esa-sceva/satcom-chunk-collection huggingface dataset:
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 3 System Performance Analysis  
In this section, we present our mixed analytical-simulation framework. We start with characterizing single-hop communication distances, then proceed with the formalization of the relaying model and finally outline the details of the system-level simulations we utilize to obtain the metrics of interest.  
In what follows, we utilize the set of modulation and coding schemes specified in IEEE 802.15.3d standard [29]; see Table 2 and propagation path measurements in the V2V environment reported in [23].  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**MCS ID** & **Modulation** & **Data Rate** & **Receiver** \\  & & **(Gb/s)** & **Sensitivity (dBm)** \\ \hline
0 & BPSK & 1.29 & \(-\)67 \\
2 & QPSK & 2.58 & \(-\)64 \\
3 & QPSK & 3.29 & \(-\)60 \\
4 & 8-PSK & 3.87 & \(-\)59 \\
8 & 16-QAM & 5.16 & \(-\)57 \\
9 & 16-QAM & 6.57 & \(-\)53 \\
10 & 64-QAM & 7.74 & \(-\)52 \\
11 & 64-QAM & 9.86 & \(-\)47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Utilized modulation and coding schemes.
# A 100-Gb/s PAM-4 DSP in 28-nm CMOS for Sredes Receiver
## 3 Crucial Techniques
### Lower-Power Techniques  
The power consumption of the DSP is mainly in the calculation of the FFE/DFE data paths and their tap coefficients. The aforementioned optimization of word length is naturally a method to balance data path performance and power consumption. In order to further reduce the power consumption of this system, this section will introduce two techniques to further optimize power consumption. One is to develop an LMS algorithm based on random selection, which enables it to reduce the power consumption of the tap weight calculation with negligible performance degradation. Another is to replace the traditional moving average filter with an average calculator based on the sigma-delta algorithm to significantly reduce the averaging hardware.
## 2 Materials and Methods
### UL Rehabilitation Platform
#### 2.1.2 Leap Motion  
The Leap Motion controller [35] is a commercial USB peripheral device designed for hand tracking. The heart of the device consists of two monochromatic infrared (IR) cameras and three infrared LEDs. These LEDs generate patternless IR light with a wavelength of 850 nanometers, which is outside the visible light spectrum. The light is reflected by the objects and reaches the two cameras, which generate almost 200 frames per second while capturing these data. The data received by the camera are read and treated with necessary resolution adjustments in the memory included in the device. At this point, the obtained data are sent through a USB to the host computer. Here, the Leap Motion service software processes the images using heavy mathematical tools. It first compensates for background objects and ambient environmental lighting. Then it reconstructs a 3D representation of what the device sees. Moreover, the tracking algorithms extract information relative to the positions of the hand and fingers. After a filtering process, the information is managed by the software and expressed as a series of frames, or snapshots, containing all of the tracking data.  
In this paper, the Leap Motion controller was selected because it is a low-cost device with a small observation area and high resolution that make it suitable for upper limb and hand rehabilitation. It is used to track index finger and thumb movement and to provide  
Figure 3: Position behavior of the thimble to the serial port commands sent by the microcontroller.  
position information from the serious games. Moreover, it allows quantitative information to be collected about the patients' movements.
## 2 Results and Discussion
### C6 Volatile Level, Odor Contribution, and Cultivar and Vintage Effect
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Volatiles**} & \multicolumn{3}{c}{**Cultivar**} & \multicolumn{3}{c}{**Vintage**} & \multicolumn{3}{c}{**Cultivar \(\times\) Vintage**} \\ \cline{2-10}  & **F Value** & \(\mathbf{p}\) & **Sig.** & **F Value** & \(\mathbf{p}\) & **Sig.** & **F Value** & \(\mathbf{p}\) & **Sig.** \\ \hline Hexanal & 75.4 & \(7.49\times 10^{-10}\) & *** & 468.3 & \(3.68\times 10^{-12}\) & *** & 64.3 & \(2.19\times 10^{-9}\) & *** \\ (\(E\))-2-Hexenal & 359.68 & \(1.65\times 10^{-14}\) & *** & 2215.01 & \(<\)\(2\times 10^{-16}\) & *** & 92.62 & \(1.86\times 10^{-10}\) & *** \\ Total C6 aldehydes & 184.85 & \(1.65\times 10^{-12}\) & *** & 1309.51 & \(3.12\times 10^{-15}\) & *** & 497.41 & \(1.20\times 10^{-8}\) & *** \\ 1-Hexanol & 12.81 & \(5.56\times 10^{-5}\) & *** & 18.41 & 0.0075 & *** & 2.55 & 0.06964 & \\ (\(E\))-2-Hexen-1-ol & 36.56 & \(9.04\times 10^{-8}\) & *** & 512.24 & \(4.88\times 10^{-6}\) & *** & 22.64 & \(1.89\times 10^{-6}\) & *** \\ (\(Z\))-2-Hexen-1-olol & 35.89 & \(1.02\times 10^{-7}\) & *** & 598.84 & \(6.87\times 10^{-8}\) & *** & 35.89 & \(1.02\times 10^{-7}\) & *** \\ (\(E\))-3-Hexen-1-ol & 32.64 & \(1.44\times 10^{-6}\) & *** & 130.75 & \(1.73\times 10^{-8}\) & *** & 16.62 & \(1.33\times 10^{-5}\) & *** \\ (\(Z\))-3-Hexen-1-ol & 70.79 & \(<\)\(2\times 10^{-6}\) & *** & 1833.20 & \(3.02\times 10^{-16}\) & *** & 293.7 & \(6.72\times 10^{-14}\) & *** \\ Total C6 alcohols & 34.15 & \(1.40\times 10^{-7}\) & *** & 58.07 & \(2.40\times 10^{-6}\) & *** & 19.84 & \(4.24\times 10^{-6}\) & *** \\ Ethyl hexanoate & 2.49 & \(0.075929\) & 23.14 & 0.0028 & *** & 2.84 & 0.05024 & \\ Hexyl acetate & 17.89 & \(7.94\times 10^{-6}\) & *** & 1.3 & 0.274 & 6.79 & 0.00155 & *** \\ (\(Z\))-3-Hexenyl acetate & 189.51 & \(1.39\times 10^{-12}\) & *** & 40.95 & \(1.66\times 10^{-5}\) & *** & 25.98 & \(8.01\times 10^{-7}\) & *** \\ Total C6 esters & 42.34 & \(3.48\times 10^{-8}\) & *** & 3.3 & 0.09098 & 7.24 & 0.00114 & *** \\ Hexanoic acid & 11.73 & \(9.11\times 10^{-5}\) & *** & 78.22 & \(4.18\times 10^{-7}\) & *** & 4.76 & 0.00764 & ** \\ \hline \hline \end{tabular} The F value of the cultivar, vintage, and their interactive effects is calculated using the volatile concentration of C6 compounds in table grapes during the different stages of development. ** and *** indicate a significant effect at \(p<0.01\) and 0.001.  
\end{table} 
Table 1: Two-way ANOVA analysis on the concentration of C6 volatiles in seven table grape cultivars during berry development.
## 1 Introduction  
It is now known that it is possible to determine the individual characteristics of the author on the basis of the writing style, since each text has a specific linguistic personality [1].  
The topic of attribution overlaps with information security [2; 3; 4; 5]. With the constant increase in volume of transmitted and received documents, there are many opportunities for the illegal use of personal data. An example is a type of fraud in which an attacker sends an employee of an organization an email on behalf of a manager asking them to perform a specific action (e.g., to divulge confidential information of the organization or to transfer funds). In addition, quite often there are situations related to hacking the victim's social media accounts and sending messages on the victim's behalf. One solution to this kind of problem is to compare the writing style of the suspicious texts with others for which it is certain that they were written by the person. As a result of the comparison, it is possible to determine the author. Establishing general differences in the documents based on the writing style is most relevant if there are no other data that would allow the author to be identified.  
One type of violation in cyberspace is a copyright infringement and related rights of a text, which can be expressed, for example, by claiming a text by another author for material gain or attempting to pass off the authorship of a created text as the authorship of another person. The effectiveness of intellectual property protection in the digital space is determined by an ability to resist such violations and threats of their occurrence. Authorship identification methods allow determining such infringements and establishing the identity of the text creator.  
Interest in the topic is also due to a growth in the volume of text data, the evolution of technology, and social networks. Thus, automatic identification of authorship is a growing area of research, which is also important in the fields of forensic science and marketing.  
In this article, we solve the problem of identifying the author of a Russian-language text using a support vector machine and deep neural networks. Literary texts written by Russian-speaking writers were used as input data. The article includes an overview of related works, the statement of the text authorship problem, a detailed description of approaches to solving the authorship identification problem, an impact evaluation of attacks on the developed approaches, and a discussion of the results obtained.
## 2 Materials and Methods
### Response Surface Method  
Applying statistical principles, RSM can not only qualitatively analyze the relationship between input variable and output variable, but also quantitatively analyze the relationship between them [37]. The best test conditions and procedures can be determined by selecting a suitable fitting model based on the experimental data. The Box Behnken design (BBD) is a RSM design method which can evaluate the nonlinear relationship between indicators and factors. BBD can reduce the number of trials and is a cost-effective method [38]. The number of experiments can be calculated by (2k \((\mathrm{k}-1)+\mathrm{n}\)), where k is the number of independent variable factors and n is the number of center points.  
In this study, the BBD design was performed using the Design-Expert 8\({}^{\text{\textregistered}}\) software (Stat-Ease, Inc., Minneapolis, MN, USA). The experimental design required 17 experimental runs. The center point was designed to be 5. The independent variables were WBR, BFC and RC. Among them, the rubber content was the percentage of replacing fine aggregate. The slump (SP), flexural strength (FS)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Water** & **Cement** & **Fine Aggregate** & **Coarse Aggregate** & **Basalt Fiber** & **Crumb Rubber** & **Superplasticizer** \\  & **(kg/m3)** & **(kg/m3)** & **(kg/m3)** & **(kg/m3)** & **(kg/m3)** & **(kg/m3)** & **(kg/m3)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 4: The mix proportion of RBFC.  
and compressive strength (CS) of the mixture were used as response variables. Based on the previous research [32; 33; 34; 39], the levels of the three preparation parameters were determined. When using BBD to design the test group, the preparation parameters were required to be three levels. In addition to the two levels of maximum and minimum, the median is taken as another level, so that the best prediction results can be achieved through different design combinations of three factors and three levels. Table 5 shows the levels of the three preparation parameters.  
According to the test data, the surface model can be fitted well, and the fitting formula is as follows:  
\[\text{R}=\beta_{0}+\sum_{1}^{k}\beta_{i}X_{i}+\sum_{1}^{k}\beta_{ii}X_{i}^{2}+ \sum_{i<j}^{k}\beta_{ij}X_{i}X_{j}+\varepsilon \tag{1}\]  
where R is the response, \(X_{i}\) and \(X_{j}\) are the coded independent variables, \(\beta_{0}\) is the mean value of response constant coefficient, \(\beta_{i}\) is the linear effect of independent variable \(X_{i}\), \(\beta_{ii}\) is the secondary effects of \(X_{i}\), \(\beta_{ij}\) is the linear interaction between \(X_{i}\) and \(X_{j}\), and \(\varepsilon\) is the random error [40; 41].
# Towards a Glass New World: The Role of Ion-Exchange in Modern Technology
## 4 Perspectives & Future Insights
### All-Glass Flexible Photonics
Another strategy to partially expose the noble metal NPs on the surface of a WGM resonant glass microcavity could be represented by a chemical etching step in an extremely dilute solution of hydrofluoric acid (HF), in order to preserve as much as possible a low value of surface roughness for the microresonator. In recent years, with the improvement of manufacturing techniques, there has been a continuous and growing interest in the realisation of new hybrid optical fibres (HOFs), in which the co-presence within a silica host fibre of multi-materials (e.g., glasses, crystals, semiconductors, etc.) and different guiding structures (e.g., non-linear waveguides, photonic crystals, semiconductor junction, optoelectronic elements, etc.) contributes to broaden their application horizon towards the achievement of an all-fibre photonic platform [221; 222; 223; 224; 225; 226]. In this context, ion-exchange in molten salts of noble metals with subsequent thermal post-process may induce the nucleation and growth of plasmon nanoparticles at the end of a possible HOF, whose core is made up of a compound glass suitable for ion-exchange (e.g., silicate, phosphate, tellurite, etc.). Alternatively, the end of a multi-mode silica optical fibre loaded with a thin layer of soft glass could be used for the same purpose. In the near future, these strategies could represent an effective low-cost solution towards the realisation of optical fibre probes for SERS applications in comparison with current lithographic techniques, such as electron beam lithography (EBL) or nanoimprinting lithography (NIL), surely more effective but definitely complicated and expensive technologies [227; 228]. Figure 12 represents some configurations for the development of novel fibre probes for SERS applications. In particular, Figure 12a reports the concept of multimaterial HOF while Figure 12b,c show two possible schemes, one based on HOF and the other on a multimode optical fibre (MOF). Finally, another application that deserves to be investigated concerns the fabrication of compound glass nanotips [229] with embedded noble metal nanoparticles--following ion-exchange and thermal annealing processes--for SERS and tip enhanced Raman spectroscopy (TERS) applications [230; 231]. Figure 11: A sketch of the formation mechanism of silver nanoparticles on the WGM microsphere surface by means of a laser beam irradiation. The nanoparticles (NPs) result partially exposed as reported in detail in the inset.
## 4 Construction and Analysis of Sliding Mode Control Based on RBF Neural Network
### Simulation and Experimental Analysis  
Quantitative gait analysis was conducted to evaluate trajectory tracking stability during exoskeleton-assisted ambulation. Participants completed 10-s walking trials while wearing the robotic system (Figure 8). Kinematic data from reflective markers were acquired at 100 Hz using a Qualisys motion capture system, and then processed through MATLAB. The resulting reference trajectories for sagittal plane movements--hip flexion/extension, knee flexion/extension, and ankle plantarflexion/dorsiflexion--are detailed in Figure 9.  
Figure 10 shows the trajectory tracking curves of the hip, knee and ankle joints, where the blue solid line is the desired gait rehabilitation training trajectory of the lower limb, the red solid line is the trajectory tracking curve of the RBF neural network sliding mode control algorithm, and the orange solid line is the trajectory tracking curve of the traditional sliding mode control algorithm. It can be seen that in the trajectory curve tracking, the control algorithms designed in this paper are better than the traditional sliding mode control, and can approximate the desired curve faster. It meets the control requirements of lower limb exoskeleton rehabilitation robot training and can ensure the safety of patients.  
Figure 8: Lower limb gait rehabilitation.  
Figure 9: Expected trajectory of the joints of the lower limbs.  
Figure 10 presents the joint angle tracking performance of the lower limb exoskeleton rehabilitation robot. Quantitative analysis demonstrates that the proposed control algorithm exhibits superior performance compared to conventional sliding mode control, achieving 32% faster convergence to the desired trajectory. The maximum tracking errors were measured at 1.77\({}^{\circ}\) (hip flexion/extension), 1.87\({}^{\circ}\) (knee flexion/extension), and 0.72\({}^{\circ}\) (ankle plantar flexion/dorsiflexion), satisfying clinical requirements for rehabilitation training while ensuring patient safety.  
Figure 11 shows the control input curves of the hip and knee joints of the lower limb exoskeleton rehabilitation robot, where the blue solid line is the control input of the RBF neural network sliding mode control algorithm, and the red dashed line is the control input of the traditional sliding mode control algorithm. It can be seen that the input fluctuation of the traditional sliding mode control is very obvious, although it can meet the requirements of tracking accuracy, it increases the wear on the motor. The control algorithm designed in this paper has a smooth input, and through the good approximation characteristics of the neural network, the jitter vibration is effectively suppressed to ensure the safety and comfort of patients. The effectiveness of the controller designed in this paper is proven.  
Figure 10: Trajectory tracking curves of each joint of a lower limb exoskeleton rehabilitation robot. (**a**) Hip trajectory tracking curve; (**b**) knee trajectory tracking curve; and (**c**) ankle trajectory tracking curve.
## 2 Materials and Methods  
Antimony-germanate-borate glasses (labeled as SGB) with the following chemical composition (mol.%): 25 Sb\({}_{2}\)O\({}_{3}\)-25 GeO\({}_{2}\)-(39.8-x)B\({}_{2}\)O\({}_{3}\)-5 Al\({}_{2}\)O\({}_{3}\)-5 Na\({}_{2}\)O\({}_{3}\)-xAgNO\({}_{3}\)-0.2 Eu\({}_{2}\)O\({}_{3}\), where \(x=0\); 0.1; 0.3 and 0.6, were synthesized by a standard melt-quenching method. All raw materials (30g) with 99.99% (Sigma-Aldrich) purity were homogenized and melted in a platinum crucible in an electrical furnace at 1350 \({}^{\circ}\)C for 30 min. in an air atmosphere. Next, in order to obtain glassy rods, the liquid glass was poured into the pre-heated brass cast and then annealed at 300 \({}^{\circ}\)C for 12 h, in order to release internal stress. Fabricated glass rods with 10 mm of diameter were cut (3 mm slides) and polished in order to meet the requirements for optical measurements. Silver ion reduction in SGB glasses was obtained by thermal-treatment at around the glass transition temperature (T\({}_{\rm hT}\) = 360 \({}^{\circ}\)C), with a time range from 1 h to 4 h in 1h gaps.  
The rod with a 10 mm diameter was used for fabrication of the glass fiber (GF) using an SG Controls drawing tower. The drawing temperature of SGB glass was estimated at 580 \({}^{\circ}\)C. The fabricated GF with 160 \(\upmu\)m diameter were also annealed in an manner analogous to glass samples in a special construction of tube furnace CZYLOK PRC 50X630-3/110 (Czylok Company, Jastrzebie Zdroj, Poland), with a 300 mm width stabilized heat zone. The temperature control accuracy was \(\pm\)1 \({}^{\circ}\)C.  
The characteristic temperatures of glass were determined by DSC measurement performed using the SETRAM Labsys thermal analyzer (Setaram Instrumentation, Caluire, France), with a heating rate of 10K/min. The absorbance spectra of glass fibers were measured in the range of 300-600 nm by Stelarnet Green-Wave spectrometer with incident light emitted from SL5-DH UV-VIS Light Source (StelarNet Inc., Tampa, FL, USA). The excitation spectra of the glasses in a range of 350-500 nm were measured using a Jobin Yvon Fluoromax4 (HORIBA, Kyoto, Japan) spectrophotometer. The luminescence spectra of europium ions were registered in measurement setup equipped with: 100 mm integrated sphere, 394 nm laser excitation, and Stelarnet Green-Wave spectrometer. The morphology of prepared samples was examined by FEI Company Nova Nano SEM 200 scanning electron microscope. The analyses were carried out in the secondary electron mode (SE). Prior to analyses, the samples were covered with a 5 nm carbon layer.
# Scale-Free Features in Collective Robot Foraging
## 2 Methods
### Robot Behavior
#### 2.1.2 Communication Modes  
We focus on the local communication between the robots and their influence on the global swarm behavior. A common approach is to restrict robot communication _only_ to the area within the nest. This approach is inspired by several natural systems, in which the communication takes place mainly inside the nest or hive, such as in the case of ants or honey bees [14; 47; 48; 49; 50]. Moreover, this approach accommodates two relevant properties of foraging systems: (i) it is common that the foraging area is significantly larger than the nest area, and hence, individual encountering rates outside the nest are negligibly low; (ii) high density of individuals within the nest leads to more accurate information about the environment due to the high encounter rate of individuals that explored different, distant parts of the foraging area.  
Regarding particular communication strategies, it is common to let robots broadcast the last exploration result only _once_, namely when the robots switch to the _resting_ state. Henceforth, we will refer to this approach as the _discontinuous communication mode_ (DCM), because after broadcasting the message once, the active communication of the robot is interrupted and is limited to listening. In contrast, we use the term _continuous communication mode_ (CCM) to refer to the mode in which robots continue broadcasting the result of their last foraging attempt at every time step until they switch back to the _exploring_ state. As we will see later, the difference between these two modes does not have substantial impact on swarm performance. However, it has a significant impact on the statistical distribution of various system features for which we study the scale-free property.
# Adaptive Threshold-Aided K-Best Sphere Decoding for Large MIMO Systems
###### Abstract  
This paper proposes an adaptive threshold-aided K-best sphere decoding (AKSD) algorithm for large multiple-input multiple-output systems. In the proposed scheme, to reduce the average number of visited nodes compared to the conventional K-best sphere decoding (KSD), the threshold for retaining the nodes is adaptively determined at each layer of the tree. Specifically, we calculate the adaptive threshold based on the signal-to-noise ratio and index of the layer. The ratio between the first and second smallest accumulated path metrics at each layer is also exploited to determine the threshold value. In each layer, in addition to the \(K\) paths associated with the smallest path metrics, we also retain the paths whose path metrics are within the threshold from the \(K\)th smallest path metric. The simulation results show that the proposed AKSD provides nearly the same bit error rate performance as the conventional KSD scheme while achieving a significant reduction in the average number of visited nodes, especially at high signal-to-noise ratios.  
Keywords:multiple-input multiple-output (MIMO); sphere decoder (SD); K-best SD (KSD) +
Footnote †: journal: Applied Physics Letters