esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation and Analysis of the Manufacturing Process of Pre-Bulged Rupture Discs
## 2 Materials and Methods  
During the production of forward and reverse acting rupture discs, the bulge-forming process plays an important role. In this process, an initially flat and circular sheet metal blank is formed into a double-curved rupture diaphragm using an aeroforming or comparable forming process. The geometric shape of the resulting meridian of the mainly axisymmetric diaphragm influences the strength and stability behavior of the discs during operation [13]. In addition to the influence on the static nominal bursting pressure, the resistance of the diaphragm to premature response under cyclic loading regimes and the creep resistance at elevated temperatures should be mentioned here as well as the vacuum resistance of forward acting rupture discs. For this reason, knowledge of the behavior during elastoplastic deformation is essential for understanding characteristics of pre-domed rupture discs. This process is therefore analyzed in the following by a detailed numerical model and accompanied by an experimental test program.
# The NEWA Ferry Lidar Experiment: Measuring Mesoscale Winds in the Southern Baltic Sea
## 2 Ship Lidar Technology
Floating Lidar Systems (FLSs), referring to a more or less stationary floating platform or buoy equipped with a commercial wind lidar device, particularly show economic benefits in an offshore wind resource assessment when compared to the state-of-the-art met masts, which as a standard, are used for the same application. An FLS can provide wind data of comparable quality as met masts but at considerably lower costs. A ship lidar cannot have the same purpose as a buoy-based measurement system since the movement of the ship has an essential impact on the obtained data, implying a non-stationarity, even if the local motions are compensated quite efficiently. Nonetheless, the development of the one technology can and has benefitted from the other. Also the motion effects on the measurements of an FLS can be described by Equation (1), though with another order of the most relevant motion impacts; while both for FLS and ship lidar heading information is most relevant to correct the wind direction data, the ship lidar wind speed data are essentially affected by the ship's translatory motions, the data of the FLS on the contrary by the tilting at a stationary position.
## 3 Numerical Verification
### Damage Identification Based on Variational Modal Decomposition-Continuous Wavelet Transform  
The previous discussion indicates that applying continuous wavelet transform to contact point acceleration response results in poor WC detection performance, mainly due to the contamination of high-frequency components in WC. To improve identification accuracy, it is necessary to filter the contact point response to make the WC trend more distinct. Therefore, this paper first applies variational mode decomposition to analyze the accel  
\begin{table}
\begin{tabular}{c c c c} \hline
**Case** & **Vehicle Mass** & **Vehicle Speed** & **Damage Location** & **Degree of Damage** \\
**Number** & **(kg)** & **(m/s)** & **(Element Number)** & **(\%)** \\ \hline
1 & 1000 & 5 & 13 & 5 \\
2 & 1000 & 5 & 13 & 10 \\
3 & 1000 & 5 & 13 & 20 \\
4 & 1000 & 5 & 13 & 30 \\
5 & 1000 & 2 & 13 & 30 \\
6 & 1000 & 8 & 13 & 30 \\
7 & 1000 & 5 & 7, 13, 18 & 30 \\
8 & 1000 & 5 & 7, 13, 38 & 30 \\
9 & 1000 & 5 & 13, 32, 38, 44, 63 & 30, 20, 15, 20, 30 \\ \hline \end{tabular}
\end{table}
Table 2: Case setup for VBI analysis.  
eration response of the contact point, decomposing the extracted acceleration signal into a series of intrinsic mode functions (IMFs) with different frequencies. Figure 4 shows the IMFs and the corresponding FFT spectra after processing the acceleration response (Case 4). Figure 4a shows the driving frequency component response and the first three components of the bridge response under variational mode decomposition, while Figure 4b presents the corresponding frequency spectra.  
The continuous wavelet transform results of the contact point response for Cases 1-4 are shown in Figure 5. As seen in Figure 5, after applying the variational mode decomposition technique, the continuous wavelet transform images become smoother, with more pronounced bulges at the bridge damage locations, which become more significant as the damage severity increases. This indicates that the variational modal decomposition-continuous wavelet transform can be used to detect the damage location and quantify the severity of the beam damage.  
Figure 4: IMFs and FFT Extracted by variational modal decomposition: (**a**) IMFs; (**b**) FFT.  
Figure 3: Continuous wavelet transform of contact point acceleration in Case 3: (**a**) wavelet coefficient plot at fundamental scale; (**b**) wavelet coefficient plot at scale = 253.  
The steps of damage identification in this study are as follows:  
1. The test vehicle drives over the bridge at speed \(v\), and the data are collected by the acceleration sensor on the test vehicle;
2. Obtain the contact point acceleration response by calculation;
3. Decompose the contact point acceleration response by variational modal decomposition transformation to obtain a number of IMFs.
4. Extract the IMFs corresponding to the first-order intrinsic frequency of the bridge, and use continuous wavelet transform for damage identification.
# Experience Replay Optimisation via ATSC and TSC for Performance Stability in Deep RL
## 3 General Framework for ERO-ATSC
### ERO and Alternating ERO
#### 3.1.2 Alternating Transition Selection Framework
When a transition meets a specified reward threshold of the TSC, it is stored in both buffers. However, if the transition does not meet the required threshold, it is only stored in H1. Again, at a specific sampling ratio, the ATSC samples a mini-batch of transitions from either H1 or H2. These transitions are vectorised and prioritised by the replay policy and uniformly sampled for training the agent. After training, the replay policy receives feedback for policy evaluation. ```
Given: An off-policy RL algorithm DDPG Sampling strategies (S1, S2) from replay where: S1 and S2 samples with ERO Return strategies (F1, F2) where F1 and F2 are FIFO and Enhanced-FIFO Reward threshold (Rt), sampling ratio (\(\lambda\)), mini-batch size (Mb)
1: Initialise DDPG and replay buffer H1, H2
2: observe state (\(S_{0}\)) and choose action (\(a_{0}\)) using DDPG
3:for episode =1, M do
4: observer (\(s_{t}\); \(r_{t}\); \(s_{t+1}\))
5: store transition (\(s_{t}\), at, \(r_{t}\), \(s_{t+1}\)) in H1 to follow ERO
6:if\(r_{t}>Rt\)then
7: store transition (\(s_{t}\), at, \(r_{t}\), \(s_{t+1}\)) in H2 to follow ERO
8:endif
9:for t = 1; T do
10:if size (H2) > Mb then
11: with S1, S2, and sampling ratio \(\lambda\), sample transition from H1 and H2
12:else
13: with S1, sample transition from H1
14:endif
15: update weight according to DDPG
16:endfor
17:if H1 is full then
18: use F2
19:endif
20:if H2 is full then
21: use F1
22:endif
23:endfor
```  
**Algorithm 1****ERO-ATSC with TSC**
## Appendix B OBR Spatial and Strain Resolution  
According to [PERSON] and [PERSON] [PERSON] and [PERSON] (2016), the spatial resolution \(L_{\text{res}}\) of the strain measurements utilizing OBR is:  
\[L_{\text{res}}\approx\frac{\lambda^{2}}{4n\cdot\Delta\lambda}, \tag{1}\]  
with \(n\) the refractive index of the fiber, \(\lambda\) the center wavelength and \(\Delta\lambda\) half the change in wavelength of the continuous tuning range of the laser used to perform the measurements. For the OBR 4600, the center wavelength of the laser is 1550 nm and \(\Delta\lambda\) is 10.478 nm. With a refractive index of the fiber at 1550 nm of 1.465 [PERSON] _et al._ (2016), it can be calculated that the theoretical spatial resolution limit can be as small as 0.039 mm. However, the strain resolution \(\varepsilon_{\text{res}}\) is connected to \(L_{\text{res}}\) [PERSON] and [PERSON] (2016):  
\[\varepsilon_{\text{res}}L_{\text{res}}=\frac{\lambda}{4n}. \tag{2}\]  
That means, to obtain a strain resolution smaller than 30 ppm, the backscattered signal needs to be evaluated over 1 cm length of the optical fiber. In the proprietary OBR 4600 software, this length is the length of the virtual strain gauge and it was 1 cm for the data presented here. To obtain a spatial resolution of 1 mm without compromising the strain resolution, the distance between two adjacent virtual strain gauges was set to 0,1 cm. Thus, the virtual strain gauges are overlapping and a running average strain value is determined in each point. By re-analyzing the provided raw data [PERSON] _et al._ (2016), the interested reader can confirm that the running average strain values are qualitatively and quantitatively the same as for non-overlapping virtual strain gauges.
## 3 Algorithmic Errors
### AI-Enhanced Navigation Systems
#### 3.5.5 Auxiliary Infrastructure  
The implementation of AI-enhanced navigation systems also necessitates substantial auxiliary infrastructure. This includes robust data collection and transmission capabilities, as well as the power and computational resources required to process large datasets and run complex AI algorithms. Ensuring reliable connectivity in remote maritime environments is crucial for real-time data transmission and decision-making [71; 72; 73]. Moreover, the power requirements for AI systems can be significant, especially for real-time data processing and analysis. Vessels must be equipped with adequate power generation and management systems to support the continuous operation of AI-enhanced navigation systems. Additionally, integrating these systems with existing onboard equipment and ensuring compatibility with legacy systems can pose technical challenges [74].
## 4 Experiments
### Throughput Results  
Regarding throughput, the conclusions that can be drawn are very similar to the ones obtained for energy consumption in Section 4.2. Table 3 show the average time required per each of the 100 HTTPS requests performed for the different RSA and ECDSA configurations. As can be observed, ECDSA outperforms RSA in all scenarios. Specifically, when running at 240  MHz and for a 128-bit security level, the secp256r1 is roughly three times faster than 3072-bit RSA. Moreover, the secp384r1, which provides a 192-bit security level, is as fast as the weaker 2048-bit RSA that provides only 112 bits of security. For each of the tested configurations, the fastest result is achieved when configuring the SoC at 240 MHz, which provides more than a 50% time per request reduction when compared to running the SoC at 80 MHz. The only exception occurs when using the secp224r1 curve, where a 46.2% reduction in time per request is achieved. This fact is related to the observed behavior of the secp224r1 curve regarding the NIST modulo \(p\) optimizations. In fact, when comparing the throughput performance of the secp224r1 curve with one obtained by the secp256r1 curve when the SoC runs at 240 MHz, the latter outperforms the former, presenting a time per request reduction of over 10%.
## 4 Q3: What Are the Methodologies Used to Overcome the Challenges in UIoT?
### Methods to Overcome the Security Challenges in UIoT
#### 4.2.6 Methods to Prevent Flooding Attacks  
The existing techniques to prevent flooding attacks in UIoT are discussed herewith. Bidirectional authentication is necessary to protect the nodes from flooding attacks in UIoT networks. In [53], [PERSON] et al. proposed a centralized scheme to protect the hello flooding attack that can be used in UIoT networks. In [113], [PERSON] et al. proposed a GEDAR, a geographical routing approach that prevents flooding attacks underwater. In the GEDAR approach, the communication is established based on the location information of UIoT nodes.
# Efficient Retrieval of Music Recordings Using Graph-Based Index Structures
## 4 Experiments
### Experimental Setup
For the HNSW graph, we use the efficient hnswlib implementation in C++ by the authors of the original paper [25] ([[https://github.com/nmslib/hnswlib](https://github.com/nmslib/hnswlib)]([https://github.com/nmslib/hnswlib](https://github.com/nmslib/hnswlib)), accessed on 12 May 2021), using the Python wrapper version 0.4.0. We use librosa 0.7.1 [42] for the audio processing pipelines and TensorFlow 1.7.0 [43] for the deep neural network implementation.
# Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning
## 1 Introduction
In [12], the bandwidth of M2M communication is allocated by taking into consideration the degradation of the quality of service (QoS) between the UE. The transmission rate control with emphasis on M2M devices is crucial as the demand for M2M communication is expected to increase rapidly. In contrast, the real-time characteristics are necessary for autonomous driving content, which is a type of M2M communication. Thus, focusing on content delay tolerance can improve the performance and quality of user experience. On the other hand, reinforcement learning has been applied in various fields in recent years [13]. Reinforcement learning is a learning method in which an agent optimizes the action through repeated trial and error [14]. It is confirmed that reinforcement learning can obtain a higher score than humans in board games by performing appropriate actions based on acquired experience [15; 16]. The method used for handling various situations based on the acquired experience may be applied to satisfy various communication demands [17; 18; 19]. For example, [17] comprises the use of reinforcement learning for power control and rate adaptation in the downlink of a network. In addition, in [18], deep reinforcement learning is applied for cost and energy aware multi flow. Although it has been assumed in previous studies related to LAN offloading problems from mobility users that the mobility pattern of the UE is known in advance, [18] has become unnecessary. In recent years, reinforcement learning has been applied in cellular networks, and the obtained results are shown in [17; 18; 19]. Therefore, there is also a possibility that reinforcement learning can be used to resolve the locality of the mobile data traffic demand. In this paper, we propose and evaluate a delay-tolerance-based mobile data offloading method using deep reinforcement learning for improving the bandwidth utilization efficiency of cellular infrastructure and solving the traffic demand locality. If an agent can learn how to balance the load properly, considering various situations such as traffic model of the UE, topology structure, and eNB loads, proper balancing can be achieved under conditions for which time-wise offloading of MDOP cannot balance the load properly. Our method performs transmission rate control by determining the priority for allocating the bandwidth to the UE as per the quality of service level (QoS level) based on the content delay tolerance of the UE and the conditions of the eNB such as the load.
## 3 Related Works
[PERSON] and [PERSON] [21] presented a hybrid deep learning approach for detecting and countering DoS/DDoS attacks in SDNs. The selection of a hybrid model that included a 1D CNN, a dense neural network (DNN), and a gated recurrent unit (GRU) took advantage of their individual strengths that synergistically addressed the intricacies of the problem. The model achieved good results when using CICDDoS 2019, where it achieved 0.9981 in accuracy, 0.9996 in precision, 0.999 in recall, and 0.9993 in F1-score. Some recent studies used a stacked autoencoder to improve DDoS detection accuracy. [PERSON] et al., 2022 [22] proposed a novel approach for detecting DDoS attacks, which involved integrating deep learning with feedforward neural networks in the form of autoencoders. The training and evaluation of the model were analyzed using two datasets, initially through a static approach and subsequently through an iterative technique. They developed the autoencoding model through a layer-by-layer stacking of the input layer and the hidden layer of self-encoding models, wherein each self-encoding model employed a hidden layer. They assessed the performance of their model by employing a three-fold data partitioning strategy comprising training, testing, and validating subsets. The test result showed that the model yielded superior accuracy for the static dataset. Specifically, for the ISCXIDS-2012 dataset, the model attained a maximum accuracy of 99.35% during training, 99.3% during validation, 99.78% for precision, 99.99% for recall, and 99.87 for F1-score. The UNSW-2018 dataset exhibited high levels of accuracy during training, with values of 99.95% for training and 99.94 for validation, and 99.99 for recall, precision, and F1-score. [PERSON] et al., 2018 [23], presented a new method (DLGraph) for detecting malware based on deep learning along with graph embedding. Their architecture for deep learning was comprised of two stacked denoising autoencoders (SDA). One SDA was able to learn the latent structure of function-call graphs in programs. The other SDA was capable of learning a latent representation of Windows API calls made by programs. They utilized the node2 vec technique when incorporating a function-call graph in a feature space. The experimental results on three distinct datasets demonstrated that the proposed DLGraph method obtained high levels of accuracy and exceeded the closely related DL4 MD method, where it gained 99.14% in accuracy for dataset 1, 99.36% for dataset 2, and 99.31 for dataset 3. Table 1 shows the comparison between these related works.
# A Distributed RF Threat Sensing Architecture
## 4 Test Environment and Experiment Setup
### Experiment Setup and Measurements
#### 4.2.1 High-Power Frequency Sweep Jamming Attack in Both 2.4 GHz and 5.8 GHz Frequency Bands  
In this scenario, the RF sensor was deployed at a distance of 200 m and 500 m from the jammer, respectively. At these specific distances and frequencies, the 1 st Fresnel zone (r1) at the two frequencies of interest is presented in Table 5 below.  
In theory, it is recommended to ensure obstacle avoidance and ground clearance at 80% of r1, while in practice a 60% clearance is typically adopted. Hence in our case, we deployed the Vivaldi antennas at a height of 2.50 m to minimize the effects of Fresnel zone disturbance. In practice, in a clear line of site setup, to avoid Fresnel zone obstructions, the sensor antenna must be placed on an elevated mast. To emulate a high-power jamming signal, an experimental jammer was developed and utilized, emitting 48 dBm EIRP at 2.4 GHz and 45 dBm EIRP at 5.8 GHz on a sweep jamming mode, within both band ranges. The high-power transmission at 500 m lasted 20 s and was repeated twice. The same experiment was also performed at 200 m.  
Based on the performed profiling measurements, presented in Tables 2 and 3, it becomes obvious that, in order to reduce the false-positive alerts, one has to consider in the ATL estimations the existing interfering signals. For example, to trigger with confidence a jamming alarm at 2.4 GHz, a \(l_{t}ol\) should be set above the environment noise/interfering level. On this occasion, a \(\ J_{t}ol=10\) dB was set for the 2.4 GHz band bringing the Alarm Threshold Level (\(ATL>-45\) dBm). Similarly, the same procedure can be carried out for the 5.8 GHz sensor and its profiled environment.  
All jamming signals were clearly detected by the sensor, as depicted in Figures 8 and 9. Consequently, an alarm was triggered and forwarded to the SOC.
# Research on VVC Intra-Frame Bit Allocation Scheme Based on Significance Detection
## 3 Model and Proposed Method
### Determining Optimal Weight Values: Decision Process
The regression analysis of the model with improved CTU enhancement magnification is shown in Figure 7a-c, and the analysis with reduced magnification is shown in Figure 7d-f.  
(a) depicts the variation of the mean square error (MSE) of Y computed by cross-validation on the training set with the number of PLS components, with the lowest MSE value and the best fit when the number of principal components is 5. (b) and (c) describe the relationship between the actual response values and the predicted values of the model in the training and test sets, respectively, where the horizontal coordinates represent the resultant data in the training set and the vertical coordinates represent the predicted values of the model. Ideally, the data points should be arranged around a straight line with a slope of 1, indicating that the predicted values match the actual values, which reflects the model's ability to generalize over unknown data, at the same time, the slope of the best-fit \begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Calculation Method** \\ \hline Proportion of improved CTUs & \(ratio=\frac{N_{c}}{N_{CTU}}\) \\ Average frame-level target bitrate & \(T_{F,avg}=\frac{1}{\lambda}\sum\limits_{i=1}^{4}T_{F,i}\) \\ Improving CTU’s average complexity & \(T_{C,avg}=\frac{1}{N_{c}}\sum\limits_{i=1}^{N}C_{i}\) \\ Mean Canny significance values for improved CTUs & \(T_{st1,avg}=\frac{1}{N_{c}}\sum\limits_{i=1}^{N}S_{1,i}\) \\ Mean color contrast significance values for improved CTUs & \(T_{st2,avg}=\frac{1}{N_{c}}\sum\limits_{i=1}^{N}S_{2,i}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PLS model feature selection. Figure 7: Regression model analysis for improved CTUs. line (red solid line) differs from 1 by a distance that is within an acceptable margin of error. Comparison of images from (b) with (c) and (d) with (e), respectively, shows that the fitting effect of using the model for the training set and the test set is close to each other, which proves that the model has a better generalization ability. Finally, in the actual bit allocation process of rate control, the feature information corresponding to the sequence is brought into the regression equation fitted by RLS to obtain the tuned CTU allocation.