esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Hosting Rich Content from Mobile Platforms with Relative Proximity Sensing
## 1 Introduction
Therefore, users will get charged for uploading their content, as well as downloading content from other object or people, thereby reducing the incentive to host content. _T-Wing_ achieves the desired properties not only in an energy and but also in a cost-efficient manner. In particular, it allows users to host content from their mobile platform. Data-intensive content can be downloaded directly from nearby mobile platforms using the high-bandwidth Wi-Fi interface. For instance, movies or other large files can be shared among nearby objects very efficiently, without requiring first uploading the data to the cloud via a cellular interface. Crucially, _T-Wing_ is energy efficient. In normal operation, the mobile platform's battery consumption is only minimally affected. One critical component underlying _T-Wing_ is a new mechanism that allows mobile platforms to turn on their Wi-Fi interface purely 'on-demand', that is, only when the mobile platform is either  
Figure 1: Overall concept diagram of Tiny-Web-Thing (_T-Wing_), a system that enables users to host and share content from their mobile devices. requesting data from nearby mobile platforms, or serving its content to any nearby mobile platform. The mechanism employs a proxy server in the cloud, which keeps track of each mobile platform's location, and is able to predict for a given mobile platform, which other mobile platforms are within its Wi-Fi connectivity range. A mobile platform can then download data-intensive content directly from any such mobile platform: using its cellular interface, a mobile platform sends a request to the proxy server, which then asks both mobile platforms to turn on their Wi-Fi interface, and assigns non-conflicting IP addresses. The two mobile platforms can then connect directly in a peer-to-peer fashion and exchange data. We have implemented _T-Wing_ on an open hardware embedded board, called BeagleBoard [17] in which each mobile platform runs a local web server. The proxy server is implemented using Apache and SQL server. We compare our system against alternative approaches to implement this functionality using only the cellular or Wi-Fi (but not both), and show that in typical usage, _T-Wing_ consumes less than 15\(\times\) the energy and is faster by an order of magnitude. The remainder of the paper is organized as follows: in Section 2, we review related works and analyze existing systems. In Section 3, we present the motivation of this work and our approach. In Section 4, we illustrate the core mechanism for on-demand Wi-Fi connection to nearby mobile platforms. In Section 5, we present the overall system design and components of the proposed system. In Section 6, we discuss the overall architecture of _T-Wing_ and additional extensibility of this work. In Section 7, we evaluate the performance of the proposed system. Finally, in Section 8, we present our conclusions.
## 3 Results
The HKS and the FO showed a positive correlation to the KAM, which means that with a larger FO and a larger HKS angle, the knee induction moment was higher. The NSA, the HKA, and the FMA showed a negative correlation such that with a smaller NSA, HKA (more varus), and FMA, the knee addiction moment increased, and vice versa. No correlations between the clinical leg parameters and the HAM were found for the involved or for the non-involved side. Furthermore, moderate-to-strong correlations between the hip abduction moments and the maximum hip adduction angles were found for both the involved and non-involved legs (\(R\) values between 0.686 and 0.790). For the involved leg, moderate correlations were found between KAM1 and HAM1 and the pelvic drop of the contralateral leg, as well as between the knee extension and HAM2. For the non-involved leg, no other correlations were found between the kinematics and the kinetics; however, more significant correlations were found between the kinematic gait parameters and the leg alignment parameters (Table 5). For the involved leg, a multiple regression analysis revealed that the FO explained 47% of KAM1 (\(R^{2}=0.471\); \(F=13.370\); \(p=0.002\); Table 6). Including the HKA significantly improved the model to 61% (\(\Delta R^{2}=0.143\), \(\Delta F=5.194\), \(p=0.039\)), whereas the NSA was excluded due to a strong correlation with the FO. Adding the pelvic drop of the contralateral side improved the model of the FO and the HKA even more and explained 78% of KAM1 (\(\Delta R^{2}=0.162\), \(\Delta F=9.368\), \(p=0.009\); Table 6).
## 3 Time-Frequency Coded Cooperative Transmission Based on MC-CDMA and Power Allocation
### Power Allocation between Source Sensor and Its Cooperative Sensor  
Generally, the battery energy of a wireless sensor is limited [28; 29; 30]. It is therefore beneficial for the performance of the WSN to allocate node power reasonably. Therefore, a power allocation algorithm is proposed to distribute the power to source sensor and cooperative sensor according to their channel conditions to the sink node. The total transmission power of a pair of partner sensors is assumed to be \(E\), \(E_{S_{k}}+E_{P_{k}}=E\). Then, the transmitted power of source sensor \(S_{k}\) and its cooperative sensor \(P_{k}\) are distributed according to their channel conditions to the sink node \(D\), which are formulated as:  
\[E_{S_{k}}=\frac{\overline{h}_{\mathbf{r_{k}},D}}{\overline{h}_{S_{k},D}+\overline{ h}_{\mathbf{r_{k}},D}}\,E \tag{11}\]  
\[E_{P_{k}}=\frac{\overline{h}_{S_{k},D}}{\overline{h}_{S_{k},D}+\overline{h}_{ \mathbf{r_{k}},D}}\,E \tag{12}\]  
where \(\overline{h}_{S_{k},D}\) is the average channel gain between source sensor \(S_{k}\) and sink node \(D\), \(\overline{h}_{S_{k},D}=\frac{1}{N_{c}}\sum_{n_{c}=1}^{n_{c}=N_{c}}h_{S_{k},D}( n_{c})\); \(\overline{h}_{\mathbf{r_{k}},D}\) is the average channel gain between cooperative sensor \(P_{k}\) and sink node \(D\), \(\overline{h}_{\mathbf{r_{k}},D}=\frac{1}{N_{c}}\sum_{n_{c}=1}^{n_{c}=N_{c}}h_{\mathbf{ r_{k}},D}(n_{c})\).  
With the proposed power allocation scheme, the power distributed between a pair of partner sensors is balanced according to their channel states. Thus, the situation that a wireless sensor with poor channel condition is allocated insufficient power which may result in a higher BER can be avoided.
## 3 Results and Analysis
### Analysis of Residual Characteristics of Projectile Penetrating into Liquid Cabin at an Attack Angle
When projectile water entry is under normal penetration, the projectile undergoes erosion-mushrooming deformation, and the greater the velocity, the more serious the erosion; when projectile water entry is under penetration at an attack angle, the projectile undergoes a large overall deformation at 15-30\({}^{\circ}\), and the overall plastic bending occurs at 1600 m/s. The reason is: an overturning moment is generated on the projectile under asymmetric loads, leading to the bending stress on the cross-section of the projectile; the critical section of the projectile reaches the yield limit under the combined action of axial compressive stress and bending stress; at this time, the plastic hinge line is formed in this section, leading to overall plastic bending deformation of the projectile; when the incident velocity is large enough, such as 2400 m/s, the critical section breaks directly, such as No. 30 and 31; however, when the initial attack angle is greater than 30\({}^{\circ}\), only side erosion occurs during the high-velocity water entry of the projectile, and certain bending deformation also occurs when the incident velocity is high enough (No. 32); when the L/D ratio of the projectile is large enough, a large part of the projectile still undergoes deformation at low velocities and small attack angles; taking No. 33 as an example, the projectile with a L/D ratio of 10 at 1200 m/s undergoes bending when the attack angle is 8\({}^{\circ}\). There are four failure modes of the projectile during its penetration into the liquid cabin: mushrooming, erosion, plastic bending and fracture. The failure phase diagram of the projectile penetrating the liquid cabin at different attack angles and initial velocities can be drawn. Figure 14 is the failure phase diagram of the penetration of the rod projectile with a L/D ratio of 4.8 into the liquid cabin. In summary, at low projectile velocities, its penetration into the front plate is the main stage of failure, and at high velocities, the penetration into the liquid is the main stage of failure. The failure modes of the projectile are determined by its initial velocity, attack angle and L/D ratio. The projectile with an attack angle undergoes asymmetric deformation. When the attack angle is in the range of 15-30\({}^{\circ}\), overall deformation is most likely to occur. At low velocities, overall plastic bending deformation occurs and with the increase in velocity, fracture will occur. When the attack angle is greater than 30\({}^{\circ}\), the failure mode of the projectile is side erosion, but with the increasing initial velocity of the projectile, the range of the attack angle that leads to the overall deformation of the projectile also expands. The larger the L/D ratio, the more easily the overall plastic bending occurs to the projectile. Figure 14: Failure phase diagram of the rod projectile with a L/D ratio of 4.8 penetrating into liquid cabin. 1
## 3 Results
It can be seen from Table 1 that the values of the \(\Phi_{HCC(RZ)}\) metric better characterize the density of hydrides at the tip of the volumetric V-type flaw, being more connected with the influencing parameters of the reorientation phenomenon of localized hydrides, namely, \(K_{I}\) and \(K_{c}\), as well as with the hydrogen concentration absorbed in the sample. The new metric defined for hydride morphology assessment at a volumetric flaw in a Zr-2.5%Nb alloy pressure tube could be used in the framework of fracture mechanics analyses by considering an \"equivalent flaw like a crack\". This equivalent flaw like a crack has an updated depth, \(\mathsf{a}_{\text{eq}}\), which also considers the influence of the \(\Phi_{\text{HCC(RZ)}}\) metric value in the following way:  
\[\mathsf{a}_{\text{eq}}=\mathsf{a}+\Phi_{\text{HCC(RZ)}}\cdot 2\mathsf{a}= \mathsf{a}\cdot\left(1+2\Phi_{\text{HCC(RZ)}}\right) \tag{8}\]  
Figure 9 shows two examples considering the \(\mathsf{a}_{\text{eq}}\) depth versus actual a depth of a volumetric flaw obtained with relationship (8) using the \(\Phi_{\text{HCC(RZ)}}\) metric. Also, one may see that there are already radial hydrides connected in the form of a crack in the \(\text{Area}_{\text{HCC1}}\) zone. In this way, the embrittlement induced by hydride reorientation at the flaw tip is more appropriate for further evaluation of the stress effect under fatigue or other loadings. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reorientation** & \(\mathbf{K_{I}}\) (\(\mathbf{MPa\sqrt{m}}\)) & \(\mathbf{K_{c}}\) (\(\mathbf{FeA}\)) & \(\mathbf{\Phi_{HCC(RZ)}}\) (\(\mathbf{Relation\ 7}\)) & \(\mathbf{HCC}\) (\(\mathbf{Relation\ 6}\)) \\ \hline \(C_{H}=60\) ppm & & & & \\ \(a=0.5\) mm & 3.22 & 9.65 & 0.58 & 0.53 \\ \(\rho=0.05\) mm & & & & \\ \(C_{H}=80\) ppm & & & & \\ \(a=0.7\) mm & 4.25 & 10.46 & 0.80 & 0.42 \\ \(\rho=0.05\) mm & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between two reorientation zones with metrics for hydride morphology. Figure 8: Examples: (**a**) metric \(\Phi_{\text{HCC(RZ)}}=0.58\) at a 60 ppm hydrogen concentration, flaw depth \(\mathsf{a}=0.5\) mm, root radius \(\rho=0.05\) mm; (**b**) metric \(\Phi_{\text{HCC(RZ)}}=0.80\) at a 80 ppm hydrogen concentration, flaw depth \(\mathsf{a}=0.7\) mm, root radius \(\rho=0.05\) mm. Thus, with this approach using the HCC(RZ), the stress intensity factor \(K_{I}\) can be defined much more accurately for an equivalent flaw like a crack instead of an actual volumetric flaw. In this way, the embrittlement induced by hydride reorientation at the flaw tip is more appropriate for further evaluation of the stress effect under fatigue or other types of thermal or mechanical loadings. Consequently, this can be used in structural integrity evaluations of CANDU pressure tubes following in-service inspections.
## 3 Results and Discussion
### Validation Results  
#### 3.2.1 Monte Carlo Dataset  
The mean prediction errors over the entire Monte Carlo validation dataset for the different numbers of selected wavelengths, \(k\), are plotted in Figure 6, with the error bars representing one standard deviation. For both the ideal and the convolved cases, very low errors (<1%) are evident for all but the \(k=3\) case. Figure 7 shows these results separated by  
Figure 5: Simulated absorption spectra for the ideal (left) and convolved (right) cases for (a) low melanin (<0.10) and (b) high melanin (>0.30) concentrations. Each plot shows example spectra for low (<0.60, blue), medium (0.60-0.80, green), and high (>0.80, red) SO\({}_{2}\) values. Note that the separations between the curves near the higher wavelength lobe is greatly diminished for the convolved case relative to the ideal case.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**k** & **Ideal (nm)** & **Convolved (nm)** \\ \hline
5 & 524, 542, 553, 585, 588 & 522, 534, 546, 552, 560 \\
7 & 521, 533, 543, 550, 556, 569, 596 & 520, 535, 543, 547, 554, 563, 596 \\
11 & 521, 537, 539, 544, 551, 556, 570, 575, 581, 583, 592 & 524, 527, 533, 535, 540, 546, 551, 556, 559, 565, 588 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Example wavelength selections.  
melanin fraction to represent light-skinned, moderately pigmented, and darkly pigmented adults. For all plots in both figures, we note the slight increase in prediction errors for the \(k=81\) case where all possible wavelengths are selected, suggesting that the inclusion of some wavelengths actually hurts the prediction accuracy due to as-yet unmodeled phenomena. This finding is consistent with other studies (e.g., [20]), which achieved higher estimation accuracy with fewer wavelengths than were available. The small, highly overlapping error bars for \(k>3\) indicate that there are no significant differences in the prediction errors for the different skin types, thus validating the proposed algorithm's ability to not only accurately predict SO\({}_{2}\), but to do so for all skin pigmentations.  
Figure 6: Mean prediction error values for each number of selected wavelengths along with one-standard-deviation error bars for the (**a**) ideal case and the (**b**) convolved case. Values are computed from the entire Monte Carlo validation dataset.  
Figure 7: Prediction errors for the Monte Carlo validation dataset separated by melanin fraction representing light-skinned (<0.10), moderately pigmented (0.10–0.30), and darkly pigmented (>0.30) adults. Mean values and one-standard-deviation error bars are given for (**a**) the ideal case and (**b**) the convolved case.
## 2 Methodology
### PaDBN Model
#### 2.3.3 Regression Model and Classification  
Two methods can be used to convert the DBN into the regression model. In the first method, the DBN is trained in an unsupervised manner and then converted into the neural network (NN) without adding any other layer. In this way, all the samples are fed forward through this NN, and the output is treated as samples for the regression model. In the second method, DBN is converted into the NN after its supervised training by adding an output layer (with two neurons) for regression on the top. Thereafter, NN is trained with the labeled dataset for fine tuning and treated as a regression model. In our work, we use the second method for the regression model. For classification, we used the sigmoid function for two-class logistic regression. Sigmoid function utilization is suitable for our binary classification problem because it maps the predicted values to probabilities. The two nodes in the output layer enable the network to select the most relevant features for representation of each image patch. Thus, the deep learning features have two dimensions. The function can be written as follows  
\[\sigma_{(z)}=\ \frac{1}{(1+e^{-z})} \tag{5}\]where \(z=\sum\limits_{i=1}^{n}w_{i}x_{i}+\text{bias}\).
# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 2 Datasets
### RSSCN7 Dataset  
The RSSCN7 dataset is a public dataset released by Wuhan University in 2015. There are seven different scene categories, including grass, field, industry, river, lake, forest, residential, and parking lot. The entire dataset includes a total of 2800 images. Each scene category of the dataset contains 400 images and corresponds to one of four different sampling ratios (1:700, 1:1300, 1:2600, and 1:5200); there are 100 images corresponding to each of these ratios. In the original dataset, all of the images have a size of \(400\times 400\) pixels. The data were acquired in different seasons and under various weather conditions. In the case of sampling differences in different proportions, classification of this dataset is a greater challenge. The different image categories are shown in Figure 2. The RSSCN7 dataset images were converted into \(224\times 224\) pixel size for transfer learning.
# Autonomous Underwater Vehicle Localization Using Sound Measurements of Passing Ships
## 3 The Localization Method Using Bearing-Only Measurements
### System Models
The bearing measurement model associated to the \(n\)-th ship (\(n\in\{1,2,\ldots,N\}\)) is  
\[\mathbf{z}_{k}^{n}=h^{n}(\mathbf{X}_{k})+\mathbf{w}_{k}^{m}. \tag{29}\]  
Here, we utilize  
\[h^{n}(\mathbf{X}_{k})=atan2(y_{k}^{r,n},x_{k}^{r,n}). \tag{30}\]  
Here, \(x_{k}^{r,n}=x_{k}^{l,n}-x_{k}^{o}\), and \(y_{k}^{r,n}=y_{k}^{l,n}-y_{k}^{o}\). In (29), \(\mathbf{w}_{k}^{m}\) is the measurement noise with following distribution: \(\mathbf{w}_{k}^{m}\sim N(\mathbf{0},\mathbf{R}_{k})\). Here, \(N(\mathbf{0},\mathbf{R}_{k})\) represents Gaussian distribution with mean \(\mathbf{0}\) and covariance matrix \(\mathbf{R}_{k}\). Recall that \(\mathbf{H}_{k}\) is the partial derivative of \(h(\mathbf{X}_{k})\) with respect to \(\mathbf{X}_{k}\) evaluated at \(\mathbf{X}_{k}=\mathbf{X}_{k}^{-}\). See Equation (6). We next present how to derive \(\frac{\partial h^{n}}{\partial\mathbf{X}_{k}}\). We utilize  
\[\frac{\partial h^{n}}{\partial\mathbf{X}_{k}}=[\frac{\partial h^{n}}{\partial x _{k}^{\alpha}},\frac{\partial h^{n}}{\partial y_{k}^{\alpha}},\frac{\partial h ^{n}}{\partial x_{k}^{\alpha}},\frac{\partial h^{n}}{\partial y_{k}^{\alpha}}]. \tag{31}\]  
We next present how to derive each term in (31). Using Equation (30), we have  
\[\frac{\partial h^{n}}{\partial x_{k}^{\alpha}}=\frac{y_{k}^{\prime,n}}{(r_{k} ^{n})^{2}}. \tag{32}\]  
Here, \(r_{k}^{n}=\sqrt{(x_{k}^{\prime,n})^{2}+(y_{k}^{\prime,n})^{2}}\). Furthermore, we utilize  
\[\frac{\partial h^{n}}{\partial y_{k}^{\alpha}}=\frac{-x_{k}^{\prime,n}}{(r_{k }^{n})^{2}}. \tag{33}\]  
Using Equation (30), we have  
\[\frac{\partial h^{n}}{\partial x_{k}^{\alpha}}=\frac{\partial h^{n}}{\partial y _{k}^{\alpha}}=0. \tag{34}\]
## 3 Materials and Methods
### Underlying Software Components
#### 3.2.3 Application Services: Microservices  
The proposed architecture in this research targeted the microservices architecture applied to the IoT framework. The architecture is made by different components including all real-time data acquisition and processing architectures in the IoT domain as shown in Figure 1. It had three key components: (1) data sources, (2) data processing, and (3) end-user data access.  
The proposed solutions of microservices for smart buildings in general and fire detection with occupancy detection are detailed in Figure 2.  
Figure 1: Proposed Deployment Architecture.  
The concept considered allowed independent mounting and unmounting of the new nodes. Every node was considered one service for data acquisition and transmission. It allowed any connected node to be updated and integrated with the management of the services manager played by Docker Swarm. The transmission protocol, data management, web services, and visualization services were managed as microservices. This allowed services reusability without affecting the whole structure.
# Multi-Access Channel Based on Quantum Detection in Wireless Optical Communication
## 3 Results and Analysis  
### Setup for Numerical Results  
To analyze the effect of quantum detection more intuitively, we need to perform numerical verification of the theoretical results. The settings for the numerical results for MED and USD are given here.  
In Section 3.2, the experiments do not involve the effect of noise, so we examine the effect of signal power on the error performance and channel capacity. Without loss of generality, the average photon number is used to measure the strength of the optical signal power. Here, \(|\mu|^{2}\) characterizes the average photon number of the signal and the output is the channel capacity. In Section 3.3, in addition to setting \(|\mu|^{2}\), the experiment also considers the effect of thermal noise power on the channel performance. We let \(N\) characterize the average photon number of thermal noise and examine the output as the inter-user detection error probability and channel capacity. In these experiments, \(|\mu|^{2}\) is taken in the range of 0.1 to 5 and \(N\) is taken in the range of 0.01 to 0.5. Other parameters, such as SNR, will be used in Section 4.2. The definitions of the symbols involved can be found in Table 1.
# Self-Organizing Maps to Assess the Recycling of Waste in Ceramic Construction Materials
## 2 Materials and Methods
### Analytical Methods  
The chemical composition of the samples was determined at Activations Laboratories (Canada). Major elements were determined by fusion-inductively coupled plasma (FUS-ICP). The contents of Cd, Cu, Mo, Ni, Pb, and Zn were determined using total digestion ICP. Contents of As and Cr were determined by non-destructive instrumental neutron activation analysis (INNA) and Ba by means of INNA/FUS-ICP. Fired brick specimens (pieces, prototypes and bricks) were analysed for their technological (physical, mechanical, and chemical) properties according to standard methods employed for characterisation of ceramic construction materials [43,44]. These characterisations included: (i) water absorption capacity (WA) [43,45], (ii) flexural strength (MOR) [46] through the modulus of rupture that was assessed using a servohydraulic apparatus equipped with a 15 Tm Suzpecar MES-150 stress cell; (iii) open porosity (OP) [45] determined by mercury intrusion porosimetry using a Micromeritics AutoPore IV 9500; (iv) Linear firing shrinkage (LFS) measured with a precision calliper; (v) weight loss during firing (WL) and (vi) bulk density (D) [45]. High quality commercial ceramic bricks typically exhibit WA below 16%, LFS values below 8%, WL values between 5 and 15% and D in the range 2-2.15 g/cm\({}^{3}\), depending on the type of brick (wall bricks, roof tiles or face bricks) [47, 48, 49], which will be considered as threshold values of the technical properties.  
The environmental properties assessed on the fired specimens were the release of potentially toxic species during their leaching of metals after disposal in landfill sites, simulating the end of life when they become construction and demolition waste. Leaching was assessed from crushed samples and sieved to a particle size below 4 mm following EN 12457-1 and 2 methods. Distilled water is used as leachant in the compliance equilibrium leaching tests (one-stage batch tests), using liquid to solid ratios of 2:1 [50] and 10:1 [51]. Leachates were analysed for critical elements included in the Landfill Directive 2003/33/CE [52]. Concentrations of Ba, Cd, Cr, Cu, Mo, Ni and Zn were analysed using ICP-OES (Perkin Elmer Plasma 400), As and Pb were determined by Atomic Absorption (Perkin Elmer 1100B).