esa-sceva/satcom-chunk-collection huggingface dataset:
# Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response +
## 5 Discussion
### Limitations  
It can be argued that the dataset created in this experiment was unchanged and fixed. The process of collecting and storing data is fast; however, making the data usable requires time-consuming pre-processing and training stages that require substantial computing resources. As this dataset is produced once the network topology has been set up, frequent updates are not required. Nevertheless, modifying the network topology may decrease its accuracy and increase the number of false positives. To ensure optimal performance, we suggest updating the dataset when a switch is added or removed from the network, or at the conclusion of a set time interval.  
The lack of a comprehensive real-attack dataset for timing attacks in SDN restricts our research. Despite our best efforts, we were unable to obtain real-world attack data for timing attacks in SDN for experimental reasons; therefore, we relied on simulated attacks instead. It is possible that these simulations did not accurately reflect real-world conditions, thereby limiting the generalisability and applicability of the results. However, the primary objective of this study is to construct and evaluate our proposed method within the constraints of existing knowledge and resources. Furthermore, the validation process of the response mechanism has the potential to introduce delays in real-time response mechanisms. In some instances, this may result in a delayed response time. The first reaction to a detected attack attempt was somewhat delayed because of the temporary rule generation. Although essential for ensuring the resilience of the solution, this delay can potentially affect the overall response time of the solution, which can lead to slower reaction times in specific situations. Another limitation of our proposed response mechanism is the granularity of the customised response rules. An adversary can potentially generate requests with different destination subnets andprotocols, which generates many customised flow entries on the switch. This could potentially overwhelm the switch flow table and deteriorate the network performance.
# Secure Internal Data Markets
## 2 Background and Related Work
Additionally, the topic of trading data independently from data markets has received some attention in the last decade [20]. In traditional IT security, attention on data markets has so far mainly focused on their usage for illegal feeds, particularly in the area of the Darknet and trading stolen data [21]. The trade-off between privacy and the return on investment in business models was also investigated in the past, as this is a field with huge potential for harm that also saw many changes with respect to the GDPR [22; 23]. Furthermore, works on security in data markets, such as [24], typically focus on the privacy aspects of the people involved in the traded data sets, which is, of course, an important topic but does not cover the whole problem field of secure data markets. Other approaches focus more on the integrity of the market and the involved data, proposing, for example, blockchain-based solutions to the problem [10; 25]. Related topics, such as data lakes [26], have been studied, for example, with regard to counterfeit protection in the event of data being changed by externally involved third parties [27]; however, these works relate more to global than internal markets with respect to their setup and especially the security issues surrounding them. Furthermore, the authors of [12] provided insight for general issues of data markets; still, the model proposed in this work is rather general and does not focus on issues specific to internal markets. The approach proposed in Section 4 differs thusly from this model in many key aspects.
## 3 Problem Statement  
For clarity and ease of comprehension, this section elucidates the ephemeral group recommendation task and delineates the notational conventions adopted throughout this paper.  
Table 1 presents a summary of the principal notations utilized in this paper. Let \(\mathcal{U}=\{u_{1},u_{2},\ldots,u_{|\mathcal{U}|}\}\) represent the set of users, \(\mathcal{I}=\{i_{1},i_{2},\ldots,i_{|\mathcal{I}|}\}\) denote the set of items and \(\mathcal{G}=\{g_{1},g_{2},\ldots,g_{|\mathcal{G}|}\}\) signify the set of groups, where \(g_{n}\in\mathcal{G}\) identifies the \(n\)-th group. These sets underpin the formulation of user-item, group-item and user-group interaction records, reflecting historical preferences and affiliations. The binary interaction matrices \(R\in\mathbb{R}^{|\mathcal{U}|\times|\mathcal{I}|}\) and \(Q\in\mathbb{R}^{|\mathcal{G}|\times|\mathcal{I}|}\) encapsulate user-item and group-item interactions, respectively, where \(r_{ui}=1\) indicates an interaction between user \(u\) and item \(i\), and, similarly, \(q_{gi}=1\) represents an interaction between group \(g\) and item \(i\).  
Group \(g\in\mathcal{G}\) encompasses a subset of users \(\{u_{1},u_{2},\ldots,u_{|g|}\}\subset\mathcal{U}\) characterized by sparse interaction records \(q_{g}\), a row vector in matrix \(Q\). The matrices \(R\) and \(Q\) serve as the raw features for users and groups, respectively, and are embedded into a latent space of dimension \(d\). The resultant user and group embeddings \(Z\in\mathbb{R}^{|\mathcal{U}|\times d}\) (produced from MLP Encoder2) and \(P\in\mathbb{R}^{|\mathcal{G}|\times d}\), respectively, are concatenated from disentangled embedding matrices \(\{Z_{1},Z_{2},\ldots,Z_{k}\}\) and \(\{P_{1},P_{2},\ldots,P_{k}\}\), corresponding to the \(k\) factors within our framework. Each one of the factor-specific embedding matrices \(Z_{k}\) and \(P_{k}\) resides in a subspace of dimension \(d/k\).  
The primary objective in this work is to address the challenges inherent in ephemeral group recommendations characterized by minimal historical interactions among group members. Our framework aims to harness limited historical group-item interactions to predict item preference rankings for ephemeral groups composed of loosely connected members. The user and group representations \(\{Z_{1},Z_{2},\ldots,Z_{k}\}\) and \(\{P_{1},P_{2},\ldots,P_{k}\}\), derived from the input feature matrix \(R\), underpin the prediction of group preference distributions, denoted by \(Y\).
# Flow-Induced Noise Characteristics Analysis of a Pipeline Structure in a Cabin Rigid Corner
## 3 The Acoustic Radiation Equation of the Pipeline Structure in a Rigid Corner  
### Cylindrical Shell Model  
The vibration and acoustic radiation problem of a cylindrical shell structure is essentially the structural vibro-acoustic radiation problem of the pipeline structure in the acoustic field. To analyze the structural-acoustic radiation problem of cylindrical shell structures, it is necessary to propose a series of appropriate assumptions: the acoustic medium is inviscid and compressible; and the acoustic medium cannot withstand stress.  
An infinite thin-walled cylindrical shell was submerged within a fluid with density \(\rho_{f}\) and acoustic speed \(c_{f}\), as shown in Figure 1. The definition of certain assumptions is as follows: the material used to make the cylindrical shell structure is both isotropic and linearly elastic; the cylindrical shell has slight deformation; and the cylindrical shell and fluid gravity force are not given sufficient attention.  
The position of the fluid domains and cylindrical shell is determined by the cylindrical coordinate system (\(x_{0}\), \(\theta_{0}\), \(r_{0}\)). The center of the cylindrical shell is where the ordinate origin of the coordinate system is located, as depicted in Figure 1.  
In the cylindrical coordinate system, the centerline of the cylindrical shell was chosen as the coordinate axis \(z\) and the coordinate axes \(r\) and \(\theta\) responded to the radial and circumferential directions, respectively. \(u_{z}\), \(u_{\theta}\), and \(u_{\rm r}\) are the orthogonal components of cylindrical shell displacement in the axial, circumferential, and radial directions, respectively. \(R\) is the mean radius of the cylindrical shell, and \(h\) is the cylindrical shell thickness. \(R\) and \(h\) satisfy \(h/R<<1\).  
According to the acoustic Helmholtz equation, in cylindrical coordinate systems, the associated form of the acoustic field in free space is expressed by solving the acoustic wave equation using the variable separation method. The far-field acoustic pressure of the cylindrical shell structure can be described as follows:  
\[p_{f}=\sum_{\zeta=1}^{\infty}\sum_{n=-\infty}^{\infty}P_{n,\zeta}\cos(n \theta)H_{n}^{(1)}(k_{r}r)\mathrm{e}^{-\mathrm{i}k_{n,\zeta}z}, \tag{6}\]  
where \(P_{n,\zeta}\) is the acoustic pressure amplitude of every \(n\) and \(\zeta\), \(H_{n}^{(1)}()\) is the \(n\)th order of the first-order Hankel function, and \(H_{n}^{(1)}()=J_{n}()+\mathrm{i}Y_{n}()\), and \(k_{r}\) are the radial and free wave numbers. The parameter \(k_{r}\) depends on the free wave number \(k_{0}\) and axial wave numbers \(k_{n,\zeta}\) and the relationship between them can be written as follows:  
\[\left\{\begin{array}{l}k_{r}=\sqrt{k_{0}^{2}-k_{n,\zeta}^{2}}\,0<k_{n,\zeta}<k_{0}\\ k_{r}=-\mathrm{i}\sqrt{k_{0}^{2}-k_{n,\zeta}^{2}}\ \ \ \,k_{0}<k_{n,\zeta}. \end{array}\right.\]
## 3 Results
### Effect of Non-Gaussianity on NRCS Simulation
Curves and symbols are the same as those in Figure 7:0  
simulated NRCS, especially under VV polarization. This might be caused by the noise contained in the Sentinel-1 data. Examples of the scattering profiles simulated based on the conventional TSM + Gaussian sea surface, the conventional TSM + Tayfun sea surface, and the modified TSM + Tayfun sea surface, the conventional TSM + Tayfun sea surface and their comparison with Sentinel-1 data at an incidence angle of 32\({}^{\circ}\) and wind speed of 10 m/s in the upwind direction under VV and HH polarizations are shown in Figure 13a,b, and those in the downwind direction under VV and HH polarizations are shown in Figure 13c,d. Figure 13 shows that the range of the fluctuation of the simulated NRCS is consistent with that of the reference data for a given wind direction and polarization at a certain wind speed and certain incidence angle. Neither the introduction of the non-Gaussianity of the Tayfun sea surface nor the introduction of the bispectrum correction substantially affects the range of fluctuation of the simulated scattering. Figure 7:1 Examples of the pdfs for scattering predicted based on the conventional TSM + Gaussian sea surface, the conventional TSM + Tayfun sea surface, and the modified TSM + Tayfun sea surface and their comparison with Sentinel-1 data and Gaussian fitting to Sentinel-1 data at a 32\({}^{\circ}\) incidence angle and 10 m/s wind speed (**a**,**b**) in the upwind direction under VV and HH polarizations and (**c**,**d**) in the downwind direction under VV and HH polarizations. Blue solid curve, conventional TSM + Gaussian sea surface; red dashed curve, conventional TSM + Tayfun sea surface; green solid curve, modified TSM + Tayfun sea surface; black solid curve, Sentinel-1 data; magenta star, Gaussian distribution fitting to Sentinel-1 data.
# Defining Blood Plasma and Serum Metabolome by GC-MS
## 5 Workflow of GC-MS Analysis of Blood Metabolome
### Sample Preparation
#### 5.1.2 Protein Cleanup  
Interfering proteins presented in plasma and serum may suppress analytical signals of metabolites. Therefore protein molecules should be removed in advance to reduce the complexity of the sample under study and improve the peak resolution of metabolomic profiling. The precipitation procedure is often used to purify plasma or serum from protein contaminants. Precipitation is accomplished by changing the pH and polarity of the  
Figure 3: Stages of standard GC-MS analysis of plasma or serum samples. In a hypothesis-driven study, a metabolomic experiment starts with formulating a hypothesis, which further work will be aimed at confirming or refuting. Preamolytic operations involve quenching enzymatic processes (stage 1) in every biological sample from a representative sample. Further, the sample is purified (stage 2) from interfering protein molecules, followed by liquid or solid-phase extraction (stage 3), which allows the release of metabolites from the plasma or serum matrix and concentrates them in a smaller volume. Next, extracted metabolites are derivatized (stage 4) to improve their volatility and thermal stability. After ensuring that the quality criteria (stage 5) are met, the researcher performs a gas chromatography-mass spectrometric experiment (stage 6). Finally, data processing (stage 7) provides the researcher with either an answer to the original question or the basis for starting a new data-driven study [68].  
solution. As a result, intramolecular interactions are disrupted, the protein denatures, aggregates, and falls out of solution [71; 72]. For plasma and serum, the excess amounts of organic solvents such as methanol or acetonitrile [72; 73] are used, followed by high-speed centrifugation and separation of the supernatant (Figure 3, stage 2).  
It is noteworthy that plasma and serum represent a hydrophilic environment with limited solubility of hydrophobic metabolites such as lipids, fatty acids, steroids, and thyroid hormones. Efficient transport and distribution of these hydrophobic compounds in blood plasma in vivo are achieved due to their interaction with proteins. During the precipitation of proteins, a certain part of the metabolites is lost. To minimize this effect, there is a technique that allows the extraction of coprecipitated metabolites due to the enzymatic cleavage of precipitated proteins; however, the reproducibility of this method requires additional evaluation [74]. The combination of adding an excess of a strong organic solvent such as methyl tert-butyl ether (MTBE) with a subsequent extraction step also increases the coverage of the metabolome [75].
## 3 Results and Discussion
### TGA Measurements  
The TGA of PHB and ZVI-PHB composite demonstrated a loss of mass as a function of temperature (Figure 4). Figure 4 shows the thermal decay profiles of the PHB and ZVI-PHB complex. Rapid weight loss occurred after heating beyond 252 \({}^{\circ}\)C, and the PHB seems to have decayed entirely once the temperature exceeded 294 \({}^{\circ}\)C. Then again, within the same temperature range, the ZVI-PHB complex lost approximately 50% of its initial mass. The remaining residue of the ZVI-PHB complex should be chiefly ascribed to the iron based species within the complex [(29)]. Therefore, according to the thermogravimetric analysis, ~50% of the mass of ZVI-PHB composites is estimated to be made of ZVI particles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Surface Area (m2/g)** & **Total Pore Volume (cm3/g)** \\ \hline Untreated PHB & 6.1 \(\pm\) 0.5 & 0.0160 \\ Treated PHB & 3.5 \(\pm\) 0.5 & 0.0095 \\ ZVI–PHB Composite & 4.3 \(\pm\) 0.5 & 0.0162 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Surface areas and total pore volumes of commercial untreated PHB (polyhydroxybutyrate), thermally treated PHB, and ZVI (zero-valent iron)–PHB composite (ZVI/PHB ratio 1:2 \(w/w\)).  
Figure 3: (**a**) N\({}_{2}\) adsorption/desorption isotherms; and (**b**) pore size distribution of commercial untreated PHB, thermally treated PHB, and ZVI–PHB composite samples (ZVI/PHB ratio 1:2 \(w/w\)).
# Human Activity Recognition Using CSI Information with Nexmon
## 7 CSI Data Preprocessing and Feature Extraction
### CSI Data Preprocessing Methodology  
#### 7.1.1 Experiment 1: Raspberry Pi 3B+, 4B Using SVM and LSTM  
CSI data are affected significantly by noise and ambiguities caused by hardware and firmware. To make the CSI data useful for the machine learning and deep learning algorithms for the classification of human activities, it required some denoising of the data and preprocessing. This section explains the method used to preprocess the CSI data to input SVM and LSTM algorithms. In the first experiment, each packet of the collected CSI data at 40 MHz contained 128 subcarriers in total, where 108 were used for payload, 6 for the pilot, and 14 as null subcarriers. The CSI data was preprocessed, then using this processed data, activity classification using machine learning and deep learning was achieved. First, the collected activity files were segmented into multiple files, where each file contained 150 packets. Second, the data was the denoised and reliable payload of the CSI data extracted by removing the null and pilot subcarriers. In the third step, the fourteen features from each segmented file were extracted for the first two PCA components, and then the feature matrix was fed for the classification using SVM and LSTM.  
The preconditioning of the CSI data aims to remove the undesired data packets and subcarriers from each type of human activity raw data file. Each collected raw CSI file was first segmented into an equal size of 150 packets. Each packet contained in total 128 subcarriers, i.e., a raw matrix of \([m\times n]\), where \(m=150\) rows and \(n=128\) columns. As not all of them are useful, it is necessary to remove the pilot subcarriers \(CSI_{p}\) indexes \(\pm 11\), \(\pm 25\), \(\pm 53\)[37], eliminating the null subcarriers \(CSI_{null}\) indexes 0, \(\pm 1\), \(\pm 59\), \(\pm 60\), \(\pm 61\), \(\pm 62\), \(\pm 63\), \(\pm 64\), and there are some unchanged subcarriers \(CSI_{un}\) indexes \(\pm 2\), \(\pm 3\). This removal of the subcarriers gives at the end a matrix of [150 \(\times\) 104] for each segmented file, see Algorithm 1. This preconditioning is further depicted in Figure 4 below.  
In Figure 4 the experimental data shows that the peaks at central frequency and waveform of pilot, null and unchanged subcarriers are removed on preprocessing the collected CSI data by eliminating the \(CSI_{p}\) = \(\pm 11\), \(\pm 25\), \(\pm 53\), \(CSI_{null}\) 0, \(\pm 1\), \(\pm 59\), \(\pm 60\), \(\pm 61\), \(\pm 62\), \(\pm 63\), \(\pm 64\), and \(CSI_{un}\)\(\pm 2\), \(\pm 3\) from the raw data.  
```
0: Raw data as CSIraw, pilot, null and unchanged subcarriers as CSIp, CSInull, and CSIun
0: CSI preconditioned as \(CSI_{precond}\)
1:\(CSI_{precond}\leftarrow\) CSIraw - CSIp - CSInull - CSIun
```  
**Algorithm 1** CSI-Data Preprocessing
## 2 Application of the Space Syntax Analysis to an Urban Context
### Spatial Indicators Assessment  
By using the axial map both of the axial or of the angular segment analysis, it is possible to calculate different spatial indicators (syntactic measures) to give a quantitative value to the obtained representation. Among the various indicators, the integration (\(I\)) and the choice (\(Ch\)) indices have been used. The integration index (\(I\)) represents the accessibility of a given street within the urban grid, while the choice index (\(Ch\)) represents the extent to which a line or a segment functions as an intermediate location within the urban grid [42; 43].  
The \(I\) index describes the mean depth of a line respect all other lines of the axial map [33], it is defined as  
\[I=(k-1)/D_{t}\]  
where \(k\) represents the total number of lines of the axial map and \(D_{t}\) represents the total depth of a line. The depth of a line is defined as the distance separating a pair of lines; it can be determined by the number of lines dividing the two lines along the shortest path between the two considered lines. The total depth of a line is the sum of the depths with respect to all the other lines (see example in Figure 2). The \(I\) index quantifies how close a given road (identified by a specific line or segment) is by all other roads. The greater the \(I\) value is, the more accessible and integrated the road results, and, vice versa, the lower the \(I\) value is, the more isolated and segregated the road results.  
The _Ch_ index counts the number of times a specific line or segment lies on the shortest path between all origin-destination pairs in the urban grid [44], if an axial map is made up of \(k\) lines, the _Ch_ standardised value is calculated with the following equation.  
\[Ch=\frac{N_{l}-(k-1)}{N_{t}-(k-1)}\]  
where \(N_{l}\) represents the number of paths containing the line under examination and \(N_{t}\) represents the theoretical maximum number of possible paths. \(N_{t}\) can be calculated as  
\[N_{t}=\left(\begin{array}{c}k\\ 2\end{array}\right)=\frac{k!}{(k-2)!\cdot 2!}\]  
The _Ch_ index represents the frequency with which a line or a segment falls within the shortest paths that connect all lines to all the others in the system, excluding those to and from the considered line (see example in Figure 2). The _Ch_ index gives a measure of the complexity of the road connections within the urban grid. When many of the shortest paths that connect all the spaces to all the others pass through the considered space there is a high value of choice. As for the \(I\) index, the _Ch_ index depends on the number of lines in which the system is composed. \(N_{l}\) and \(N_{t}\) are both calculated not considering the paths starting or finishing with the considered line. The minimum value of the _Ch_ index corresponds to the lines or segments that are only on the paths that affect them as terminal lines; while the maximum value corresponds to the case of the lines that are on all the paths that reciprocally connect each pair of lines or segments.
## 4 Results
### Simulation Study  
#### 4.2.1 Validity and Efficiency Measures  
The validity of the CAFT system was evaluated by computing the measures of the conditional measurement bias (CBIAS), conditional root mean squared error (CRMSE), and test overlap rate (T) across all areas of ability (Table 2). The values of CBIAS, CRMSE, and T across all \(\emptyset\) areas are compared in terms of four different stopping rules (i.e., the adaptive testing is stopped if the criterion of the stopping rule is met). The algorithms of the stopping rules are based on the standard error of a learner's ability. Since the value of the stopping rule corresponds to the value of the standard error, a smaller stopping rule means that there is less standard error in the learner's estimated ability, and therefore higher measurement precision. The CBIAS and CRMSE were computed by the discrepancy between the learner's abilities as estimated by CAFT and the simulated learner's true abilities. The values of CBIAS, CMSE, and CRMSE decreased as the stopping rules became smaller. This means that the measurement precision of CAFT was higher as the stopping rules of CAFT were made stricter. In addition, the test overlap rate was computed to count the number of items used in CAFT. The test overlap rate increased as the stopping rules decreased. This is because the stricter the stopping rule became; the more items were used from the item bank. Therefore, the results found that CAFT functions that as the stopping rules decreased, the measurement accuracy and precision increased based on the measures of the CBIAS and CRMSE and the number of items used from the item bank also increased.