esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 4 Conclusions  
### Empirical Insights  
* MTD aims to enhance security by shifting the attack surface rather than eliminating all vulnerabilities in system components. This represents a departure from traditional security goals, which have focused on eliminating vulnerabilities entirely. With the addition of machine learning, MTD can provide an affordable, optimized, and self-adaptive defense mechanism that enhances system security without requiring the replacement of existing techniques. By actively guiding this development trend, we can further balance the relationship between defense costs, system security, and system availability in multiple dimensions, enabling MTD to move toward large-scale applications. * Defense measures are not mutually exclusive, and MTD is actively seeking to integrate with existing security defense mechanisms. However, it is crucial to consider how introducing MTD may alter the existing network configuration, which is often relatively fixed when existing network security defense measures are in place. This change can increase resource consumption, reduce network availability, and potentially interfere with existing network security defenses, ultimately reducing overall defense capability. * To leverage existing techniques and maximize the effectiveness and efficiency of MTD, it is significant to combine MTD with other emerging techniques such as SDN, cloud computing, and machine learning to achieve better active defense. By using different types of MTD that can be tailored to specific application domains, we can enhance the overall defense capability of MTD. * While game-theoretic MTD approaches are commonly utilized in MTD strategy selection research, the emergence of machine learning has shown that relevant algorithms can be considered to address the affordability, self-adaptation, and other limitations of existing MTD techniques [82]. Machine learning can also help construct better protection mechanisms for existing MTD systems. * In our survey, we did not present MTD techniques measurement metrics due to our focus on MTD and intelligent development, but this does not mean that measurement metrics are unimportant. Many MTD techniques have applied MTD-related metrics to assess the effectiveness of their own techniques. Unfortunately, these evaluation metrics differ among different MTDs, making it difficult to integrate various techniques effectively. Several quantitative metrics have been proposed to assist in uniformly assessing MTD techniques, but they have only had limited success. We believe that researchers should continue striving to develop a universal metric that covers all
# Nanofabrication Techniques in Large-Area Molecular Electronic Devices
## 4 Summary and Outlook  
The use of molecules in electronic devices as critical functional elements in circuitry is expected to result in a novel technology opening the path to future industrial processes for high-value products. The use of functional organic materials has attracted a great deal of attention due to the numerous and very appealing advantages of using molecules as functional units. Importantly, the use of molecules in electronic devices is expected to result not only in the further miniaturization of transistors, but also in diminished power consumption and new functionalities, due to the quantum effects that govern the properties at the nanoscale. Researchers are currently immersed in the exploration of two different paradigms. On the one hand, the study of single-molecule junctions, which is of primary importance to understand charge transport through molecules. On the other hand, the fabrication and characterization of large-area devices in which assemblies of molecules are located between two (or three) electrodes. The fabrication of these electrode \(|\) monolayer \(|\) electrode systems can be done through simple, soft and scalable technologies that result in high-quality and reproducible monolayers, making possible the manufacture of thousands of devices. Many advances towards the deposition of a continuous top-contact electrode, without short-circuiting or damaging the monolayer, have also been made in the last years. This potential processability and scalability in the fabrication of large-area molecular electronic devices pave the way towards industrial mass production. In this review, progress in the fabrication of large-area molecular electronic devices in recent years has been presented, with particular emphasis on the techniques used for the fabrication of well-ordered and tightly-packed monolayers, as well as top-contact deposition methodologies. An overview of the current nanofabrication techniques clearly evidences that there is still a long way to go before having a well-defined manufacturing route to get integrated, high yield, robust, stable, scalable, and reproducible molecular devices, produced at a reasonable cost. A multidisciplinary approach with the collaboration of chemists, physicists, and engineers is being made in research groups all over the world to overcome these difficulties and put in the hands of the next generation an emerging technology that is expected to improve people's quality of life.  
This research was funded by Ministerio de Economia y Competitividad from Spain and fondos FEDER in the framework of projects MAT2016-78257-R and PID2019-105881 RB-I00. [PERSON], S.M. and P.C. also acknowledge projects LMP33-18 and E31_20R (Platon research group) funded by Gobierno de Aragon/Fondos FEDER (construyendo Europa desde Aragon). The authors declare no conflict of interest.
# Evaluation of Conifer Wood Biochar as Growing Media Component for Citrus Nursery
## 2 Materials and Methods
### Growing Media Composition and Experimental Design  
Seven growth media (gm A-G) were prepared; each contained 50% sandy volcanic soil and the remaining 50% was made up as follows: A = black peat and perlite 1:1; B = biochar 1; C = black peat,perlite, and biochar 0.5:1:0.5; D = black peat and biochar 1:1; E = black peat, compost, and biochar 0.5:0.5:1; F = black peat, perlite, compost, and biochar 0.5:0.5:0.5:0.5; G = black peat and lapillus 1:1. According to the United States Department of Agriculture (USDA) scheme [26], the used volcanic soil is classified as sand. Perlite (Perlite Italiana Srl, Corsico (Milan), Italy) and peat (Baltic peat, Varena, Lithuania) were purchased from commercial enterprises. A homemade mature compost from a mixture of residues of citrus industries and citrus-tree pruning wastes was used. Medium G, previously adopted by the hosting nursery, was the control. The biochar was obtained from conifer forest wood waste and produced by slow pyrolysis at 400 \({}^{\circ}\)C for 48 h. Lapillus is an inert porous material coming from volcanic rocks and obtained through industrial calcination at high temperatures. All growth medium components were thoroughly mixed before filling the containers. Treatments consisted of a randomized complete-block design with three replicates, where each replicate comprised 20 plants, for a total of 420 plants (7 media \(\times\) 3 reps \(\times\) 20 plants).
# Synthesis of Si-Fe Chondrule-like Dust Analogues in RF Discharge Plasmas
## 2 Materials and Methods  
### Experimental Setup  
The experimental setup for the synthesis of Si-Fe nanoparticles, schematically shown in Figure 1a, was upgraded [24]. In a stainless steel vacuum vessel, an Ar-Fe-HMDSO plasma was generated between 12 cm diameter electrode discs with RF discharge. Radiofrequency energy was applied to an electrode at the top of the chamber, which was electrically isolated from the grounded side and bottom walls of the chamber. In all experiments, Ar (99.9 percent purity) was continuously supplied to the discharge chamber using a Bronkhorst mass-flow controller (Ruurlo, The Netherlands), and the vapour supply of HMDSO (Sigma-Aldrich, St. Louis, MO, USA, NMR grade, 99 percent purity) and ferrocene was controlled using a needle valve. The pressure was kept constant during the experiments using a pumping system. The argon flow rate was 50 sccm, and the total working pressure was determined by adding the partial pressures of HMDSO and ferrocene vapours.  
Once synthesised, the Fe-Si films formed in the RF discharge, as an additional experiment was tested for interaction with a high-energy pulsed plasma beam. The exposure to the highly energetic plasma beam induces a fast heating of the deposited Fe-Si nanopowders on the substrate surface. The schematic of the pulsed plasma beam setup is shown in Figure 1b, and the detailed characterisation of the device is given in [25]. The plasma beams are formed in a coaxial pulsed plasma acceleration device with its own self-generating magnetic field. Table 1 summarises the main operating parameters of the pulsed plasma beam accelerator. After plasma beam irradiation, the morphology of the samples was studied using a Quanta 3D 200i SEM (FEI company, Hillsboro, OR, USA), while the mineralogy was investigated using Rigaku MiniFlex 600 X-ray diffractometer (Tokyo, Japan).  
Figure 1: Experimental setup: **(a)** RF discharge plasma (red line means heating); **(b)** Plasma accelerator.
# CitiusSynapse: A Deep Learning Framework for Embedded Systems
## 2 Background and Related Works
### Accelerated Libraries for Deep Learning  
The operation process of deep learning involves forward and backward operations, which are mostly vector or matrix operations. BLAS consists of a set of routines for performing general linear algebraic operations, such as vector or matrix operations. BLAS generally uses the specifications defined in Netlib [22], but there are various BLAS libraries optimized for each HW type, such as a CPU or GPU.  
CPU-based BLAS libraries include the Linear Algebra Package (LAPACK) [24], Automatically Tuned Linear Algebra Software (ATLAS) [25], and OpenBLAS [26]. LAPACK and ATLAS only support single-core CPUs, whereas OpenBLAS supports multi-core CPUs.  
CUDA-based BLAS libraries include cuBLAS [27], CUTLASS [28], and KBLAS [29], in which cuBLAS is a BLAS library officially provided by Nvidia. A CUDA-based BLAS library is only operable on a system equipped with an Nvidia GPU, and is inoperable in embedded systems, such as mobile devices.  
In addition to the BLAS library, the Nvidia CUDA Deep Neural Network Library (cuDNN) can perform acceleration operations which are based on a GPU, for each layer of a DNN. Based on CUDA, cuDNN supports forward and backward operations in convolution, pooling, and softmax layers, which are essential layers of a DNN. Similar to CUDA, cuDNN is operable only in systems which are equipped with an Nvidia GPU.  
OpenCL-based BLAS libraries are typically conducted using open-source code, because OpenCL is an open, universal parallel computing framework. Well-known OpenCL-based BLAS libraries include clBLAS [30], ViennaCL [31], and CLBlast [32]. The OpenCL-based BLAS library is currently optimized for the PC environment, thus failing to guarantee an optimized performance in embedded GPUs. In addition, insufficient research has been conducted on OpenCL-based convolutional neural network (CNN) accelerator libraries that support forward and backward operations of a layered label, as in cuDNN. The ARM compute library (ACL) [33] is a deep learning accelerated library for ARM CPUs and GPUs, managed by ARM Ltd. The ACL is only operable in ARM CPUs and GPUs; Caffe or TensorFlow models can be used when ArmNN [34] is used, but ACL alone cannot be linked with conventional deep learning frameworks.
# A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception
## 6 Simulation Analysis
### Separation Performance Analysis
Compared with all the above algorithms, the NCGWO proposed in this paper has high accuracy and a fast convergence speed, making it very suitable to solve CVBSS problems. Figure 6 presents the average similarity coefficients under different SNRs and JSRs. The comparison diagram of similarity coefficients under different SNRs reveals the following observations. (1) As the SNR increases, the separation performance of each algorithm improves. The proposed algorithm exhibits a relatively slow deterioration in performance as the SNR decreases. Notably, when the SNR is between 0 and 10 dB, the proposed algorithm outperforms other algorithms, indicating better separation performance at a low SNR. (2) The performance order of each algorithm is as follows: Sobi \(<\) cFastICA \(<\) RobustICA \(<\) JADE \(<\) FAGD \(<\) Proposed. The similarity coefficient diagram of different JSRs shows the following trends. (1) As the JSR increases, the separation performance of each algorithm decreases. However, the proposed algorithm exhibits a relatively slow deterioration in performance as the JSR increases. (2) When the JSR is between 20 and 30 dB, the proposed algorithm again outperforms other algorithms, indicating better separation performance under a high JSR. (3) The performance order of each algorithm remains the same: Sobi \(<\) cFastICA \(<\) RobustICA \(<\) JADE \(<\) FAGD \(<\) Proposed. Figure 7a shows the mean square error of DOA estimation under different SNRs. With the increase in SNR, the DOA estimation RMSE decreases. Obviously, compared with traditional methods, the proposed algorithm is closer to the real value and has higher estimation accuracy. Figure 7b is the BER under different SNRs after coherently demodulating the separated QPSK signal. When the SNR is greater than 10 dB, the BER is 0, so the BER of 0-10 dB is given in the figure. With the increase in SNR, the BER decreases. Figure 5: Objective function curves of different optimizers. Figure 6: Average similarity coefficients under different SNR and JSR. (**a**) Average similarity coefficient under different SNR, (**b**) average similarity coefficient under different JSR. Because of the high separation accuracy of the proposed algorithm, the BER under the same SNR is lower than that of other algorithms.
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
## 5 Related Work  
[PERSON] [1] first proposed a CTC framework to enable wireless devices with different physical layers to communicate with each other through modulating the length of packets. HoWiES [7] is aimed at saving WiFi energy with assistance from ZigBee-modulated CTC messages through a number of consecutive packets. WIDE [20] utilizes digital emulation to reduce emulation errors from WiFi to ZigBee. FreeBee [8] enables communications among WiFi, ZigBee, and Bluetooth. The recent CTC designs, including WEBee [10] and SymBee [11], achieve 250 Kbps and 31 Kbps throughput from WiFi to ZigBee and ZigBee to WiFi, respectively. LongBee [9] is another improved CTC work of WEBee. LongBee extends the communication range of CTC to support long-range IoT applications. X-MIMO [12] supports MIMO for cross-technology communication. SDR-lite [13] supports ZigBee to WiFi high-throughput CTC. However, SDR-lite is destructive to the WiFi communication. Ref. [2] is proposed to support the MAC layer for CTC. Despite the good performance, they need to sacrifice ongoing WiFi and ZigBee traffic, which is destructive. C-LQI [21] proposes a new link metric and a joint link model that considers both the emulation error and the channel distortion. Based on the link model, a lightweight approach is proposed to estimate the quality of the CTC link. CoWBee [22] proposes a network-layer spectrum allocation technique deployed in existing WiFi infrastructure from WiFi to ZigBee. In this paper, our design overcomes the low-throughput and transparency issue by creating a chip-level side channel for ZigBee-to-WiFi CTC.
## 3 Fibre Bragg Grating
### Modeling as a Petri Network  
A very good introduction to the theory of Petri nets and a detailed derivation and description of the notation used thereby is given in [54]. A Petri network \(N\) is formally described by a six-tuple equation  
\[N=(P,T,A,\omega,\kappa,M_{0}), \tag{6}\]  
wherein,  
\(P\) is a finite set of points in a graph, \(P=\{p_{1},p_{2},...,p_{n}\}\),  
\(T\) is a finite set of transitions \(T=\{t_{1},t_{2},...,t_{n}\}\),  
\(A\subseteq(P\times T)\cup(T\times P)\) is the flow relation for edge definition,  
\(\omega:A\to N\) is the weight function of the edges,  
\(\kappa:P\to N\) is the capacity function of the task, and,\(M_{0}:P\to C\cup\{0\}\) is the initial marking function.  
As a model for the control of the FBG, the Petri net is used in Figure 5. It contains six points and six transitions. The initial marking of the graph is \(M_{0}=(101000)\). Mathematically, the graph in Figure 5 fully described by the so-called incidence matrix \(I_{\text{FBG}}\) (Equation (7)) [54]. It specifies as to when and which value of weight can be provided to the transitions.  
\[I_{\text{FBG}}=\begin{pmatrix}-1&1&-1&0&0&0\\ 1&-1&1&0&0&-1\\ 0&0&0&-1&1&0\\ 0&0&0&1&-1&0\\ 0&0&1&-1&0&0\\ 0&0&0&0&0&1\end{pmatrix} \tag{7}\]  
In the graph,...  
\(p_{1}\): FBG is set to the idle/neutral state (available for adjusting the Bragg wavelength)  
\(p_{2}\): FBG is set to a setting process  
\(p_{3}\): OSA is in idle/neutral state and is ready to measure  
\(p_{4}\): OSA is in measurement mode, waiting for the results  
\(p_{5}\): Auxiliary/Help status for timeout  
\(p_{6}\): FBG-Error due to its Bragg wavelength setting.  
Since in a Petri network no fixed time for the switching behaviour of the transitions must be determined, this model can be universally used for the control. It first serves as a template for programming a control characterization and later as a model for the automated control of the overall system.
# Thermal and Mechanical Assessment of PLA-SEBS and PLA-SEBS-CNT Biopolymer Blends for 3D Printing
## 3 Results
### Results of PLA-SEBS-CNT Nanocomposites
However, visual inspection of the torn specimens shows (Figure 18) that the PLA and 1% SEBS specimens broken along a line in the full cross-section, perpendicular to the longitudinal axis, while the CNT-containing specimens again show fiber separation and there is no well-defined fracture line. The reason for the decrease in values may be the separation of the fibers, which can be prevented or reduced by changing the printing parameters; for example, by raising the printing temperature. These are the subject of further investigation. Figures 19-21 show the results of bending test, the flexural modulus, the flexural stress at maximum and the deflection at maximum stress. Figure 17: Tensile strain at break for PLA, 1% SEBS, 0.06 and 0.1% CNTs specimens. Figure 18: Specimens after tensile test: (**a**) PLA, (**b**) 1% SEBS, (**c**) 0.06\({}^{\circ}\)CNT, (**d**) 0.1% CNT. The change in the results of the bending test is similar to that observed in the tensile test. The values decrease in all cases of the flexural modulus, the maximum flexural stress and the deflection at the maximum stress. The 1% SEBS sample shows lower values compared to the original PLA and the values further decrease if the CNTs are also mixed into it. Increasing the carbon nanotube content slightly increases the values but is still lower compared to samples with pure PLA or 1% SEBS. However, if we look at the specimens after the test (Figure 22), we can see that the pure PLA was broken in the full cross-section, the 1% SEBS sample suffered partial fracture, but the samples containing different amount of CNTs did not fracture. In addition, not only did they not break, but they also did not suffer a partial fracture, and they almost completely regained their straight shape after the test. The same cannot be said for the 1% SEBS sample, as it suffered from permanent deformation and remained in its bent state. Figure 19: Flexural modulus of PLA, 1% SEBS, 0.06 and 0.1% CNTs specimens. Figure 20: Flexural stress at maximum for PLA, 1% SEBS, 0.06 and 0.1% CNTs specimens. The type of test is three-point bending, which means that the specimens are not fixed anywhere and can move freely. The free movement of the specimens made it possible to show that the carbon nanotube increases the degree of elastic deformation, pushing out the occurrence of residual deformation. In the case of a tensile test, the fixed grip and the test method lasting until complete failure prevented its detection. However, this also indicates that the resulting flexural stress value is not a strength value but only a limit stress, and this limit stress is still less than the stress required for the residual deformation. This means that PLA samples reinforced with 0.06 and 0.1% carbon nanotubes and containing 1% SEBS are not only able to withstand a high degree of bending stress but can also be said to be raw materials suitable for shape memory prototypes or product printing.
# Occlusion-Aware Worker Detection in Masonry Work: Performance Evaluation of YOLOv8 and SAMURAI
## 3 Experimental Evaluation  
This section presents the evaluation results of YOLOv8 and SAMURAI for worker detection in brick transportation and brick laying tasks. The analysis focuses on detection performance across different occlusion levels, comparing the two models based on their ability to identify workers under varying degrees of visibility. The evaluation follows a structured time-based analysis, ensuring a comprehensive comparison of detection trends.  
To systematically compare the detection performance, the evaluation framework categorizes the results into three occlusion cases: Case 1 (non-occlusion), Case 2 (partial occlusion), and Case 3 (severe occlusion). For each case, detection accuracy is recorded at predefined time intervals to analyze temporal detection consistency. The results are presented in a tabular format, with additional visual representations highlighting key detection outcomes at different occlusion stages.  
The results are divided into two sections based on experimental tasks. Section 3.1 analyzes detection performance in brick transportation, where occlusion occurs as the worker moves behind obstacles, such as a trolley loaded with bricks. Section 3.2 evaluates detection performance in brick laying, where occlusion progressively increases as more brick layers are stacked in front of the worker. Each subsection details the detection trends observed for YOLOv8 and SAMURAI, offering insights into their object detection consistency across time and occlusion conditions. A final summary in Section 3.3 consolidates the findings and provides a comparative analysis of the detection effectiveness of both models.