esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Sine Sweep Test Results  
#### 3.3.1 Comparison between Steel and NiTi WMDs  
SS 304 and NiTi WMDs made with the same relative density of 0.31 were subjected to a sine sweep test from 20 to 200 Hz with accelerations of 0.25 g, 0.5 g, 1 g, 1.5 g, and 2 g. The transmissibility function of the two types is shown in Figure 16. To better examine the vibration isolation performance of the WMDs, the natural frequency, peak transmissibility, and dissipation factor of both types of WMDs are calculated using Equations (7) and (8), then plotted as a function of input acceleration, as shown in Table 11 and Figure 17, respectively.  
As shown in Figure 16, the transmissibility functions of both types of WMDs have some characteristics in common. As the input acceleration increases, the resonant peak of the two types of WMDs shifts to lower frequencies. Furthermore, the acceleration transmissibility and loss factor reach their minimum and maximum first and then tend to return to their original levels.  
Figure 16: Comparison of the vibration isolation performance of WMDs fabricated with different materials: (**a**) SS 304; (**b**) SE NiTi.  
Figure 15: Characterization of the dynamic hysteresis behavior of NiTi WMDs heat-treated for different times: (**a**) dynamic stiffness; (**b**) loss factor.  
The common features described above can be attributed to the elastic porous structure of the WMD. As the input acceleration increases from 0.25 g to 1 g, the increasing dynamic load leads to a larger motion amplitude and a greater friction dissipation among dry friction joints, which yields a lower natural frequency, a lower acceleration transmissibility, and a greater loss factor. This mechanism remains in effect unless the friction dissipation is not significant enough to attenuate the input acceleration. As for input acceleration higher than 1 g, the transmissibility increases, and the loss factor decreases again.  
Given the same mesh density and excitation condition, the difference between the frequency response of two types of WMDs can be viewed as a result of stress-induced martensite transformation. The NiTi WMD exhibits lower natural frequencies than that of the SS 304 WMD for all acceleration inputs due to its lower dynamic stiffness, as discussed previously in Section 3.2.1. Furthermore, the NiTi WMD demonstrates lower transmissibility and higher dissipation factor than the steel WMD only if the input acceleration is lower than 1 g. This advantage over SS 304 WMD gradually disappears as the input acceleration is higher than 1 g.
## 5 Prospect of Colloidal Silica as a Grouting Material  
### Other Applications for CS in Civil Engineering  
CS is a kind of promising nanomaterial and has various potential applications in other branches of civil engineering. Apart from ground improvement and liquefaction mitigation, CS has been investigated by some researchers to be used for controlling water production and fluid flow in the petroleum industry, reducing permeability and fixing contaminants in environmental engineering, and preventing water ingress in underground and tunneling construction.  
[PERSON] and [PERSON] (1991) used CS in a practical reservoir fluid-flow control system for workovers at an oilfield [81]. Eleven wells were treated with CS for water injection profile modification, water production control, and remedial casing repair. The results show that CS treatment works well in water production control and remedial casing repair. Zones that have been previously hydraulically fractured can be perfectly treated using CS. Moreover, CS has also been verified to be suitable as a permeation grout for barrier systems in contaminated sites [82; 83; 84; 85]. [PERSON] et al. (1995) used CS when injecting in a heterogeneous unsaturated sand layer at 10-14 feet in depth. It was shown that CS could effectively permeate through this porous media and form a fairly uniform plume. A post-treatment test showed a four order of magnitude permeability reduction for the treated layers. Thus, CS is a potential material that can remarkably decrease hydraulic conductivity and, therefore, can be adopted for use in contaminant barrier systems [82]. [PERSON] et al. (2001) injected CS gel into large sand tanks to determine in situ hydraulic conductivity. Consequently, a permeation test indicated that by grouting CS, a core approximately 60 cm in diameter was produced, with a remarkable reduction in saturated hydraulic conductivity [85]. Besides, the soil-water characteristic curves of grouted soils further verified the use of CS as a grouting material to isolate activated soils in the vadose zone.  
In addition, CS can also be adopted as grouting agent to permeate through hard rocks in tunneling and underground engineering. Geotechnical procedures were adopted to analyze silica soil's mechanical behaviors over time and in different storage conditions, representing different environments encountered in tunnels [86; 87]. It was shown that the initial strength of silica soil increases faster at high temperatures or low humidity, indicating sufficient strength can be obtained in this instance to withstand most grouting conditions. Moreover, when sufficiently confined, silica soil is ductile. These properties indicate that it can suffer loading and unloading cycles in fairly large deformations. Meanwhile, a hydraulic conductivity between \(10^{-10}\) and \(10^{-11}\) m/s was observed for silica soil, which implies that silica soil is a kind of low permeation material and is a suitable material to be grouted in hard rocks in tunneling practice.
## 4 Estimating Shear Stress Versus \(m\)  
We seek to derive an analytical (mathematical) relationship between \(f^{\prime\prime}(0)\) and \(m\). To that end, recall the approximations in (14) and (15) around \(z=0\). In particular, observe that, if \(h(0)=(f^{\prime\prime}(0))^{n}\
eq 0\), we then have  
\[h(z)\approx((h(0))^{\frac{n+1}{n}}-mmz)^{\frac{n}{n+1}}=((f^{\prime\prime}(0) )^{(n+1)}-mmz)^{\frac{n}{n+1}}. \tag{18}\]  
Now, to determine a relationship between \(m\) and \(f^{\prime\prime}(0)\) close to the point \((m_{0},0)\), consider the rate of change of \(f^{\prime\prime}(0)\) with respect to \(m\) at \(m=m_{0}\) (note that \(f^{\prime\prime}(0)=0\) at this cut-off value of \(m\)). We must consider a small change in \(m\), say \(\Delta m\). To this end, fix some \(z\) close to \(0\); we seek that \(h(z)\) (at that point) does not change as \(m\) changes, for the following explanation. First, note that this shall require that \(\Delta m\) be proportional to \((f^{\prime\prime}(0))^{n+1}\). Observe that this will then imply that the corresponding changes in \(h(z)\) and \(h^{\prime}(z)\) are, mathematically, of orders higher than \(1\) in \(\Delta m\). It is not difficult to see that, past this point, the change (variation) in \(h\) over the solution interval \((z,1)\) should remain within orders higher than \(1\) in \(\Delta m\) (see Equation (10)). Thus, the overall change in \(h\) over the interval \((z,1)\) will be of higher orders of \(\Delta m\). Consequently, we must have \(h(1)=0\) satisfying the boundary condition at the right end. We point out that the mathematical details can be worked out with the techniques of the calculus of variations. However the details are of an abstract mathematical nature, and would not be of direct relevance to our main conclusions and results, so we choose to omit them.  
The above discussion and mathematical arguments provide the approximate relationship for \(f^{\prime\prime}(0)\) versus \(m\) (for \(m\) close to \(m_{0}\)) up to orders of \(\Delta m\) greater than \(1\):  
\[f^{\prime\prime}(0)\approx\beta(m-m_{0})^{\gamma},\quad\gamma\approx\frac{1}{ n+1} \tag{19}\]  
**Remark 4**.: _Observe that the above approximation provides an exact relationship for infinitesimal changes in the parameters. Additionally, note that \(h^{\prime}\) remains finite for \(z\) close to \(z=1\) for \(n>1\), while it remains integrable around \(z=1\) for \(n<1\), as discussed earlier (see [26]). This shows that the provided approximation is anticipated to be better for larger values of \(n\) (but should stay valid for all \(n\))._
## 2 Materials and Methods  
### Study Area  
The Tianshan North Slope, located in the Xinjiang province, China, is a typical arid area covering \(148.8\times 10^{3}\) km\({}^{2}\) (\(43.0^{\circ}\) N/\(79.8^{\circ}\) E-\(46.2^{\circ}\) N/\(91.6^{\circ}\) E). It has a typical arid landscape that contains mountains and deserts and covers the major vegetation types of the arid Central Asia. The topography is very complex, with an elevation varying from 170 m in the Jungggar Basin to 5216 m in the mountainous region (Figure 1a). It has an annual mean temperature of 2 \({}^{\circ}\)C and annual potential water surface evaporation around 2000 mm. From the basin to the mountains, the annual precipitation increases from 100 mm in the desert to 500 mm in the mountains, while the annual mean temperature has an opposite relationship, decreasing from 5 \({}^{\circ}\)C in the desert to \(-5\)\({}^{\circ}\)C in the mountains [43]. Followingthis water and heat distribution, the spatial pattern of the vegetation from the desert to the mountains is desert xeric shrubs, crops, grassland, coniferous forest, and alpine meadows.  
In order to detect the responses of different vegetation types to the future climate change and CO\({}_{2}\) fertilization effect, we divided the vegetation into eight types, namely, temperate needle-leaf forest (TNF), temperate broadleaf forest (TBF), grass (GRS), non-pheratophyte shrub (NPS), phreatophyte shrub (PS), crop (CRP), summer pasture (SPT), and settlement (STL) (Figure 1b). In our study area, the dominant vegetation type is GRS, which is widely spread in the low mountainous area and covers 28.0% of the total area. The CRP is widely distributed on alluvial fans between the desert and mountains, and covers 16.4% of the total area. The third vegetation type, the NPS (mainly dominated by _Haloxylon ammodendron_) is widely distributed in the desert and covers 15.5% of the total area. The SPT is widely spread in the mountainous area with an elevation higher than 1600 m and covers the 12.9% of the total area. The other four types, namely, TNF, TBF, PS, and STL cover only 4.1% of the total area. The other 23.1% of the total area is not covered by any vegetation. It includes bare soil, rocks, glaciers, and water bodies, and is excluded from this study.
## 2 Concept
### Single- and Multi-Target Scenarios
#### 2.2.3 Scenario III  
Scenario III is the first scenario that provides two targets in the same range resolution cell, so that a time gating target separation is not possible. Moreover, targets #3 and #4 are located in two neighboring main beam areas, leading to the following RX signal:  
\[s_{\mathrm{r,III}}(t)=s_{\mathrm{t}}(t)*h_{\mathrm{sys}}(t)*h_{\mathrm{fs}}(t- \tau_{\mathrm{V}})*(h_{1}(t)+h_{3}(t)) \tag{9}\]  
As the unique coding filter transfer functions superimpose equally, this signal is comparable to scenario II. Therefore, a simple spectral analysis cannot discriminate between scenario II and scenario III. Unfortunately, this may lead to ambiguities. For further discrimination, both scenarios' features, e.g., the time course of the radar measurement, must be taken into consideration. Moreover, in real scenarios, the target RCSs will not be equal and it is very unlikely that both signals are weighted with the same antenna gain. This will result in different behavior, making the target discrimination and classification easier.
# Stochastic Modeling and Optimal Time-Frequency Estimation of Task-Related HRV
## 1 Introduction
We investigated the advantages of non-resting HRV recordings with respect to usual measurements with spontaneous breathing and the consequent use of non-stationary spectral analysis methods in [13], where the time-frequency marginals of the spectrogram and Wigner-Ville distribution are extracted and divided into the corresponding low-frequency and high-frequency bands. Assuming stationarity on a local scale is a practical approach to model a non-stationary signal, for instance as a Locally Stationary Process (LSP) [19]. We consider an extension of this model, known as Locally Stationary Chirp Process (LSCP) [20; 21], which account for the presence of an increasing instantaneous frequency in the signals. The proposed inference method divides the problem into lower-dimensional sub-problems, by using the instantaneous respiratory frequency to estimate the chirp before proceeding with the estimation of the other parameters from the task-related HRV. The model parameters can be used both for the evaluation of an optimal time-frequency estimator and as the response in a regression analysis to investigate the predictive power of physiological variables over the model parameters. Preliminary results of the regression analysis were presented in a previous conference paper [22], based on a data-set of 47 TPs. Additionally to extending the data-set, we have now refined the inference method and included the derivation of the MSE optimal model-based kernel for the TF estimates. The paper is structured as follows. The methods are outlined in Section 2, including the procedure for data acquisition and preprocessing, the mathematical background for LSCPs, the statistical inference method and the optimal time-frequency estimator, as well as the regression analysis approach. In Section 3, the results from the statistical inference are presented, the different time-frequency estimates are compared, and the significant predictors for each model parameter are discussed. Section 4 is dedicated to comments on the significance of this work and directions for further research.
# Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey
## 8 Challenges and Future Directions
### Privacy Concerns in Unsupervised Learning for Anomaly Detection  
Despite the potential benefits of unsupervised learning in anomaly detection, privacy concerns are likely to arise [62]. As these algorithms often operate without explicit labels or supervision, they may inadvertently reveal sensitive information or patterns that compromise user privacy [36]. For example, clustering algorithms that group similar transactions or behaviors may disclose patterns of user activity, potentially enabling deanonymization or profiling attacks [36]. Similarly, dimensionality reduction techniques applied to blockchain data may expose underlying relationships or correlations, raising concerns about user privacy and data confidentiality [63].  
Addressing privacy concerns in unsupervised learning for anomaly detection requires careful consideration of data anonymization techniques, differential privacy mechanisms, and privacy-enhancing technologies [198]. Techniques such as data masking, noise injection, and anonymization algorithms can help mitigate the risk of privacy breaches while preserving the utility of the data [199]. Moreover, integrating privacy-preserving models with federated learning approaches can enable collaborative anomaly detection without compromising individual user privacy.  
While unsupervised learning algorithms offer promising solutions, it is imperative to address privacy concerns to ensure the ethical and responsible use of these technologies [199]. By using privacy-enhancing techniques and adopting privacy-preserving models, stakeholders can harness the benefits of unsupervised learning while safeguarding user privacy and data confidentiality.
## 3 Results
### Factors Influencing Quality of Life  
The statistical analysis between skin lesion severity indices (PASI, BSA, sPGA, PPSI, GPPSI) and the DLQI indicated that patients with more severe psoriasis had more impaired QoL (Table 2). Higher DLQI scoring indicating a more impaired QoL was also observed in patients suffering from pruritus within previous three days compared to subjects without pruritus (DLQI in the group with pruritus: 13.5 \(\pm\) 7.5 points vs. DLQI in the group without pruritus: 8.0 \(\pm\) 8.0 points; \(p<0.001\)), with the highest DLQI scoring observed in patients with generalized pruritus (mean DLQI: 16.9 \(\pm\) 7.6 points). As shown in Table 2, DLQI correlated with all pruritus intensity measurements (NRSmax, NRSaverage, 10-PSS). Interestingly, a low, albeit significant, correlation was also observed between DLQI and BMI (Body Mass Index: \(\rho\) = 0.17, \(p\) = 0.004). However, DLQI scoring was independent of gender (females: 12.4 \(\pm\) 7.7 points vs. males: 12.8 \(\pm\) 8.1 points, \(p\) = 0.66), age (\(p\) = 0.03, \(p\) = 0.61), psoriasis duration (\(\rho\) = 0.03, \(p\) = 0.6) and the coexistence of psoriatic arthritis (with psoriatic arthritis: 13.4 \(\pm\) 8.7 points vs. without psoriatic arthritis: 12.6 \(\pm\) 7.8 points, \(p\) = 0.59).  
Figure 2: Degree of psoriasis impact on quality of life based on Dermatology Life Quality Index (DLQI) scoring in various clinical variants of psoriasis (0–1—no effect, 2–5—small effect, 6–10—moderate effect, 11–20—very large effect, 21–30—extremely large effect on patient’s life).  
The marked influence of pruritus intensity on QoL may be, at least partially, explained by sleeping problems caused by pruritus. Difficulties in falling asleep, occurring even rarely, similarly to awakenings during the sleep, meaningfully impacted the QoL (Table 3). Patients using sleeping pills also had more reduced QoL than those who did not (Table 3).  
The multiple regression analysis considering all above-mentioned parameters indicated that the involvement of body area by psoriasis (BSA), intensity of pruritus measured with 10-PSS and the occurrence and frequency of awakenings during the night caused by itching are all independent factors determining QoL in psoriatic patients.
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 5 Results and Validation
### Performance Comparison
#### 5.3.6 Delay Variation Comparison  
In Figure 7, the performance of different routing algorithms in terms of delay variation is compared. Dijkstra, which does not take into account delay variation attributes, is not part of the comparison. A comparison is made between TBPPO, TBDRSIR, PPO, and DRSIR. On the NSFNet-14 data, as depicted in Figure 7a, the TBPPO algorithm starts to converge around 60 ms after roughly 1300-episode iterations, showing a clear trend towards convergence. PPO, on the other hand, converges around 60 ms after 600-episode iterations. TBDRSIR reaches approximately 125 ms after 1700-episode iterations, with significant fluctuations. DRSIR, however, fails to converge, with an average delay of around 70 ms. According to the shaded area in the graph, it continues to fluctuate between 15 ms and 175 ms even after about 3000-episode iterations. These results suggest that TBPPO, while emphasizing security, maintains a competitive delay performance compared to PPO.  
Figure 6: Performance comparison of the trust value of five algorithms: (**a**) NSFNet-14, and (**b**) Germany-50.  
Figure 7: Performance comparison on delay inequality: (**a**) NSFNet-14, and (**b**) Germany-50.  
In our performance analysis of the Germany-50 data, when comparing delay variations (as shown in Figure 7b), we evaluated TBPPO, TBDRSIR, PPO, and DRSIR. Dijkstra was omitted from this comparison due to its lack of multiple delay-aware paths. As illustrated in Figure 7b, the TBPPO algorithm exhibits a clear convergence trend, starting to converge around the 1500 th iteration to an approximate 50 ms delay. In contrast, PPO achieves convergence earlier, at around 1000-episode iterations, with an average delay of approximately 140 ms. TBDRSIR fails to reach convergence, while DRSIR exhibits more noticeable convergence at around 210 ms. Interestingly, even though TBPPO prioritizes security, its delay variation performance is not significantly inferior to PPO. In the context of Germany, TBPPO demonstrates the benefits of secure route discovery by reducing the number of attacks, resulting in lower delay compared to PPO and showcasing improved real-time communication capabilities.
# Coverage Analysis and Efficient Placement of Drone-BSs in 5G Networks +
## 6 Conclusions  
Drone-BSs deployment in 5G networks is currently an important subject with extreme potential in the mobile communication networks industry. Placement optimization is a complicated task that requires automation algorithms to find the optimal solution for the placement of drone-BSs. In this work, we studied a 3D deployment of drone-BSs in a 5G network using GWO algorithm. Firstly, we analysed the downlink coverage probability according to SINR and altitude. Next, we proposed a mathematical model in order to maximize coverage under a minimum number of drone-BSs, altitude and total limiting available bandwidth. The results obtained showed a minimization of the total number of drone-BSs deployed with a coverage reaching 82.5%. Our study does not take into consideration the blocking effect and overlap between drones that are important for the handoff, but these factors will be the focus of research in future work.  
Conceptualization, M.A.O. and G.B.; methodology, C.G.; software, M.A.O.; validation, M.-E.O. and C.G.; formal analysis, M.A.O.; investigation, G.B.; resources, G.B.; writing--review and editing, C.G. and M.-E.O.; supervision, M.-E.O.; All authors have read and agreed to the published version of the manuscript.  
This Project is financed by Politehnica University Timisoara, Timisoara 300006, Romania.  
Not applicable.  
The authors wish to acknowledge the technical support, infrastructure and documentation provided by Nokia Networks.  
The authors declare no conflict of interest.
## 4 Multi-Site Micro-Scale Diversity Scheme  
In this section, a methodology to generate rain attenuation time series spatially correlated for multiple sites based on Copulas functions is presented. It is generally uncommon for micro-scale satellite communication systems to simultaneously perform in situ beacon measurements. Therefore, the use of a synthesizer to generate rain attenuation time series  
Figure 8: Downlink outage capacity statistics for SISO and SIMO systems operating at Ka- (blue lines) and Q-band (red lines), respectively, for a clear-sky SNR value of 30 dB.  
Figure 9: Uplink outage capacity statistics for SISO and MISO systems operating at Ka-band for clear-sky SNR values equal to 10, 20 and 30 dB.  
and then calculate the capacity time series for multiple spatially separated links could be of significant importance.  
In the proposed model, the synthesizer is based on the assumption that joint rain attenuation statistics in the temporal and the spatial domain are modeled using Gaussian copulas [34; 36]. The advantages of using Gaussian copulas to model the joint statistics in the spatial domain are that they are simple to apply and can easily be extended for multiple spatially separated links. Moreover, the synthesizer based on copula functions does not require the proof of any assumption regarding the distribution of the rain attenuation induced on a particular single link [36].