esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient and Secure Management of Medical Data Sharing Based on Blockchain Technology
## 1 Introduction
Given any data record, not only can the authenticity of the data be verified, but also the data provenance algorithm provided can be used to obtain the original data, the processing operations performed, and the intermediate data generated. Similarly, given any data, the platform can identify the operational behaviors that were performed to process the data into new data. After obtaining authorization, users can access all data within the scope of their permissions through smart contracts. This paper describes a medical data processing management platform based on the open-source blockchain platform Fabric [7]. The platform is designed to support the trustworth storage of medical data, processing data, authorization data, and query records from multiple medical institutions. The platform also supports regulators to monitor all transaction data within the platform using smart contracts. Based on business requirements, two different provenance algorithms were designed to support provenance of medical data and operational behaviors. Finally, the effectiveness of the proposed data provenance and query methods was verified in real-world scenarios and medical data. The experimental results show that the designed platform can facilitate the management of medical data by regulatory departments and also verify the efficiency of data queries and provenance. The main contributions of this paper are as follows:  
1. We designed and implemented a medical data storage, processing, sharing, and regulatory platform based on Fabric. 2. Based on the characteristics of medical and operational behavior data stored on the blockchain, we propose a DAG-based forward tracking and backward tracing method to ensure the efficiency of tracking and tracing within the limits of accessible block intervals and time intervals. 3. The throughput of data transaction storage on the designed blockchain platform was verified using medical data and operational behaviors data, as well as the efficiency of data provenance algorithms and query methods. The rest of the paper is organized as follows. In Section 2, we discuss the research work related to this paper. In Section 3, we design the structure of the data management platform based on Fabric from the perspective of medical data management and provenance business requirements. In Section 4, we introduce the data structure of medical data and operational behavior and elaborate on the two provenance algorithms proposed. In Section 5, we design experiments based on the designed blockchain platform, test the throughput of transaction storage, provenance, and query efficiency, and analyze the results of the tests. In Section 6, we summarize the entire paper and highlight possible future research work.
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 3 Fundamentals of Reinforcement Learning (RL)
### Reinforcement Learning Techniques  
In this section, the most important RL methods, including dynamic programming (DP) and deep reinforcement learning (DRL) and their features, are presented [14,30]. These schemes are briefly presented in Table 1.  
* Dynamic programming (DP) assumes that there is a complete model of the environment, such as the Markov decision process (MDP). DP consists of a set of solutions that are used to compute the optimal policy according to this model.
* Monte Carlo (MC) approaches are known as the free-model RL techniques, meaning that they do not need to know all the features of an environment. These approaches interact with the environment to achieve experiences. MC methods solve the reinforcement learning problem by averaging sample returns. They are episodic. As a result, MC assumes that the experience is divided into episodes. At the end step, all episodes will be finished no matter what action is selected. Note that the agent can only change values and policies at the end of an episode. Therefore, MC is an incremental episode-by-episode method.
* Q-learning is one of the most important RL algorithms. In this algorithm, the agent tries to learn its optimal actions and store all the state-action pairs and their corresponding values in a Q-table. This table includes two inputs, state and action, and one output called Q-value. In Q-learning, the purpose is to maximize the Q-value.
* State-action-reward-state-action (SARSA), similar to Q-learning, tries to learn MDP. However, SARSA, dissimilar to Q-learning, is an on-policy RL technique that chooses its actions by following the existing policy and changing Q-values in a Q-table. In contrast, an off-policy RL method such as Q-learning does not pursue this policy and selects its actions using a greedy method to maximize the Q-values.
* Deep reinforcement learning (DRL) uses deep learning to improve reinforcement learning and solve complex and difficult issues. Deep learning helps RL agents to become more intelligent, and improves their ability to optimize policies. Compared to other machine learning techniques, RL does not need any dataset. In DRL, the agent interacts with the environment to produce its dataset. Next, DRL uses this dataset to train a deep network.
# The Effect of Apple Juice Concentration on Cider Fermentation and Properties of the Final Product
## 3 Results and Discussion  
### Effect of Juice Concentration on Free Amino Acid Composition  
The concentration of individual free amino acids (FAA) was measured in the juice and reconstituted apple juice concentrate prior to the start of fermentation and free amino nitrogen content (FAN) content was calculated (Table 1). Knowing the amount of added nutritional supplementation and FAN/NH4\({}^{+}\) contained therein, the approximate amount of yeast assimilable nitrogen (YAN) in supplemented samples at the start of fermentation was also determined.  
The most abundant free amino acids in the apple juice used in the study were asparagine, aspartic acid, and glutamic acid. This correlates with what has been shown previously in other studies [23; 24]. Overall statistical significance according to the analysis of variance showed that the juice and reconstituted concentrate can be considered as different (\(p<0.05\)), the latter containing much more free amino acids. Thus, the concentration of serine, glycine, threonine, ornithine, lysine, tyrosine, isoleucine, and leucine was deemed statistically significantly higher (\(p<0.05\)) and, hence, attributed to the process of juice concentration. The industrial concentration of juices is a high-pressure boiling process that has been reported to possess several adverse effects on the juice. One of them is thermal degradation and evaporation of volatile compounds which may increase the proportion of free amino acids [25; 26; 27; 28]. The increase could also be attributed to partial peptidolysis/proteolysis that may have led to the release of individual amino acids. Finally, the increase of free amino acids concentration could also be attributed to a shift in proportions of the components of the juice matrix during reconstitution-the loss of some intrinsic constituents during clarification pre-treatment shifts the proportions in favor of amino acids when the water is added back according to % Brix.
## 4 Experimental Results  
The proposed SATCOM RX phased array was fabricated as is detailed in Figure 14. One fabricated PCB was cut cross-sectionally in order to display the interior copper paste sintering structure that can be seen in Figure 14a. The copper paste used for making interconnections between the PCB layers was surrounded by green solder material to prevent the copper paste from re-flowing while the two parts were sintered together. The 16 identical 128-element subarrays were attached to a metal structural component using screws. The heat generated by the beamformer chips was transferred to this metallic structural component and dissipated by the fans behind the array. The total size of the RX phased array was 76.8 \(\times\) 45 cm\({}^{2}\), where the effective aperture of the antenna was 76.8 \(\times\) 38.4 cm\({}^{2}\). The array was controlled by a computer and measured using a \(Ku\)-horn antenna and a E8363C vector network analyzer (VNA) in a far-field anechoic chamber.
# An Efficient Grid-Based Geocasting Scheme for Wireless Sensor Networks
###### Abstract  
In a wireless sensor network (WSN), geocasting is a location-based routing protocol used for data collection or information delivery. In geocasting, a target region usually contains many sensor nodes with limited battery capacity, and sensor nodes in multiple target regions need to transmit data to the sink. Therefore, how to use location information to construct an energy efficient geocasting path is a very important issue. FERMA is a geocasting scheme for WSNs based on Fermat points. In this paper, an efficient grid-based geocasting scheme for WSNs, which is called GB-FERMA, is proposed. The scheme uses the Fermat point theorem to search for the specific nodes as Fermat points in a grid-based WSN, and it selects the optimal relay nodes (gateways) in the grid structure to realize energy-aware forwarding. In the simulations, when the initial power 0.25 J, the average energy consumption of GB-FERMA is about 53% of FERMA-QL, 37% of FERMA, and 23% of GEAR; however, when with the initial power 0.5 J, the average energy consumption of GB-FERMA is about 77% of FERMA-QL, 65% of FERMA, and 43% of GEAR. The proposed GB-FERMA can effectively reduce the energy consumption and thus prolong the lifetime of the WSN.  
F 2023 23 2783 2023 2023 23 2023 23 2023  
WSN. We propose an efficient geocasting scheme with a grid-based shared tree path for WSNs, called GB-FERMA.  
The contributions of the proposed GB-FERMA consist of three parts. First, an area of interest is defined as a target region, and then the data for each target region is collected into the region head. Second, the GB-FERMA constructs an efficient grid-based geocasting tree for data transmission. The grid-based shared tree path is constructed for multiple region heads and the sink. A cell head is elected from each cell for data sending, data forwarding, and data fusion, and then the cell head is used for data transmission in grid-based geocasting. Finally, GB-FERMA selects the optimal relay nodes (gateways) for data transmission, reducing the energy consumption of sensor nodes.  
The rest of this paper is organized as follows. Section 2 introduces the related background of this work. In Section 3, we describe the proposed grid-based geocasting scheme for WSNs. Simulation results are presented in Section 4. Section 5 draws conclusions.
## 2 Materials and Methods
### GB-RAR Basic Principles  
A ground-based real aperture radar (RAR) system is a stepped-frequency continuous-wave (SF-CW) radar that emits a continuous wave with different progressive frequencies within a given frequency band. The frequency bandwidth B provides the range resolution \(\Delta\)R of the radar according to the relationship, Equation (2):  
\[\Delta R=\frac{c}{2B} \tag{2}\]  
where c is the speed of electromagnetic waves in vacuum. The corresponding echoes, backscattered by the scene, give rise to the raw data. Table 2 summarizes the main technical specifications of the IBIS-F/L(r) GB-RAR, IDS S.p.a, Pisa, Italy, used in this experiment.  
The inverse Fourier transform of the raw signal acquired by the radar, normalized and converted to a logarithmic scale, provides the normalized radar cross-section (NRCS) profile, which shows the amplitude of the radar signal backscattered by targets located within the scene, discriminating them with the range resolution (2).  
The interferometric phase is computed as follows:  
\[\Delta\varphi_{1,2}=\mathrm{atan}\{\mathrm{S}_{2}\cdot conj(\mathrm{S}_{1})\} \tag{3}\]  
where S1 and S2 are two coherent complex-values radar data acquired at times t\({}_{1}\) and t\({}_{2}\), respectively, during the data acquisition. The line-of-sight (LoS) displacement D\({}_{1,2}\) of a point P, occurring in the time interval [t\({}_{1}\), t\({}_{2}\)], is related to the interferometric phase \(\Delta\varphi_{1,2}\), computed in Equation (3), by the relationship:  
Figure 1: Sketch of the TLS functioning in line scan mode. The distance d is computed in Equation (1).  
\[D_{1,2}=\frac{\lambda}{4\pi}\Delta\varphi_{1,2} \tag{4}\]  
where \(\lambda\) is the radar wavelength. The precision of displacement measurements depends on the precision of phase measurements and it is a fraction of millimeter.  
In the case of RAR interferometric applications, the radar acquires data with a sampling time in the order of a fraction of second, usually of a few milliseconds. This means that the radar can accurately track in time the deformation profile of the target. The vibration frequency spectrum is obtained by a spectral analysis of the displacement profile, computed in Equation (4). The vibration frequency spectra can be visualized as 2D maps. For each target, discriminated in range with a range resolution \(\Delta\)R, the frequency spectra and displacement profiles are displayed vs. frequency and time, respectively.
# Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications
## 3 Method
### Assessment of Potential Applications Using Visual Image Interpretation  
Visual image interpretation was used to discern land use and land cover features distinguishable from the images to recommend potential remote sensing applications for nSight-1 CubeSat images. [PERSON] [41] defines image interpretation as the process of examining images by identifying objects and judging their significance. Image interpretation is a fundamental step in most image analysis and classification procedures and has been taking place since the advent of aerial photography and persists to present-day remote sensing [41; 42; 43]. Elements of image interpretation such as tone, colour, size, shape, texture, pattern, shadow height, depth, volume, slope, aspect and higher-order elements of the site, situation, and associations were used to perform accurate image interpretation. Recently, [PERSON] [44] used the primitive elements of image interpretation to describe the physical characteristics of slums. The scholars subsequently used the observed image characteristics as a precursor in developing advanced image classification algorithms. In our current study, we used collateral information such as global land use and land cover maps, Google Earth, geological maps, and street maps as the search method to correlate our image interpretation. Image interpretation keys were also used to aid the analysis.
# Object-Based Approach for Adaptive Source Coding of Surveillance Video
## 3 Proposed Adaptive Coding Method Using Object Dynamics  
The proposed adaptive bitrate control method employs a statistical model that describes the linear relationship between PSNR reduction and object dynamics. In this method, the bitrate is controlled adaptively when a new object appears in the video and is increased to sustain PSNR quality based on the prediction of the statistical model. This section explains the proposed model estimation algorithm and the prediction method employed to adaptively increase the bitrate.
# On Orthogonal Double Covers and Decompositions of Complete Bipartite Graphs by Caterpillar Graphs
## 4 Use Case
### On-Chip Communication Networks  
Complete bipartite graphs are widely used in various areas of communication networks [26; 46]. At the same time, if the network is large, there is a problem of finding routes and managing traffic in such networks [1; 47]. It is solved, for example, by using the principle of small-world networks [48; 49] by grouping nodes on a regular basis that are interconnected by the intensive communication traffic [32]. When referring to on-chip communication networks, the principles of organizing global networks using network-on-chip technology are then still supplemented by the use of communication buses [50; 51; 52], which have a very developed support at the level of CAD, IP cores, and their interfaces, and also are well-standardized.  
The mathematical apparatus proposed in this paper allows for performing the decomposition of large graphs into simpler caterpillar graphs, whose features are the presence of the main route between nodes and branches from it. This route is very suitable for its implementation as a bus [53]. That is, in large graphs, separate sections (caterpillar subgraphs) which can be organized locally using communication buses can be distinguished.  
Let us observe this concept with an example. It is known that in on-chip networks, there can be communication links of different bandwidths designed to transmit the traffic of different priorities and implement different levels of virtual channels [54]. The natural structure for complete bipartite graphs is to allocate circulant subgraphs for such tasks [55]. For example, in \(K_{12,12}\), a circulant subgraph of the form \(C(12;1,3,5)\) can be distinguished. The use of a ring circulant allows reflecting the local connectivity of neighboring routers in terms of location on the chip. Although such a circulant is much simpler than a complete bipartite graph, it nevertheless contains quite a lot of links, and the question is how to implement it as a communication environment on chip. Additionally, to implement routing, each router must store a routing table [56] for 12 nodes.  
We propose to segment the graph along the outer ring into pairs of nodes, selecting from them \(C_{2}(3,3)\); the main path is implemented by a high-performance bus [53], and the outer connections are implemented by links with a lower bandwidth. Thus, it is possible to reduce the size and number of routing tables (the number of segments (I, II, III, etc.) become 2-times less than nodes; one common table is stored per segment for two nodes). An illustration of such a partition is shown in Figure 19.  
This approach can be applied to graphs with an even number of nodes. The length of the main path in caterpillar graphs must be less than the length of the second circulant generator.
# Advances in Computer-Aided Medical Image Processing
## 4 Neural Network Design for Medical Imaging
### Utilization of Multilevel Information of Medical Images
#### 4.2.2 Multimodal Feature Fusion  
In medical imaging, different MRI modes are usually used to add more hierarchical features. Taking brain segmentation research as an example, as shown in Figure 9, T1-weighted images can produce good contrast between gray matter and white matter, while T2-weighted images can help to visualize abnormal tissue lesions. FLAIR sequences can more sensitively detect lesions near the brain tissue CSF junction. Therefore, it is very important to consider multidimensional image information to obtain accurate diagnosis results. [PERSON] et al. [75] took U-Net as the main framework, extracted multiscale features of different modes, fused multimodal features at the decision-making level, and used MRI images of the liver from the McGill University Health Center to build a dataset, with a segmentation accuracy of 81.98%. [PERSON] et al. [45] proposed a multimodal segmentation network based on a 3D FCN. The network uses two independent V-Net architectures to extract high-dimensional features from PET and CT images with a segmentation accuracy of 89.10%.
# Numerical Analysis of Human Cancer Therapy Using Microwave Ablation
## 3 Results
### Absorbed Microwave Power  
During thermal ablation of cancer cells, tissue destruction occurs when tissues are heated to sufficient temperatures via a microwave source. The MCA connected to a microwave generator and inserted into the biological tissue is the source of the energy of the microwave. The energy emitted by the antenna propagates into the liver tissue, which allows heat to dissipate in a volume of tissue close to the antenna. This internal heating leads to the killing of cancer cells due to the vibration and rotation of water molecules. It is important to evaluate the microwave power density absorbed by the liver. This quantity is equal to the volumetric source term in the heat equation \(Q_{ext}\).  
Figure 7 depicts the microwave power density dissipated in the liver tissue at \(t=300\) s for three different values of the input microwave power, namely, \(P_{in}=10\) W, \(P_{in}=45\) W, and \(P_{in}=75\) W. For \(P_{in}=10\) W, the maximal value of the microwave power density is about \(34\) W/cm\({}^{-3}\). However, \(Q_{ext}\) carries rapidly through the system. To better observe the spatial variations of \(Q_{ext}\), we limited  
Figure 6: Validation of the temperature distribution in the tissue liver against the experimental data offered by [PERSON] et al. [36].  
the values of this quantity in Figure 7 to the value \(1\) W/cm\({}^{-3}\). In other terms, Figure 7 presents the quantity \(\min\)\(\big{(}Q_{ext},1\) W/cm\({}^{-3}\big{)}\).  
From the above figure it is clear that for the three cases of the power microwave, the microwave power density absorbed is very high near the slot of the antenna and that it decreases with distance. The extension of the heated zone increases with the input power. This extension takes place mainly in the vicinity of the tumor and has an oblong and stretched form.  
For \(P_{in}=10\) W, a very small area of the normal tissue is heated, and, at the same time, not all the tumor is heated. When \(P_{in}\) increases, all of the tumor is heated, but, unfortunately, a large area of the normal tissue is also heated. A certain compromise should be made to optimize the treatment.