esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Command and control (C2) servers are essential in network administration, cybersecurity operations, and adversarial engagements [1]. From remote system administration and ethical hacking (red team operations) to cybercriminal activities, C2 servers have a varied history. As cyber threats evolve, so must the infrastructure supporting security professionals in defending networks and developing resilient defenses [2].  
C2 servers are crucial for remote command execution, data exfiltration, system monitoring, and network penetration testing. Traditional C2 frameworks, however, struggle with scalability, particularly in the face of modern cloud and hybrid infrastructures. Their centralized architecture creates a single point of failure, making them vulnerable to takedowns and forensic analysis. Furthermore, their common implementations and lack of scalability make them easily detectable by Endpoint Detection and Response (EDR) solutions. Finally, most C2 frameworks rely on well-known and often outdated post-exploitation techniques, limiting their adaptability.  
This work introduces a new C2 server designed to overcome these limitations. It incorporates modern data exfiltration, persistence, and command execution techniques, along with a decentralized architecture, to improve scalability, stealth, and operational security (OpSec). This system, named C3 (covert C2), provides a robust alternative to legacy C2 frameworks for ethical cybersecurity professionals [3]. Overall, C3 [4] integrates advanced scalability and command execution features, high evasion rates, adaptability, and a decentralized architecture for resilient operations against modern defensive countermeasures.  
In more detail, while existing well-known C2 frameworks like Cobalt Strike [5], Mythic [6], and Sliver [7] offer valuable capabilities but often lack stealth, automation, and adaptability, C3 distinguishes itself through the following:  
* Enhanced stealth: C3 employs uncommon techniques for direct, undetected system command execution.
* Decentralized architecture: Victims' independent communication with multiple C2 servers enhances resilience.
* Optimized performance: C3 minimizes resource consumption while maintaining high-speed command execution and data transfer.
* Increased adaptability and expandability: Using infrequent techniques and direct command execution, C3 adapts to new environments, offers diverse post-exploitation and persistence methods, and enables flexible expansion.
* Out-of-the-box persistence: Persistence is quite crucial in a red team engagement. As a result, C3 offers out-of-the-box persistence to the post-exploitation attack.  
The rest of the paper is structured as follows. Section 2 describes the threat model. Section 3 overviews the proposed scheme. Section 4 details the testbed and scenarios. Section 5 evaluates C3's detectability against popular EDRs. Section 6 analyzes C3's post-exploitation behavior and defenses. Section 7 compares C3 with existing C2 frameworks. Section 8 discusses related work. Finally, the paper concludes with future directions.
# Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
## 3 Results and Discussion
### Application of NDVI and TBI to Wavelength and Water Status
#### 3.5.2 TBI  
Compared with NDVI, almost the same characteristic wavelength bands were obtained for the TBI of the wavelength and water status (Figure 10). Better performance of the TBI regression model for WC was obtained with DIFF, where the model is \(y=-56.752x+109.12\), the coefficient of determination for calibration is \(R^{2}=0.6369\), the coefficient of determination for validation is \(r^{2}=0.4057\), and the root-mean-square error is RMSE =1.39 at the wavelengths \(\lambda_{1}~{}=1620\) nm and \(\lambda_{2}=1390\) nm. Better performance of the TBI regression model for MC was observed with RAW at wavelengths \(\lambda_{1}~{}=1310\) nm and \(\lambda_{2}=1300\) nm, where the model is \(y=0.0202x+4.7953\) (\(R^{2}=0.7886\), \(r^{2}=0.6389\), RMSE = 0.2720).  
Overall, for WC assessment, the spectral preprocessing affected the model accuracy to varying degrees. The best model was obtained using TBI with DIFF (Table 2). It is considered that the combination of first-order differential processing and TBI helped improve the model performance. For MC assessment, the best model performance was obtained using NDVI with RAW; LOG and DIFF preprocessing did not help improve the regression model performance.
## 2 Materials and Methods
### Data and Data Processing  
The monthly total precipitation and mean temperature between 2000 and 2020 were collected from the National Meteorological Information Center ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 8 May 2022) and interpolated into 1 km resolution raster data using ANUSPLIN 4.3 [37], which includes the incorporation of covariate variables to improve accuracy [38]. Validation of our interpolated meteorological data using data from multiple meteorological observation stations, such as Damxung and Nagqu, showed that our interpolated data explained over 90% of the actual observed data for both temperature and precipitation [38; 39]. The MODIS data of the enhanced vegetation index (EVI) and the land surface temperature (LST), as well as imagery data for the land surface water index (LSWI), were downloaded from the Land Processes Distributed Active Archive Center ([[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)), accessed on 12 July 2022) and converted to raster data with a spatial resolution of 1 km. The data on grassland type were obtained from the vegetation atlas of China, which is available at the Resource and Environment Science and Data Center of the Chinese Academy of Sciences ([[https://www.resdc.cn/data.aspx?DATAID=122](https://www.resdc.cn/data.aspx?DATAID=122)]([https://www.resdc.cn/data.aspx?DATAID=122](https://www.resdc.cn/data.aspx?DATAID=122)), accessed on 25 December 2021). The GPP, ER, and NEP data from different CMIP6 (Coupled Model Intercomparison Project Phase 6) earth system models were downloaded from the World Climate Research Programme ([[https://esgf-node.llnl.gov/search/cmip6/](https://esgf-node.llnl.gov/search/cmip6/)]([https://esgf-node.llnl.gov/search/cmip6/](https://esgf-node.llnl.gov/search/cmip6/)), accessed on 10 September 2022).
# RSTSRN: Recursive Swin Transformer Super-Resolution Network for Mars Images
## 2 Related Works
### Single-Image SR Algorithms
#### 2.2.2 Deep Learning-Based SR Algorithms  
The emergence of deep learning technology has made up for the shortcomings of traditional shallow learning algorithms. At present, deep learning-based SR algorithms have become the mainstream algorithms in the industry.  
[PERSON] et al. applied the convolutional neural networks to image SR reconstruction for the first time (SR Convolutional Neural Network, SRCNN) [42], and they divided the network into three stages: image block extraction, nonlinear mapping, and image reconstruction, and then these three stages are unified into a CNN (Convolutional Neural Network) framework to realize end-to-end learning from LR images to HR images.  
Classic deep learning SR networks include FSRCNN (Fast SRCNN) [43], ESPCN (Efficient Sub-Pixel Convolutional neural Network) [44], VDSR (Very Deep networks for SR) [45], DRCN (Deeply Recursive Convolutional Network) [46], SCN (Sparse-Coding-based Network) [47], DEGREE (Deep Edge Guided REcurrent rEsidual) [48], MemNet (Memory Network) [49], LapSRN [10], DRRN (Deep Recursive Residual Network) [50], SRDenseNet (Super-Resolution Dense convolutional Network) [51], SRGAN (Super-Resolution Generative Adversarial Network) [52], RDN (Residual Dense Network) [53], SwinIR [54], NGswin (N-Gram Swin Trans-former) [55], DAT (Dual Aggregation Transformer) [56], etc.  
In recent years, some deep learning-based SR algorithms for Lunar and Mars images have been published and achieved good results [39; 40; 57; 58; 59; 60; 61; 37; 39; 61]. This provides a useful reference for this work. The author's team also made some beneficial explorations in the aspect of remote sensing image SR [62; 11; 63] and carried out research work in several aspects such as network structure and lightweight, loss function and how to realize unsupervised learning. We previously proposed Lightweight Laplacian Pyramid Recursive and Residual Network (LRN) in [64] for Mars images.
# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 4 Proposed Energy-Aware Path Planning (EAPP) Approaches
### Computational Complexity
#### 4.4.3 Computational Complexity of GWO-Based Approach  
The computational complexity of the GWO-based approach is \(O(n^{2})\) of \(O(N\times m)\) time, where \(N\) represents the population size and \(m\) represents the dimension of the problem. Given a maximum number of iterations, the total time complexity of the DGWO and GWO is \(O(N\times m\times MaxIter)\), where \(MaxIter\) indicates the maximum number of iterations.
###### Abstract  
From a very brief speech, human listeners can estimate the pitch range of the speaker and normalize pitch perception. Spectral features which inherently involve both articulatory and phonatory characteristics were speculated to play roles in this process, but few were reported to directly correlate with speaker's F0 range. To mimic this human auditory capability and validate the speculation, in a preliminary study we proposed an LSTM-based method to estimate speaker's F0 range from a 300 ms-long speech input, which turned out to outperform the conventional method. By two more experiments, this study further improved the method and verified its validity in estimating the speaker-specific underlying F0 range. After incorporating a novel measurement of F0 range and a multi-task training approach, Experiment 1 showed that the refined model gave more accurate estimates than the initial model. Based on a Japanese-Chinese bilingual parallel speech corpus, Experiment 2 found that the F0 ranges estimated with the model from the Chinese speech and the model from the Japanese speech produced by the same set of speakers had no significant difference, whereas the conventional method showed significant difference. The results indicate that the proposed spectrum-based method captures the speaker-specific underlying F0 range which is independent of the linguistic content.  
underlying F0 range; F0 range estimation; spectral features; pitch perception Article  
Estimation of the Underlying F0 Range of a Speaker from the Spectral Features of a Brief Speech Input  
[PERSON], [PERSON], [PERSON]  
Likewise, in speech technologies such as automatic speech recognition/understanding and automatic assessment of pronunciation, F0 normalization is very important and hence an automatic estimation of the speaker-specific F0 range is necessary, especially for tone languages where F0 plays more important roles than in non-tone languages [9; 10]. However, in most previous studies a calculation of F0 range is accomplished by a direct statistical analysis of F0 variation from a lengthy speech input [10; 11; 12; 13], which apparently does not coincide with the way human listeners estimate F0 range in the condition of a very brief speech input. To mimic the auditory mechanism of human listeners, a recent study of ours [14] proposed a model of estimating F0 range from spectral features. As a follow-up study, this paper proposes a refined model, and further investigation of the validity of the method in estimating the speaker-specific underlying F0 range.
# Intelligent Transportation System Technologies, Challenges and Security
## 2 Intelligent Transportation Systems
### Intelligent Transportation Communication Technologies
#### 2.2.5 Low-Power Wide-Area Network  
Technologies, distributed over a wide range of areas, offer wireless communication for low-power machine-to-machine (M2M) and IoT devices. Some of the Low-Power Wide-Area Network (LPWAN) technologies are NarrowBand IoT, Long Range (LoRa), and Sigfox. It uses the Global System for Mobile (GSM) network. In this technology, sensors and cameras are used. All end devices can collect their data in a central place with different communication technologies [26].
## 2 Results
### Production of VO Defects Versus Ge Dopant Concentration  
Figure 5 shows the production of the VO defect as a function of the Ge dopant concentration in the Si samples. As seen in Table 3, we used samples with Ge concentrations in the range 1 \(\times\) 10\({}^{17}\)-2 \(\times\) 10\({}^{20}\) cm\({}^{-3}\). Since the concentration of C\({}_{\rm S}\) in the samples also varies, and as it is known that C affects the production of VO, as we shall discuss later, we divided the samples into three groups. Samples with a low initial C\({}_{\rm S}\) concentration smaller than 2 \(\times\) 10\({}^{16}\) cm\({}^{-3}\), such as the samples Cz-Si0, Ge-2 and Ge-6, labeled as carbon-lean samples, formed group I. Next, samples Ge-1, Ge-3, Ge-4 and Ge-7, with an initial C\({}_{\rm S}\) concentration in the range of 2 \(\times\) 10\({}^{16}\)-1 \(\times\) 10\({}^{17}\) cm\({}^{-3}\), labeled as carbon-moderate doped samples, formed group II. Finally, samples Ge-5 and Ge-8, with an initial C\({}_{\rm S}\) concentration around 2 \(\times\) 10\({}^{17}\) cm\({}^{-3}\), labeled as carbon-rich doped samples, formed group III. Regarding group I, we observed in Figure 5 an increase in VO concentration as Ge concentration increased. At first, as we mentioned above, Ge dopant acted as a temporary trap of V in the course of irradiation, and, thus, increased the VO concentration. Apparently, the larger the Ge concentration, the larger the availability of V, thus the larger the VO concentration. This explains the observed VO versus Ge curve for C-lean samples in Figure 5. Regarding group II, we had, in addition, the effect of C on VO production. As we also mentioned above, the latter impurity acted as a trap for the self-interstitials produced by irradiation, thus suppressed their annihilation with the vacancies. This behavior of C increased the availability of V and consequently, further enhanced the production of VO defect. As a result, the production curve of VO in C-moderate samples had a similar behavior to C-lean samples, but it was replaced at higher VO concentration, as depicted in Figure 5. Regarding group III, a reduction in VO was observed for sample Ge-8 with a high Ge concentration of 2 \(\times\) 10\({}^{20}\) cm\({}^{-3}\), in comparison with sample Ge-5 with a Ge concentration of 1 \(\times\) 10\({}^{19}\) cm\({}^{-3}\). Earlier photoluminescence studies have shown [145] that the presence of Ge in such concentrations causes changes in the spectra which are attributed to the inhomogeneous distributions of Ge atoms and the appearance of Ge clusters. The induced strains in the lattice lead to the trapping of V and Si\({}_{\rm I}\)'s in these clusters, facilitating their mutual annihilation. Thus, the availability of V produced by irradiation is reduced, which leads to a decrease in the VO defects [142]. Apparently, the higher the Ge concentration, the larger the VO decrease, as depicted in Figure 5 for the two samples Ge-5 and Ge-8.  
Figure 5: The production of the VO defect as a function of the Ge dopant concentration in the Si samples. Reproduced with permission from [37]. Elsevier, 2009.
## 2 Materials and Methods  
The employed laser was ns pulsed laser (Quanta System, PILS-GIANT, 6 ns pulse duration) operating with the fundamental (1064 nm) and the second harmonic (532 nm), a repetition rate varying between 1 and 10 Hz and with energy varying between 50 mJ and 217 mJ, depending on the experiment performed. The processes of laser ablation in liquid were studied during the production of NPs with high temporal and spatial resolution techniques, as described in further paragraphs. The laser ablation in liquid was performed by focusing the laser beam on a target immersed in deionized water using a stainless steel chamber with a set of optics for laser transmission and the collection of the plasma emission and/or shadowgraph detection. For the optical emission spectroscopy, a spectrometer (Jobin Yvon TRIAX 550) equipped with an intensified charge-coupled device (ICCD) as the detector (Jobin Yvon i3000) was employed. For the shadowgraph technique a collimated light source obtained by a set of lenses and a fast ICCD detection system (New iStar, Andor) coupled with a pulse generator (Stanford inc. DG 535) for the laser pulse-ICCD synchronization were employed. The laser scattering measurements during the PLAL processes were performed with a continuum laser DPSS at 532 nm (Cobolt Samba, 25 mW, CW) by employing the same spectroscopic system used for the shadowgraph experiment. For each presented experiment, the laser focal position onto the target surface and the water were changed after each measurement to always have a flat target and fresh water, respectively. The laser crater on the target had a diameter of 1 \(\pm\) 0.2 mm.  
The targets were titanium (99.995% pure, 1\({}^{\prime\prime}\) diameter \(\times\) 0.125\" thick, [PERSON]) and silver (99.99% pure, 1\({}^{\prime\prime}\) diameter \(\times\) 0.125\" thick, [PERSON]).  
More details on the experimental set-ups can be found in Refs. [7; 8; 9]. Each figure caption reports detailed experimental conditions relative to the shown measurements.
# Path Planning of a Mobile Robot Based on the Improved RRT Algorithm
## 2 Background
### Basic RRT Algorithm  
The RRT algorithm builds a search tree by sampling randomly in the configuration space. Let the initial state \(x_{init}\) be the root node of the tree. Then, the following steps are followed until a path is found or the maximum number of iterations is reached: (1) A state \(x_{rand}\  
\(x_{new}\) and the edge \(\{x_{near},x_{new}\}\) are added to the tree (line 6-8 in Algorithm 1). Otherwise, this expansion is discarded and the process returns to Step (1). If the search tree achieves the target region \(X_{goal}=\left\{x\middle|\middle|\middle|x-x_{goal}\right|_{2}\leq r\right\}\), \(x_{new}\) and \(x_{goal}\) are connected and returned to the path (line 9-11 in Algorithm 1). If no path is found after the maximum number of iterations, the planning fails. Figure 1 illustrates the expansion process of RRT.
## 5 Application of the Approach
### Classification of Relevant Measures  
To showcase the potential of the risk reduction measures, the four measures (Table 6) from the prioritized paths were selected for the measures catalog to be integrated into the production schedule. The division of the measures into preventive and reactive is based on the allocation of the presented categorization of energy flexibility measures. For example, M5 can be assigned to the energy flexibility measure \"adjust resource allocation\" and is, therefore, a reactive measure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Description** & **Type** & **Effect on the Process** \\ \hline M4 & Preparation of an additional core & Reactive & Altering \\ M5 & Adjusting furnace utilization if an & Reactive & Supplementing \\ M6 & Adjust process start furnace & Preventive & Supplementing \\ M7 & Adjust parameter melting & Preventive & Supplementing \\  & temperature and duration of furnace & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Catalog with four categorized measures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Marginal Utility Score**} \\ \cline{2-4}
**Criteria** & **Cost** & **Energy Flexibility** & **Risk Reduction** & **Final Utility Score** \\ \hline
**Weight** & **0.0082** & **0.2700** & **0.7218** & \\ \hline No. 120 & 0.3930 & 0.8689 & 1.0000 & 0.9596 \\ No. 73 & 0.3930 & 0.3107 & 1.0000 & 0.8089 \\ No. 114 & 0.5028 & 0.5677 & 0.7507 & 0.6992 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Marginal and final utility scores of the three best performing paths.  
M7 is assigned to the energy flexibility measure \"adjust process parameters\" for which a decision must be made depending on the production situation. In the application, the process parameters should only be adjusted if a fault actually occurs and is therefore defined as a reactive measure.
## 5 Performance Evaluation
### Performance Analysis  
In this section, we discuss the impact of varying different network parameters on the NR V2X performance. Note that the simulation has been conducted until a 95% confidence interval was achieved. With no loss of generality and to better illustrate the performance of the 5G NR V2X communication technology, let us model the performance metrics, PIR, PRR, and throughput, using Gaussian random variables \(T\), \(R\), and \(P\), respectively. Their corresponding probability density functions are defined by  
\[f_{T}(t) =\frac{1}{\sigma_{T}\sqrt{2\pi}}e^{-\frac{1}{2}\left(\frac{t-T}{ \sigma_{T}}\right)^{2}}, \tag{1a}\] \[f_{R}(r) =\frac{1}{\sigma_{R}\sqrt{2\pi}}e^{-\frac{1}{2}\left(\frac{t-R}{ \sigma_{R}}\right)^{2}},\text{ and}\] (1b) \[f_{P}(p) =\frac{1}{\sigma_{P}\sqrt{2\pi}}e^{-\frac{1}{2}\left(\frac{t-P}{ \sigma_{P}}\right)^{2}}, \tag{1c}\]  
where \(t\in[330,375]\) ms, \(r\in[0.96,1.0]\), \(p\in[14,17]\) KB. The mean and standard deviations used to conduct real-time simulations of PIR, PRR, and throughput are given in Table 3.  
Since these metrics are evaluated on a discrete set of values, we determined their corresponding cumulative distributions functions, \(F_{T}(t)\triangleq\Pr[T\leq t]\), \(F_{R}(r)\triangleq\Pr[R\leq r]\), \(F_{P}(p)\triangleq\Pr[P\leq p]\), \(\forall t\in\mathbb{R}\), \(r\in\mathbb{R}\), and \(p\in\mathbb{R}\), defined as  
\[F_{T}(t) \triangleq\Pr[T\leq t]=\int_{-\infty}^{t}f_{T}(\alpha)\,\mathrm{d} \alpha\,,\]these output performance metrics, namely the PIR, PRR, and throughput of the networked 5G new radio in the framework of V2X communication.
# Successful Tests on Detecting Pre-Earthquake Magnetic Field Signals from Space
## 3 Results and Discussion
### Extended Validation Analyses as the Base for an OEPS
Fourth, we can finally integrate this method with the analysis of other kinds of precursors, such as ground, atmospheric, and other kinds of ionospheric precursors [49], in order to improve the reliability of the OEPS as much as possible.
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 5 Results  
This section presents the results obtained from the aforementioned use case simulations. The objective is to compare the performance of the proposed architecture (case B) with a legacy network (case A) when there is a failure. The failure consists in a link of the core network crashing at about 150 s after the beginning of the simulation.  
Looking at Figure 5, when the failure happens, in case B there is a negligible additional latency of 1.8 ms, while in case A, the connection stops for about 50 s. In addition, Figure 6 depicts how the overall data rate increases in uplink and downlink when the SDN path-computing application (DisPaC) and MPTCP are applied. There is a 6.15% increment for the uplink data rate and 0.43% for the downlink one. It is considered that the improvement rate resulted from this comparative analysis is independent of the simulation platform and that the comparative values obtained could be extrapolated to a real scenario.  
Figure 5: Delay measurement in use cases A and B when a link in the core network goes down at 150 s.