esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 3 Results and Discussion
### E-Plane and H-Plane Radiation Patterns  
The proposed antenna was equipped with an 18 GHz anechoic chamber, and the measured and simulated radiation patterns in the E-plane with \(\mathcal{O}=0^{\circ}\) and in the H-plane with \(\mathcal{O}=90^{\circ}\) for a full range of \(\theta\) from \(0^{\circ}\) to \(360^{\circ}\) at three resonating frequencies of 3.62 GHz, 7.82 GHz, and 10.58 GHz are represented in Figure 15a-c. It is noticed that, for the proposed antenna with the FSS, the radiation pattern in the E-plane is leaf-shaped, while, in the H-plane, it is bidirectional and follows the shape of a figure of eight (\"8\"). The measured and simulated values are found to exhibit an excellent match with each other at all resonating frequencies. However, while the shapes of the radiation patterns in the E-plane and H-plane are nearly identical, they have differences in their gain values and magnitudes.
## 4 Problem Space for DL Applications
### IoMT Architecture Layers
#### 4.1.2 Network Layer
By leveraging advanced wireless technologies, distributed AI models, and blockchain security, the network layer ensures seamless, efficient, and privacy-preserving healthcare communication, paving the way for next-generation smart healthcare ecosystems. To enhance network efficiency in IoMT systems, DL can be applied to reduce redundant data transmission, optimize bandwidth use, and improve timely data processing. Federated learning allows IoMT devices to process data locally, instead of continuously transmitting raw data to centralized servers. This way can reduce network congestion and latency while maintaining diagnostic accuracy [136]. Additionally, DL-based data compression techniques can encode high-resolution medical images and real-time monitoring data in a more compact format without compromising critical diagnostic features. Edge computing integrated with DL further optimizes data flow by preprocessing, filtering out redundant information, and only transmitting essential insights [91]. This can not only reduce bandwidth consumption, but also lead to faster decision making and real-time responsiveness in critical care scenarios. Anomaly detection models powered by DL can analyze data streams in real time, identifying and discarding outliers or duplicated information before transmission, minimizing unnecessary network load. Additional research should develop adaptive DL models that dynamically adjust data transmission rates based on network conditions, further enhancing the scalability and reliability of IoMT systems. Table 5 indicates related works concentrating on the DL in network infrastructures. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Home** & **Wearables** & **Hospital** \\ \hline \multirow{4}{*}{Precision Augmentation} & Dementia analysis [137]. & Cardiovascular monitoring for COVID-19 patients [138]. & Disease screening and enhancement of smart healthcare [139]. \\ \cline{2-3}  & Cloud-based biomedical applications [140]. & Deep belief network for heart disease diagnosis [141]. & Reinforcement learning for healthcare workflows [142]. \\ \hline Risk Assessment & & Tracking vital signs of patients [143]. & Remote robotic surgery [144]. \\ \hline \multirow{4}{*}{Personalized Treatment} & Smart Dental Health–IoT platform [145]. & Elderly healthcare framework [146; 147]. & Smart hospital operations [147; 148]. \\ \cline{2-3}  & Remote patient monitoring systems [149]. & Deep learning framework for data analytics [150]. & AI-IoMT systems for disease prognosis [151]. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Related works in network layer.
# A Heterogeneous Multi-Satellite Dynamic Mission Planning Method Based on Metaheuristic Algorithms
## 3 Algorithm Design
As mentioned in reference [24], which discusses extremely large-scale multiple-input multiple-output systems, compressed sensing techniques incur prohibitive computational complexity, especially when both the transmitter and the receiver are equipped with a very large number of antennas. To address this challenge, they propose a multidimensional orthogonal matching pursuit relying on a parameter refinement algorithm to reconstruct the near-field channel with lower complexity. This approach serves as a valuable reference for our discussion about optimizing algorithmic complexity in satellite communication systems, highlighting the importance of reducing algorithm complexity.
# A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
## 2 Intra-Vehicle Security  
Securing a vehicle in terms of its intra-vehicle networks is not a trivial task. Indeed, a vehicle can be intended as an ecosystem in which mechanical, electrical, and software-based technologies exhibit mutual interactions: in such a complex context, the security failure of a single element can often induce effects in other elements--and this is what malicious actors typically aim to as a first objective. Nonetheless, when inspecting the possible issues that may affect the intra-vehicle security, it can be observed that some of them appear as their counterpart in common computer networks (e.g., traffic filtering, packet inspection) while other ones are peculiar to the vehicular context (e.g., CAN-bus issues). In the following sub-sections we review the state-of-the-art in securing intra-vehicle networks.
# Detecting Urban Mobility Structure and Learning Functional Distribution with Multi-Scale Features
## 5 Discussion
### Urban Functional Distribution  
In Figure 10, we conducted a visual analysis of the functional distribution prediction of different regions in the city. The results show that the urban functional distribution prediction model demonstrates good performance in most regions and can reflect the actual distribution of urban functions relatively accurately, indicating that residents' travel activities and the distribution of urban functions are in a relatively balanced state. We  
Figure 9: Statistics of residents’ activities in different communities.  
selected four regions especially and conducted an in-depth visualization study on the predicted values of their functional distribution. Among them, the cosine similarity between the model outputs of regions (a) and (b) and the real situation exceeds 0.9, indicating that the model can effectively fit the POI type distribution of these regions with the help of the active structure vectors. However, the cosine similarity of regions (c) and (d) is less than 0.5, and the model shows significant overprediction biases in the predictions of the three types of POIs, namely daily life service, shopping, and food and beverages, in these regions.  
To explore the reasons behind it, we conducted further statistics and analyses of the four regions of travel flow conditions, and the results are shown in Figure 11. Both region (a) and region (b) are commercial areas with diverse functions. The flow of residents' activities is not only regular but also large in scale, which provides rich characteristic information for the model and enables it to effectively conduct functional distribution learning. In contrast, region (c) is a tourist attraction. Although the flow of residents' activities in the region is also considerable, due to the lack of other regions (tourist attractions) with similar functional distribution as a reference and comparison, this undoubtedly increases the difficulty of accurate prediction by the model. Region (d), on the other hand, is an industrial area mainly composed of factories, whose activity flow is inherently at a relatively low level, making it difficult to extract sufficient effective features from the urban mobility structure vector. From the perspective of urban planning, there are indeed unreasonable aspects in the functional services distribution of region (d), lacking basic living supporting facilities such as daily life.  
Figure 10: Visualization of the ground truth and predicted value. The left panel is the visualization of cosine similarity, and the right panel is the ground truth and predicted value. Among them, the cosine similarity of **(a,b)** is greater than 0.9, and the cosine similarity of **(c,d)** is less than 0.5.
## 4 Applications of Plasmonic Photocatalysts in Environmental Protection
### Air Purification  
Indoor and outdoor air pollutions are also major issues to be addressed for the sake of human health. Typical air pollutants are mainly divided into three aspects, (1) inorganic and organic chemical compound gas, including oxynitride (NO, NO\({}_{2}\)), oxysulfide (SO\({}_{2}\), SO\({}_{3}\)), toluene and volatile organic compounds (VOCs), (2) pathogens (bacteria, viruses) and (3) suspended particulate matter (PM2.5). Hence, it is necessary to develop multifunctional air purification technology to realize broad-spectrum air pollution treatments [116; 117; 118]. In recent years, various types of air purification filters made by one-dimensional nanostructure have been made, including metal nanowires [119; 120], carbon based-materials [121] and polymer nanofibers [122; 123], due to their high active surface areas, mechanical stability and functionalization. Furthermore, they are effective in various air pollutant purifications by being combined with PMH. Figure 12d shows a multilayers air purification filter based on hierarchical Ag@MWCNTs/porous Al\({}_{2}\)O\({}_{3}\)[124]. In such structures, the network like distribution of Ag@MWCNTs placed at the pores of Al\({}_{2}\)O\({}_{3}\) serves as an effective medium for catalytic reactions. Compared to Al\({}_{2}\)O\({}_{3}\), Ag@MWCNTs and MWCNTs/Al\({}_{2}\)O\({}_{3}\), the large active surface area, high catalyst mediated by plasmonic Ag and retention of particles, without decreasing the air pressure drop significantly, resulted from these hierarchical structures, and a highly efficient elimination of VOCs and aerosol microorganisms was achieved simultaneously (Figure 12e,f).  
Apart from the above, \"Greenhouse Effects\" mediated by high-ratio CO\({}_{2}\) in air cannot be neglected, and the question of how to solve this critical problem is also a hot topic in modern society. A good deal of studies, combined with PMH photocatalysts, have been reported for the effective reduction of CO\({}_{2}\) from different point of views, including light absorption, the separation and diffusion of carriers and the activated adsorption of reactants [125; 126]. Based on this, the rational designing routes [127; 128], such as doping, alloying, band engineering, lattice control, introduced defects, and so on, are raised for the reduction and conversion of CO\({}_{2}\) into new fuels (methane, methanol and formic acid), which in practice is deemed to be of great value.
## 2 Materials and Methods
### Statistical Analysis  
The normality of the distribution of the calculated extreme values was checked using the Shapiro-Wilk test, which showed distributions different from normal in most cases. Statistical analysis was performed using PQStat 2021 software v. 1.8.2.238 (PQStat Software, Poznan, Poland).  
The statistical analysis was conducted in several stages due to the results of the calculation procedure described above. The first stage was to check whether the joint angles, torques, and GRF curves differ for treadmill gait between the young and elderly groups. For SPM \(t\)-test, a statistical parametric map SPM(t) was created by calculating the univariate t-statistic at each point of the gait curve [30]. Afterwards, Random Field Theory allowed an estimation of the critical threshold of 5% (\(\alpha=0.05\)) [31]. If at any time, an SPM(t) crossed the critical threshold, a supra-threshold cluster was created, indicating a significant difference between two motion patterns in a specific location of the gait cycle. All analyses were performed using open-source SPM1d code (www.spm1D.org) in Matlab R2021a. The second step involved a comparison of extreme values between groups (Y and O) within each strategy and treadmill gait separately. For this purpose, the U Mann-Whitney test was used. To compute the effect size for a U Mann-Whitney test, the following formula was used: \(\mathrm{r}=\frac{Z}{\sqrt{\mathrm{N}}}\), where Z is the Z-score from the test, and N is the total number of observations. In this case, N = 42. The effect size interpretation was as follows: \(\mathrm{r}<0.3\)-small effect, \(0.3\leq\mathrm{r}\leq 0.5\)-medium effect, and \(\mathrm{r}>0.5\)-large effect [32].  
In the last step in both groups, the number of strategies separately for the right and left lower limbs were counted to see which occurred most often.
## 4 Discussion
### Correlations between NDVI, PRI and Leaf Pigments  
Diurnal trends of NDVI and PRI showed no significant changes, with the exception of the aforementioned decreases during the morning. As expected, pigment sampling of avocado leaves at SHF showed no significant diurnal changes, with the exception of the photoprotective xanthophyll pigments (i.e., increase in zeaxanthin and decrease in violaxanthin). Significant increases in the DEPS at each sampling time point confirmed the engagement of slowly reversible photoprotective mechanisms one and two hours after direct illumination and recovery to lower levels by sunset. These changes in xanthophyll pigments are expressed in the PRI index [15]. Published leaf-level experiments have presented correlations between PRI, DEPS and NPQ [47]. PRI has also been shown to track changes in DEPS under stable chlorophyll and carotenoid concentration [21; 38; 48]. The morning decrease in PRI observed here corresponded with a significant DEPS increase in avocado leaf samples sampled one hour after light exposure. However, the high DEPS was found to be maintained in avocado leaf samples collected two hours following light exposure, whereas during this period PRI had returned to the pre-exposure level. This suggests that observed morning changes in PRI were not representing changes in xanthophyll composition. We hypothesise that it may be related to the physical and optical properties of avocado leaves, which require more detailed examination based on dedicated experiments. Unchanged daily leaf chlorophyll content indicates that the photosynthetic pigments could not be responsible for the significant morning decrease in NDVI observed for avocado leaves. Therefore, the morning drop, seen in both NDVI and PRI, does not result from plant physiological processes, but a temporal measurement error. We deduced that it is most likely caused by the LIFT instrument casting a light shadow on sampling spots during the early morning hours. The intensity differences between white reference and sample reflectance measurements subsequently triggered the variability in optical indices.
## 2 Related Work
### Parallel Transmission MAC Protocols for Underwater Acoustic Networks
Collisions will also occur more frequently as the number of nodes participating in the handshake process increases in this method. The multi-channel contention-based MAC protocols use more than one channel for communication. Recent studies showed that such a parallelism can improve the performance of underwater acoustic networks effectively [22]. Multi-channel MAC protocols can be classified into two categories: single rendezvous] and multiple rendezvous [23]. In single rendezvous protocols [23; 24; 25; 26], all control packets are exchange over one common control channel. This method can solve the problem that the source may fail to shake hand with the destination since they reside on different channels. However, the control channel will become a bottleneck of the network when the traffic gets high. In multiple rendezvous protocols [27], nodes can simultaneously shake hands on multiple channels. This method hasn't bottlenecks, but special coordination is required to guarantee that the source and its correspond destination reside on the same channel when the handshaking happens.
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 5 Conclusions  
Reflector antennas offer notable advantages for spaceborne SAR systems, including high gain, lightweight construction, and excellent radiation efficiency, making them well suited for applications in military reconnaissance, environmental monitoring, and commercial remote sensing. This paper has presented the fundamental principles of reflector antennas, examined various structural types, and reviewed commonly used deployable reflector configurations and feed architectures. In addition, this paper surveyed representative spaceborne SAR systems employing reflector antennas and discussed emerging techniques, such as DBF, staggered SAR, and SweepSAR, as well as the technical challenges associated with reflector antenna-based SAR platforms.  
To further advance reflector antenna-based SAR systems, future research directions include HRWS imaging, multi-mode SAR functionality, lightweight structural designs, and novel deployment mechanisms. As technological capabilities advance, reflector antennas are expected to play an increasingly critical role in next-generation spaceborne SAR systems, enabling more accurate, efficient, and versatile remote sensing solutions across a broad range of applications.  
Conceptualization, W.Y. and Y.H.; writing--original draft preparation, Y.H.; writing--review and editing, W.Y., Y.H., Q.L., W.L. and Y.E.; supervision, W.Y. and Q.L.; project administration, W.Y.; funding acquisition, W.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
## 3 CSCL-WVE-UDI: Method for Identifying Unknown IoT Devices  
### Logical Architecture of CSCL-WVE-UDI Method  
As shown in Figure 1, the logical architecture of the CSCL-WVE-UDI method comprises five components. The data-preprocessing component extracts session-level payload data from raw traffic. Generating PNSPs converts the payload data into grayscale image samples using an improved Nilsimsa algorithm and further applies the permutation sample-pairing strategy to generate the corresponding positive-negative sample pairs for each type of IoT device. The embedding-vector database-construction component, which is based on the CSCL-Siamese network, employs the proposed CSCL-Siamese network to train the set of PNSPs, thus enabling the identification of known IoT devices and the construction of an embedded vector database for known IoT devices. The filtering of unknown IoT device embedding-vector component inputs the traffic features of unknown IoT devices into the CSCL-Siamese network to obtain the corresponding embedding vectors, which are then compared against the known IoT device embedded vector database. By setting a similarity threshold, unknown IoT devices with features distinct from known devices are filtered out. Finally, the clustering-ensemble component integrates the clustering results of multiple unsupervised clustering algorithms through weighted voting and clusters the embedding vectors of unknown IoT devices, thereby completing the identification of unknown IoT devices. Each component is discussed in detail below.
## 3 Proposed Handover Scheme
### Handover Decision
#### 3.4.1 Confidence Probability  
In order to estimate the unnecessary handover probability, we define the confidence probability \(\Pr_{con}(i)\) as the probability that at time index \(i\) a BSN actually enters the coverage of the target AP, conditioned on the BSN being predicted to be within the coverage of the target AP. As such, the unnecessary handover probability, which is the probability that the BSN would be back to the previous AP at the time index \(i\), can be expressed as (\(1-\Pr_{con}(i)\)).  
To calculate \(\Pr_{con}(i)\), we map the predicted position \(s_{i}\) along the orthogonal and parallel directions of the AP boundary, as shown in Figure 4. Denote its orthogonal mean and standard deviation as \(\Delta\tilde{s}_{i}\) and \(\tilde{\sigma}_{i}\) respectively. Note that only the orthogonal factors contribute to the handover decision. The confidence probability is expressed as  
\[Pr_{con}(i)=1-Q\left(\frac{\Delta\tilde{s}_{i}}{\tilde{\sigma}_{i}}\right) \tag{7}\]  
where \(Q(\cdot)\) is the complementary distribution function of the standard Gaussian [25]. Denote \(P\) as the mapping vector that is orthogonal to the AP boundary. \(\Delta\tilde{s}_{i}\) and \(\tilde{\sigma}_{i}\) are calculated as follows:  
\[\Delta\tilde{s}_{i}=(\hat{s}_{i}-s_{k})\,P=(i-k)T\bar{v}P \tag{8}\]  
\[\tilde{\sigma}_{i}^{2}=E[P^{T}(s_{i}-E[s_{i}])^{2}P]=P^{T}\sigma_{s_{k}}^{2}P +\frac{(i-k)^{2}T^{2}}{4}P^{T}\sigma_{a}^{2}P \tag{9}\]  
where \(\sigma_{s_{k}}^{2}\) is the uncertainty of the current point \(s_{k}\).  
Figure 4: Confidence probability prediction.  
As can be observed in Equation (7), \(\Delta\tilde{s}_{i}/\tilde{\sigma}_{i}\) increases with \(i\), thus \(\Pr_{con}(i)\) also increases with \(i\). It means that the farther the predicted position of the user is from the boundary of the serving AP, the higher the probability that the user is actually within the coverage of the target AP. In Figure 4, \(\Pr_{con}(i)\) is the shadow part under the probability density distribution (PDF) of \(s_{i}\) at AP2 side. \(\Pr_{con}(k+1)\), \(\Pr_{con}(k+2)\), \(\Pr_{con}(k+3)\) are the confidence probability at the time index \(k\), \((k+1)\), and \((k+2)\) respectively.
## 4 Experiments and Discussions
### Synthetic Data Experiments
#### 4.1.3 Effect of Heart Rate Ratio  
According to the statistics of the experimental results in Sections 4.1.1 and 4.1.2, the heart rate ratio range of the 105 mixed signals is 1.2 to 2.8. The experimental statistical results show that the heart rate ratio has no significant effect on the performance of the proposed method, except when the heart rate ratio is equal to 1 and the FECG and MECG are exactly coincident (see Figures 9 and 10).  
Figure 8: Correlation coefficient and SNR improvement results against the FECG and MECG amplitude ratio (_FMAR_) using the proposed method for 105 synthetic signals. The red, blue, and black lines present the maximum, minimum, and average values, respectively.  
Figure 9: The change of SNR improvement with the FECG and MECG heart rate ratio (FMHRR) when FNR is fixed.