esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Structural Model Validation
The associated determination coefficients (R\({}^{2}\)), as well as path coefficients, are presented in  
In this new TAM extension, taking into account the results of the initial TAM validation, the relationship between PEOU and ATT was removed as it was found to be not significant. However, PEOU was found to have a moderate influence on farmers BI to use the e-learning tool (\(\beta\) = 0.274). PEOU is influenced strongly by SE (\(\beta\) = 0.480) and EXP (\(\beta\) = 0.454), which jointly explain approximately 72.9% of its variance. The main determinant of PU was found to be CQ (\(\beta\) = 0.632), and both CQ and PEOU (\(\beta\) = 0.238), are responsible for 58.3% of PU variance. PU is strongly associated with farmers' attitude towards use (\(\beta\) = 0.742) and explains 55.1% of ATT variance. In general, the model is able to explain 73.2% of farmers' intention to use the platform. The strongest determinant of BI is ATT (\(\beta\) = 0.694). Based on this new TAM extension and considering the criteria established by [PERSON] [50], self-efficacy and experience have a **strong** positive causal relationship with PEOU (H6b and H2b). CQ has a **strong** positive impact on PU (H1a). PU showed a **strong** effect on ATT (H9a), which in turn has a **strong** effect on BI (H11). The casual relationship between PEOU and BI was not included in any of the hypothesis, but it was found to exist and be **moderate**. This adjusted and validated TAM extension allows the identification of critical drivers of acceptance for FARMER 4.0. Furthermore, the revised model offers a more reliable perspective when designing e-learning systems in future agricultural applications to foster users' acceptance.
# Autonomous Underwater Vehicle Localization Using Sound Measurements of Passing Ships
## 2 Preliminary Information
### Derive the Maximum Sensing Range Associated to a Ship  
As the preliminary information of this paper, we introduce the propagation model which simulates a sound field in underwater environments [41]. While the surface target's sound signal propagates in underwater environments, a sound field generated from the target gets complicated due to environmental interference [42]. Numerical propagation models, such as ray theory and parabolic equation, were widely utilized to simulate a sound field [41].  
In the parabolic equation, a small-angle approximation approximates the elliptic wave equation obtained from the Helmholtz equation. However, this results in error at steep angle spaces. RAM reduces this error utilizing a wide-angle parabolic equation with Pade approximation [39; 40; 41]. In this way, RAM can simulate a sound field accurately.  
Next, several definitions in [42] are discussed. Source Level (\(SL\)) indicates the sound strength generated by a source. We assume that the approximate \(SL\) of a ship is known a priori. Transmission Loss (\(TL\)) is the reduction of sound while the sound emanates from the source. \(TL\) profile is the function of both target range and depth.  
\(TL(C,target)\) represents \(TL\) of the target's signal as the AUV is at a position \(C\) and the source is at a position \(target\). \(TL(C,target)\) is constructed utilizing the RAM. Let \(M(C)\) denote the sound strength as the sound signal from the target at \(target\) reaches the AUV in \(C\). \(M(C)\) is estimated as  
\[M(C)=SL-TL(C,target). \tag{9}\]  
Note that the AUV at \(C\) fails to measure the sound of the target ship at \(target\) in the case where \(M(C)\) is less than zero. Using Equation (9), the AUV cannot measure the ship's sound in the case where  
\[SL<TL(C,target) \tag{10}\]is met. The AUV's depth can be measured using pressure sensors. Thus, the maximum sensing range can be derived by searching for a target range, which satisfies Equation (10).
## 4 Experiments and Results
### Experimental Setting  
MLNet is implemented using the PyTorch backend and executed on two NVIDIA GeForce RTX 4070 Ti GPUs with a memory capacity of 12 GB each. During the training process, the batch size is set to 8, the initial learning rate is set to 1 \(\times\) 10\({}^{-4}\), and the learning rate scheduler is a power of 0.9 of the polynomial. The network is trained using the Adam optimizer [34], and a total of 500 epochs are conducted. The weight decay rate is set to 2 \(\times\) 10\({}^{-2}\).
## 3 Results
### Comparison of the Performance of a Surge Arrester with a Degraded Cable and a Nominal One  
The surge arrester configuration (Figure 6) compliant with the specification (Table 4) was used to simulate the surge \(U_{\text{WE1}}=6\) kV 1.2/50 us through a degraded RE-2Y(ST)Y 2 \(\times\) 2 \(\times\) 0.75 mm\({}^{2}\) (CT1) cable, as this type of cable was most often used for the short-distance connection of wheel detectors. The parameters of the degraded cable model were determined from the results presented in [27]. A summary of the input cable parameters for the simulations is presented in Table 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Designator** & **Specification** & \(U_{max}\) [V] & \(I_{max}\) [A] & \(P_{mean}\) [W] & \(P_{max}\) [W] \\ \hline C\({}_{4}\) & 1 nF & 469.71 & 0.49 & 0.43 & 26.17 \\ \hline GDT & SG75 & 469.71 & 0.00 & 0.00 & 0.03 \\ \hline GR\({}_{9}\) & 2.2R (\(\times\)3) & 216.62 & 32.82 & 360.50 & 7109.8 \\ \hline GR\({}_{10}\) & 2.2R (\(\times\)3) & 216.60 & 32.82 & 360.50 & 7108.7 \\ \hline D\({}_{4}\) & SMDJ28 CA & 36.50 & 31.89 & 74.98 & 1163.9 \\ \hline R\({}_{5}\) & 1R & 3.40 & 3.40 & 1.80 & 11.58 \\ \hline R\({}_{6}\) & 1R & 3.40 & 3.40 & 1.80 & 11.58 \\ \hline D\({}_{1}\) & SMDJ5.0 CA & 7.12 & 3.40 & 5.00 & 24.23 \\ \hline D\({}_{2}\) & SMDJ6.0 CA & 4.38 & 0.02 & 0.00 & 0.03 \\ \hline D\({}_{3}\) & SMDJ6.0 CA & 5.70 & 0.02 & 0.00 & 0.08 \\ \hline \hline \end{tabular} Number of resistors in Groups R\({}_{9}\) and R\({}_{10}\):  
\end{table} 
Table 4: Parameters for the proposed specification of the surge arrester \(U_{\text{WE2}}=650\) V. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Cable** & **R\({}_{W}\) [m\(\Omega\)]** & **Lw [\(\mu\)H]** & **R\({}_{5}\) [m\(\Omega\)]** & **L\({}_{5}\) [uH]** & **C\({}_{11}\) [pF]** & **C\({}_{12}\) [pF]** & **C\({}_{23}\) [nF]** & **C\({}_{45}\) [nF]** & **C\({}_{710}\) [nF]** \\
**Status** & **(20 Hz)** & **(10 kHz)** & **(20 Hz)** & **(20 Hz)** & **(10 kHz)** & **(10 kHz)** & **(10 kHz)** & **(10 kHz)** & **(10 kHz)** \\ \hline Nominal & 364.25 & 14.50 & 280.00 & 10.20 & 490.00 & 475.00 & 1.50 & 1.49 & 0.80 \\ \hline Degraded & 378.23 & 14.50 & 329.88 & 10.75 & 724.10 & 713.30 & 3.18 & 3.28 & 2.45 \\ \hline \(\delta\) (\%) & **3.84\%** & 0.00\% & 17.81\% & 5.38\% & 47.78\% & 50.17\% & 111.80\% & 120.27\% & 206.23\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of CT1 nominal and aged cable parameters. The \(\delta\) parameter was represented in the equation:  
\[\delta=\frac{x-x_{0}}{x_{0}}\times 100\%,\]  
where \(x_{0}\) stands for nominal parameters of the cables and \(x\) describes the degraded parameters of the cables affected by penetrating water. The biggest changes in \(\delta\) are observed with respect to the capacitive parameters due to the ingress of water between cable parts. The result of the approximation of the cable degradation process (1) was the source of the verification of the critical parameters, using the criteria presented in Table 1. The analysis of surge arrester parameters is shown in Table 6.
# Implementing and Testing a U-Space System: Lessons Learnt
## 3 The U-Space Services
### Mission Management
Moreover, the number of waypoints in the trajectory should be as reduced as possible, since some drones have a limit in the number of points that can be stored in the flight controller.
# Knee Osteoarthritis Classification Using 3D CNN and MRI
## 3 Results  
### 3D CNN Using MRI Data  
Table 2 shows the performance of our 3D CNN model in a confusion matrix of actual vs. predicted level for the 5-category classification. It should be noted that results presented in this subsection and the following two subsections are based on the validation set. The testing set was used once as a final evaluation step in Section 3.4 only. Observing the results in Table 2, we found that the two boundary categories, i.e., KL = 0 and KL = 4, are relatively easier to classify with a high accuracy of 70% and 90%, respectively. The middle categories are more difficult to classify. For category KL = 1, the accuracy is only 45%. However, most misclassified cases (15 out of 22 misclassified ones) were for KL = 0, which is less severe than those misclassified into higher KL grades, since clinically, both KL = 0 and KL = 1 are considered as non-OA. Category KL = 2 had the lowest accuracy 37.5%, and most of these misclassified cases went to KL = 0 and KL = 1. It is worth further examining why this category was considered more similar to non-OA class by the model when clinically this category is considered as an OA class.  
Using the same 3D CNN architecture, we trained another model with the input da-ta labeled as non-OA (KL \(\leq\) 1) and OA (KL \(\geq\) 2). Table 3 shows the confusion matrix of the 2-category model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Act.\(\backslash\)Pred.** & **P0** & **P1** & **P2** & **P3** & **P4** & **Total** & **Acc.** \\ \hline A0 & 28 & 7 & 5 & 0 & 0 & 40 & 70.0\% \\ A1 & 15 & 18 & 2 & 5 & 0 & 40 & 45.0\% \\ A2 & 7 & 10 & 15 & 7 & 1 & 40 & 37.5\% \\ A3 & 0 & 2 & 2 & 24 & 12 & 40 & 60.0\% \\ A4 & 0 & 0 & 0 & 4 & 36 & 40 & 90.0\% \\ Total & 50 & 37 & 24 & 40 & 49 & 200 & 60.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix for the 5-category KL classification by the 3D CNN model on the validation set. A0 denotes samples that are actually with KL = 0 and P0 denotes samples that are predicted as KL = 0 by the model.
# Non-Contact Dermatoscope with Ultra-Bright Light Source and Liquid Lens-Based Autofocus Function
## 5 Conclusions and Outlook  
In this work, we present a prototype of a non-contact dermatoscope with a built-in autofocus system. The auto-focus is based on a liquid lens in combination with an infrared laser distance sensor. This setup is sufficiently fast for dermatoscopy enabling a focus refresh rate of 30 Hz for image acquisition, which is double the maximal frame rate of the camera. The resolution of the dermatoscopic system is slightly lower than the resolution of the system without the liquid lens, but it is still within the required minimal resolution of 30 \(\upmu\)m. In the current alignment of the components, the system is optimized for a working distance of 45 cm \(\pm\) 3 cm. If the surface to be examined is not strongly curved (over the range of the depth of field) the system delivers sharp images at even larger distances as the infrared laser spot is reflected on the image plane. The prototype is largely independent of the ambient light due to its ultra-bright LED-light source illuminating the area of interest on the target at a distance of 45 cm with a luminous flux of 35.8 lm, which is equivalent to 162,000 lx. This makes images takenunder different ambient light conditions comparable. The system also allows for color calibration to obtain more realistic color representation  
In the next step, the prototype will be tested in a clinical environment. This could reveal further diagnostic advantages. A study will be designed to collect a larger set of images from skin lesions correlated to specific diagnostic data. The focus will be placed on a number of common inflammatory diseases in order to identify (new) diagnostic features that are revealed by the dermatoscopic images and to better evaluate the diagnostic potential of the non-contact remote approach.  
Conceptualization, D.F. and M.W.; Funding acquisition, B.R.; Investigation, E.D. and D.F.; Project administration, B.R., Resources, A.H., T.W. and B.R.; Software, D.F.; Supervision, M.W. and B.R.; Writing--original draft, D.F.; Writing--review & editing, D.F., M.W. and B.R.  
This project is funded by the Lower Saxony Ministry for Culture and Science (MWK) through the program Tailored Light and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453).  
The authors declare no conflicts of interest.
## 7 Discussion
### Implications of Scalability  
In the context of the expansion of modern grid networks, scalability represents a crucial feature for STLF models. The analysis indicates that models designed with scalability in mind are capable of handling increasing volumes of data from diverse sources without a significant loss in performance. This scalability is of great importance in adapting to the growth of the smart grid infrastructure and the integration of distributed energy resources.
# Joint Power Charging and Routing in Wireless
## 2 System Model and Problem Formulation
### Routing Constraints  
We assume each sensor node has the same sensing rate \(\lambda_{n}\) (\(n\in\mathcal{N}\)) and define the real variable \(f_{ij}^{n}\) (and \(f_{ij}^{n}\)) as the data flow traveling over link \(e_{ij}\) (and over \(e_{ji}\) ) for node \(n\), where \(e_{ij},e_{ji}\in\mathcal{E}\). We have the following constraints when designing the routing tree:  
For the source node \(n\), we have \[\sum_{e_{ij}\in\mathcal{E}}f_{ij}^{n}=\lambda_{n}(i=n) \tag{1}\]  
For node \(i\), as an intermediate relay node for sensor \(n\), that is, \(i\
eq n,i\
eq Sink\), we have  
\[\sum_{e_{ij}\in\mathcal{E}}^{k\
eq Sink}f_{ki}^{n}=\sum_{e_{ij}\in\mathcal{E}}^ {j\
eq n}f_{ij}^{n}\left(i\
eq n,Sink,i\in\mathcal{N}\right) \tag{2}\]  
For node \(n\), as the sink node of sensor \(n\), we have  
\[\sum_{e_{ij}\in\mathcal{E}}f_{ji}^{n}=\lambda_{n}\left(i=Sink\right) \tag{3}\]  
On the basis of the above definition, the energy consumption of each sensor \(i\) can be calculated as  
\[P_{i}=C_{i}^{r}D_{i}^{r}+\sum_{j\in\mathcal{N}}C_{ij}^{t}D_{ij}^{t} \tag{4}\]  
where \(C_{i}^{r}\) is the energy consumption per receiving data rate of node \(i\), \(D_{i}^{r}\) is the total received rate of node \(i\), \(C_{ij}^{t}\) is the energy consumption per transmitting data rate from node \(i\) to \(j\), and \(D_{ij}^{t}\) is the total transmitted rate from node \(i\) to \(j\). In Equation (4), \(C_{ij}^{t}\), \(D_{i}^{r}\) and \(D_{ij}^{t}\) are given as  
\[D_{i}^{r}=\sum_{n\in\mathcal{N}}\sum_{k\in\mathcal{N}\setminus i,n}f_{ki}^{n} \tag{5}\]  
\[D_{ij}^{t}=\sum_{n\in\mathcal{N}}f_{ij}^{n} \tag{6}\]  
and  
\[C_{ij}^{t}=\beta_{1}+\beta_{2}d_{ij}^{\alpha} \tag{7}\]  
where \(\beta_{1}\) is a distance-independent constant term, \(\beta_{2}\) is a coefficient of the distance-dependent term, and \(\alpha\) is the path-loss index.
## 7 Results and Discussion
### Convergence Time  
Convergence time is critical for network resilience, stability, and performance. During a link failure between Branch-1 and Branch-2 via the primary provider (ISP-A), an alternative route through a secondary provider (ISP-B) was established. The goal was to  
Figure 19: Packet loss using 13 routers—EIGRP vs. OSPF.  
Figure 18: Network jitter using 13 routers—EIGRP vs. OSPF.  
measure how quickly the network responded to the link loss and rerouted traffic through the new connection.  
Without authentication, EIGRP took between 125 and 140 s to establish an alternate path, while OSPF required 150 to 170 s. On average, EIGRP recovered from a link failover in 134.2 s, whereas OSPF needed 159.1 s. This demonstrates that EIGRP outperforms OSPF in backup path determination and link-failure recovery under these conditions. Figure 20 illustrates the convergence time without authentication.  
With authentication, EIGRP required 13 to 15 s to generate an alternate path, while OSPF needed 20 to 27 s. On average, EIGRP recovered in 14.3 s, compared to 24.3 s for OSPF. These results further highlight EIGRP's superiority in handling backup path determination and link-failure recovery with authentication enabled. Figure 21 presents the convergence time data with authentication.  
Figure 21: Convergence time with authentication.  
Figure 20: Convergence time without authentication.  
Additionally, the study notes that a relatively short hold time in BGP, particularly less than 10 s, can lead to frequent disconnections, causing network instability. To mitigate this, a hold time of at least 10 s or more is recommended to ensure a stable and continuous BGP connection, thereby enhancing network efficiency.
###### Abstract  
The development of the Fifth-Generation (5G) of cellular communications considers bands in millimeter waves (MMW) for indoor, short-range links. The propagation of MMW is affected by atmospheric and weather conditions, specular reflections from surfaces, and the directivity of the antennas. The short wavelength enables utilization of a quasi-optical propagation model for the description of indoor multi-path scenarios. A study of MMW propagation in tunnels, long corridors, or canyons is carried out using ray-tracing to evaluate the link budget and group delay. The analysis considers radiation patterns of both transmitting and receiving antennas, deriving a criterion for the number of dominating rays. Error analysis demonstrates the convergence of the method, while using a finite number of reflected rays. Experiments in a small-scale tunnel model demonstrate the accuracy of the analysis.  
Keywords: indoor millimeter wave propagation; 5G; ray tracing model; MMW +
Footnote †: journal: Accepted: 25 April 2020; Published: 26 April 2020
# FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs
###### Abstract  
Vehicular ad hoc networks (VANETs) are used for vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. They are a special type of mobile ad hoc networks (MANETs) that can share useful information to improve road traffic and safety. In VANETs, vehicles are interconnected through a wireless medium, making the network susceptible to various attacks, such as Denial of Service (DoS), Distributed Denial of Service (DDoS), or even black hole attacks that exploit the wireless medium to disrupt the network. These attacks degrade the network performance of VANETs and prevent legitimate users from accessing resources. VANETs face unique challenges due to the fast mobility of vehicles and dynamic changes in network topology. The high-speed movement of vehicles results in frequent alterations in the network structure, posing difficulties in establishing and maintaining stable communication. Moreover, the dynamic nature of VANETs, with vehicles joining and leaving the network regularly, adds complexity to implementing effective security measures. These inherent constraints necessitate the development of robust and efficient solutions tailored to VANETs, ensuring secure and reliable communication in dynamic and rapidly evolving environments. Therefore, securing communication in VANETs is a crucial requirement. Traditional security countermeasures are not pertinent to autonomous vehicles. However, many machine learning (ML) technologies are being utilized to classify malicious packet information and a variety of solutions have been suggested to improve security in VANETs. In this paper, we propose an enhanced intrusion detection framework for VANETs that leverages mutual information to select the most relevant features for building an effective model and synthetic minority oversampling (SMOTE) to deal with the class imbalance problem. Random Forest (RF) is applied as our classifier, and the proposed method is compared with different ML techniques such as logistic regression (LR), K-Nearest Neighbor (KNN), decision tree (DT), and Support Vector Machine (SVM). The model is tested on three datasets, namely ToN-IoT, NSL-KDD, and CICIDS2017, addressing challenges such as missing values, unbalanced data, and categorical values. Our model demonstrated great performance in comparison to other models. It achieved high accuracy, precision, recall, and f1 score, with a 100% accuracy rate on the ToN-IoT dataset and 99.9% on both NSL-KDD and CICIDS2017 datasets. Furthermore, the ROC curve analysis demonstrated our model's exceptional performance, achieving a 100% AUC score.  
attacks; intrusion detection; machine learning; security; VANET
## 5 Performance Evaluation
### Experimental Results and Analysis
#### 5.2.1 Pipeline Parallel Training
It uses pipelines to parallel training, overlapping communication and computing tasks, which can reduce the communication time obviously. The experiment counted the accuracy and training time change about the three models under different training methods for 100 epoch training for which the time unit was the hour. The experimental results are shown in Figure 5.  
Figure 4: Experimental results of the proportion of communication time. It can be seen from Figure 5 that the model acceleration effect ranked TAPP as the fastest, Pipe-torch second, and BSP the slowest. TAPP had the shortest training time among the three model trainings. The total training time was shortened by about 1.5 h, 10 h, and 1.6 h, respectively, and training accuracy consistent with BSP was achieved. BSP is affected by slow nodes and network bandwidth, so the acceleration effect was not obvious. TAPP and Pipe-torch use pipeline parallel training, which overlaps computing and communication tasks well, to achieve significant acceleration. The acceleration effect was the best in the VGG-16 model training. VGG-16 has many parameters, strong model fitting ability, and long training time. It took about 52 h for BSP to train the VGG-16 model for 100 epochs. TAPP can predict the optimal task allocation plan, which can alleviate expensive data communication and shorten the training time of the VGG-16 model by 1.24 times, achieving the best training acceleration effect. The experimental results prove that TAPP can effectively accelerate the model training speed and achieve model training accuracy consistent with data-parallel BSP.
## 5 Simulation Study
### Results and Discussions
#### 5.3.1 Dropped Packets  
Whilst transmitting all application traffic types simultaneously, Figures 5-8 show the packets transmitted and dropped for each application traffic type: EV charging data, meter reading, power quality and DR. The OLSR multiple metrics managed to keep the number of dropped packets low when compared to other OLSR versions. \(OLSR_{MM}\) does not only reduce dropped packets, but also enables the transmission of a target traffic type that requires certain network parameters provided by a particular link metric.  
Figure 5 shows the transmitted and dropped EV charging traffic, which transmitted the highest number of CBR traffic (about 25 packets per second) of all traffic. Though a high number of packets have been dropped, it did not replicate the number of losses incurred on other OLSR versions. This can be attributed to better path selection by the multiple metrics algorithm and the alternative paths it provides to other application traffic types to reduce congestion and bottle neck problems on specific intermediate nodes.  
In Figure 6, the transmitted and dropped CBR packets for the meter reading application are presented with the \(OLSR_{MM}\) recording the lowest number of packet losses. Compared to ETX, which has the lowest packet losses of all other conventional link metrics, a reduction of 20% in packet loses was achieved.  
Figure 5: Transmitted and dropped CBR packets for EV application.  
Figure 7 shows the CBR packets transmitted and dropped for the power quality measurement data. The packet losses recorded for this application are also lower than that of other OLSR versions. It shows that an improvement of about 13% of dropped packets is achieved by using \(OLSR_{MM}\), compared to other versions of OLSR.  
Contrary to the other application traffic types transmitted upstream towards a data concentrator, the DR traffic is multicast traffic, downstream towards the smart meter. While using the OLSR multiple metric to simultaneously transmit application traffic to the destination, in Figure 8, the \(OLSR_{MM}\) also showed lower losses on the DR multicast compared to other OLSR versions.