esa-sceva/satcom-chunk-collection huggingface dataset:
# Relaxed Data Types as Consistency Conditions +
## 7 Application: Hybrid Consistencies
_Each Peek instance_ \(op\) _with_ \(\Pi=\pi.op.\rho\) _returns a value such that_ \(\pi^{\prime}.op\) _is legal, where_ \(\pi^{\prime}\) _is obtained by deleting a contiguous sequence of up to_ \(k\) _instances from a minimal-length sequence equivalent to_ \(\pi\)_. That is,_ \(op\) _returns one of the_ \(k\) _oldest elements currently in the queue and may return_ \(\bot\) _if there are fewer than_ \(k\) _elements currently in the queue._  
Exactly as with the Out-of-Order consistency condition, we can express this as a relaxation, but, in this case, we only need to relax the \(Peek\) operation, not all operations. **Definition 21** (Augmented FIFO Queue with Relaxed \(Peek\)). :
1. \(OPS=\{Enqueue,Dequeue,Peek\}\)
2. \(\mathcal{L}\) _is the set of all sequences of instances of operations in_ \(OPS\) _such that each Dequeue instance returns the argument of the first_ \(Enqueue\) _instance whose argument has not previously been returned by a Dequeue instance or_ \(\bot\) _if none exists. Each Peek instance returns the argument of one of the first_ \(k\) _Enqueue instances whose arguments have not been returned by a previous Dequeue instance or possibly_ \(\bot\) _if there are fewer than_ \(k\) _such_ \(Enqueue\) _instances._  
By construction, this relaxation is equivalent, under Linearizability, to an unrelaxed queue under the previous consistency condition. In both of these cases, hybrid consistency is arguably attempting to express something that is probably more naturally expressed as a relaxation of certain operations. In fact, the queue with relaxed \(Peek\) has appeared in the relaxed data type literature [15; 16] already. It seems intuitive that per-operation conditions like these hybrid consistencies are easier as relaxations of operations of the underlying data type. We are still limited in expressing them as such by the fact that we cannot currently express all consistency conditions as relaxations, but this provides both incentive and possible insight for extending the equivalence between the two models.
# Production and Processing of a Spherical Polybutylene Terephthalate Powder for Laser Sintering
###### Abstract  
This work describes the production of a spherical polybutylene terephthalate (PBT) powder and its processing with selective laser sintering (SLS). The powder was produced via melt emulsification, a continuous extrusion-based process. PBT was melt blended with polyethylene glycol (PEG), creating an emulsion of spherical PBT droplets in a PEG matrix. Powder could be extracted after dissolving the PEG matrix phase in water. The extrusion settings were adjusted to optimize the size and yield of PBT particles. After classification, 79 vol. % of particles fell within a range of 10-100 \(\upmu\)m. Owing to its spherical shape, the powder exhibited excellent flowability and packing properties. After powder production, the width of the thermal processing (sintering) window was reduced by 7.6 \({}^{\circ}\)C. Processing of the powder on a laser sintering machine was only possible with difficulties. The parts exhibited mechanical properties inferior to injection-molded specimens. The main reason lied in the PBT being prone to thermal degradation and hydrolysis during the powder production process. Melt emulsification in general is a process well suited to produce a large variety of SLS powders with exceptional flowability.  
selective laser sintering; powder bed fusion; additive manufacturing; PBT; powder; melt emulsification; powder production; polymer processing +
Footnote †: journal: Applied Physics Letters
# QESIF: A Lightweight Quantum-Enhanced IoT Security Framework for Smart Cities
## 4 Simulation Setup and Experimental Results
### Security Analysis (QBER and Attack Detection Rate)  
The QBER analysis conducted across 100 QKD sessions under three distinct channel conditions--clean (1% error), noisy (7% error), and eavesdropped (15% error)--demonstrates the adaptive capability of the QESIF. In the clean channel scenario, the average QBER remained exceptionally low at approximately 0.0103, well below the QESIF rejection threshold of 0.11, allowing all the generated keys to be accepted for encryption. Under noisy channel conditions, the QBER increased moderately to an average of 0.0714 yet still remained within the secure operational range, showcasing the QESIF's tolerance to en  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Parameter** & **Description** \\ \hline Number of IoT Devices & 100 nodes per scenario (distributed across transport, energy, healthcare) \\ \hline Number of Edge Gateways & 10 QKD-enabled gateways for local key management and routing \\ \hline Traffic Model & Exponential traffic generation, mean inter-arrival time = 100 ms \\ \hline IoT Protocols & MQTT (publish–subscribe), CoAP (request–response) \\ \hline Quantum Channel Noise Conditions & Clean (1\% QBER), Noisy (7\% QBER), Eavesdropped (15\% QBER) \\ \hline IoT Device Specification & ARM Cortex-M3 equivalent: 32 MHz CPU, 64 KB RAM \\ \hline Gateway Specification & Quad-core 1.4 GHz CPU, 2 GB RAM (edge computing node) \\ \hline Simulation Tools & QulSP (QKD simulation), OMNeT++ with INET and Castalia (IoT network stack) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation configuration parameters.  
vironmental quantum noise. In contrast, the eavesdropped scenario yielded a significantly higher QBER, averaging 0.1528, thereby exceeding the safety threshold in over 95% of the sessions, as depicted in Figure 2.  
The comparative analysis of Attack Detection Rate (ADR) across three benchmark datasets--TON_IoT, Edge-IIoTset, and Bot-IoT--demonstrates the superior detection capability of the proposed QESIF. As illustrated in Figure 3, the QESIF achieved the highest detection performance, recording 98.1% on TON_IoT, 98.7% on Edge-IIoTset, and 98.3% on Bot-IoT. In contrast, the next-best performer, QuIDS, achieved 93.5%, 94.1%, and 93.9%, respectively, across the same datasets. QRHE showed moderate performance, ranging from 92.3% to 92.8%, while HyQuSec and QLSN recorded lower ADRs between 89.1% and 91.2%. These results validate that the QESIF's Q-IDS, which incorporates entropy deviation, QBER response, and key drop monitoring, is more effective in identifying advanced threats, especially in high-noise and adversarial IoT environments.
# Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal
## 3 Results
### Split Frame Length Selection  
From Figure 5, it can be observed that there is an interaction between the split frame length and the reconstruction parameter \(\tau\) which probably influences the performance of classifier. Therefore, four split frame lengths (5 s, 8 s, 10 s, and 15 s) and two \(\tau\) values (11 and 12) were paired to extract TDA features. Five ML classifiers were used to predict VF, and the superior classifiers are listed in Table 4 with their accuracy.  
As shown in Table 4, DT and RF demonstrate superior classification performance. For the value of \(\tau\), the accuracy of 12 is generally higher than that of 11, so optimal \(\tau\) was selected as 12. Moreover, with increasing split frame length, the accuracy gradually decreases; as a result, optimal split frame length was chosen as 5 s.  
Figure 5: Box plots for four different split frame lengths. (**a**) 5s-length split frame: the median of the first local minimum of the MI function is obtained as 12; (**b**) 8s-length split frame: the median of the first local minimum is 10.5; (**c**) 10s-length split frame: the median of the first local minimum is 12.5; (**d**) 15s-length split frame: the median of the first local minimum is 11. And the “+” represents the outlier.
## 3 Results  
### Structure, Porosity, and Chemical Analysis of PLGA/HA+ \(\beta\)-TCP Scaffolds  
Control scaffolds showed an interconnected pore structure with round morphology (Figure 11). The solvent casting and particle leaching technique (SC/PL) produced highly porous structures with heterogeneous pore sizes of 120, 220, and 680 \(\upmu\)m. In addition to high porosity, the composites showed high interconnectivity, resulting in a larger surface area and greater fluid retention capacity than the control group. The ceramic particles were dispersed in the matrix forming agglomerations, which increased by increasing the amount of ceramic; these agglomerations were mainly detectable in groups G4, G5, and G6, whereas in groups G1, G2, and G3, a great portion of the ceramic particles was coated by collagen (Figure 1).  
The collagen coating (groups G1, G2, and G3) covered the exposed ceramic particles and rounded the irregular pores within the PLGA matrix (Figure 2).  
EDS analysis revealed the presence of carbon (C), oxygen (O), calcium (Ca), and phosphorus (P) (Figure 3). The carbon derived from the polymeric matrix as well as from the collagen (in G1, G2, and G3). Oxygen was present within the matrix and in the calcium phosphate particles. Calcium and phosphorus derived exclusively from calcium phosphates.
## 2 Research Background
### Optimization Methods Developed for RIS Analysis
The authors in [20] and [43; 48] proposed a few techniques to improve the phase shifts at the RIS and beamformer at the AP while considering the highest possible spectral efficiency [48]. Other works related to RIS-based for SIMO, MIMO, were proposed in [54; 55].
# A Survey of Vehicular VLC Methodologies
## 2 Visible Light Communication
### VLC Receiver  
The receiver device is one of the essential elements in VLCs, where the received light signal is detected using photodiode detection devices (PDs), which work to extract the electrical signal from the received light signal. These devices are either stand-alone or may be in the form of an image sensor (or the so-called camera sensor), which consists of several diodes arranged in an array. Moreover, image sensors are also used in mobile devices (smartphones). However, the throughput rate realized is limited (kilobits per second) [12].  
Figure 2: VLC block diagram.  
In the meantime, photodiode (non-imaging) devices have a higher data rate when receiving the signal (up to hundreds of megabits per second), so the PDs used in VLCs vary widely. Furthermore, four types of photodiodes are usually used in VLC applications. The first type is the Avalanche photodiode (APD), the second type is intrinsic positive-negative (PIN), the third type is the metal semiconductor metal PD, and the final is the photo-conductor. Therefore, both PIN and APD photodiodes are the most popular and widely used because of the features that enable them to implement the requirements of VLC systems [45]. All types of photodiodes used in VLCs work to obtain the electrical current from the received light. Then, the received electric current is amplified before it reaches the equalizer. On the other hand, the optical condenser can be used in the future by focusing the received rays in the photodiode, as seen in Figure 2. At the same time, the VLC receiver contains an optical filter that removes unwanted noise to enhance the efficiency of the VLC system [43].
## 4 Case Study
### Test Plan of the Addressed Weibull Distribution  
Here, we numerically perform the vibration test plan given in Appendix C of the GMW3172 users guide. The steps to perform this test plan for the addressed bilinear Weibull distribution are as follows.  
Step 1. Determine the six test plan elements. They are: (1) Required reliability \(R(t)\). (2) Required confidence level \(CL\) for the desired \(R(t)\) index. (3) The sample size \(n\) to be tested. (4) The testing time \(t\). (5) The Weibull (\(\beta\) and \(\eta\)) parameters. And (6) The testing conditions.  
Step 2. Determine the required sample size \(n\) corresponding to the addressed \(\eta\) value. Based on Pina-Monarrez [18], it is given by:  
\[n=\frac{-1}{\ln(R(t))} \tag{14}\]  
Step 3. Determine the required testing time. It is the time that corresponds to the desired reliability \(R(t)\) index. From [19], and using the \(n\) value of Equation (14) and the bilinear Weibull parameters of step 1, it is given by:  
\[t=\frac{\eta}{n^{1/\beta}} \tag{15}\]  
Step 4. Determine the \(n_{2}\) value that corresponds to the required \(R(t)\) and \(CL\) indices from step 1 as:  
\[n_{2}=\frac{\ln(1-CL)}{\ln(R(t))} \tag{16}\]  
Step 5. Using the \(n_{2}\) value, determine the corresponding upper Weibull eta \(\eta_{U}\) parameter as:  
\[\eta_{U}=n_{2}^{\frac{1}{\beta}}t \tag{17}\]  
Step 6. From Pina-Monarrez [18], determine the lower \(\eta_{L}\) parameter as:  
\[\eta_{L}=\frac{\eta^{2}}{\eta_{U}} \tag{18}\]  
Step 7. Using the testing time \(t\) from step 3 and \(\eta_{U}\) from step 5 in Equation (3), determine the corresponding reliability index.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Percentile** & **50\%** & **55\%** & **1\(\sigma\)** & **80\%** & **90\%** & **95\%** & **2\(\sigma\)** & **97\%** & **99\%** & **3\(\sigma\)** \\ \hline \(\eta\)L & 1445.7 & 1437.8 & 1411.6 & 1393.9 & 1367.6 & 1346.2 & 1327.9 & 1315.3 & 1307.0 & 1257.0 \\ \(\eta\)U & 1445.7 & 1453.6 & 1480.6 & 1499.4 & 1528.3 & 1552.5 & 1573.8 & 1589.0 & 1599.1 & 1662.6 \\ \hline \end{tabular}
\end{table}
Table 8: Upper and lower limits of \(\eta\) for several failure percentiles.  
Step 8. Determine the P-S-N percentile corresponding to the required \(CL\) value from step 1 as:  
\[k_{a}=-\ln\left(\left(\frac{\eta_{L}}{\eta u}\right)^{\frac{1}{2}}\right)*\left( \frac{\eta}{\sigma_{\eta}}\right) \tag{19}\]  
where \(\sigma_{\eta}\) was determined in Step 10 of Section 4.1, using data from the Fisher matrix in Equation (9). The numerical application is presented as follows.
## 3 The Security Mechanism
### Local Detection  
The difference between LD-1 and LD-2 is whether nodes exchange their neighbor lists or not. To put it simply, the replicas of the compromised nodes could be detected directly in the communication range of nodes in LD-1, while in LD-2, replication attacks detection involves nodes' two-hop neighbors, which could be detected by exchanging their neighbor lists.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 3 Methodology  
### Overall Description  
A new HC-MMK approach (Hierarchical Classification based on Multi-source and Multi-temporal data and geo-Knowledge) was proposed in this study to produce the 30 m-resolutionCLC-SW2010 product, which is essentially an object-oriented classification method. The multi-source and multi-temporal satellite images and field data were firstly collected and preprocessed. After object-oriented multiresolution segmentation, a series of hierarchical classification trees for each mapping unit were built by decision tree algorithm and were then conducted to produce the preliminary classification results. Then, five-step knowledge-based interactive quality control was used to further improve the quality of classification results. The CLC-SW2010 product was independently validated by a third-party group. The construction of hierarchical classification trees and the five-stepknowledge-based interactive quality control are the major components of this proposed approach. The flowchart of this proposed approach can be seen in Figure 2.
# Layout Model of Bike-Sharing Facilities in the Transfer-Influenced Area of a Subway Station
## 2 Methodology
### Alternative Facility Location Prediction
#### 2.3.2 Location Prediction  
A large number of cycling-origin and destination data reflect the spatial movement characteristics of bike-sharing travel. Given that the end of the last trip and the starting point of the next trip of bike-sharing are mostly coincident, the starting point of travel was selected as the research object in this paper, and the K-means two-dimensional spatial clustering analysis method was used to perform cluster analysis on the bike-sharing order data to discern the spatial points where the number of cycling transfers was relatively frequent and dense. On identifying the cluster center, the location of the candidate point for the bike-sharing facility was obtained.  
When a traveler chooses to use bike-sharing, if the walking distance for finding a bike exceeds their acceptable range, they may give up using shared bikes and switch to other transfer modes. According to the analysis of the transfer cost, when the transfer costs of bike-sharing and walking are equal, the distance is the maximum acceptable walking distance for bike-sharing mode. Correspondingly, the maximum acceptable walking distance to find a shared bike is the maximum service radius of the candidate points in the cluster analysis [26]. In this paper, the service radius of alternative facilities locations was utilized as the evaluation index of the bike-sharing clustering result, and the clustering result needed to fulfill the requirement of maximum radius. The specific clustering process was as follows:Filtering the bike-sharing order data of the destination coordinates within 50 m of each exit of the subway station. The purpose of these bike-sharing orders was considered to transfer to rail transit in this paper.
2. Filtering the bike-sharing order data again to obtain the coordinates of the origin located in the transfer-influenced area of the subway station, and modifying the acquired order data in the transfer-influenced area based on the actual land characteristics.
3. Clustering the origin coordinates of the bike-sharing order data, and adjusting the clustering value K continuously, so that the area of the convex hull composed by the origin coordinates of the same class was small enough to be within walking distance.
4. Integrating the resulting data into ArcGIS, marking the cluster center, and drawing the cluster boundary.
# Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach
## 2 Existing Methods for Detection and Identification of Objects in an Image
### Selective Searching  
One of the most effective ways to find subregions containing an object in an image is an algorithm called selective searching. As reported in [14], this algorithm is based on three main assumptions:  
1. Capturing all possible scales in the image--using a hierarchical algorithm, selective searching attempts to take into account all possible scales of the objects;
2. Diversification--since objects in the analyzed area are subject to different changes such as illumination, shadows, and other, selective searching does not use a uniform strategy for a subregion search;
3. Calculation speed--since the step of subregion searching is only a preparation for the object recognition itself, this algorithm is designed to not cause any decrease of calculation speed.  
An example of subregion searching in a static image using a selective searching algorithm is shown in Figure 2.
# The Status of Space Weather Infrastructure and Research in Africa
## 2 Distribution of Space Weather Infrastructure in Africa
### Space Weather Products and Services  
There is a need to develop thresholds of space weather threats on a variety of technological systems in Africa. This includes the aviation industry since ICAO now recommends the provision of space weather alerts as part of regulations and standards for enhanced safety in civil aviation. Except for SANSA Space Science in South Africa, the rest of Africa has not developed space weather products and services for the aviation industry and other industrial sectors. Commercialization of space weather research findings will help mitigate economic losses brought about by space weather events, and it could also possibly provide funding streams to support space weather research. At the moment, there is a huge disconnect between industry and academia in Africa.
# Application of a Novel High-Order WENO Scheme in LES Simulations
## 2 WENO-ZQ Scheme and Numerical Tests
### Unsteady Convergence Compared with WENO-JS Scheme
This indicates that the WENO-ZQ scheme can resolve  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grid Size** & **Dimensionless Time Step** & **Cores** & **WENO-JS5 Wallclock Time (h)** & **WENO-ZQ5 Wallclock Time (h)** \\ \hline \(32^{3}\) & 0.001\(t_{c}\) & 2 & 0.9 & 0.8 \\ \(64^{3}\) & 0.0005\(t_{c}\) & 6 & 5 & 4.5 \\ \(128^{3}\) & 0.00025\(t_{c}\) & 32 & 15 & 13 \\ \(256^{3}\) & 0.000125\(t_{c}\) & 128 & 80 & 70 \\ \hline \hline \  
Figure 9 displays the time evolution of average kinetic energy (AKE) for different WENO schemes compared to reference solutions obtained using a pseudo-spectral code [49]. Overall, higher-order WENO reconstruction schemes yield results closer to the reference solutions for the same grid resolution. Among schemes with the same order and grid resolution, the WENO-ZQ scheme consistently provides results that are closer to the reference values than the classical WENO-JS scheme. Notably, the AKE results for the WENO-ZQ scheme with a \(64^{3}\) mesh are comparable to those of the WENO-JS scheme with a \(128^{3}\) mesh, indicating superior grid convergence of the WENO-ZQ scheme. Since computational cost scales linearly with the number of grid cells, the WENO-ZQ scheme can significantly reduce computational costs while maintaining accuracy. Figure 10 illustrates the kinetic energy dissipation rate (KEDR) results for various schemes using a \(256^{3}\) mesh. The peak dissipation rate of the reference solution [49] occurs at \(t=9t_{c}\), and both fifth-order WENO schemes successfully capture this peak. The differences between the WENO schemes are quantitatively assessed using the root mean square error (RMSE) relative to the reference solution. In Figure 10a, the RMSE for the WENO-JS3 scheme is \(1.365\times 10^{-3}\), whereas the WENO-ZQ3 scheme achieves an RMSE of \(6.25\times 10^{-4}\), indicating better accuracy. Similarly, in Figure 10b, the RMSE for the WENO-JS5 scheme is \(5.353\times 10^{-4}\), while the WENO-ZQ5 scheme has an RMSE of \(1.91\times 10^{-4}\), demonstrating closer agreement with the reference values. The WENO-ZQ scheme consistently yields predictions that closely approximate the reference values, with the WENO-ZQ3 scheme performing comparably to the WENO-JS5 scheme. These findings demonstrate that the high-order finite-volume WENO-ZQ scheme offers reduced computational costs while capturing more detailed fluid physics compared to the WENO-JS scheme [18]. Figure 10: Evolution of KEDR by different WENO schemes. Figure 9: Evolution of the AKE by various WENO schemes on different meshes.
# A Symbolic Encapsulation Point as Tool for 5G Wideband Channel Cross-Layer Modeling
## 3 Methods Applied
### Finite State Markov Chain Constitution  
#### 3.3.1 State Classification Model  
One application worth discussing is the constitution of the FSMC model, as in [34]. In this work, normalized signal level is used to be the channel state classification benchmark, as depicted in Figure 3a.  
This means that for appropriate signal level range, a two-state Markov model can be modeled (Figure 3b). This useful model can further be used to predict either PER, throughput, etc. Suppose the presence of a mobile user in a network which, according to the measured value of signal level, can be classified in one of K specified signal level regions, assumed to have the same traffic properties and hence, same QoS requirements. Physically, it might represent classes of users with proximate velocities, device classes, users inside similar traffic densities, characterized with some guaranteed QoS values inside \(k^{\text{th}}\) region. This approach is usually used for adaptive modulation and coding (AMC) algorithms employed by higher network layers. We suggested here one realization, defining two possible states: the signal belongs to (varies in limits of) class \(k\), and signal does not belong to class \(k\), as depicted in Figure 3b. These states are described with steady state probabilities \"in state \(k\)\", \(p_{k}\), \"out of state \(k\)\", \(p_{\alpha}\), and transition probabilities \(p_{k\alpha}\), \(p_{\alpha k}\). The channel state transition matrix configured this way is  
\[\mathbf{P}_{\text{{Ch}}}=\left|\begin{array}{cc}p_{k}&p_{k\alpha}\\ p_{\alpha k}&p_{\alpha}\end{array}\right| \tag{17}\]