esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Brake forces and maximum static road friction coefficients for each wheel of the vehicle are essential information for vehicle safety systems including adaptive cruise control, electronic stability control (ESC), and collision avoidance system, etc. Many studies have been performed to estimate brake force and road friction using well-known model-based approaches, but none have unambiguously guaranteed an accurate performance in all ranges of driving conditions and road ones. In addition, the investigation of the integrated estimation approach of road friction and brake force including mass estimation has not been clearly addressed so far. Therefore, in this study, a novel integrated estimation strategy based on a data-driven technique and artificial neural network (ANN) classifier along with a compact mass identification has been proposed to acquire the accurate road friction and brake force of individual wheel. Specifically, it includes an instant mass estimation by monitoring static suspension deflections, an artificial neural network (ANN) classifier for road friction coefficient based on the average data set from available standard sensors, and a brake force estimation using the data-driven technique. The performance of the proposed technique is validated by a co-simulation environment between Carsim and MATLAB/Simulink. It is found that the integrated estimation strategy guaranteed an accurate estimation of brake forces and road friction for a wide range of variations of road frictions, vehicle velocities, and masses. This work will be a valuable asset for those who wish to develop an integrated estimation system for such crucial parameters of the vehicle system.  
vehicle dynamics; estimation; brake force; road friction coefficient; vehicle mass estimation; integrated estimation system; artificial neural network (ANN) classifier; data-driven technique Article
## 3 The Proposed Method
### Generation of the First Constrained Polygonal Space
#### 3.3.2 Deviation in Length from an Optimal Path  
The proposed method is a global PP approach, and all information about the obstacles geometries is known in advance. By utilizing the obstacles geometry information, we can estimate the path length without calculating the actual path. We estimate length of an optimal path \(L_{o}\), where \(L_{o}=\overline{l_{o}}\) as an optimal path and estimate the deviation in it that can occur due to obstacles that are on \(\overline{l_{o}}\). We call it deviation \(D^{{}^{\prime}}\) from the optimal path and formalization used to estimate is explained below.
* Computing the optimal path \(L_{o}\) that is a straight line between \(s\) and \(t\) using Equation (6). \[L_{o}=\sqrt{(x_{t}-x_{s})^{2}+(y_{t}-y_{s})^{2}+(z_{t}-z_{s})^{2}}\] (6)
* Calculating the deviation \(D^{{}^{\prime}}\) in optimal path \(L_{o}\) due to the presence of obstacles on the \(\overline{l_{o}}\) between \(s\) and \(t\) locations. The \(D\) in the paths length due to an obstacle (e.g., \(O_{i}\)) is calculated using Equation (7). \[D_{i}=min(\frac{O_{W}}{2},O_{H})\] (7)
* Estimating the total deviation \(D^{{}^{\prime}}\) that can likely occur due to the presence of the obstacles between \(s\) and \(t\) in the CPS using Equation (8). \[D^{{}^{\prime}}=\sum_{i=1}^{n}D_{i}\] (8)
* Calculating the length of the estimated paths (\(L_{1}\)) avoiding all obstacles that are on the \(\overline{l_{0}}\) using Equation (9). \[L_{1}=L_{o}+\sum_{i=1}^{n}D_{i}\] (9) where \(L_{1}\) denotes the lengths of the paths avoiding obstacles in the selected space, \(L_{0}\) denotes the Euclidean distance between \(s\) and \(t\) locations, \(n\) represents obstacles' strength in the selected space, and \(D_{i}\) denotes the degradation in path length due to obstacles.
* Computing the complexity \(C_{p}\) of the estimated path that can assist in analyzing the \(S_{1}^{3}\) complexity using Equation (10): \[C_{p}=\frac{L_{o}}{L_{1}}\] (10)  
The value of the \(C_{p}\) ranges between 1 and 0. We use this value to represent the estimated path complexity in terms of length in overall CPS \(S_{1}^{3}\) complexity evaluation in Equation (20).
###### Abstract  
Geophysical techniques are widely applied in the archaeological field to highlight variations of the physical behaviour of the subsoil due to the presence of ancient and buried remains. Considerable efforts are required to understand the complexity of the relationship between archaeological features and their geophysical response where saturated conditions occur. In the case of lacustrine and wetland scenarios, geophysical contrasts or electromagnetic signal attenuation effects drastically reduce the capabilities of the geophysical methodologies for the detection of structures in such conditions. To identify the capability of the electrical and electromagnetic methods in different water-saturated scenarios, an experimental activity was performed at the Hydrogeosite CNR laboratory. The test allowed us to analyze the limits and potentialities of an innovative approach based on the combined use of the ground-penetrating radar and 2D and 3D electrical resistivity tomographies. Results showed the effectiveness of the ground-penetrating radar for detecting archaeological remains also in quasi-saturated and underwater scenarios despite the em signal attenuation phenomena; whilst the results obtained involving the resistivity tomographies offered a new perspective for the archaeological purposes due to the use of the loop-loop shaped array. Moreover, the radar signal attenuation, resolution and depth of investigation do not allow to fully characterize the archaeological site as in the case of the scenarios with a limited geophysical contrast (i.e., water-saturated and arid scenarios). The experimental tests show that these limits can be only partially mitigated through the integration of the geophysical methodologies and further efforts are necessary for improving the results obtainable with an integrated use of the adopted geophysical methodologies.  
ground penetrating radar (GPR); electrical resistivity tomography; archaeogeophysics; subwater geophysics; multi-parametric analyses; 3D geophysical imaging; full-scale laboratory test; loop-loop electrical resistivity tomographies (LLERT); water-saturated scenarios; analogue model +
Footnote †: journal: Applied Sciences
# Design of a Digital Array Signal Processing System with Full Array Element
## 2 System Architectural Design
### System Hardware Architecture Design
#### 2.1.4 Design of Digital HIF Architecture  
In traditional array signal processing systems, the baseband signal processor generates a lower IF signal. When the operating band is high, a multi-stage RF up/down conversion to the RF band is required. This increases the size and power consumption of the RF front end.  
The RFSoC enables a digital HIF architecture due to its ultra-wide analog bandwidth, from 0-6 GHz, and internal DUC and DDC, as shown in Figure 7. Meanwhile, compared with the traditional RF transceiver implementation of the HIF architecture [11], the digital HIF architecture can realize the numerically controlled oscillator (NCO) reset, which solves the problem of the traditional HIF architecture being unable to synchronize the phase of each channel's local oscillation [15].  
The digital quadrature up/down conversion can generate two perfectly quadrature local oscillation signals, and their local oscillation frequencies and phases can be precisely controlled. The frequency accuracy \(\Delta f\) is calculated using Equation (1). In contrast, the analog quadrature up/down conversion used in the conventional architecture generates two quadrature local oscillation signals with certain phase deviation, which leads to an I/Q imbalance in the up/down conversion and generates a mirror frequency that is difficult to suppress [16].  
\[\Delta f=\frac{F_{s}}{2^{48}} \tag{1}\]
# Children's Safety on YouTube: A Systematic Review
## 4 Approaches and Existing Methods
### Audio Content Analysis Approach  
Some researchers have attempted to use an audio content analysis that might be an important addition to the video material that helped determine whether a video is suitable for children through transcription analysis/transcribed text. The sound language and auditory cues were both present in a video's signal. Automatic speech recognition algorithms were utilized to extract the linguistic component of an audio source using NLP methods. [PERSON] in [2] claimed that the content filtering approaches that were based on meta-data attributes were not appropriate. Therefore, real-time content filtering is proposed to keep undesired content from kids. Their method relies on analyzing video and audio content to add more restricted filtrating. The collected videos were annotated into two classes: appropriate and inappropriate for kids. The thin-slicing theory was utilized to extract audio and image frames in order to reduce computational time and resources. The audio was passed to automatic speech recognition techniques for linguistic content analysis. Additionally, the audio signal was analyzed to extract scenes and events. The image frames were analyzed to detect and avoid appropriate scenes. Several features were extracted from each image frame, including color, shapes, objects, and motion features. The proposed method utilized a fusion of three modalities (video, audio, and text transcription from audio) in order to increase the performance of the classification process. [PERSON] and [PERSON] [81] proposed deep neural network models to detect and classify the violent content appearing in videos. The purpose of their study was to examine the impact of several deep network architectures with different numbers of hidden layers and hidden nodes. These architectures were implemented based on the try-error method to assess the effect of the number of hidden layers and hidden nodes on the classification performance. The VSD2014 dataset was used in the experiments, where 86 videos with 434 attributes (features) were selected. Among these features, eight of them were chosen to represent audio features: Amplitude Envelope (AE), Zero-Crossing Rate (ZCR), Root-Mean-Square Energy (RMS), Band Energy Ratio (BER), Frequency Bandwidth (BW), Spectral Centroid
## Appendix A Full Questionnaire (Updated Version--60 Questions)
### Documentation Debt--Questions 24 to 28  
Question 24: Is Requirement Documentation available?  
Stakeholder: Organizers  
Score: 5  
Justification: Requirement documentation is crucial for understanding the goals and objectives of the project, as well as the functionalities expected from the system. Withoutit, development might lack direction or focus, leading to potential misunderstandings and misalignments.  
Example: If the platform's matchmaking algorithm requirements are not well-documented, developers might implement incorrect or suboptimal features that could require significant rework.  
Question 25: Is Technical Documentation available?  
Stakeholder: Organizers  
Score: 5  
Justification: Technical documentation provides insight into the system's architecture, components, and functionalities from a technical perspective. It aids developers in understanding how different parts of the system interact and how to implement or modify them effectively.  
Example: If documentation is lacking, integrating a new game into the platform may take significantly more time and increase the risk of mistakes during the process.  
Question 26: Is End-user Documentation available?  
Stakeholder: Organizers and Participants  
Score: 5  
Justification: Clear end-user documentation--including tutorials and help guides--plays a key role in enhancing user satisfaction and minimizing the need for ongoing support.  
Example: When features are not well-documented, users may become frustrated or confused, resulting in more support requests and potentially harming the platform's reputation.  
Question 27: Is the documentation clear?  
Stakeholder: Organizers and Participants  
Score: 5  
Justification: Clear documentation is key to effective communication. When instructions are easy to follow and free from ambiguity, both users and developers can interact with the system more efficiently, improving overall usability.  
Example: For instance, if the process for submitting competition scores is not clearly explained, participants may interpret it differently--leading to inconsistent data and compromising the accuracy of the leaderboard.  
Question 28: Is the documentation up to date?  
Stakeholder: Organizers and Participants  
Score: 5  
Justification: Maintaining up-to-date documentation is essential, as outdated information can misguide both developers and users. This may result in the use of obsolete features or incorrect platform versions, creating unnecessary setbacks.  
Example: For example, if recent updates to competition rules are not reflected in the documentation, participants may unknowingly follow outdated instructions--potentially causing confusion or disqualification.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 3 Proposed Protocol
### Cluster Formation Phase
#### 3.2.2 Node Joining a Cluster
Thus,  
\[CH_{min}=\text{Min}[\delta(S_{i},CH_{i})], \tag{4}\]  
where \(CH_{min}\) is the minimum distance \(CH\), \(S_{i}\) represents the \(i\)th candidate node, and \(CH_{i}\) is the \(i\)th cluster in the sample space. Using our modified k-medoids technique, Algorithm 2 outlines how a node might join a specific cluster. The inputs for this algorithm are \(k\), \(M\), and \(S\). \(k\) is the number of the clusters obtained in response to a Hello message, \(M\) is the set of CHs, and \(S\) is the set of applicant nodes. The algorithm's output consists of \(k\) clusters created or modified by adding \(N\) nodes, where \(N\) stands for the collection of member nodes in the cluster. The algorithm's complexity is on average \(\Theta(\text{n})\). The output of the algorithm is \(K\) clusters each time. ```
0:\(K\), \(M\), \(S\)
0:\(K\) cluster with \(N\) nodes
1. Begin:
2. repeat:
3. if\(K\
eq 0\)then
4. for each member of \(M\)do
5. Compute \(\eta_{i}\) using (1)
6. Compute \(\kappa_{i}\) using (2)
7. Compute \(\delta_{i}\) using (3)
8. \(T_{i}\Leftarrow\delta_{i}\)
9. \(CH_{min}\leftarrow\text{Min [T]}\) using (4)
10. \(CH_{min}\leftarrow\text{S}_{i}\)
11. \(N_{min}\leftarrow\text{S}_{i}\)
12. endfor
13. else
14. \(K\Leftarrow K+1\)
15. \(CH_{i}\gets S_{i}\)
16. \(M_{i}\gets CH_{i}\)
17. endif
18.until: All the members of \(S\) are clustered
19.end
```  
**Algorithm 2**Candidate Node Joining a Cluster.
## 3 Results and Discussion  
### Static Characteristic Analysis Results and Discussion  
According to the static method of Section 2.3, the deformation rule of the milling condition is solved, as shown in Figure 10:  
As shown in Figure 10, when the milling force and \(Y\)-axis angle are equal to about 60\({}^{\circ}\), the maximum deformation is 2.9772 \(\upmu\)m. This value is the maximum deformation value of the direct-drive turntable milling condition, and its corresponding position is the maximum deformation position. When the milling position is closer to the top of the workpiece, the deformation of the workpiece is larger. Therefore, in the milling process of the direct-drive turntable, the maximum deformation value can be obtained when the angle between the working point of milling force and the \(Y\)-axis is 60\({}^{\circ}\) and is located on the top surface of the workpiece. The stress nephogram of the maximum deformation position is shown in Figure 11:Analyzing the stress nephogram of the maximum deformation position, the maximum stress value of the direct-drive turntable under milling force is 2.6791 MPa. The maximum value appears in the milling force addition side of the turntable, and the stress is mainly concentrated at the bottom of the workpiece. The stress of the turntable surface's top and the mounting sleeve's bottom is small. The material of the turntable surface is HT200, and the yield limit is 200 MPa. The maximum stress of the turntable is far less than 200 MPa. The maximum deformation of the direct-drive turntable occurs on the workpiece, and the maximum deformation is 2.9772 \(\upmu\)m. According to the processing requirements, the maximum deformation of the workpiece of the direct-drive turntable should be less than 10 \(\upmu\)m. The results meet the job requirements.  
According to the same research method as that used for the milling condition, the maximum deformation variation law of the turning condition and the stress nephogram and deformation nephogram of the maximum deformation position are obtained, as shown in Figures 12 and 13:  
As shown in Figure 12, when the turning force and \(Y\)-axis angle are equal to about 20\({}^{\circ}\), the maximum deformation is 4.5239 \(\upmu\)m. The maximum deformation value of the direct-drive turntable turning condition is 4.5239 \(\upmu\)m, and its corresponding position is the maximum deformation position. The stress nephogram at the maximum position of machining deformation is shown in Figure 13. When the direct-drive turntable is turning, the overall stress is small, and the maximum stress is 5.5303 MPa; this is far less than the material yield limit of the turntable surface, whether turning or milling the direct-drive turntable. It is not necessary to worry that the maximum stress will destroy the direct-drive turntable. The direct-drive turntable meets the stiffness and accuracy requirements under the two working conditions.  
Figure 11: The milling stress nephogram of the maximum deformation position.  
Figure 10: The maximum deformation at different loading positions under milling condition.
## 3 Simulated and Measured Results
### Electro-Magnetic Behavior
Concerning the performance of _Dev3_, as reported in Figure 6a,b, it is possible to notice that the measured return loss (S22) curve of Figure 6b shows more limited oscillations, ranging from \(-20.22\) dB (at 18.94 GHz) to \(-27.08\) dB (at 28.22 GHz). The measured insertion loss (S12) curve of Figure 6b shows a minimum of \(-3.35\) dB (at 30 GHz), and it does not exceed \(-2\) dB up to 15.7 GHz, with a rather constant discrepancy compared to the simulated curve, a discrepancy which is smaller than 1.88 dB along the entire interval. Following the pattern characterizing the previous devices, the measured isolation (S12) curve of Figure 6a approximates the simulation curve at lower frequencies (roughly up to 12 GHz). In the subsequent portion of the considered spectrum, the measurement curve is generally better than the simulated one, and it reaches a minimum of \(-28.41\) dB (at 23.56 GHz) and a maximum of \(-15.49\) dB (at 30 GHz). According to the reported measurements, the electromagnetic behavior of the considered devices is generally satisfying. In fact, among the three investigated devices along the 5-30 GHz interval, the return loss is always better than \(-20.22\) dB, the insertion loss is better than \(-3.35\) dB, and the isolation is better than \(-14.86\) dB.
## 4 NAEEA for Clustering Optimization of AUSCNs
### Mutation  
In order to further reduce the energy consumption of AUSCNs, the NAEEA introduces some random mutation strategies. The purpose of these mutation operations is to increase the diversity of the population. In order to keep the number of cluster head nodes unchanged, when the 0 in the individual is converted to 1, the random 1 in the individual will also become 0 accordingly. The mutation operation will use a certain mutation probability, Q, to replace random bits in the individual with other non-duplicate nodes. These newly selected sensor nodes will be regarded as cluster head nodes. In the processing of each individual, a random number, Qr, is generated by using chaotic mapping, and then a mutation operation is performed. As shown in Figure 8, the code of an individual is '0100100100'; when changing the 1 of the second position to 0, it needs to change a random position 0 to 1, so the new code is '0001100100' (Take 0 at the fourth position and change it to 1). The specific formula expression is as follows:  
\[i_{n,j}=\left\{\begin{array}{l}i_{n,j},\ Qr>Q\\ i_{new},Qr<Q\end{array}\right.,j\in[1,L],n\in[1,N] \tag{12}\]  
In Equation (12), \(i_{new}\) represents the newly generated cluster head node and \(i_{new}\) does not overlap with the existing cluster head nodes in the individual.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges  
Although substantial progress has been made in IMI strategies for B5G networks [226, 227, 228], several gaps and limitations remain, especially in the context of large-scale deployments, RT responsiveness, and integration with emerging network paradigms. This section provides a critical assessment of the reviewed techniques and outlines open research challenges that need to be addressed for the practical and scalable implementation of IMI in future wireless systems.
# An Improved Image Filtering Algorithm for Mixed Noise
## 2 The Filtering Algorithm for Mixed Noise with Salt and Pepper + Gaussian Noise  
In this section, we will introduce and analyze the proposed filtering algorithm with mixed noise in detail. This algorithm involves the improvement of three sub-algorithms:  
1. We made improvement to the traditional median filtering algorithm. Compared with the Adaptive Median Filtering (AMF) algorithm, the weighted multilevel median filtering algorithm, and the multilevel nonlinear weighted mean median filtering algorithm, the improved algorithm has better filtering effect for salt and pepper noise.
2. We improved the wavelet threshold denoising algorithm's threshold function and threshold. This improved algorithm has better removal effect on Gaussian noise than the traditional hard threshold denoising, soft threshold denoising, and semi-soft threshold denoising algorithms.
3. We made improvements in the Euclidean distance function that measures the similarity of image blocks in the NLM algorithm, and the distance weight function between similar image blocks. The improved algorithm performs better in filtering Gaussian noise than the NLM algorithm and can better preserve the edge and detail information of the image.  
The three improved sub-algorithms are combined to denoise salt and pepper + Gaussian noise according to a certain process. Experimental results show that the algorithm is robust and has excellent denoising ability and detail recovery ability.