esa-sceva/satcom-chunk-collection huggingface dataset:
# Correlation of Bone Textural Parameters with Age in the Context of Orthopedic X-ray Studies
## 2 Materials and Methods  
### Dataset  
The initial dataset comprised 3782 radiographic images of the hip and knee regions, captured using an X-ray machine and stored in PNG8 format with 8-bit color depth. The images were acquired from a group of 241 individuals of both sexes, with ages ranging from 26 to 91 years. Following an initial screening, all radiographs that did not portray the hip joint bones were excluded from the subsequent analysis. The images depicting the hip joint were obtained prior to endoprosthesis surgery or in a condition indicating the need for reoperation. In addition, images with artifacts from elements such as the mattress or endoprosthesis were also excluded. Finally, 481 radiographs from 132 patients were used for analysis, where 93 patients had a longitudinal study (several X-rays taken at intervals), and 39 patients had a single study (having only one image at one time point).
## 2 Related Work
### Traditional Methods  
Friend link prediction is a core problem in social network analysis, essentially predicting potential links by calculating the similarity between nodes. Traditional methods for friend link prediction mainly rely on the local topological structure of nodes. Common similarity measurement metrics include Jaccard [40], AA Adamic-Adar (AA) [41], Preferential Attachment (PA) [42], Resource Allocation (RA) [43], and the Salton index (SI) [44]. These methods evaluate similarity by analyzing the adjacency relationships of nodes. For example, CN assumes that two nodes with more common neighbors are more likely to be similar. Additionally, some path-based methods, such as Local Path (LP) [45], the Katz index [46], and Shortest Path (SP), consider path lengths and weights. For instance, the Katz index weights all paths and gives shorter paths higher importance, making the contribution of short path nodes to similarity more prominent.  
In recent years, several methods have been proposed to improve the accuracy and efficiency of link prediction. [PERSON] et al. [47] proposed the CNDP algorithm, which combines topological features such as the network's average clustering coefficient and the number of shared neighbors, and calculates node similarity using an adaptive penalty method, enhancing prediction accuracy. [PERSON] et al. [48] proposed the DGLP method inspired by [PERSON]'s law of gravitation, which addresses the cold-start problem for new users joining the network, enhancing the model's adaptability to new nodes. [PERSON] et al. [49] combined the Katz index and the AA index, using local path information and local information within a specified distance to further improve prediction accuracy. [PERSON] et al. [50] introduced a non-parametric similarity measure combining local and global features to reduce the impact of high node degrees on prediction, effectively reducing biases when node degrees are large. In practice, these methods are often used in combination, with some studies combining local and global similarity metrics or introducing other network features (such as node degree centrality, closeness centrality, betweenness centrality) to enhance accuracy and robustness.  
Although these traditional methods improve prediction performance to some extent, they mainly rely on the local topological structure. They also have clear limitations when dealing with the rich heterogeneous data and spatiotemporal characteristics in LBSNs. As the complexity of LBSN data increases, the effectiveness of traditional methods gradually diminishes, particularly when considering heterogeneous nodes and edges, where the prediction accuracy significantly declines. Therefore, how to incorporate geographic, social,and interest information from LBSNs into graph structures for friend link prediction has become a key problem in the current research.
# Management of Acute Life-Threatening Asthma Exacerbations in the Intensive Care Unit
## 4 Acute Life-Threatening Asthma Advanced Management in the ICU
### Mucolytics
#### 4.8.2 Recombinant Human Deoxyribonuclease (rhDNase/Domase Alfa)  
Airway obstruction by viscous mucus is one of the primary pathophysiological features of fatal asthma exacerbations. Recombinant human deoxyribonuclease (rhDNase) has been used to treat several respiratory conditions, most notably cystic fibrosis, for many years. The lysis of inflammatory cells leads to free DNA that is thought to be a primary contributor to the viscosity and adhesiveness of mucus. rhDNase is reported to liquefy these components by depolymerising extracellular DNA and thus reducing its size, leading to transformation of viscous mucus into a flowing liquid [131; 132]. rhDNase is currently only licensed for use in cystic fibrosis as a solution of 2500 units delivered up to twice per day via a jet nebuliser. A small study of 50 patients with symptomatic asthma in the emergency department did not show any clinical improvement [133]. The British Thoracic Society guidelines on the management of asthma do not support its use due to a lack of sufficient evidence [10]. Much of the literature involving rhDNase is focused on treating patients with cystic fibrosis, with only a minority of studies looking at its use in asthma. Case studies dating back to the 1990s have reported excellent effects with its use in specific patients who had proven atelectasis or lobar mucus plugging [134]. However, case reports suggest potential beneficial effects in mechanically ventilated asthmatic patients, and further studies are needed to evaluate the potential clinical benefits of both nebulised and bronchoscopic administration of rhDNase on clinical outcomes [135; 136].
## 2 The ProbPlot via IR-AVPCA Technique for Bearing Fault Detection and Diagnosis (BFDD)  
The use of an image recognition technique in the field of BFDD requires only the generation of the features as images to be the inputs (database) of this image recognition technique. Thus, no feature extraction phase is needed; instead, only a feature (image) generation phase is required, which is less complex than the former. Many possible choices exist for generating the vibration signal features in the form of images to BFDD: (i) the features (images) can be obtained directly in time domain from the original raw data of the vibration signals under the different bearing health conditions; (ii) they can be obtained in frequency domain by, for example, capturing the FFT of the vibration signals as in [23]; and (iii) or they can be obtained by generating the ProbPlot of the vibration signals as proposed for the first time in this paper, which has the advantage of not needing computation or analysis compared, unlike the FFT-based method. Further, contrary to the limitation of using the FFT to generate the features (images) for BFDD, since it is only suitable under steady state conditions but not under nonstationary conditions (i.e., variable speed, current, and/or torque), because the information about time will be lost [34], the proposed ProbPlot-based method can deal with the nonstationary conditions, which are more realistic work conditions, especially when dealing with bearings. A detailed description of the proposed ProbPlot via IR-AVPCA technique to BFDD is given below.
## 1 Introduction
Another advantage of this system is that it can be used to monitor several users in the same area at the same time using a single wireless network; hence, maximization of the number of modules supported by the wireless network is desirable. The traffic generated by the developed motion capture system is used in this paper to provide the application scenario where the performance of the eLPRT protocol and the IEEE 802.15.4 schemes are compared. The monitoring of some data-intensive biomedical signals, such as ECG, is an example of another application area with similar traffic characteristics and QoS requirements [8]. New mechanisms to improve protocol performance, namely a reallocation mechanism and a frequency hopping mechanism are enhancements introduced by eLPRT to the original LPRT protocol. This paper describes and analyzes the main features of the eLPRT protocol, comparing each one to the corresponding functionality provided by the GTS scheme. This paper is organized as follows. The next section presents an overview of the IEEE 802.15.4 standard. The features provided by the eLPRT protocol are described in Section 3. Section 4 describes the input parameters and simulation models used to evaluate the performance of the protocols. Section 5 demonstrates the inadequacy of the IEEE 802.15.4 unslotted CSMA/CA scheme relative to the motion capture application scenario. Section 6 presents an analysis of the eLPRT protocol features, comparing each one to the corresponding functionality provided by the GTS scheme. Section 7 describes the implementation of the eLPRT protocol in the context of the developed motion capture system and provides experimental results. Finally, Section 8 presents the conclusions.
# Secure On-Off Transmission in UAV Relay-Assisted mmWave Networks
## 2 System Model
### Channel Model  
Due to the blockage effects, the air-to-ground links can be LOS or non-line-of-sight (NLOS). Similar to [31], the occurrence probabilities of LOS and NLOS are given as  
Figure 1: System Model.  
\(1/\left\{1+a\exp\left[-b\left(\arctan\left(H/r\right)-a\right)\right]\right\}\), and \(p_{N}\left(r\right)=1-p_{L}\left(r\right)\), where \(r\) is the horizontal distance from UAV to the ground node, and \(a\) and \(b\) are the environment dependent constant values. Moreover, the propagation characteristics for LOS and NLOS links are different in mmWave bands [32; 33]. Giving the link of length \(d\), the path loss can be given as \(L\left(d\right)=C_{j}d^{-a_{j}}\) for \(j\in\left(L,N\right)\), where \(C_{j}=10^{-\zeta_{j}}\) is the intercept of the link with parameter \(\zeta_{j}\), \(a_{j}\) is the path loss exponent, and \(L\) and \(N\) denote that the link is LOS or NLOS. In addition, the small-scale fading for each link is modeled as independent Nakagami-m fading [34]. More specifically, the small-fading channel gain between two nodes follows normalized Gamma random variable, and for a LOS link, it is expressed as \(\left|h_{ij}\right|^{2}\sim\Gamma\left(N_{L},1\right)\); for a NLOS link, \(\left|h_{ij}\right|^{2}\sim\Gamma\left(N_{N},1\right)\), where \(i,j\in\left\{S,R_{x},D,E_{e}\right\}\).  
Based on the above assumptions on antenna and channel models, the signal to noise (SNR) at the legitimate receiver is written as  
\[\gamma_{ij}=\frac{P_{t}G_{ij}\left|h_{ij}\right|^{2}L\left(d_{ij}\right)}{N_ {0}},i\in\left\{S,R_{x}\right\},j\in\left\{R_{x},D\right\}, \tag{1}\]  
where \(P_{t}\) is the transmit power, \(R_{x}\in\Phi_{U}\) is the available UAV relay at position \(x\), \(d_{ij}\) is the distance from node \(i\) to \(j\) and \(N_{0}\) denotes the noise power. In addition, we focus on the non-colluding eavesdropping case, where all eavesdroppers process the received signals independently. As such, the secrecy performance is dominated by the largest SNR at the most malicious eavesdropper, which is written as  
\[\gamma_{E}=\underset{E_{e}\in\Phi_{E}}{\text{max}}\left\{\frac{P_{t}G_{R_{0}E_ {e}}\left|h_{R_{0}E_{e}}\right|^{2}L\left(d_{R_{0}E_{e}}\right)}{N_{0}}\right\}. \tag{2}\]
## 2 Background on Physical-Layer Security in LoRaWAN and WSNs  
Physical-layer security is a critical requirement in WSNs and LoRaWAN, where vulnerabilities such as spoofing and jamming pose significant threats. These attacks exploit the open nature of wireless communication, where signals can be intercepted, manipulated, or disrupted without physical access to the network. Traditional cryptographic methods are commonly used to secure upper layers of network communication. However, these approaches impose high computational and energy costs, making them unsuitable for resource-constrained IoT environments like LoRaWAN and WSNs [11]. PLA offers a lightweight alternative by using device-specific physical-layer attributes for authentication and anomaly detection [8].  
Spoofing attacks compromise network integrity by allowing malicious nodes to impersonate legitimate devices using forged attributes such as RSSI or MAC addresses. Similarly, jamming attacks disrupt communication by introducing interference, severely impacting system reliability. These threats are exacerbated by the lack of encryption at the physical (PHY) layer in LoRaWAN, as shown in Figure 1, where the red-highlighted section is labelled as the \"Not Protected Layer\" in the LoRaWAN communication stack.  
Figure 1 visually demonstrates the vulnerabilities in the LoRaWAN communication stack, specifically in the PHY layer, due to unencrypted data transmission. This absence of encryption leaves the PHY layer highly susceptible to critical security threats, including  
Figure 1: LoRa layer stack highlighting the “Not Protected Layer”—created based on Ref. [12].  
spoofing attacks, where malicious actors impersonate legitimate devices, and jamming attacks, which disrupt communication by introducing intentional interference. These vulnerabilities underscore the fundamental weakness of the PHY layer, as it lacks intrinsic mechanisms for data protection or authentication, making it a critical target for adversaries aiming to compromise network integrity and reliability [12].
## 3 Results
Specifically, seedable clouds occurred for about 36 days over the course of one year (approximately 17 days in winter, 7 days in spring, 5 days in summer, and about 7 days in fall). However, this figure is simply the occurrence frequency of seedable clouds. Figure 6: Annual frequency distribution of seedable clouds (**a–c**) around the Korean Peninsula. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Water Phase**} & \multicolumn{8}{c}{**Ice Phase**} \\ \cline{3-14}  & & **Ac** & **As** & **Ns** & **Cu** & **Sc** & **St** & **N** & **Ac** & **As** & **Ns** & **Cu** & **Sc** & **St** & **N** \\ \hline \multirow{3}{*}{**Winter**} & Freq. & 0.47 & 12.85 & 0.03 & 13.23 & 73.33 & 0.09 & & 14.75 & 19.69 & 0.03 & 26.39 & 39.12 & 0.03 & \\  & CTH & 3.26 & 3.07 & 3.20 & 2.06 & 2.14 & 2.42 & 1,386,094 & 3.45 & 3.39 & 3.67 & 2.71 & 2.48 & 1.84 & 651,549 \\  & CTT & \(-\)2.72 & \(-\)2.45 & \(-\)2.88 & \(-\)1.61 & \(-\)1.86 & \(-\)2.15 & & \(-\)12.13 & \(-\)10.40 & \(-\)7.77 & -11.14 & -11.30 & -9.46 & \\ \hline \multirow{3}{*}{**Spring**} & Freq. & 2.84 & 16.99 & 0.04 & 10.13 & 70.01 & \(<\)0.01 & & 24.08 & 23.87 & 0.01 & 21.86 & 30.18 & \(<\)0.01 & \\  & CTH & 3.54 & 3.22 & 3.33 & 2.29 & 2.26 & 2.47 & 1,792,845 & 3.58 & 3.49 & 3.63 & 2.94 & 2.76 & 3.19 & 703,956 \\  & CTT & \(-\)2.22 & 0.25 & \(-\)1.46 & 0.84 & 2.95 & 3.94 & & \(-\)10.31 & \(-\)11.29 & \(-\)8.88 & \(-\)10.22 & \(-\)11.26 & \(-\)6.28 & \\ \hline \multirow{3}{*}{**Summer**} & Freq. & 1.65 & 11.23 & \(<\)0.01 & 10.65 & 76.47 & \(<\)0.01 & & 35.39 & 13.85 & - & 39.44 & 11.31 & - & \\  & CTH & 3.53 & 3.22 & 2.71 & 2.32 & 2.28 & 2.36 & 2,062,96 & 3.62 & 3.64 & - & 2.89 & 2.93 & - & 214,432 \\  & CTT & 3.98 & 6.14 & 11.21 & 11.23 & 11.41 & 14.74 & & \(-\)5.74 & \(-\)5.57 & - & \(-\)5.78 & \(-\)6.02 & - & \\ \hline \multirow{3}{*}{**Fall**} & Freq. & 4.32 & 12.13 & 0.02 & 16.43 & 67.09 & 0.02 & & 32.31 & 18.18 & 0.02 & 34.27 & 15.20 & 0.03 & \\  & CTH & 3.51 & 3.15 & 3.04 & 2.36 & 2.22 & 2.58 & 1,497,370 & 3.63 & 3.58 & 3.56 & 2.84 & 2.85 & 3.07 & 246,
# Correlation of Microstructure and Mechanical Properties of Metal Big Area Additive Manufacturing
## 2 Methods
### Mechanical Testing  
After printing, the walls were machined for either tensile testing, Charpy impact toughness testing, or metallography. The tensile and impact specimens were machined according to ASTM standards [(29; 30)] and taken from three orientations, i.e., with the long axis' parallel, 45\({}^{\circ}\), and perpendicular to the build direction. Three sets of three specimens were machined for the perpendicular and 45\({}^{\circ}\) orientations, i.e., three specimens at the bottom, middle, and top of the build. The parallel orientation had eight sets, numbered 1-8, of three samples. The center of sample set 1 contained the interface between the build and the base plate (note that this is the only interface that will be referenced in the research). The center of sample set 2 was 63.5 mm from the interface. The subsequent parallel samples were taken from the build height at a center to center distance of 63.5 mm (see Figure 4a). The tensile tests were conducted on a 111 kN capacity MTS SHM-07, 4 pillar tensile frame. All the samples were tested at room temperature using a strain rate of 0.076 s\({}^{-1}\).  
The Charpy specimens were machined, from two orientations, with the long axis parallel to the build direction (Z) and the long axis perpendicular to the build direction (Y). Three sets of samples were machined in the parallel directions (Z1, Z2, Z3) and four sets of samples were machined in the perpendicular direction (Y1, Y2, Y3, Y4). The first perpendicular samples were machined 12.7 mm from the interface and the subsequent samples alternated directions up the build height (see Figure 4b). The Charpy specimens were tested on a Tinius Olsen Charpy Impact Machine with 407 J capacity. Samples were tested at temperatures of \(-80\)\({}^{\circ}\)C, \(-60\)\({}^{\circ}\)C, \(-40\)\({}^{\circ}\)C, \(-20\)\({}^{\circ}\)C, and \(25\)\({}^{\circ}\)C (room temperature).
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 3 Communication Protocols
### Multi-Access Protocols  
The ALOHA-MAC protocol can become a bottleneck for the performance of LoRaWANs in terms of reliability and scalability as the network size increases. The reason behind this lies in the fact that devices transmit asynchronously without performing any carrier sensing. As a result, in a dense network, collisions are expected to occur at a pretty high rate. Each collision causes at least one additional retransmission; thus, it increases the energy consumption. Motivated by this, numerous multi-access solutions have been proposed in order to improve the network performance in terms of energy efficiency and reliability in dense networks. When networks become denser, the increase in collisions leads to a significant waste of energy and eventually network collapse. Collisions and packet loss rate can be minimized through guaranteed access protocols, where nodes are granted access to the medium or subdivision of resources (time, frequency, code). Further, enhanced ALOHA studies make minor modifications to the standard ALOHA protocol in order to avoid collisions and improve network scalability.  
The aim of the proposed protocols is to reduce collisions, which, in turn, succeeds in reducing energy consumption. Additionally, some studies address energy efficiency by optimally selecting transmission parameters (e.g., SF) and having nodes going into sleep mode, assuming that the energy consumption in sleep mode is negligible. However, Enhanced ALOHA protocols increase energy consumption because they continuously listen to the channel and require maintaining the synchronization with the GW. Additionally, time/frequency division multiple access (T/FDMA) protocols increase energy consumption due to synchronization as the end-devices need to listen for the beacon from the GW before sending a packet; thus, a beacon-skipping mechanism is crucial for energy efficiency. Carrier sensing multiple access (CSMA) protocols perform carrier sensing, which is another major cause of energy waste due to channel listening, but overall reduce energy consumption due to fewer collisions on networks larger than 1000 devices [39]. Finally, energy consumption should be considered, as a reasonably long network lifetime is always desirable.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 3 Background
### GPRS Tunneling Protocol in 5G  
This study collected GTP tunneling packets to be transmitted to a 5G core and detected DDoS attacks through them. Therefore, the GTP uplink process for a network packet in 5G is described.  
Figure 2 shows the uplink flow through the user plane of the 5G network traffic. The IP packet generated by the UE is forwarded to the gNB for transmission to the DN. When the gNB receives an IP packet, it encapsulates the IP packet in the GTP-U header. Subsequently, the packet is encapsulated inside the IP and UDP headers and is then transmitted to the UPF. The encapsulated IP packet and GTP-U header make up the G-protocol data unit (G-PDU). The GTP-U header consists of the message type, next extension header type, tunnel end-point identifier (TEID), and QoS flow identifier (QFI). The message type is fixed to decimal number 255 (0 xff), indicating the G-PDU, and the next extension header type is fixed to binary number '1000 0101' (0x85), indicating the PDU session container. The transmitted packet in the QoS flow is identified by the QoS flow identifier. TEID is an identifier uniquely assigned to each GTP user connection on each node [12].  
Figure 1: The 5G system architecture.
## 4 Results and Discussion
### Stress Component Calculation
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Weld**} & \multirow{2}{*}{\(\sigma_{m}\)} & \multirow{2}{*}{\(\sigma_{a}\)} & \multirow{2}{*}{\(\sigma_{min}\)} & \multirow{2}{*}{\(\sigma_{max}\)} & \multirow{2}{*}{\(R\)} & \multirow{2}{*}{1/\(R\)} & \multirow{2}{*}{\(M_{\sigma}\)} & \multirow{2}{*}{[\(a_{\perp}\)]} & \(\sigma_{min}\) & \(\sigma_{max}\) \\
2 & & & & & & & & & & & \\
1 & 19.01 & 3.29 & 15.72 & 22.31 & 0.70 & 1.42 & 0.30 & 22.80 & 7 & 28 \\
2 & 5.50 & 1.26 & 4.24 & 6.76 & 0.63 & 1.59 & 0.30 & 24.66 & 24 & 11 \\
3 & 10.02 & 2.29 & 7.73 & 12.31 & 0.63 & 1.59 & 0.30 & 7.31 & 8 & 27 \\ \hline \multicolumn{1}{c}{**Weld**} & \multirow{2}{*}{\(\sigma_{m}\)} & \multirow{2}{*}{\(\sigma_{a}\)} & \multirow{2}{*}{\(\sigma_{min}\)} & \multirow{2}{*}{\(\sigma_{max}\)} & \multirow{2}{*}{\(R\)} & \multirow{2}{*}{1/\(R\)} & \multirow{2}{*}{\(M_{\sigma}\)} & \multirow{2}{*}{[\(a_{\parallel}\)]} & \(\sigma_{min}\) & \(\sigma_{max}\) \\
2 & & & & & & & & & & \\
1 & \(-\)22.49 & 3.90 & \(-\)26.39 & \(-\)18.59 & 1.42 & 0.70 & 0.30 & 29.98 & 27 & 8 \\
2 & \(-\)8.15 & 2.22 & \(-\)10.37 & \(-\)5.93 & 1.75 & 0.57 & 0.30 & 23.70 & 11 & 24 \\
3 & \(-\)9.98 & 2.23 & \(-\)12.21 & \(-\)7.76 & 1.57 & 0.64 & 0.30 & 33.73 & 25 & 10 \\ \hline \multicolumn{1}{c}{**Weld**} & \multirow{2}{*}{\(\tau_{m}\)} & \multirow{2}{*}{\(\tau_{a}\)} & \multirow{2}{*}{\(\tau_{min}\)} & \multirow{2}{*}{\(\tau_{max}\)} & \multirow{2}{*}{\(R\)} & \multirow{2}{*}{1/\(R\)} & \multirow{2}{*}{\(M_{\tau}\)} & \multirow{2}{*}{[\(a_{\tau}\)]} & \(\tau_{min}\) & \(\tau_{max}\) \\
2 & & & & & & & & & & \\
3 & & & & & & & & & & & \\ \hline \multicolumn{1}{c}{**Weld**} & \multirow{2}{*}{\(\sigma_{a}\)} & \multirow{2}{*}{\(\sigma_{a,znl}\)} & \multirow{2}{*}{\(a_{\perp}\)} & \multirow{2}{*}{\(\sigma_{a}\)} & \multirow{2}{*}{\(\sigma_{a,znl}\)} & \multirow{2}{*}{\(a_{\parallel}\)} & \multirow{2}{*}{\(\tau_{a}\)} & \multirow{2}{*}{\(\tau_{a,znl}\)} & \multirow{2}{*}{\(a_{\tau}\)} & \multirow{2}{*}{**Assessment**} \\
2 & & & & & & & & & & & \\
1 & 3.29 & 22.80 & 0.14 & 3.90 & 29.98 & 0.13 & 1.03 & 24.40 & 0.04 & 0.20 & Pass \\
2 & 1.26 & 24.66 & 0.05 & 2.22 & 23.70 & 0.09 & 0.72 & 17.14 & 0.04 & 0.10 & Pass \\
3 & 2.29 & 7.31 & 0.31 & 0.25 & 33.73 & 0.01 & 1.63 & 17.14 & 0.10 & 0.33 & Pass \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fatigue evaluation assessment results.