esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview on Push-Based Communication Models for Information-Centric Networking
## 3 Push-Based Approaches in ICN
### Push Communication via Long-lived Interests  
Long-lived Interests are a method that has been proposed to assist in reducing the cost of supporting push-based communications in local environments [15; 19]. PIT entries for Long-lived Interests are associated with a specific time interval. The goal is to allow producers to send multiple data packets to consumers during such time interval, without the need to generate additional Interest packets.  
Long-lived Interests are implementable via special Interest packets that carry a _Time Threshold Value (TLV)_[16]. Hence, IoT consumers may use Long-lived Interests to express their willingness in receiving specific data for a certain amount of time (e.g., 3 days).  
Long-lived Interests are therefore relevant in terms of reducing the PIT caching size, the network traffic, and the usage of resources by resource-constrained devices: sensors can produce and push data as soon as it is produced, without having to stored it while waiting for incoming Interest packets. This means that data producers can spend more time in Idle mode, thus sparing battery.  
Although Long-lived Interests lock PIT entries for longer periods of time, this does not raise an issue, given that applications should generate Long-lived Interests only to support periodic data transmissions, and not sporadic ones. Nevertheless, ICN/NDN routers may implement a policy allowing the release of PIT entries of long-lives Interests for which no data has been forwarded during a configured period of time.  
The interruption of data reception implicitly informs consumer applications about the end of the respective time period. The application resumes the regular ICN process, under the assumption that data is being generated with a frequency lower than initially expected.
# A Framework for Constructing a Secure Domain of Sensor Nodes
## 4 Procedures for Creating a Secure Domain of Sensor Nodes
### Procedure for Refreshing the Node Status  
During the procedure described in Section 4.3, the node's data in the RAM are restored. On the M node, the status fields of the other domain nodes have also been restored, but the current status of these nodes requires confirmation. For this purpose, the procedure for refreshing the node's status should be used for each node.  
The sequence diagram of the procedure for refreshing the node status is shown in Figure 16.  
Description of the most critical steps of the procedure are as follows:  
**(1) Prepare a request to update the status of the R node**. Do the following:  
* Read the data: N_ID, RN, NSK, and NAD descriptions of the verified R node stored in the EEPROM of the M node;
* Prepare the _status_refresh_req_ packet (Figure 17) containing the following data: N_ID, RN, and SQ of the M node. The packet is encrypted using the NSK key of the R node;
* Send a _status_refresh_req_ packet from the M node using NAD as the target address of the R node.  
**(3) Verify the correctness of the node description**. Do the following:  
* On the R node, decrypt the received _status_refresh_req_ packet and check the correctness of the N_ID and RN fields. If they are not correct, print the message and stop the procedure;
* On the R node, prepare the _status_refresh_ans_ packet (Figure 18) and send it to the M node. The SQ from the _status_refresh_req_ packet is increased by 1. The packet is encrypted using the NSK key of the R node.  
Figure 16: The sequence diagram for the procedure for refreshing the node status.  
**(5) Acquire node status**: on the M node, update the _Stat_ field to the _Active_ value in RAM in the description of the verified R node.
# Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier
###### Abstract  
Transmission line fault classification forms the basis of fault protection management in power systems. Because faults have adverse effects on transmission lines, adequate measures must be implemented to avoid power outages. This paper focuses on using the categorical boosting (CatBoost) algorithm classifier to analyse and train multiple voltage and current data from a 330 kV and 500 km-long simulated faulty transmission line model designed using Matlab/Simulink. From it, 93,340 fault data sizes were extracted. The CatBoost classifier was employed to classify the faults after different machine learning algorithms were used to train the same data with different parameters. The trainer achieved the best accuracy of 99.54%, with an error of 0.46% for 748 iterations out of 1000. The algorithm was selected for its high performance in classifying faults based on accuracy, precision and speed. In addition, it is easy to use and handles multiple data-sets. In contrast, a support vector machine and an artificial neural network each has a longer training time than the proposed method's 58.5 s. Proper fault classification techniques assist in the effective fault management and planning of power system control thereby preventing energy waste and providing high performance.  
fault classification; CatBoost classifier; transmission line; machine learning; fault management
## 5 Test Results and Interpretation
### AVTs on Pylon Foundations  
In order to provide useful information for the FE model refinement and calibration, the contribution of the soil-foundation compliance on the overall bridge dynamics is investigated, extending the measurements of vibrations to the foundation basements [33; 34]. The results of the AVTs that were performed on the pylon foundations according to the configuration of Figure 6, are shown in Figure 22 in terms of frequency content of the accelerometer recordings.  
In particular, the PSDs of sensors that were placed on SP1 and NP1 (V1-F, V2-F, V3-F, T-F, and L-F) are plotted together with the first SVD of the bridge deck, which summarizes the overall dynamics of the unloaded bridge. As can be seen, the higher frequency content  
Figure 21: Dynamics of the tested truck: (**a**) frequency content, and (**b**) AVT layout.  
Figure 22: Frequency content of the foundation system and relevant pictures.  
of the foundation system is mainly detectable for frequencies that are higher than 1.5 Hz, hence the soil-foundation compliance has greater effects on the bridges higher modes, namely the fourth, fifth, and sixth, than the first three. This may partially justify problems that are encountered in the numerical interpretation of these vibration modes, considering that soil-structure interaction effects are not included in the FE model. Indeed, frequency contents are significantly different between the north and south pylons, probably due to their different restraint conditions. Actually, the downstream side antenna of the south pylon (SP1) is located within the riverbed and has a reduced backfill which gives it a higher deformability with higher peaks in the PSDs. This also affects the behavior of the whole structure for frequencies that are higher than 1.5 Hz and could explain the splitting of the second torsional mode of the deck into the fourth and fifth modes of the overall bridge.  
However, the acceleration amplitudes that were recorded on the foundations are overall much lower than those that were recorded on the deck. Thus, it can be concluded that the stiffness of the soil-foundation system is so high that its deformability does not significantly affect the bridge dynamics. Consequently, the fixed base hypothesis that was adopted in the numerical model can be acceptable.
# Physiological Evaluation of User Experience in Unstable Automated Driving: A Comparative Study
## 2 Research Methodology
### Experiments with a VR Simulator
#### 2.1.3 Automated-Driving Implementation  
Two scenarios were implemented to evaluate how unstable automated driving affects users. The first scenario (A.D(S)) uses automated-driving technology alone, while the second scenario (A.D(C)) employs cooperative automated driving for stable navigation in traffic-obstructed sections. Both scenarios operate identically under normal conditions but show different behaviors when encountering obstacles like roundabouts, merging sections, unsignalized intersections, and blind-spot pedestrian crossings.  
As shown in Table 1, considering the characteristics of the Korean road environment, the driving speeds for both scenarios were set at 60 km/h for urban roads and 80 km/h for highways. The acceleration and deceleration values were applied based on the automated-driving characteristics preferred by the majority in an automated driving-preference evaluation study [51].  
Figure 3: Overview of virtual road design and implementation of key sections.  
The key distinction between scenarios lies in gap acceptance at roundabouts, highway merges, and unsignalized intersections. A.D(S) requires gaps of 5 s or more, while A.D(C) requires 4 s or more. While gap acceptance standards for automated driving are not yet clearly defined, the Highway Capacity Manual (HCM) suggests a basic critical gap of 5.19 s [57], and some studies propose a minimum of 3.6 s [58]. Currently, automated-driving vehicles being tested in Korea typically wait 5 s when encountering traffic-flow obstacles [59].  
For this study, A.D(S) uses a 5 s gap acceptance time, reflecting conservative behavior based on existing practices [57; 59]. A.D(C) uses 4 s, considering its cooperative capabilities and research on critical gaps in advanced automated driving [57; 58]. These thresholds balance operational efficiency with safety--the longer 5 s gap in A.D(S) compensates for sensor-only operation limitations, while A.D(C)'s 4 s gap reflects enhanced awareness from cooperative systems while maintaining safety. This difference allows for a comparison of how each scenario's behavior affects users.
## 6 Computational Study
### Experimental Design
In both cases, the control system was required to track an input sinusoidal reference with amplitude 10 and period 20 s for 3,600 s. The Integral of Squared Error (ISE) between the process output and the closed-loop reference model (19) was measured in both cases, giving a measure of the nominal (error free) performance of the control loops. Following this, the experiments were repeated under fault injection. In order to simulate the effects of burst errors, simple Markov model (see [20] for details) was employed to inject burst errors on the HIL PC links (in both sensor-controller and controller-actuator links) at an accelerated rate, with mean burst inter-arrival of 5 s and mean burst duration of 0.4 s. For the RC design, a buffer size of 10 samples in each link was employed ensuring that \(\approx 95\%\) of burst lengths would be covered by the buffer. For the proposed compensator, the same buffer size of \(H=10\) samples was used with a sensor node packet deadline of \(t_{s}=0.05\) s employed in the controller node. The experiments were again executed for 3600 s, with the ISE measured in both cases and with identical patterns of burst errors employed. The ratio of the ISE value obtained under error conditions to the nominal ISE value therefore gives a measure of the ability of the method to tolerate the errors, with a larger value indicating worse closed-loop performance. During the course of the experiments, CPU execution times on the embedded controller node were recorded using an on-chip timer with 0.1 \(\mu\) second resolution.
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 5 Estimation of Tilted Global Irradiation (GTI) from Horizontal Global Irradiation (GHI)  
In sizing or simulation software for solar systems, the solar collector inclination is introduced as an input and the horizontal solar data (generally hourly) collected from several meteorological stations are \"tilted\". The accuracy and quality of GTI used as an input in these software have an impact on the reliability of the results.  
It is difficult to develop a simple model for converting GHI into GTI [6] because the radiation received by a tilted plane includes the radiation reflected by the ground and scattered by the sky; this last component is difficult to estimate; when the collector is inclined, it sees only a part of the sky; moreover, the sky diffuse radiation depends on the inclination or orientation of the collector, on the elevation and azimuth of the sun, but also on the sky state with complex anisotropic effects [7; 8]. The larger the time-step is, the more this anisotropy decreases (time-averaging and compensating effect) and tends towards an isotropic distribution; the shorter the time-step is, the more it is difficult to realize this conversion with good accuracy. The conversion of GHI to GTI is a complex issue often dealt with in the scientific literature [7; 42; 43; 44; 45; 46].
# Tomato Leaf Disease Classification by Combining EfficientNetv2 and a Swin Transformer
## 3 Experimental Results and Analysis
### Ablation Experiment  
Regarding the effectiveness of the attention mechanism selected for the CNN branch, this study conducted experiments on various attention mechanisms on tomato leaf disease and pest image datasets to compare their impacts on the network, including the initial SE, CBAM, ECA, and CA methods used in this paper, as shown in Table 1.  
The experimental results indicate that the CA exhibits different improvements in recognition accuracy compared to the CBAM, SE, and ECA attention modules. As the CA attention mechanism not only captures inter-channel information but also considers directionally relevant positional information; it demonstrates greater efficiency and better performance in handling global contextual dependencies and channel spatial relationships than SE, CBAM, and ECA. Therefore, the CA attention mechanism shows better effectiveness in classifying tomato leaf diseases and pests with the Eff-Swin dual-stream network, achieving a network model accuracy of 99.70%, which was 1.16% higher than that of the original SE method.  
Furthermore, to further validate the effectiveness of the CNN branch and Swin Transformer branch proposed in this paper, detailed ablation experiments were conducted on tomato leaf disease and pest image datasets. Table 2 presents the results of the ablation experiments for the proposed methods, where the CNN branch is based on the improved EfficientNetV2 framework, and the backbone network of the Transformer branch is the Swin Transformer. The experimental results demonstrate that the accuracy of the Eff-Swin model, which combines the improved EfficientNetV2 and Swin Transformer, is 0.49%-3.68% higher than the accuracy of the individual branches. This indicates that the Eff-Swin algorithm can effectively extract and integrate both local and global information from tomato disease and pest images, making the feature representation more discriminative and further improving the accuracy of tomato disease and pest classification.
## 3 Crashworthiness Parameters  
Energy absorbing structures are assessed based on their performance efficiency, employing multiple criteria including specific energy absorption capability, crush force efficiency, and overall energy efficiency [44; 45]. Among these criteria, specific energy absorption (_SEA_) capability emerges as the paramount evaluation metric for energy absorbing  
Figure 1: Arrangement of (**a**) nested tube-only and (**b**) nested-tube-reinforced EPP foam block specimens.  
structures. SEA, which is defined as the amount of energy absorbed per unit weight, is articulated as illustrated in Equation (1).  
\[SEA=\frac{EA}{m} \tag{1}\]  
where \(m\) denotes the aggregate mass of the energy absorbing structure. Furthermore, \(EA\) represents the cumulative energy absorbed, which can be determined by calculating the area under the force-displacement curve of the structure. This calculation is facilitated through the utilization of the formula presented in Equation (2).  
\[EA=\int\limits_{0}^{S}F(\delta)\cdot d\delta \tag{2}\]  
The parameters \(\delta\) and \(F\) represent displacement and force, respectively. The concept of the crushability ratio is elucidated as the quotient obtained by dividing the displacement value, recorded at the conclusion of the experimental procedure, by the initial diameter of the structure under investigation. This is formally expressed in Equation (3).  
\[e_{g}=\frac{l}{d} \tag{3}\]  
The parameters of \(l\) and \(d\), denoting the crushed and original length of the absorber, are critical in this evaluation. The concept of work effectiveness (_Weff_) encompasses both the specific energy absorption capacity and the crush efficiency indicator. This parameter is essential in evaluating the performance of energy absorbers, offering a comprehensive measure that combines both energy absorption efficiency and structural integrity post deformation.  
\[W_{eff}=SEA\times e_{g} \tag{4}\]
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 4 Localization Techniques
### Device-Based Localization
#### 4.1.4 Camera-Based Localization  
In camera-based localization inside the building [51], the smartphone camera can be used as a sensor to computer vision for the position estimate. This technique combines distance approximation with image identification to attain the coordinates of the object to be positioned. To minimize the realization cost and for improving flexibility, unified sensor nodes are preferred. An optical camera and an alignment sensor node are proposed in [52], where adjacent nodes are identified with the help of fingerprints of the Wi-Fi signal. An improved localization method [53] features capture image and coordinates information to structure out fingerprints. This method works very well for indoor and outdoor environments and in regions with shadows.
# Multi-Stage Quantum Secure Direct Communication Using Secure Shared Authentication Key
## 5 Example of the Proposed Protocol
### Message Sharing Procedure
\begin{array}{rcl}\varphi_{1}&=&\left[\begin{array}{cccc}1&0&0&0\\ 0&cos(4(30))&sin(4(30))&0\\ 0&sin(4(30))&-cos(4(30))&0\\ 0&0&0&-1\end{array}\right]\times\left[\begin{array}{c}1\\ 1\\ 0\\ 0\end{array}\right]\\ &=&\left[\begin{array}{c}1\\ -0.5\\ 0.8660254\\ 0\end{array}\right]\end{array} .\] (13)
2. [PERSON] encrypts the \(\varphi_{1}\) with the authentication key, which is previously shared in the initial authentication procedure, \(\varphi=20^{\circ}\). \[ \begin{array}{rcl}\varphi_{2}&=&\left[\begin{array}{cccc}1&0&0&0\\ 0&cos(4(20))&sin(4(20))&0\\ 0&sin(4(20))&-cos(4(20))&0\\ 0&0&0&-1\end{array}\right]\times\left[\begin{array}{c}1\\ -0.5\\ 0.8660254\\ 0\end{array}\right]\\ &=&\left[\begin{array}{c}1\\ 0.76604444\\ -0.64278761\\ 0\end{array}\right].\end{array} \] (14)3. [PERSON] then applies the transformation associated with the encoded bit. In the case of bit 0 is being sent, the angle is set at \(\theta_{X}=0^{\circ}\) or \(\theta_{X}=45^{\circ}\) if bit 1 is being sent. \[ \begin{array}{rcl}\varphi_{3}&=&\left[\begin{array}{cccc}1&0&0&0\\ 0&cos(4(0))&sin(4(0))&0\\ 0&sin(4(0))&-cos(4(0))&0\\ 0&0&0&-1\end{array}\right]\times\left[\begin{array}{c}1\\ 0.76604444\\ -0.64278761\\ 0\end{array}\right]\\ &=&\left[\begin{array}{cccc}1\\ 0.76604444\\ 0.64278761\\ 0\end{array}\right].\end{array} \] (15)
4. [PERSON] receives \(\varphi_{3}\) and decrypts it by rotating it back with the angle \(-\theta_{A}\). \[ \begin{array}{rcl}\varphi_{4}&=&\left[\begin{array}{cccc}1&0&0&0\\ 0&cos(4(-30))&sin(4(-30))&0\\ 0&sin(4(-30))&-cos(4(-30))&0\\ 0&0&0&-1\end{array}\right]\times\left[\begin{array}{c}1\\ 0.76604444\\ 0.64278761\\ 0\end{array}\right]\\ &=&\left[\begin{array}{cccc}1\\ -0.5\\ -0.8660254\\ 0\end{array}\right].\end{array} \] (16)
5. [PERSON] receives \(\varphi_{4}\) and decrypts it by rotating it back using the angle of authentication key \(\mathcal{O}_{A}\). [PERSON] receives the original message. \[ \begin{array}{rcl}\varphi_{5}&=&\left[\begin{array}{cccc}1&0&0&0\\ 0&cos(4(-20))&sin(4(-20))&0\\ 0&sin(4(-20))&-cos(4(-20))&0\\ 0&0&0&-1\end{array}\right]\times\left[\begin{array}{c}1\\ -0.5\\ -0.8660254\\ 0\end{array}\right]\\ &=&\left[\begin{array}{cccc}1\\ 1\\ 0\\ 0\end{array}\right].\end{array} \] (17)
# Research on Coal and Gangue Recognition Model Based on CAM-Hardswish with EfficientNetV2
## 2 Coal and Gangue Recognition Network Model
### Setting of Network Structure Hyperparameters  
From input to the classification network, images go through a series of convolution and pooling operations, and a final output feature map results from this process of abstracting information. Several structural hyperparameters of the image affect the effectiveness of network training, such as image resolution, network depth, network width, number and size of convolutional kernels, and number of convolutional layers. Most of the ImageNet classification datasets use image resolutions of 224 pixels \(\times\) 224 pixels for the classic image classification network model. However, the EfficientNet network differs because the image size required to feed the network is too large.  
Using a deeper network structure and larger input image size in the EfficientNet network results in high video memory usage and slower training speed for different datasets. To further improve network training speed, network structure hyperparameters are modified by exploring them without compromising model accuracy. The structural parameters of the EfficientNetV2 network that were modified to improve training speed are shown in Table 1.  
In this paper, the network that integrates the CAM attention mechanism module and the Hardswish activation function and modifies the hyperparameters of the network structure is named EfficientNetV2-CAMHardswish.