esa-sceva/satcom-chunk-collection huggingface dataset:
# Passive Intermodulation at Contacts of Rough Conductors
###### Abstract
Their sources and interlinks are summarised in Figure 1 and include  
* Charge tunnelling and diffusion at MIM junctions of conductor asperities;
* Current constriction at asperities of rough contact surfaces;
* Self-heating and thermal expansion of the contact surfaces and asperities;
* Electro-thermal effect due to ohmic losses in conductors and contacts;
* Asperity deformations of the conductors with rough surfaces and their contacts subjected to mechanical stresses, expansion and creep. These nonlinearities exist in conductor joints concurrently and influence each other despite their notably different time scales. The underlying physical mechanisms are intrinsically linked as discussed in Sections 2-4 and are influenced by the layouts and profiles of the contact surfaces. The frequency and phasing of the high-power carrier signals also have a notable effect on the generated PIM products. For example, the positions of the hot spots in the antenna beamforming networks vary with the carriers' phasing. As the result, the observed pattern of PIM products is affected by a profile of the contact area, its deformations and heat flow. This implies that the locations of the PIM hot spots can change with the contact pressure that flattens sharp asperities and expands the contact area. This entails reduction of the current density at the MIM junctions and the lesser effect of their nonlinearity. In this paper, we review the main types of nonlinearities at contact joints of good conductors and the related mechanisms of PIM generation. The effects of electrical nonlinearity in the conductor contacts are discussed in Section 2. Thermal and electro-thermal nonlinearities are the subject of Section 3. The effects of surface roughness and mechanical deformations of the conductor joints on PIM products are considered in Section 4. The main properties of different mechanisms of nonlinearities, their effect on PIM at contact joints of conductors with rough surfaces are summarised in the conclusion.
## 4 Experimental Results  
### Data Generation and Preprocessing  
To effectively analyze SCAs on embedded systems, a dataset was generated using cryptographic algorithms, including RSA, DES, and Hash functions [40]. The reason for selecting these algorithms in this study is that these algorithms are widely used in embedded systems and IoT environments and have been frequently targeted by recent SCAs. In particular, RSA and DES are suitable examples, given their cryptographic stability and widespread use in various industries [14,40]. The data collection was performed using a controlled simulation environment based on QEMU and GDB, allowing for the extraction of detailed assembly-level execution data.  
The generated dataset underwent multiple stages of preprocessing to enhance its usability for DL models. Initially, the extracted instruction data was normalized to ensure consistency across different algorithm implementations. The dataset was then augmented by incorporating variations in memory access patterns and execution timing data, thereby capturing a more comprehensive representation of cryptographic behavior. This preprocessing step was crucial in facilitating robust model training and improving classification accuracy [39].  
The dataset was structured to maintain an equal distribution of original and dummy instructions, ensuring a balanced training set. This was essential for assessing the resilience of DL-based classifiers against SCAs and validating the effectiveness of dummy power traces. Table 6 presents the dataset distribution, highlighting the number of original and dummy instructions per cryptographic algorithm.  
Although each cryptographic algorithm was executed the same number of times during data generation, the total number of instructions collected per algorithm varied. This variation in instruction count primarily arises from the intrinsic structural differences among the algorithms. DES performs a large number of repetitive operations within each encryption cycle, resulting in a higher instruction count per execution compared to RSA and hash functions. In contrast, RSA involves modular exponentiation and hash functions operate on fixed-length data blocks, both of which result in fewer instruction traces per execution.  
Since the primary objective is to classify dummy versus original instructions rather than to compare different algorithms, this difference in instruction count does not compromise the validity of the dataset. Instead, a balanced distribution of original and dummy instructions was maintained to ensure consistency with the experimental objective.  
Maintaining such a balanced dataset was crucial for simulating realistic attack scenarios and assessing the effectiveness of countermeasures. Additionally, a thorough evaluation of instruction dependencies and opcode frequency distributions was performed to identify potential biases in data generation, thereby ensuring the robustness of the dataset used for classification tasks.
## 2 Literature Review
### Target Tracking  
The tracking and localization of perforated robotic arms are primarily dependent on sophisticated methodologies within the domain of deep learning, with a particular emphasis on the architectural configuration of twin networks, more specifically Siamese Networks. Twin network trackers have attracted considerable attention due to their capacity to maintain high tracking accuracy while meeting real-time performance requirements. In 2016, [PERSON] et al. proposed SiamFC (Siamese Fully Convolutional Network) [15], which learns relevant similarities.  
The measurement function is conducted through a pre-trained deep twin network that passes through a full convolutional layer to evaluate the similarity between targets and potential candidates. This is designed to allow flexible processing of templates and search images of different sizes; however, it is less robust to complex scene processing such as significant appearance changes or occlusion of targets. Furthermore, due to the lack of an online update mechanism in SiamFC, significant changes in target appearance may result in performance degradation over time. In 2018, [PERSON] et al. proposed SiamRPN (Siamese Region Proposal Network) [7], which integrates a region proposal network (Region Proposal Network (RPN)) on top of the base twin network structure. This improvement predicts the target location with greater accuracy and significantly enhances the tracking performance in situations such as target size change and occlusion. Nevertheless, SiamRPN is also deficient in terms of online learning mechanisms, and the long-term stability of tracking performance remains a significant challenge. In the same year, [PERSON] et al. proposed DaSiamRPN [16], which introduced a mechanism based on SiamRPN that was designed to enhance the model's ability to distinguish between targets and non-targets (interference terms), a process that the authors refer to as \"distractor-aware\". However, the complex structure of the DaSiamRPN model and its reliance on a substantial quantity of labeled data to identify interfering items restrict its applicability in data-scarce perforated robotic arm scenarios. Despite these algorithms demonstrating considerable advancement in real-time performance and accuracy, further research and optimization are still necessary to achieve enhanced performance and stability when confronted with intricate industrial environments.
## 4 Research Methodology
### Data Analysis
#### 4.2.1 Quantitative Analysis  
[PERSON]'s alpha coefficient is used to calculate the internal consistency of the different variables in order to determine the strength of the five-point scales. The value for [PERSON]'s alpha coefficient in this study is 0.876, which is greater than the threshold of 0.7, which suggests that the data are reliable for further statistical analysis [111]. Furthermore, the mean value technique is used to determine the relative importance of individual barriers. The mean values of individual barriers are computed, ranked, and compared between the three groups (contractors, consultants, clients). Mean value analysis is a technique used to effectively identify key factors among various factors [112]. In addition, [PERSON]'s coefficient of concordance (Kendall's W) is also calculated to measure the agreement of responses in particular groups [113]. The range of the value of [PERSON]'s coefficient of concordance (W) is from 0 to 1. The higher value of W indicates the high level of consensus among the respondents within the group [114; 115]. In addition, chi-square analysis should be carried out, if the number of items is greater than seven [116]. In addition, the [PERSON]'s rank correlation coefficient was introduced to calculate the strength of a relationship between two groups [117]. The range of the [PERSON]'s rank correlation coefficient (\(r_{s}\)) is from \(-1\) to \(+1\). The higher the positive/negative value of \(r_{s}\), the stronger positive/negative linear correlation [90].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Description** & **Number of Participants** & **Percentage (\%)** \\ \hline \multirow{2}{*}{Gender} & Male & 20 & 100 \\  & Female & - & - \\ \hline \multirow{3}{*}{Educational qualification} & Bachelors & 3 & 15 \\  & Masters & 5 & 25 \\  & PhD & 6 & 30 \\  & Professional & 6 & 30 \\  & Engineers & 6 & 30 \\ \hline \multirow{3}{*}{Working experience} & 5–7 years & 4 & 20 \\  & 8–9 years & 9 & 45 \\  & More than 10 years & 7 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Participant demographics (\(n=20\)).
# Aerial Tele-Manipulation with Passive Tool via Parallel Position/Force Control +
## 6 Conclusions and Future Works  
The topic of aerial physical interaction using a typical under-actuated VTOL UAV equipped with a passive tool was considered in this paper. We use a simple passive light weight tool, instrumented with a compliant end-effector, rigidly attached to the top of an aerial robot. We propose a control method in a bilateral teleoperation scheme to let a human operator drive the aerial manipulator in a remote environment, controlling the position of the robot in free flight and regulating the force applied to the object in contact interaction while maintaining the flight stability and keeping the contact. For this purpose, a parallel position/force controller is utilized, which also uses the rotational dynamic axes, that is, yaw motion, to generate the desired force that allows maintenance of the contact with a wider range of forces. On the one hand, theoretical proof of the stability of controlled system is derived and presented. On the other, experiments on driving the aerial robot toward the desired point, docking, and applying the desired forces to stationary and movable objects represented the feasibility and efficacy of the proposed solution. The human operator is provided with force haptic feedback proportional to the velocity in free flight and the applied force in contact, allowing for an improved situational awareness.  
Future work will involve vision based outdoor implementation of the proposed approach, and investigating bandwidth and delay effects in haptic teleoperation based on passivity theories such as [36]. Performing more complex tasks cooperatively with a team of aerial manipulators will also be considered.  
The following are available at [[https://www.mdpi.com/article/10.3390/app11198955/s1](https://www.mdpi.com/article/10.3390/app11198955/s1)]([https://www.mdpi.com/article/10.3390/app11198955/s1](https://www.mdpi.com/article/10.3390/app11198955/s1)), Video S1: The video of the experiments in the multimedia.  
Conceptualization, M.M., A.F., D.B. ([PERSON]) and D.P.; Funding acquisition, A.F. and D.P.; Investigation, A.F. and D.P.; Methodology, M.M., A.F., D.B. ([PERSON]) and D.P.; Project administration, A.F. and D.P.; Software, M.M. and D.B. ([PERSON]); Supervision, A.F. and D.P.; Visualization, D.B. ([PERSON]); Writing--original draft, M.M., D.B. ([PERSON]), A.F., D.B. ([PERSON]) and D.P.; Writing--review & editing, M.M., D.B. ([PERSON]), A.F. and D.P. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the European Union's Horizon 2020 research and innovation program grant agreement ID: 871479 AERIAL-CORE.  
Not applicable.  
The authors declare no conflict of interest.
# Use of Surface Acoustic Waves for Crack Detection on Railway Track Components--Laboratory Tests
## 1 Introduction  
### Head Checks--A Specific Type of Rail Damage  
Near-surface defects arising from rolling contact fatigue (RCF) are a major source of concern in railway operations worldwide. Among the different damage types related to RCF, head checks have become the most prominent since they pose a high risk of complete rail failure and are a major driver of maintenance costs. Head checks form a closely spaced network of almost parallel-aligned cracks on the gauge corner on high rails at turns. These surface cracks initially grow at a shallow angle to the rail, posing a challenge for detection at an early stage. A description of head checks and how they can be generated in a laboratory setup, i.e., a 1:1 wheel rail test rig can be found in reference [1].
# Three Steps towards Better Forecasting for Streamflow Deep Learning
## 2 Methodology
### Model Performance Evaluation
#### 2.6.3 Nash-Sutcliffe Efficiency (NSE)  
_NSE_ measures the relative differences between the observed and predicted values. A higher value of _NSE_ indicates the model's superiority. When _NSE_ is 1, it means a perfect match of the observed and predicted. Otherwise, if _NSE_ is 0, the predicted values are similar to the average of the observed values [49]. The model accuracy can be categorized as very good for 0.75 < _NSE_\(\leq\) 1, good for 0.65 < _NSE_\(\leq\) 0.75, satisfactory for 0.50 < _NSE_\(\leq\) 0.65 or unsatisfactory for _NSE_\(\leq\) 0.50 [50].  
The mathematical representation of NSE is as follows:  
\[NSE=1-\frac{\sum_{i=0}^{n}(Y_{i}-Y_{t})^{2}}{\sum_{i=0}^{n}\left(Y_{i}-\overline {Y}\right)^{2}}\,-\infty<NSE\leq 1 \tag{12}\]  
where:  
\(Y_{i}\) is the predicted values of the criterion;  
\(Y_{t}\) is the measured value of the criterion variable (dependent) variable Y;  
\(\overline{Y}\) is the mean of the measured values of Y;  
\(n\) = sample size.
## 4 Result
### The Residual Zenith tropospheric Delay (dZTD)
These nine models can be divided into four groups, that are saas+norm/GPT, EGNOS/UNB3/UNB3m, saas+GPT2/GPT2w/GPT3, and VMF3. The model stabilities are comparable within each group, and significantly increase among groups. Specifically, RMSs of EGNOS/UNB3/UNB3m are nearly 30% smaller than saas+GPT, and are 20% larger than saas+GPT2/GPT2w/GPT3. RMSs of VMF3 are the smallest, which are improved by about 70% compared to the GPT2/GPT2w/GPT3 model. The results above show that the correction accuracies of different ZTD models could be remarkably different, and the model accuracies are usually lower in summer. Generally, newer, more elegant models have higher accuracy than those older and more complicated one. However, operational burdens for the former are also heavier. As shown in Figure 5, the average run time of eight models (saas+norm/GPT/GPT2/GPT2w/GPT3, EGNOS, UNB3, UNB3m) was counted by using MATLAB software. It counted data from 400 stations with a time slot of 28 days, and the data sampling rate was 5 min. It showed that the computation efficiency of the Saastamoinen model is 100 times higher than saas+GPT/GPT2/GPT2w/GPT3, and 30 times higher than EGNOS/UNB/UNB3 models. For real-time practical applications, users should make balance between higher accuracy and higher computation efficiency. Empirical models, such as Saastamoinen, are still popular when the requirement for positioning accuracy is not so stringent, but higher computation efficiency is necessary. In the next subsection, biases on SPP solutions produced by the uncorrected residual ZTD when adopting different ZTD models are evaluated globally and the spatiotemporal characteristics are analyzed. With these results, custom-grade SPP users are capable of anticipating the positioning deviation from the unknown real location when using different tropospheric delay correction models. Figure 5: Running time of the eight models (saas+norm/GPT/GPT2/GPT2w/GPT3, EGNOS, UNB3, UNB3m) (unit: \(\upmu\)s).
## 4 Discussion
### Early-Season Classification Performance for Separate Target Year
Transformers can exploit temporal dependencies and handle variable time series lengths as inputs [66], making them highly suitable for early-season mapping as a single model can be used for progressive mapping throughout the season [28; 29]. Ref. [29] demonstrated the effectiveness of a transformer-based deep learning architecture for early-season crop mapping, highlighting its temporal (and spatial) generalizability. However, their approach requires a substantial amount of labeled data to train this transformer-based model, an architecture that often requires larger datasets in comparison to CNNs to achieve better performance [67]. Ourstudy already faced the constraint of limited sample availability with CNNs, so it can be questioned how useful this specific model would be for our catch crop classification. Fortunately, recent research also addresses this aspect of limited labeled data availability. Here, transfer learning, a machine learning technique that transfers knowledge between related tasks, emerged as a strategy to alleviate the need for labeled data in environmental remote sensing [67]. For example, ref. [28] demonstrated pre-trained models, providing a strong starting point for early-season crop mapping in a separate target year for which no data are yet available, even when the vegetation trend differs between years. Reference [30] similarly demonstrated the effectiveness of transfer learning for the transferability of crop mapping models on Sentinel-1 data. Therefore, there is a need for the development of transferable models or even foundation models in remote sensing, which are large pre-trained and often transformer-based models that can be applied to a variety of tasks downstream through transfer learning [67]. As such, future work can explore transfer learning for early-season catch crop classification--for example, with a recently developed pre-trained transformer model for remote sensing time series by [68]--as this approach seems to generalize well even with limited training data.
# PIFNet: 3D Object Detection Using Joint Image and Point Cloud Features for Autonomous Driving
## 3 Proposed Point-Image Fusion Network (PIFNet)
### Two Stream Network Architecture
#### 3.1.1 Image Feature Extraction Stream  
The camera RGB images are processed by an Encoder-Decoder Fusion (EDF) module as shown in Figure 2. The EDF module consists of a set of convolution and deconvolution layers. The encoder module uses four convolution blocks to reduce the resolution of the feature maps. Then, the feature maps in the decoder are up-scaled via deconvolution layers and then combined with the corresponding feature maps of the same resolution in the encoder through element-wise accumulation.  
In the encoder module, the low-level feature maps have high resolution but poor semantic features. On the contrary, the high-level feature maps have rich sematic features but low resolution. The EDF module operates on the camera images and effectively extracts the semantic features and fuses the low-level feature maps and high-level feature maps to achieve good performance.  
The encoder module consists of a series of convolutional blocks. Specifically, each convolutional block consists of two 3 \(\times\) 3 convolution layers and is followed by a batch normalization layer and a ReLU activation function. Each convolution block produces a feature map \(E_{m}\)\((m=1,\ldots,4)\). The decoder module consists of a series of deconvolution layers with successively increasing resolution. Each deconvolution module produces a feature map \(F_{n}\)\((n=1,\ldots,4)\). \(E_{4}=F_{4}\) and \(F_{n}\) have an increased resolution than that of the previous feature maps \(F_{n+1}\), for \(n=1,2,3\). We exploit the abundant texture provided by camera images by using the EDF module and fed the four feature maps into the fusion module to associate with point-wise features.
###### Abstract  
This paper investigates the cooperative anti-jamming distributed channel selection problem in UAV communication networks. Considering the existence of malicious jamming and co-channel interference, we design an interference-aware cooperative anti-jamming scheme for the purpose of maximizing users' utilities. Moreover, the channel switching cost and cooperation cost are introduced, which have a great impact on users' utilities. Users in the UAV group sense the co-channel interference signal energy to judge whether they are influenced by co-channel interference. When the received co-channel interference signal energy is lower than the co-channel interference threshold, users conduct channel selection strategies independently. Otherwise, users cooperate with each other and take joint actions with a cooperative anti-jamming pattern under the impact of co-channel interference. Aiming at the independent anti-jamming channel selection problem under no co-channel interference, a Markov decision process framework is introduced, whereas for the cooperative anti-jamming channel selection case under the influence of co-channel mutual interference, a Markov game framework is employed. Furthermore, motivated by Q-learning with a \"cooperation-decision-feedback-adjustment\" idea, we design an interference-aware cooperative anti-jamming distributed channel selection algorithm (ICADCSA) to obtain the optimal anti-jamming channel strategies for users in a distributed way. In addition, a discussion on the quick decision for UAVs is conducted. Finally, simulation results show that the proposed algorithm converges to a stable solution with which the UAV group can avoid malicious jamming, as well as co-channel interference effectively and can realize a quick decision in high mobility UAV communication networks.  
interference-aware; cooperative anti-jamming; Markov decision process; Markov game; Q-learning +
Footnote †: journal: Applied Mechanics and Applications
# An Improved Image Filtering Algorithm for Mixed Noise
## 2 The Filtering Algorithm for Mixed Noise with Salt and Pepper + Gaussian Noise
### The Improved Median Filtering Algorithm
#### 2.1.2 Second-Level Detection of Noise Point  
As mentioned above, local extreme points are not necessarily noise points. If all local extreme points are taken as noise points for median value replacement, the details will inevitably be lost. For a natural smooth image, the pixel values of the smooth area are similar or even equal. Only at the edge of the image, or in the area with rich details, are the difference of image pixel values relatively large. According to human visual characteristics, it is more sensitive to the noise in the smooth area than the noise in the detailed area. If the possible noise point is still an extreme point in a larger window, this point is considered as a noise point. Using a larger window to judge noise points can effectively improve the accuracy of noise detection.  
In this paper, a 7 \(\times\) 7 window is used for secondary confirmation of possible noise points. The 7 \(\times\) 7 window is marked as \(w^{7}\); \(w^{7}_{max}\) and \(w^{7}_{min}\) represent the maximum value and the minimum value of window \(w^{7}\), respectively, and \(f_{i,j}\) represents the possible noise of window \(w^{3}\). If \(f_{i,j}=w^{7}_{max}\) or \(f_{i,j}=w^{7}_{min}\), then \(f_{i,j}\) is marked as the noise point; otherwise, \(f_{i,j}\) is marked as the signal point.  
The following formula is constructed:  
\[flag(i,j)=\left\{\begin{array}{ll}1,(f_{i,j}=w^{3}_{max}\text{ and }f_{i,j}=w^{7}_{max})\text{ or }(f_{i,j}=w^{3}_{min}\text{ and }f_{i,j}=w^{7}_{min})\\ 0,&\quad\text{otherwise}\end{array}\right. \tag{2}\]