esa-sceva/satcom-chunk-collection huggingface dataset:
# Framework Based on Simulation of Real-World Message Streams to Evaluate Classification Solutions
## 2 Proposed Framework
### Evaluation  
Evaluation evaluates the performance of the learners used to classify the stream by measuring accuracy, precision, recall, positive true, positive false, negative true and negative false. The evaluation module receives the output of the classifiers used and the real classes of each message in the stream generated in the simulation. From this information, measures of accuracy, precision, recall, true positives, false positives, true negatives and false negatives are applied to obtain the performance of the different classification solutions. This allows comparisons to be made with other classifiers using the same input parameters.  
The evaluation module allows you to design and run experiments to evaluate message stream analysis solutions and analyse the results. Designing experiments involves organising the following initial elements: setting the simulation time, configuring the message stream generator, configuring text processing (if enabled), configuring classification and configuring the evaluation process.  
By setting the simulation time it is possible to regulate the duration of the experiment to be carried out. The configuration of the message stream generator consists of defining and/or creating the profile instances that will constitute the source that generates the message streams to be processed in the simulation. Instances may exist previously or be created in the same design of the experiment, although the instance of training examples is unique, as explained in the description of the Message Stream Simulation module. Existing instances can be selected without further adjustment of the \(sub\) parameters, although they can be changed at the experimenter's discretion. To create a profile instance, it is first named, classified according to profile types, and it is decided which messages will form the corpus. Parameter values are then set to adjust the various \(sub\) of the profile instance to suit the experimenter.  
In the processing configuration, you can choose whether to use the processing module or not. The classification solutions to be evaluated may include the processing stage and therefore the use of this module would not be necessary. On the other hand, the choice to use the processing module may be due to the use of a basic processing that allows to focus only on the evaluation of the classification process, or to evaluate a processing solution by comparing it with other solutions and seeing the classification behaviour when receiving the outputs of the different preprocessors that can be compared by one or more available classification solutions.
## 4 Simulation Analysis
### Single-Component Vibration Error Compensation
This is caused by the residual period-modulated phase greater than \(\pi/4\), which is shown in the red line of Figure 7. Meanwhile, the green line of Figure 7 shows that the residual phase is no greater than \(\pi/4\), but is still periodically modulated. Therefore, the imaging quality of Figure 6c is further improved, but its side lobes are still relatively high. Fortunately, the remainder phase error is smaller than \(\pi/4\) and no longer periodically modulated after compensation by the proposed method. Therefore, the paired echoes are perfectly suppressed, as shown in Figure 6d. Finally, we performed a quantitative analysis to assess image quality. The center point target of Figure 6b-d is intercepted using a rectangular window. Figure 8 shows the resulting azimuth-dimensional amplitude profiles. Table 3 lists the impulse response width (IRW), peak sidelobe ratio (PSLR) and integral sidelobe ratios (ISLR) for all vibration compensation methods. It is clear from Figure 8 and Table 3 that the STFT-based rough estimation method with \(h=20\) is too poor to improve image quality. Correspondingly, after vibration error compensation by the STFT with \(h_{op}=26\), the image quality was improved, but its PSLR and ISLR were still high, as shown in red line of Figure 8 and Table 3. The great thing is that there are no longer paired echoes in the SAR images and the imaging quality indices are consistent with the theoretical ones after vibration error compensation by the STFT-based fine estimation method, which validate that the improved method can solve the problem of the former two methods and has the best performance. Figure 5: True and estimated single-component vibration error. Figure 6: Comparison of compensation effect. (**a**) No compensation; (**b**) compensation based on STFT with \(h=20\); (**c**) compensation based on STFT with \(h_{op}=26\); (**d**) compensation based on STFT with \(h_{op}=26\) and RANSAC. Figure 7: Phase estimate error.
## 2 Materials and Methods
### hiPSCs Neural Commitment and Differentiation into Astrocytes  
hiPSCs were induced along the neural lineage and differentiated using the protocol published by [PERSON] and colleagues [24; 25], with minor modifications. Briefly, hiPSCs were enzymatically detached using StemPro Accutase (A1110501; ThermoFisher, Waltham, MA, USA) and plated at 3-4 \(\times\) 10\({}^{5}\) cells/well into a vitronectin-coated six-well plate, in Essentials medium supplemented with Revitacell, for \(\sim\)16 h, and then kept in culture in Essentials without Revitacell Supplement, for \(\sim\)8 h. The medium was then replaced (day in vitro 0, DIV0) with Neural Induction Medium, and the cells were fed daily until DIV7, then with N2 Medium from DIV8 to DIV12. At DIV12, adherent cells were lifted using the StemPro EZPassage Disposable Stem Cell Passaging Tool (23181010; ThermoFisher, Waltham, MA, USA), cultured in suspension into low-attachment dishes, in order to favor sphere formation, and were fed every other day in N2B27 medium until DIV20, then until DIV30 with PDGF Medium (see Table S1 for media composition). On DIV30, spheres were picked and plated onto six-well plates coated with 0.1 mg/mL poly-L-omithine (PO, P3655; Sigma-Aldrich, Saint Louis, MO, USA), followed by the application of 10 mg/mL laminin (Lam, L2020; Sigma-Aldrich, Saint Louis, MO, USA), at the density of 20 spheres/well. Plated spheres were gently fed with PDGF medium (2/3 media changes) every other day. At DIV70-80, spheres and cells migrated out of the spheres were dissociated with StemPro Accutase for 30 min, passed through a 70 \(\upmu\)m cell strainer and sorted for CD49f-positive (CD49f+) cells [26]. CD49f+ sorted astrocytes were then plated at a density of 3000/cm\({}^{2}\) onto PO/Lam coated u-Slide 8 well ibiTreat (80826; Ibidi, Grafelfing, Germany), cultured for three days in PDGF medium, and then fixed with 4% paraformaldehyde(PFA) in 0.1 M sodium phosphate buffer (PB). Samples were washed two times with PBS and stored at 4 \({}^{\circ}\)C until immunofluorescence analyses.
## 3 Methodology and Comparative Experimental Setup
### Comparative Experimental Setup
#### 3.2.2 Evaluation Metrics  
The performance evaluation of a model by a single metric is usually limited [39], so we referred to the previous literature and synthetically selected four famous metrics for performance evaluation [27; 40]. The Nash-Sutcliffe efficiency (NSE), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the peak percentage of threshold statistics (PPTS) were used to evaluate the prediction performance of the models. The NSE can evaluate the predictive capability of a model by measuring how close the simulated process values are to the observed values. The closer the value is to 1, the better the model performance. MAPE can assess the predictive accuracy of the smoothed component in the runoff series, with a value of 1 in the ideal case. RMSE is used to measure the variability of the data, and the smaller the value, the better the accuracy of the model [41]. To calculate the PPTS values, the original data samples are arranged in descending order and the predicted data samples are arranged in the same order. The parameter \(\gamma\) indicates a threshold, which can be chosen as 5% or 10%. The parameter \(G\) is  
Figure 5: Principle of the quantile mapping method. (**a**) Cumulative distribution function. (**b**) Probability density function.  
the number of values above this threshold level. For example, PPTS (10) is the top 10% of flows or peak flows evaluated according to the PPTS criteria. A lower PPTS value indicates a more accurate peak flow prediction [42]. These four metrics are calculated as follows:  
\[NSE=1-\frac{\sum_{i=1}^{N}\bigl{(}y_{i}-y_{i}^{*}\bigr{)}^{2}}{\sum_{i=1}^{N} \bigl{(}y_{i}-\overline{y}\bigr{)}^{2}} \tag{16}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{N}\bigl{(}y_{i}^{-}-y_{i}^{*}\bigr{)}^{2}} \tag{17}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{N}\bigl{|}\frac{y_{i}-y_{i}^{*}}{y_{i}}\bigr{|} \times 100\% \tag{18}\]  
\[PPTS(\gamma)=\frac{100}{\gamma}\frac{1}{n}\sum_{i=1}^{G}\bigl{|}\frac{y_{i}-y _{i}^{*}}{y_{i}}\bigr{|}\times 100\% \tag{19}\]  
where \(y_{i}\) is the measured value, \(y_{i}^{*}\) is the simulated value, and \(\overline{y}\) is the average of the measured values.  
According to \"Standard for Hydrological Information and Hydrological Forecasting\" (GB/T22482-2008) [43], when NSE \(\geq\) 0.90, the accuracy is set to Grade A; when 0.70 \(\leq\) NSE \(<\) 0.90, the accuracy is Grade B; and when 0.50 \(\leq\) NSE \(<\) 0.70, the accuracy is Grade C. If a forecast program's accuracy reaches Grade A or B, it can be used to issue official forecasts; if a program's accuracy only reaches Grade C, it can be used for reference forecasts but not official forecasts; and if a program's accuracy is below Grade C, it can only be used for reference estimates.
# Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources
## 2 Background and Related Work
### Hosting vBBU on MEC Servers
#### 2.2.1 Linux as the Host OS for Running vBBUs  
Over recent years, several mechanisms have emerged to provide RT support within the Linux Kernel. Key examples comprise of RT Linux [4], the low-latency patch [5], and the PREEMPT_RT [6] patch. This development has positioned Linux to be used in RT systems [25], particularly in the domain of RT signal processing for vBBU functions [7]. For instance, Linux RTAI (Real-Time Application Interface) [26] has found applications in mobile system testbeds [7; 27; 28; 29; 30]. Studies in [29; 30] demonstrate the performance of vBBUs using the PREEMPT_RT patch in the context of Cloud-RAN. The Low-Latency  
Figure 1: Cloud Radio Access Network architecture.  
Figure 2: 3 GPP functional split of the LTE–BBU functionality [20].  
Kernel patch [5], integrated into the mainline code of the Ubuntu distribution, has gained widespread adoption, particularly among researchers using the OAI code [31; 32; 33]. This popularity stems from the optimization of OAI's code for seamless compatibility with the Low-Latency Kernel.
## 3 The Proposed NSPSO Sampling Algorithm
### Particle Memory Strategy
 \tag{19}\]  
where \(\vspace{-0.1 cm}x_{i}(t+1)^{\prime}\) is the revised location after location boundary check; \(\vspace{-0.1 cm}x_{max}\) and \(\vspace{-0.1 cm}x_{min}\) are the predefined upper and lower bounds for all particles in the variable space, respectively. Step 9: Calculate particle densities and perform the elite breeding strategy as described in Section 3.4. Step 10: Stop iteration if convergence criterion was met. Otherwise, repeat steps 2-9. Convergence criterion was set as: function values of 95 percent of all particles is smaller than a predefined acceptable error and the densities of all particles is bigger than \(M_{d}\)  
Step 11: Archive the particle positions and use them as candidate samples in the DOEs. Pseudocode for the NSPSO sampling algorithm was shown in Algorithm 1. ```
Input :\(Np-\)population size \(Ec-\)the allowable error \(v_{-}-\)the velocity range of values for population \(G_{max}-\)maximum iteration number  DB\(-\)database for store sampling points \(R_{out}-\)truncation radius \(v_{max}-\)maximum velocity \(ns-\)neighborhood size \(f-\)fitness of population  SM\(-\)surrogate model Output :\(x,f\)
1 /* Initialization */
2 \(x\leftarrow\)RandomInitialize(Np);
3 \(v\leftarrow\)RandomInitialize(Np);
4 DB append (\(x\)/\((x)\));
5 Evaluate \(f(x)\);
6 repeat
7 Update \(p_{i}\);
8 Update \(p_{i}\) using Equation (14);
9 for\(i\!\!-\!1\)toNpdo
10 Calculate \(N\): using Equation (8);
11 Calculate \(r\): using Equation (11);
12 Calculate \(re\) using Equation (13);
13 Update \(v\) using Equations (15) and (18);
14 Update \(x\) using Equations (17) and(19);
15 Evaluate \(f(x)\);
16 DB append (\(x\)/\((x)\));
17 end for
18\(x\leftarrow\)elitbreeding(\(x\), \(R_{out}\), \(n_{0}\))
19 until maximum iteration is reached or stopping condition is true;
20 Train SM with DB;
```  
**Algorithm 1** Pseudocode for the NSPSO sampling algorithm
# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 4 Collaborative Computing in IoV
### Big-Data-Oriented Task Offloading
Specifically, the cost function is established using the weighted sum of MEC energy consumption and the response time of the application, while combining SDN and fog computing to optimize the computational task. In the MEC-cloud collaboration, [PERSON] et al. [91] made the Unmanned Aerial Vehicle (UAV) the air relay, combined with the ground RSUs deploying MEC to process the vehicle's computing tasks,which reduced the processing delay. However, this may increase the energy consumption, so they proposed a multivariate optimization method to efficiently manage the energy of battery-supported RSUs and EVs. Energy issues and sustainable development are significant parts of IoV, which are crucial to the reliability and effectiveness of the system, and will continue to receive attention [92]. In the vehicle-edge-cloud collaboration, some researchers introduce digital twins (DT) to solve the problem of computational task offloading and resource allocation. DT technology establishes a real-time updated and interactive digital copy of the physical world, called a virtual model, which can be applied to mine information and simulate processes to obtain favorable results and feedback on the physical model, so as to achieve the purpose of optimization or provide new visualization services [93]. [PERSON] et al. [94] adopted the power of digital twins to intelligently screen edge devices and offload tasks. Specifically, in the DT network, blockchain technology is utilized to ensure the consistency of information, and then computing devices with good communication quality and reliability are selected to allocate tasks. In order to solve the offloading balancing problem in edge computing of IoV, [PERSON] et al. [95] chose edge computing devices as agents to apply deep reinforcement learning on the basis of DT technology, and the experiment verified that the proposed method could improve the level of service QoS. Another study [96] combined digital twins, game theory, and a distributed incentive mechanism to solve the problem of computing resource allocation from the perspective of energy efficiency and user recognition.
## 2 Problem Statement
### Parameters  
We are given a planning horizon-partitioned equally into \(H\) periods. Every period is denoted by \(t\), \(t=\{1,2,...,H\}\). The following parameters are used in the mathematical formulation of the model.  
* \(N\): number of potential UDCs to subcontract
* \(H\): number of periods
* \(C_{t}\): cost of transportation for one product
* \(C_{tr_{i}}\): cost to contract the distribution of one product out to UDC\({}_{i}\).
* \(C_{tp_{i}}\): cost of pollution generated by contracting one product out to UDC\({}_{i}\).
* \(C_{p}\): cost of one product delayed
* \(Y_{ii}\): maximal number of products to be delivered by platform \(i\) in each period
* \(d_{t}\): demand in each period
* \(C_{\text{max}}\): maximum capacity that can be delivered by the main platform
* \(D_{t}\): the sum of the demand and the delayed products.
* \(L_{p}\): the amount of decreased capacity due to a preventive maintenance action
* \(L_{r}\): the amount of decreased capacity due to a corrective maintenance action
* \(M_{p}\): preventive maintenance cost
* \(M_{c}\): corrective maintenance cost
* \(\lambda_{t}(k)\): the failure rate
* \(\Delta t\): the length of distribution period
* \(\lambda_{n}(t)\): the nominal failure rate
* mu: monetary unit  
Decision variables:  
* \(x_{t}\): quantity transported in each period
* \(y_{it}\): quantity of products subcontracted to platform \(i\) in each period \(t\)
* \(Q_{t}\): quantity of products delayed in each period \(t\)
* \(AF_{it}\): allocated platform \(i\) in each period \(t\)
* \(P\): optimal number of preventive maintenance actions
* \(T\): the optimal period of intervention
* \(\zeta_{M}(x,P)\): the excepted number of failures
# Similarity-Based Malware Classification Using Graph Neural Networks
## 3 Proposed Problem-Solving Mechanism
### Similarity Model
#### 3.5.1 Siamese Networks  
The Siamese network refers to a class of neural network architectures that contain numerous unanimous subnetworks. The subnetworks are mirrors of each other, having the same architecture, parameters, and weights. Traditionally, neural networks learn to predict multiple classes, but they must change and be retrained using all available data to change their prediction targets. A Siamese network learns a function that measures the similarity of its inputs by comparing their embedding vectors. Therefore, it can identify whether two inputs are the same or not and face the new class data without being entirely retrained. Figure 7 displays the Siamese network architecture.  
The Siamese network usually makes pair-wise learning that does not use cross-entropy loss of learning. The loss function of the Siamese network involves contrastive loss [40], a distance-based loss, rather than a more conventional error prediction loss. This loss is used to learn embedding in the same classes and different classes are far away from each other. Equation (1) defines contrastive loss; \(Y\) is zero when the pair are of the same class; otherwise, \(Y\) is one. \(Dw\) represents the distance between the subnet's outputs in the Siamese network. The value \(m\) is greater than zero's margin value that distinct classes outside this margin will not contribute to the loss. This means the loss for different classes only exists between zero and the margin.  
\[\textit{Contrastive Loss}=(1-Y)\frac{1}{2}(Dw)^{2}+(Y)\frac{1}{2}\{max(0,m- Dw)\}^{2} \tag{1}\]  
Figure 8 presents an example of contrastive loss, in which the colors of the circles represent a class. The class associated with the red circles differs from that of the blue circles. Contrastive loss is high when the blue circles are far from each other or the red circles are close to the blue circles. The loss is low when the blue circles are close to each other or the red circles are far from the blue circles. Therefore, the model depends on the  
Figure 7: Siamese network architecture.  
contrastive loss to update its weights by backpropagation, learning how to reduce loss as in the example.
# Detection of Decision-Making Manipulation in the Pairwise Comparison Method
## 4 Problem Statement
### Naive Algorithm  
The first manipulation algorithm is the simplest one. Assuming that the goal is to increase the priority of alternative \(a_{p}\), we will try to increase all elements in the \(p\)th row of the _PC_ matrix \(\mathbf{C}\).  
Let \(\alpha\) be a positive real number such that \(\alpha>\min\{\max\{c_{ij}\},n\}\) where \(i,j\in\{1,\ldots,n\}\). In the first step of the algorithm, we update all elements in the \(p\)th row (except \(c_{pp}\), which must be 1) to \(\alpha\) and all elements in the \(p\)th column to \(1/\alpha\). Without loss of generality, we may assume that \(p=2\). Thus, the initial form of \(\mathbf{C}\) and the final form, denoted by \(\mathbf{C}^{\prime}\), are as follows:\[\mathbf{C}=\begin{bmatrix}1&c_{12}&\cdots&c_{1n}\\ c_{21}&1&&c_{2n}\\ \vdots&&\ddots&\vdots\\ c_{n1}&c_{n2}&\cdots&1\end{bmatrix}\xrightarrow[\text{attack}]{}\begin{bmatrix}1&1/ \alpha&\cdots&c_{1n}\\ \alpha&1&&\alpha\\ \vdots&&\ddots&\vdots\\ c_{n1}&1/\alpha&\cdots&1\end{bmatrix}^{\prime}=\mathbf{C}^{\prime}\]  
The \"naive\" algorithm can be written compactly as follows:  
```
1:functionattack(it takes matrix \(\mathbf{C}\) and index \(p\) on input)
2: set the initial value for \(\alpha\) and generate a list of indexes to modify
3: For each index and in the list to be modified, set as element \(c_{pi}\) the value of \(\alpha\) and, due to the reciprocity preservation, its inverse i.e. \(c_{ip}\) set to \(\alpha^{-1}\).
4:endfunction
```  
**Algorithm 1** Naive algorithm  
Figure 1 shows the effect of the _naive_ algorithm in the form of a colored PC matrix (i.e., heat map) (the values in the heat map correspond to the \(c_{ij}\) values. The color changes continuously from purple (lowest value) to yellow (highest value)). It is visible that the 8 th row and column have been modified, while the rest of the values remain unchanged.
# A Cost-Optimized Model for Dynamic Port Hinterland
## 4 Experimental Design and Results
### Calculating the \(H(x,y)|_{(p_{n},o_{n})}\)
#### 4.1.4 Details of Costs of the Different Items  
Here the data collection of costs of the terms indicated in the section of methodology to each product and origin selected is described.  
* Freight Costs  
Freight costs were obtained from commercial services, encompassing port management, border management, fuel costs, and freight taxes. These costs represent the total amount paid by the importer for goods delivered outside the destination port. Based on the acquired rates, transportation from the ports of origin (South Africa and South America) to the principal ports of the Iberian Peninsula yielded the tariffs presented in Table 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Product** & **Volume (Tons)** & **Medium Lifetime (Weeks)** & **Classification** \\ \hline Bananas & 1,052,861.47 & 1 to 3 & Very Perishable \\ Oranges & 745,875.81 & 8 to 12 & Semi-Perishable \\ Apples & 694,893.13 & 8 to 30 & Non-Perishable \\ Kiwi & 432,556.69 & 8 to 14 & Semi-Perishable \\ Melon & 411,952.76 & 8 to 12 & Very Perishable \\ \hline \hline \end{tabular}
\end{table}
Table 7: Consumption of Perishable Goods in Spain 2023 with lifetime and classification. Source: Spanish Ministry of Agriculture.  
These rates encompass a comprehensive set of logistics costs, including freight, customs and border management fees, destiny port handling charges, fuel surcharges, and applicable freight taxes. This inclusive pricing structure indicates that the container is available for truck collection immediately upon completion of customs clearance procedures at the destination port.
* Road Transport Cost This is calculated using the reference data published by the Spanish Ministry of Transport: 1.53 EUR/km [93].
* Train Transport This is the cost of transit by train from the nearest station to a city where the hinterland is calculated from the closest station of the port.  
The public offer of RENFE (Spanish public and main train operator) has not been published since 2012, but it was used for this example because, as published by the Spanish Ministry of Transport, the revenue per ton is similar [94]. Table 6 displays RENFE's public rates for multi-client and multi-product rail transportation from origin stations (rows) to destination stations (columns). These rates include cargo handling operations at the stations.  
* Haulage Costs The haulage costs were determined by identifying the minimum rate at which truck operators offer services for very short-distance transport. This value was derived from commercial offers and established at EUR 50. This value represents the baseline cost for minimal-distance haulage operations.