esa-sceva/satcom-chunk-collection huggingface dataset:
# Metal-Organic Frameworks as Promising Textile Flame Retardants: Importance and Application Methods
## 6 Conclusions  
The textile chain has a great capacity for product innovation, contributing to the development of multifunctional substrates with specific performance. In this paper, the reasons why the search for new flame retardants has become a focus of research are outlined, highlighting the severe reduction in brominated composites. Flame retardant finishes on cotton fabrics become considerable due to the composition of the fiber, mainly cellulose, which lowers its oxygen limit index and thus favors rapid ignition and high smoke generation.  
Current research has focused on metal-organic materials as promising flame retardants. This class of material is widely investigated due to its porosity characteristics, large specific area, chemical stability, and ease of synthesizing and incorporating new functionalities. MOFs have attracted the attention of researchers because of their flame retardant mechanism since they absorb the gases that fuel combustion and also generate a layer of charcoal that inhibits the evolution of the flame. The application of these emerging material als has been reported, presenting three main methods: dip-pad-dry, direct synthesis (in situ growth), and layer-by-layer (LBL) assembly. From the perspective of developing new flame retardant materials, this study serves as inspiration for the search for substitutes for brominated compounds to encompass the trend towards the use of versatile structures such as MOFS. Finally, to strengthen textile innovation, MOFs should be increasingly explored as a component of substrate functionalization, with the aim of linking their characteristics to application methods in order to improve the performance of textile articles.  
Conceptualization, M.P.M., M.J.L. and F.M.B.; formal analysis, G.A.G.; investigation, E.K.T.S.V. and W.B.V.; resources, M.P., M.J.L. and F.M.B.; writing--original draft preparation, E.K.T.S.V. and V.B.V.; writing--review and editing, S.S., M.M., M.J.L., M.P.M. and F.M.B.; visualization, S.S., M.M., G.A.G. and M.P.; supervision, F.M.B. and M.P.M.; project administration, M.J.L.; funding acquisition, M.J.L. and F.M.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CNPq--Conselho Nacional de Desenvolvimento Cientifico e Tecnologico, of the Ministry of Science, Technology and Innovation from Brazil, Chamada Publica MCTI/CNPq n\({}^{\text{o}}\) 14/2023--Apoio a Projectos Internacionais de Pesquisa Cientifica, Tecnologica e de Inovacao, grant number 201166/2024-0.  
CNPq, UPC and the Laboratorio Multiusuario (LAMAP) at UTFPR Apucarana campus.  
Author [PERSON] was employed by the company Gde I+D+i, S.L. Author [PERSON] was employed by the company Spoint Soluoes Tecnologicas. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 6 Experimentation and Performance Analysis
### Computational Battery Performance Analysis  
Because the UE can be mobile and battery-operating, we measure the power and energy performance using two different lightweight platforms; the results using the Raspberry Pi are shown in Figure 5a and the results of the Mini-PC are shown in Figure 5b. We measure the power values in watts (W) and then derive and calculate the energy values in milliwatt-hours (mWh); the energy is a product of the power measurements in this section and the time measurements in Section 6.2.  
In Figure 5a, when simulating the UE with a Raspberry Pi, the power consumption of each cipher is closely comparable, ranging from a minimum of 8.58 W for secp256r1 to a maximum of 9.01 W for X25519, with Kyber-512 falling in between at 8.8 W. However, when considering energy consumption, the results are incomparable due to the cumulative computational time of the public-key generation and shared-key generation. Kyber-512 proves to be more efficient, requiring only 0.299 mWh due to a low computational time, which is notably 69.49% and 76.08% lower than the energy consumption of the X25519 and secp256r1, with 0.98 mWh and 1.25 mWh, respectively.  
Similarly, for the Mini-PC, the power consumption is comparable and the values are closely related, with 1.39 W, 1.29 W, and 1.34 W for secp256r1, X25519, and Kyber-512, respectively. While the energy consumption shows variation, Kyber-512 shows better energy efficiency on the Raspberry Pi, consuming only 0.136 mWh. This is significantly  
Figure 4: CPU performance using cycles count at user equipment (Raspberry Pi and Mini-PC). The average values and the 95% confidence intervals are shown, although they may not be too visible.  
lower than the energy consumption of secp256r1 and X25519, which consume 0.61 mWh and 0.49 mWh, respectively, as described in Figure 5b.  
Additionally, Figure 5a,b illustrate that power and energy consumption are higher on the Raspberry Pi compared to the Mini-PC, attributed to the lower computing resources of the Raspberry Pi.  
**Summary:** We aim to compare the power and energy performances of all three ciphers on resource-constrained devices. The power consumption among all three ciphers is closely comparable on both the Mini-PC and Raspberry Pi, while post-quantum KEM Kyber-512 demonstrates superior energy efficiency, consuming notably less energy compared to pre-quantum ciphers, secp256r1 and X25519, on both Mini-PC and Raspberry Pi platforms, helping to conserve battery life, thereby allowing users to use their devices for longer periods without needing to recharge.
## 4 Discussion
Furthermore, after the end of the measurement campaign, the CPC was checked \"as found\", and the 23 nm efficiency was 63.6% and the calibration factor \(+\)5%. Even though the overestimation seems small (\(+\)5%), the small change of the cut-off size in combination with the different penetration of the system compared to the others can result in high differences when many small particles exist. The calibration discrepancies have been discussed in the literature for both thermal pre-treatment systems [16] and condensation particle counters [22; 41] and will not be repeated here. Taking all comparisons together, the conclusion is that \"cold\" proportional dilution, fixed \"cold\" dilution, and fixed \"hot\" dilution are equivalent; at least for the majority of the cases that were examined. This was supported also by the fact that the repeatability of the measurements was similar. It should be noted that all comparisons were done with partial flow systems. More research with full dilution tunnel would be desirable. However the equivalency of partial and full flow systems has been shown in the past (e.g., [6; 8; 11; 42]). The data that were collected for concentrations of particles below 23 nm showed that there were engines with a small fraction, but also others with concentrations seven times higher, as also shown elsewhere [7]. This means that the current methodology with the 23 nm cut-off size in some cases captures only 12% of the emitted particles. Thus, for a better characterization of the emissions, a lower size (e.g., 10 nm as in this study) is recommended. However, a lower size has higher risk for higher differences between the various systems. This was not shown in the repeatability of the measurements that remained at the same levels for \(>\)23 nm and \(>\)10 nm measurements. Additionally, the differences between the two concepts of proportional and fixed \"hot\" dilution were in most cases within 25%. Thus, the different concepts seem equivalent also for the lower size of 10 nm. However, there might be cases where the fixed \"hot\" dilution with catalytic stripper measures lower concentrations. This can be either due to avoidance of nucleation and condensation in the first place, more efficient removal of the volatiles and less re-nucleation downstream of the evaporation tube, or smaller growth of particles that do not reach the lower size of the counters. However, the cases in which this happened were limited, not necessarily representative of reality and the evaporation tube systems were used with low dilution that can enhance the artefacts. Thus, the necessity of a catalytic stripper needs further studies. In general, the literature supports the use of a catalytic stripper, because the artefacts are minimised [16].
# A Network Traffic Abnormal Detection Method: Sketch-Based Profile Evolution
## 3 Sketch-Based Profile Evolution for Network Traffic Abnormal Detection  
The difficulty in analyzing network behavior lies in modeling dynamic traffic behavior. Traffic is characterized by few attributes, a large amount of data, a large number of human influence factors, and no typical behavior characteristics. The existing anomaly detection has many characteristics, but is built on a specific system. When the scope is extended to the actual communication data in the network, anomaly detection cannot be applied. Based on the analysis of the most common network packets in the network, this paper generates a Profile of user behavior through the network traffic structuring process, including Profile Evolution to describe the change of user behavior. Finally, the anomaly detection is completed by calculating the offset.
## 4 Experiments and Analysis
### Experimental Results and Analysis
From the perspective of objective evaluation, the fusion result obtained from the proposed method performs best on all six quantitative indices. In general, the comprehensive evaluation based on objective evaluation and subjective visual effects can show that the proposed method has superior spatial detail retention characteristics and spectral retention characteristics.
# Optimization of AZ91D Process and Corrosion Resistance Using Wire Arc Additive Manufacturing
## 1 Introduction  
The application of ceramics, polymers, and polymer-matrix composites for bone healing procedures has been continuously increasing [1; 2], especially in metals such as stainless steel, titanium and cobalt-based alloys, which are the most widely used composites to heal or replace damaged bones [3]. However, devices with these composites have a higher strength moduli. This may cause stress shielding to occur around the area where the device is implanted. The reduction in stress leads to a decrease in bone density on the areas close to the metallic component, which may lead to a deficient healing process [4]. Furthermore, limitations arise from the possible liberation of harmfulmetallic ions inside the organism due to corrosion mechanisms, as well as debris formation because of wear and tear that lead to inflammation in the areas near the biomedical device [5].  
For alternative bone healing purposes, magnesium has been studied due to its mechanical properties and degradability inside the human body. As shown in Table 1, the elastic modulus and yield strength of magnesium are closer to those of human bone, reducing the stress shielding phenomena [6]. As the implant decomposes, magnesium elements will progressively pass through the human body upon completing the assigned task (bone healing) until no trace of the original element is present inside the organism [7]. The body requires magnesium ions for both metabolic reactions and biological mechanisms. Typically, a 70 kg person would have approximately 35 g of magnesium [8]. Excess magnesium ions are excreted through urine with no harm on the metabolic system [9].  
However, one of the main shortcomings of magnesium alloy implants is fast degradation via corrosion [10; 11; 12]. A new manufacturing process to produce magnesium implants can assist in corrosion resistance. This study demonstrates that magnesium produced by Wire and Arc Additive Manufacturing (WAAM) technique creates impurity-free and void-free structures that will increase the corrosion resistance [12]. AZ91D was selected since it is a low-cost, readily available alloy, which mainly consists of aluminum, zinc, and magnesium as referenced in Table 2[13]. To determine the microstructure and corrosion performance of AZ91D produced by WAAM, it was studied with electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM), X-ray diffraction (XRD), energy dispersive spectroscopy (EDS), and optical microscopy.
## Appendix A Autocorrelation Correlation Function Derivations  
First, we will derive ACF for the undesired signal. Since the signal is assumed cyclostationary, a two-parameter ACF instead of one-parameter (a single time difference between a stationary signal and its lagged version) ACF is developed here.
## 4 Performance Analysis
### The Steady Phase  
In the steady phase of network operation, since the sensor spatial distribution converges uniformly, we may describe the network topology using the Averaged Model (AM) and the statistic properties of neighboring sensor density. The AM approximates the number of neighboring sensors that will eventually join the new cluster by the expectation of the number of neighboring sensors of each sensor in the network. Thus, a simple formula for predicting the number of clusterheads is  
\[N_{ch}\simeq\frac{n}{1+E[N_{i}]}. \tag{26}\]  
Based on the reference cluster size \(z\), upper bound _UB_, and the lower bound _LB_ of the cluster size, the number of clusters \(N_{ch}\) is bounded by the following inequality, which yields  
\[\frac{n}{UB}\leq N_{ch}\simeq\frac{n}{z}=\frac{n}{\alpha\left(1+\frac{\pi R_{A} ^{2}}{A}\cdot(n-1)\right)}\leq\frac{n}{LB}, \tag{27}\]  
where \(\alpha\geq 1\).  
Given the estimated number of clusters \(N_{ch}\) and due to the close relationship between the cluster formation and gateway selection, each cluster may be symbolized as a sensor and utilize the neighboring information in the new formed network in order to determine the number of gateways in a cluster. Hence, a random network of \(N_{ch}\) sensors with transmission range \(R_{\mathcal{M}}^{2}\cdot(\pi N_{ch})\approx c^{2}\cdot\ell^{2}\cdot\log(N_{ ch})\) described in (9) may be generated to abstract the behavior of gateway selection and may approximate the number of distributed gateways in the network. Therefore, the average number of gateways in a cluster is  
\[N_{g(\mathrm{avg})}=\frac{1}{N_{ch}}\sum_{i=1}^{N_{ch}}N_{i}^{(g)}\simeq\frac{ (\lceil E[N_{i}]\rceil+1)}{n}\sum_{i=1}^{N_{ch}}N_{i}^{(g)}, \tag{28}\]  
where \(N_{i}^{(g)}\) is the number of the neighboring sensors of sensor \(i\) in a random network of \(N_{ch}\) sensors. The comparison of the performance of the SAMCA and the simplified models is shown experimentally in Section 6.  
Energy Consumption and Complexity Analysis  
This section considers the energy consumption of the SAMCA assuming homogenous sensors. The total power requirements include the power required to transmit messages \(E_{T}\), the power required to receive \(E_{R}\), and the power for processing messages \(E_{P}\). Moreover, the communication and time complexity are derived to assess the performance of the proposed scheme.
# Efficient Coverage Path Planning for a Drone in an Urban Environment
## 5 SST--An Efficient Heuristic Algorithm for the GPP Problem
### Candidate Waypoint Set--\(\mathcal{V}\)  
As part of the planning process (SST), the second step is to select the set of waypoints \(\mathcal{W}^{h}\subset\mathcal{V}\) that will form the trajectory in order to ensure that it visits high-probability areas. In the absence of any preliminary operations, the search space is very large since any point included in the map boundaries and within the free space is valid.  
In order to reduce computation times for the planning stage, the search space is narrowed down to a set of candidate waypoints \(\mathcal{V}\) that are found in a pre-processing stage. This set of points is created by dividing the free space into clusters such that the area of each cluster is fully covered by the footprint of the drone when hovering above its center. As opposed to the ES algorithm, this time, the entire free space is clustered, regardless of the probability values. Moreover, the clustering operation occurs only once, and the footprint size considered is the one obtained at the lowest flight altitude as it increases with height. The set of candidate waypoints \(\mathcal{V}\) is the collection of all cluster centers. With this set of waypoints as a baseline, any point in the free space can be covered by the drone footprint at any height by simply selecting the cluster center closest to that point of interest:  
\[\forall c_{m,n}\subset\mathcal{A},\ \exists v\in\mathcal{V}:\ c_{m,n} \subset\mathcal{F}(v) \tag{5}\]
## 3 The Hybrid Distributed Topology Control Scheme (HDTC)
### Phase III: Reforming the Communication/Routing Group
#### 3.3.2 Relay Reselection  
Due to the vehicle movements, adaptive reselection of the relay member is necessary for information dissemination. Therefore, one simple reselection strategy is described as follows: When \(R_{u}\) is empty, we have:  
\[p^{{}^{\prime}}=\arg\min_{p^{{}^{\prime}}\in G_{u}}D_{p^{{}^{\prime}}\rm sink} \tag{8}\]  
where the relay member is selected from \(G_{u}\). Otherwise, the relay vehicle is selected by:  
\[p^{{}^{\prime}}=\arg\min_{p^{{}^{\prime}}\in R_{u}}D_{p^{{}^{\prime}}\rm sink} \tag{9}\]  
where the relay member is selected from \(R_{u}\). The procedures of the HDTC scheme and information routing are summarized in Table 1.  
Figure 8: A typical example of the proposed HDTC scheme for information routing (**Left**); one-hop communication protocol (**Right**).
## 4 Discussion
### Decelerations  
Similarly, five deceleration intervals were established for U14 players: \(<-\)0.26, \(-\)0.27 to \(-\)0.63, \(-\)0.63 to \(-\)1.22, \(-\)1.22 to \(-\)2.545, and >\(-\)2.54 m/s\({}^{2}\). Results showed that 96.64% of deceleration actions fell within low to moderate intensity (0 to \(-\)1.222 m/s\({}^{2}\)), with only 3.36% classified as high-intensity decelerations (>\(-\)1.223 m/s\({}^{2}\)). In contrast, senior players demonstrated deceleration intervals of <0.37, 0.37-1.13, 1.14-2.07, 2.08-3.23, and 3.24-4.77 m/s\({}^{2}\)[5], further highlighting the distinct physical demands experienced by younger athletes that are lower than those of professional players.  
From a dataset comprising 53 million deceleration data points, 96% of actions were recorded below \(-\)1.222 m/s\({}^{2}\), a value markedly lower than the previously reported U14 average of 3.1 \(\pm\) 1.6 m/s\({}^{2}\)[50]. This discrepancy suggests that, in real-game contexts, U14 players rarely reach high deceleration intensities, potentially due to developmental limitations in strength, coordination, or tactical demands. These findings highlight the importance of progressively developing deceleration capacity in youth athletes, as this component may be undertrained relative to its critical role in performance and injury prevention. Coaches should therefore implement age-appropriate training strategies that emphasize controlled deceleration mechanics and gradually introduce higher-intensity deceleration drills. Such an approach can help young players build the neuromuscular control required to safely manage the more demanding deceleration loads encountered at higher competitive levels.
# No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System
## 3 Materials and Methods
### Proposed Method
#### 3.2.1 Dissimilarity Measure Based on Disparity Index
\(Th_{eng}\) also determines whether the disparity estimate is reliable, which can be determined for the averaged filter tolerance coefficient (\(\alpha\)), as shown in Figure 4, where the error energy threshold is given as  
\[Th_{eng}(i,j)=\alpha\cdot E_{eng}(i,j) \tag{5}\]  
where \(\alpha\) is a tolerance factor to adjust the reliability of the filtering process; smaller values make \(\widetilde{D}(i,j)\) more reliable. On the other hand, decreasing \(\alpha\) erodes the disparity map by removing more disparity points in the map. Then, the disparity index can be calculated as the average of \(\widetilde{D}(i,j)\) as  
\[D_{n}=Mean[\widetilde{D}(i,j)] \tag{6}\]  
A stereoscopic video maintains a nearly constant \(D_{n}\) ratio between left and right views. Considering the assumption that there are no scene changes in the video, this statement will be explained and analyzed in the experimental part. If there is any packet loss or distortion in either or both views, \(D_{n}\) will deteriorate, thereby demonstrating dissimilarity between the views which is measured as  
\[S_{m}=\left|\left(\sum_{i=n-p}^{n-1}D_{i}\right)-\left(D_{n}\cdot P\right) \right|\times\frac{D_{n}}{10} \tag{7}\]  
where \(D_{n}\) is the disparity index of the current frame and \(P\) the number of disparity indices of the previous candidate frames being compared with it to measure the dissimilarity (\(S_{m}\)). The \(D_{n}\) value is divided by 10 to get the final \(S_{m}\) score within the range from 0 to 1 and produce an equal ratio (weight factor) of \(S_{m}\) with the perceptual edge-based measure which equally contributes to the final QA score of \(QAOM_{3}D\), as discussed in a later section.
# MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
## 3 Materials and Methods
### Loss Function  
Road extraction can be formulated as a pixel-wise binary classification task in semantic segmentation. Cross-entropy is defined as a measure of the differences between two probability distributions for a given random variable. In the deep learning domain, the binary cross-entropy (BCE) loss function is used to optimize models in binary classification tasks. Assuming that the size of the input image is \(H\times W\), then the BCE loss function is calculated as follows:  
\[L_{BCE}=-\frac{1}{N}\sum_{i=1}^{N}[y_{i}\cdot\log p_{i}+(1-y_{i})\cdot\log(1- p_{i})] \tag{8}\]  
where \(y_{i}\) is the ground truth denoting road or background for a given pixel in position \(i\), \(p_{i}\) is the corresponding probability predicted by the model and \(N=H\times W\). The BCE loss function separately evaluates the predicted classes of each pixel and then averages all pixels, so it can be considered that all pixels are learned equally. Thus, it is difficult to learn the features of road pixels when there is a great imbalance in which there are far fewer road pixels than background pixels. The dice-coefficient loss function is introduced to alleviate the above problem caused by sample imbalance. Compared with the BCE loss function, the dice-coefficient loss function directly supervises the similarity of prediction and ground truth [33], which can be calculated as follows:  
\[L_{Dice}=1-\frac{1}{N}\sum_{i=1}^{i=N}\frac{2\cdot y_{i}\cdot p_{i}}{y_{i}+p_{ i}} \tag{9}\]  
Due to the imbalance of road and non-road pixels, a simple combination of binary cross-entropy (BCE) and dice-coefficient (Dice) loss functions is used to train deep learning models to alleviate this problem in previous work [11; 23], which can be defined as:  
\[L_{loss}=L_{BCE}+L_{Dice} \tag{10}\]  
This simple combination can be considered to have the same weight, which may lead to suboptimal training results in road extraction tasks. Therefore, the final loss function used in this paper is modified as follows:  
\[L_{loss}=K\cdot L_{BCE}+(1-K)\cdot L_{Dice} \tag{11}\]  
where the adjustment factor \(K\) is set to balance the loss contribution of the BCE and dice loss function.