esa-sceva/satcom-chunk-collection huggingface dataset:
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 2 Basic Principles
### Convolutional Neural Network
#### 2.1.1 Convolutional Layer  
The convolution kernel is the core of the convolutional layer for feature extraction. It extracts higher-order features by traversing the input data for convolutional operations. The convolutional layer is characterized by local awareness and weight sharing. That is, the weight matrix of the same convolution kernel is shared to reduce parameters and decrease model complexity. The convolution operation is shown in the following equation:  
\[y_{i}^{l}(j)=w_{i}^{l-1}\,*\,x^{l-1}(j)+b_{i}^{l-1} \tag{1}\]  
where \(y_{i}^{l}(j)\) is the convolution result of layer \(l\), \(w_{i}^{l-1}\) is the weight matrix of layer \(l-1\) (the weight matrix of the \(i\)th convolution kernel of layer \(l-1\)), and \(x^{l-1}(j)\) denotes the \(j\)th region where the convolution operation is performed in layer \(l-1\). \(b_{i}^{l-1}\) denotes the bias term, which is the bias of the \(i\)th convolution kernel of layer \(l-1\). The symbol \(*\) denotes the convolution operation. The principle of the convolution kernel is shown in Figure 2.  
Figure 1: Structure of a CNN.  
Figure 2: Schematic diagram of the convolution principle.  
The convolutional layer employs a predefined convolutional kernel to perform a local convolution on the input features in step \(s\). After traversal, a feature map is generated. For instance, a 4 \(\times\) 4 plane is computed by a 3 \(\times\) 3 convolution kernel to obtain a 2 \(\times\) 2 feature map. The parameters of the convolution kernel are multiplied with the corresponding parameters in the window and then summed to obtain the values in the feature map.
# Fault Diagnosis of HV Cable Metal Sheath Grounding System Based on LSTM
## 2 Model of High Voltage Cable Grounding System  
The metal sheath of HV cable grounding system adopts the cross-bonded grounding method, which can effectively weaken the induced voltage on the sheath and thus can have the induction current on the sheath suppressed. The model of HV cable grounding system is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Literature** & **Full Range of Fault Types** &
\begin{tabular}{c} **Simple Measuring** \\ **Equipment** \\ \end{tabular} & **Simple Models** & **High Accuracy** \\ \hline SGCC corporate & & \(\surd\) & \(\surd\) & \\ standard [9] & & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. [10] & & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. [11] & & \(\surd\) & \(\surd\) & \\ [PERSON] al. [13] & & \(\surd\) & \(\surd\) & \(\surd\) \\ Proposed & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of existing diagnostic methods.  
As shown in Figure 1, the HV cable grounding system model is mainly composed of 12 cable joints (JA0, JA1,..., JC3), 9 minor sections of metal sheath (A1, A2,..., C3), and two link boxes (J1, J2) and two grounding boxes (G1, G2). \(I_{\rm san}\) (\(n\) = 1, 2, 3, 4, 5, 6) indicates the sheath current flowing into the grounding box at the first and last ends. The metal sheaths are connected by coaxial cables transposed in the link box to form three sheath loops L1 (A1-B2-C3), L2 (B1-C2-A3), and L3 (C1-A2-B2), as shown in Figure 2. The sheath current is the current flowing through the metal sheath loops, including leakage current and sheath induced current.
## 5 Conclusions  
This study proposed a novel heart rate estimation technique based on a six-axis Heart Rate Sensing Device (HRSD) that leverages ballistocardiographic (BCG) principles. By utilizing accelerometer and gyroscope data commonly available in wearable devices, our method enables continuous, non-invasive heart rate monitoring without requiring optical sensors.  
Experimental results from 20 participants demonstrated that a 4 s analysis window yielded the highest estimation accuracy, with a mean absolute error (MAE) of 4.75 bpm. The signal processing pipeline--including sensor fusion, high-resolution sampling, filtering, PSD analysis using the Welch method, and dominant frequency extraction--proved effective in isolating heartbeat-induced vibrations, even within short analysis windows.  
Compared to optical PPG-based systems, our approach offers three major advantages: (1) lower power consumption due to the use of embedded inertial sensors; (2) robustness against environmental factors such as lighting or skin tone; and (3) suitability for long-term monitoring in mobile or immersive environments such as VR/AR headsets.  
Despite these strengths, limitations remain. The method's sensitivity to sensor placement and motion artifacts needs further refinement. Additionally, broader validation across diverse populations and real-world scenarios is required to enhance generalizability. Future research will focus on improving robustness through AI-based signal enhancement, adaptive windowing, and sensor placement optimization.  
Ultimately, this study contributes a promising and scalable method for heart rate monitoring in next-generation wearable systems, particularly those requiring non-contact sensing in dynamic environments.  
Figure 9: [PERSON]–[PERSON] plot comparing HR derived from six-axis sensor and PPG.  
**Author Contributions:** Conceptualization, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); formal analysis, [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); data curation, [PERSON] ([PERSON]) and J.K. ([PERSON]); writing--original draft preparation, J.K. ([PERSON]) and J.K. ([PERSON]); writing--review and editing, J.K. ([PERSON]); visualization, J.K. ([PERSON]); supervision, J.K. ([PERSON]); project administration, J.K. ([PERSON]); funding acquisition, J.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Simulation Result and Comparison
Results indicate that in the presence of the ATCTs, voltage stability and profile for entire distribution system can be improved. Meanwhile, it can maintain voltage at a proper and statutory range by installing ATCTs in less than half nodes.
## 4 Data Analysis and Results
### Correlation Between UCS and BTS
Finally, [PERSON] [97], using data obtained from the sedimentary rocks, established an excellent logarithmic correlation between UCS and BTS with an r of 0.97. However, in the present study, a linear correlation equation with r equal to 0.81 was obtained between the UCS and BTS of igneous rocks. In this respect, there are differences in equation type and r values of correlations developed between UCS and BTS by various researchers. These differences could be due to differences in the tested rock classes, range of physical characteristics, UCS, and BTS of the samples, mineralogical composition and textural characteristics of the samples, the specimen conditions used to test (i.e., dry or saturation), and number and dimensions of test specimens. Figure 4: Correlation between UCS and BTS. As two statistical indices, the variance account for (VAF) and mean absolute percentage error (MAPE) were used to assess the validity of the correlation equation between the UCS and BTS. \[\text{VAF }=\left[1-\frac{\text{var}(\text{y}-\text{y}^{\prime})}{\text{ vary}}\right]\times 100 \tag{3}\]  
\[\text{MAPE }=\frac{\sum_{i=1}^{\text{N}}|\text{y}-\text{y}^{\prime}|}{\text{N}} \times 100 \tag{4}\]  
where y and y\({}^{\prime}\) are the actual and predicted values of the UCS, respectively, \(\bar{y}\) and \(\bar{y}^{\prime}\) are the mean values of the y and y\({}^{\prime}\), respectively, and N is the number of the dataset. The VAF measures the proportion of the total variance in the actual values that is accounted for by the variance in the predicted values. On the other hand, the MAPE calculates the average percentage difference between the actual and predicted values. A higher VAF, indicates better accuracy of the correlation equation in predicting the unknown parameter. In addition, a lower MAPE indicates higher predictive accuracy. Thus, a correlation equation has excellent performance in predicting the unknown parameter using the one that is known (in the present study: UCS and BTS, respectively) if VAF = 100% and MAPE = 0%. Based on Equations (3) and (4), VAF and MAPE values equal to 62.97 and 29.43%, respectively, were obtained for the correlation equation between the UCS and BTS. These values are in an acceptable level, indicating a meaningful correlation between the UCS and BTS.
## 2 Materials and Methods
### Adaptive Knot Selection  
The iterative method of fitting continued fractions allows for an adaptive method of  
error are likely to be very close to each other, we impose the condition that we take the samples with the highest error, but they must have alternating signs.  
In this way, we select \(k\) knots for \(g_{i}(\mathbf{x})\) and \(i>0\), with the first knot at the location of the sample with the highest absolute error computed from the model \(g_{i-1}(\mathbf{x})\). For the rest of the knots, the \(j\)th knot is selected at the sample's location with the next highest absolute error after the sample used for the \((j-1)\)th knot, and only if the sign of the (non-absolute) error of that sample is different from the sign of the (non-absolute) error of the sample used for the \((j-1)\)th knot. Otherwise, we move on to the next highest absolute error sample, and so on, until we fulfill this condition. This knot selection procedure is shown in Algorithm 2. Note that we let \(g_{0}\) be a linear model, as there is no previous model from which to obtain the knot locations.  
```
Input:\(\epsilon_{i}\) /* Given the vector of residuals \(\mathbf{ffl_{i}}\) of the spline model at depth \(i\), select the knot placements for the next spline model at depth \(i+1\) */ /* Sort by indices of highest absolute error */ 1 abs_error \(\leftarrow\) elementWiseAbsoluteValue(\(\mathbf{ffl_{i}}\)) highest_error_indices \(\leftarrow\) argsortDecreasing(abs_error) /* Take the top \(k\) highest order indices, such that each error term has opposite sign of the last */ current_sign \(\gets null\) knots_added \(\leftarrow\) 0 for each i in highest_error_indices do ifknots_added \(\geq k\)then break end if ifsign(\(\epsilon_{i}[\mathbf{i}]\
eq\) current_sign)then current_sign \(\leftarrow\) sign(\(\epsilon_{i}\)) knot_indices.append(\(\epsilon_{i}[\mathbf{i}]\)) knots_added \(\leftarrow\) knots_added + 1 end if end returnknot_indices
```  
**Algorithm 2**SelectKnots (Adaptive Knot Selection)  
The goal of using additive spline models with the continued fraction is to take advantage of the continued fraction representation's demonstrated ability to approximate general functions (see the discussion on the relationship with Pade approximants in [9]). The fraction's hierarchical structure allows for the automatic introduction of variable interactions, which is not included individually in the additive models that constitute the fraction. The iterative approach to fitting makes for a better knot selection algorithm.  
An example of this algorithm modeling the well-known gamma function (with standard normally distributed noise added) is demonstrated in Figure 1. Here, we show how the fitting to gamma is affected by different values of depths (3, 5, 10, 15) in the _Spline Continued Fraction_. As desired, it is evident from the figure that the _Spline Continued Fraction_ with more depth has a better fit with the data.
# A Lightweight AI-Based Approach for Drone Jamming Detection
## 5 Model Performance  
### Parameters Tuning Phase  
The first phase in our testing methodology involves tuning the model's hyperparameters by varying them between a minimum and a maximum value, evaluating the impact on the performance metrics (detailed in Section 4.4) and then choosing the best possible values. In our scenario, a value is considered good if it provides a high metric value while at the same time not impacting the execution time too drastically. In cases where improving the value of a metric requires a significant increase in execution time, we chose the value that provides the best trade-off between the two.  
Among the parameters available in the _Scikit-learn_ implementation of the Isolation Forest, we decided to tune the following:  
* **n_estimators:** the number of base estimators in the ensemble, i.e., the number of isolation trees used to compute the anomaly score of each data point.
* **max_samples:** the max number of samples to draw from the dataset to train each tree.
* **contamination:** the amount of outliers present in the training dataset.  
During the tuning phase, the untested hyperparameters were set to the default values of the _Scikit-learn_ implementation of the Isolation Forest (Table 3).  
The model was evaluated using input composed of normal traffic samples concatenated with jamming attack samples (see Algorithm 1). This was conducted to simulate a real-world scenario where the model has to be able to correctly classify anomalous samples but also reduce the number of false positives during normal operation.  
```
1:normalTraffic\(\leftarrow\) ReadAndParseFile(NORMAL_TRAFFIC_FILE, normal_traffic_size)
2:jamming\(\leftarrow\) ReadAndParseFile(JAMMING_FILE, jamming_size)
3:testInput\(\leftarrow\) Concatenate(normalTraffic, jamming)
```  
**Algorithm 1** Test input definition  
The selected values for _normal_traffic_size_ and _jamming_size_ are shown in Table 4.  
The dataset is intentionally unbalanced between normal data points and anomalous points, as jamming attacks are usually rare events compared to normal network operation. In Figure 5, we can see a representation of the input signal in the case of a constant jamming attack. The proposed results, unless otherwise specified, are based on the input signal shown in Figure 5, which employs constant jamming. Periodic jamming has always shown comparable trends as constant jamming in all the performed tests.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **Size** \\ \hline normal\_traffic\_size & 20,000 \\ jamming\_size & 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dataset sizes used for the tuning and testing phases.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Default Value** \\ \hline n\_estimators & 100 \\ max\_samples & ‘auto’ \\ contamination & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scikit-learn Isolation Forest hyperparameter default values.
## 5 Discussion and Future Directions
### Limitations of SCI Device-Based Therapies  
#### 5.2.1 Power Supply and Device Longevity  
Many of the SCI devices mentioned rely on electrical stimulation and motorised components, making power supply a universal challenge. Implantable devices such as FES, EES, and BCIs are often dependent on internal batteries with limited lifespans of around 3-5 years, which means they require surgical replacement, increasing the procedural risk. External systems, such as exoskeletons and surface FES, need frequent charging as the battery life is limited. The time needed to charge the devices creates interference in the therapy and reduces overall efficacy. Lastly, emerging technologies such as ultrasonically powered stimulators and triboelectric nanogenerators offer alternative power sources; however, these methods are still in early development phases and face the limitation of energy density and reliability [48].
# Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free +
## 4 Discussion
### Scalability of the Community Fitness Function
Therefore, in multilevel networks, only the lowest community level can be detected, while all intermediate levels are invisible. In contrast, when \(\beta>1\), it rescales the whole resolution range by a multiple factor \(\beta\), which amplifies the resolution scales of different community levels, and effectively enables our detections on all levels of communities. As a result, for the RB hierarchical networks, our method successfully detects up to five levels of communities with \(\beta=2\), which to our knowledge, has not been done by any other methods reported in previous literature. Yet the deficiency of our scaling factor \(\beta\) is that it rescales the resolutions unevenly: comparing to the lowest and highest (but trivial) levels, expansions for the resolution scales of the intermediate community levels are relatively minor. As a result, for networks having too many community levels, our method fails to detect some of them. Additionally, when the network size is too large, it becomes more and more difficult for the original Louvain algorithm to converge to a best solution. Improved methods and algorithms are to be studied in the future.
# A Single-Rate Multicast Congestion Control (SRMCC) Mechanism in Information-Centric Networking
## 4 Rate Adaptation
### Rate Calculation
#### 4.2.4 Heavy Occupancy Stage  
At this time, the queue occupancy ratio has exceeded the maximum threshold \(O_{th2}\), and network congestion is prone to occur or has occurred. In order to alleviate network congestion, the usual approach is to decrease the rate immediately. However, if the current queue is mainly occupied by other competing streams, continuously reducing the multicast sending rate will cause the multicast throughput to drop-to-zero. To maintain fairness, it is necessary to determine whether the heavy occupancy is caused by the multicast traffic itself. We innovatively introduce the minimum fair shared rate \(R_{fair-shared}\) as the demarcation, and the calculation rate for the next time interval is adjusted according to Equation (9).  
\[R(t+1)_{cal}=\left\{\begin{array}{ll}min\Big{(}R(t+1)_{cal},\frac{1}{2} \times R(t)\Big{)},&R(t)\geq R_{fair-shared}\\ 2.0\times R(t),&R(t)<R_{fair-shared}\end{array}\right. \tag{9}\]  
As shown in Equation (9), when \(R(t)\) is larger than or equal to \(R_{fair-shared}\), the heavy occupancy state is caused by multicast traffic. Therefore, the multicast rate should be reduced to alleviate congestion. At this time, \((O_{th}-O(t))\) is a negative value, and \(w\) is set to 1.0 in Equation (3) to speed up the decline of the calculation rate. When \(R(t)\) is less than \(R_{fair-shared}\), the situation is caused by other competing traffic, and the multicast rate should be increased to preempt bandwidth and prevent throughput from dropping to zero.  
Finally, \(R(t+1)_{min}\) or \(R(t+1)_{max}\) are adjusted for the next round according to Equation (4).
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 4 Data Aggregation Using Compressed Sensing Theory  
### Data Aggregation  
Data aggregation is an essential process in wireless routing for collecting the data from various sources in the network [15]. It aggregates the sensor's data, which removes the data redundancy and reduces the number of data transmissions. Thus, it saves the energy on the network nodes in IoT. Data fusion clubs the sensor's data and removes the noise from various sources. Finally, it generates accurate data for data transmission. The data aggregation process is shown in Figure 3.  
Figure 1: Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) Network Model.  
Figure 2: Data collection and aggregation from node PN1 to Destination Oriented Directed Acyclic Graph (DODAG) root using compressed sensing (CS) theory.
## 9 Future Research Directions and Challenges
### Cooperative Localisation  
With V2X technology, many heterogeneous multi-robot applications comprising UGVs and UAVs can be envisioned. This enables more efficient use of robots in diverse scenarios such as coverage, formation, task distribution, etc. For example, in a search and rescue operation, a group of heterogeneous robots may collaborate, while some act as an anchor in the absence of a signal for some or all team members. On the other hand, sharing information by robots operating in the same area would endow robustness and accuracy to the localisation system, as each robot can benefit from the knowledge and estimation of its neighbour(s).
## 2 Materials and Methods
### Collection of Ground Truth Data  
This research utilized the ground truth data prepared in the previous study [54]. The ground truth data were prepared by field survey, accompanied with an existing vegetation survey map (1:25,000 scale), and visual interpretation of the time-lapse images available in Google Earth. The sample points (longitudes and latitudes) were prepared from homogenous areas belonging to each land cover and vegetation type concerned. In total, 1200-2400 sample points were prepared for each land cover and vegetation type for each site. The vegetation types were classified by adopting the genus-physiognomy-ecosystem system developed for satellite-based classification of plant communities in the previous study [55]. The ground truth data were prepared by considering unchanged areas between 2018-2020. However, since the study sites were usually devoid of human disturbances and landscape changes, the ground truth data could represent a few more nearby years. The distribution of the ground truth data size for each site is shown in Figure 2. The sample points were prepared by considering the proportional coverage of the land cover and vegetation types in each site. Therefore, vegetation types with large coverage, such as Abies ECF, Quercus DBF, and Fagus DBF, consisted of large sample sizes; whereas low-coverage vegetation types such as Acer DBF and Quercus Shrub received small sample sizes. This research dealt with the classification and mapping of 16 land cover and vegetation types in Hakkoda, 23 land cover and vegetation types in Zao, and 12 land cover and vegetation types in Shiranuka.  
Figure 1: Location map of the study sites: Hakkoda, Zao, and Shiranuka.