esa-sceva/satcom-chunk-collection huggingface dataset:
# Infrared Thermal Imaging Analysis in Screening for Toddler's Fracture: A Proof-of-Concept Study
## 1 Introduction  
A fracture results in a complete or incomplete break in the anatomic continuity of a bone. This could be due to an external force or impact such as a fall, or the effect of a medical condition such as osteoporosis, which reduces bone density. During fracture healing, fracture fixation induces direct bone formation whereas moderate stability causes endochondral ossification [1].  
Bony fracture repair can be described as occurring in four phases [2]. During the initial inflammation or granulation phase, hematoma is formed in the injured bone, caused by bleeding from the site and the periodest vessels formed within the medullary canal and beneath the peritoneum [3]. During the proliferative phase, a soft callus is developed, characterized by the formation of connective tissues, including cartilage and new capillaries from pre-existing vessels [2]. During the maturing or modeling phase, a hard callus is formed, leading to woven bone, either directly from mesenchymal tissue or via an intermediate stage of cartilage. At this stage, the osteoblast-formed woven bone is mechanically weak but bridges the fracture [2]. During the final or remodeling phase, the woven bone is remodeled into stronger lamellar bone, facilitated by osteoclastic bone resorption and osteoblastic bone formation [2].  
Bones are highly vascularized, receiving around 10-15% of resting cardiac output [4]. Blood supply is provided by an extensive network of arteries, arterioles, and capillaries [5].  
A fracture causes inflammation and disturbs the blood flow at the injured site. The changes in the blood flow cause the temperature to change at the affected area, including the overlying skin. There is evidence of an increase in the temperature of surrounding tissues at the site of a fracture due to increases in metabolism and blood flow [6]. A temperature change can accurately be measured and analyzed by high-resolution infrared thermal imaging (HRIT).  
In the following sections, studies related to applications of infrared thermal imaging to screen for bone fracture are reviewed, a brief description of the toddler's fracture is provided, the study's methodology is described, and the results are explained.
## 3 Instrument Characteristics and Tests
### Time bandwidth product
#### Accuracy of brightness temperatures  
The uncertainty \(\sigma T_{\rm B}\) of a brightness temperature \(T_{\rm B}\) measured is proportional to the uncertainty \(\sigma U_{\rm RM}\) scaled with the radiometer gain \(G_{\rm RM}\):  
\[\sigma T_{\rm B}=\frac{\sigma U_{\rm RM}}{G_{\rm RM}} \tag{14}\]The uncertainties \(\sigma T_{\rm B}\) given in the right three columns of Table 3 are expected for the indicated \(T_{\rm in,\ RM}\) and \(\tau_{\rm rec}\). The uncertainties \(\sigma T_{\rm B}\) for \(\tau_{\rm rec}\geq 3\) s become smaller than 0.1 K for all input brightness temperatures that can be expected in applications of the radiometer. Therefore, a record duration of \(\tau_{\rm rec}=3\) s, is recommended for operating ELBARA II.
# LR-MPIBS: A LoRa-Based Maritime Position-Indicating Beacon System
## 3 Results and Discussion
### Validation of Extracting of Key Positioning Data  
This paper verifies the key positioning data extracting algorithm based on test data obtained from actual sea trials, and the results are shown in Figure 12. The green region illustrates the algorithm's identification of positioning data of relative static, and the pink region demonstrates the specific process of small-angle turning recognition. The simulation results showed that 83.72% of key positioning data could be identified through wide-angle turning features. Combining the analysis of small-angle turning features, the recognition rate of drifting trajectory turning key positioning data reached 97.67%. Utilizing these data facilitates the rescue team in predicting the drift trajectories of distressed individuals [33].  
Figure 11: Communication performance tests result of LR-MPIBS.  
Figure 12: Test of the extracting of key positioning data.
## 2 Background
### Smart Farming and Precision Agriculture  
Smart farming can be understood as the application of supplementary technologies to agricultural production techniques to help minimize waste and increase productivity. The application of IoT in farming allows to boost the production process through plantation monitoring, soil and water management, irrigation scheduling, pest control, delivery tracking; using data from temperature, luminosity, humidity, pressure, ground chemical concentration, and images, which are collected by sensors and cameras, using unmanned flying equipment (UAV), agricultural information management systems, global positioning systems (GPS) and communication networks. This integration results in the optimization of scarce resources.  
Precision agriculture has its concept linked direct to precision, which means, to manage precisely resources, such as energy, soil, water, fertilizers, herbicides and pesticides, among others, using different methods and techniques [14]. To achieve the best management, the most correct information is fundamental. Thus, IoT is noted as a solution to optimize agriculture and for precision agriculture.
## 4 The System Proposal: An Introduction
### ABCDE Method: On-Chain Subsystem
#### 4.3.7 Decentralised IDentities and IoT Devices  
As already mentioned, not only do entities represented by legal or natural persons contribute to associating files to a given building/DID but they also appropriate IoT devices, located in strategic points inside the building. These devices belong to the DID, and the DID sets them as its delegates through the _addDelegate_ method of the _EthereumDIDRegistry.sol_ contract. Hence the delegates are the addresses of the IoT devices that are delegated by the DID owner for a specific time to perform a function on behalf of its identity. The DID owner sets the type of function that IoT devices must perform. For example, the DID owner can set this variable equal to _attestor_ to attest to the authenticity of the send data from this device. The system could derive a unique identifying key for each IoT device exploiting the manufacturing variability of the devices' SRAM chips, through the so-called Physically Unclonable Function (PUF) [37] to identify reliably the devices. The IoT device subsystem interacts both with the Blockchain subsytem and with the information management platform. The several sensors acquire data and, through the IoT subsystem, forward it to the information management platform, hence also to the client/Ethereum node, which in turn communicates them to the smart contract at time intervals pre-established or at the request of some stakeholders, through remote procedure calls.
## 3 Automated Diagnosis of COVID-19
### Image-Level Diagnosis: Classification-Based Models  
#### 3.1.1 Overview  
To give a formal definition of the image-level diagnosis of COVID-19, suppose we have a dataset \(\mathcal{D}=\{(X_{i},Y_{i})\}_{i=1}^{N}\) with \(N\) samples, where \(X_{i}\) and \(Y_{i}\) are the \(i\)th input image and the class label of the corresponding image. Our goal is to learn a function \(f\), which  
Figure 3: **Top**: CT image contains multiple channels, whereas X-ray image only has a single channel. **Bottom**: the proportion of CT/X-ray-based methods and datasets.  
is usually a Convolutional Neural Network (CNN) [39], to predict the label from a given image accurately. In other words, the results of classification can be written as \(\hat{Y}_{i}=f(X_{i})\) and we expect a low prediction error \(d_{i}=\hat{Y}_{i}-Y_{i}\) for each \(i\).  
This formulation is adopted by most researchers. Here, we first define a commonly adopted pipeline that consists of several steps, then introduce techniques that have been used in each step in the following sections (Sections 3.1.2-3.1.5). As shown in Figure 4, this pipeline is a combination of the following steps: first, the lung scanning images (CT or X-ray) are preprocessed by data augmentation or lung segmentation; then, the feature vectors are extracted by Convolution Neural Networks (CNN) or other feature extractors. The classifier predicts the label that corresponds to the input image.  
In Figure 4, we use a ResNet-50 as the feature extraction backbone, which is adopted by most researchers according to our statistics in Section 3.1.3. The ResNet-50 backbone can be divided into four stages, and each of them has three, four, six and three blocks. The detailed structure of a block is shown on the right side of Figure 4. It is an important method to ensure that deep learning does not degenerate with the increasing number of network layers.
## 2 Three-Dimensional Dubins Path
### Two-Dimensional Dubins Path
#### 2.1.1 RSR and LSL  
For the RSR case, a trajectory consists of turning right on a right circular arc at waypoint \(\mathbf{p}_{i}\), going straight, and then turning right again on a right arc until the next waypoint \(\mathbf{p}_{i+1}\) is reached. Figure 4 shows the pull-out point \(\mathbf{q}_{0,i}\) at waypoint \(\mathbf{p}_{i}\), which is a transition point from a circular arc to a straight line, and the wheel-over point \(\mathbf{q}_{1,i}\), which is a transition point from a straight line to a circular arc at waypoint \(\mathbf{p}_{i+1}\), and a straight line connecting them, respectively.  
The pull-out and wheel-over points are computed as follows:  
\[\mathbf{q}_{0,i} =-R\mathbf{e}_{rot,i}\times\mathbf{e}_{c,i}+\mathbf{c}_{i} \tag{4}\] \[\mathbf{q}_{1,i} =-R\mathbf{e}_{rot,i}\times\mathbf{e}_{c,i}+\mathbf{c}_{i+1}\]  
where \(\mathbf{e}_{rot,i}=-\mathbf{e}_{n,i}\) is the rotation axis of the right circular arc, \(\mathbf{c}_{i}=\mathbf{c}_{r,i}\), \(\mathbf{c}_{i+1}=\mathbf{c}_{r,i+1}\), and \(\mathbf{e}_{c,i}\) is an unit direction vector from \(\mathbf{c}_{i}\) to \(\mathbf{c}_{i+1}\).  
The rotation angle \(\theta_{0,i}\) to traverse from point \(\mathbf{p}_{i}\) to \(\mathbf{q}_{0,i}\) is determined by  
\[\phi_{0}=\cos^{-1}\biggl{(}\frac{(\mathbf{p}_{i}-\mathbf{c}_{i} )\cdot(\mathbf{q}_{0,i}-\mathbf{c}_{i})}{R^{2}}\biggr{)} \tag{5}\] \[\theta_{0,i}=\begin{cases}\phi_{0},&\text{if }\mathbf{e}_{i} \cdot\mathbf{e}_{d,0}\geq 0\\ 2\pi-\phi_{0},&\text{else}\end{cases}\]  
Figure 4: RSR path.  
Figure 3: Left and right circle at a waypoint.  
where  
\[\mathbf{e}_{d,0}=\frac{\mathbf{q}_{0,i}-\mathbf{p}_{i}}{\|\mathbf{q}_{0,i}-\mathbf{ p}_{i}\|_{2}}.\]  
Similarly, the rotation angle \(\theta_{1,i}\) to traverse from point \(\mathbf{q}_{1,i}\) to \(\mathbf{p}_{i+1}\) is given as follows:  
\[\begin{split}&\phi_{1}=\cos^{-1}\Big{(}\frac{(\mathbf{p}_{i+1}- \mathbf{c}_{i+1})-(\mathbf{q}_{1,i}-\mathbf{c}_{i+1})}{R^{2}}\Big{)}\\ &\theta_{1,i}=\begin{cases}\phi_{1,i}&\text{if }\mathbf{e}_{i+1} \cdot\mathbf{e}_{d,1}\geq 0\\ 2\pi-\phi_{1,}&\text{else}\end{cases}\end{split} \tag{6}\]  
where  
\[\mathbf{e}_{d,1}=\frac{\mathbf{p}_{i+1}-\mathbf{q}_{1,i}}{\|\mathbf{p}_{i+1}- \mathbf{q}_{1,i}\|_{2}}.\]  
For the LSL case, a trajectory consists of turning left on a left circular arc, going straight, and then turning left again on a left arc until the next waypoint is reached. Figure 5 shows the pull-out and wheel-over points and a straight line connecting them, respectively.  
The pull-out, wheel-over points, and rotation angle are computed by the same Equations (4)-(6) with the following replacements:  
\[\mathbf{e}_{rot,i}=\mathbf{e}_{n,i} \tag{7}\] \[\mathbf{c}_{i}=\mathbf{c}_{i,i}\] \[\mathbf{c}_{i+1}=\mathbf{c}_{i,i+1}\]  
The distance \(s_{i}\) between two waypoints along the Dubins path is computed by summing the arc lengths along the circumference and straight line.  
\[s_{i}=R\ \theta_{0,i}+\|\mathbf{q}_{1,i}-\mathbf{q}_{0,i}\|_{2}+R\ \theta_{1,i} \tag{8}\]
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 2 Watermark System Model on 5G NR
### Performance Analysis for Bit Error Rate  
In this section, the bit error rate of the proposed watermarking scheme in the Gaussian channel and in the Rayleigh fading channel is analyzed theoretically. In the watermarking scheme proposed in this paper, the modulation mode of the user's signal is 16 QAM, the base sequence of DMRS is QPSK signal, and the watermark is embedded in the base sequence of DMRS by phase rotation. This section mainly analyzes the symbol error rate of users' signals and the bit error rate of watermark embedded bits under ideal conditions, that is, assuming that synchronization is completely accurate and the channel estimation by DMRS is also completely accurate. In actual communication, the signal will be attenuated. To make the system more suitable for practical applications, in this paper, DMRS is used for channel estimation in both the Gaussian channel and the Rayleigh fading channel. In the Gaussian channel, the fading coefficient should be constant.
## 3 Experimental Results and Discussion
### Change in the Velocity Field Caused by DBD-PA Actuation
The third trend is observed in the cases of Figure 17b with \(F^{+}\) = 10 and (c) with \(F^{+}\) = 50, where the boundary layer separation is suppressed and the separation point moves downstream, as shown in Figure 14. Both distributions exhibit a negative \(C_{p}\) peak at the leading edge. These enhancements in negative pressure increased the lift. This trend corresponds to the third phenomenon. These results indicate that the first and third trends appear to be independent of each other; however, the second trend can depend on these trends and occur simultaneously. The two separation points of the boundary layer, evaluated based on the \(C_{p}\) distributions and PIV measurements, were consistent under conditions of high \(F^{+}\), high \(Re\), or no DBD-PA actuation. However, it is difficult to determine the separation point in the \(C_{p}\) distributions under relatively low \(Re\) and \(F^{+}\) conditions. These results can explain the shedding of high-energy vortices into the flow above the wing because of burst actuation of the DBD-PA under such conditions. According to previous studies [65], a local low pressure area in the \(C_{p}\) distribution flows unsteadily downstream when vortices flow above the airfoil. Simultaneously, the separation point moved unsteadily; therefore, the correspondence between the separation point determined based on the time-averaged PIV results and time-averaged \(C_{p}\) distribution was weakened.
## 3 Recent Methods for General Applications  
In the literature, various articles that involve the most recent research on classification and regression algorithms, which can be used in different areas of application, have been presented. [PERSON] et al. [117] present a method based on filtering and revision stages to delete samples that have little influence on the learning results of a SVM, where the goal is to reduce training time without losing accuracy. This strategy could be used for handling and reducing huge databases before the application of any other algorithm. [PERSON] et al. [118] tackle the challenging problem of classification in the presence of label noise. In this regard, they propose a Markov chain sampling framework that robustly learns effective classifiers and accurately identifies mislabeled instances. [PERSON] and [PERSON] [119] propose a prototype-based classification to select some data from a dataset for development of learning rules and prediction, demonstrating that the proposed approach overcomes other classifiers such as the Bayes classifier and the nearest neighbor. Regarding the Bayesian approaches, [PERSON] et al. [120] present a probability density estimation approach based on the nonparametric kernel mixing model in order to estimate reliable class-conditional probability functions; in general, the proposed Bayesian classifier consists of three steps: Partitioning, structure learning, and estimation of probability density functions. [PERSON] et al. [121] propose a learning scheme that offers a recursive algorithm to explore the distribution of class density for the Bayesian estimation and an automated approach to select powerful discriminant functions for the classification of high-dimensional data, while [PERSON] [122] proposes a unified visual approach to compare and classify a large subset of Bayesian confirmation measures. In the work of [PERSON] et al. [123], analytical and approximate inference methods are discussed to calculate the marginal probabilities of Bayes factors, providing guidance on the interpretation of results and offering new types of analysis to study sequential data in many application areas. Regarding regression analysis, [PERSON] et al. [124] present the geometric-based online Gaussian process that could scale with massive datasets, guaranteeing that the proposed algorithm produces a good enough solution (close to the optimal one) and a fast-online regression. [PERSON] and [PERSON] [125] present an information theory-based approach using the Kolmogorov complexity and the principle of minimum description length to provide a practical solution to the problem of inferring the direction of causal dependence of observational data. [PERSON] and [PERSON] [126] analyze two uplift modeling approaches for linear regression and identify the situations in which each model works best; in fact, they propose a third model that combines the benefits of both approaches. [PERSON] et al. [70] propose the model called heterogeneous-target robust mixture regression that addresses the challenges and practical concerns of joint learning for multiple objectives/multi-tasking learning by managing mixed
# Energy Efficient Routing Protocol for an IoT-Based WSN System to Detect Forest Fires
## 5 Comparative Analysis
### Energy Consumption in EE-MAC  
In EE-MAC, the energy consumed by an ordinary sensor node is denoted by \(\text{E}_{\text{ON}\_\text{EEMAC}}\) and is computed as shown in Equation (7).  
\[\text{E}_{\text{ON}\_\text{EEMAC}}=\text{E}_{\text{RE}\_\text{ADV}}+\text{E}_{ \text{SE}\_\text{JOIN}}+\text{E}_{\text{RE}\_\text{ACK}}+\text{E}_{\text{RE}\_ \text{SCH}}+\text{E}_{\text{SE}\_\text{DATA}} \tag{7}\]  
Here, \(\text{E}_{\text{SE}\_\text{JON}}\) denotes the energy spent in sending join-request messages to CHs. \(\text{E}_{\text{RE}\_\text{ACK}}\) is the energy spent by the ordinary node in receiving acknowledgement packets from CHs. \(\text{E}_{\text{CH}\_\text{EEMAC}}\) denotes the energy consumed by the CH and is calculated using Equation (8).  
\[\text{E}_{\text{CH}\_\text{EEMAC}}=\text{E}_{\text{BR}\_\text{ADV}}+\text{E}_{ \text{RE}\_\text{JOIN}}+\text{E}_{\text{BR}\_\text{ACK}}+\text{E}_{\text{BR} \_\text{SCH}}+\text{E}_{\text{RE}\_\text{DATA}}+\text{E}_{\text{SE}\_\text{DATA}} \tag{8}\]  
\(\text{E}_{\text{BR}\_\text{ACK}}\) denotes the energy spent by the CH in broadcasting acknowledgement packets. The total energy spent in a cluster in an EE-MAC frame is denoted by \(\text{E}_{\text{EEMAC}}\) and is calculated using Equation (9). Here, \(\text{E}_{\text{AON}\_\text{EEMAC}}\) denotes the energy spent by all the ordinary nodes in the cluster.  
\[\text{E}_{\text{EEMAC}}=\text{E}_{\text{AON}\_\text{EEMAC}}+\text{E}_{\text{ CH}\_\text{EEMAC}} \tag{9}\]  
EE-MAC saves more energy compared to LEACH and ES-MAC. EE-MAC uses four parameters to save energy. The RE of a node is compared with a TE value to decide if the node can participate in the CH selection process. The PT ensures that CHs are not located too close to each other in the network. The MCS puts a limit on the maximum size of clusters in the network. The VP aids in reducing idle listening in CHs.
## 3 Results and Discussion
### Adsorption of Phenol during the Immersion Process
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Modified Model of Langmuir**} & \multicolumn{3}{c}{**Langmuir Model**} \\ \hline
**Samples** & \(\mathbf{K_{1}}\) & \(\mathbf{K_{2}}\) & \(\mathbf{K_{HB}}\) & \(\mathbf{R}\) & \(\mathbf{Q_{0}}\) & \(\mathbf{K_{L}}\) & \(\mathbf{R}\) \\ \hline  & \(\mathbf{L}\)-\(\mathbf{mmol^{-1}}\) & \(\mathbf{mmol\cdot g^{-1}}\) & \(\mathbf{mmol\cdot g^{-1}}\) & & \(\mathbf{mmol\cdot g^{-1}}\) & \(\mathbf{L}\)-\(\mathbf{mmol^{-1}}\) & \\ \hline G & 0.336 & 0.578 & \(-\)0.0263 & 0.993 & 0.612 & 0.238 & 0.989 \\ GN & 0.359 & 0.559 & \(-\)0.0216 & 0.993 & 0.583 & 0.269 & 0.990 \\ GNA & 0.270 & 0.786 & \(-\)0.0271 & 0.977 & 0.870 & 0.189 & 0.973 \\ GP & 0.400 & 0.621 & \(-\)0.0332 & 0.976 & 0.657 & 0.270 & 0.971 \\ GC\({}_{1073}\) & 0.183 & 0.606 & \(-\)0.0018 & 0.967 & 0.618 & 0.174 & 0.970 \\ GC\({}_{1173}\) & 2.273 & 0.668 & \(-\)0.0096 & 0.997 & 0.693 & 0.236 & 0.997 \\ GC\({}_{1273}\) & 0.229 & 0.623 & \(-\)0.0010 & 0.967 & 0.625 & 0.225 & 0.967 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Setting parameters to the Langmuir and Langmuir models modified for the adsorption of phenol from aqueous solution for the process of adsorption of 2 h.  
Figure 4: The adsorption isotherms of phenol from aqueous solution for the adsorption process in the calorimeter. The lines correspond to the adjustment of the data in the Langmuir Modified Model. solution (Table 3, chemical parameters obtained from Boehm Titration and pH in point of zero charge, pH\({}_{\text{PZC}}\)) [21], while for the activated carbon GNA, some acid groups were utilised to incorporate nitrogen as a different heteroatom to oxygen. Nitrogen has a couple of unpaired electrons that allow it to form a link with hydrogen by hydrogen bonds; that is to say, the adsorption of phenol on the GNA sample is greater than other activated carbon samples, because there are more hydrogen bonds between the phenol and the activated carbon surface. Table 2 also shows the constant K\({}_{\text{HB}}\) values; this represents the non-specific interactions between the adsorbate and adsorbent. The modified Langmuir model includes two terms: the first has the general shape of the Langmuir model, with the difference being that the adsorption capacity of Langmuir is represented by the product of the constants K\({}_{1}\) and K\({}_{2}\). The second term corresponds to the K\({}_{\text{HB}}\) constant; when its value is lower, the hydrophobic interactions are lower too. In this case, the lower values were obtained for G, GN, GNA, and GP samples, which have a higher number of oxygenated groups on the surface. The carbonised samples GC\({}_{1073}\), GC\({}_{1173}\), and GC\({}_{1273}\) presented fewer negative values for K\({}_{\text{HB}}\). These results are in agreement with those obtained by [PERSON] [3], which show that the constant K\({}_{\text{HB}}\) increases as the fraction of uncovered area of activated carbon is incremented.