esa-sceva/satcom-chunk-collection huggingface dataset:
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 4 Methodology
### Improved Cosine Text Similarity Implementation
#### 4.5.1 Multiple Runs Algorithm  
The algorithm of multiple runs was implemented to allow the elimination of words that are frequent in the text and superimpose to other important words. Between runs, the most common encoded words (i.e., words with frequency \(\geq 2\) occurrences in the top-5 ICD-10 codes, based on their similarity to a given EHR text) are included in the blacklist, thus allowing the encoding of other disease entities present in the EHR.  
As an example, let us consider the case where for a single run, the top-5 similarity, between the ICD-10 codes and a given EHR text, is \"J17--Preumonia in diseases classified elsewhere\" (0.63 similarity), \"R0602--Shortness of breath\" (0.62 similarity), \"P23--Congenital pneumonia\" (0.58), \"P239--Congenital pneumonia, unspecified\" (0.54) and \"J1289--Other viral pneumonia\" (0.49). The word \"pneumonia\" has frequency \(\geq 2\) occurrences among the top-5, so it will be included in the blacklist, and in the next run it will not be used for similarity calculation. If it was not removed, then the word \"pneumonia\" would superimpose, making it difficult to encode other ICD-10 codes which are still present but have lower similarity scores because of the influence of the word pneumonia. In the second run, after the removal of \"pneumonia\", the encoded top-5 is \"R0602--Shortness of breath\", \"J44--Other chronic obstructive pulmonary disease\", \"J81--Pulmonary edema\", \"J449--Chronic obstructive pulmonary disease, unspecified\" and \"J811--Chronic pulmonary edema\". The next run may add the word \"pulmonary\" to the blacklist, thus providing a way to possibly find other ICD-10 codes.  
Also, if the disease entity does not appear with frequency \(\geq 2\) occurrences in the top-5, but appears in the top-5 of two consecutive runs, it is also included in a temporary blacklist. To exemplify this fact, consider the previous example, where \"R0602--Shortness of breath\" appears in both runs. Its words are also included in the temporary blacklist, since it was already encoded. From the previous example, after the removal of \"shortness of breath\", the third run does not find this ICD-10 code, thus allowing other codes to surface: \"R11--Nausea and vomiting\", \"R112--Nausea with vomiting, unspecified\", \"R0603--Acute respiratory distress\", \"R110--Nausea\", \"R1111--Vomiting without nausea\".
# Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association
###### Abstract
One is the maximum number of consecutive updates without measurements, and the other is the minimum target speed. The criterion of the minimum target speed is very effective as shown in [22] when high clutter occurs on false targets that are not in motion. Finally, the track is confirmed as a valid track if it continues longer than the minimum track life length. Figure 1 shows a block diagram of detecting and tracking vehicles by a multirotor at a long distance. In the experiments, a drone captured a video while flying at a height of 400 m. The drone camera pointed directly downwards. A total of 55 moving vehicles appeared or disappeared for about 20 s. Figure 2 shows sample objects of the video. The object resolution is very low and sometimes they are occluded or move close to each other. The background can include the road lane and mark. Four methods are compared: IMM-CV with track association and directional track association, and IMM-CA with track association and directional track association. The IMM-CA with the directional track association outperforms other methods with an average total track life (TTL) of 91.7% and an average mean track life (MTL) of 84.2%. Figure 1: Block diagram of moving object detection and multiple target tracking. Figure 2: Sample objects in the video captured by a multirotor. The remainder of the paper is organized as follows: moving object detection is discussed in Section 2. Section 3 demonstrates multiple target tracking. Section 4 presents experimental results, and the conclusion follows in Section 5.
## 2 FMCW LiDAR
### FMCW LiDAR Principle of Operation
#### 2.2.1 Triangular FMCW  
The operational mechanism of triangular FMCW LiDAR unfolds in a three-step sequence. Initially, the frequency of the transmitted signal linearly varies up and down periodically with time, the rise time is equal to the fall time, and the average value of the frequency is \(f_{C}\). The transmission delay from the collimator to the target and then scattering back to the receiver is \(\tau\), and the range of the signal frequency is the bandwidth \(B\). The frequency difference consists of the amount of frequency change \(f_{R}\) and the Doppler shift \(f_{D}\) introduced by the propagation delay \(\tau\), and the frequency difference \(\Delta f_{1}\) and \(\Delta f_{2}\) between \(f_{R}\) and \(f_{D}\) can be obtained by coherent demodulation of each of them. The frequency difference \(\Delta f_{1}\) and the frequency difference \(\Delta f_{2}\) generated at the rising and falling edges of the frequency each differ by \(f_{D}\) from \(f_{R}\). Subsequently, the frequency difference \(f_{R}\) and the Doppler shift \(f_{D}\) introduced by the distance can be calculated using \(\Delta f_{1}\) and \(\Delta f_{2}\) through coherent demodulation, as illustrated in Figure 3. The formula for calculating the frequency difference is as follows [7]:  
\[\left\{\begin{array}{c}f_{R}=\frac{\Delta f_{1}+\Delta f_{2}}{2}\\ f_{D}=\frac{|\Delta f_{1}-\Delta f_{2}|}{2}\\ \gamma=\frac{2B}{T}\\ \tau=\frac{f_{R}}{\gamma}=\frac{(\Delta f_{1}+\Delta f_{2})T}{4B}\end{array} \right.\quad\rightarrow\left\{\begin{array}{c}v=\frac{f_{D}\cdot c}{f_{c}}= \frac{|\Delta f_{1}-\Delta f_{2}|\cdot c}{2f_{c}}\\ R=\frac{c\cdot\tau}{2}=\frac{(\Delta f_{1}+\Delta f_{2})\cdot c}{4\gamma} \end{array}\right. \tag{6}\]  
This formula defines \(\Delta f_{1}\) as the frequency difference generated on the rising edge of the frequency, \(\Delta f_{2}\) as frequency difference generated on the falling edge of the frequency, \(v\) as the target the velocity projection on the line between LiDAR and the measured target, \(R\) as the distance between the FMCW LiDAR and the target object, \(c\) as the speed of light, and \(f_{c}\) as the central frequency of the optical wave in the transmitted signal. \(\Delta f_{D}\) represents the Doppler shift. The symbol \(\tau\) is used to denote the time taken for the collimator to emit radiation toward the target object and then scatter back to the receiver. \(\Delta f_{R}\) represents the amount of frequency change, while \(\gamma\) denotes the rate of frequency change of the signal. \(B\) represents the range of frequency change of the signal, i.e., the bandwidth, which is generally 3 GHz, and \(T\) denotes the period of a chirp, which is generally 10 \(\mu\)s to 50 \(\mu\)s.
# A Generative Adversarial Network-Based Fault Detection Approach for Photovoltaic Panel
## 3 ppFDetector Solution
### Loss Functions in GAN Model Training
#### 3.2.1 Adversarial Loss  
The generator network \(G\) needs to update the training parameters according to the classification accuracy of discriminator network \(D\) (to determine whether the input is the original PV panel image or the reconstructed image). This section only refers to the generator part, but meanwhile, the discriminator is also trained and its weights are updated. In this paper, we update the generator network \(G\) based on the features of the intermediate layer of the discriminator denoted as \(\varphi(x)\), as shown in Figure 3. Assuming that the independent variables \(x\) and \(\widetilde{x}=G(x)\) both obey the positive sample data distribution \(P_{x}\), the adversarial loss function \(L_{adv}\) is the distance between the original PV panel image \(x\) and its reconstructed image \(\widetilde{x}\) using \(smooth_{L1}\) distance, it is defined as follows:  
\[L_{adv}=E_{x\sim P_{x}}||\varphi(x)-\varphi(G(x))||_{smooth_{L1}} \tag{2}\]  
where \(P_{x}\) is the data distribution of positive normal PV panel images; \(\varphi(x)\) is the output feature of the input PV panel image \(x\) in the intermediate layer of the discriminator; and \(G(x)\) is the reconstructed image of \(x\) in the generator. This loss is only activated with negative samples in the discriminator.
[3] mentioned aerodynamic focusing and conducted a numerical study on the micro particle flow in plane-shaped, axisymmetric and three-dimensional supersonic micronozzles with the Euler-Lagrangian method. Particle beam collimation occurred in two different ranges of particle sizes. [PERSON] and [PERSON] [10] also noted this phenomenon and continued their previous research, studying particle-laden flows in a micronozzle with rectangular cross-sections and a convergent/divergent sidewall. The obtained results agreed well with previous data for axisymmetrical and plane-shaped micronozzles. [PERSON] et al. [3] also considered the fact that the aerodynamic focusing of particles in the supersonic convergence-divergence nozzle significantly increased the speed of the collimated beam. Table 1 is the summary of the previous studies on aerosol focusing. Although many studies have been carried out on particle dynamics in micronozzles, far too little attention has been paid to the impact of non-spherical shape characteristics and rarefied gas on particle motion to date. Based on the microscopic observation of the morphology of particles, most of the micro particles are irregular and non-spherical [11; 12]. \begin{table}
\begin{tabular}{c c c c} \hline
**Authors** & **Particle Size** & **Method** & **Findings** \\ \hline [PERSON] et al. [5] & 0.12–1.3 μm & Experiment & 1. Aerosol focusing is the result of the inertial effect. \\  & & & 2. Particle velocity is up to 190 m/s. \\ [PERSON] et al. [8] & 100 μm & Experiment and a mathematical model & The Saffman force acting on aerosol particles becomes significant, causing a noticeable migration of particles \\  & & & toward the center line of the capillary. \\  & & & 1. The influence of the Magnus force on \\ [PERSON] et al. [9] & 2–6 μm & CFD numerical simulation & 2. A silver particle velocity of 600 m/s can be reached and the aerosol beam width is as thin as 50 μm. \\ [PERSON] et al. [3] & 0.05–5.0 μm & CFD numerical simulation & Particle beam collimation occurred in two different ranges of particle sizes for the plane-shaped and axisymmetrical nozzle. \\ [PERSON] and [PERSON] [10] & 0.05–5.0 μm & CFD numerical simulation & Particle beam collimation occurred in two different ranges of particle sizes in the nozzle with a rectangular cross-section. \\ \hline \end{tabular}
\end{table} 
Table 1: The previous studies on aerosol focusing in nozzles. [PERSON] et al. [13] numerically studied the non-spherical silicon particles in different gas streams, and the results showed that the particle impact velocity and temperature can be altered by the change of the sizes and shapes of sprayed particles. Therefore, based on the N-S equation with the first-order slip boundary conditions and drag force equation for non-spherical particles, we combine the Euler-Lagrangian method to discuss the particle trajectory and diffusion range in a pressure-driven micronozzle in this paper. The obtained results are important for optimizing the performance of micronozzles, such as controlling the speed of the submicron particle beam in a micronozzle and forming a collimated submicron particle beam.
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
## 3 Results
### Identification and Composition of Bullet Residue Particles with SEM  
SEM provides high-resolution imaging and chemical composition analysis of particles, making it a valuable tool for solving complex forensic cases and obtaining detailed evidence. In the analysis of bullet particles, SEM is used to identify such residues at crime scenes. This technique allows for the examination of bullet residue particles to determine their chemical composition and morphology. This helps forensic experts identify the type of ammunition used and correlate the residues with specific weapons. These particles may contain metals such as lead, copper, or antimony, which are key elements for forensic identification [30; 31].  
SEM is also crucial for the analysis of the morphology and size of bullet residue particles. By examining the shape and size of these particles, forensic investigators can differentiate between different types of ammunition and determine shooting distances. The morphology of the particles can vary depending on the type of weapon and projectile used [32].  
In explosive residue analysis, SEM plays a critical role in identifying the chemical components of explosives through their morphology and composition. This technique provides valuable insights into the type of explosive used and its production method, contributing to investigations into explosions and terrorist attacks [33]. Additionally, SEM allows forensic experts to study the damage caused by explosives on materials and surfaces. By analyzing the explosive residues and structural damage on affected surfaces, it is possible to gather information about the strength and nature of the explosion and the manner in which the explosive was used [34].  
Regarding metal analysis, SEM combined with EDS spectroscopy is widely used to identify the metals present in bullet residues. Common elements include lead (Pb), copper (Cu), antimony (Sb), and barium (Ba). The presence and concentration of these elements can provide information on the manufacturing process of the projectile and help compare the residues with known samples [35].  
SEM also plays a significant role in the study of the crystalline structures of residue particles. Certain metals and compounds form characteristic crystalline structures that can be used to determine the ammunition type and firing conditions. For instance, lead may appear in different crystalline forms depending on its origin and the treatment it underwent [17].  
SEM offers several advantages in the analysis of bullet residues. Its very high resolution allows for the visualization of the minute details of residue particles, which is particularly beneficial when analyzing small amounts of residue that may not be visible using other techniques [36]. Furthermore, SEM provides both morphological and chemical information in a single analysis. The ability to combine high-resolution imaging with detailed chemical analysis makes SEM a powerful tool for identifying and characterizing bullet residues [37].
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 2 Background and Related Work
### Opportunistic Networks  
Opportunistic networks have evolved from MANETs (mobile ad hoc networks), which have the significant drawback that they store routing information and update frequently. On the other hand, opportunistic networks are dynamically built when mobile devices collaborate to form communication paths while users are in close proximity, without requiring state information such as routing tables, being instead based on a store-carry-and-forward paradigm. ONs are a type of delay-tolerant network (DTN). DTNs are composed of independent internets with connectivity inside the networks, but only occasional connectivity between them. They consist of DTN regions and DTN gateways. While DTN gateways bridge together DTN regions that may operate on different protocol stacks, ONs are more flexible environments.  
To illustrate the flexibility of ONs, here are their main characteristics:  
* Nodes can communicate with each other, although there is no route between them;
* Nodes do not require information about the network topology in order to be able to communicate;
* The routes between nodes are built dynamically
* any node can be used as the next hop for a message, if it may bring the message closer to the destination;
* Since the paths between nodes are assumed to be dynamic, this aspect may increase the delivery delay for a message;
* Each node acts as a gateway for a message, exploiting its local knowledge at that time in order to determine the best next hop.  
An opportunistic protocol is an algorithm that operates under the constraints just mentioned. This protocol may try to achieve end-to-end message delivery or to disseminate information into the network based on the interests of the nodes and their social relationships.  
ONs apply to many real-life scenarios such as wildlife tracking [1], sensor networks [5], military networks [6], intermittent Internet connectivity [7], crowd management [8], emergency crises [9], etc.  
Large mass events pose a tremendous load on the classic network infrastructure due to the large number of devices concentrated in a relatively small geographical area. Where the traditional means of communication may fail, ONs come to light. The next two sections will discuss applying ON routing protocols at festivals that fall under the umbrella of large mass events.
# ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
## 7 Discussion  
**Performance degradation in high dynamic scenario:** Although ACE has low transmission latency and high PDR in the most common usage scenarios of Bluetooth Mesh (e.g., smart home, smart building). However, the topologies of these network are relatively static. The Figure 10 shows the ACE consume considerable time to achieve route recovery. As for the high-dynamic scenarios, such as the nodes are carried with humans or pets, the performance of ACE will decrease. To address this problem, the ACE can set up some proxy nodes which compatible with Bluetooth Mesh and ACE. The high-dynamic nodes can transmit data to the proxy nodes with Bluetooth Mesh protocol, and the proxy nodes forward the data packets into ACE network.  
**Various routing policies:** For ACE, we choose the route which minimize the number of hops. Each node can utilize the TTL filed in the heartbeat message to infer the hops to other nodes autonomously, which is simple and effective. However, if other routing policy (e.g., minimize latency, minimize energy consumption) is applied, this method may fail. An intuitive solution is each node maintains a routing table with global information. But this will result in much control overhead during the route establishment and maintenance phase. Inspired by the THREAD protocol standard [17], ACE can adaptively choose a leader node which collects the global information of the network. The leader node should be placed where the network density is the highest to reduce the control overhead. The leader node can choose the optimal route or each node with a corresponding optimization model. For example, to minimize the transmission latency, the leader node can optimize the route by building a routing tree [18]. To optimize the energy consumption, the leader node should consider the energy consumption of channel hopping to assign optimal scanning channel for each node, then the energy-aware routing algorithm [19] can be used to generate the routes with minimum energy consumption. This improvement is reserved for future work.  
**Single link is susceptible to interference:** In the ACE, we propose autonomous channel scheduling scheme to assign each node with a single scanning channel. However, without channel-hopping mechanism, the link between two nodes is vulnerable to the large amount of interference on 2.4 GHz ISM band. To solve the problem, we can assign three channels to each node, and the channel hopping mechanism can be applied with these three channels. The channel assign method can be varied. An intuitional way is to assign three continuous channels around the scanning channel assigned with autonomous channel scheduling scheme. Although the three channels are continuous, the link layer of BLE will mapped to three discontinuous physical channels. We reserve the channel-hopping based channel scheduling scheme as future work.
# Modal Verification and Strength Analysis of Bladed Rotors of Turbine in Rated Working Conditions
## 3 Results and Discussion
### Analysis of Aerodynamic Force of TRT Rotor
After the external force result is obtained, each external force is applied to the windward side of each moving blade, as shown in Figure 19.  
In this analysis, to examine the composition effect of aerodynamic force and centrifugal force, besides the aerodynamic force resulting from the flow field, the structure is given a 3600 rpm rotation effect in the aerodynamic analysis. The detailed loading and boundary conditions are illustrated below in Figure 20.  
Figure 19: Upper and lower surface positions. Figure 20: TRT rotor boundary conditions and speed load. When the complete loading and boundary are applied to the finite element model, the aerodynamic force analysis is used for evaluating the stress distribution and intensity on the blades, as shown in Figure 21.  
According to the analysis results, the position of maximum stress on the TRT rotor in normal working conditions is coincident with the centrifugal force analysis simulation, both in the first section dovetail slot of the blade. According to the simulation results of the 1 st stage blade, the stress on the first section dovetail slot of the blade is 282.90 MPa, as shown in Figure 22.  
The maximum stress on the 2 nd stage blade also occurs in the first section dovetail slot of the blade; the stress is 318.50 MPa. The stress is the maximum stress on the TRT shaft system, and the stress distribution result is shown in Table 4.0  
Table 4.1 Aerodynamic analysis result of TRT rotor. Table 4.2 The stress distribution of the 1 st stage blade. According to the aerodynamic force analysis result, the position of maximum stress on the blade is similar to the position in the centrifugal force result; the maximum stress of the TRT shaft system increases from 286.21 MPa of centrifugal force analysis to 318.50 MPa. If the maximum stress is simply used as an evaluation criterion, the TRT structure is stable and safe. As the aerodynamic external force on the TRT rotor in operation is a simple harmonic external force, after long-term usage, the blade structure is likely to have metal fatigue due to aerodynamic external force which may occur in the first section dovetail slot of the blade, as shown in Table 4.3