esa-sceva/satcom-chunk-collection huggingface dataset:
# Arrays of Sub-Terahertz Cryogenic Metamaterial
## 3 Metamaterial Matrices of Microwave Filters: Design, Fabrication, and Experimental Studies
### Fabrication Technologies  
The fabrication process can be divided into two stages: the first is shaping the FSS, wiring and contact pads, and the second stage is the fabrication of the SINIS detectors. The technology of the normal metal-insulator-superconductor (NIS) tunnel junctions is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Array \#** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline
**Parameter, \(\upmu\)m** & & & & & & \\ \hline Outer radius & 33 & 33 & 33 & 33 & 33 & 50 \\ Inner radius & 30 & 30 & 3 & 15 & 30 & 3 \\ Connecting wire & 4 & 29 & 29 & 29 & 8 & 3 \\ Period & 70 & 95 & 95 & 95 & 74 & 103 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of six types of electrically small antennas.  
Figure 19: Spectral response calculated for six different matrices of electrically small antennas: (**a**) Mode 1; (**b**) Mode 2. Number labels are the same as in Table 1.  
described in detail in our review article [31], and in this section, we present some specific features of these fabrication recipes.  
The first layer with FSS can be fabricated using a lift-off process. After cleaning, the substrate is spin-covered with double-layer or inversed photoresist and baked. The resist is exposed by a laser or e-beam lithographer and developed. A thin film of FSS material (usually 10 nm Ti/100 nm Au/20 nm Pd) is deposited by thermal evaporation, e-beam evaporation, or magnetron sputtering. The first Ti layer improves the adhesion to the substrate, the second Au layer is low-loss material for the antenna, and the third Pd buffer layer is intended to reduce the resistance between the Au and the aluminum electrodes of the detector. The first three-layer sandwich fabrication process is schematically presented in Figure 20.  
The second stage is the fabrication of SINIS detectors (see the schematic in Figure 21). The incoming radiation is absorbed by a thin film of normal (non-superconducting) metal. The temperature variation of the absorber is measured by tunnel NIS junctions connected to the edges of the absorber. High-quality tunnel junctions can be fabricated in one vacuum cycle using the shadow evaporation technique. One method is the Dolan technique with a suspended bridge of resist [32], another is the so-called bridge-free technique with evaporation into orthogonal grooves [33]. Later, we developed a process with magnetron sputtering, two lithography steps, and reactive ion etching [34]. Furthermore, we also developed a novel process of suspended bolometer fabrication (see Figure 21d). A detailed description of the recipes is presented in [31], and photos, in Figure 22.  
Figure 20: Schematic of the first three-layer fabrication process: (**a**) profile of inversed and (**b**) double-layer positive resists, and deposited films underneath; (**c**) optical image of developed resist; and (**d**) deposited Ti/Au/Pd film comprising annular antenna and wiring.
## 4 The WADGPS Master Station Processes
### Ionospheric Corrections
The MS generates the grid model and its confidence with feedback information to ensure that GIVE covers 99.9% of the corrected ionospheric residuals statistically. Therefore, the MS uses the grid model to estimate the vertical ionospheric delays of the RSs and their confidences (UIVE). Then, the master station can determine if the UIVE bounds the difference of ionospheric delays from the grid  
Figure 6: The feedback algorithm for GIVE. model (based on above user algorithms, Equations (17)) and that from the RSs' own dual-frequency measurements. If not, the MS must increases the GIVEs of the four grid points surrounding the IPP measurement. After checking all IPPs from the entire network, the GIVEs are guaranteed to cover 99.9% of the corrected ionospheric residuals statistically [11]. Figure 6 summarizes this process.
# Indirect Assessment of Railway Infrastructure Anomalies Based on Passenger Comfort Criteria
## 5 Case Study: Portuguese Northern Line Condition Assessment  
To validate and verify the accuracy of the developed methodology, vibrations were recorded at the Portuguese Railways Northern Line in a downward direction between the Porto (Campanha) and Lisbon (Oriente) stations, a distance comprising a total of 275 km. Multiple AP and IC railway vehicles were monitored while performing regular passenger service. Then, the results were compared with those reported by the Portuguese infrastructure manager (IP) obtained by the EM 120 track inspection vehicle.
## 5 Results and Discussion
### Comparison of Results with Other Approaches
#### 5.4.1 Identification  
The face recognition identification results are presented in Table 6. In the first three conditions our network yielded better results than the other approaches, although there is a slight difference of accuracy values between the models in some cases. The last two conditions showed a performance gap between the deep learning models and th statistical approaches; the multitask network and Face ResNet gave competitive results close or above 90% while Eigenfaces and Fisherfaces presented a considerable performance drop. This indicates that statistical models have difficulties recognizing faces that present variations during the acquisition process. The standard deviation values obtained from Fisherfaces are considerably greater than those obtained from deep learning approaches and Eigenfaces. Regarding speaker identification, the best results of our approach obtained a 100% accuracy in most cases with almost no dispersion. Although SincNet delivers competitive results comparable to our proposed system, its performance is greatly affected by noisy speech samples. Moreover, x-vectors yielded the lowest values, which were slightly greater than 90% and presented a performance drop comparable to SincNet when dealing with noisy speech samples.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SNR (dB)** & **WER (\%)** \\ \hline Noiseless & 0.9 \(\pm\) 0.55 \\
0 & 4.9 \(\pm\) 0.63 \\
5 & 2.75 \(\pm\) 0.62 \\
10 & 1.79 \(\pm\) 0.55 \\
15 & 1.45 \(\pm\) 0.56 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Speech recognition results in terms of WER (%).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & & & & & \\
**Transformations** & **No** & **Brightness** & **Horiginal** & **Rotation** & **Combination** \\
**Signal to** & **Transformation** & & & & **of Transformations** \\
**Noise Ratio (dB)** & & & & & \\ \hline Noiseless & 0.74 \(\pm\) 0.65 & 0.97 \(\pm\) 0.95 & 1.34 \(\pm\) 1.09 & 3.44 \(\pm\) 0.71 & 3.27 \(\pm\) 1.46 \\
0 & 0.93 \(\pm\) 0.98 & 0.97 \(\pm\) 0.57 & 1.82 \(\pm\) 2.02 & 3.31 \(\pm\) 0.97 & 3.06 \(\pm\) 0.96 \\
5 & 0.83 \(\pm\) 0.98 & 0.97 \(\pm\) 0.7 & 1.74 \(\pm\) 1.43 & 2.53 \(\pm\) 0.66 & 3.32 \(\pm\) 1.66 \\
10 & 1.01 \(\pm\) 0.68 & 1.18 \(\pm\) 0.47 & 2.06 \(\pm\) 2.11 & 2.67 \(\pm\) 0.44 & 3.57 \(\pm\) 1.35 \\
15 & 0.44 \(\pm\) 0.55 & 0.97 \(\pm\) 0.58 & 1.55 \(\pm\) 1.54 & 2.87 \(\pm\) 0.84 & 3.82 \(\pm\) 1.44 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Text prompted verification results in terms of EER (%)
## 5 Result and Discussion
### Thermal Performance
When \(T_{\text{g1}}\) is 558.15-563.15 K, the optimal HTL evaporation temperature reaches the upper limit, and the optimal HTL superheat degree is 0 K; furthermore, the HTL evaporation temperature is suitable for \(T_{\text{g1}}\), and the exergy efficiency increases rapidly with \(T_{\text{g1}}\). When \(T_{\text{g1}}\) increases to 568.15 K, the HTL evaporation temperature is unsuitable for increasing \(T_{\text{g1}}\), and the optimal HTL superheat degree is not 0 K; with the increase in \(T_{\text{g1}}\), the irreversibilities between exhaust gas and HTL increase, and the exergy efficiency decreases slowly. When cyclohexane is used for HTL, and \(T_{\text{g1}}\) is 523.15-598.15 K, the optimal HTL evaporation temperature does not reach the upper limit, and the PPTD of the HTL evaporator occurs at VPP; in addition, the exergy efficiency increases with \(T_{\text{g1}}\). When \(T_{\text{g1}}\) increases to 603.15 K, the PPTD of the HTL evaporator occurs at PPP, and the optimal HTL evaporation temperature reaches the upper limit; moreover, \(T_{\text{g3}}\) cannot decrease, although the increase in \(T_{\text{g1}}\) will improve the net power output and will increase the irreversibilities between exhaust gas and the HTL given the HTL evaporation temperature upper limit, and the exergy efficiency begins to decrease slowly. Figure 7b depicts that, when benzene is used for HTL, and \(T_{\text{g1}}\) is 523.15-613.15 K, the optimal HTL evaporation temperature does not reach the upper limit, and the exergy efficiency increases with \(T_{\text{g1}}\). When \(T_{\text{g1}}\) increases to 618.15 K, the optimal HTL evaporation temperature reaches the upper limit, and the PPTD of the HTL evaporator occurs at VPP. The HTL evaporation temperature is suitable for \(T_{\text{g1}}\) at the moment; with the increase in \(T_{\text{g1}}\), \(T_{\text{g3}}\) rapidly decreases, the increment rate of net power  
Figure 7: Effects of \(T_{\text{g1}}\) on the exergy efficiency. output increases, and then increment rate of exergy efficiency increases. When toluene is used for the HTL, and \(T_{\rm g1}\) is 523.15-623.15 K, the optimal HTL evaporation temperature does not reach the upper limit, and the PPTD of the HTL evaporator occurs at VPP; furthermore, with the increase in \(T_{\rm g1}\), the exergy efficiency increases, but the increasing trend is unchanged; when R1234 ze(E) is used for LTL, the exergy efficiency is much smaller than that of other systems because the PPTD is higher in the condenser/evaporator than in \(\Delta T_{\rm pp2}\). When R601a is used for LTL, the exergy efficiency is maximal; when R1234 ze(E) is used for LTL, the exergy efficiency is minimal.
## 4 System Design
### Task-Object Semantic Modeling and Inference  
A knowledge base of semantic models of the proposed system is maintained. The knowledge base is stored using the Protege ontology editor. The knowledge base contains individuals for tasks, virtual objects, tags, and their relationship information. The inference system based on tag mapping provides the candidate virtual object for the task. The semantic representation of the ontology is provided in Figure 5. In Protege, every class is a subclass of the ow:thing class. In this architecture, there is a class for the mission which represents the semantic modeling of the mission and its ontology. A mission has a goal which must be achieved. In addition to the goal, a mission has a set of policies such as security, maximum latency, reliability, etc. Similarly, the Quality of Service (QoS) of the mission is also one of the major things that need to be considered. A mission is composed of many services, each of which consists of tasks. Each task is mapped onto a certain virtual object. A virtual object has two subclasses, namely sensor and actuator. There are several individuals created for the task, mission and sensors. The corresponding XML representation for the Ontology Web Language (OWL) graph is shown in Figure 5b.  
Figure 4: Flow of operation in planning missions and task–object mapping.
# Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification
## 1 Introduction  
Images are an important source of information sharing and have been used for many decades to represent actions, events, things, and scenes, etc. It is generally believed that an image speaks a thousand words and has served through its wide use in newspapers, posters, magazines, and books. Images containing different actions are easily understood by humans. Automatic image recognition isan important area of research and a great deal of time and effort has been invested to achieve this goal. Human actions are a series of gestures generated by the human body, and their recognition includes analyzing them by matching the specified patterns. The recognition of human actions is widely used in solving different research problems like surveillance, activity analysis, and event recognition, etc. Over the last two decades, a lot of efforts have been made to recognize human actions from video content that has sufficient temporal and spatial information. However, a little work has been done for the same purpose using still images due to its challenging nature as static images have less information than video sequences.  
Human detection is the first step in the recognition of human actions. Many well-known efforts are made for human detection using a histogram of oriented gradients [1; 2; 3]. However, in the last decade, it has been observed that the deep learning-based classifier has been performing better than the conventional feature extraction based classification. [PERSON] et al. [4] used a faster regional-convolutional neural network (Faster R-CNN) to detect and locate the objects. This may be applied for the detection of a specific object like human detection and localization. Much of the work carried out in the field of action recognition includes silhouette-based features through depth images or vision-based generic features for 2D images with inherent limitations. Geometric features can be a good source of information used to solve the classification problem [5] and domain-specific features may perform better than the generic nature of global or local features [6].  
This article presents a technique to address the problem of recognizing human actions from still images. The task of human detection and localization is accomplished through the use of faster R-CNN followed by background modeling (BM) and segmentation algorithm (SA). Geometric features have been extracted from the human blob and adopted in the context of human body size proportions science. The computed features are used to model the classification rules for six human actions: single-hand side wave, single-hand top wave, walking, standing, both hands side wave, and both hands top wave.  
The rest of the paper has following organization: Section 2 presents the existing research, Section 3 presents the proposed research, Section 4 presents results and discussion over the produced results, while Section 5 concludes the presented research.
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Blockchain and GDPR: An In-Depth Analysis
#### 5.3.1 Reconciling Blockchain Technology with GDPR Compliance Challenges  
The GDPR assumes the presence of identifiable data controllers for each personal data point, a notion challenged by blockchain's decentralization, which replaces a unitary actor with multiple players. This decentralization complicates the allocation of responsibility and accountability, particularly in light of the uncertain definitions of (joint) controllership under GDPR. This lack of clarity is further compounded by recent case law developments that have yet to interpret these concepts definitively. Moreover, the GDPR's provisions, particularly Articles 16 and 17, assume that data can be modified or erased as needed to comply with legal requirements. However, blockchain technologies are typically designed to make data modifications purposefully onerous, ensuring data integrity and network trust. This design principle creates a significant challenge in aligning with the GDPR's requirements for data amendment and erasure, a topic widely discussed in recent years [32]. In addition, the principles of data minimization and purpose limitation, central to GDPR, pose challenges when applied to blockchain technologies. Blockchains, being append-only databases that continuously grow with each new data entry, inherently conflict with the principle of data minimization. The application of the purpose limitation principle is also unclear in the blockchain context, particularly regarding whether it includesonly the initial transaction or the continued processing of personal data for other purposes, such as consensus or storage on the chain. These aspects underscore the complexities and challenges of reconciling the operational principles of blockchain technologies with GDPR requirements [32]. The intricate nature of these challenges, especially concerning the allocation of responsibility, data modification, data minimization, and purpose limitation, illustrates the need for detailed and nuanced approaches to ensure compliance, as comprehensively compared in Table 4.
## 6 Scenario Study
### Scenario A  
The investigations and results of the capability of the frequency control support strategies for different input wind speeds are described and presented in this subsection. Figure 14 shows the resulting power output and frequency of the WPPs and battery. The minimum point of the frequency after the disturbance is 59.728 Hz in the proposed system incorporating the battery control system, as seen in Figure 14a. The MPPT technique with 59.521 Hz is less than the proposed technique by 0.207 Hz, Technique I with 59.624 Hz is less than the proposed technique by 0.104 Hz, Technique II with 59.697 Hz is less than the proposed technique by 0.031 Hz, and the proposed system without the battery control system with 59.709 Hz is less than the proposed technique by 0.019 Hz. These results show that the proposed system with the battery releases comparatively more energy stored from the synchronous generators' rotating masses and the battery regulating the power output references.  
The results of Scenario A can be seen in Figure 15. The WPP\({}_{1}\) power output of the suggested technique (red and blue lines) is greater than Technique I, Technique II, and the MPPT control operation, as shown in Figure 15a. As seen in Figure 15b, the output power of WPP\({}_{2}\) in the suggested technique and Technique II is similar because of the power limit. As seen in Figure 15c, the Battery\({}_{2}\) power output of WPP\({}_{2}\) is less than that of WPP\({}_{1}\) because the rotor speed, \(\omega_{r}\), of WPP\({}_{1}\) is less than that of WPP\({}_{2}\). Hence, as shown in Figure 15d, the \(SOC\) of Battery\({}_{1}\) rapidly decreases compared to that of Battery\({}_{2}\).  
Figure 13: IEEE 14-bus system with three WPPs.  
Figure 16a,b shows that \(\Delta P\) in the suggested technique is enormously higher than in the typical schemes, Technique I and Technique II. This is due to \(K(\omega_{r},\,\Delta f)\) in the suggested technique being bigger than the gain in the typical schemes, as indicated in Figure 16c,d. Consequently, \(\omega_{r}\) in the suggested technique is smaller than that in the typical schemes, which can be seen in Figure 16e,f.  
Figure 14: Scenario A: (**a**) frequency; (**b**) total output power.  
Figure 15: Results for Scenario A: (**a**) WPP\({}_{1}\) and battery\({}_{1}\) power output; (**b**) WPP\({}_{2}\) and battery\({}_{2}\) power output; (**c**) battery’s power output; (**d**) battery’s \(SOC\).
# Integrated Information in Relational Quantum Dynamics (RQD)
## 5 Hierarchical Decomposition: The Integration Dendrogram
In effect, one can read off which groups of subsystems form coherent modules and which connections are tenuous. For example, a branch of the dendrogram might show qubits 1,2,3 forming a tight sub-cluster (high \(\Phi\) internally) that only weakly connects (low \(\Phi\) cut) to another cluster of qubits 4,5, etc. * **Algorithmic Simplicity:** Building the dendrogram requires solving the bipartition optimization at each level, which is the same type of problem as computing \(\Phi(\rho)\) in the first place. While finding the optimal partition is NP-hard in general (since one might have to try all splits), focusing on pairwise splits at each level yields a manageable procedure. There are \(O(2^{n})\) possible bipartitions of an \(n\)-element set, so a brute-force search at each level is exponential in the size of the current subset. However, since we do at most \(n-1\) levels, the overall worst-case complexity is \(O(n\cdot 2^{n})\), which is exponential but not super-exponential. In practice, many splits will involve smaller subsets and thus fewer possibilities. Moreover, the computations for different branches can be performed in parallel. This is far more tractable than attempting to search among all partitions of all sizes simultaneously (a number that grows faster than \(2^{n}\)). Thus, the hierarchical approach breaks the problem into \(n-1\) manageable pieces. * **Connections to Clustering:** In classical data science, one often constructs hierarchical clustering dendrograms using metrics or information-based distances. Here we have the quantum analog: a dendrogram based on a rigorous information metric (\(\delta\)) applied not to classical data points but to the quantum state itself. Rather than clustering individual data samples, we are clustering subsystems of a single quantum state based on their entanglement structure. This opens the door to using visualization and cluster-identification techniques from classical analysis in quantum many-body systems. For instance, one could visualize the dendrogram with node heights proportional to \(\Phi\) values, giving a clear picture of the \"integration profile\" of the state across scales. Branch lengths indicate how much correlation binds subsystems at that split. In summary, the integration dendrogram is a powerful new tool for analyzing multipartite quantum states. It yields a unique, stable, multi-scale decomposition of a state's entanglement structure without requiring any ad hoc choices beyond the definition of \(\Phi\). By reading the dendrogram, one can immediately identify which subsystems form natural groupings or communities (high internal \(\Phi\)) and where the \"weak links\" between those groupings are (low \(\Phi\) between them). This has potential applications in understanding complex quantum networks or many-body systems: for example, identifying modules in an interacting spin chain, or coarse-grained functional units in a quantum neural network, etc. The dendrogram encapsulates an entire hierarchy of quantum integration within a single object.
# Routing Schemes in FANETs: A Survey
## 7 Conclusions  
This paper presents a review of the limited work on routing in flying ad hoc networks (FANETs), which possess unique characteristics (e.g., movement with high mobility degree in the three-dimensional space) that distinguish itself from the traditional mobile ad hoc networks and vehicular ad hoc networks. Routing in FANETs aims to achieve the objectives of enhancing route stability, network coverage, as well as routing performance and quality of service while addressing the challenges of high dynamicity, high cost, and low residual energy. Various routing metrics, such as mobility metrics, link expiration time, geographical location, and residual energy, are used to select routes; for instance, routes with higher link expiration time are selected. Due to the limited research done on routing in FANETs as well as the limited focus on the unique characteristics of FANETs, there remains a large amount of future work, and this paper has laid a solid foundation to motivate investigations of the open issues raised in this topic.  
All the authors contributed to the work and wrote the article. [PERSON] proposed the idea, designs, and writing. [PERSON], [PERSON] and M.A.I. suggested directions for the detailed designs as well as coordinated the work.  
This work was part of the project entitled \"A Novel Clustering Algorithm based on Reinforcement Learning for the Optimization of Global and Local Network Performances in Mobile Networks\" funded by the Malaysian Ministry of Education under Fundamental Research Grant Scheme FRGS/1/2019/ICT03/SYUC/01/1.  
The authors declare no conflict of interest.