esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Crystallinity  
The analysis of the crystalline structures of the various raw materials was evaluated by XRD. According to the peak's characteristics obtained in the diffractograms, it was possible to verify whether each material presented a predominant crystalline or amorphous physical structure. Figure 2 illustrates two diffractograms of different solid structures--crystalline (ondansetron) and amorphous (PVP K17). The XRD diffractograms of the other raw materials are presented in the Supplementary Materials (Supplementary Figures S1, S3, S5 and S7).  
In the Figure 2 diffractogram a, the peaks were short and very broad. This demonstrates the recording of low intensity refractory beams, indicating the existence of a predominantly amorphous solid structure. This type of structure was observed in the diffractograms of PVP K17, PVP K25, PVP K90 and PVA Mw30. The diffractogram b of the Figure 2, corresponding to the ondansetron, presented well-defined peaks. The presence of well-represented diffraction maxima (peaks), with narrow and high symmetry, indicates the existence of a crystalline solid structure.  
The three APIs presented mainly a crystalline solid structure, although in different degrees (ondansetron higher than 99%; esomeprazole Mg (2H\({}_{2}\)O) approximately 82% and esomeprazole Mg (3H\({}_{2}\)O) around 72%). The MCC exhibited a diffractogram compatible with a mixed structure composed by crystalline and amorphous phases (Supplementary Figure S1). These results are consistent with other ones reported in the literature [34, 35, 36]. In the study  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Property**} & \multicolumn{4}{c}{**Circle Equivalent Diameter (\(\mu\)m)**} \\ \cline{2-5}  & **Average** & **D10** & **D50** & **D90** \\ \hline MCC & 41.2 & 17.8 & 38.6 & 66.8 \\ PVP K17 & 66.0 & 26.6 & 64.2 & 104.0 \\ PVP K25 & 71.3 & 23.6 & 68.4 & 116.0 \\ PVP K90 & 143.0 & 63.7 & 142.0 & 224.0 \\ PVA Mw30 & 139.0 & 53.6 & 139.0 & 217.0 \\ Esomeprazole Mg (2H\({}_{2}\)O) & 6.7 & 2.7 & 6.1 & 12.0 \\ Esomeprazole Mg (3H\({}_{2}\)O) & 19.8 & 6.4 & 17.1 & 36.9 \\ Ondansetron & 114.0 & 41.0 & 101.0 & 208.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Particle size analysis of raw materials by scanning electron microscopy (distribution by number).  
reported by [PERSON] et al. [34], the excipient PVA was found to have a partial crystalline structure. Ingredients with amorphous solid structures can be advantageous given that amorphous solids have a higher dissolution rate [37].  
Figure 1: SEM images of particles (**a**) MCC; (**b**) PVP K17; (**c**) PVP K25; (**d**) PVP K90; (**e**) PVA Mw30; (**f**) Esomeprazole Mg (2H2O); (**g**) Esomeprazole Mg (3H2O); (**h**) Ondanestron.
## 3 Proposed Architecture  
### DFU-Helper Overall Framework  
The overall framework, DFU-Helper, depicted in Figure 3, illustrates the proposed approach for monitoring the treatment progress of a diabetic foot ulcer patient, as initiated by a medical practitioner.  
The following gives an exhaustive description of the proposed DFU-Helper framework:  
1. [label=0., ref=0]
2. Obtain a representative dataset of diabetic foot ulcers (DFU) that provides a comprehensive description of different types of ulcers;
3. Train a Siamese Network using similarity learning on the dataset. This network will learn to distinguish between dissimilar images and group similar images together;
4. Test the SNN to validate its performance in terms of regrouping similar items closer and dissimilar items further. We plan to perform this step by testing it on a classification task and comparing it with known models;
5. Create anchor points for each significant class, including healthy, ischemia, infection, both, and none;
6. When a medical practitioner examines a patient for the first time, they capture an image of the wound, which is represented by the first image in Figure 3. This image is marked with a blue border, indicating the pre-treatment stage. The practitioner then initiates an individualized treatment protocol;  
Figure 3: Overall schematics of the DFU-Helper framework for DFU longitudinal evaluation of DFU before and after the start of treatment by a medical practitioner.  
During the subsequent visit, the doctor performs the standard evaluation procedure and captures another image of the wound. This new image is then fed into the system, along with the initial image, for further analysis and comparison;
7. The system generates a radar chart that plots the distances between the images and each of the anchor classes, as well as a table with the distances from the anchors;
8. In Figure 3, we can observe that the blue line representing the initial image shows a closer similarity to the infection class. In the second image, the blue line is plotted as being more similar to the healthy class, indicating an improvement and a movement towards the normal class.  
Having introduced the Siamese Neural Network that implements similarity learning, the subsequent section will delve into an explanation of what exactly similarity learning entails.
## 3 Congestion Recognition Based on the ACA
### Overview of Congestion Identification Algorithms  
This paper introduces a method for identifying congested nodes using reverse reasoning. The method relies on the observation that if two optimal paths intersect and subsequently deviate from their initial overlapping section after a period of stability, this suggests the presence of congestion during that timeframe. Such congestion triggers a change in the initial path selection by the ACA. Therefore, the skipped section of the initial overlapping path is considered the congestion path, with its nodes identified as congestion nodes. The network congestion identification method based on the ACA involves the following main steps:  
To begin with, the initialization and assignment of parameters for the ant colony model and M/M/1 queuing model are carried out. Then, utilizing the Waxman model proposed by [PERSON] [26], the network topology model and the corresponding distance information between nodes are randomly generated [27]. This allows for the determination of the propagation delay between network nodes and the calculation of data  
Figure 1: The flowchart of the optimized ACA.  
transmission delay. Consequently, a network transmission delay matrix is constructed [28]. The propagation delay is defined as the ratio of the distance between network nodes to the data propagation speed. Additionally, employing the M/M/1 queuing model, the data processing delay and queuing delay of each network node in the network topology are generated, resulting in the construction of a network queuing delay matrix. To amplify the effect, the data can be scaled according to a specified proportion. Finally, the total network delay matrix is constructed by combining the network transmission delay matrix and the network queuing delay matrix. It is important to note that in cases of congestion, the network transmission delay becomes significantly smaller or even negligible compared to the network queuing delay. The queuing delay at network nodes becomes the crucial factor in determining the optimal path.  
By comparing several pairs of data from the original optimal path set and the current optimal path set, it becomes evident that each pair of current optimal paths avoids or bypasses the previous intersection point or a segment of the previous intersection path. These bypassed sections precisely correspond to the previously adjusted and simulated congestion paths. This observation indicates that the congested nodes are located along these bypassed paths, ultimately leading to the ACA selecting a new path. This reverse reasoning approach provides a logical and reasonable method for determining the location of congestion paths. By carefully comparing and analysing the nodes along the bypassed paths, it becomes possible to infer the specific locations of the congested nodes. Figure 2 illustrates the flowchart of the congestion identification method.  
Figure 2: The flowchart of the congestion identification method.
## 1 Introduction and Related Work
However, USP does not support the UAV replacement scheme when the UAV runs out of its battery energy. In this paper, we consider the problem when multiple roads in the disaster suffer communication link failures, called events here, simultaneously. Due to the limited communication range and available battery energy of each UAV, the ground control station (GCS) proposed in this paper adopts the proposed energy-aware multi-UAV dispatch algorithm (EAMUD) to dispatch different numbers of UAVs to corresponding locations on each link-broken road. After every UAV arrives at its assigned location, it first forms the flying ad hoc networks (FANET) with nearby UAVs and then builds a two-layer network with existing VANET or 4G/5G cellular network to recover communication links of each event with the maximum event communication time. Table 1 compares the aforementioned work in terms of important characteristics for dispatching UAVs to recover the communication link of the disaster area. Most of them focus on dispatching a UAV to serve a single event without considering multiple events, UAV energy consumption, or subsequent handover process. Only the proposed EAMUD provides solutions for all of them. It has the following contributions:  
1. We first propose the mobile GCS to carry UAVs to the disaster area for recovering broken links. GCS adopts the UAV pool to record information of all UAVs and schedules all events stored in the proposed event queue for efficient multi-UAV dispatching. 2. Then we propose the energy-aware multi-UAV dispatch algorithm (EAMUD) to dispatch multiple UAVs to the disaster area for building the FANET, by considering the UAV battery charging progress and energy consumption for flying and communication. Further, EAMUD performs the proposed Event-Based Dispatching (EBD) algorithm to check whether communication durations of all dispatched UAVs overlap or not to maximize the event communication time. 3. For continuing communication of the broken link before the dispatched UAV drains its remaining energy, we propose the UAV handover process of EAMUD such that GCS can dispatch an optimal UAV to hand over the dispatched one. Further, whenever GCS cannot find enough available UAVs in the UAV pool to serve the current sub-event, two exception handling methods, i.e., Drop and Postpone, are proposed in EAMUD to schedule the scarce UAV resources efficiently, which in turn extends the event communication time. 4. For given events, we propose the linear programming optimization equations to compute the optimal event communication time as the performance benchmark for dispatching UAVs in the ideal scenario. The rest of the paper is organized as follows. Section 2 describes details of the proposed event structure and flows of the EAMUD and EBD algorithms. Section 3 describes how to formulate linear programming equations for maximizing the event communication time in the ideal scenario. Section 4 shows simulation results of EAMUD in the ideal and real scenarios. Section 5 concludes this paper.
# Blockchain for Future Wireless Networks: A Decade Survey
## 2 Background
### Security in Wireless Networks (From 4G to 6G)
#### 2.2.3 Security in 6G  
The main objective of 5G WN is to connect different smart devices to offer automation in smart cities, smart industries, and smart vehicles systems. The next-generation WN, i.e., the 6G network, presents a fundamental shift by unifying digital technologies and the human world to provide intelligent systems that redefine human lives. Evidently, the expansion of the 6G network is based on the technologies of the 5G network, such as AI, machine-to-machine (M2M) communication, digital twins, and many more. This enables the 6G developers to analyze the physical world closely by simulating it and take practical actions back to the physical world. It offers high data rates of up to 1 Tbps, high reliability (99.99999%), high scalability, low latency (1 ms) using further enhanced mobile broadband (FeMBB), extremely reliable and low-latency communication (ERLLC/eURLLC), and enhanced ultra-reliable low latency communication between different service providers and end-users. The low power consumption feature of 6G makes it 10 times more energy efficient compared to 5G. However, the security concern of the 6G network is still a matter of discussion, where malicious adversaries can impede the tight coupling between a 6G network and modern technologies. Some of the security threats in 6G are the privacy of users and data, artificial intelligence (AI) attacks, and trust violations [3].  
Since the 6G network is still not developed and publicized, very minimal literature is available that specifies the security and privacy of the 6G network. Here, we incorporated some of the contribution that researchers have presented to tackle the security issues in a 6G network. For instance, [PERSON] [46] identified key areas of 6G, such as intelligent radio, 3D intercoms, distributed AI, and real-time intelligent edge. Moreover, they presented a survey on security and privacy issues in 6G networks. [PERSON] [3] discussed 6G security challenges with various 6G applications, such as trust management problem in smart grid, scalability and automation issue industry 5.0, fake experiences in extended reality, privacy protection in holographic telepresence, and unarmed aerial vehicle (UAV)-based mobility. Furthermore, they explored distributed ledger technology, quantum computing, and scalable AI/machine learning (ML) technologies with 6G and identified how it affects the security and privacy of 6G [47]. Despite the fact that the 6G network has indispensable capabilities, the security problem continues to hinder the end-user experience. The reason behind that is that the 6G network integrates many modern technologies such as AI, IoT, optimization, data analytics, and many more discuss the intelligent use cases of 6G, but an essential part of security is primarily missing from their fundamentals. Therefore, there is a strong requirement for a technology, i.e., blockchain that blends with 6G networks to provide a proactive mechanism to secure 6G WN against numerous security threats.
## 4 Results and Comparison
### Pre-Trained Model Experiments  
As shown in Figure 9, there were 100 clusters when \(T_{2}\) took 165. In general, an increase in \(T_{2}\) is associated with a decrease in the size of the clusters, whereas the impact of \(T_{1}\) on cluster formation is minimal. Figure 10 illustrates the convergence of pre-trained model associated with varying values of MFCC label clusters: (1) Convergence of the pre-trained model becomes particularly challenging when the clusters value is increased. It is evident that WavLM achieves convergence more readily compared to HuBERT. (2) Both HuBERT and WavLM face increased difficulty in fitting the data as the number of clusters grows. From the perspective of model refinement, a larger number of clusters generally corresponds to improved back-end modeling capabilities. (3) It is crucial to select appropriate values for \(T_{2}\) and \(T_{1}\) to accurately determine the number of clusters. In the subsequent experiment, the optimal number of clusters was found to range between 60 and 90 when \(T_{2}\) is set to approximately 180. This finding serves as a useful guideline for selecting the number of clusters in similar scenarios.  
Figure 9: Number of clusters corresponding to \(T_{1}\) and \(T_{2}\).
# Hardware Platform-Aware Binarized Neural Network Model Optimization
## 5 Experimental Results and Discussion
### Analysis and Discussion  
The optimal model selected can be different depending on the target cost estimation. For FPGA, as shown in Table 9, the model with three layers has the fewest flip-flops, while the model with four layers has the fewest LUTs and consumes the lowest power consumption. If the power consumption is considered the most critical resource, the model with four layers will be the optimal candidate.  
Table 11 shows the real hardware cost for our best model. Compares the hardware costs of the selected model with **md1**, which has the fewest MAC operations, we can see that the proposed method can reduce the power consumption of the target hardware compared to using MAC operations. In addition, according to Table 9, the estimation method gives valuable data related to the hardware cost compared to actual hardware overhead. Therefore, the network designs can be considerably optimized on the critical cost when applying the proposed method for complex networks and datasets. For example, in our experiments, the model **md4** with six convolution layers has almost 1.5 times more MAC operations than the model with three layers but has similar hardware statistics as the model with three layers. This result is proof that MAC operations can be misleading and not a reasonable estimate for actual hardware performance.  
Finally, the optimal BNN models found by our method for the different target hardware in this paper are provided in Table 12. Based on this table, there is a conclusion that the optimal network architecture is different for each hardware, and it is not possible to find an optimal network for any hardware using generalized metrics, such as FLOPs or MAC operations. Each hardware has different critical resources, and we need hardware-specific metrics to find an optimal model for the target hardware.
## 3 Numerical Model  
### The Modeling of the Physical System  
With the presence of a cavity in the tube, the acoustic and fluid fields mutually interfere with each other. The treatment of this interaction is a key element to the numerical calculation process.  
In the Cartesian coordinate system, the physical model can be simplified as the two-dimensional mathematical model shown in Figure 1. The model is divided into two domains; \(\Omega_{1}\) is the acoustic computing region that contains the cavity. In order to simulate the unbounded domain, the PML is used in \(\Omega_{2}\), which is used to absorb sound waves. All the geometric parameters were dimensionless, with reference height \(H_{ref}^{*}\), \(H^{*}\) being the height of the tube.
# A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking
## 4 Simulations
### Example 2: CA Motion Model
(**a**) The actual measurements, actual trajectories and estimated trajectories obtained after filtering by the SM-GM-PHD filter for \(T_{s}=2\) s (\(T_{i}=4\) ms, \(\lambda=52\), \(p_{D,k}=0.96\)). (**b**) The actual measurements, actual trajectories and estimated trajectories obtained after filtering by the SM-LGM-PHD filter for \(T_{s}=2\) s, and the different trajectories are denoted by different color dots. (**c**) The actual measurements, actual trajectories and estimated trajectories obtained after filtering by the SM-GM-PHD filter in x and y coordinates for \(T_{s}=2\) s. (**d**) The actual measurements, actual trajectories and estimated trajectories obtained after filtering by the SM-LGM-PHD filter in x and y coordinates for \(T_{s}=2\) s.  
Figure 16: The average OSPA of different filters in example 2 (CA motion model) for 100 sample runs, when \(T_{s}=2\) s (\(T_{i}=4\) ms, \(\lambda=52\), \(p_{D,k}=0.96\)). **(a)** Average OSPA distances of different filters. **(b)** OSPA locations of different filters. **(c)** OSPA cardinalities of different filters. Figure 15: The average OSPA of different filters in example 2 (CA motion model) for 100 sample runs when \(T_{s}=1\) s (\(T_{i}=2\) ms, \(\lambda=104\), \(p_{D,k}=0.8\)). **(a)** Average OSPA distances of different filters. **(b)** OSPA locations of different filters. **(c)** OSPA cardinalities of different filters.
## 4 Results and Discussion
### Impact of Attitude Model  
#### 4.1.1 Attitude Modeling  
First, we give the definition of the satellite body-fixed (SBF) of GRCC and CS01. The origin of GRCC's SBF is the center of mass of the Super STAR Accelerometer proof mass, which almost coincides with GRCC's center of mass. The +X axis (roll axis) is defined from the origin to the phase center of the K/Ka band horn; the +Y axis (pitch axis) comprises a right-handed triad with the +X and +Z axis; +Z (Yaw Axis) is normal to the plane of the main equipment platform and positive towards the satellite radiator [17]. For CS01, the origin of SBF is the center of mass. The +X axis is aligned to the satellite positive velocity direction; the +Z axis points toward the Earth's center of mass, and the +Y axis is perpendicular to the XOZ plane, completing the right-hand coordinate system. GPS observations involve the geometric range between the antenna phase center location of the GPS satellite (transmitting end) at signal emission time and the antenna phase center location of the LEO satellite (receiving end) at signal reception time, whereas the final POCD results are based on the center of mass (COM). Meanwhile, the PCO values usually are defined in SBF, but the orbits should be computed in a conventional terrestrial system (CTS). Therefore, the attitude transformation matrix \(R\) between SBF and CTS is of great importance to the accuracy of LEO satellites' orbit. The GRACE-FO satellites provide measured attitude model data in the form of rotation quaternions that are composed of four elements, \(q_{1}\), \(q_{2}\), \(q_{2}\) and \(q_{4}\). The first three are vector parts, providing the orientation along the roll, pitch and yaw axis, respectively, and the last one is the scalar part. Therefore, we can obtain the transformation matrix \(R\) based on the quaternions, which can be expressed as follows:  
\[R=\left(\begin{array}{ccc}{q_{4}}^{2}+{q_{1}}^{2}-{q_{2}}^{2}-{q_{3}}^{2}&2( q_{1}q_{2}-q_{3}q_{4})&2(q_{1}q_{3}-q_{2}q_{4})\\ 2(q_{1}q_{2}+q_{3}q_{4})&{q_{4}}^{2}-{q_{1}}^{2}+{q_{2}}^{2}-{q_{3}}^{2}&2(q_{ 2}q_{3}-q_{1}q_{4})\\ 2(q_{1}q_{3}-q_{2}q_{4})&2(q_{2}q_{3}-q_{1}q_{4})&{q_{4}}^{2}-{q_{1}}^{2}-{q_{2 }}^{2}+{q_{3}}^{2}\end{array}\right)\]  
Note that, in the testing period in this paper, GRCC is in the leading position and the +X axis of the GRCC satellite is in the anti-flight direction. For CS01, the measured attitude data are not available. However, in SHAOOD software, transformations between CTS and SBF can be performed using measured or nominal attitude [42] according the actual needs. Therefore, this part of the research on the attitude model will mainly focus on GRCC.
# A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation
## 1 Introduction
In the segmentation step, initially the brain images are segmented using four different clustering methods. The two clustering methods are based on a traditional fuzzy set and the other two are based on an intuitionistic set. In the traditional fuzzy set category, Robust Spatial Kernel FCM (RSKFCM) [28] and Generalized Spatial Kernel FCM (GSKFCM) [29] are employed. On the other hand, in the intuitionistic fuzzy set category, two variants of Modified Intuitionistic Fuzzy C-Means [20] are employed. Furthermore, the results of four individual clustering methods are combined using a voting schema. The proposed approach is evaluated on two publicly available MRI datasets: OASIS and IBSR18 Dataset, and the results are compared using the results with state-of-art methods. The primary contributions of this paper are as follows:  
* Proposed a consensus clustering method for MRI brain tissue segmentation. * The results of four variants of fuzzy clustering methods are combined to achieve better results. * To check the efficacy of the proposed method, we conducted experiments on two standard brain segmentation datasets. The remainder of the paper is as follows: Section 2 presents the methodology of the proposed method. We then introduce the datasets and the evaluation metrics alongside the implementation details and discussions on the performance of the proposed method in Section 3. Finally, the conclusion of the paper is presented in Section 4.