esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results  
### Genotyping of Patients with Dementia and CN Volunteers, Association of PRS with Dementia  
The study included 348 patients with dementia and 519 cognitively normal volunteers. In total, 867 DNA samples were genotyped for 23 SNPs, including 21 polygenic risk markers according to the model of [PERSON] et al. [9], as well as rs429358 and rs7412 that define the \(\varepsilon\) alleles of the _APOE_ gene. A multiplex assay utilizing hybridization on low-density hydrogel microarrays was the primary method for SNP genotyping. The genotype and allele frequency data based on the results of SNP identification are presented in Table S1. The distribution of genotypes for all studied markers conformed to the Hardy-Weinberg equilibrium. Genotyping results for 21 PRS markers from 100 DNA samples (50 from dementia patients and 50 from CN volunteers) were validated using high-resolution melting analysis and selectively confirmed via Sanger sequencing. Additionally, _APOE_ alleles were identified via real-time PCR using hydrolysis probes for all samples. The genotyping results obtained by various methods were consistent for 100% of the samples.  
For each sample, the PRS was calculated. The PRS values ranged from \(-1.148\) to 1.096. A PRS less than \(-0.268\) corresponded to the first quartile, while a PRS greater than or equal to 0.1725 corresponded to the fourth quartile. The thresholds for the first and top deciles were \(-0.4598\) and \(0.3924\), respectively.  
The distribution of PRS in both patients with dementia and CN volunteers is displayed in Figure 1. A significant difference was observed in the mean PRS between patients with dementia and CN volunteers (\(t\)-test, \(p\)-value = 0.001). The logistic regression analysis indicated an association between dementia and both PRS and _APOE-\(\varepsilon\)4_ for both homozygotes and heterozygotes, as shown in Table 1. Since the \(\varepsilon\)2-allele carriage was not a significant factor in this study, it was not included in the further analysis of how genetic factors impact the risk of dementia.  
Figure 1: Distribution of PRS in patients with dementia and CN volunteers. The boxplots show the median, with the two hinges corresponding to the first and third quartiles and two whiskers. The upper and lower whiskers extend from the hinges to the largest value no further than 1.5 \(\times\) IQR from the corresponding hinge (where IQR is the interquartile range). Points beyond the whiskers indicate the outliers.  
Adjusted for _APOE-e4_, individuals with a PRS falling within the fourth quartile or tenth decile had a higher risk of dementia compared to those in the first quartile/decile: OR = 1.85 (95% CI = 1.25-2.74; _p_-value = 0.002) and OR = 2.27 (95% CI = 1.22-4.28; _p_-value = 0.01), respectively.  
Next, we examined the extent of the impact of _APOE-e4_ as a risk factor in individuals with varying PRS values. Compared with non-carriers, carriers of _APOE-e4_ with the fourth quartile of PRS had a greater risk of dementia, while the presence of the _APOE-e4_ in individuals with the first quartile was not a significant risk factor in our cohort (Table 2).
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 3 Communication Protocols
### Multi-Access Protocols
#### 3.2.5 Code Division Multiple Access (CDMA)  
CDMA has been explored for CA in LoRaWAN. CDMA allocates orthogonal codes to each end-device individually and allows for several end-devices to send data simultaneously over a channel. In [65], the authors propose an improved CA algorithm that combines CDMA and ALOHA. Specifically, a certain orthogonal spreading code is assigned to each end-device, and the end-device transmits encoded data using its own spreading code. End-devices can transmit simultaneously at the same frequency, time, and space without collision because different SFs are used. A number of end-devices may use the same spreading code, in the case the number of end-devices is greater than the spreading codes. A collision occurs when end-devices using the same spreading code transmit data at the same time. In this case, ALOHA can be used to switch the end-device to back-off mode. This algorithm was evaluated and compared with slotted ALOHA, 1-persistent, non-persistent, which shows that it increases the network throughput.  
To sum up Table 5, summarizes the basic features of each multi-access protocol in LoRaWAN.
## 8 Application Challenges and Strategic Directions for Digital Twin Integration
### Cybersecurity Considerations for Connected Digital Twins  
As DTs increasingly operate in connected environments--integrated with in-vehicle networks, cloud-based analytics platforms, and over-the-air (OTA) update systems--they introduce a new class of cybersecurity risks. These include  
* Data spoofing attacks, where manipulated sensor inputs are injected into the DT, leading to invalid conclusions or inappropriate responses;
* Model leakage and IP theft, as adversaries could reverse-engineer simulation models or extract system parameters through observation of DT outputs;
* Attacker training, as attackers could practice and evaluate attacks virtually with little risk of detection;
* Unauthorized access to test interfaces, which potentially compromises the integrity of the test environment or allowing command injection into safety-critical systems.  
These risks necessitate a cybersecurity-first approach to digital transformation (DT) architecture. To mitigate vulnerabilities, future DT platforms should implement
* Encrypted communication protocols to safeguard data exchange between components and external systems;
* Isolated model execution sandboxes to prevent cross-domain interference;
* Secure and restricted interface layers to limit access strictly to authenticated and validated simulation APIs.  
These risks underscore the importance of embedded emerging automotive cybersecurity regulations, such as ISO/SAE 21434 [51], into DT lifecycle management and test documentation practices. Aligning DT architectural practices with these standards ensures compliance with both industry expectations and related regulations, such as UNECE R.155 [52], which mandates certification of cybersecurity management systems for type approval.
# Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach
## 4 Use Case
### Observed Anomalies  
Beyond quantitative metrics, a qualitative evaluation of the enriched transport routes was performed to identify typical system-level errors, modality mismatches, and correction patterns. The IAES application exhibited some edge cases during route enrichment, which were analyzed in detail for future improvements:  
1. Structural anomalies: A total of six self-loop routes were identified, where the starting and ending nodes were identical (A \(\rightarrow\) A). These are typically unintended and indicate either a geocoding fallback failure or incorrect source-to-destination mapping. These routes do not negatively affect subsequent graph operations.
2. Mode-to-node mismatches: Several semantic mismatches between the mode and waypoint type were detected. Twelve bidirectional air routes were incorrectly initiated from locations classified as seaports. A prominent example is the case of the Port of Newark, where the generated waypoint shares coordinates with New York International Airport. While the geographic location is technically accurate due to the spatial overlap of port and airport facilities in major logistics hubs like New York, the assigned waypoint type was semantically incorrect. The LLM classified the node as a seaport, yet assigned air as the transport mode. This mismatch illustrates the need for more precise type validation logic beyond coordinate proximity alone. Additionally, two bidirectional rail routes originating from customer nodes were identified as using rail transport. Upon manual verification using Google Maps, this assignment appears valid, as the customer locations indeed have direct railway connections.
3. Incorrect node reuse and name-based confusion: In 12 cases (pairs), routes were assigned between one incorrectly named and classified waypoint (e.g., \"Truck Terminal Munich\" was in reality \"Airport of Munich\"). This problem is based on incorrect localization by the LLM for road waypoints, which in the SourceSearcher later matched with the wrong location due to a proximity-based distance match.
4. Planner-based correction for modality conflicts: In two notable cases (pairs), the route planner suggested the mode \"River\" for inland nodes (e.g., \"Bratislava Seaport\"). In this case, the RoutePlanner suggested the mode correctly after the SourceSearcher (LLM) recognized that Slovakia is a landlocked country and responded that no valid waypoint could be generated and changed the mode type to river.
5. Location-based filtering: Another 12 routes pairs were removed due to coordinate errors, typically when the generated node could not be geolocated within a valid  
Figure 8: The top 15 waypoints by the number of connected routes: the diagram illustrates the centrality of major waypoints, based on the number of connected inbound and outbound routes.  
proximity to known transport infrastructure. These were flagged and discarded during the post-enrichment validation step.
# Optimization and Experimental Verification of Lower
## 1 Introduction  
Globally, the expansion of sustainable and eco-friendly urban public transportation is being pursued, with trans increasingly being adopted as a solution [1; 2; 3]. The introduction of tram vehicles is on the rise, as trans operate on electric power, which does not contribute to air pollution, making them a highly regarded environmentally friendly transportation option [4; 5; 6]. Low-floor terms, which consider passenger convenience during boarding and aligning, are predominantly being introduced [7; 8; 9]. In the Republic of Korea, recent decisions have confirmed the introduction of trans, and domestic rail vehicle manufacturers are designing and producing low-floor terms. However, one of the major systems in low-floor trams, the gangway system, remains reliant on foreign products [10]. The low-floor tram gangway system consists of upper and lower articulation, bellows, and turntables. Among these, the lower articulation is classified as a key component of the gangway system [11]. It not only supports the vehicle and passenger loads, but also needs to accommodate loads in the fore-aft and vertical directions while providing flexible lateral rotation during operation. Therefore, a lower articulation is crucial for the gangway system. Optimization of design and manufacturing research are broadly being conducted to address the structural strength, durability, and weight reduction of trans [12; 13; 14].  
Techniques commonly applied in optimization design include topology optimization, experimental design methods, and meta-models. These techniques generally aim to reduce the structural volume and thickness while maintaining the required strength level through shape modifications [15; 16; 17; 18]. We viewed studies that have applied these methods: [PERSON] [15] performed topology optimization to enhance the stiffness of a control arm by changing its geometric dimensions and structural properties, resulting in a 30% reduction in component weight. [PERSON] [16] applied topology optimization to the front upright structure for weight reduction in formula car suspensions, achieving a mass reduction ofover 60% and confirming performance through simulations and validations. [PERSON] [17] optimized an upper control arm using response surface modeling (RSM) and Kriging models based on FEM analysis, resulting in a weight reduction of 4.13% and 5.22%, respectively. [PERSON] [18] compared heuristic optimization and meta-models for concrete bridge design, reducing computational costs according to optimization techniques. Despite these various optimization design studies, there is a lack of experimental validation through the structural testing of products produced via optimization design.  
This paper aims to address this gap by focusing on the lower articulation applied to low-floor terms. Using meta-model techniques, we will derive structural analyses and optimization design parameters, produce three optimized models of the lower articulation, and validate the feasibility of the optimization design through structural and fatigue testing.
## 5 Our Proposed Scheme
### Cluster Head Selection Process  
The primary metric for selecting CHs is the residual energy of a node, as it is considered an optimal parameter for achieving better performance, while the distance to the BS is considered a secondary metric in the selection process of the CHs. In this regards, the following three cases are inspected. When a node is elected as a CH in the second round, it generates an ADV message and broadcasts it towards its CMs, as shown in Figure 6a.
## 6 Simulation Evaluation of the Dynamic VWSN
### Evaluation Metrics  
In this paper, we define that reliability of a VWSN topology consisting of its robustness and adaptivity. The robustness of a VWSN topology, which is defined as the ability to keep its connectivity high and path length short even when nodes are removed, is shown in our previous work [13]. Here, therefore, we evaluate the adaptivity of a VWSN topology. In this paper, we define adaptivity as the ability to quickly find an alternative path after node failure. To evaluate the adaptivity of the VWSN topology, we evaluate the time (\(T_{recovery}\)) needed for the data delivery ratio to recover to at least 99.9% after the first sensor node fails. We also evaluate the number of control packets that were sent to find an alternative path (\(CP_{recovery}\)) after node failure. We evaluate the data delivery ratio from \(t-500\) (in s) to \(t\), where \(t\) is an arbitrary time in the simulation. As we did in our previous paper, we assume two removal modes: random failure and targeted attack. The node to be removed at the next time step is selected randomly in random failure mode, and the node with the highest degree is selected in targeted attack mode.
# Simulation of Calibrated Complex Synthetic Population Data with XGBoost
## 3 Calibration of a Population
### Post-Calibration with Combinatorial Optimization
* Convergence of the SA is very slow because households are replaced using simple random sampling. Applying this algorithm to real-world applications, a population of tens of millions of people will yield costly run times and possibly fail to converge. * Parameters \(T\), \(c\) and \(n\) can strongly influence the convergence of the SA and adopting the choice of these parameters when the problem is split into multiple smaller subgroups is not supported. ```
1:Set starting values regarding:
2:- Maximum number of iterations
3:- Starting temperature \(T\), cooldown rate and maximum number of cooldowns \(c\)
4:- Initial swap rate \(n\)
5:- Allowed relative error \(\epsilon>0\)
6:- Initialize a first selection \(\Delta=(\delta_{1},\ldots,\delta_{N^{\prime}})\), calculate initial synthetic margins \(\mathbf{m}\), and the value of the objective function: \[Obj=f(\mathbf{t},\hat{\mathbf{t}})=\sum_{m=1}^{M}|t_{m}-\hat{t}_{m}|\]
7: Randomly add and remove \(n\) persons, including all household members, resulting in a new selection \(\Delta_{new}\). 8: Calculate the new value of the objective function \(Obj_{new}\) using \(\Delta_{new}\). 9:if\(\frac{Obj_{new}}{N}\leq\epsilon\)then
10: Convergence reached, terminate the algorithm and return \(\Delta_{new}\). 11:endif
12:if\(Obj_{new}<Obj\)then
13: Update \(\Delta\leftarrow\Delta_{new}\). 14:else
15: Update current selection \(\Delta\) with new selection \(\Delta_{new}\) if \(u<\exp\Bigl{(}-\frac{Obj_{new}-Obj}{T}\Bigr{)}\) where \(u\) is drawn from \(U(0,1)\); otherwise, keep \(\Delta\). 16:endif
17: Increment counters and reduce temperature, if needed. 18:if Maximum number of iteration or minimum temperature reached then
19: Terminate the algorithm and return current selection \(\Delta\). 20:else
21: Go to step 3. 22:endif
```  
**Algorithm 3** Calibration of the Synthetic Population using Simulated Annealing  
In [29], applying the SA algorithm to a synthetic population of the Bangladesh Coastal Zone revealed various challenges. The population, which consists of 37.2 million individuals, was generated using a micro data set from the 2011 Bangladesh National Census through IPUMS (see [30]). The improvements to the SA algorithm discussed here are largely influenced by its application to this real-life data set. Subsequently, we will present the SA improvements in detail.
## Materials and Methods  
### System Configuration of SS-OCT  
The rice leaf specimens were imaged using a commercial SS-OCT system (Thorlabs. Inc, OCS1310V1 OCT). The OCT system uses a MEMS-VCSEL sweep source laser. Figure 2 depicts the system configuration of the utilized SS-OCT system. The OCT system operates at a center wavelength of 1300 nm \(\pm\) 15 nm and average output power of 20 mW with a broadband tuning range of more than 97 nm. The swept laser source used in this OCT system has A-scan line sweep rate of 100 kHz. A numerical aperture of the objective scan lens was 0.11, and the sensitivity of the OCT system was 105 dB. The interference signal of the interferometer is received from the detector and sent to the DAQ. The interferogram is sampled with equal temporal intervals, where the data set is evenly distributed in time domain but not necessarily in the frequency or wavenumber domain. Unless the frequency/wavenumber is tuned linearly with time, direct FFT on this data set, which is non-equidistantly distributed in wavenumber domain leads to significant transform errors. Hence, each pair of OCT signal and clock signal were examined by the nearest neighbor check algorithm for crossing points, which can be performed according to methods reported in [45; 46]. Next, the OCT data set was indexed by referencing these searched points prior to FFT process [47]. The axial resolution of the system is 12 um in tissue and 16 um in air, and the lateral resolution including the objective lens (Thorlabs. Inc, LSM03) is 25 um in tissue and 33 um in air at focus. The inset images of Figure 2 illustrate the horizontal and vertical scanning directions of leaf specimens. The maximum field of view of the OCT system is \(10\times 10\times 3\) mm. The detailed configuration of the system is reported elsewhere [48].
## 3 Data and Data Processing
### Satellite Images  
Images from the LANDSAT 7(ETM+) satellite were utilized to analyze the LULC. On 15 April 1999, NASA launched LANDSAT 7, which is equipped with the ETM+ (Enhanced Thematic Mapper plus) with four modes: SWIR (Shortwave Infrared), TIR (Thermal infrared--Thermal infrared range), PAN (Panchromatic--Panchromatic range), and VNIR (Visible and Near Infrared). The Thermal spectral bands are provided by the LANDSAT 7 ETM+ instruments, which offer an improvement from 120 m to 60 m. From GloVis, EarthExplorer, and Landsat-Look Viewer, users can access the LANDSAT 7 ETM+ scenes.  
The research study has been conducted by LANDSAT 7 images from [https://www. usgs.gov/landsat-missions/landsat-data-access]. On every occasion the image is oriented North Up, there is an overall of 11 bands or projection units in meters. Band 4 provides vegetation-related geographical features in TIFF format. The scenes that are pulled are TIFF files that are RASTER pictures of Bands 1, 2, 3, 4, 5, and 7. Multiple pages and layers, as well as red, blue, and green, are supported by the scenes. Multiple layer images with a depth of either 8 bits per channel or 16 bits per channel can be found in a single TIFF file. LANDSAT 7 scenes were taken from the time period of 2014 to 2022 for the analysis of the LULC over the years.
## 2 Solar-Hydrogen Storage System
### Workflow of Solar-Hydrogen Storage System  
A solar-hydrogen storage system's workflow consists of a number of interrelated procedures that allow solar energy to be converted into hydrogen gas, which is then stored and used for energy production or other purposes [128; 129]. The following is a detailed breakdown of the standard procedure:  
* Solar Energy Harvesting: Using solar panels or concentrators, solar energy is first captured to start the process. Through the process of the photovoltaic effect, solar panels, which are made up of photovoltaic cells, directly transform sunlight into electricity. Solar dishes and parabolic troughs are examples of concentrators that direct sunlight onto a receiver in order to produce heat [130; 131; 132]. * Electricity Generation and Heat Production: Depending on the solar energy harvesting technology being employed, the solar energy collected by the solar panels or concentrators is transformed into either heat or electricity. The following phase of the operation is then fed with the produced heat or energy [133; 134; 135]. * Electrophysic for Hydrogen Production: Once the electrolyzer, which separates water (H\({}_{2}\)O) into its component parts of hydrogen (H\({}_{2}\)) and oxygen (O\({}_{2}\)), is powered by the electricity or heat produced, water in the electrolyzer undergoes electrolysis when an
## 2 Rheology of Starch--Its Measurement, Instrumentation, and Limitations
These drawbacks can be associated with a poor or inconsistent definition of both the measured parameters or protocols that are utilized in each laboratory, the time-consuming nature of some of the current methods (e.g., the time required for sample preparation), the difficulties in the interpretation of the results (e.g., results based on the interpretation of a single parameter or the effect of moisture), as well as issues associated with sample requirements (e.g., the number of replicate samples, sample particle size--including preprocessing of the sample prior to analysis, such as grinding--or the effect of the temperature or harvest conditions) [15, 16, 17, 18, 20, 21]. Figure 1 illustrates the typical RVA profile obtained after analyzing different rice flour samples with varying concentrations of AMY. It has been observed that the start and end of each stage or step in the RVA profile depends on the temperature gradient (black dotted line) of the pasting method utilized. Overall, a standard RVA profile yields the following parameters: the pasting temperature (PT), peak viscosity (PV), Time to Peak (PIi), breakdown (BD), Trough/Minimum Viscosity (TV), setback (STB), and final viscosity (FV) (Figure 1) [15, 16, 17, 18]. While the definitions and significance of these parameters remain consistent among researchers and applications of this technique, different abbreviations are often used, and some of these are summarized in Table 1.  
Figure 1: Typical Rapid Visco Analyser (RVA) profile of different rice flows with varying amylose contents, indicating the main parameters that are calculated during the analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Abvs. ** & **Definition** & **Significance** \\ \hline \multirow{4}{*}{ \begin{tabular}{c} **Pasting** \\ **Temperature** \\ \end{tabular}  } & \multirow{4}{*}{PT, PTemp} & The temperature at which the sample begins to thicken, and the viscosity starts to rise. It marks the onset of starch gelatinization as the granules absorb water, swell, and disrupt. & PT indicates the gelatinization temperature, which is important for understanding the processing conditions required for starch-based products. \\ \hline \multirow{4}{*}{\ \begin{tabular}{c} **Peak Viscosity** \\ **Peak Viscosity** \\ \end{tabular}  } & \multirow{4}{*}{PV} & The highest viscosity reached during the heating phase of the RVA analysis. It occurs when starch granules are fully swollen and before they start to break down. & PV reflects the maximum water-holding capacity and swelling of the starch granules. It is crucial for determining the thickening potential of starch in a product. \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Time to Peak** \\