esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## 4 IEEE 802.11 Technology for TSN
### Low-Latency Technology  
The authors of [34; 35] proposed a packet size reduction technique and a new queuing technique to achieve the bounded low latency. In [36], multiple physical layer interfaces are used in a wireless NIC to reduce the packet transmission time by transmitting multiple packets simultaneously. 802.11e proposed four access categories based on service characteristics, and the priorities for each category are distinguished [37]. For the service with the highest priority, frequent channel access is provided. In addition, multiple transmission opportunities are provided through one channel access, therefore reducing the channel contention overhead and delay time.  
OFDMA proposed in 802.11 ax divides a single channel into several sub-channels called resource units (RUs) to support simultaneous transmission from multiple stations [2; 6; 7]. Figure 6 shows an example of the OFDMA transmission. In the figure, the AP transmits a  
Figure 5: Fine timing measurement (FTM) mechanism [30].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Latency**} & **Reliability** & **Scheduling** \\ \hline \multirow{4}{*}{802.11 ax} & OFDMA & O & \\ \cline{2-4}  & SR & O & \\ \cline{2-4}  & TWT & & O \\ \cline{2-4}  & TWT + OFDMA & O & O \\ \hline \hline \end{tabular}
\end{table}
Table 3: Relationship between TSN features and 802.11 ax.  
trigger frame (TF) including association identifier (AID), which represents stations assigned to each RU. After receiving the TF, the stations transmit packets in the same RU as their AID. An RU with an AID of 0 is used for random access. A station with an AID of 0 transmits packets when their OFDMA backoff (OBO) counter reaches 0. Because OFDMA enables simultaneous transmission from multiple stations, the queuing delay decreases, and therefore the transmission delay decreases.
## 4 Experiments and Results
### Absorption Losses in 2.4 GHz in an Underwater Environment  
Whenever EM signals propagate underwater, the EM radiation gets absorbed and the signal strength decreases strongly leading to absorption loss. We estimated the amount of absorption losses when 2.4 GHz is transmitted underwater specifically in freshwater.  
Figure 8 shows the absorption loss as 2.4 GHz signal propagates in freshwater. The conductivities of freshwater range from 0 S/m to less than 1 S/m. As the signal propagates and the water conductivity increases, the absorption losses are also increased. These losses become more significant when the water conductivity increases. Thus, Figure 9 shows the absorption losses at 2.4 GHz in river water and seawater conductivities.  
From Figures 8 and 9, the following observations have been made. The absorption losses are about 9.8 dB to 24 dB in freshwater, when water conductivity ranges from 0 S/m to 0.9 S/m at 2.4 GHz. The absorption losses are increased to 19 dB to 24 dB in river water conductivities at 2.4 GHz, respectively. These absorption losses increase to up to 25 dB to 30 dB in seawater conductivities at 2.4 GHz. The losses at 2.4 GHz are extremely high. Accordingly, we test MHz radios and estimate the losses in different water conductivities.
## Methods  
### Changes in Front Position  
We multi-looked the master SLC images of TanDEM-X data by a factor of 5 in range and azimuth to generate speckle-reduced intensity images. The intensity images were geocoded using their corresponding TDX DEMs in an iterative approach (Section 4.3) and sampled with a spatial resolution of 10 m \(\times\) 10 m. In case of unavailability of corresponding TDX DEMs, we geocoded the intensity images using the TDX DEM of 03/02/2012. The existing RGI 4.0 glacier inventory [21] was updated and the front position was regularly delineated using the time-series of intensity images. We used the terminus position of 06/18/2011 as a reference for measuring changes in front position during 2011-2016 (Figure 1). The errors are composed of a 5.4% error from RGI 4.0 glacier outline [22] and an uncertainty of \(\sim\)2 pixels in the manual delineation of the glacier front. The approximate terminus width of the main branch and the west branch are \(\sim\)8 km and \(\sim\)3 km respectively. This leads to an estimated upper-bound error of about 5.5% for the area change measurements.
## 5 Discussion  
### Scale-Invariant Assumption  
The scale-invariance assumption is a core hypothesis in this study's methodology. Its fundamental concept is that statistical models established using low spatial resolution images or large-scale data can continue to be applicable to high spatial resolution or small-scale imagery. Scale issues have long been one of the most important research focuses in the field of remote sensing [35]. Building on previous studies, many scholars have widely applied the scale-invariance hypothesis to various research areas, such as land surface temperature retrieval, and have demonstrated the hypothesis's strong applicability [36, 37]. In order to verify the applicability of this assumption in the methodology of this study, a set of parallel sections are uniformly plotted on each island in this study to compare the 2-m resolution bathymetry data obtained from the inversion based on the GF data with the 10-m-resolution bathymetry data obtained from the inversion based on the Sentinel-2 data. The specific sampling method is: from the westernmost to the easternmost side of the island, a set of north-south section straight lines are selected every 1000 m, and uniform sampling points are generated on each section with 10-m resolution (Figure A1). The final obtained bathymetry data were presented through a scatterplot, as shown in Figure 6. From the scatter plot, it can be observed that the water depth results retrieved using the proposed method  
Figure 5: Comparison results of bathymetric data obtained from inversion with ICESat-2 data in the four islands, where the red dashed line is the 1:1 line, the blue dashed line is the fitted line, and N is the number of data points from ICESat-2 used for validation.  
exhibit a high correlation with the water depth data obtained from Sentinel-2, with \(R^{2}\) values exceeding 0.91. Additionally, the high-density data points across the four study areas are approximately aligned with the 1:1 line, and the slope of the fitted line approaches 1. This indicates that the statistical model established based on 10-m spatial resolution data remains effective in reflecting water depth information when applied to 2-m spatial resolution GF data, thereby validating the applicability of the scale-invariance hypothesis in this study.  
However, the scatter also reveals some discrepancies between the two methods, suggesting that there is still room for improvement in the water depth results obtained in this study. This discrepancy may be attributed to the fact that the method proposed by [PERSON] et al. utilizes multi-year median composite images from Sentinel-2 for water depth retrieval, whereas the method in this study is based on single-scene GF-1 image data. The accuracy of the retrieval is thus dependent on the imaging quality of the single scene. [PERSON] et al. (2019) also mentioned the effectiveness of increasing the number of images for improving accuracy, which represents one of the potential directions for enhancing the method proposed in this study.
## 3 Results and Discussion
### Statistical Analysis of Survey Results
The most significant statistically significant differences were observed between odour intensity values between wind direction groups: SW and NW (pDumnett = 0.009, pGames-Howell = 0.002) and SW and S (pDumnett = 0.009, pGames-Howell = 0.004). The results are presented in Figure 15.  
The non-parametric Kruskal-Wallis test was used to verify these results due to a failure to meet the basic assumptions of the ANOVA test. The results of this test confirmed the ANOVA analysis--the \(p\)-value was 0.006, indicating significant differences between group means for odour intensity. The multiple (two-sided) comparison test showed statistically significant differences between group mean values of odour intensity (grouping variable: wind direction) for the same pairs of wind direction values (SW and NW and S and SW) as the Dumnett and Games-Howell tests. The Kruskal-Wallis test also confirmed the hypothesis that there were no statistically significant differences between the group mean values of odour annoyance for the group variable wind direction. Figure 15: ANOVA test and post hoc test results (decomposition of effective hypotheses). Previous studies focusing on pollution measurements did not take into account information from residents and their perception of the odour impact. Survey research allows for a broader look at the problem of odour nuisance and ensuring the comfort of life in an urban agglomeration. The research shows the main odour source as the refinery.
## 2 Literature Review
### Optimization of Parcel Locker Location Selection  
The location of PLCs is influenced by factors such as availability, accessibility, security, environmental impact, site occupancy, cost, usage methods, regulations, and others [7; 18]. Previous studies on optimizing the site selection of PLCs have thoroughly discussed the environmental impact, the optimal time window for distribution, the cost-efficiency of distribution, and the sensitivity of users' pickup distance to the selection of PLCs [19; 20; 21]. Despite significant progress in PLC location optimization, its limitations still constrain practical applications:  
(1) Neglect of user behavior: Most studies aim at cost minimization or coverage maximization without integrating spatiotemporal trajectory data to enhance efficiency.  
(2) Static assumption bias: Traditional models presume fixed demand distributions, overlooking time-varying user activity patterns (e.g., commuting tidal effects), exacerbating spatiotemporal mismatches.  
(3) Excessive parameter simplification: Current KDE applications are largely confined to spatial dimensions, failing to couple parcel physical attributes (e.g., weight-volume-urgency) and limiting decision-making granularity. These shortcomings highlight the need to develop a dynamic, user-centric, and multi-parameter collaborative PLC location framework.  
With the advancement of mobile terminals, apps, and geographic information technology, it is now possible to leverage user spatiotemporal trajectories to select suitable PLCs, thereby improving user convenience [12; 22; 23].
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 3 Problem Formalization
### Formal Security Definitions  
By defining two games, we reduce the security of PPDB to difficult assumptions, that is, if PPDP is not secure, the difficult assumptions can be solved.  
For the confidentiality of the data in our scheme, we define the following game.  
**Setup:** The challenger \(\mathcal{C}\) first generates the system parameters \(param\) and private key \(sk\), and sends \(param\) to the adversary \(\mathcal{A}\).  
Figure 2: System model.  
**Challenge:** The adversary \(\mathcal{A}\) chooses two pieces of data \(m_{0}\) and \(m_{1}\), and sends them to the challenger \(\mathcal{C}\), where \(|m_{0}|=|m_{1}|\). The challenger \(\mathcal{C}\) randomly chooses \(b\in\{0,1\}\), computes \(C^{*}=ENC(param,m_{b})\) and sends it to the adversary \(\mathcal{A}\).  
**Output:** The adversary \(\mathcal{A}\) outputs a guess \(b^{\prime}\). If \(b^{\prime}=b\), then the adversary \(\mathcal{A}\) wins the game.  
The advantage of the adversary \(\mathcal{A}\) to win the game can be defined as \(Adv_{e,\mathcal{A}}(l)=\left|pr(b=b^{\prime})-\frac{1}{2}\right|\).  
We say that the scheme enjoys indistinguishability under chosen plaintext attacks (IND-CPA) if any PPT adversary \(\mathcal{A}\) has only negligible advantage in \(\varepsilon\) in winning the above game.  
We continue to define the EU-CMA security of scheme.  
**Setup:** The challenger \(\mathcal{C}\) first generates the system parameters _param_ and private key \(sk\) and sends \(param\) to the adversary \(\mathcal{A}\).  
**Signature Query:** The adversary \(\mathcal{A}\) adaptively selects data \(m_{i}\) and sends them to the challenger \(\mathcal{C}\). The challenger \(\mathcal{C}\) executes the signature algorithm to generate a signature \(\sigma_{m_{i}}\) and sends it to the adversary \(\mathcal{A}\).  
**Output:** The adversary \(\mathcal{A}\) outputs a forged signature \(\sigma_{m^{*}}\) for a message \(m^{*}\), where \(m^{*}\) has never been executed as a signature query before. If \(\sigma_{m^{*}}\) is a valid signature for \(m^{*}\), then the adversary \(\mathcal{A}\) wins the game.  
We say that the scheme enjoys existential unforgeability against chosen message attack (EU-CMA), if any PPT adversary \(\mathcal{A}\) has only negligible advantage in \(\varepsilon\) in winning the above game.
## 7 Overview of Deep Learning in 5G Security
### Deep Learning Applications in 5G Security  
The practical applications of deep learning in the context of 5G security are vast and diverse. As 5G networks increase in complexity and scale, the role of advanced techniques such as deep learning in ensuring network security becomes increasingly pivotal. Various deep learning models have been successfully applied to areas such as anomaly detection, threat prediction, and even intrusion detection systems in 5G networks [106].  
**Anomaly detection:** One of the main applications of deep learning in 5G security is anomaly detection. Anomaly detection in network traffic refers to identifying patterns that do not conform to expected behavior, which could be indicative of a potential security threat or attack. Deep learning algorithms, especially unsupervised learning models like autoencoders, have proven effective in detecting anomalies in high-dimensional data. They can model the 'normal' behavior of the network and can effectively identify deviations or anomalies from this established norm.  
**Threat prediction:** Deep learning can also be used for threat prediction in 5G networks. By analyzing historical network data, deep learning algorithms can identify patterns and trends that might suggest a future attack. RNNs, in particular, are very effective at this task due to their ability to process sequential data and remember past inputs, which is critical for understanding time-dependent patterns and trends in network data [95].  
**IDSs:** Deep learning-based IDSs are another important application in 5G security. IDSs can monitor network traffic for suspicious activity or violations of policy. A deep learning-based IDS can learn and evolve with the ever-changing threat landscape, thereby improving its detection capabilities over time. Furthermore, deep learning algorithms can classify different types of attacks, thus enabling the system to respond appropriately to the specific threat at hand [140].
## Appendix A Examples of Area Calculation Error of the Scattering Ellipse  
In Table 11 we list some examples of area calculation error of the scattering ellipse, with the presented approximation presented in this paper, the error is under 0.1%. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Scenario** & **Precise Area (m\({}^{2}\))** &
\begin{tabular}{l} **Approximate** \\ **Area (m\({}^{2}\))** \\ \end{tabular} & **Calculation Error (\%)** \\ \hline Rising t = 0 s & 5.3767 \(\times\)\(10^{5}\) & 5.3729 \(\times\)\(10^{5}\) & 0.071 \\ Rising t = 700 s & 6.0180 \(\times\)\(10^{6}\) & 6.0178 \(\times\)\(10^{6}\) & 0.0023 \\ Cruising t = 0 s & 1.4728 \(\times\)\(10^{7}\) & 1.4728 \(\times\)\(10^{7}\) & 0.0026 \\ Cruising t = 900 s & 1.4651 \(\times\)\(10^{7}\) & 1.4651 \(\times\)\(10^{7}\) & 0.00064 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Examples of Area Calculation Error of the Scattering Ellipse \begin{table}
\begin{tabular}{l l l l} \hline \hline
**t** & **Fitting Functions** & **Coefficients** \\ \hline \(t=400\) s & Polynomial & \(c_{1}=-0.3246\), \(c_{2}=0.00763\), \(c_{3}=0.01599\), \(c_{4}=0.005074\), \(c_{5}=0.02261\) \\ \(t=600\) s & Polynomial & \(c_{1}=-0.5116\), \(c_{2}=0.01115\), \(c_{3}=-0.0005945\), \(c_{4}=0.004889\), \(c_{5}=0.02343\) \\ \(t=800\) s & Polynomial & \(c_{1}=-0.7453\), \(c_{2}=0.01103\), \(c_{3}=-0.01604\), \(c_{4}=0.003993\), \(c_{5}=0.02392\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Coefficients in Fitting Functions of normalized Doppler shift \(\gamma_{\text{DIF}}\) (Rising). Appendix B The Calculation of \(l_{\text{l,T}}\left(\tau_{\text{DIF}},t\right)\) and \(l_{\text{s,T}}\left(\tau_{\text{DIF}},t\right)\)  
\[l_{\text{l,T}}\left(\tau_{\text{DIF}},t\right)=y_{\text{D}\max}\left(\tau_{ \text{DIF}},t\right)-y_{\text{D}\min}\left(\tau_{\text{DIF}},t\right) \tag{11}\]  
where \(y_{\text{D}\max}\left(\tau_{\text{DIF}},t\right)\) and \(y_{\text{D}\max}\left(\tau_{\text{DIF}},t\right)\) are calculated as Equations (12) and (13), where  
\[
# Adaptive Versioning in Transactional Memory Systems +
## 4 Basic Adaptive Versioning
### Adaptive for Non-Persistent Transactional Memories
Let \(\tau_{Lazy}\) be the total amount of time spent while (i) versioning data from the original memory locations to the redo log area and (ii) writing the data from the redo log area back to the original memory locations. The first kind of operations are shown as 1 in Figure 1(b) and the second kind of operations are shown as 2 in Figure 1(b), respectively. The first kind of operations are always done in lazy versioning and the second kind of operations are done only when the transaction commits. That means, for a committed transaction, data movement is performed two times. Therefore, for lazy versioning,  
\[\tau_{Lazy}=(2N_{commit}+N_{abort})\cdot\alpha \tag{15}\]  
Based on 3 different cases below, we can see 3 scenarios for \(\tau_{Eager}\) and \(\tau_{Lazy}\):  
* Case 1: **If**\(N_{commit}=N_{abort}\), **then**\(\tau_{Eager}=\tau_{Lazy}\)
* Case 2: **If**\(N_{commit}>N_{abort}\), **then**\(\tau_{Eager}<\tau_{Lazy}\)* Case 3: **If**\(N_{commit}<N_{abort}\)**, then**\(\tau_{Eager}>\tau_{Lazy}\)  
Moreover, equation for \(\tau_{Eager}\) suggests that in eager versioning, total time spent for an aborted transaction is twice as much as the time spent for a committed transaction. Then it is immediate that the eager versioning performs better until \(N_{commit}\geq 2N_{abort}\); i.e.,  
\[\frac{N_{abort}}{N_{commit}}\leq\frac{1}{2} \tag{16}\]  
Thus, we get \(Threshold_{Eager}=\frac{1}{2}\) and switch to lazy versioning when \(ACR_{Eager}>\frac{1}{2}\). Equation for \(\tau_{Lazy}\) suggests that the lazy versioning performs better until \(2N_{commit}\leq N_{abort}\); i.e.,  
\[\frac{N_{abort}}{N_{commit}}\geq 2 \tag{17}\]  
Then, we get \(Threshold_{Lazy}=2\) and switch to eager versioning when \(ACR_{Lazy}<2\).
## 3 Results and Discussion
### Adsorption Studies
#### 3.2.2 Effect of Contact Time  
The time needed for the system (adsorbent + adsorbate) to reach equilibrium is a key factor that must be assessed. The effect of contact time on the adsorption of copper(II) ions for all biomaterials was examined over a range from 2 min to 2 h at an initial concentration of 200 mg L\({}^{-1}\), under the optimal pH of 4.0 (Figure 6).  
All investigated materials exhibit excellent affinity towards Cu(II) ions in aqueous media. For MO, equilibrium was reached within ten minutes. The adsorption process is notably faster for LA, M, and L, where adsorption equilibrium was achieved within the first two minutes, highlighting the strong affinity of those plant materials towards Cu(II) ions in aqueous solutions and their highly efficient adsorption rate.  
Three kinetic models were applied as follows: pseudo-first-order, pseudo-second-order, and the Weber-Morris diffusion model, described in detail in [PERSON] et al. [38]. The kinetic parameters were applied solely to MO. For the other sorbents, due to the very fast adsorption, this approach is not applicable. The calculated parameters are presented in Table 2. A comparison of the pseudo-first-order and pseudo-second-order kinetic models shows that the adsorption of Cu(II) ions onto MO fits better with the pseudo-second-order model. The findings imply that chemisorption is likely the rate-limiting step in the adsorption process for the Cu(II)-MO system.  
Figure 5: Influence of pH on the amount of adsorbed Cu(II) ions onto MO, M, LA, and L.  
Analysis using the intraparticle diffusion model indicates that the adsorption process involves multiple stages, as evidenced by two distinct linear regions in the kinetic plot. The initial phase exhibited a fast rate (K\({}_{\text{id1}}\) = 0.449), while the subsequent phase proceeded at a significantly slower rate (K\({}_{\text{id2}}\) = 0.009). This difference is likely due to the initial phase corresponding to the diffusion of copper ions onto the MO surface, whereas the latter phase is associated with intraparticle diffusion. Nonetheless, neither of these two linear phases intersects the origin, suggesting that although intraparticle diffusion played a role in the adsorption process, it was not the sole rate-limiting factor.
## 4 Description of the Approach
### Prioritization of the Risk Treatment Paths with a Hybrid MCDM Approach
#### 4.1.1 Normalization of the Decision Matrix
Risk reduction can thus be understood as the added value of the risk treatment approach and is defined as the absolute difference between the risk potential of the original plan and the resulting risk in the risk-threatened plan:  
\[Risk\;reduction=|risk\;potential-resulting\;risk| \tag{2}\]  
The risk potential of a path, on the other hand, is defined as the product of the probability of occurrence of the risks \(P_{Risk}\) it contains and the costs caused by the risks \(\Delta C_{tot}{}^{R}\), i.e., the damage [44; 45; 46]:  
\[Risk\;potential=P_{Risk}\cdot\Delta C_{tot}{}^{R} \tag{3}\]  
The resulting risk considers all path elements and thus consists of the product of the probability of occurrence of the risks and measures P and the total risk and measure costs of the path\(\Delta C_{tot}\):  
\[Resulting\;risk=P\cdot\Delta C_{tot} \tag{4}\]  
A high rating for the risk reduction is desired whereas the worst possible outcome is a risk reduction of zero, implying that no measures were integrated, and thus no potential risk was reduced. Thus, all three criteria are in line with criteria (3), namely, independency. As for costs, a differentiation between measure and risk-induced costs is introduced, and the energy flexibility only considers cost-independent factors. Furthermore, all three criteria are quantified evaluations of the regarded system and thereby fulfill principle (4). To achieve comparability across the criteria that utilize different scales, normalization of the criteria ratings \(r_{ij}\) is necessary (criteria (5)). Depending on the direction of the criteria, Equation (5) or Equation (6) is applied [25], resulting in a normalized rating \(r_{ij}^{*}\), with a value of one corresponding to the best possible alternative \(i\) and zero being the worst possible alternative \(i\) for the respective criterion \(j\):  
\[r_{ij}^{*}=\frac{r_{ij}-\min\bigl{(}r_{ij}\bigr{)}}{\max\bigl{(}r_{ij}\bigr{)} -\min\bigl{(}r_{ij}\bigr{)}},\;\;for\;maximizing\;criteria \tag{5}\]  
\[r_{ij}^{*}=1+\frac{\min\bigl{(}r_{ij}\bigr{)}-r_{ij}}{\max\bigl{(}r_{ij}\bigr{)} -\min\bigl{(}r_{ij}\bigr{)}},\;for\;minimizing\;criteria \tag{6}\]The cost indicator \(\Delta C_{tol}M\) is to be minimized, and the energy flexibility indicator EF and the risk indicator risk reduction are to be maximized. The three criteria can now be graphically represented in a trilemma and are summarized in the decision matrix D with \(r_{ij}^{*}\) being the normalized rating for alternatives \(i=1\ldots n\) with respect to the criterion \(j=1\ldots m\):  
\[D= \begin{bmatrix}r_{11}^{*}&\cdots&r_{1m}^{*}\\ \vdots&\ddots&\vdots\\ r_{n1}^{*}&\cdots&r_{nm}^{*}\end{bmatrix}  \tag{7}\]