esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review  
### Factors Affecting Slope Stability  
Under specific geological background circumstances, slope instability typically arises as a result of the excitation of initiating events like intense earthquakes and heavy rains [12]. A hillslope's stability can be affected by a variety of causes. Geological material, geological structure, topographic and environmental conditions, and engineering aspects are the four main causes of landslides according to [PERSON] [13].  
* Geological material: The slope stratum is mainly composed of single or multiple geological materials. The cementation, particle size, and composition of the geological materials directly affect the stability of the slope.
* Geological structure: Weak planes (such as strike and dip angle) and slope surfaces (such as dip slope, anti-dip slope, and cross-dip) are the main factors affecting slope stability. Other geological structures, such as faults, joints, folds, layers, and other discontinuous surfaces, transform the slope's rock and soil into discontinued or fragmented stones. These structures reduce rock and soil strength and increase weathering, affecting the slope's stability.
* Topographic and environmental conditions: Topography includes the slope's general form and rough surface terrain. While slope terrain has its own height and gradient, surface terrain relates to the characteristics of the geological structure. Groundwater, earthquakes, and rainfall are examples of environmental elements that affect slope stability.
* Engineering aspects: Slope stability is affected directly or indirectly by human factors such as freeways, tunnel excavation, blasting, overdevelopment of slopes, substandard building or site selection, poor slope drainage systems, and inadequate maintenance of protective structures.  
To prevent road slopes from collapsing without warning, Taiwan's Ministry of Transportation and Communications uses slope patrols, slope monitoring, and ground anchor inspections to closely monitor slope stability. According to the \"Freeway Maintenance Manual\" [11], slopes are divided into the following four categories: A, B, C, and D. The classification criteria and disposal countermeasures are described below.  
* Class A slope: Slopes classified as class A exhibit clear indications of instability. Appropriate actions must be implemented right away, and cooperation with careful observation and monitoring is needed.
* Class B slope: Due to the possible indication of instability, additional patrols, monitoring, maintenance, reinforcing, and remediation are needed.
* Class C slope: The slope needs regular patrol or maintenance, with monitoring as necessary, even if there are no obvious signs of instability.
* Class D slope: Patrolling is still necessary, even though it is steady.
## 3 Optimization Algorithms for Equalizers with Traditional Structures
### Adam  
Adaptive moment estimation (Adam), obtains individual adaptive step sizes for each tap coefficients from estimates of first and second moments of the gradients [18]. The biased first and second moment estimates \(\mathbf{m}_{t}\) and \(\mathbf{v}_{t}\) of \(\mathbf{g}(\mathbf{\omega}_{t})\) are initialized as zeros vector, which are updated as  
\[\mathbf{m}_{t}=\beta_{1}\times\mathbf{m}_{t-1}+(1-\beta_{1})\times\mathbf{g}( \mathbf{\omega}_{t}), \tag{10}\]  
\[\mathbf{v}_{t}=\beta_{2}\times\mathbf{v}_{t-1}+(1-\beta_{2})\times\mathbf{g}( \mathbf{\omega}_{t})\odot\mathbf{g}(\mathbf{\omega}_{t}) \tag{11}\]  
where \(\beta_{1}\) and \(\beta_{2}\) are set to 0.9 and 0.999, respectively. However, they are biased towards zero, especially during the first few steps. Bias-corrected first and second moment estimates counteract biases,  
\[\hat{\mathbf{m}}_{t}=\mathbf{m}_{t}/\left(1-\beta_{1}^{t}\right), \tag{12}\]  
\[\hat{\mathbf{v}}_{t}=\mathbf{v}_{t}/\left(1-\beta_{2}^{t}\right). \tag{13}\]  
Tap coefficients are also updated by  
\[\mathbf{\omega}_{t}=\mathbf{\omega}_{t-1}-\frac{\theta}{\delta+\sqrt{\hat{\mathbf{v}} _{t}}}\odot\hat{\mathbf{m}}_{t}. \tag{14}\]
# Comparing Application-Level Hardening Techniques for Neural Networks on GPUs
## 1 Introduction
The key contributions of this article are as follows:  
* The proposal of a systematic evaluation of different application-level software-based HTs applied to a representative set of NNs from Edge AI domains. The evaluations have been conducted through hardware-aware FI campaigns. * The proposal, for the first time of qualitative and quantitative evaluation metrics specially developed to compare objectively different HTs for NNs, considering the engineering effort, the execution time overhead, and the impact on the accuracy of the NN. * The proposal of a general training strategy enabling the adaptation of HT across different NN architectures. Such training is crucial in cases where the HT needs either fine-tuning or retaining stages. * The proposed evaluation indicates that each HT is not always suitable for all the NN architectures considering both the perspectives of the HT implementation impact evaluation and their capabilities of counteracting faults. We applied the evaluation strategy using five HTs (i.e., adaptive clipper [32], Ranger [14], Swap ReLU6 [33], median filter [13] and fine-grain TMR [13]) implemented on five NN architectures (Lenet5 [34], Mnasnet [35], Mobilenet V2 [36], Resnet18 [37], and Squeezenet [38]) evaluated on three datasets (i.e., MNIST [39], Cifar10 [40] and STL10 [41]). The results show that reliability enhancement depends on the error model and test bench used in the evaluations. For example, Swap ReLU6 on Resnet18 experiences a maximum accuracy degradation of 13% in WSBF FI campaigns compared to a fault-free scenario, while the same technique leads to over 35% degradation during NBER FI on the same NN architecture. In NBER FI campaigns, implementing Swap ReLU6 on Mobilenet V2 results in a 16% accuracy decrease. At the same time, when it is implemented on Resnet18, it achieves only 1.5% of degradation. In general, range-restriction-based HTs, like Adaptive Clipper and Ranger, offer better trade-offs, with Adaptive Clipper scoring up to 6/10 in effort, and only a 4.5% accuracy degradation. Inference time overhead is modest for Adaptive Clipper (up to 3.52%), while for Ranger, it reaches 7.54%. Notably, these HTs can effectively mask fault effects in up to 58.23% of cases during fault injections. Section 2 covers backgrounds on application-implemented HTs, error propagation modeling and related works on the HT effectiveness assessment. Then, Section 3 describes the proposed hardware-aware application-level evaluation strategy. The experimental setup is presented in Section 4, followed by the experimental results in Section 5. Subsequently our results are summarized and compared with the state of the art in Section 6. Finally, Section 7 outlines conclusions and future remarks for our work.
# A Comparative Analysis of High Availability for Linux Container Infrastructures
## 3 Materials and Methods
### High Availability  
Reliability and high availability are important factors in Linux containers and container orchestration. High availability means the ability of the system to continue operating despite node failures in the cluster or hardware and software updates. Fault tolerance is a key technique to achieve high availability. Replication management is one way to implement fault tolerance. In this way, the required number of containers can be maintained, and continuous operation can be ensured. Health checking is used to identify faulty containers and then remove them, deploying additional containers to ensure that the desired number of replicas is maintained. High availability is essential for Linux containers because it provides continuous operation of applications even if parts of the system fail. Replica management and high-availability controllers are tools that allow one to achieve high availability and guarantee stable operation of containerized applications. High-availability controllers provide the ability to configure multiple orchestration administrators to ensure continuous control of the application even if an orchestrator node fails or becomes overloaded. Their implementation is important in the design and implementation of container systems to minimize failures and ensure reliable operation [42; 43; 44].
# A Metamaterial Solution for Soundproofing on Board Ship
## 4 Experimental Sound Transmission Loss Results
### Real-Case Scenario on Board: Ceiling Covering  
The optimized Metasolution was mounted and tested in a real-case scenario representative of applications on board ship, as a ceiling covering for acoustic insulation between decks. Two different configurations were chosen for testing, varying the distance of the sound insulation panel from the ceiling. The first configuration had an air gap between the panel and the ceiling, and the second one had no air gap (Figure 11).  
The solution was fixed to the ceiling (covered with two layers of mineral wool of different heights) with elastic suspensions (Figures 11 and 12).  
In these configurations, the Rw was evaluated by performing sound transmission loss measurements, with the sound source placed in the room under the ceiling and the receiving microphones located in the room above the deck.  
The Rw of the Metasolution in the first configuration was higher by 6 dB with respect to the Rw in the second configuration (Figure 13). These values broadly satisfy the limit reported in the Code On Noise Levels On Board Ships [22] in cabins.  
Figure 11: Scheme of the deck covering assembly in the two configurations (image adapted from technical report).  
Figure 12: Deck covering mock-up with the 450 mm air-gap configuration.
## 3 Results
### Analysis of Interviews
#### Involvement of External Stakeholders  
All interviewees agreed that a network organisation should involve external stakeholders and identified several possible roles and modalities. The views on involving governments differ. Two interviewees stated that governmental (political) stakeholders need to be involved, while one asserted that \"representation [on network bodies] of organisations with political or governmental affiliation should be avoided\".
# A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer
## 3 Methodology and the System Design Procedure
### Overall Steps of Synergetic Observer Design Procedure  
The synergetic observer design consists in using of some mathematical algorithm that connects the values of the observed (measured) variables with the estimates of the unobserved variables, i.e., the observer performs restoring the complete state variables vector from the available signal and some a priori information. In the design procedure of a synergetic observer, both disturbing and setting actions, as well as object parameters, can act as unobserved variables.  
Figure 10: Fourier series plot for random signal (white noise). Black line: Fourier series; red lines: Savitzky-Golay filter.  
Figure 9: Fourier series plots for \(Z(t)\). Black line: Fourier series; red lines: Savitzky-Golay filter; (**a**) idle transmission; and (**b**) transmission with useful signal.  
In the general case, the mathematical model of an extended system consists of observable and unobservable terms, and is represented as [40]:  
\[\begin{array}{l}\dot{x}(t)=g_{0}(\mathbf{x})+\mathbf{G}_{1}(\mathbf{x})\,\mathbf{y};\\ \dot{\mathbf{y}}(t)=\mathbf{h}_{0}(\mathbf{x})+\mathbf{H}_{1}(\mathbf{x})\,\mathbf{y},\end{array} \tag{6}\]  
where \(\mathbf{x}\) is the observed state variables vector, \(\dim\mathbf{x}=n\); \(\mathbf{y}\) is the vector of unobservable variables (unmeasured physical variables, parameters, setting and disturbing actions, etc.), \(\dim\mathbf{y}=k;\mathbf{g}_{0}\) and \(\mathbf{h}_{0}\) are the continuous nonlinear vector functions, \(\dim\mathbf{g}_{0}=(n\times 1)\), \(\dim\mathbf{h}_{0}=(k\times 1)\), \(\mathbf{G}_{1}(\mathbf{x})\) is the functional matrix, \(\dim\mathbf{G}_{1}=(n\times k)\), \(\mathbf{H}_{1}(\mathbf{x})\) is the functional matrix, \(\dim\mathbf{H}_{1}=(k\times k)\).  
The main steps of the synergetic observer design procedure [40] are:  
_Main Step 1._ Synthesis of a synergetic observer. The synthesis task is to construct an asymptotic estimation \(\hat{\mathbf{y}}\) of the vector \(\mathbf{y}\) from the observed values of the vector \(\mathbf{x}\).  
_Main Step 2._ Replacing unobserved variables \(\mathbf{y}\) with their asymptotic estimates \(\hat{\mathbf{y}}\).  
In our case the equations of the asymptotic observer have the form:  
\[\begin{array}{l}\dot{\mathbf{z}}=L\mathbf{z}-L\int_{0}^{x}\Gamma(\mathbf{x})\,dx-\mathbf{h} _{0}(\mathbf{x})+\Gamma(\mathbf{x})\,g_{0}(\mathbf{x});\\ \hat{\mathbf{y}}=\int_{0}^{x}\Gamma(\mathbf{x})\,dx-\mathbf{z},\end{array} \tag{7}\]  
where \(\mathbf{z}\) is the observer state variable vector; \(\mathbf{L}\) is the numerical matrix, \(\dim\mathbf{L}=(k\times k)\), selected based on the observer's stability conditions; \(\Gamma(\mathbf{x})\) is the functional matrix, \(\dim\Gamma=(k\times n)\), \(\Gamma(\mathbf{x})\) is the determined from the following matrix equation:  
\[\mathbf{H}_{1}(\mathbf{x})-\mathbf{L}=\Gamma(\mathbf{x})\mathbf{G}_{1}(\mathbf{x}). \tag{8}\]
## 3 Method
### Point Cloud Projection and Back-Projection  
The density of point clouds varies with different laser beams and rotating speeds of LiDAR, resulting in different vertical angular resolution and horizontal angular resolution.  
Figure 3: The images related to the Ruby32-128 dataset. (**a**) The equipment we built for data acquisition; (**b**) a bird’s-eye view of the 128-line point cloud data in Ruby32-128.  
Figure 2: The images related to the CARLA32-128 dataset. (**a**) The scene of the CARLA simulator; (**b**) a bird’s-eye view of the 128-line point cloud data in CARLA32-128.  
Based on the 3D coordinates \((x,y,z)\) of point clouds and the vertical angle \((\omega)\), azimuth angle \((\alpha)\), and offset \((\delta)\), we project the point clouds in 3D space onto a two-dimensional plane according to Equation (1), making it easier to process these data using deep learning methods based on two-dimensional images. Subsequently, we use Equation (2) to back-project the processed prediction results from the model into 3D space.  
\[\begin{split} r=&\sqrt{x^{2}+y^{2}+z^{2}},\\ \alpha+\delta=arctan(y/x),\\ \omega=arcsin(z/r).\\ \end{split} \tag{1}\]  
\[\begin{split} x=&\,cos(\omega)sin(\alpha+\delta),\\ y=&\,cos(\omega)cos(\alpha+\delta),\\ z=&\,rsin(\omega).\\ \end{split} \tag{2}\]  
Figure 4 illustrates the results of projecting 3D point cloud data from CARLA32-128 and Ruby32-128 onto 2D images. It can be observed that the widths of the corresponding 2D images are the same for CARLA-32 and CARLA-128 because of their identical horizontal FOV and angular resolution during data acquisition. However, the difference lies in the heights of the 2D images, with the image for CARLA-128 being four times taller than that for CARLA-32, as shown in the first and second rows of Figure 4. Similarly, Ruby-32 and Ruby-128, shown in the third and fourth rows of Figure 4, share the same characteristics. Therefore, our TSE-UNet model aims to recover dense point clouds from sparse ones.
## 4 Experiments and Analysis
### Ablation Experiment
#### 4.5.4 Effect of Loss Function  
To evaluate the applicability of the CE-Dice in PSANet, we conducted comparative experiments by replacing CE-Dice loss with Focal loss [40] or Dice loss during the training process. Focal loss modifies the cross-entropy loss function by introducing a modulation factor, enabling it to assign greater weight to samples with fewer instances. Therefore, these loss functions can address the imbalance between the number of target-area and non-target-area pixels. Figure 11 shows the experimental results, from which it can be seen that CE-Dice loss outperformed the other two loss functions in terms of IoU, F1-Score,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Strategy** & **IoU** & **Recall** & **Precision** & **OA** & **F1-Score** \\ \hline \multirow{3}{*}{Jiangsu Province} & CBAM & 70.85 & 82.49 & 83.38 & 94.01 & 82.94 \\  & Replaced with PSAM & 70.91 & 80.55 & 85.54 & 94.16 & 82.97 \\  & Replaced with BAM & 71.14 & 81.81 & 84.51 & 94.14 & 83.12 \\  & FRM & 71.38 & 82.23 & 84.39 & 94.18 & 83.31 \\ \hline \multirow{3}{*}{Anhui Province} & CBAM & 73.54 & 83.61 & 85.94 & 96.53 & 84.75 \\  & Replaced with PSAM & 73.44 & 82.43 & 87.06 & 96.56 & 84.68 \\ \cline{1-1}  & Replaced with BAM & 74.13 & 83.81 & 86.51 & 96.61 & 85.14 \\ \cline{1-1}  & FRM & 74.31 & 84.52 & 86.01 & 96.63 & 85.26 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The assessment metrics derived from different strategies (%).  
Precision, and OA in both regions. Focal loss may pose difficulties when dealing with small targets, as inappropriate focus factor settings can lead to insufficient learning of small targets. In addition, since Dice loss is calculated based on the degree of overlap, if the overlap between the ground truth and the predicted value is small in the early stages of training, it may affect the convergence speed and overall performance of the model. In comparison, CE-Dice loss effectively combines the advantages of cross-entropy loss and Dice loss, where cross-entropy loss ensures classification accuracy, while Dice loss enhances the focus on small targets, thereby improving segmentation accuracy and ensuring better training stability. This indicates that CE-Dice loss is more suitable for PSANet with regard to prediction accuracy and stability.
# A Software-Defined Radio Platform for Teaching Beamforming Principles
## 4 Beamforming Platform Validation and Testing
### Design and Characterization of an Array of Patch Antennas
The factors detailed above also explain the differences observed for the coupling between the patch antenna elements, as quantified by the S\({}_{12}\) parameter (Figure 13b). The coupling effect of consecutive patch elements was observed to be most significant for consecutive patch elements, as the case of S12 presented, and less significant for patch elements spaced further. Even though the antenna efficiency is not ideal, one must keep in mind that the antenna is intended for short-distance transmission, so authors appreciate that this design will be sufficient for the assumed objective. Further antenna optimization is possible but does not constitute the main effort of this paper as it is important to balance design efforts with real-world needs.
## 1 Introduction
### Contributions  
The detailed contributions in this paper are summarized as follows:  
* We demonstrate that [PERSON] et al.'s protocol cannot withstand various attacks such as masquerade, SG devices stolen, and session key disclosure attacks. We also show that their protocol does not ensure secure mutual authentication.
* We present a privacy-preserving lightweight authentication protocol for the SG system using pseudo-identity and secret parameter to enhance the security weaknesses of [PERSON] et al.'s protocol. The proposed protocol can withstand against masquerade, session key disclosure, replay, and MITM attacks, as well as achieve secure mutual authentication and anonymity. Thus, the proposed protocol is more secure and efficient than [PERSON] et al.'s protocol because it utilizes only hash and XOR operations.
* We performed the widely known Burrows-Abadi-Needham (BAN) logic analysis [13] to prove that the proposed scheme provides secure mutual authentication. We utilized informal security analysis to prove the safety of the proposed protocol against potential attacks and also proved the session key security of proposed scheme utilizing ROR model [14].
* We performed formal security analysis utilizing the widely adopted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool to evaluate that the proposed scheme is secure against replay and MITM attacks. Moreover, we present the performance analysis of the proposed protocol with existing protocols.
# Building Wireless Control Applications with XBee and LabVIEW
## 3 Description of the Control Layout
API frames were divided into four sections--(1) start delimiter, all frames started with the same byte, 0x7E; (2) length of the frame--the next two bytes; (3) API-specific Structure--which followed the structure of the application protocol used, and could contain as many bytes as designed; and (4) checksum--which was the calculated checksum of the bytes in the API-specific structure. As a matter of example for the sake of describing the structure of the frames, Figure 2 shows a typical frame. The whole size of the frame was 12 Bytes. The API-specific structure, represented by red lines, defines the application protocol for sending/receiving the values to/from the sensors or actuators. This frame has been specifically designed for control applications similar to the one presented in this work. In the example frame shown in Figure 2, the API-specific structure included the following bytes--(Byte 4) frame type, in which 0x10 meant that the frame was a Zigbee Transmit Request; (5) frame ID was 0x00, which disabled the response frames; (6-7) 16 bits for the destination address--in this case 0x222--divided into its MSB and LSB; (8) byte for options, in this case 0x01, which disabled the retries; (9-10) 16 bits for the data sent and the reading of a sensor (in the example the 0x10F1 value was sent); and (11) sequence number, which was used to detect whether all frames were delivered (in the example this was 0x01), since this was the first frame sent. Finally, in byte 12 the checksum of the message was included. This information was used for detecting whether the received message was corrupted. In such a case, the message was discarded. In order to connect any device (sensor or actuator), the precision of the ADC (Analog-to-Digital Converter) at the XBee module was analyzed. The results have been presented in the next section. Figure 1: Layout of a wireless networked control system.
# Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images
## 3 Ore Rock Fragmentation Calculation Method
### Image Preprocessing  
Image preprocessing is a vital prerequisite and foundational step for subsequent algorithms, encompassing key procedures, such as grayscale conversion and median filtering.  
Considering the minimal color variations in rock images, the RGB color image undergoes conversion to a single-channel grayscale image, which can reduce the algorithm's complexity and enhance computational efficiency. The calculation is performed as follows:  
\[I_{g}(i,j)=R(i,j)\times 0.299+G(i,j)\times 0.587+B(i,j)\times 0.114 \tag{1}\]  
where \(I_{g}(i,j)\) is the grayscale value of the grayscale image \(I_{g}\) at the \((i,j)\) pixel, and \(R(i,j)\), \(G(i,j)\), and \(B(i,j)\) are the values of the \(R\), \(G\), and \(B\) channels of the color image at the \((i,j)\) pixel, respectively.  
Median filtering is utilized to replace the gray value of the central point with the median gray value of all pixels within the local neighborhood. This technique effectively reduces surface texture variations in ore rock and eliminates isolated noise points. The computation can be expressed as follows:  
\[I_{0}(i,j)=median\big{\{}I_{g}(i-a,j-b)\big{\}},(a,b)\in S, \tag{2}\]  
where \(I_{0}(i,j)\) represents the grayscale value of the pixel after median filtering and \(S\) denotes the filtering template window.