esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Dataset Description and Characterization  
The dataset employed in this study originated from the DEFCON Capture The Flag 22 event [19], recognized as one of the most prestigious and technically demanding cybersecurity competitions worldwide. Held in Las Vegas from 8 August to 10 August 2014, the event featured 20 finalist teams from countries such as the United States, Taiwan, South Korea, China, Russia, Australia, Germany, Japan, France, Denmark, and Poland, participating in a traditional attack-defense format. This competitive and high-pressure environment enabled the collection of full network traffic, later released in PCAC (packet capture) format, facilitating in-depth forensic analysis. Each team was assigned a distinct CIDR subnet and tasked with defending a designated.2 server IP address within their range. The vulnerable services deployed in the competition included  
* eliza: a text-based apce economy simulation game;
* wdub: a web service;
* justify: a constraint solver;
* imap: an email server based on the IMAP protocol. The CTF infrastructure was carefully designed to test both offensive and defensive capabilities, requiring teams to secure their own services while simultaneously attempting to exploit vulnerabilities in their opponents' infrastructure. The dataset was composed of 272 game rounds, each lasting 5 min, capturing bidirectional traffic in libpcap format, including both packet headers and full payloads. Unlike typical network environments where benign traffic predominates, this dataset was uniquely characterized by a high proportion of malicious traffic, given the adversarial nature of the CTF scenario. Additionally, embedded within the traffic were 26-character validation tokens--comprising letters and digits--which teams were required to extract in order to score points. Table 1 provides an overview of the participating teams, detailing their assigned subnets, defended servers, and the total number of alerts generated during the competition. According to [PERSON] et al. (2018) a detailed analysis of network traffic and team behaviors during the DEFCON CTF revealed key insights into the strategies that contributed to varying levels of team performance. In terms of offensive tactics, the most successful teams, such as PPP, HITCON, and Blue-Lotus, implemented automated systems to launch exploits at regular intervals. This approach likely enabled a consistent pace of attacks while allowing human team members to focus on tasks requiring adaptive decision-making. Furthermore, high-performing teams exhibited superior situational awareness by dynamically modifying their exploits during the competition in response to evolving defensive strategies. Exploitation rates among teams varied widely, ranging from fewer than 30 to over 400 exploits per round. However, this variation did not show a direct correlation with final rankings. Defensive strategies also played a critical role. Teams that ranked in the top five managed
## 3 SRB Impact on Environments and Humans
### The Relationship between Iron Corrosion and SRB Presence  
Another material that corrodes as a result of SRB action is metallic iron (Fe\({}^{0}\)), including as a component of steel. Iron corrosion in the presence of SRB is accelerated [53]. Corrosive SRB prefer the direct consumption of electrons derived from iron than from H\({}_{2}\)[78; 79]. According to this theory, the iron in contact with water loses positive ions and thereby is polarized. In an anoxic environment, the released electrons then combine with protons dissociated from water to form H\({}_{2}\). This remains in equilibrium on the metal surface. However, this equilibrium is perturbed by SRB utilizing H\({}_{2}\), resulting in a driving of the reaction instead toward further metal oxidation. Other possible corrosion mechanisms have been proposed. More than a decade ago, a study suggested that bacterial strains that use iron as a direct electron donor without a hydrogen intermediate can also be involved in corrosion [80; 81]. These strains include species later called _Desulfovibrio ferrophilus_ and _Desulfopila corrodens_. Electrons are also gained through the formed crust of iron sulfate (FeS) and ferrous carbonate (FeCO\({}_{3}\)), and they form a conductive mixture. Since SRB damage worldwide pipelines and result in hundreds of millions of dollars lost annually, bacteriophage treatment of SRB can reduce these costs significantly. It is well known that corrosion control is a big economic issue [82].
## 3 Experimental and Simulation Setup
### Experimental Setup
#### 3.1.1 Network Architecture  
The network architecture encompassed advanced components in both the RSU and OBU (see Figure 2). The RSU featured a General Purpose Compute Unit (GPCU) with an Intel Xeon E5-2620 processor, 32 GB RAM, and a 1 TB SSD. In addition, it included a Cohda MK5 RSU and Cohda MK6c EVK for IEEE 802.11p and Cellular V2X (C-V2X) connectivity, both equipped with built-in Global Navigation Satellite System (GNSS) receivers. A Universal Software Radio Peripheral (USRP) N310 Software-Defined Radio (SDR) facilitated high-speed data transfer with multiple antennas and a wide frequency range, while a Septentrio AsteRX-m2 ensured high-precision GNSS reception.  
Figure 1: 5G testbed at the University of Antwerp Campus Groenenborger in Antwerp.  
Figure 2: RSU architecture.  
Similarly, the OBU incorporated an Intel Nuc 7i7 DNKE with an Intel Core i7-8650U processor, 8 GB RAM, and a 250 GB SSD. The Cohda MK5 OBU and Cohda MK6c EVK provided IEEE 802.11p and C-V2X connectivity, complemented by a USRP B210 SDR for flexible communication. An NVidia Jetson AGX Xavier contributed a robust GPU for processing tasks, enhancing the overall communication capabilities. Finally, the Septentrio AsteRX-m2a delivered high-precision GNSS data.
## 5 Experiments and Results
### Comparison of Proposed LR-TED Approach with the Static-Dictionary-Based Approach  
We independently evaluated all thirteen features used in the static approach and the newly added feature. The results are compared with the static-dictionary-based approach [14] for each disaster type and feature category. The F-Scores comparison of the LR-TED approach by adopting the ANN deep-learning model for classification and the F-scores of the dictionary-based approach is shown in Table 11. The category names are denoted as; \"Ew\" for eyewitness, \"Un\" for unknown, and \"NEw\" for non-eyewitness. It can be observed from Table 11 that [PERSON]'s approach achieved the best f-score of \"0.92\" for the earthquake type. The LR-TED approach, using the grammar rules, secured the best F-score of \"0.93\" for the same disaster type. Furthermore, the results for both eyewitness and non-eyewitness classes for earthquake events were higher than those produced by [PERSON]'s approach. However, this work eventually identifies eyewitness reports from tweet content without utilizing any static dictionary and human intervention.  
In the hurricane disaster category, LR-TED has shown a better F-score for eyewitnesses (\"0.60\") as compared to [PERSON]'s approach, which is \"0.51\". Similarly, low F-scores were observed for wildfire disasters by [PERSON]'s approach. Likewise, comparative results were observed for flood and wildfire disaster categories by the LR-TED approach. Overall, it is evident from the comparative results that the LR-TED approach generated competitive results for the eyewitness category but did not beat the results secured by [PERSON]'s approach in all categories. The reason for such results could be the training dataset, which contains a small number of tweets to train the model. To implement the concept of a generalized approach, such variations in the performance of the LR-TED approach are acceptable.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Earthquake**} & \multicolumn{3}{c}{**Flood**} & \multicolumn{3}{c}{**Hurricane**} & \multicolumn{3}{c}{**Wildfire**} \\ \hline  & **EW** & **Un** & **NEw** & **EW** & **Un** & **NEw** & **EW** & **Un** & **NEw** & **EW** & **Un** & **NEw** \\ \hline LR-TED & 0.93 & 0.07 & 0.75 & 0.45 & 0.51 & 0.65 & 0.60 & 0.36 & 0.80 & 0.26 & 0.55 & 0.86 \\ [PERSON]’s & 0.92 & 0.39 & 0.71 & 0.53 & 0.73 & 0.63 & 0.51 & 0.64 & 0.81 & 0.38 & 0.73 & 0.90 \\ Approach & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of LR-TED and [PERSON]’s Approach.
## 3 Methods
### Synthetic Data Generation Techniques
#### 3.4.3 Linf-Projected Gradient Descent  
An iterative gradient-based technique for creating adversarial examples is the Linf-projected Gradient Descent (LinfPGD) [23]. This technique may be modified to create synthetic data for model extraction assaults. An expansion of the Fast Gradient Sign Method (FGSM) is LinfPGD which aims to create more robust and effective synthetic samples. Given a victim model \(f\), an input sample \(x\), a loss function \(\mathcal{L}(\cdot,\cdot)\), and a maximum perturbation budget \(\epsilon\), Under the \(L_{\infty}\) norm constraint, LinfPGD iteratively updates \(x\) in the path of the direction of the gradient of the loss function to produce a synthetic sample \(x^{\prime}\):  
\[x^{\prime}t+1=\Pi\epsilon\big{(}x^{\prime}_{t}+\alpha\cdot\text{sign}(\
abla_ {x}\mathcal{L}(f(x^{\prime}_{t}),y))\big{)}\]  
where \(y\) is the ground truth label or target output for \(x\), \(\alpha\) is the step size, \(\text{sign}(\cdot)\) is the sign function applied element-wise to the gradient, and \(\Pi\epsilon(\cdot)\) is the projection operator that projects the disturbance to the \(L_{\infty}\) sphere with radius \(\epsilon\).  
The iterative nature of LinfPGD allows for more sophisticated and effective synthetic sample generation compared to FGSM. The synthetic sample is updated often in the path of the loss function gradient, LinfPGD can potentially explore a larger region of the input space and generate samples that are more informative and challenging for the target model. In order to preserve the semantic and structural characteristics of the input data, the \(L_{\infty}\) norm constraint makes sure that the synthetic samples stay boundedly close to the original input sample.  
LinfPGD offers several advantages over FGSM, including improved robustness to gradient masking or obfuscation techniques, and the ability to generate more diverse and informative synthetic samples. However, it is computationally more expensive than FGSM due to the iterative nature of the algorithm. Various modifications and extensions to LinfPGD can be explored, such as adjusting the step size, incorporating momentum or restart strategies, or combining it with other synthetic data generation techniques to further enhance its effectiveness for model extraction.
## 5 Gas Extraction Process Optimization and Field Test Validation
### Analysis of Gas Extraction Processes
#### 5.2.2 Upper Corner Gas Concentration Monitoring  
Due to the influence of wind leakage, the upper corner of the working face is often prone to gas concentrations that exceed the limit, which seriously affects the safe production of coal mines. To verify the effect of directional long borehole management, the upper corner gas concentration was monitored for 30 days, every 2 days, and the specific monitoring values are shown in Figure 24.  
Figure 23: Pure gas extraction diagram of 1–4# directional long boreholes.  
Figure 22: Diagram of 4# directional long borehole extraction.  
From the upper corner gas concentration monitoring values in Figure 24, it can be seen that when directional long boreholes are used for extraction, the maximum upper corner gas concentration is 0.44% and the minimum is 0.28%, which is much smaller than the 0.8% stipulated for mine operation. This achieves high-efficiency coal and rock gas extraction, effectively ensures the safety of the working face extraction, and improves the production efficiency of the mine.
# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 2 Introduction to Related Technologies
### Symmetric encryption
Asymmetric encryption is typically classified into three main types: the Integer Factorization Problem, where encryption relies on the product of two large prime numbers, the Discrete Logarithm Problem, which exploits the computational difficulty of solving discrete logarithms and uses strong one-way hash functions for asymmetric distributed encryption, and Elliptic Curve Cryptography, which uses elliptic curves on a plane to generate specific asymmetric encryption values [62]. As Algorithm 5 shows, asymmetric encryption uses a pair of keys: a public key for encryption and a private key for decryption. It begins by generating two large primes \(p\) and \(q\), computing \(n=p\times q\) and [PERSON]'s totient \(\varphi(n)=(p-1)(q-1)\). A public exponent \(e\) is selected such that \(\gcd(e,\varphi(n))=1\), and the private exponent \(d\) satisfies \(e\times d\equiv 1\pmod{\varphi(n)}\). Encryption transforms plaintext into ciphertext via \(c=m^{c}\bmod n\), while decryption recovers the original message with \(m=c^{d}\bmod n\). The security relies on the computational difficulty of factoring \(n\). ```
1:Initialization:
2: Select two large prime numbers \(p\) and \(q\). 3: Compute modulus \(n=p\times q\) and [PERSON]'s totient \(\varphi(n)=(p-1)(q-1)\). 4:procedureKeyGeneration
5: Choose public exponent \(e\) such that \(1<e<\varphi(n)\) and \(\gcd(e,\varphi(n))=1\). 6: Compute private exponent \(d\) such that \(e\times d\equiv 1\mod\varphi(n)\). 7: Output public key \((e,n)\) and private key \((d,n)\). 8:endprocedure
9:procedureEncryption(\(plaintext\))
10: Convert \(plaintext\) to an integer \(m\). 11: Compute ciphertext \(c=m^{c}\bmod n\). 12: Output \(c\). 13:endprocedure
14:procedureDecryption(\(ciphertext\))
15: Compute message \(m=c^{d}\bmod n\) using private key. 16: Convert integer \(m\) back to \(plaintext\). 17: Output \(plaintext\). 18:endprocedure
19:procedureSecurity Assurance
20: The security relies on the computational difficulty of factoring \(n\) into \(p\) and \(q\). 21: Only the private key holder can efficiently decrypt the ciphertext. 22:endprocedure
```  
**Algorithm 5** Asymmetric Encryption Process
## 3 Results and Discussion
### Multivariate Analysis  
Spectral angle mapper (SAM) is a widely used method for pixel-by-pixel hyperspectral image screening, requiring the assignment of a reference spectrum (endmember) to calculate the angle between reference spectra and the spectra of each pixel in the target image. Herein, individual SAM models were developed for each adulterant to identify adulterant particles in spiked samples, with the obtained images shown in Figure 5. The intensities of each pixel in the obtained rule images represent the SAM angles between the endmember and the spectrum extracted from a given hyperspectral pixel [21], with pixel darkness being proportionate to the similarity between these spectra. Paprika powder showed less dark pixels (larger angles) than those of the adulterants owing to the higher dissimilarity of the former and the reference spectrum. Thus, the dark pixels in Figure 5 represent adulterant particles, with the obtained results confirming the high potential of SAM for identifying and locating the spatial positions of individual adulterant particles based on the Raman characteristics of these adulterants. Since the rule images showed a distinct difference between adulterant and food powder pixels, the obtained images could be subjected to thresholding to develop a quantitative analysis model.  
Figure 4: Preprocessed Raman band images and multiplication image of a 0.75% adulterated paprika powder.  
Figure 5: Resulting rule images of both adulterants from spectral angle mapper (SAM).
# Point Cloud Repair Method via Convex Set Theory
## 2 Methods
### Discretization of Regional Space  
In general, the smaller the hole area is, the higher the repair accuracy. In view of this, we discretized the 3D space around the hole boundary points and divided it into k subunits. We defined \(X\) as the set of boundary points. \(X=\{x_{1},x_{2},\cdots,x_{m}\}\), \(m\) is the number of hole boundary points. We first selected \(T\) (\(T\geqslant 3\)) adjacent boundary points \(x_{i},\cdots,x_{i+(T-1)}\) and took midpoint \(\hat{x}_{i}\) of \(x_{i}\) and \(x_{i+(T-1)}\) as the base point to define 3D space \(S\) with a size of \(L\times W\times H\), and \(\hat{x}_{i}\) was the central point of \(S\). \(x_{i+(T-1)}\) is denoted by \(x_{j}\) for convenience in the description. Then, we discretized 3D space \(S\), so that the resolution of each spatial unit was \(d_{L}\times d_{W}\times d_{H}\). After discretization, the 3D tensor of shape \(\frac{L}{d_{L}}\times\frac{W}{d_{W}}\times\frac{H}{d_{H}}\) was obtained. Because we needed to include \(x_{i}\) and \(x_{j}\) in the spatial unit, we took \(d_{L}=L=||x_{i}-x_{j}||^{2}\) and divided \(S\) into \(k\) (\(k=9\)) subunits, \(S=\{s_{1},s_{2},\cdots,s_{k}\}\). After discretization, the 3D points contained in \(S\) were also allocated to different subunits. We took subunit-containing points \(x_{i}\) and \(x_{j}\) as the central unit \(s_{1}\), and the subunits surrounding the central unit were neighborhood subunits \(s_{k}(s_{k}\
eq 1)\). With the increasing number of iterations, the specific subunits gradually moved to the center of the hole region until the hole had been completely repaired.
# Real-Time Image Detection for Edge Devices: A Peak Fruit Detection Application
## 3 Results and Discussions
### Inference Time  
Table 4 lists the inference times of the models for the target CPU and TPU devices. The model with the lowest latency was SSD MobileNetV1 at 47.6 ms (average). The SSD MobileNet EdgeTPU model exhibited the highest latency (50.5 ms). The maximum difference between the models was 2.9 ms. An important finding was that the inference speed was 20 times faster on average when the model was running on the TPU device and the models designed (native) to run on the CPU (MobileNetV1 and MobileNetV2); however, it was optimized to run on the TPU, and it performed inferences slightly faster than the models designed to run on TPU devices.
## 4 Discussion
### Limitations and Strengths  
The current study has some limitations that should be taken into account. Firstly, the sample size was limited, including only three observed subjects. However, this is a very common issue in studies involving goalkeepers who compete in elite soccer [22; 33]. Secondly, the study included goalkeepers from one club; therefore, the results can only be applied to similar samples of subjects and levels of competition. Third, as only one-game microcycles were analyzed, the findings are not applicable to elite-level teams playing more than one game per week. However, this study also offers several strengths. Thus, this is one of the first studies to evaluate ETL among elite-level goalkeepers in competitive microcycles using goalkeeper-specific measures. Also, to the best of our knowledge, goalkeepers' loads during the specific actions are described for the first time in the current study. Finally, this study is the first to utilize multivariate techniques to assess differences in goalkeepers' daily demands. Future studies should analyze a larger sample of goalkeepers from multiple elite-level clubs, including microcycles consisting more than one game per week.
## 4 Results and Analysis
### Change Trend Analysis of the Indicators  
The change profiles of the MODIS NIR BRDF_R index, NDVI, average wind speed, and in-situ AR from April to September in 2013-2014 are shown in Figure 3.  
The four parameters of the MODIS NIR BRDF_R index, MODIS NDVI, average wind speed, and in-situ AR all show apparent variations in vegetation growth for the monitored period from 2013 to 2014. Variations in the NDVI (average value: 0.442; variation range: 0.113 to 0.671) directly reflect the growing process of the grass, and the variation trends in the NIR BRDF_R index (average value: 0.067; variation range: 0.009 to 0.159) are opposite to that of the NDVI. Figure 2 indicates that as the grass grows, the NDVI and AR will increase and the NIR BRDF_R index and average wind speed will decrease. The AR (average value: 0.0101 m; variation range: 0.0028 to 0.0234 m) shows similar variation trend as the NDVI, and the average wind speed (average value: 2.27 m/s; variation range: 1.785 to 2.967 m/s) shows consistent variation trend with the NIR BRDF_R index.
## 3 Result and Discussion  
### Characterization of Fiber Media Filter  
Figure 2 shows the surface morphology, hydrophobicity, and surface charge determined via FE-SEM, water contact angle measurements, and zeta potential measurements, respectively, of the fiber media filter. FE-SEM images (Figure 2a) show that the surfaces of the filter were very rough, owing to the twisted polyethylene multilines on the monofilament loop comprising the filter. The zeta potential measurements revealed a negative charge value for the media filter over a wide pH range, and the isoelectric point of the filter occurred at pH 5.09 (Figure 2b). Additionally, the water contact angle of the fiber media filter indicated that the filter was extremely hydrophobic; an average water contact angle of 116.8\({}^{\circ}\) was measured. This hydrophobicity can have a significant effect on the potential for clogging, leading to an increase in hydrophobic attraction between contaminants and the filter, resulting in easy deposition of contaminants on the surface of the filter.
# External Validation of Accelerometry-Based Mechanical Loading Prediction Equations
## 2 Materials and Methods
### Data Processing  
Force plate and accelerometer data were processed using MATLAB (version 2022a; MathWorks, Natick, MA, USA). Due to differing sampling frequencies (1000 Hz for the force plates and 100 Hz for the activity monitor), resampling was necessary. To attenuate noise, data were filtered using a Butterworth fourth-order low-pass filter with a 20 Hz cut-off frequency, determined through Fast Fourier Transform (FFT) and visual examination of the signal's frequency spectrum. The resultant vectors for ground reaction force and acceleration (\(r_{i}=\surd x_{i}^{2}+y_{i}^{2}+z_{i}^{2}\)) were calculated. Afterwards, both ground reaction force and acceleration signals were adjusted by their timestamp and synchronized through the maximum cross-correlation coefficient. Then, the signals were visually inspected for confirmation, and the synchronization was manually adjusted, if necessary.  
Walking and running peak acceleration were identified based on two criteria: They had to exceed the average acceleration measured in the trial by at least 1 standard deviation (SD), with a minimum gap of 0.2 s between peaks. For drop, box, countermovement, and horizontal forward jumps, peak acceleration was determined as a minimum height of 2 SD above the mean acceleration and a separation of at least 0.4 s. Finally, for the side hops and continuous jumps, peak acceleration was identified by a minimum height of 2 SD above the mean acceleration and by a separation of at least 0.3 s. The peak ground reaction force was defined as the highest ground reaction force value within 0.1 s before and after each peak acceleration.  
Rates of change were computed using a centered derivative, and peak loading rate and peak acceleration rate were defined as the maximum values in the array. For each movement type and accelerometer placement, mean values of peak ground reaction force, peak acceleration, peak loading rate, and peak acceleration rate for the resultant vector and its vertical component were obtained and used in all following analyses.