esa-sceva/satcom-chunk-collection huggingface dataset:
# Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands
## 2 Additional Attenuation of Vegetation
### Tree Alley Perspective
#### 2.2.1 Diffraction at Tree Edge  
The wave that impinges on the edge of the tree corona can be diffracted. Then, it can travel along the tree corona and be diffracted at the second corona edge. It is possible that the signal transmitted by the antenna is diffracted on four sides of the tree: top and bottom, left and right. Furthermore, in the tree path, the signal can propagate along the edge of the entire alley.  
In that case, the diffraction of the signal can also be modeled as a phenomenon occurring twice. Then, the well-known knife-edge diffraction model [22] given by Formula (17) can be extended to the two-edge diffraction model. Diffraction loss for a single edge is  
\[C_{\rm diff}=6.9+\log_{10}\left(\sqrt{(v-0.1)^{2}+1}+v-0.1\right), \tag{17}\]  
where \(C_{\rm diff}\) is the diffraction loss in dB, which is the approximation of a complex Fresnel integral valid for \(v\) greater than \(-0.78\). \(v\) is the parameter depending on the wavelength \(\lambda\) and geometry of the obstacle with height \(h\) and diffraction angle \(\psi\):  
\[v=\sqrt{\frac{2h\psi}{\lambda}}. \tag{18}\]  
In case diffraction is present at two edges, the double-edge diffraction loss (\(C_{\rm d-diff}\)) method can be applied from [23]. Then, the total loss of diffraction is  
\[C_{\rm d-diff}=C_{1}+C_{2}+C_{\rm corr}, \tag{19}\]  
where \(C_{1}\) and \(C_{2}\) are the diffraction losses for the first and the second edges, respectively. \(C_{corr}\) is the correction coefficient since in the case of low separation between edges, the loss \(C_{\rm ddiff}\) is underestimated [24]. Then the correction coefficient can be found as [17]  
\[C_{\rm corr}=10\log_{10}\biggl{(}\frac{(d_{1}+d_{2})(d_{2}+d_{3})}{d_{2}(d_{1} +d_{2}+d_{3})}\biggr{)}, \tag{20}\]  
where \(d_{1},d_{2}\), and \(d_{3}\) are specific distances given in Figure 4. In addition, the parameter \(h_{2}^{\prime}\) in Figure 4 is the height of the obstruction above the line of sight of the signal diffracted from the highest obstruction.  
Figure 3: Considered modeling for foliage attenuation.
## 4 Results and Discussion
### Performance Analysis
#### 4.5.2 Performance Analysis Based on MyNursingHome Dataset  
Figure 8 depicts the analysis of the MyNursingHome dataset. Figure 8a denotes the accuracy analysis of FP-SFOA-DQN-FL. For time step = 200 s, the accuracy of FP-SFOA-DQN-FL at epochs 20, 40, 60, 80, and 100 is 0.844, 0.856, 0.872, 0.873, and 0.894. The loss analysis of FP-SFOA-DQN-FL is given in Figure 8b. The loss of the FP-SFOA-DQN-FL at epochs 20, 40, 60, 80, and 100 is 0.205, 0.190, 0.188, 0.184, and 0.172 for time stamp = 140 s. Figure 8c denotes the MSE analysis of FP-SFOA-DQN-FL. For time step = 80 s, the MSE  
Figure 7: Performance evaluation based on YOLO object-detection dataset. (**a**) Accuracy, (**b**) Loss, (**c**) MSE, (**d**) RMSE, (**e**) FPR, (**f**) Mean average precision.  
of FP-SFOA-DQN-FL at epochs 20, 40, 60, 80, and 100 is 0.172, 0.161, 0.155, 0.155, and 0.143. The RMSE analysis of FP-SFOA-DQN-FL is given in Figure 8d. The RMSE of FP-SFOA-DQN-FL at epochs 20, 40, 60, 80, and 100 is 0.463, 0.462, 0.441, 0.441, and 0.436 for time stamp = 200 s. Figure 8e denotes the FPR analysis of FP-SFOA-DQN-FL. For time step = 100 s, the FPR of FP-SFOA-DQN-FL at epochs 20, 40, 60, 80, and 100 is 0.206, 0.193, 0.191, 0.180, and 0.179. The mean average precision analysis of FP-SFOA-DQN-FL is given in Figure 8f. The mean average precision of FP-SFOA-DQN-FL at epochs 20, 40, 60, 80, and 100 is 0.850, 0.859, 0.878, 0.882, and 0.887 for time stamp = 200 s.
## 2 Related Works
### Deep Learning Models
#### 2.1.2 ResNet  
The residual network (ResNet) [15] architecture is a deep learning model that is primarily composed of residual blocks, and it introduced the concept of residual learning. In traditional deep learning architectures, as the input passes through multiple convolutional layers, information can be lost or degraded due to the vanishing gradient. Residual learning deals with this issue by introducing skip connections (or shortcut connections), which allow the original input features to be added back to the output of a convolutional layer. This residual connection carries the original information directly to the subsequent layers.  
By incorporating residual connections, the model can learn to focus on the residual, or the difference between the original input and the feature representation, rather than trying to learn the entire representation from scratch. This enables the model to more effectively capture and utilize fine-grained details and features that may have be lost or difficult to learn through conventional convolutional layers.  
The ResNet architecture is named based on the number of layers in the network. For example, ResNet-50 consists of 50 layers, including residual blocks, while ResNet-101 has 101 layers. These deeper architectures have shown improved performance in various computer vision tasks, including facial expression recognition, image classification, object detection, and semantic segmentation.  
ResNet and its variants have become popular choices as initial architectures in deep learning models, serving as a foundation for transfer learning and feature extraction in various domains. The use of pre-trained ResNet models, such as ResNet-18, ResNet-50, and ResNet-101, has significantly boosted the performance and efficiency of many computer vision applications.
## 4 Analysis of Vibration Characteristics of Cross-Type Hydraulic Pipeline
### The Effect of Elastic Modulus on the Vibration Characteristics of Cross-Type Branch Pipeline  
In this section, the elastic modulus of materials is taken as the research variable, and the elastic modulus of all metal materials is considered without loss of generality. The range of elastic modulus is determined as 70-230 GPa, and every 20 GPa is a calculation point. The modal shape and harmonic response results are solved, and the calculation results are analyzed. Figure 10 depicts the variation of the first six natural frequencies under the elastic modulus parameters in the above range. Figure 11 shows the harmonic response analysis of a cross-type branch pipeline with an elastic modulus of 70 GPa.  
Figure 10: Natural frequency variation curve of pipeline under different elastic modulus.  
From the above analysis, it can be concluded that the first six natural frequencies of the cross-type branch pipeline increase when the elastic modulus increases, and the higher-order modes are more significantly affected by the elastic modulus. With the increase in solid elastic modulus, the pipeline's stiffness will go up. So the self-excited vibration period of the vibration system decreases, and the natural frequency increases. The response of the cross-type branch pipeline with an elastic modulus of 70 GPa under the action of this harmonic force shows maximum values at frequencies of 198 Hz, 342 Hz, and 678 Hz. Comparing the response of the cross-type branch pipeline in the X, Y, and Z directions, it was found that the displacement response of the cross-type pipeline in the x direction was the highest, with a displacement response of 0.019 mm. By comparing the modal analysis, it is found that the natural frequencies of the branch tube are very close to the first-order natural frequency of 198.52 Hz, the second-order natural frequency of 342.17 Hz, and the fifth-order natural frequency of 678.15, respectively. This indicates that under external excitation, the first, second, and fifth order modes of the cross-type tube with this elastic modulus are sensitive to external excitation forces, and the response value is the highest in the first order mode. When conducting modal analysis on pipelines, we will find that the amplitude of vibration varies at different orders of modes. Therefore, when subjected to external excitation, although the magnitude of the excitation force is the same, sensitivity may vary due to the different amplitudes of the modes in which the structure is located.  
In passage [26], branch pipelines have been calculated with the transfer matrix method. By comparing its calculation results with those of finite element calculation, it was found that the transfer matrix method has a greater error in calculation. Therefore, it can be proven that the finite element method is superior at solving vibration problems in branch pipelines.
# An Ensemble of Convolutional Neural Networks for Audio Classification
## 4 Data Augmentation Methods
### Time-Scale Modification (TSM)  
This protocol applies the five algorithms contained in the time-scale modification (TSM) toolbox [47]. TSM methods (samples in Figure 2) are commonly used in music production software to change the speed of signals without changing their pitch. Since two different constant stretching factors (0.5 and 1.8) were used for each TSM method, this augmentation approach produced ten new images. For a detailed description of the TSM toolbox, see [48]. A brief description of the five TSM algorithms follows:  
1. OverLap Add (OLA): this algorithm is the simplest TSM method. It covers the input signals with overlapping windows of size \(H_{a}\) and maps them into overlapping windows of size \(H_{s}\). The number \(H_{a}\) depends on the implementation of the algorithm, while the ratio \(\alpha=\
icefrac{{H_{s}}}{{H_{a}}}\) is the speed-up factor, which can be optionally set. The settings investigated in this study were 0.8 and 1.5. These same values were used for each TSM method;
2. Waveform Similarity OverLap Add (WSOLA): this is a modification of OLA where the overlap of the windows is not fixed but has some tolerance to better represent the output signal in cases where there is a difference of phase;
3. Phase Vocoder addresses the same phase problem as WSOLA. However, it exploits the dual approach by matching the windows in the frequency domain: first, the Fourier transforms of the signal are calculated; second, the frequencies are matched, and the signal is pulled back into the time domain;Phase Vocoder with identity phase locking: this TSM method is a modification of Phase Vocoder where the frequencies are matched as if they were not independent of each other. This modification was introduced by [PERSON] and [PERSON] [49];
5. Harmonic-Percussive Source Separation (HPSS): this augmentation technique decomposes an audio signal into its harmonic sound components, which form structures in the time direction, and its percussive sounds, which yield structures in the frequency direction. After decomposing the signal in this way, the phase vocoder is applied with the identity phase locking to the harmonic component, and OLA is applied to the percussive component. Finally, these two components are merged to form a new signal.
# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 6 Discussion and Findings
### DL and UL Use Case Results: Transport Assignment Process
The assignment result exhibited an unpredictable correlation between time and error rate values in determining the UE traffic and transport network selection process. Comparable results were also noted for the output from the selection process implemented in the cluster-transport assignment in Section 5.1. This study provided additional findings, in which the outcome pattern only produced disputable assignments due to low transport network capacity. From the findings, DOCSIS cable was selected as one of the transport options because of its ability to handle low traffic. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Traffic Type** & \begin{tabular}{c} **Optical Network: TR1** \\ **(Assigned Use Case)** \\ \end{tabular} & \
\begin{tabular}{c} **Satellite Network: TR2** \\ **(Assigned Use Case)** \\ \end{tabular} &
\begin{tabular}{c} **DOCSIS Cable: TR3** \\ **(Assigned Use Case)** \\ \end{tabular} \\ \hline
1 & Max DL & 0\% & 100\% (eMBB) & 0\% \\ \hline
2 & Min DL & 0\% & 0\% & 100\% (mMTC) \\ \hline
3 & Max UL & 40\% (mMTC) & 48\% (mMTC) & 12\% (mMTC) \\ \hline
4 & Min UL & 38\% (URLLC) & 52\% (URLLC) & 10\% (URLLC) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The composition of DL and UL traffic size (use case) to the transport network: Scenario 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Traffic Type** & \begin{tabular}{c} **Optical Network: TR1** \\ **(Assigned Use Case)** \\ \end{tabular} & \
\begin{tabular}{c} **Satellite Network: TR2** \\ **(Assigned Use Case)** \\ \end{tabular} &
\begin{tabular}{c} **DOCSIS Cable: TR3** \\ **(Assigned Use Case)** \\ \end{tabular} \\ \hline
1 & Max DL & 100\%, (eMBB) & 0\% & 0\% \\ \hline
2 & Min DL & 0\% & 0\% & 100\% (mMTC) \\ \hline
3 & Max UL & 48.33\% (mMTC) & 3.33\% (mMTC) & 48.33\% (eMBB) \\ \hline
4 & Min UL & 51.67\% (mMTC) & 0\% & 48.33\% (eMBB) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The composition of DL and UL traffic size (use case) to the transport network: Scenario 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Clustering Based on Use Cases (eMBB, mMTC, and** **URLLC) against the Assigned Transport Types** & **Performance Matrix** & **DL** & **UL** \\ \hline
1 & Total traffic size vs. link capacity & Cluster BW \textless{} TR Capacity & 100\% & 87\% \\ \hline
2 & Maximum PDB vs. link RTT & Cluster PDB \textless{} Tr RTT & 100\% & 100\% \\ \hline
3 & Minimum PER vs. link PER & Cluster PER \textgreater{} Tr PER & 33\% & 47\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The performance of the clustering process (use case): Scenario 2.
## 4 Results
### Evolution of RIP
#### 4.1.1 Technological Advancements
The feeding current does not consistently flow through the inductive sensor, unlike the method used by [PERSON]. The PAM technique considerably reduces power consumption while maintaining sensitivity due to the huge feeding current. Contemporary developments have completely diminished the use of an LC oscillatory circuit by using a high-performance, low-power embedded microprocessor that instantly digitizes the inductance and therefore requires no calibration and allows the storage of data locally and simultaneously, transmitting it wirelessly by computer and cloud using PneuRIPTM[10]. As a result, it is recommended that users account for advancements in RIP devices prior to applying them for the intended function.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 4 Analysis of Performance Results
### Simulation Results
#### 4.2.1 PER for 802.11p and 802.11 bd Protocols  
The reliability and data rate of the protocols depends on some parameters such as MCS options, the SNR in the receiver and the channel-fading conditions. To improve the DR, a higher-order MCS can be used as this allows a greater number of bits to be transmitted per symbol. However, higher-order MCS requires a higher received SNR since the Euclidian distance decreases with the increase in the modulation order.  
As depicted in Figure 4, the PER is analyzed, as a function of the SNR and the distance between vehicles. In Figure 4A, the PER is analyzed as a function of SNR curves. It is viewed that the protocol 802.11 bd performs better than 802.11p, for all SNR values. This translates into a better signal quality and a more reliable communication of 802.11 bd. For example, for MCS2 (QPSK), with 5 dB, 802.11 bd has a PER of 0.4%, while 802.11p has a value of 0.78%.  
Figure 4B analyzes the PER curves versus the distances between vehicles. In general, for all the MCS options analyzed, regardless of the increase in the distance, 802.11 bd has lower PER values than the 802.11p protocol, which means a better performance. For example, for a distance of 400 m, the PER value for 802.11 bd is 0.4%, and for 802.11p, it is 0.8%, the latter meaning that there is a significant packet loss. Therefore, the 802.11 bd protocol performs better because it has the characteristic of having higher diversity gains for frequency-selective channels and due to the use of midambles for channel estimation.
# Autonomous Service Drones for Multimodal Detection and Monitoring of Archaeological Sites
## 3 Materials and Methods
### Proposed Framework for Archaeological Aerial Monitoring  
The central unit of the proposed framework is a DJI Mavic Dual Enterprise drone controlled using a GCS (Ground Control Station) to inspect an archaeological landscape and monitor architectural cultural heritage sites. The drone integrates an on-board RGB and thermal cameras and an external depth camera for low-altitude data acquisition. The on-board cameras are a visual 4K Ultra HD sensor with a bitrate of 100 Mbps and a thermal sensor with a resolution of 160 \(\times\) 120. The external depth camera is an Intel RealSense Depth Camera D435 with a resolution of up to 1280 \(\times\) 720, connected to a controlling unit and fixed to the bottom of the drone. As depicted in Figure 5, the framework begins with flight planning where a set of waypoints is assigned using a mission planning software on the GCS to control the flight path. During the scheduled flights, the drone captures calibrated RGB, depth, and thermal videos for the inspected area. After the completion of an inspection flight, recorded videos are uploaded to a cloud server for further processing and analysis. We retrieve the videos recorded and apply a multimodal two-stage depth-to-RGB and thermal-to-RGB mosaicking algorithm to generate three orthomosaics. We use the generated mosaics for built cultural heritage detection, 3D modeling, temporal tracking, and anomalies reporting. Analysis reports are accessible to the end user through a user interface, as explained in Section 4.3.  
Figure 5: Proposed archaeological aerial monitoring system framework.
## 6 Evaluation  
We evaluate the estimation accuracy of the failure factor estimation algorithm. The performance evaluation is conducted using the scan response results acquired through a computer simulation using ns-3 (version 3.30) [40]. The scanning mechanism used in our simulator is similar to that of state-of-the-art scanning tools, such as Masscan, which is capable of transmitting up to 10 million packets per second. The scan response data in the \"normal\", \"low SNR\" (a scenario in which the propagation loss of a certain terminal is large), and \"congestion\" scenarios are acquired assuming a cellular system (LTE). This simulation does not consider \"port blocking\" and \"no network connection\". A port that is blocked by a firewall is considered safe and is reported as \"filtered\" or \"blocked\", while the result of the network scan when there is \"no network connection\" will result in a time-out, and no actual data can be gathered when these causes of scan failure/delay occur.  
Figure 4 shows the target network in our study. In this evaluation, only the LTE network is considered. In the future, we will expand the cause estimation to the low-power wide-area (LPWA) (Wi-SUN/LoRa) networks.
# On Combining DeepSnake and Global Saliency for Detection of Orchard Apples
## 3 Target Fruit Detection Based on Fusion Improved Saliency Calculation  
Since the accuracy of the DeepSnake algorithm is affected by the accuracy of the initial detection box, it is very necessary to improve the accuracy of the initial target fruit detection algorithm. [PERSON] et al., used CenterNet for the initial detection of the target [23]. This detection algorithm can detect the target object more accurately, but it takes a lot of time and cost to label the data to train the model. Considering that there is currently a lack of public apple data sets in the natural environment, and the initial detection box has a greater impact on the accuracy of the DeepSnake algorithm, this paper combines the classic color difference method, the saliency detection based on the global brightness difference and the histogram back projection algorithm to obtain the saliency map, and further proposes a dynamic adaptive overlapping fruit separation algorithm to separate overlapping fruits and label the target fruits. Considering different color spaces can describe the object from different angles and the lighting conditions of the fruit under natural conditions, in this paper, Lab color space were applied for target feature extraction. First, the RGB color space was changed into Lab color space, performed global L component contrast calculation on the image in the Lab color space and dynamic threshold binarization on the result of saliency detection. After binary processing, the target fruit region was obtained by the maximum connected domain analysis method, and the rectangular region was reduced as a template according to a certain scale. The template was used for RGB histogram back projection. Finally, the result of the histogram back projection was merged with the result of the saliency detection, did binarization processing again. The maximum connected domain is obtained by the maximum connected domain analysis method, and the dynamic number threshold corrosion operation was performed on the largest connected domain, and the corrosion result was obtained. The maximum connected domain analysis was performed again to obtain the maximum connected domain, and finally expanded the maximum connected domain with structural elements such as the number of corrosion operations. The circumscribed rectangle of the largest connected domain was used to achieve the calibration of the fruit. The method can realize fast detection and positioning of the fruit target, provide preconditions for the implementation of the mechanical action of the picking robot, and realize the effective separation of the overlapping fruits, thereby greatly improving the working efficiency of the apple picking robot.
## 4 ARCOMEM Data Management
### ARCOMEM Storage
#### 4.2.1 The ARCOMEM Object Store  
The object store is responsible for storing and serving the raw content harvested by the crawler. It relies on Apache HBase [16], a NoSQL database written in Java and modeled after Google's BigTable [17]. Essentially, each table is a sparse map storing values in cells defined by a combination of a row and a column key. In order to provide high availability of data, HBase keeps its data in a distributed filesystem called Hadoop Distributed File System (HDFS) [18]. This approach also diminishes the impact of a node failure on the overall performance.  
A sample row of the object store is shown in Figure 5. The URL of the fetched Web object, whether it is text, image or video, will serve as the row key (www.cnn.com in our case), while the actual binary content will be stored under \"content\". Extra attributes can be stored in other table columns. Moreover, it is possible to store many timestamped versions of a fetched Web object under the same row key. This is important, since the crawling procedure is likely to discover altered version of already stored Web objects in different crawls. It is also important to note that the object store allows the _a posteriori_ addition of attributes, as well, allowing the data model to be enriched on demand.  
Querying is performed using either the HBase Get API, which retrieves a particular Web object by its URL, or the HBase Scanner API, which sequentially reads a range of URLs. In both cases, the user can control which version of the datum is to be retrieved. The object store can be queried from a client over the network or using distributed MapReduce computations. For this purpose, Java classes, as well as a RESTful interface are provided.