esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Methodology  
This study adopts a systematic literature review methodology to investigate security concerns in 5G networks. The search strategy encompasses several academic databases, such as IEEE Xplore, ACM Digital Library, and Google Scholar, focusing on recent peer-reviewed articles and conference proceedings. Specific keywords, such as \"5G Security\", \"5G vulnerabilities\", \"Machine learning in 5G security\", and \"Network attacks in 5G\", were employed to narrow the scope of the inquiry. Upon identifying potential articles, an initial screening process was conducted based on titles and abstracts to assess relevance. The selected articles then underwent a more rigorous full-text evaluation to ascertain their alignment with the research objectives. Quality metrics include the reputation of the publication outlet, the rigor of the methodologies employed, and citation frequency, among others. Data extracted from each vetted article include research objectives, methods, key findings, and implications. Thematic analysis was subsequently employed to identify and interpret patterns related to 5G security and machine learning and deep learning in enhancing 5G security, answering this study's research questions. This approach ensures that the findings are rigorous and pertinent to the domain of 5G security.  
The rest of the paper is organized as follows. Section 2 discusses 5G telecommunications technology and architecture. Section 3 examines in detail the mobile network security evolution and the evolution of the threat landscape. Section 4 explores and analyzes the security threat landscape in 5G networks. Section 5 discusses the current and prospective solutions to enhance 5G security. Sections 6 and 7 discuss the role of machine learning and deep learning in 5G security and the cutting-edge technologies being applied or proposed to address 5G security concerns. Section 8 discusses the technical and ethical considerations for the effective implementation of 5G security. Finally, Section 9 draws this study's conclusions and guidelines for future work.
## 3 Results and Discussion
### Effect of Heat Pump Set-Point Temperature
The figure presents the required power consumption of the vacuum pump (VP) for the vacuum of 0.26 bar, the heat pump (HP) consumption and the total power consumption of the system as the recirculation ratio increases for the set-point of the heat pump at 75, 80 and 85 \({}^{\circ}\)C. The lower consumption is identified for the case of 75 \({}^{\circ}\)C where the maximum water production is 0.13 m\({}^{3}\)/h, the lowest compared to the other cases. It is observed that, when the recirculation ratio is up to 80%, both the vacuum pump and the heat pump's compressor power consumption increase, leading to an increased total power consumption with a maximum of 40 kW. When the recirculation ratio takes values higher than 80%, the vacuum pump power consumption remains almost constant, while the heat pump and total power consumption decrease, since the largest percentage of the recirculating brine is already hot and the heat pump switches off. A similar behavior can be observed for the set-point at 80 \({}^{\circ}\)C, presenting higher total power  
Figure 5: Water production (**a**) and percentage of water recovery of the flash evaporation (**b**) as recirculation ratio increases, for three different set-points of the heat pump. consumption, while the heat pump reaches the set-point at a recirculation ratio of 93%. Finally, when the set-point of the heat pump is 85\({}^{\circ}\)C, the vacuum pump, heat pump and the total power consumption show the highest values and the heat pump does not seem to reach the set point. Figure 8 presents the respective coefficient of performance (COP) for the three cases. Figure 6: Salinity of the output liquid brine as recirculation increases for varying set-point temperature of the heat pump. Figure 7: Vacuum pump (VP), heat pump (HP) and total power consumption as recirculation ratio increases for the set-points of 75, 80 and 85 \({}^{\circ}\)C.  
The specific energy consumption in terms of total electricity absorbed by the system is presented in Figure 9 for the three set-points of the heat pump, showing the total energy consumed for each cubic meter of produced water. It can be observed that, for the specific vacuum (0.26 bar) provided by the vacuum pump, the set-point of 75 \({}^{\circ}\)C presents the lowest specific energy consumption, while the specific energy consumption of 80 and 85 \({}^{\circ}\)C present similar values for the recirculation ratio up to 95%.
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 2 Materials and Methods
### Demodulation
#### 2.4.3 Join & Downsample  
Frame detection and symbol decision require information about the compressed pulse peak positions in time, which are difficult to establish in one matched filter branch, e.g., only the up chirp compression result, as there may be no peaks present, if the signal hypothetically only consists of down chirps. The JDS block first resamples the sequence to an integer fraction by \(N_{\text{res2}}\) to the sample rate  
\[y_{\text{res2}} =\text{resample}\{y_{\text{fhx}},\,f_{\text{s1}},\,f_{\text{s2}}\},\, \text{where} \tag{25}\] \[f_{\text{s2}} =\frac{f_{\text{s1}}}{N_{\text{res2}}},\] \[N_{\text{jds}} =\left\lfloor\frac{N_{\text{fhx}}}{N_{\text{res2}}}\right\rfloor,\]  
then creates the sum and difference  
\[y_{\text{sum}}[n,\,c] =y_{\text{fhx}\uparrow}[n,\,c]+y_{\text{fhx}\downarrow}[n,\,c], \tag{26}\] \[y_{\text{dfi}}[n,\,c] =y_{\text{fhx}\uparrow}[n,\,c]-y_{\text{fhx}\downarrow}[n,\,c].\]  
This operation requires coherence, since a phase difference between the up and down chirp compressed sequences leads to sub-optimal symbol detection. This condition will be fulfilled only if no Doppler shift is present, so sender and receiver do not move relative to each other [50]. For this work, we exclusively considered stationary conditions. The sum and difference sequences are stored in a joint array \(y_{\text{jds}}\) of size \(\left\{N_{\text{jds}},\,N_{\text{lo}},\,2\right\}\).
# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
## 4 Experimental Studies
### Dataset Description
#### 4.1.8 Mutagenesis3_Chains Dataset  
The mutagenesis3_chains dataset is designed to classify molecules as mutagenic or non-mutagenic based on chain-level structural information. It contains 188 molecules, with 125 positive (mutagenic) and 63 negative (non-mutagenic) labels. Each molecule is described using 24 features, which capture the properties of molecular chains. This dataset is used to improve predictive models in fields like environmental science and pharmaceuticals, where identifying potentially mutagenic substances is vital.
# An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series
## 5 Conclusions  
In this study, we mapped planted forests in the NHAR by developing the OASB technique that uses the inter-annual Landsat time series (1991-2020). This novel method was an effective integration of image segmentation and time series classification, taking advantages in (1) no sample requirements and (2) interpretability. A desirable performance of the method was confirmed by the two results: (1) the planted forest map in 2020 with a high accuracy (OA = 87.3%, Kappa = 0.82); and (2) the planting year calendar (RMSE = 2.46 years) that coincided with the implemented afforestation activities in the most recent three decades. Overall, the OASB provided new insights for distinguishing the planted forests from the natural forests and estimating their planting years over large areas; the resultant maps were essential information for evaluating the effectiveness of forestry eco-engineering programs, supporting policymaking decisions, and facilitating the sustainable development of forest ecosystems.  
Conceptualization, X.X. and X.L.; methodology, X.X.; software, X.X. and C.W.; validation, X.X. and C.W.; investigation, Y.M. and X.X.; resources, X.L.; writing--original draft preparation, X.X.; writing--review and editing, Q.Y., L.T., L.Z. and X.X.; visualization, X.X.; supervision, X.L.; funding acquisition, X.L. and C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Flexible Introduction Team of the Ningxia Hui Autonomous Region, grant number 2020 RXTDLX03; Remote sensing monitoring and evaluation of ecological status in Ningxia, grant number NXCZ20220203.  
Not applicable.  
**Acknowledgments:** Special thanks to the reviewers and editors for providing constructive suggestions and comments to improve this manuscript.  
The authors declare no conflict of interest.
# Real-Time Geo-Localization for Land Vehicles Using LIV-SLAM and Referenced Satellite Imagery
## 4 Experiments
### Ablation Experiments
#### 4.3.1 Comparison of Matching Algorithm  
The proposed matching algorithm is compared against other matching methods, including Scale Invariant Feature Transform (SIFT), omniGLUE [36], MI [37], and CFOG [38]. Among them, SIFT is a classic local feature-based algorithm, while omniGLUE is a more recent and deep learning-based feature matching method that can be applied to cross-modal matching tasks. Both MI and CFOG are designed for cross-modal matching. Similar to our method, they adopt a translation-based full-search strategy for the matching. Two variants of our method are included in the comparison: ASD, a spatial-domain search based on the absolute difference metric (4), and CFD, a frequency-domain search utilizing the double-angle cosine similarity metric (6).  
A total of 100 matching pairs, each consisting of a real-time projection image and the corresponding reference search region, were randomly sampled from 14 sequences. A matching result is considered incorrect if its distance to the ground truth exceeds 5 m. The results in Table 3 demonstrate that our method achieves the best performance in terms of both matching accuracy rate and computational efficiency. Examples of the above matching method are provided in Figure 9.  
Figure 8: Performance of OAGS when entering indoor environments. (**a**) Erroneous observations from image matching lead to significant localization errors; (**b**) The result after applying the proposed OAGS mechanism.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy** & **Processing Time (s)** \\ \hline SIFT & 0\% & 1.67 \\ OMNIGLUE & 1\% & 126.59 \\ MI & 76\% & 265.32 \\ CFOG & 80\% & 0.31 \\ Ours (ASD) & 96\% & 5.50 \\ Ours (CFD) & 96\% & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of matching performance.
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 5 Numerical Experiments, Results, and Discussion
### Single Wideband Signal Scenario  
A scenario where there is a single signal in a single time window was first simulated. The length of the window \(N=2048\) and sampling frequency \(f_{s}=48\) MHz. Furthermore, the signal was simulated as a wideband plane wave \(s(t)=\delta(t-1024/f_{s})\) with harmonic order \(N_{h}=3\) and coming from \(\Omega=(90,\ 0)^{\circ}\). In addition, spherical harmonic coefficientsin the time domain were computed as presented in Section 4.1 using an \(N\)-size discrete Fourier transform. The signal covariance matrix for a plane wave is  
\[\tilde{\mathbf{R}}_{ss}=\sum_{k=1}^{N}||H_{u}(k)||^{2}\triangleq E_{eq} \tag{64}\]  
and \(E_{eq}\approx 0.88\) is the equalized signal energy in total.  
The RMSE of an individual technique over 100 Monte Carlo samples for different noise variances \(\sigma^{-2}\) is shown in Figure 3. Moreover, Figure 3 shows the theoretical CRLB performance for the deterministic and stochastic cases. For one signal scenario, it can be observed that both techniques have almost the same performance. The RMSE of the techniques adheres to the CRLB when \(SNR\leq-10\) dB. The elevation angle estimation has a smaller RMSE, just as in the CRLB prediction.
## 2. Mature Technologies for Water and Wastewater Remediation
### Ozonation
#### 2.2.3 Advantages and Disadvantages of Ozonation  
Ozonation is an excellent disinfectant and oxidising agent [22] that effectively removes odours and colours from water [23]. Furthermore, since excess ozone rapidly decomposes into oxygen, the purified water does not have a residual scent or taste, which justifies its popularity in the food and beverage industry. Industrial ozonation processes can purify between 10000 and 60000 L/h of water, which is relatively quick [17]. In these applications, ozone is generated on-site, so external chemicals are not usually required, unless liquid oxygen is utilised [21]. This eliminates the risk associated with the transportation, handling and storage of chemicals.  
Ozonation also fulfils its primary role as a disinfectant in tertiary water purification. It virtually eliminates organics such as phenols [24] and can effectively oxidise inorganics (such as Mn and Fe) into insoluble particles [25]. It is also highly effective against pathogens, including _E. coli_, _Giardia_ and _Cryptosporidium_ (unlike chlorination) and viruses.  
However, there are also disadvantages. Ozone produces DBPs when it reacts with endocrine-disrupting chemicals, even at high doses [26]. However, these are formed in small amounts and little is known about them. Ozone also produces HAAs and THMs, but in significantly smaller amounts than chlorination. The third and most problematic DBP is bromate, because it can be carcinogenic [27]. However, if the water contains a low bromide concentration, then the concentration of bromate produced may still be within acceptable levels. Other disadvantages include the nature of ozone: it requires vast amounts of energy [28] and capital [21], but because it rapidly decomposes in water, the excess ozone cannot be measured, so operators cannot minimise the amount of ozone used. Furthermore,this rapid decomposition makes it unfeasible for long distribution pipelines, which could introduce contaminants downstream [22]. Finally, ozone must be safely contained within the operating environment, as the inhalation of ozone can cause severe lung damage [29].
# Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
## 6 Conclusions and Future Work  
In this paper, we presented the formulation of the adaptive allocation problem for multi-radio, multi-channel wireless mesh networks (WMN) with a practical constraint on the number of NICs per AP. The adaptive allocation algorithm is made of the initial phase and the dynamic phase, the dynamic phase introduces a newly defined decision function. The viability of our methodology is checked through our modified version of the NS-2 network simulation. The notable enhancement of the performance is seen by picking a suitable \(\lambda\) with a diminishing number of changes. In future works, we shall present a mathematical estimator for \(\lambda\), and perform more studies for wireless mesh networks with multiple GWs to justify our methodology.  
Conceptualization, W.H. and T.F.; methodology, W.H.; validation, W.H. and T.F.; formal analysis, W.H. and T.F.; investigation, W.H. and T.F.; resources,T.F.; data curation, W.H.; writing--original draft preparation, W.H.; writing--review and editing, T.F.; visualization, W.H. and T.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
FCA Fixed Channel Allocation  
ACA Adaptive Channel Allocation  
GW Gateway  
NIC Network Interface Card
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 5 Singular Pulses
### Square Cosine Pulse  
A smoother pulse profile is the square cosine one. In this case, the discontinuity occurs in the second derivative level, but the function itself and its derivative are both continuous. Therefore, this pulse can simulate pulses, which were generated by discontinuous dielectric media. Let  
\[A(z=0,t)=\begin{cases}A_{0}\cos^{2}(\pi t/\theta)&|t|<\theta/2\\ 0&|t|\geq\theta/2\end{cases} \tag{40}\]  
be the signal's profile at one end of the medium; then, since it can be rewritten as  
\[A(z=0,t)=\frac{1}{4}A_{0}\Big{[}\exp\Big{(}2i\frac{\pi}{\theta}t\Big{)}+\exp \Big{(}-2i\frac{\pi}{\theta}t\Big{)}+2\Big{]}[u(-t+\theta/2)-u(-t-\theta/2)] \tag{41}\]  
its dynamics can be derived, again using Equation (24), to yield the result at the second end  
\[\begin{split} A(t,z)=\frac{1}{4}A_{0}\Big{[}erfc\Big{(}\frac{t- T/2}{\sqrt{-2i\beta_{2}z}}\Big{)}-erfc\Big{(}\frac{t+T/2}{\sqrt{-2i\beta_{2}z}} \Big{)}\\ \exp\Big{(}i\frac{\beta z}{2}\big{(}\frac{2\pi}{\tau}\big{)}^{2} \Big{)}\Big{[}\exp(i2\pi\frac{t}{\tau})erfc\Big{(}\frac{t-T/2+2\pi\beta_{2}z/ T}{\sqrt{-2i\beta_{2}z}}\Big{)}+\exp\big{(}-i2\pi\frac{t}{\tau}\big{)}erfc \Big{(}\frac{t-T/2-2\pi\beta_{2}z/T}{\sqrt{-2i\beta_{2}z}}\Big{)}-\\ \exp(i\pi\frac{t}{\tau})erfc\Big{(}\frac{t+T/2+\pi\beta_{2}z/T}{\sqrt{-2i \beta_{2}z}}\Big{)}-\exp\big{(}-i\pi\frac{t}{\tau}\big{)}erfc\Big{(}\frac{t+T/ 2-\pi\beta_{2}z/T}{\sqrt{-2i\beta_{2}z}}\Big{)}\Big{]}\end{split} \tag{42}\]  
Figure 14 presents the dynamics of the square cosine pulse.  
Figure 14: Similar to Figure 12, but for the square cosine pulse (Equation (42)).  
Figure 13: Same as Figure 2, but for the intensity \(\big{(}|A(t,z)|^{2}\big{)}\) of the pulse presented by Equation (39).  
A comparison between Equations (27), (39), and (42) is presented in Figure 15. The three pulses represents three levels of singularities. Equation (27) represents a case of amplitude discontinuity, Equation (39) represents discontinuity in the pulse's amplitude's derivative, and Equation (42) represents discontinuity in the amplitude's second derivative.  
As can clearly be seen, the higher is the level of the singularity (i.e., the discontinuity occurs at higher derivatives), the faster the pulse decays in accordance with Ref. [22; 23].
## 3 Results and Discussion
### Gold Adsorption from Chloride Media
#### 3.3.6 Separation of Gold (III) from Selected Base Metals  
Since base metals are normally found in the gold-HCl bearing solutions, the selectivity of the present adsorption system against the presence of various metals in the aqueous phase was investigated by using an adsorbent dosage of 0.25 g L\({}^{-1}\) and the aqueous phase containing 5.1 \(\times\) 10\({}^{-5}\) M Au(III), Fe(III), Cu(II) in 0.1 M HCl media. From the results obtained on the basis of binary solutions Au(III)-metal (Table 8), it is inferred that gold was preferably adsorbed over these base metals. The selectivity with respect to gold seems to be enough to separate this precious metal selectively from the base metals.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dosage, g L\({}^{-1}\)** & **\% Gold Adsorption** & **Metal Uptake, mg g\({}^{-1}\)a** \\ \hline
0.5 & 99.5 & 10.0 \\
0.25 & 92.0 & 18.4 \\
0.13 & 51.0 & 19.6 \\
0.05 & 31.0 & 25.8 \\ \hline \end{tabular}  
* After 2 h.  
\end{table}
Table 6: Influence of nanotubes dosage on gold(III) adsorption.  
\begin{table}
\begin{tabular}{c c c} \hline
**HCl, M** & **\% After 30 min** & **\% After 2 h** \\ \hline
0.1 & 99.5 & 99.5 \\
1 & 94.0 & 99.5 \\
10 & 66.5 & 99.5 \\ \hline \end{tabular}  
\end{table}
Table 7: Influence of initial HCl concentration on gold(III) adsorption.
# Vehicle Motion Control for Overactuated Vehicles to Enhance Controllability and Path Tracking
## 6 Results and Discussion
### U-Turn Maneuver
Consequently, the path tracking errors \(e\) remain similar across the acceleration levels for the overactuated configuration. Figure 10: U-turn maneuver at varying reference acceleration levels \(|a_{\text{ref}}|\). Results are shown for the dual-motor configuration (dotted), the overactuated configuration (solid) and the reference trajectory (red with circle markers). In Figure 11, the actuator commands for the overactuated configuration for the U-turn at \(|a_{\text{ref}}|=8\,\text{m}/\text{s}^{2}\) are plotted. The figure displays the steering response on the front axle \(\delta_{f}\) and the rear axle \(\delta_{r}\) and the wheel torques \(T_{w,i}\). During the transition to straight-line running, the MPC applies negative steering corrections \(\delta_{f}\) at \(\approx 260\,\text{m}\). These corrections are apparent also in both the phase plane near the origin and the gg diagram at the maximum \(a_{x}\) in Figure 10.  
For comparison, the ideal steering response \(\delta_{f,\text{ref}}\) based on the reference vehicle behavior is plotted in the left plot of Figure 11. The generated steering command of the proposed architecture closely follows the desired steering response, indicating good MF behavior with a similar understeer gradient. The positive rear-wheel steering compensates for the otherwise larger vehicle sideslip angle \(\beta\). In the drive torque plots of the individual wheels \(T_{w,i}\) in Figure 11 (right), the braking, cornering and accelerating phases are marked. During the braking phase, the CA distributes torque to the front wheels, especially to the highly loaded front right tyre (\(i=2\)). This aligns with the allocation criterion (11e) that aims to minimize tyre slips \(\kappa\). During this phase, an additional outward-turning yaw moment is generated with the rear right wheel (\(i=4\)) to compensate for oversteer under braking. During the cornering phase, torques \(T_{w,i}\) are predominantly used for TV, generating an inward-turning yaw moment to compensate for limit understeer. During the acceleration phase at approximately \(260\,\text{m}\), the MPC performs steering corrections supported by an additional inward-turning yaw moment to compensate for increased understeer during out-of-corner acceleration, visible through the positive torque split in this phase. This is followed by a longitudinal acceleration phase at \(270\,\text{m}\), during which the rear wheels contribute the majority of the driving torque, once again to ensure minimal slip \(\kappa\).