esa-sceva/satcom-chunk-collection huggingface dataset:
# Image-Based Malicious Network Traffic Detection Framework: Data-Centric Approach
## 3 Proposed Detection Framework
### Data Preprocessing and Feature Engineering
#### 3.3.3 Information Gain-Based Feature Selection
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Rank** & **Feature Name** & **LG Value** \\ \hline
1 & src\_bytes & 0.7814411 \\
2 & dst\_bytes & 0.6248539 \\
3 & service & 0.6197358 \\
4 & flag & 0.4727718 \\
5 & diff\_srv\_rate & 0.4604546 \\
6 & same\_srv\_rate & 0.4500181 \\
7 & dst\_host\_srv\_count & 0.4269103 \\
8 & dst\_host\_same\_srv\_rate & 0.390421 \\
9 & dst\_host\_diff\_srv\_rate & 0.3721759 \\
10 & logged\_in & 0.3601069 \\
11 & count & 0.3390986 \\
12 & dst\_host\_server\_rate & 0.3373162 \\
13 & error\_rate & 0.3313643 \\
14 & dst\_host\_srv\_server\_rate & 0.3287723 \\
15 & sr\_server\_rate & 0.3114433 \\
16 & dst\_host\_srv\_diff\_host\_rate & 0.2479778 \\
17 & dst\_host\_count & 0.1850297 \\
18 & dst\_host\_same\_src\_port\_rate & 0.1840325 \\
19 & srv\_diff\_host\_rate & 0.1400392 \\
20 & dst\_host\_srv\_rerror\_rate & 0.0910595 \\
21 & error\_rate & 0.0786418 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The information gain values of Nsl-Kdd features. The information gain results on the NSL-KDD dataset reveal that traffic volume features (_src_bytes_ and _dst_bytes_) and connection behavior features (_service_ and _flag_) provide the most discriminative power. This aligns with known attack signatures, where abnormal data transfer sizes and unusual service usage patterns often indicate malicious activity. Features with IG values near zero contribute negligible information for classification, and their removal reduces noise while improving computational efficiency. We removed the bottom five features with the smallest IG values to reduce the number of data dimensions, resulting in the remaining 36 features. The reason why we selected 36 features is to convert the final generated feature vector to a 6 \(\times\) 6 square image with 36 pixels through the data preprocessing process. Algorithm 1 summarizes the overall process of the data preprocessing and feature engineering module. This systematic preprocessing pipeline includes categorical feature transformation using label encoding to prevent dimensionality expansion, logarithmic normalization to mitigate bias from varying feature scales, and dimensionality reduction via information gain-based feature selection. As a result, the raw NSL-KDD dataset is effectively converted into a compact and informative 36-dimensional numeric feature vector optimized for subsequent grayscale image generation.
# Performance Analysis of FSO-UWOC Mixed Dual-Hop Relay System with Decode-and-Forward Protocol
## 2 System and Channel Modeling  
A mixed dual-hop FSO-UWOC transmission system experiencing hybrid fading on each propagation link is depicted in Figure 1. The system comprises three nodes: the transmitter node S, the relay node R, and the destination node D. The transmitter node S communicates with the underwater destination node D via a surface relay node R employing a DF relay protocol. The source signal at S is transmitted through the FSO link to node R, where it is decoded and reconstructed before being transmitted through the UWOC link to the destination node D. In the channel modeling process, both the FSO and UWOC links take into account the composite impacts of path loss, zero-boresight pointing errors, and turbulence. This is designed to offer a more comprehensive and accurate analysis for the mixed dual-hop system's performance.  
The received signal model at relay R can be expressed as \(y_{SR}=\sqrt{P_{1}}h_{1}s+n_{1}\), where \(P_{1}\) is the transmitted power of the S-R link; \(h_{1}\) denotes the channel fading under the influence of atmospheric turbulence fading \(h_{a}\), zero-boresight pointing error \(h_{pa}\) and atmospheric path loss \(h_{l1}\), \(h_{1}=h_{a}h_{pa}h_{l1}\); \(s\) is the transmitted binary modulation symbol with average energy equal to 1; and \(n_{1}\) is a Gaussian random variable with a mean of 0 and a variance of \(\sigma_{1}^{2}\).  
Figure 1: FSO-UWOC mixed dual-hop transmission system.  
When the DF relay protocol is used for the R-D link, the received signal at the destination node D in the R-D link is \(y_{RD}=\eta\sqrt{P_{2}}h_{2}\cdot\delta+n_{2}\), where \(\eta\) is the photodetector conversion coefficient of the UWOC link, \(P_{2}\) is the transmit power of the link, channel fading \(h_{2}=h_{f}h_{p}h_{l2}\) is affected by a combination of underwater turbulence fading \(h_{f}\), zero-boresight pointing error \(h_{po}\) and path loss \(h_{l2}\), \(\delta\) represents the corresponding binary modulation and decoded reconstructed signal at node R, and \(n_{2}\) is a Gaussian random variable with a mean of 0 and a variance of \(\sigma_{2}^{2}\).
## 3 Results and Analysis
### Spatial Variability of Soil Nutrients  
#### 3.3.1 Geostatistical Analysis of Soil Nutrients  
The semi-variogram parameters for the soil nutrient fitting model in Fengtai County are detailed in Table 4. The nugget value, C\({}_{0}\), represents the variability arising from random factors. In contrast, the sill value (C\({}_{0}\) + C) captures the impact of structural factors on the indices. The ratio C\({}_{0}\)/(C\({}_{0}\) + C), termed the nugget-to-sill ratio, quantifies the relative contribution of random variability to the total variability [30]. A nugget-to-sill ratio in the range of 0-25% indicates a strong spatial correlation, signifying the minimal influence of randomness on the index. When the ratio lies between 25 and 75%, it reflects a moderate spatial correlation, suggesting that both random and structural factors contribute to the index's variability. Conversely, a ratio spanning 75-100% denotes a weak spatial correlation, with the index predominantly being influenced by randomness [31]. Analysis reveals that the available potassium in Fengtai County's soil has a moderate spatial autocorrelation. This underscores that its spatial variability stems from both structural elements, such as parent material and topography, and random influences like human activities and agricultural methods. On the other hand, other nutrient indices like pH, organic matter,  
Figure 5: Spatial distribution of various soil nutrients in Fengtai County.  
Total N, alkali-hydrolyzed N, Olsen-P, and slowly released K mainly exhibit weak spatial autocorrelations, implying their variations are primarily driven by random factors. The range, which signifies the spatial correlation scale, delineates the span of spatial variability of the variables under study. For the seven nutrient indices, the spatial correlation scales, in decreasing order, are alkali-hydrolyzed N > Olsen-P > organic matter > total N > slowly released K > available K > pH. Their respective scales are 777 m, 756 m, 462 m, 415 m, 237 m, 199 m, and 165 m. Notably, alkali-hydrolyzed N exhibits the broadest range, signifying a weaker spatial autocorrelation within its 777 m radius. In contrast, pH has the most confined range, indicating that local environmental factors predominantly drive its spatial variability on a more restricted scale.
## 4 Factors to Study in VANETs
### Mobility Model  
One of the challenges posed by the study of VANETs is the definition of a vehicular mobility model [30] providing an accurate and realistic vehicular mobility description at both macroscopic and microscopic levels [31]. To perform realistic simulations, it is especially important that the chosen mobility generator is able to obtain a detailed microscopic traffic simulation by importing network topologies from real maps. Our mobility simulations are performed with SUMO [32], an open source traffic simulation package that has interesting microscopic traffic capabilities, such as collision free vehicle movement, multi-lane streets with lane changing, junction-based right-of-way rules, traffic lights, _etc_. SUMO can also import roadmaps directly from map databases such as OpenStreetMap [33] and TIGER [34].  
Our mobility simulations account for areas with different vehicle densities. In a real town, traffic is not uniformly distributed; there are downtowns or points of interest that may attract vehicles. Hence, we include the ideas presented in the _Downtown Model_[35] to add points of attraction in realistic roadmaps.  
To generate the movements for the simulated vehicles, we used two different mobility models available in SUMO: (i) the [PERSON] mobility model [36] with some modifications to allow multi-lane behavior [37]; and (ii) the [PERSON] mobility model [38]. The Krauss model is based on collision avoidance among vehicles by adjusting the speed of a vehicle to the speed of its predecessor using the following formula:  
\[v(t+1)=v_{1}(t)+\frac{g(t)-v_{1}(t)\tau}{\tau+1}+\eta(t) \tag{12}\]  
where \(v\) represents the speed of the vehicle in m/s, \(t\) represents the period of time in seconds, \(v_{1}\) is the speed of the leading vehicle in m/s, \(g\) is the gap to the leading vehicle in meters, \(\tau\) is the driver's reaction time (set to 1 second in our simulations) and \(\eta\) is a random numeric variable with a value between 0 and 1.  
The Wagner model, unlike most driving models that assume an instantaneous or even delayed reaction of the driver to the surrounding situation, considers two important features of human driving and of human actions in general. Firstly, humans usually plan ahead, and secondly, the type of control that humans apply is not continuous, but discrete in time: they act only at certain moments in time. These specific moments are known as action-points.
## 2 Methodology  
### System Description  
From 2014-2018, nine SD-GHPs (SG-1\(\sim\)9) and eight MD-GHPs (MG-1\(\sim\)8) applied for space heating have been field tested and monitored. Table 1 lists the basic information on those projects. The total space heating area is 1.13 million m\({}^{2}\), where 0.61 million m\({}^{2}\) are equipped with SD-GHPs, and 0.52 million m\({}^{2}\) are equipped with MD-GHPs. The building functions include 11 residential projects and six commercial projects, such as schools, office buildings, hospital and mall.  
Figure 1 shows the typical system diagram of heat pump systems applied for space heating and its measuring points. It can be seen that the system forms of the SD-GHPs and MD-GHPs are nearly the same, and the main differences exist in the GHEs. As for the SD-GHPs, the U-type ground heat exchangers are mainly applied. However, for the MD-GHPs, since the depth of GHEs reaches more than 2000 m with higher pressure and greater construction difficulties, the enclosed coaxial DBHEs are commonly used in practical application, and U-type GHEs are still under study [21]. As shown in Figure 2, the DBHE is composed of an inner tube and outer tube. During the operation, the heat transfer medium flows down to the DBHE through the outer tube and extracts heat from soil and rocks around. Then it flows upward through the inner tube, while the heat is transferred to the medium in the outer tube, since the thermal resistance of the inner tube is not high enough [16]. As for the heat transfer medium, water is applied in MD-GHPs, while glycol solution is commonly applied in SD-GHPs for anti-freezing.  
\begin{table}
\begin{tabular}{c} 2 heating \\ seasons \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information on field test systems.
# Investigation of Microseismic Monitoring of and Precursor Information on Roof Collapse
## 4 Data Analysis and Discussions
### Case Study  
Between May and August 2019, a significant number of MS incidents were detected near the surface, leading to roof collapse accidents. Figure 5 illustrates the spatiotemporal evolution of the MS event distribution above 530 m during this period. By analyzing the range of roof collapse and surface fractures, the collapse boundary was determined. According to the monitoring results, in May 2019, the overlying goals of the 30# and 32# ore bodies had not yet connected. Roof fractures occurred within a certain range between 8 May and 30 May and again from 31 May to 10 June. However, the extent of the roof fractures was smaller than the previous occurrence, suggesting an impending connection. MS events near the surface exhibited significant concentration, propagating from the middle of the 30# ore body toward the 32# collapse area and east. In July 2019, the two goaf areas gradually connected, leading to further upward collapse of the roof.  
Figure 4: Typical MS cluster categories. (**a**) Phase I; (**b**) Phase II; (**c**) Phase III; (**d**) field crack pattern. Different colors represent different clusters.  
From Figure 5e, it is evident that after the roof crack penetration in 2019, MS events occurred around the penetrated crack, particularly at the 980 m surface location. The mining activity caused the original goat of the 30# ore body to be influenced by the mining of the 32# ore body, leading to gradual crack expansion. This resulted in the connection of the 32# roof with the goat, subsequently impacting the surface. As mining operations continue, surface cracks will continue to deepen and expand. Therefore, further assessment and treatment of crack zoning within the 980 m elevation area are necessary.
# An Innovative Enhanced JAYA Algorithm for the Optimization of Continuous and Discrete Problems
## 3 Research Methodology (Proposed Algorithm)
### Standard Version of JAYA Algorithm
#### 3.1.1 First Step: Setting the Parameters  
Undoubtedly, there are certain parameters associated with each algorithm that require tuning. These parameters include lower and upper bounds (LX and UX), decision variables (DVs), the maximum cycle repetition (MI), and the size of the population (PS).
## 2 Tracking Error Model of Azimuth Axis Tilt Influence
### Tracking Error Model of the SDC System
\right]^{\mathrm{T}} \tag{10}\]  
\[\mathrm{N_{f2}}=\mathrm{N^{\prime}}_{\mathrm{f2}}\cdot\mathrm{Rot}(\mathrm{N_{ e2}},\beta) \tag{11}\]  
To facilitate the derivation, let \(\mathrm{N_{e2}}=[\mathrm{d_{1}},\mathrm{d_{2}},\mathrm{d_{3}}]\) and \(\mathrm{N^{\prime}}_{\mathrm{f2}}=[\mathrm{e_{1}},\mathrm{e_{2}},\mathrm{e_{3 }}]\), substitute them into Equation (11), and then simplify to obtain Equation (12):  
\[\mathrm{N_{f2}}=\left[ \begin{array}{c}(\mathrm{e_{1}}-\mathrm{c_{1}})\cdot \mathrm{cos}\beta-(\mathrm{e_{2}}\mathrm{d_{3}}-\mathrm{e_{3}}\mathrm{d_{2}}) \cdot\mathrm{sin}\beta+\mathrm{c_{1}}\\ (\mathrm{e_{2}}-\mathrm{c_{2}})\cdot\mathrm{cos}\beta+(\mathrm{e_{1}}\mathrm{ d_{3}}+\mathrm{e_{3}}\mathrm{d_{1}})\cdot\mathrm{sin}\beta+\mathrm{c_{2}}\\ (\mathrm{e_{3}}-\mathrm{c_{3}})\cdot\mathrm{cos}\beta-(\mathrm{e_{1}}\mathrm{ d_{2}}-\mathrm{e_{2}}\mathrm{d_{1}})\cdot\mathrm{sin}\beta+\mathrm{c_{3}}\end{array}  \right]^{\mathrm{T}} \tag{12}\]  
In Equation (12), \(\mathrm{c_{1}}\), \(\mathrm{c_{2}}\), and \(\mathrm{c_{3}}\), respectively, are:  
\[\mathrm{c_{1}}=\mathrm{e_{1}}\mathrm{d_{2}^{2}}+\mathrm{e_{2}}\mathrm{d_{1}} \mathrm{d_{2}}+\mathrm{e_{3}}\mathrm{d_{1}}\mathrm{d_{3}}\]  
\[\mathrm{c_{2}}=\mathrm{e_{2}}\mathrm{d_{2}^{2}}+\mathrm{e_{1}}\mathrm{d_{1}} \mathrm{d_{2}}+\mathrm{e_{3}}\mathrm{d_{2}}\mathrm{d_{3}}\]  
\[\mathrm{c_{3}}=\mathrm{e_{3}}\mathrm{d_{3}^{2}}+\mathrm{e_{1}}\mathrm{d_{1}} \mathrm{d_{3}}+\mathrm{e_{2}}\mathrm{d_{2}}\mathrm{d_{3}}\]  
(3) The angle between the sun ray vector \(\mathrm{N_{s}}\) and the focal axis vector \(\mathrm{N_{f2}}\), i.e., the SDC system tracking error, can be calculated by Equation (14):  
\[\mathrm{e_{all}}=\arccos(\mathrm{N_{s}}\mathrm{N_{f2}}) \tag{14}\]
# Development of a GNSS Buoy for Monitoring Water Surface elevations in Estuaries and Coastal Areas
## 2 Methodology
### Determination of Directional Wave Spectra Using ATC Data  
Equation (B9) in Appendix B is used to calculate the directional wave spectrum from the ATC data. In the equation, the power spectral density (PSD) of the water surface elevation is estimated using the method described in Appendix A. The coefficients in the directional spreading function are calculated based on the formulas provided by [PERSON] [1]. The nearly vertical acceleration, pitch, roll, and azimuth of the ATC sensor were utilized in the analysis.
## 3 Results and Discussion
### Classification Results and Model Performance  
Building upon the optimal DDE + ESM2 feature combination, we employed a LambdaRank-based feature selection strategy to further refine the feature set and enhance model performance. This advanced selection process identified a highly informative subset of features, effectively reducing dimensionality, mitigating the risk of overfitting, and improving model generalizability. Subsequently, we trained our final XGBoost classifier, previously identified as the top-performing algorithm, on this optimized feature subset. Figure 6 illustrates the relationship between the number of selected features and the corresponding classification accuracy.  
The final optimized feature set, derived from the DDE and ESM2 embeddings, resulted in a **216-dimensional** input vector for the XGBoost classifier. To rigorously assess the model's robustness and generalizability, we performed 10-fold cross-validation on the training set. The model achieved an impressive average accuracy of 96.52% across the 10 folds, with a peak accuracy of 97.26% in 1 fold. This consistently high performance across different data partitions underscores the model's strong ability to generalize to unseen data, indicating its robustness and reliability.  
The performance of our final model, named PLM-FRP, was validated on the independent test set. As shown in Table 3, PLM-FRP achieved a remarkable accuracy of 96.09%, significantly surpassing the performance of existing FRP prediction methods.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Sn (\%)** & **Sp (\%)** & **MCC** \\ \hline [PERSON] et al. (2010) & 91.52 & 92.61 & 90.43 & 0.831 \\ DDE + ESM2 & 94.78 & 95.22 & 94.35 & 0.896 \\ PLM-FRP (Optimized) & **96.09** & **96.52** & **95.65** & **0.922** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of various methods using traditional features and feature selection strategies.  
Figure 6: The accuracy of selected features using the XGBoost classifier.  
These exceptional results, corroborated by the high accuracy in 10-fold cross-validation, highlight the power of combining PLM embeddings with a robust feature selection strategy to achieve state-of-the-art performance in FRP prediction. This approach not only improves prediction accuracy but also yields a more focused and interpretable model, enabling a deeper understanding of the features driving the prediction.
## 5 Experiments and Results
### Dataset Description  
The NGSIM dataset, comprising public vehicle trajectory data, is widely utilized for research on traffic flow and vehicle movements models [38]. As our study objective focuses on urban intersection scenes, data from Lankershim Boulevard in Los Angeles, CA, and Peachtree Street in Atlanta, GA, in the NGSIM are spontaneously chosen for training and verifying the proposal we develop, as shown in Figure 6. The size of the used dataset is 10,329 frames collected at 10 HZ with a total of 30 min of samples available. The dataset involves two time periods (12:45 P.M. to 1:00 P.M. and 4:00 P.M. to 4:15 P.M.), including scenarios with high and sparse vehicle density. Table 1 illustrates some of the data fields and explanations. In addition to the typical vehicle trajectory attribute, the dataset also contains signal phase information for each interaction, which greatly benefits our experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial No.** & **Data Fields** & **Explanations** \\ \hline
1 & Vehicle\({}_{ID}\) & Vehicle ID \\
2 & Local\({}_{X}\) & X value in regional coordinate frame \\
3 & Local\({}_{Y}\) & Y value in regional coordinate frame \\
4 & v\({}_{Length}\) & Vehicle length \\
5 & v\({}_{Width}\) & Vehicle width \\
6 & v\({}_{Gel}\) & Vehicle velocity \\
7 & v\({}_{Acc}\) & Vehicle acceleration \\
8 & Lane\({}_{ID}\) & The lane number of the vehicle \\
9 & Int\({}_{ID}\) & The number of intersection \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of key elements in the dataset.  
Figure 5: Future potential vehicle trajectory prediction.
## 3 Results and Discussion
### Delamination Factors and Optimization Process  
Three types of delamination factors, including Fd, Fa, and SF, were considered in this study and the optimization of each parameter will be explained separately. The drilling process of a CFRP composite was optimized with different feed rates and rotational speed levels and two angles of layers of the composite were considered to minimize the maximum drilling force, one- and two-dimensional delamination factors, and to maximize the shape factor.
# MSLCFinder: An Algorithm in Limited Resources Environment for Finding Top-\(k\) Elephant Flows
## 2 Background and Related Work
### Review of Related Research
#### 2.2.2 Top-\(k\) Flows Finding Technology  
Although the probability of top-\(k\) comes from the discovery of frequent items in massive data in the database research field, the top-\(k\) lookup in the network environment differs from the task in the database. The top-\(k\) elephant flow identification method has two basic strategies: _count-all_ and _admit-all-count-some_[7], but the core problem is still elephant flow detection, including heavy hitter [37] detection and heavy change [37,38] detection. Table 1 summarizes the research on top-\(k\) elephant flow identification methods in related work.  
Due to memory limitation, hash conflicts will inevitably occur in processing massive data, making the measured value more extensive than the actual value. Research has found that the statistical error in the sketch can be effectively reduced by separating the elephant and mouse flows.  
Cold Filter [39] has designed a two-layer sketch structure. When data packets arrive, it first uses the first layer of the sketch to filter out most of the mouse flows, and then sends the filtered elephant flows to the second layer of the sketch for storage. This classified statistical method reduces the super error between elephant flows and mouse flows and also reduces the false positives in the statistical results. HeavyKeeper [7] uses the attenuation formula to make the elephant flows decay slowly, and the mouse flows violently decay to screen out the elephant flows. HeavyGuardian [40] divides each bucket in the hash table into a heavy part and a light part. The heavy part stores key-value pairs of multiple elephant flows, and the light part only stores the count values of mouse flows. High measurement accuracy is achieved with limited memory. The ActiveKeeper [41] uses a two-mode active counter to record flow sizes, reducing memory usage while ensuring high accuracy. ActiveKeeper also avoids the waste of mouse flow on the counter. The FastKeeper [42] can identify large real-time flows with a primary goal of simultaneously achieving low overhead, high performance, and high accuracy. FastKeeper employs a sliding-window-based algorithm for accurate measurement of real-time flow rates and a bitmap-voting algorithm for the timely replacement of flows that have become small in the measurement data structure. [PERSON] et al. [43] presented a universal online lightweight sketch named ActiveCM + for tracking heavy hitters, which ensures that per-packet overhead is a small constant (four hash and four memory accesses) in the worst case, making it much more suitable for online operations, especially for pipeline implementation.  
In summary, those researchers mainly studied the method of separating elephant and mouse flows to optimize the counters counting or reduce the collision probability of the hash function.