esa-sceva/satcom-chunk-collection huggingface dataset:
# Antiference: New Concept for Evolutive Mitigation of RFI to GNSS +
## 2 Background  
The detection of interference has been a topic of research for a long time. Most of the classical ways to detect jamming are based on signal processing or statistical techniques, such as automatic gain control (AGC) monitoring, spectral monitoring, time-domain statistical analysis, and signal/carrier to noise ratio (CNR) monitoring. It is trickier to detect spoofing, but the most common methods use Position/Velocity/Time (PVT) estimate consistency checks, navigation data estimate consistency checks, the monitoring of AGC for anomalies, the monitoring of multiple correlation peaks and distortions of the Auto-correlation function (ACF), using additional equipment like inertial measurement units (IMU) for position check, signal polarization, or multi-antenna arrays for directing arrival detection.  
Machine learning (ML)-based methods have also been explored to detect jamming and spoofing signals. In some cases, they use similar features to the classical techniques as a basis for the detection of interference. Some of the features used can be summarized as Power Law Detector (PLD), such as the monitoring of AGC-gain values; Power Spectral Density (PSD) Analysis, including methods such as the computation of Power Spectral Density (PSD), filter banks, and wavelet Transformations; Probability Density Function (PDF) Analysis, such as Kurtosis and the Gaussian White Noise test; Short Term Fourier Transform (STFT) for the detection and classification of jamming; and Cyclo-stationary analysis to detect spoofed conditions, such as pre-correlation Spectral Power Contents  
Using the pre-processed data as features is only part of the solution; the other part is the choice of model and its training and testing data and methods. Jamming detection has been implemented successfully using Support Vector Machines (SVM) in [4; 5; 6], while Neural networks (NN) such as Convolutional Neural Networks have been used in [7]. RFI classification has used SVMs [8; 9], Long Short-Term Memory (LSTM) has been used in [3], and Convolutional Networks has been used in [8] with good results. Spoofing detection is carried out using SVMs in [10; 11], using NNs in [11; 12], and using the LASSO technique in [13]. Signal Characterization in the cognitive radio domain is carried out using different NNs [14; 15; 16; 17; 18] and SVMs [19].  
The mitigation of jamming is performed using multiple classical techniques depending on the types of attack. The most used techniques can be summarized as the Bandpass filter (for out-of-band interfering signals), the Notch Filter (NF) [20], the Adaptive Notch Filter (ANF) [21; 22], Frequency Domain Adaptive Filtering (FDAF), Pulse Blanking [20], Wavelet-based methods (such as [23]), and Karhunen-Loeve-based methods [23].
## 2 GPS Attitude Baseline Determination
### Integer Ambiguity Resolution
If the determined baseline parameters are correct, the differences between the observed and the calculated DD carrier phases are the integer ambiguities. Therefore, the result of the ARF should be close to the number of the available DD carrier phases (\(n\)) if the correct candidate is tested. Beside the AFM, we also consider the lengths and the residuals of the determined baseline parameters to check the correctness of the ambiguities. As can be seen in Figure 1 Step (iii) of the ambiguity resolution process is also applied to fixed solutions from Step (i) or (ii), to validate these results. Thus, due to the combination of Steps (i), (ii) and (iii), a fast and reliable ambiguity resolution can be performed at the same time.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{3}{*}{**Conctex Characteristics**} & \multirow{3}{*}{
\begin{tabular}{c} **Heavy** \\ **Non-Activated** \\ **Concrete** \\ \end{tabular} } & \multicolumn{6}{c}{**Lightweight Fiber Concrete**} \\ \cline{3-8}  & & \multicolumn{6}{c}{**Processing Time in VLA, s**} \\ \cline{3-8}  & & **0** & **50** & **60** & **70** & **80** & **90** & **100** \\ \hline Density, kg/m\({}^{3}\) & 2439 \(\pm\) 110 & 2080 \(\pm\) 94 & 2087 \(\pm\) 94 & 2097 \(\pm\) 94 & 2089 \(\pm\) 94 & 2091 \(\pm\) 94 & 2095 \(\pm\) 94 & 2086 \(\pm\) 94 \\ Cubic compressive strength, MPa & 61.1 \(\pm\) 3.9 & 43.2 \(\pm\) 2.8 & 45.1 \(\pm\) 2.9 & 49.2 \(\pm\) 3.2 & 52.3 \(\pm\) 3.4 & 54.7 \(\pm\) 3.6 & 52.8 \(\pm\) 3.4 & 50.3 \(\pm\) 3.3 \\ Prismatic compressive strength, MPa & 45.8 \(\pm\) 2.8 & 32.1 \(\pm\) 2.0 & 34.8 \(\pm\) 2.1 & 36.9 \(\pm\) 2.3 & 39.2 \(\pm\) 2.4 & 41.0 \(\pm\) 2.5 & 39.6 \(\pm\) 2.4 & 37.2 \(\pm\) 2.3 \\ Tensile strength in bending, MPa & 11.0 \(\pm\) 0.8 & 7.0 \(\pm\) 0.5 & 8.1 \(\pm\) 0.6 & 8.9 \(\pm\) 0.6 & 9.4 \(\pm\) 0.6 & 11.3 \(\pm\) 0.8 & 10.1 \(\pm\) 0.7 & 9.9 \(\pm\) 0.7 \\ Axial tensile strength, MPa & 6.7 \(\pm\) 0.4 & 5.0 \(\pm\) 0.3 & 5.1 \(\pm\) 0.3 & 5.4 \(\pm\) 0.3 & 5.8 \(\pm\) 0.3 & 6.6 \(\pm\) 0.4 & 6.1 \(\pm\) 0.4 & 5.8 \(\pm\) 0.3 \\ Ultimate deformations during axial compression, mm/m \(\times\) 10\({}^{-3}\) & 2.23 \(\pm\) 0.12 & 2.89 \(\pm\) 0.16 & 2.89 \(\pm\) 0.16 & 2.97 \(\pm\) 0.16 & 2.98 \(\pm\) 0.16 & 3.08 \(\pm\) 0.17 & 3.01 \(\pm\) 0.17 & 3.01 \(\pm\) 0.17 \\ Ultimate deformations under axial tension, mm/m \(\times\) 10\({}^{-3}\) & 4.65 \(\pm\) 0.28 & 4.86 \(\pm\) 0.29 & 4.89 \(\pm\) 0.29 & 4.86 \(\pm\) 0.29 & 4.97 \(\pm\) 0.30 & 5.31 \(\pm\) 0.32 & 5.09 \(\pm\) 0.31 & 5.02 \(\pm\) 0.30 \\ Elasticity modulus, GPa & 36.3 \(\pm\) 2.0 & 31.3 \(\pm\) 1.7 & 32.5 \(\pm\) 1.8 & 33.2 \(\pm\) 1.8 & 33.6 \(\pm\) 1.8 & 35.1 \(\pm\) 1.9 & 34.7 \(\pm\) 1.9 & 33.5 \(\pm\) 1.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Influence of the processing time in the VLA of the cement-sand mortar on the growth of strength of lightweight fiber-reinforced concrete specimens.
## 4 Results and Discussions
### Skin Friction Angle Analysis in Pile-Soil Interfaces  
Table 5 illustrates the relationship between sand relative density and skin friction angles for different pile materials, specifically examining the interface behavior between sand and concrete-steel versus concrete-timber combinations. This information is crucial for understanding pile-soil interaction in deep foundation design. For concrete-steel interfaces, the skin friction angle significantly increases from 12.8 degrees in very loose sand to 32.8 degrees in very dense sand. This progressive increase reflects the enhancement in mechanical interlocking and friction development as sand density increases. The relationship appears to be relatively linear, with each increment in relative density corresponding to a proportional increase in friction angle. The concrete-timber interface exhibits consistently higher skin friction angles across all relative density values, ranging from 20.2 degrees in very loose sand to 37.4 degrees in very dense sand. This higher friction angle can be attributed to timber materials' natural surface roughness and texture, qualities which typically provide better mechanical interlocking with sand particles compared to smoother concrete-steel surfaces. The difference in skin friction angles between the two interface types is most pronounced in very loose sand conditions, where timber interfaces show approximately 7.4 degrees higher friction than concrete-steel interfaces. This difference is important for pile design in loose sandy soils, where timber piles might offer superior shaft resistance despite their generally lower structural capacity.  
The influence of relative density on skin friction angle is more pronounced for concrete-steel interfaces, showing a total increase of 20 degrees from very loose to very dense conditions, compared to the increase of 17.2 degrees for concrete-timber interfaces. This suggests that the concrete-steel interface is more sensitive to changes in sand density, an important consideration in foundation design for varying soil conditions. These relationships have significant practical implications for pile design, particularly in terms of calculating shaft resistance and overall pile capacity. The higher skin friction angles associated with timber interfaces might make them preferable in certain applications, especially where shaft resistance is a critical design consideration and where soil conditions are suitable for timber pile implementation. The systematic variation in skin friction angles with relative density provides valuable input for foundation engineering, numerical modeling, and analytical calculations. This enables more accurate predictions of pile behavior under various soil conditions. This understanding is essential for optimizing pile design and ensuring adequate foundation performance in different geotechnical scenarios.
# Investigating and Modeling of Cooperative Vehicle-to-Vehicle Safety Stopping Distance
## 4 Communication-Assisted Stopping Distance  
Stopping Distance is the term given to describe the distance a vehicle will traverse until a full complete stop; this includes time for the driver or machine to react to a problem and for the vehicle to fully decelerate after the brakes are applied. This can be split into three categories leading to total distance [35; 36; 37; 38];  
* Thinking Distance--this is categorized as the distance a vehicle will travel before the driver notices a problem.
* Reaction Time--this describes the time taken for the driver to initialize a reaction to the encountered problem, for example applying the brakes.
* Braking Distance--this is the distance the vehicle will travel after the reaction has been made, such as engaging the brakes, before the vehicle will come to full stop.  
These different timings will be broken down in our results with the reaction time being substituted to our communication time. We also omit the Thinking Distance parameter as this would be an automatic braking system based on communication the driver of the vehicle would not be counted upon to react.
## 4 Experiments
### Discussion and Relevance  
#### 4.5.1 Classification Performance of the Two Models on Three Dataset  
In this study, we constructed three distinct datasets (Data1, Data2, and Data3) and trained both ResNet50+ArcFace and ResNet50 models on each dataset. As shown in Table 4, the ResNet50+ArcFace model consistently achieved higher Top1 accuracy than the standard ResNet50 across all test datasets, with performance improvements of 0.6%, 1.5%, and 1.6%, respectively. These results demonstrate the superior performance of the ResNet50+ArcFace algorithm on all three datasets. We attribute this enhancement primarily to the more effective ArcFace loss function.
# A Generic Automated Surface Defect Detection Based on a Bilinear Model
## 3 Experiments
### Datasets Description
#### 3.2.1 DAGM_2007 Defect Dataset  
The first open dataset is the DAGM_2007 surface defect dataset, which is manually generated and can be used for surface defect detection. The dataset contains six types of surface defects with different textures, where in each of these defects has 1000 defect-free and 150 defective grayscale images, the sizeof the image is \(512\times 512\) pixels and the pixel precision is 8 bits. The ground truths of all defective images are provided in the dataset. Examples of defect images are shown in Figure 4.
## 4 Proposed Method and Problem Formulations
### Problem Formulations
CG data-sets are available in Appendix A */
1  Input: G \(\leftarrow\) net_data, G \(\leftarrow\) cg_data, foreign gateway FG, controller_capacity \(\leftarrow\) 1.1 /* controller capacity is considered to be 1.1 million requests per second */
2 Issep_sp_lists \(\leftarrow\) LSSP(G,FG) // Algorithm 1 returns the list of routers in LSSP
3 G \(\leftarrow\) CG_data // load customer gateways and attach to network graph G
4 R \(\leftarrow\) median_router(Issp) // median router is identified from LSSP based on Equation (2) RSS \(\leftarrow\) migrate(R) // this function migrates the legacy router R into a SoIDP6-capable switch
5 add_link(rss,bgp) // this attaches the root source switch (RSS) to the ONOS/BOP speaker (_lgp_) (border gateway protocol) speaker
6 forsp_routers in sp_lists do
7 R \(\leftarrow\) median_router(sp_routers) // median router identification per shortest path
8 BFT_list \(\leftarrow\) BFTravel(sp_routers,R) /* function to implement breadth-first traversal starting search from median router R, and returns the ordered list into BFT_list */
9 forrouter in BFT_list do
10 ifrouter is not SoIDP6 capable then
11 migrate(router) // legacy router becomes a SoIDP6-capable switch
12 sdn_switches \(\leftarrow\) get_switches(G) // retrieve SoIDP6-capable switches
13 sw_latency \(\leftarrow\) 0.0 // initializes switch latency
14 fors in sdn_switches do
15 sw_latency+ = \(\Delta_{s,c}\) /* propagation latency from switch \"y\" to controller \"C\" (i.e., bgp) is cumulatively added */
16 store_switch_latency(sw_file_name) /* this stores the switch numbers and latency records in a data file */
17 switch_gws \(\leftarrow\) find_gateways(G) /* once a switch is migrated, the directly attached router is transformed into BOP peer gateways. This function identifies the BOP peer gateways. Ref. Equation (6) */
18 gw_latency \(\leftarrow\) 0.0 // initialize gateway latency
19 forg in switch_gws do
20 gw_latency+ = \(\Delta_{g,c}\) /* propagation latency from gateway \"g\" to controller \"g\" i.e., bgp is cumulatively added */
21 store_gateway_latency(gw_file_name) /* this stores the gateway numbers and latency records in a data file */
22 controller_load \(\leftarrow\)control_switch_gateway(s,g) /* this function records the total load to the controller by switches and gateways */
23 store_control_load(control_load_file) // stores controller load records into a file
24 ifcontroller_load \(>\) controller_capacity then
25 notify_controller_addition() // raises warning of controller overloading
26 forR in G do
27 migrate(R) // migrates all stub routers to SoIDP6-capable switches
28 record_latency(S,gw) /* this function records the latency to corresponding file due to upgraded stub routers and gateways */
```  
**Algorithm 2**Network migration implementation and controller placement with breadth-first router replacement (BFR)
## 6 Application of Algorithm to Various Scenarios for Fault Identification: Case Studies
### Effect of Noise  
Power system distribution and transmission lines pass through the terrain where the possibility of interference from external factors is high. This may introduce noise which might affect the performance of the protection schemes and result in false tripping. Hence, the algorithm was tested for the identification of all types of faults by adding a noise level of 10 dB signal to noise ratio (SNR) to the current signals. The results of the phase-A to ground fault are detailed in Figure 11. The WD-index corresponding to phase-A has a high magnitude during the incidence of a fault, as depicted in Figure 11b, whereas the magnitude of this index corresponding to phases-B & C is low. Figure 11c illustrates that the ALN-index has a high magnitude corresponding to all the phases at the time of fault incidence. Figure 11d illustrates that the fault index corresponding to the faulty phase (phase-A) is high compared to the threshold, whereas this FI has a lower magnitude than the threshold corresponding to the healthy phases (phase-B & C). Hence, the algorithm effectively detects the AG fault and also identifies the nature of phases in the presence of a high level noise of 10 dB SNR.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Phase Name** & \multicolumn{4}{c}{**Fault Index Magnitude**} \\ \hline  & \(Y-Y\) & \(Yg-Yg\) & \(Y-\triangle\) & \(\triangle-\triangle\) & \(Yg-\triangle\) \\ \hline Phase-A & \(3.9\times 10^{8}\) & \(2.6\times 10^{8}\) & \(3.1\times 10^{8}\) & \(2.5\times 10^{8}\) & \(2.4\times 10^{8}\) \\ \hline Phase-B & \(7.2\times 10^{4}\) & \(2.4\times 10^{3}\) & \(9.1\times 10^{3}\) & \(1.9\times 10^{3}\) & \(1.5\times 10^{3}\) \\ \hline Phase-C & \(2.5\times 10^{4}\) & \(1.7\times 10^{3}\) & \(7.4\times 10^{3}\) & \(1.4\times 10^{3}\) & \(1.1\times 10^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fault index with different types of transformer windings (AG fault).  
Figure 11: Effect of noise (10 dB SNR) on the detection of phase-A to ground fault on node 671 (**a**) Current waveform (**b**) WD-index (**c**) ALN-index (**d**) Fault index.
# TCCCD: Triplet-Based Cross-Language Code Clone Detection
## 7 Discussion
### Effectiveness of TCCCD on a Small Dataset  
We conducted an effectiveness analysis of TCCCD on datasets of various sizes, including 10%, 30%, 50%, 70%, and 90% of the original data, and investigated the impact of dataset size on its results. Figure 3 shows the results of the effectiveness of TCCCD on a small dataset. When using 70% of the original data, TCCCD's precision, recall, and F1-score tended to stabilize. On the reduced dataset containing 30% of the original data, its effectiveness was slightly lower than that on the full dataset, but still surpassed the effectiveness of existing baseline methods.  
When evaluating on a small-scale dataset containing only 10% of the original data, TCCCD achieved relatively lower effectiveness with a precision, recall, and F1-score of 0.95, 0.91, and 0.88, respectively. However, as the training data were increased to include 30% of the original data, the precision remained unchanged, while the recall and F1-score both improved, reaching 0.90 and 0.92, respectively. This indicated that TCCCD can maintain a high level of effectiveness and recall even with reduced data, highlighting its robustness and generalization capability on small-scale datasets.  
When the data were further increased to include 70% of the original data, the method's precision, recall, and F1-score became relatively stable. It is worth noting that our approach's precision showed relatively stable effectiveness across various percentages of the original dataset. The F1-score gradually improved as the dataset percentage increased from 10% to 50%, while the recall exhibited a significant improvement in two ranges: from 10% to 30% and from 50% to 70% of the original dataset.  
Figure 3: Effectiveness on different dataset percentages.  
The ability of TCCCD to perform well on small-scale datasets is crucial. This indicates that our approach does not excessively rely on large amounts of data, making it applicable in scenarios where data collection resources are limited or challenging. Furthermore, the consistent effectiveness on the reduced datasets demonstrated that TCCCD remained a reliable solution even in situations with limited data availability.  
Overall, these results demonstrated the efficacy and robustness of TCCCD, making it a promising approach for cross-language code clone detection even in resource-constrained environments.
# GPU Algorithms for Structured Sparse Matrix Multiplication with Diagonal Storage Schemes +
## 3 Storage Schemes  
In this section, we define the terminologies and notations used throughout the paper. Also, we discuss the storage schemes used in this work.  
Let \(A\in\mathbb{R}^{n\times n}\) be a banded matrix, where \(k_{l}\) and \(k_{u}\) are lower and upper bandwidths, respectively. Here, \(a_{ij}=0\) if \(i-j>k_{l}\) or \(j-i>k_{u}\), where \(a_{ij}\) refers to an element of matrix \(A\) and \(i\), \(j\) denote the row index and column index of matrix \(A\), respectively. Note that both indices \(i\) and \(j\) start with 0. Define \(\overline{A_{k}}=\left\{a_{ij}|j-i=k\right\}\) and \(\underline{A_{k}}=\left\{a_{ij}|i-j=k\right\}\) as the \(k^{th}\) superdiagonal and \(k^{th}\) subdiagonal of \(A\), respectively.  
Figure 1(a) displays a banded matrix where \(k_{u}=2\) and \(k_{l}=1\). Figure 1(b) displays the two-dimensional array representation used in BLAS (Basic Linear Algebra Subroutine), where \(*\) represents a placeholder that is neither initialized nor referenced by any routine. The diagonals are placed in the following order: superdiagonals (i.e., \(\overline{A_{2}}=[a_{02},a_{13}]\) and \(\overline{A_{1}}=[a_{01},a_{12},a_{23}]\)), main diagonal (i.e., \(A_{0}\)), and subdiagonals (i.e., \(\underline{A1}=[a_{10},a_{21},a_{32}]\)), preserving the column indexing. Figure 1(c) presents a variant of banded matrix storage by diagonals, where the nonexisting elements are pushed to the right such that the column indexing in the original matrix is not preserved.  
Unless \(k_{l}+k_{u}<<n\), the superfluous padding required along with the nonzero entities of super- and subdiagonals will outweigh the benefit of these compact storage schemes.  
Not only does the data structure become more complicated due to the additional storage needed for the nonexisting elements, but accessing the true matrix elements requires testing for sentinels, thereby fragmenting the control flow. This latter issue can become a significant performance bottleneck in contemporary many-core computer architectures.
# PM2.5 Concentration Prediction Based on CNN-BiLSTM and Attention Mechanism
## 3 Experiment
### Dataset and Preprocessing  
The dataset consists of 35,064 climate and pollution records from between January 2013 and February 2017 in Shunyi District. It covers 13 variables given in Table 1, including PM2.5 concentration, PM10, SO\({}_{2}\), NO\({}_{2}\), CO, O\({}_{3}\), temperature (TEMP), air pressure (PRES), dew point temperature (DEWP), rainfall (RAIN), wind speed (WDSP), year, and month. Meanwhile, the mean interpolation is adopted to fill in the missing values of the dataset for improving its data quality. Variables of the dataset are given in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories** & **Input Variables** & **Unit** \\ \hline Pollutant & PM2.5 & \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\) \\ Climate Variables & PM10 & \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\) \\  & SO\({}_{2}\) & mg/\(\mathrm{m}^{3}\) \\  & NO\({}_{2}\) & mg/\(\mathrm{m}^{3}\) \\  & CO & mg/\(\mathrm{m}^{3}\) \\  & O\({}_{3}\) & mg/\(\mathrm{m}^{3}\) \\  & TEMP & \({}^{\circ}\)C \\  & PRES & KPa \\  & DEWP & \({}^{\circ}\)C \\  & RAIN & mm \\  & WDSP & km/h \\ \hline Time Variables & Year & - \\  & Month & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variables contained in the dataset.  
Figure 4: The process of the attention mechanism.  
The horizontal ordinate in Figure 5 represents the time in hours from 1 January 2013 to 28 February 2017, and the longitudinal coordinate represent the value of each variable. It can be seen clearly that these variables have obvious periodicity. Therefore, year and month are also chosen as features for achieving higher accurate prediction of PM2.5 concentration. To further determine the period, the distribution of PM2.5 concentration for 30-day and 10-day are displayed. It can be seen from Figure 6 that there are three obvious peaks in the 30-day PM2.5 concentration distribution, and there is one peak in the 10-day graph, so 10 days are determined as a cycle.  
Since the variables used in predicting the PM2.5 concentration have different dimensions, the value of PM2.5 concentration ranges from 2 to 900, and the value of WDSP ranges from 0 to 13. If these data are directly used as the input of the neural network, large  
Figure 5: Distribution of different variables.  
Figure 6: The trend of PM2.5 concentration.  
deviations will affect the results. Therefore, the min-max method, given in Equation (5), is used to make the data more concentrated. Additionally, in the experiment, 80%, 10%, and 10% of the dataset are used as the training set, validation set, and testing set of the proposed model, respectively.  
\[x=(x-min)/(max-min) \tag{5}\]