esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
## 6 Infrastructure Control Policy
### Local Policy: Reinforcement Learning Scaling Policy
* \(c_{resource}\) accounts for the cost of the active computing nodes throughout the next time interval. For simplicity, we assume that we have a constant cost per node. Using the _Simple Additive Weighting_ technique [50], we define single cost function \(c(s,a,s^{\prime})\) as the weighted sum of the different costs (normalized in the interval \([0,1]\)):  
\[c(s,a,s^{\prime}) = w_{d}\frac{\mathds{1}_{\{M_{r}>0\}}c_{demand}}{c_{demand}}+w_{ r}\frac{(k+a)c_{resource}}{K_{max,r}c_{resource}} \tag{1}\] \[= w_{d}\mathds{1}_{\{M_{r}>0\}}+w_{r}\frac{k+a}{K_{max,r}}\]  
where \(\mathds{1}_{\{\cdot\}}\) is the indicator function, \(M_{r}\) refers to the number of resource acquisition requests coming from the applications that are unsatisfied, and \(w_{d}\) and \(w_{r}\), with \(w_{d}+w_{r}=1\), are non-negative weights for the different costs. Intuitively, the cost function allows us to _instruct_ the RM to discriminate between the _good_ system configurations and actions and the _bad_ configurations and actions (the larger the cost, the worse the configuration). As the RM aims at minimizing the incurred cost, it is encouraged by the cost function: (i) to reduce the number of active nodes; and (ii) to avoid rejecting resource acquisition requests coming from the application. The different weights allow us to express the relative importance of each cost term. Differently from the simple solution described in Section 6.1, this policy directly optimizes metrics that represent the ICS objectives. Therefore, by appropriately setting the weights, we expect the the local cost function to guide the agent towards meeting the global performance goals in a decentralized way.
# Machine Learning Algorithms for the Prediction of the Seismic Response of Rigid Rocking Blocks
## 3 Machine Learning Algorithms
### Binary Classification  
For the binary classification sub-problem, the following algorithms were considered:  
* Artificial neural network (ANN): An artificial neural network consists of a set of nodes (neurons) arranged in layers. The first layer is the input layer, where the input vectors are passed. Subsequently, each neuron takes as input the output of the previous layer and produces a weighted sum of all its inputs, which it then passes through a so-called activation function. This procedure introduces non-linearities, which allow the model to learn complex structures in the data. The final (output) layer for a binary classification problem consists of a single node with a specialized activation function, usually a step function, sigmoid, or _tanh_, which gives the probability that the given input belongs to the positive class and classifies it according to a user-defined probability threshold. The algorithm learns by minimizing the differences between its predictions and the true values in an iterative process known as backpropagation [66]. The implementation of the ANN classifier was carried out in MATLAB.
* Gradient boosting classifier: The gradient boosting classifier is a meta classifier [67], i.e., it combines weaker models, which are usually decision trees [68]. At each iterative training step, it improves its performance using the current predictions and a user-defined loss function [69]. However, even though this is a powerful algorithm, its training can be very demanding, both in time and computational resources, especially in large datasets with continuous features, such as the one used in the present study. This is because, for the creation of each individual tree, the algorithm must consider each feature and each distinct value separately [70]. To alleviate this, histogram-based methods have been developed [71; 72], which partition the numerical values of each continuous feature into bins before training. The implementation was carried out in Python programming language using the dedicated machine learning library scikit-learn[73].
## 2 Related Work
### Lightweight and Efficient Attention Mechanisms  
The attention mechanism dynamically allocates channel-wise weights to achieve adaptive adjustment across channels, thereby enhancing the model's responsiveness to critical feature channels. The introduction of this mechanism significantly improves the discriminative power of feature representations and promotes overall model performance optimization. Reference [23] improved the conventional Squeeze-and-Excitation (SE) mechanism by integrating multi-scale convolutions for weighted channel fusion and introducing an adaptive channel attention mechanism to enhance focus on key features while effectively suppressing irrelevant information. This approach offers a novel perspective for audio denoising tasks. Reference [24] proposed an extension of the SE module into a three-dimensional framework (Time-Frequency-Channel SE), expanding the traditional two-dimensional SE module--primarily operating along the channel dimension--to simultaneously address time, frequency, and channel dimensions. This enables finer-grainedcapture of spatiotemporal characteristics of audio events, significantly improving detection accuracy. Moreover, this mechanism dynamically adjusts weights across different positions, strengthening the representation of salient signals while reducing the impact of background noise. References [25; 26; 27] present various enhancements to the SE mechanism, achieving notable performance gains. Building upon these advances, the present work designs a lightweight SE mechanism that retains essential core components and incorporates reward and penalty strategies during training, thereby effectively improving training stability and model robustness.
###### Abstract  
In the ever-evolving landscape of modern technology, integrating communication and sensing systems has become increasingly essential for a wide range of applications, from military and defense to autonomous vehicles and beyond. The integration offers a convergence of capabilities that enhances operational efficiency and provides adaptability in complex environments. In this paper, we develop, in simulation and experiment, an integrated communication and sensing system, exploring the cutting-edge utilization of spread spectrum and radio-over-fiber (RoF) photonic technologies. RoF technology inherits the benefits of optical fibers, which include low attenuation and longer reach distance compared to other media. First, we consider the integration of communication and sensing functions using a spread spectrum-binary phase-shift keying waveform. In this integrated system, the sensing function is performed using a radar system. The performance of the proposed system is evaluated in terms of the peak-to-sidelobe ratio of the radar correlator output and the bit error rate for the communication system. The results are obtained through extensive MATLAB simulations. Next, we consider the realization of the proposed integrated communication and sensing system using photonics technology. This phase commences with the utilization of specialized photonics-based software for extensive simulations at different fiber lengths, which is an essential foundational step toward the practical implementation of the proposed system using photonics. Lab experiments are also presented to validate the simulation results.  
Keywords:integrated sensing and communication (ISAC); joint radar and communication (JRC); spread spectrum; radar; photonics +
Footnote †: journal: _Photonics_
# Research on Cloud Task Scheduling Algorithm with Conflict Constraints Based on Branch-and-Price
## 5 Cloud Task Scheduling Algorithm Based on Branch-and-Price
### Specific Implementation Steps of the Algorithm  
Figure 3 shows the flow chart of BPTSC, and the overall process of the algorithm is as follows:  
Step 1 By the initial solution generation algorithm, we identify all task conflict maximum cliques, and generate the initial solution and the upper bound to form the root node.  
Step 2 Initialize the branch-and-price search tree and add the root node.  
Step 3 Select a node by the depth first strategy and initialize the problem model with the information of this node.  
Step 4 Solve the Lagrange relaxation problem through column generation. If the solution is an integer solution, skip to Step 6; otherwise, skip to Step 5.  
Step 5 Calculate the feasible integer solution generated by this node, and if it is less than the current upper bound, update the upper bound value. Compare the Lagrangian relaxation optimal solution at this node with the upper bound value. If it is less than the upper bound, branch based on the above branching strategy and add the new nodes to the set of unsolved nodes.  
Step 6 If the set of unsolved nodes is not empty, skip to Step 3. Otherwise, the current upper bound is the optimal integer solution, and the algorithm is terminated.
# Understanding and Enhancement of Internal Clustering Validation Indexes for Categorical Data
## 4 Internal Clustering Validation Index: CUBAGE
### Cibage Index
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Dataset** & _CUBAGE_ & \(-F\) & \(-E\) & \(\textit{CUI}_{1\textit{k}}\) & _Clope_\({}_{1}\) & _Clope_\({}_{2}\) & _Clope_\({}_{3}\) & **R** \\ \hline Voting & (1) 0.489 & (7) 0.292 & (7) 0.292 & (1) 0.489 & (1) 0.489 & (1) 0.489 & (1) 0.489 & (6) 0.396 \\ Breast & (1) 0.704 & (7) 0.337 & (7) 0.337 & (1) 0.704 & (1) 0.704 & (1) 0.704 & (1) 0.704 & (6) 0.609 \\ Mushroom & (5) 0.362 & (6) 0.339 & (6) 0.339 & (3) 0.368 & (8) 0.256 & (1) 0.412 & (1) 0.412 & (3) 0.368 \\ Soybean & (1) 1 & (4) 0.745 & (4) 0.745 & (1) 1 & (6) 0.669 & (6) 0.669 & (3) 0.878 & (6) 0.669 \\ Car & (4) 0.031 & (1) 0.053 & (1) 0.053 & (3) 0.05 & (4) 0.031 & (4) 0.031 & (4) 0.031 & (4) 0.031 \\ Heart & (1) 0.216 & (5) 0.167 & (5) 0.167 & (1) 0.216 & (1) 0.216 & (5) 0.167 & (5) 0.167 & (1) 0.216 \\ Dermatology & (1) 0.687 & (4) 0.597 & (4) 0.597 & (1) 0.687 & (6) 0.473 & (6) 0.473 & (1) 0.687 & (6) 0.473 \\ Zoo & (1) 0.85 & (2) 0.764 & (2) 0.764 & (4) 0.741 & (5) 0.522 & (5) 0.522 & (5) 0.522 & (5) 0.522 \\
**Average NMI \({}^{*}\)** & 0.542 & 0.412 & 0.412 & 0.532 & 0.42 & 0.433 & 0.486 & 0.411 \\
**Average Rank \({}^{*}\)** & 1.875 & 4.5 & 4.5 & 1.875 & 4 & 3.625 & 2.625 & 4.625 \\ \hline \end{tabular} \({}^{*}\) Averaged over datasets (average values of each column).

\end{table} 
Table 6: Normalized mutual information (NMI) values of the hierarchical partitions chosen from layers 2–10 by each internal CVI. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Dataset** & _CUBAGE_ & \(-F\) & \(-E\) & \(\textit{CUI}_{1\textit{k}}\) & _Clope_\({}_{1}\) & _Clope_\({}_{2}\) & _Clope_\({}_{3}\) & **R** \\ \hline Voting & (1) 0.557 & (7) 0.142 & (7) 0.142 & (1) 0.557 & (1) 0.557 & (1) 0.557 & (6) 0.337 \\ Breast & (1) 0.808 & (7) 0.18 & (7) 0.18 & (1) 0.808 & (1) 0.808 & (1) 0.808 & (1) 0.808 & (6) 0.72 \\ Mushroom & (5) 0.288 & (7) 0.161 & (7) 0.161 & (1) 0.375 & (6) 0.286 & (3) 0.318 & (3) 0.318 & (1) 0.375 \\ Soybean & (1) 1 & (7) 0.431 & (7) 0.431 & (1) 1 & (4) 0.501 & (4) 0.501 & (3) 0.781 & (4) 0.501 \\ Car & (1) 0.066 & (7) & (7) & (7) & (6) 0.008 & (1) 0.066 & (1) 0.066 & (1) 0.066 & (1) 0.066 \\ Heart & (1) 0.289 & (5) 0.061 & (5) 0.061 & (1) 0.289 & (1) 0.289 & (5) 0.061 & (5) 0.061 & (1) 0.289 \\ Dermatology & (1) 0.563 & (4) 0.359 & (4) 0.359 & (1) 0.563 & (6) 0.33 & (6) 0.33 & (1) 0.563 & (6) 0.33 \\ Zoo & (1) 0.872 & (3) 0.522 & (3) 0.522 & (2) 0.715 & (5) 0.42 & (5) 0.42 & (5) 0.42 & (5) 0.42 \\
**Average ARI \({}^{*}\)** & 0.555 & 0.232 & 0.232 & 0.539 & 0.407 & 0.383 & 0.447 & 0.38 \\
**Average Rank \({}^{*}\)** & 1.5 & 5.875 & 5.875 & 1.75 & 3.125 & 3.25 & 2.5 & 3.75 \\ \hline \end{tabular} \({}^{*}\) Averaged over datasets (average values of each column).

\end{table} 
Table 7: Adjusted Rand index (ARI) values of the hierarchical partitions chosen from layers 2–10 by each internal CVI. \begin{table}
## 3 Results
### Condenser Temperature  
The effect of condenser temperature on net power production for the proposed novel cycle and performance improvement in comparison with the reference cycle for each fluid is given in Figure 14. As shown in Figure 14, net power decreases with the increasing condenser temperature. Higher condenser temperatures increase the quality values of evaporator inlets, leading to a decrease in \(\Delta\)h at evaporators and, an increase in \(\dot{m}_{11}\) and \(\dot{m}_{13}\). Higher \(\dot{m}_{11}\) increases the power consumption of Compressor 1, as it operates under the same conditions regardless of the condenser temperature. Additionally, Compressor 2 consumes more power due not only to higher \(\dot{m}_{11}\) and \(\dot{m}_{13}\) but also to an increase in pressure ratio caused by the rising condenser temperature and pressure. Furthermore, the power produced by the turbine decreases as the condenser temperature and pressure increase. Within the research range of the working fluids in this study, the highest \(\dot{W}_{net}\) is achieved by R290 at \(T_{Cond}\) of 30 \({}^{\circ}\)C, while the lowest is observed at \(T_{Cond}\) of 48 \({}^{\circ}\)C for R601. Although R290 yields higher net power production compared to other fluids, it becomes thermodynamically infeasible to operate the cycle with this fluid at condenser temperatures above 30 \({}^{\circ}\)C while the other parameters are in main operating conditions. This limitation arises from exceeding the allowable pinch point temperature difference in the ORC evaporator. As the condenser temperature increases, the temperatures at both the pump inlet and outlet also increase, which leads to a higher temperature for the refrigerant entering the ORC evaporator. Heat transfer between the geothermal source and the refrigerant is designed to maintain at least a 5 \({}^{\circ}\)C temperature difference at the pinch point, defined at the location where the refrigerant begins to evaporate. However, as the condenser temperature increases for R290, the temperature difference between the geothermal water outlet and the refrigerant inlet becomes smaller than 5 \({}^{\circ}\)C at ORC evaporator. This exceeds the required pinch point condition and makes the cycle configuration unacceptable beyond this limit. The condenser temperature investigation reveals that, for R1234 ze(E), the proposed novel cycle produces 9.6% more net power than the reference cycle at a condenser temperature of 30 \({}^{\circ}\)C. When the condenser temperature is increased to 48 \({}^{\circ}\)C, this improvement increases to 45%. These results indicate that as the condenser temperature increases, the net power improvement of the proposed novel cycle increases. Although the net power output improvement compared to the reference cycle shows slight variations among different working fluids, the overall trend remains consistent. The variation in net power improvement among the fluids is limited to approximately 5%, suggesting that the comparative benefit of the novel cycle is largely independent of the working fluid selected and primarily results from its structural features.
## 2 Materials and Methods
### Optimization Design Based on Radial Basis Interpolation of Surrogate Models
#### 2.2.2 Evaluation Index for the Accuracy of the Nozzle Profile  
The flexible plate's elastic curve is formally characterized by a series of straight lines between the support points [10]. These straight lines can be overlapped in the curvature diagram of the pneumatic profile, which is a graphical representation of the curve's shape and position. The diagram also encompasses an area that represents the elastic curve and the theoretical curve between the coincident error. The smaller the enclosed area, the more accurate the fit. The optimal method for determining the precise area is to employ the integration method, whereby the position of the support point with the smallest encircled area is identified as the optimal position.  
It should be noted that not all aerodynamic curves are able to be obtained using a continuous slope curve. In the case of a known aerodynamic profile, the curve resulting from the two differentials is frequently discontinuous, thereby making it challenging to accurately evaluate the degree of profile fit using a curvature curve.  
In addition to the curvature deviation, the deviation of the elastic curve from the aerodynamic contour at the corresponding position of the support point can also be used as an evaluation index. This reflects the accuracy of the profile overlap more intuitively. However, the position of the driving point does not reflect the degree of overlap of the complete surface. Furthermore, this method has certain limitations. The root mean square error (RMSE) is a comprehensive measure that accounts for all the discrepancies between the observed data and the theoretical curves. This approach allows for a more precise characterization of the degree of overlap between the actual profile and the aerodynamic contour across the entire profile. Consequently, this study proposes the use of RMSE as a profile evaluation index.  
\[R=\sqrt{\frac{\sum(Y_{i}-Y_{i}^{\prime})^{2}}{N}}, \tag{21}\]  
where \(Y_{i}\) is the measured profile coordinates of the ith point, \(Y_{i}^{\prime}\) is the theoretical profile coordinates of the ith point, and N is the total number of points involved in the calculation.
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
###### Abstract  
Vendors implement the MAC address randomization technique to prevent IEEE 802.11 client station devices from being tracked. Although it conceals device identity, it cannot hide its occurring data transmission. This paper presents a novel covert channel that leverages the MAC address randomization technique to create a covert channel to hide data transmission inside IEEE 802.11 networks. The secret data are a disposable random MAC address generated by the IEEE 802.11 station as part of the probe request frame while scanning the network. The paper presents the concept of the covert channel, its implementation, and performance metrics. The study covers diverse scenarios, including the adaptation of the modified Selective Repeat ARQ protocol to alleviate the impact of the number of client stations and their offered loads on the covert channel. The results show that with the appropriate parameter selections, we can adapt the covert channel to produce excellent throughput, efficiency, delay, and jitter according to the environment in which it is installed.  
IEEE 802.11 networks; covert channels; MAC address randomization; ARQ protocol 2023
## 2 SPME-GC/MS Methods
### Wine Volatile Sulfur Compounds  
Various sulfur compounds are present in wine, such as thiols, sulphides, thioesters, and heterocyclic compounds. Thiol and thio-type compounds are in general associated with flavor defects of wine and they are classified as \"light\" (boiling point \(<\) 90 \({}^{\circ}\)C) and \"heavy\" (b.p. \(>\) 90 \({}^{\circ}\)C) compounds [25; 29; 89; 90; 91]. Sulfur compounds can be formed through several enzymatic and non-enzymatic processes, such as yeast fermentation and chemical, photochemical, and thermal reactions occurring in winemaking andduring wine storage [89,90]. Most prevailing are ethylmercaptan (EtSH; onion as aroma descriptor), dimethyl sulfide (DMS; grassy/truffle-like note), 2-furanmethanethiol (FFT; roasted coffee), diethyl sulfide (DES; cooked vegetables, onion, garlic), dimethyl disulfide (DMDS; cooked gabbage, intense onion), diethyl disulfide (DEDS; garlic, burnt rubber), methyl thioacetate (MTA), ethyl thioacetate (ETA), 2-mercaptoethanolethanol (ME; burnt rubber), 2-(methylthio)-1-ethanol (MTE; cauliflower), 3-(methylthio)-1-propanol (MTP; sweet, potato), 4-(methylthio)-1-butanol (MTB; earthly-like scent), benzothiazole (BT; rubber), and 5-(2-hydroxyethyl)-4-methylthiazole (HMT) [29,91]. Also, 2-methyl-3-furanthiol (MF; cooked meat), a very odoriferous compound with an odor threshold of 0.4-1.0 ppt, was found [92].  
A SPME method for analysis of 13 volatile sulfur compounds in wine (_i.e._, DMS, EtSH, DES, MTA, ETA, ME, DMDS, DEDS, BT, HMT, MTB, MTP, and MTE) with b.p. ranging from 35 \({}^{\circ}\)C to 231 \({}^{\circ}\)C by using a CAR/PDMS/DVB 50:30 \(\upmu\)m 2 cm length fiber, was developed [90]. By addition of MgSO\({}_{4}\) (1.0 M) to increase ionic strength of solution and performing the extraction at 35 \({}^{\circ}\)C, the method showed a high sensitivity for all the analytes.  
3-mercaptoethanol-1-ol (3-MH; passion fruit, grapefruit), 3-mercaptoethanol acetate (3-MHA; Riesling type note, passion fruit, box tree), and 4-methyl-4-mercaptoethanol-2-one (4-MP; blackcurrant or box tree note) are tropical fruit scenting volatiles present in wines at ng/L level [29]. A SPME-GC/MS method for analysis of these compounds by using a CAR/PDMS/DVB fiber was proposed [93]. HS-SPME conditions were optimized by performing the extraction of wine adjusted to pH 7 at 40 \({}^{\circ}\)C for 40 min [93].  
A method for analysis of thiols in wine by synthesis of the pentafluorobenzyl derivatives was also proposed. Derivatization was performed directly on the PDMS/DVB fiber (65 \(\upmu\)m), LODs achieved were 0.05 ng/L, 0.03 ng/L, 0.11 ng/L, 0.5 ng/L, and 0.8 ng/L for FFT, 3-MHA, MF, 4-MP, and 3-MH, respectively [94].
## 2 Suspension Platform Structure and Control Unit Model  
### Suspension Platform  
The PEMS platform structure is shown in Figure 2. The system is composed of a non-ferromagnetic frame, a non-ferromagnetic suspension platform, a rail permanent magnet, a platform permanent magnet, an electromagnetic coil, a transverse and longitudinal limit wheel and an angle-limit iron and laser displacement sensor; the electromagnetic coils at the four corners of the suspension platform correspond to four suspension control units. The track permanent magnet of the suspension system is composed of two groups of several permanent magnets according to the Halbach array, which are located under both sides of the suspension platform. The same Halbach permanent magnet structure is used on both sides of the platform permanent magnet. The mutual repulsion force between the track permanent magnet group and the platform permanent magnet group is used to realize the suspension of the platform. Through the dynamic adjustment on the magnetic force of the system generated by the electromagnet in the four suspension control units, the control mode of a \"permanent magnetic field as the basic bearing and electromagnetic control as the auxiliary means\" is formed, effectively improving the dynamic and static performance and maximum bearing capacity of the system.  
Figure 1: China red rail system.  
The structure of suspension control unit is shown in Figure 3, which is composed of a controller, a power amplifier, a laser sensor and an electromagnet and permanent magnet suspension group. The controller is used to adjust the conduction time and direction of the chopper in real time, according to the given value of the air gap and the signal value of the air gap collected by the laser sensor, so as to dynamically adjust the magnitude or direction of the generated electromagnetic force, which is superimposed with the static magnetic field generated by the permanent magnet and, together, makes the floating plate stable and has certain robustness.