation of a virtual representation of physical entities, including hardware platforms like AUVs. The Internet of Things Architecture (IoT-A) reference model provides an interesting domain model that includes the virtualization of physical entities by associating their capabilities to resources that are exposed as service [52]. Figure 7 illustrates this part of the IoT-A domain model.  
In the MTRR we have adopted internally a similar approach for the AUVs and unmanned surface vehicles (USVs) used in a mission. Figure 8 shows an overview of the internal virtualization used.  
The physical vehicle has an onboard computer. In this onboard computer there is a software robot supervision module and depending on its capabilities, a robot planner module. On the other hand, running inside the MTRR (hence identified as a \"network resource\" following the IoT-A terminology), there is the \"plan controller\", accessed as a part of the virtual vehicle that represents the physical one.  
The virtual representation of a vehicle is created when the MTRR receives a new mission plan. As part of the start mission procedure, the MTRR parses the vehicles assigned to the mission, and extracts its corresponding mission plan, creating an internal virtual representation. The purpose of this virtualization is to abstract the dispatcher part of the MTRR regardless of whether the vehicles have an on-board planner or not. In this way, the dispatcher part of the MTRR only has to request to dispatch the next task in the plan for the virtual representation of the vehicle, and it is this virtual representation that is responsible for selecting if it has to follow the high-level or low-level tasks of the corresponding part of the mission plan.  
Figure 7: Internet of Things Architecture (IoT-A) domain model restricted to the virtual representation of a physical entity [52].
# Distance Protection for Coexistence of 5G Base Station and Satellite Earth Station
## 3 Distance Protection Methods
### Restriction Zone and Interactive BS Tx Power Control Scheme  
Up to this point, we have considered the distance protection method, using the exclusion zone to protect the FSS ES from interference from a single BS, where the value of I/N is less than, or equal to, the threshold. Outside the exclusion zone, the interference from a single BS is lower than the threshold; however, the aggregate interference from the other BSs exceeds the threshold. Therefore, a restriction zone is introduced to manage the aggregate interference from the cellular network to within the threshold. The derivation of the restriction zone and Tx power control list are as shown in Figures 8 and 9, respectively. Based on the exclusion zone, we consider different target areas to deploy cellular networks, as shown in Figure 8. Then, we calculate the number of BSs in the restricted area, so the interference margin (\(-12.2\) dB) can then be evenly allocated to the BS outside the restricted area to derive the restriction zone outline. Subsequently, according to the relationship between interference (\(x,y\)) and the restriction zone threshold, two kinds of BS Lists are used in the Tx power control inside the restriction zone.  
Figure 8: Flowchart for the distance protection method: Restriction zone.  
Figure 9: Flowchart for distance protection method: Interactive BS Tx power control scheme.  
To control the Tx power of the BS, so that the total interference of all BASs is lower than the interference margin, this paper proposes the Iterative BS Tx Power Control Scheme inside the restriction zone, as shown in Figure 9. Iterative BS Tx Power Control Scheme inside the restriction zone is based on the previous method and determines whether to start Interactive Power Control Scheme by calculating the initial mean interference margin. After starting the scheme, first, we reduce the Tx power of the BS in the List_PowerControl BS by 1 dB and clear the List_NoControl BS, to achieve the effect of updating the interference of the \(BS_{s}\) in the List_PowerControl BS. If the interference of \(BS_{i}\) in the updated List_PowerControl BS is less than, or equal to, the mean interference margin, it needs to be moved to the List_NoControl BS, otherwise the Remain \(BS_{i}\) in List_PowerControl BS. If no BS remains in the List_PowerControl BS after relisting, the procedure ends. Otherwise, we need to update the mean interference margin, and then continue with the previous steps, knowing that there is no BS left. Compared with the restriction zone of the simple uniform BS Tx power control scheme, the advantage of the iterative power control scheme is its fairness. In this scheme, the BSs that create relatively lower interferences can be removed from the power control earlier, which results in lower Tx power reduction. Additionally, the BSs that generate relatively higher interferences remain in the power control list for an additional iteration of power reduction.
## 6 Evaluation
### Learning Performance and Computational Cost
#### 6.1.2 Accuracy Comparison for Independent and Identically Distributed Data (IID)  
Both the MNIST [29] and Fashion-MNIST [30] datasets contain 60,000 samples for training. In this experiment, we randomly and equally distributed the training data to all clients to ensure IID. We compared 2, 5, 10, and 20 clients. Regarding server numbers, because FedAvg [2] has only one server in its design, we mainly compared Fedshare [8] and our method. We performed 2 experiments with a total number of servers being 5 and 10. Additionally, the method we proposed is \(t\)-out-of-\(n\), and we set \(t\) to 2 for the experiments.  
The experimental results are shown in Figures 6 and 7. We included the accuracy rate from the most basic machine learning model as a baseline for comparison. It can be seen from the four figures that regardless of whether it is the MNIST [29] or Fashion-MNIST [30] dataset, the accuracy decreases slightly as the number of clients increases. However, the number of servers does not affect the accuracy of the model after training. The accuracy of the three federated learning methods differs from that of the basic machine learning model. This may be related to the number of local training iterations and the dataset distribution. Figure 7 shows that there is a slight, but insignificant, difference in accuracy between 5 and 10 servers. In addition, our proposed method has basically the same the accuracy as FedAvg [2]. This shows that our method can protect the gradient without affecting the accuracy of federated learning.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 4 Two Case Studies of the Proposed Framework
### Cab Dispatch System  
This subsection presents our second case study: an event-driven cab dispatch system designed for edge computing environments. Such environments demand systems capable of efficiently handling diverse and bursty data streams, effectively utilizing multicore architectures, and dynamically adapting to evolving sensor networks. Our prototype system tackles these challenges and showcases the suitability of Elixir for edge computing applications through its robust concurrency and real-time data processing capabilities.  
Figure 3 provides an overview of the system architecture. The detailed implementation aspects of this architecture are discussed in the subsequent section. User-facing passenger and driver applications act as event producers. Passengers initiate ride requests, while drivers simulate location updates, both publishing lightweight messages to a central MQTT broker. A dedicated Elixir-based dispatch service acts as an event consumer, subscribing to these messages and dynamically assigning drivers to passengers based on real-time location and predefined rules.  
Elixir's strengths excel in this context, enabling the system to meet the demands of edge computing:  
* Lightweight process model: Efficiently manages high concurrency arising from numerous passengers and drivers, eliminating performance bottlenecks.
* Asynchronous messaging: Facilitates real-time responses, ensuring a quick and efficient dispatching experience with minimal latency.  
By leveraging these key advantages, our prototype demonstrates the effectiveness of Elixir in latency-sensitive edge computing applications. It paves the way for wider adoption of Elixir in similar scenarios, particularly those requiring robust real-time processing and reliable service delivery.  
Figure 3: Utilization of the publish/subscribe model in the cab dispatch system.
## Abbreviations  
The following abbreviations are used in this manuscript:  
ATM  
Air Traffic Management  
CPS  
Cyber-Physical System  
ATC  
Air Traffic Control  
CNS  
Communication, Navigation, and Surveillance  
ICAO  
International Civil Aviation Organization  
GNSS  
Global Navigation Satellite System  
ADS-B  
Automatic Dependent Surveillance-Broadcast  
SWIM  
System-Wide Information Management  
DDoS  
Distributed Denial of Service  
KGC  
Key Generation Center