esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, a new method is proposed to investigate GS between two identical FHN and HR neurons with unidirectional coupling. Based on the auxiliary system approach, GS between the drive and response systems occurs if synchronization between the response and auxiliary systems is achieved. Different from other researchers, the synchronization problem is solved in this paper by using the Laplace transform and the convolution theorem, as well as the iterative method in the theory of Volterra integral equations. An analytical criterion is ultimately obtained to determine the occurrence of GS in the drive-response system. Numerical simulations are carried out for the drive-response system consisting of two FHN neurons and two HR neurons to prove the correctness and efficiency of the analytical criterion.  
The functional relation in GS between two identical neurons is very special, referring to the situation where there is a time difference between the trajectories of the drive and response systems. Furthermore, the time difference has nothing to do with the GS condition, which depends on the coupling function between two neurons. According to [PERSON]'s expansion, one can easily construct a coupling function between two neurons by using the current signal of the drive system to predict its future signal or make its past signal reappear. Numerical simulations show that the coupling function based on [PERSON]'s expansion is valid to not only the FHN neural system exhibiting periodic spike behavior but also the HR neural system exhibiting burst-spike behavior.  
Drive-response synchronization techniques had typical applications in designing secure communication systems because they are typically similar to the transmitter-receiver structure. Some methods designing chaos-based secure communication systems require real-time synchronization between drive and response systems. However, time delay in signal transmission between the transmitter-receiver structure is inevitable. Our study provides an approach to predict future signals of a chaotic system based on its current signal, which may be used to overcome the problem of delay of signal propagating channels.  
Conceptualization, B.Z.; methodology, B.Z.; software, R.L.; investigation, B.Z. and R.L.; writing--original draft preparation, B.Z. and R.L.; writing--review and editing, B.Z.; visualization, R.L.; supervision, B.Z.; project administration, B.Z.; funding acquisition, B.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China grant number 11672185.  
Not applicable.  
The study did not report any data.  
The authors greatly appreciate the anonymous referees for their valuable suggestions and questions. The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China grant number 11672185.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Selected Metrics for Evaluating the Methods  
In order to evaluate the differences between the measured (\(T_{measured}\)) and predicted (\(T_{predicted}\)) operating temperature of the PV modules, three statistical metrics have been applied:  
* The correlation coefficient \(R^{2}\) defines the relationship between the estimated and measured data as the following expression: \[R^{2}=\frac{\big{(}\sum_{i}\Big{[}\Big{(}T_{predicted}-\overline{T}_{predicted }\Big{)}\big{(}T_{measured}-\overline{T}_{measured}\big{)}\Big{]}\big{)}^{2}}{ \sum_{i}\Big{(}T_{predicted}-\overline{T}_{predicted}\Big{)}^{2}\big{(}T_{ measured}-\overline{T}_{measured}\big{)}^{2}},\] (9)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Groups** & **Correlations** & **Comments** & **Ref.** & **Equation** \\ \hline \multirow{8}{*}{With wind} & Sandia: \(T_{m}=T_{a}+G_{g}\exp(a+bW_{s})\) & \(a=-3.56\) & \multirow{2}{*}{[3]} & \multirow{2}{*}{(2)} \\  & & \(b=-0.075\) s/m & & \\  & Faiman: \(T_{m}=T_{a}+\frac{G_{g}}{U_{0}+U_{1}W_{s}}\) & \(U_{0}=30.2\) W/m\({}^{2}\)/\({}^{\circ}\)C & & \\  & & \(U_{1}=6.28\) W/m\({}^{3}\)/\({}^{\circ}\)C & & \\  & PVSyst1: \(T_{c}=T_{a}+G_{g}\frac{\alpha(1-\eta_{m})}{U_{0}+U_{1}W_{s}}\) & \(U_{1}=1.2\) W/m\({}^{3}\)/\({}^{\circ}\)C & & \\  & & \(\alpha=0.9\), \(\eta_{m}=0.1\) & & \\  & Akhsassi: & & \(U_{L}=U_{0}+U_{1}W_{s}\) & \\  & & \(U_{L}=24.68\) W/m\({}^{2}\)/\({}^{\circ}\)C & & \\  & & \(U_{L}=6.13\) W/m\({}^{3}\)/\({}^{\circ}\)C & & \\ \hline \multirow{8}{*}{Without wind} & Lasnier: \(T_{m}=30\)\({}^{\circ}\)C + & \multirow{8}{*}{[14]} & \multirow{8}{*}{(6)} \\  & & & \\ \cline{1-1}  & 0.0175 \({}^{\circ}\)C\(\frac{\mathrm{m}^{2}}{\mathrm{W}}\left(G_{g}-300\frac{\mathrm{W}}{\mathrm{m}^{2}} \right)+1.14(T_{a}-25\)\({}^{\circ}\)C) & & \\ \cline{1-1}  & PVSyst2: \(T_{c}=T_{a}+G_{g}\frac{\alpha(1-\eta_{m})}{U_{0}+U_{1}W_{s}}\) & \(U_{0}=29\) W/m\({}^{2}\)/\({}^{\circ}\)C & & \\ \cline{1-1}  & Akhsassi: \(T_{m}=T_{a}+G_{g}\frac{\alpha(1-\eta_{m})}{U_{0}+U_{1}W_{s}}\) & \(U_{1}=0\) Ws/m\({}^{3}\)/\({}^{\circ}\)C & & \\ \cline{1-1}  & & \(\alpha=0.9\), \(\eta_{m}=0.1\) & & \\ \cline{1-1}  & Akhsassi: \(T_{m}=25\)\({}^{\circ}\)C + & & & \\ \cline{1-1}  & 0.123 \({}^{\circ}\)C\(\frac{\mathrm{m}^{2}}{\mathrm{W}}\left(G_{g}-200\) W/m\({}^{2}\right)+1.04(T_{a}-20\)\({}^{\circ}\)C) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Literature models for validating proposed models.  
* The root mean square error, used to evaluate the fluctuations around the model and defined by the expression: \[RMSE=\sqrt{\frac{\sum_{i}^{n}\left(T_{measured}-T_{predicted}\right)^{2}}{n}},\] (10)
# Towards a Reference Architecture for Cargo Ports
## 4 Patterns in a Cargo Port
### Cargo Port Drayage
#### 4.1.5 Structure  
Figure 6 presents the class diagram of a cargo port drayage operation. _Quay_ represents the platform for loading and unloading ships. _GateAccess_ represents the entrance to the terminal. Each quay has only one GateAccess since each port terminal has only one gate for entrance. _DrayageTransaction_ represents a single drayage transaction, where a transaction implies the delivery or pickup of a _container_ to/from the quay. DrayageTransaction consists of entities _truck_, _truck driver_, and _container_. For each drayage transaction, there is one truck driver in charge of one truck and one to two containers, depending on the size of the containers and whether they drop off and pick up a different one. GateAccess must verify that the drayage operation is a legitimate one. _ImportArea_ and _ExportArea_ represent the surfaces on the terminal assigned to stack the containers. Each container has a unique location in the storage yard, which is represented by _ImportLocation_ or _ExportLocation_. These unique locations are kept and updated in the log.
# Asymmetric Dual-Band Tracking Technique for Optimal Joint Processing of B1I and B1C Signals
## 4 The ASYM-DBT Method and Thermal Noise Performance Analysis
### The ASYM-DBT Method
#### 4.1.1 Correlator
\tag{14b}\]  
where \(R_{B1I}(\Delta\tau)\) and \(R_{B1C}(\Delta\tau)\) are the normalized ACFs of the B1I and B1C signals, respectively; \(d_{B1I}\) and \(d_{B1C}\) are the correlator spacing; T represents the pre-detection integration time; \(\Delta\tau=\tau-\hat{\tau}\), \(\Delta\theta_{s}=\theta_{s}-\hat{\theta}_{s}\), \(\Delta\theta_{c}=\theta_{c}-\hat{\theta}_{c}\) are the residual errors for code, subcarrier, and carrier phases; and \"...\" denotes the previous estimates of the variable. Besides this, \(\alpha=\pi(\mathrm{f}_{c}-\hat{\mathrm{f}}_{c}-\hat{\mathrm{f}}_{s}+\hat{ \mathrm{f}}_{s})\mathrm{T}\) and \(\beta=\pi(\mathrm{f}_{c}-\hat{\mathrm{f}}_{c}+\mathrm{f}_{s}-\hat{\mathrm{f}}_ {s})\mathrm{T}\) are introduced here to simplify the expressions. \(D\) represents the navigation message bit of the B1I signal because it does not have a pilot channel. Note that the amplitudes and ACFs of B1I and B1C are not the same due to the unequal power and different modulation schemes, and correlator spacing \(d\), as an adjustable parameter, may be set to different values for two groups of correlators.
# CoMP-Aware BBU Placements for 5G Radio Access Networks over Optical Aggregation Networks
## 3 Problem Formulation
### Decision Variables  
The decision variables of the BBU placement problem are described as follows in Table 2;  
Figure 1 shows the difference between the physical link and the virtual link. In this example, the virtual link, A-G, is routed over physical links A-B, B-C, and C-G. Therefore, \(Y^{AG}_{AB}\), \(Y^{AG}_{BC}\), and \(Y^{AG}_{CG}\) equal one, while \(Y^{AG}_{ij}\) for \(ij\
eq AB,BC,CG\) equal zero.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Inputs** & **Description** \\ \hline \(N\) & Set of nodes in the physical network, \(i,j,m,n\in N\) \\ \(S_{p}\) & Set of physical links, \(i-j\in S_{p}\) \\ \(S_{p}\) & Set of virtual links, \(m-n\in S_{p}\) \\ \(d_{ij}\) & The propagation delay introduced by the physical link \(i-j\) \\ \(D\) & The maximum allowable delay between cell site and the BBU pool \\ \(W\) & The number of wavelengths per each physical link \\ \(C_{q}\) & The computational effort in GOPS needed by a pool if it serves q RRHs \\ \(C\) & The maximum computational effort in GOPS that can be accommodated by a pool \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input sets and parameters.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Variable** & **Description** \\ \hline \(K_{i}=1\) & If node \(i\) hosts a BBU pool (binary) \\ \(X_{i,m}=1\) & If cell site m is assigned to a BBU pool at node \(i\) (binary) \\ \(Y^{\text{min}}_{ij}=1\) & If virtual link \(m-n\in S_{v}\) between cell site placed at node \(m\) and BBU pool placed \\  & at node \(n\) is routed over physical link \(i-j\in S_{p}\) (binary) \\ \(b_{i,q}=1\) & If the BBU pool hosted by node \(i\) serves \(q\) RRHs (binary). \\  & If node \(i\) does not host a pool, then \(b_{i,0}=1\) (binary). \\ \hline \hline \end{tabular}
\end{table}
Table 2: Decision Variables.
## 2 Materials and Methods
### Case Study Description  
Profiting from the database made available by the regional environmental monitoring institution (ARPA Lombardy Agency [24]), it was possible to retrieve air quality and weather data from a single, but relevant, data source (i.e., Milano via Juvara) near a university district in the city of Milan, Italy (Figure 1). These data were collected and elaborated only for 2019, given the yearly data's completeness and recentness, as described in the following sections.  
The weather and air quality station Milano via Juvara is located within the Buenos Aires-Venezia Local Identity Unit (NIL), a neighborhood characterized by low green area  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**O3 (ppm)** & **PM10 (ug/m3)** & **PM2.5 (ug/m3)** & **NH3 (ug/m3)** & **AQI** & **Category** \\ \hline
0.000–0.064 & 0–54 & 0.0–15.4 & 0–200 & 0–50 & Good \\
0.065–0.084 & 55–154 & 15.5–40.4 & 201–400 & 51–100 & Moderate \\
0.085–0.104 & 155–254 & 40.5–65.4 & 401–600 & 101–150 & Unhealthy for sensitive groups \\
0.105–0.124 & 255–354 & 65.5–150.4 & 601–800 & 151–200 & Unhealthy \\
0.125–0.374 & 355–424 & 150.5–250.4 & 801–1200 & 201–300 & Very unhealthy \\  & 425–504 & 250.5–350.4 & 1201–1800 & 301–400 & Hazardous \\  & 505–604 & 350.5–500.4 & \textgreater{}1800 & 401–500 & Hazardous \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters needed for \(AQI\) calculation for an 8 hour or 24 hour exposure period (extracted and edited from the Environmental Protection Agency (EPA) guidelines [31]). BP, Break Point.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pollutant** & **Demographic Groups at Higher Risk** \\ \hline Ozone (\(O_{3}\)) & People with lung disease, children, elderly, and people who are active outdoors \\ Particulate matter (\(PM_{2.5}\)) & People with heart or lung disease, elderly, and children \\ Particulate matter (\(PM_{10}\)) & People with heart or lung disease, elderly, and children \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensitive groups at risk by pollutant type when the \(AQI\) goes above 100 for an 8 hour (\(O_{3}\)) or 24 hour (\(PM_{10}\)) average exposure period (extracted and edited from the EPA guidelines [31]).  
coverage and mid-to-high built density, as documented by the Municipality of Milan [32] (see Figure 1). It was expected that the data collected would provide evidence to assume that the area has a low heat and pollution management capacity, which puts its inhabitants at a relevant increasing temperatures and air pollution SLODs risk.
# Fast Spectral Approximation of Structured Graphs with Applications to Graph Filtering
## 4 Approximate Graph Spectrum  
Now that we know there exist graphs with good (recursive) separators, what is left to answer is the following: _how well can we approximate the spectrum of a graph given a graph separator?_  
To address this question, we introduce our first result.  
**Theorem 1**: _Let \(\mathcal{G}=(\mathcal{V},\mathcal{E})\) be a graph for which a \((\alpha,\beta)\)-separator exists. Further, consider \(\mathcal{H}=(\mathcal{V},\mathcal{E}^{\prime})\) as the graph induced by removing the edges that connect the partition elements of \(\mathcal{G}\) obtained by the \((\alpha,\beta)\)-separator. If \(\max_{v\in\mathcal{V}}\deg(v)\in\mathcal{O}(1)\), then_  
\[W_{1}(\lambda,\lambda^{\prime})\in\mathcal{O}\left(\frac{f(n)}{n}\right), \tag{3}\]  
_where \(\lambda\) and \(\lambda^{\prime}\) are the spectra of \(\mathcal{G}\) and \(\mathcal{H}\), respectively, and \(W_{1}(\lambda,\lambda^{\prime})\) is the Wasserstein distance between the discrete spectra \(\lambda\) and \(\lambda^{\prime}\)._  
See Appendix B.  
This result motivates the use of graph separators for graph spectrum approximation. It shows that for families of graphs with _good separators_, i.e., \(f(n)\in o(n)\) and a maximum degree independent of the size of the graph, e.g., in a road map the number of highways connecting cities does not increase with the number of cities, we can obtain a good approximation of the spectrum of \(\mathcal{G}\) through the spectrum of \(\mathcal{H}\). The main advantage of computing the spectrum of \(\mathcal{H}\) instead of the spectrum of \(\mathcal{G}\) is that the spectrum of \(\mathcal{H}\) is disjoint. That is, its spectrum is the union of the spectra of its components, i.e., the matrix representation of \(\mathcal{H}\) is a block diagonal matrix (after node permutation). This leads to a divide-and-conquer approach for approximating the spectrum of \(\mathcal{G}\) through the one of \(\mathcal{H}\). By computing the spectrum of the reduced-size graphs induced by the partition, and stitching the result afterwards (conquer), an approximation of the spectrum of \(\mathcal{G}\) can be obtained. In the following, we exploit this idea to provide an algorithm, as well as theoretical guarantees, for approximate fast graph spectrum computation of families of graphs that accept _good graph separators_. Furthermore, we also extend this method for families of graphs with _good recursive separators_ which accept a hierarchical decomposition.
## 4 Performance Evaluation
### Input Signal Length Selection  
It is recommended to fix \(t\) for implementation of the method for real-world application. The SoundNet-based predictive functions exhibited a lower performance in all inter-floor noise identification except in \(\mathcal{T}_{\text{P,APT\,2}}\), as well as significantly low F1 scores for **s** at \(t=0.152\) and \(0.501\) s. Therefore, VGG16-based predictive function is considered alone in this discussion. The source type classifiers and locators with \(t=1.50\) and \(2.00\) s exhibited the best F1 scores most frequently. Additionally, the locators with \(t=0.501\) s showed clear effectiveness for localization knowledge transfer.
# Real-Time Tracking and Position Control of an Elastic Cable-Driven Winch System
## 4 Simulation Results  
The simulation results obtained in the MATLAB/Simulink environment are presented in Figure 3. In this section, the performance of the proposed control methods is evaluated under different test cases. Test cases include the minimum, medium, and maximum load conditions shown in Figure 3.  
* Subfigures (a), (d), and (g) illustrate the trajectory tracking performance;
* Subfigures (b), (e), and (h) present the tracking error between the reference trajectory and the controller output;
* Subfigures (c), (f), and (i) depict the position control performance.  
In all the scenarios, the ESMC method demonstrates a more successful performance compared to PD control, withfewer errors and a faster settling time.  
Figure 3: Simulation results. Case 1: Min. payload (0.077 kg) (**a–c**); Case 2: Medium payload (0.177 kg) (**d–f**); and Case 3: Max. payload (0.277 kg) (**g–i**).  
These simulations contributed to a better understanding of the system dynamics and enabled the validation of the control algorithms as well as a preliminary evaluation of the system behavior prior to the implementation of real-time experiments.  
Moreover, the ESMC method and the classical SMC approach are presented in Figure 4.  
In this comparison, it is observed that ESMC provides a smoother and more continuous convergence to the sliding surface. This characteristic significantly reduces the high-frequency switching (chattering) often encountered in classical SMC, thereby enhancing the system's stability. In this respect, ESMC offers a more stable and practically applicable control structure compared to the conventional SMC. Furthermore, in the experimental studies, only the ESMC method was implemented, and the obtained results support the effectiveness of the proposed control approach.
# Real-Time Detection for Cache Side Channel Attack using Performance Counter Monitor
## 5 Real-Time Detection Using PCM
### The proposed Detection Method
We implemented the softmax classification model by using TensorFlow, an open source-based framework for machine learning algorithms provided by Google. We specifically configured the TensorFlow parameters as follows: the learning rate was set to 0.1, the epochs to 100, and the batch size to 100. Moreover, we used the Adam optimization [17] technique to improve detection accuracy by minimizing the cost. The training model showed about a 99.54% accuracy for the training cases. The prediction model we configured using softmax classification detected and classified the three attacks and the normal condition with input values, that is IPC and four counter values. The output was one of the labels designated by softmax classification. Based on the classification model, we implemented a detection program to defend against three cache side channel attacks. Since the model was built with TensorFlow, we implemented the detection program by using Python for compatibility. Algorithm 1 presents the pseudo-code of the overall execution process of the detection program. Once the program was executed, it first forked the process by using a fork function (Line 1 of the algorithm). As a result, child and parent processes could run simultaneously. In Line 3, the child process was invoked and continued to execute our modified PCM. The PCM tool was provided with an option of using CSV file format. On the other hand, the parent process performed appropriate initialization process and then began execution by loading the machine learning model that we built (Line 5). The PCM tool performed monitoring and continuously output the collected counter values including the IPC, L1 miss, L2 miss, L3 miss, and the speculative and retired counters to the CSV file. Then, the program loaded the values in the CSV file and ran the model with the collected values as inputs to predict the current situation (Line 8). If the current situation was under attack, the specified label (A1: Flush+Reload, A2: Flush+Flush, and A3: Prime+Probe) was used to display the result of the attack (Lines 9-14). Finally, the CSV file was managed by flushing it. This prevented it from being slowed down when reading the CSV file because it was accumulating data. ```
1\(P=fork()\)
2 ifP is child process
3 Run PCM with an option of using CSV format
4 else
5\(model=\) Load machine learning model file and initialize. 6 While(always)do
7\(I=\) open and read CSV file
8\(Result=model\gets I\)
9 if Result is A1
10 Detect Flush+Reload
11 else if Result is A2
12 Detect Flush+Flush
13 else if Result is A3
14 Detect Prime+Probe
15 flush CSV file
```  
**Algorithm 1** Pseudo-code of the detection program
## 1 Introduction
* Using the rough-fuzzy C-means (RFCM) algorithm, we can determine the anchor location, and a membership table will be built according to the sensors' membership  
Figure 1: Basic cluster structure in the network. to the lower approximate and boundary region of anchors. The membership table can help the system activate the appropriate sensor set. * The activated sensor set temporarily forms a dynamic cluster, and the CH is selected employing delay broadcasting. The linear 0-1 programming is used to schedule the state of CMs to reduce the transmission of redundant data in the cluster. The proposed method generates a dynamic cluster with an energy-efficient topology by decentralizing the tasks of CHs. The uniqueness of the method is that tracking anchors, rather than CHs, are selected in the network. The tracking anchors consume little energy and do not need to be rotated frequently. There will be only one temporarily-formed cluster in the network at each time step, and the CH is also temporarily determined by sensor volunteering. In this way, the tasks of the CH will be undertaken by different sensors so that load balancing can be achieved in the network. Moreover, CMs will be scheduled into appropriate states so that the CH will not collect redundant sensing data. The remainder of this paper is structured as follows: Section 2 describes some related works. The network model and the energy model are presented in Section 3. Section 4 is dedicated to the presentation of our proposed clustering method. Section 5 provides the simulation results. Finally, Section 6 concludes this paper.