esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Calculation Results and Analysis
### Analysis of the Results of Accelerated Motion from Propulsion  
The variation of the submerged drag with respect to time was similar to the step response of a second-order system with critical damping, which was nearly proportional to the square of the variation of submerged speed as shown in Figure 11a. The drag curves showed the same change trend in different acceleration processes; both of them tended to be stable and close to a maximum after 7.5 s.  
Figure 11: Variable acceleration characteristic parameters with time: (**a**) submerged drag; (**b**) submerged net thrust; (**c**) submerged acceleration.  
Figure 10: The time constant with rotation speed.  
The net thrust and acceleration increased sharply at the beginning and then decreased gradually to zero, especially at different rotation speeds of the water-jet pump, as shown in Figure 11b,c. As is known, the apparent thrust was related with the rotation speed of the pump. The higher the rotation speed, the greater the apparent thrust. So, the net thrust and acceleration of the propulsion increased sharply with increasing pump speed. The increase in the submersible drag or submersible speed was a relatively slow process and required a relatively long time, resulting in the net thrust and acceleration increasing firstly and then decreasing gradually.
# In-Plane Structural Analysis of Coated Masonry Walls via a Homogenized Model
## 2 Background on the Homogenization Process  
A paradigmatic model is employed to address coated masonry walls with a running/header bond pattern, as depicted in Figure 1. The masonry consists of bricks, with dimensions \(a\times b\times h\), and mortar beds and joints, with a thickness \(c\). Thin layers of SFRM, each with a thickness of \(t\), are bonded on both sides of the masonry wall. The fundamental elements of the system, namely brick, mortar, and SFRM coatings, are considered to be homogeneous and isotropic; their elastic properties are characterized by Young modulus and Poisson ratio, denoted as \((E_{b},\
u_{b})\), \((E_{m},\
u_{m})\), and \((E_{s},\
u_{s})\), respectively. However, based on the assembly geometry, the resulting composite material is _non-homogeneous_ and _orthotropic_.  
Such a system has been homogenized in [30], where it is substituted by an 'equivalent' homogeneous orthotropic material, whose constitutive law, in planar linear elasticity, is:  
\[\begin{pmatrix}\varepsilon_{x}\\ \varepsilon_{y}\\ \gamma_{xy}\end{pmatrix}=\begin{bmatrix}1/E_{x}&-v_{xy}/E_{y}&0\\ -v_{yx}/E_{x}&1/E_{y}&0\\ 0&0&1/G_{xy}\end{bmatrix}\begin{pmatrix}\sigma_{x}\\ \sigma_{y}\\ \tau_{xy}\end{pmatrix} \tag{1}\]  
In the following, to preserve the consistency of the paper, the homogenization process used in [30] to identify the equivalent five elastic constants (\(E_{x}\), \(E_{y}\), \(v_{xy}\), \(v_{yx}\), \(G_{xy}\)), is briefly recalled. It is denoted as (xy,z)-homogenization and consists of the following two steps:  
* Step 1, xy-homogenization: it is carried out in-wall-plane (\(x,y\)) and is applied to the unreinforced masonry only;
* Step 2, z-homogenization: it is performed out-of-wall-plane (along the normal direction \(z\)) and is applied to a three-layer medium, standing for the coated wall.  
It is worth mentioning that in [30], another homogenization strategy, denoted as (z,xy)-homogenization and characterized by an inverse step order, has also been developed. Such decomposition of the two processes of homogenization does not commute. This property is analogous to that found in formulating 2D homogeneous models of plates with periodic structure, namely that in-plane and transverse homogenizations do not commute (see, e.g., [31]). However, the analyses performed in [30] have shown that the two homogenization strategies yield very similar numerical results.  
Below, while the homogenization performed in Step 1 is only briefly mentioned, the z-homogenization of the Step 2 is described with slightly more detail because the subsequent Section 3 is based on it.
## 2 Materials and Methods  
### Data  
Four distinct sequences of images are used to develop an accurate microscale object perception method. The first sequence of images, \(I^{iw}(x,y)=\{I_{1}^{iw}(x,y),...,I_{N_{1}}^{iw}(x,y)\}\), contains microspheres (beads) and irregular-shaped amoeba cells suspended in water. Note that the shapes of the amoeba cells change dynamically following a wave-like pattern with a leading side and a trailing side as they move around in the medium. Each image is represented as a matrix of gray scale intensity values at every \((x,y)\) pixel location. The second sequence of images, \(I^{sw}(x,y)=\{I_{1}^{sw}(x,y),...,I_{N_{2}}^{sw}(x,y)\}\), contains beads and regular-shaped (mostly spherical) human endothelial cells (hECs) dispersed in water. The third sequence of images, \(I^{sm}(x,y)=\{I_{1}^{sm}(x,y),...,I_{N_{3}}^{sm}(x,y)\}\), contains beads and hECs in Matrigel, a commonly used extracellular matrix for cell culture and engineered tissue development. It is worth pointing out that the composition of Matrigel changes spontaneously during cell manipulation at room temperature due to polymerization or gel formation. This change in composition alters the background contrast of the bright field images. The fourth sequence of images, \(I^{cm}(x,y)=\{I_{1}^{cm}(x,y),...,I_{N_{4}}^{cm}(x,y)\}\), comprises dense aggregates (clusters) of beads in Matrigel. Example images from all the sequences are shown in Figure 2.  
\(I^{iw}(x,y)\), \(I^{sw}(x,y)\), \(I^{sm}(x,y)\), and \(I^{cm}(x,y)\) are provided as inputs to the perception method. The method first categorizes an imaged object to be either a bead or a cell. It then computes the centroids and diameters of the beads, and the centers, dimensions and orientations (with respect to the horizontal axis) of the bounding boxes for the cells. It also traces the beads boundaries and the cells contours. For the objects that are present along the image boundaries and are only partially visible, the method does not attempt to categorize them into any type, but still computes their centroids and traces their boundaries. We describe the method in detail in the next sub-Section, and use \(I_{j}^{in}(x,y)\), \(I_{j}^{sw}(x,y)\), \(I_{j}^{sm}(x,y)\), and \(I_{j}^{cm}(x,y)\) as examples to illustrate the result of each method step.
# Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery
## 2 Study Area and Data  
### Study Area  
Hefei, the capital of Anhui Province, is situated in the center of Anhui Province, eastern China (31.51 N, 117.17 E). It is also included as a key city in the national strategy for the development of central China. Hefei covers a total area of 11,445 square kilometers and has municipal districts (Yaohai, Luyang, Shushan, and Baohe), one county-level city (Chaohu city), and four counties (Changfeng, Feidong, Feixi, and Lujiang). As of 2018, the city's permanent population was 8.087 million, and the main urban population was 3.938 million. Figure 1 is the area change data of each land type in Hefei in 15 years obtained from the Geographical Information Monitoring Cloud Platform ([[http://www.dsac.cn/DataProduct/Detail/20091601998301300](http://www.dsac.cn/DataProduct/Detail/20091601998301300)]([http://www.dsac.cn/DataProduct/Detail/20091601998301300](http://www.dsac.cn/DataProduct/Detail/20091601998301300))). During the past 15 years, the vegetation coverage of Hefei City has been decreasing year by year, and the urban area has been expanding. Urbanization has brought many environmental problems in the city, and the UHI effect is one of them. This prompts us to ask how to maintain a balance between urban development and livability. Based on this background, this paper takes Hefei city as an example to analyze the four municipal districts, Yaohai, Xiangyang, Lushan, and Baohe, as shown in Figure 2.  
Figure 1: The changes in area of each land type of Hefei during the past 15 years.
# Information-Aware Secure Routing in Wireless Sensor Networks
## 3 Model and Methods
### Attack Behavior Description  
The trust value of a node comprises the statistics of historical behaviors and the probability prediction of future behaviors of the node. In our approach, the trust value of a node is evaluated according to its historical communication behaviors. The behavior of a node is divided into normal behavior and abnormal behavior, which is established by evaluating whether the node forwards packets normally.  
In this work, sensor nodes can be divided into four categories: Normal nodes, malicious nodes, fault nodes, and dead nodes. According to the attack model in Section 3.2, normal nodes forward the received packets to the next-hop nodes normally, while malicious nodes discard some or all packets deliberately. To simplify the model, we view both fault nodes and dead nodes as malicious nodes because all three types have a significantly increased packet loss ratio.
# Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems
## 3 Machine Learning Algorithms and Techniques
### Dbscan  
DBSCAN is a clustering unsupervised algorithm that groups together data points that are close to each other based on a distance measurement and a minimum number of points [29]. It also marks the data points that are in low-density regions as outliers. Basically, this requires two parameters: the first one specifies how close data points should be to each other to be considered part of the cluster; and the second parameter is to define the minimum number of data points to form a dense region.
## 4 Setting and Context  
Most of the papers reviewed focused on locations in low and middle-income countries (LMICs), low-resource settings (LRS), or rural settings in developed countries. Among these papers, the primary focus was on LRS in African countries, with one classifying South Sudan as an extremely low-resource setting [34], three focusing on the improvement in patient outcomes in Uganda with the usage of portable ultrasound [31; 32; 38], two based in Sierra Leone [42; 43], and two with the setting of Rwanda, both of which classify the setting as an LMIC. More specifically, out of the two papers based in Sierra Leone, one assessed the accuracy of POCUS in obstetric cases [42] and the other examined the impact of POCUS on differential diagnoses at the Holy Spirit Hospital [43]. One of the studies based in Rwanda compared resident-performed lung ultrasound with a clinician-interpreted chest X-ray for pneumothorax in patients after a tube thoracostomy [59]; and the other analyzed the impact of POCUS on diagnosis and diagnostic accuracy in dyspnea patients [61]. Additionally, one of the papers based in Uganda focused on temporary outreach clinics in the Masindi region, West Rural Uganda [32]. The remaining papers included in the review that were based in Africa consisted of Zambia [40], Tanzania [57], Malawi [45], Mozambique [49], and Ethiopia [60].  
In addition to the studies centered around African countries, other papers were concentrated in Central/South American and Caribbean countries such as Nicaragua [33], Guatem [35; 37], Haiti [39], Panama [47], and Brazil [55]. Furthermore, developed countries with western medicine, who incorporated POCUS into their medical institutions in Portugal [46], United States [48; 56], New Zealand (rural hospitals) [50; 51; 52; 53; 54], United Kingdom [23], Newfoundland and Labrador, were analyzed [58]. More specifically, all of the New Zealand-based papers that were reviewed selected five to six rural hospitals to examine, many of which were dispersed to represent various communities within the rural areas of New Zealand [53; 54; 55; 56; 57]. The rest of the studies contained settings from Asia in countries such as Bangladesh [44], Laos [41], the Philippines [45], and India [23]. The studies carried out in Bangladesh and Laos were from urban areas, with the Bangladesh study focusing on a large tertiary government hospital (Chittagong Medical College, Chittagong, Bangladesh), and the Laos study focusing on Vientiane, the capital of Laos [41; 44]. The geographical distribution of the countries included in this study is visualized in Figure 2.  
Overall, when analyzing the studies from developed countries, most of them compared the applications and outcomes of POCUS implementation between remote, rural areas to large, urban areas. In every country, most studies were conducted in hospitals or clinical settings, examining outcomes in emergency departments or intensive care units where patients presented with life-threatening conditions.  
Figure 2: Geographical distribution of studies conducted.
## 3 Validations of the Analytical Approach
### Comparison and Validation  
#### 3.3.1 Joint with Elastic Gasket  
For the joint with EG, the stress-strain relationship of the CGC composite material in the analytical approach can be calculated by using Equations (7) and (8), and the stress-strain relationship of the RGR composite material can be calculated by using Equations (7), (8) and (10). If the influence of the EG is ignored, the stress-strain relationship of the composite material can be simplified as the CC model, which can be calculated directly by Equation (1). As presented in Figure 8, it is obvious that the CGC composite material is much softer than the CC material. That means the EG can have an effect on softening the joint, and by increasing the EG thickness, this effect tends to be more obvious. However, if the composite is simulated by the RGR model, the equivalent compression stiffness of the composite increases rapidly by increasing the compressive stress, and this is obviously inconsistent with the actual situation. Therefore, the EG, which is an important part of the joint, should be properly reflected in the calculation of the joint rotational stiffness.  
Figure 8: Stress–strain relationship of the composite.  
Figure 7: Numerical model of the segmental joint with EG.  
By applying an axial force to the joint, the rotation angle of the joint can be estimated by using the proposed analytical approach. According to the analytical results in Figures 9 and 10, in the condition that the eccentricity (\(e\)) remains at 150 mm, with increasing the axial force constantly, the joint rotation angles tend to increase obviously in both the sagging-moment scenario and hogging-moment scenario. The experimental results and numerical results are also presented in Figures 9 and 10. It can be seen that the differences between most of the numerical results and the corresponding experimental results are within 10%, which validates the reliability of the numerical simulations. According to the comparison, the curves from the analytical approach with the CGC model agree well with those from the experimental tests, for which the differences are within 13%. However, by increasing the axial force, the rotation angles calculated by the analytical approach with the RGR model tend to be much smaller than the experimental results; especially when the axial force increases to more than 500 kN, the maximum difference exceeds 20%. Therefore, the proposed analytical approach with the CGC model is more suitable than the RGR model for simulating the rotational behavior of the joint with EG.
# Neural Network DPD for Agramizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance
## 2 Neural Network-Based DPD Architecture
### NN Model Characteristics and Design  
As far as structure of the NN is concerned, there are two NNs proposed and used in this project whose description is given below:  
1. A DPD NN is utilized to predistort the \"real\" RoF link.
2. An RoF NN model is required to train the DPD NN.  
The NN utilized in this work was a feedforward fully connected (FC) NN. The FC-NN contained \(N\) hidden layers and \(K\) neurons per hidden layer. The schematic of the structure utilized in NN is shown in Figure 4. Considering the complex baseband signal, the NNs have two inputs and two outputs, i.e., one for the real and the other for the imaginary part. For the hidden layer (one layer at least), ReLu was used. There are different sets of nonlinear activations that can be applied in the hidden layers; however, a rectified linear unit (ReLu) was chosen due to its lower complexity.  
The output for the first hidden layer is expressed below in (3):  
\[l_{1}(n)=f\left(W_{1}\left[\begin{array}{c}\mathbb{R}(x)\\ \mathbb{I}(x)\end{array}\right]+b_{1}\right) \tag{3}\]  
Here, \(l_{1}\) represents the first hidden output layer, \(f\) represents the nonlinear activation function and \(W_{1}\) and \(b_{1}\) represent the weight and bias for the 1 st output layer in NN.  
Similarly, a general output for the ith layer can be expressed as:  
\[l_{i}(n)=f(W_{i}h_{i-1}(n)+b_{i}) \tag{4}\]  
Figure 3: Schematic showing RoF system utilizing a neural network (NN) digital predistortion (DPD) system and RoF NN model. After transmitting through the RoF link, the input and output of RoF are used to model RoF NN model \(\hat{I}\). Once trained, by backpropagating the error through \(\hat{I}\), training of \(I^{-1}\) is performed. This DPD RoF model is then used with the RoF link to perform linearization. Predistortion is carried out in a digital baseband so ADC and DACs are omitted.  
where \(i\in\mathbb{N}\) : \(2\leq i\leq N\) The final output after hidden layers \(N\) for the network is expressed in (5):  
\[\hat{x}(n)=W_{N+1}h_{N}+b_{N+1} \tag{5}\]
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 2 Background
### mmWave Testbeds and Frameworks  
New mmWave technologies are often tested using expensive, custom-built experimental prototypes; as a result, most academic mmWave research is limited to simulation [68]. Several emerging solutions promise a lower-cost, open-source alternative to make mmWave experimentation more accessible. In [69], a reconfigurable MIMO-SAR testbed is presented using COTS mmWave sensors and motorized rail platforms. This solution is reconfigurable but has only been implemented for SAR applications. In [68], an affordable, SDR-based testbed called _MillimeTera_ was proposed but not yet implemented. Despite the established need for mmWave sensing and communication testbeds, there are only a few accessible solutions.
## 1 Introduction  
### 1.1 Health Monitoring in Energy Storage  
Several factors account for the success of Li-ion rechargeable batteries, including the rise in demand for electric mobility (e-mobility) and grid energy storage for alternative power sources. Although use of the battery is an environmentally friendly and safe strategy, abusing the technology to the extreme usage may pose a health risk due to possible exposure to its hazardous chemical substances. Therefore, increasing efforts are made to control and monitor the technology by various implementations of Battery Management Systems (BMSs). With emphasis on reducing the overall weight and complexity in e-mobility, BMS technology adopts power line communication (PLC) [1, 2, 3], wireless monitoring [4, 5, 6, 7] and hybrid techniques combining both methods [8]. Figure 1 illustrates the current research focus on BMS communication related to the rechargeable battery sub-hierarchical levels and the potential connected e-mobility representative loads.  
BMS communication goes in hand with the growing interest in embedding sensors and in-situ cell monitoring. These range from various standard measurements such as voltage, current and temperature [9] to the mapping of light levels through advanced optical chromatic sensors [10, 11] for preventing thermal runaway [12, 13] or for battery state-of-health (SOH) diagnosis [14, 15, 16]. One of the major advantages of low-level monitoring at the cell level is the state-of-the-art demonstrated possibility of faster charging for current Li-ion batteries [17, 18] or possible access to thermal modulation required for new fast-charging cells [19].  
The wireless battery monitoring as a small network has been successfully demonstrated for 900 MHz [20] or 2.4 GHz [21; 22] placing the RF transceiver outside the battery cell's structure. However, wireless transmission from inside a small factor cell's metal enclosure such as the ones encountered in 18650 (18 mm diameter), 21,700 (21 mm diameter) and 26,650 (26 mm diameter) models, requires more preliminary testing steps than for the plastic enclosures used in pouch and lead-acid batteries. A recent study demonstrates a wireless transmission data link by embedding inside a 21,700 cell the sensors and transceiver circuitry illustrating the potential and advantages of remotely reading data during cell's cycling tests [23]. To the best of the author's knowledge, a study investigating a possible wireless range for building a link budget that leads towards future network implementation has not been found despite acknowledging the usefulness of such a remote management approach. Moreover, due to its constrained space on millimetre scale between the cell's top lid and sandwiched electrode, the antenna placed inside the cell is coupling with the metal enclosure. As all embedded RF circuits transmitting on the microwave spectrum and below will experience the same phenomenon, it will be also of interest to examine the radiation pattern by means of electromagnetic simulation since it has not been examined in prior studies.