esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Preserving Solutions in Blockchain-Enabled Internet of Vehicles
## 2 Background
### Privacy in IoV  
Privacy is the ability of a person or a group to preserve themselves and confidential information from unauthorised people [9]. There are numerous technologies and strategies [10] for preserving privacy. Pseudonyms, homomorphic encryption, k-anonymity via cloaking techniques and zero knowledge proofs, which are reviewed in this paper, are some of them. Especially for IoV, it is important to protect the privacy of involved users, as it is a dynamic heterogeneous system consisting of numerous participants which do not trust each other. Furthermore, as vehicular services expand, the exchangeable data grows too. Therefore, privacy by design [11] must be one of the key features of every vehicular technology. The privacy of participants in a IoV ecosystem can be divided into the following types:  
* Identity privacy: It prevents real identity disclosure of a user, mainly protected by using pseudonyms during communication in a vehicular system [3].
* Location privacy: It protects user's location and can be generally protected by cloaking/clustering methods [3].
* Data privacy: It protects the disclosure of user personal data, such as vehicle's trajectory, speed and ad-hoc messages between vehicles. Data privacy can be achieved, for example, with homomorphic encryption or federated learning [12; 13; 14].  
In addition, malicious users or attackers may attempt to cause problems to the system or to gain personal data of users. As participants do not trust each other and malicious users may occur, a fully anonymous network is not feasible. If a malicious user is detected, it should be removed from the network, considering that, these users have acquired a partial pseudonym and are subject to conditional privacy.
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## 6 Example of Application of the Energy-Efficiency Protocol
### Coupled Multi-Level Tank
Based on the criterion mentioned in [53] Chapters 8 and 9, a recommended sampling time for level tank control is \(T_{S}\in[5,\ 10]\) s. Thus, since the value of \(T_{S}\) is inversely proportional to the minimization of the energy consumption (see Equation (17)), the sampling time chosen in this experiment was \(T_{S}=10\) s.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(g\) & \(9.8\,\mathrm{m}/\mathrm{s}^{2}\) & gravitational acceleration \\ \(A_{r}\) & \(138.92\,\mathrm{cm}^{2}\) & the  
The measurement vector \(y(k)\in\mathbb{R}^{2}\) has two packets associated with independent communication level sensors: \(\bar{y}_{1}(k)=H_{1}(k)\) and \(\bar{y}_{2}(k)=H_{2}(k)\). Regarding the control signal \(u(k)\in\mathbb{R}^{2}\), two packets associated with two independent actuators are considered: \(\bar{u}_{1}(k)\) and \(\bar{u}_{2}(k)\), respectively, representing the control inputs in the first and second tanks. Using the semi-reliable network modeling developed in Section 4.2, one has \(n_{a}=n_{g}=2\) and \(\sigma_{u}=\sigma_{y}=2^{2}=4\); two partial transition matrices \(\mathbb{P}^{u}\) and \(\mathbb{P}^{y}\in\mathbb{R}^{4\times 4}\); and, consequently, the transition matrix that governs the jumps among all modes is given by \(\mathbb{P}=\mathbb{P}^{u}\otimes\mathbb{P}^{y}\in\mathbb{R}^{16\times 16}\). Furthermore, it is assumed that only the modes associated to \(y(k)\) are accessible and, as a consequence, the 16 operational modes are organized in four clusters \(\sigma_{y}=4\) in function of \(\mathbb{P}^{y}\), and the partially mode-dependent controller (13) is designed such that \(i\in[1,\ldots,\sigma_{y}]\). Concerning the communication failure, the Zero-input approach [54; 55; 56] is adopted in this experiment, that is, the control matrices \((B_{i},D_{zi})\) or the output matrices \((C_{y_{i}}\) and \(E_{y_{i}})\) of system (1) are equal to zero in the operation modes \(i\in\mathbf{K}\) associated to failures. An example of application of this approach in dynamic output-feedback control problem can be seen in [57; 58]. Besides solving the tank level control problem by using Lemmas 2 and 3, the proposed Energy-Efficiency Protocol optimizes the energy consumption of the network by limiting the maximum number of allowed transmissions of the network nodes and, at the same time, assuring the closed-loop stability and providing an acceptable \(\mathcal{H}_{\infty}\) closed-loop performance.
# Constructing a Microstrip Phase Shifter with Low Phase Error Using a Y-Resonator
## 4 Result and Discussion  
In order to demonstrate the proposed phase shifter, a broadband phase shifter with a center frequency of 3.6 GHz, a relative bandwidth of 60.8%, and a phase shift of 90\({}^{\circ}\) are designed and fabricated. The proposed phase shifter is realized on a single-layer microstrip structure. The substrate used is Rogers 4003 (\(\epsilon\)r = 3.55, tan\(\delta\) = 0.0027, h = 1.524 mm). After further analysis and optimization using ADS2020 software, the instrument used for the measurements was a Ceyear 3672C. The final layout of the phase shifter is shown in Table 1, and the processing test shot is shown in Figure 8.  
The magnitude response and phase response of the designed microstrip phase shifter are shown in Figures 8 and 9. From Figure 8, it can be seen that the 3 dB bandwidth is 60.8% in the frequency range of 2.56-4.75 GHz, the minimum return loss is better than 16.5 dB, the insertion loss is less than 1.2 dB, and due to the use of input-output cross-coupling, two transmission zeros are introduced near the passband cutoff edge at 1.68 and 5.77. As can be seen in Figure 8, the phase shifter has a phase shift value of 90\({}^{\circ}\pm 1.2^{\circ}\) in the frequency range of 2.7-4.3. The measured results are largely consistent with both the schematic and layout simulation results. However, because the physical connections at the SMA interface and solder joints are idealized in the simulation, this can lead to slight variations in the resonant frequency, resulting in a measured frequency response that displays minor multiple resonances. To facilitate comparison with other reported studies, Table 2 has been prepared. It clearly shows that the phase error, insertion loss, return loss, and relative bandwidth in [5; 9] are inferior to those of the current phase shifter. In [6], the alignment of the center frequency with the current phase shifter is more informative; although its insertion loss (better than 0.6) surpasses that of the current phase shifter (better than 0.84), its phase error, return loss, and relative bandwidth are not as favorable. This highlights the excellent phase shift accuracy of the proposed phase shifter. The phase shift range in [8] can achieve 0-360\({}^{\circ}\), and its insertion loss and relative bandwidth are slightly superior to those of the current phase shifter. However, with a phase error of up to 10\({}^{\circ}\), the advantage of the current phase shifter in terms of minimal phase error becomes more pronounced. Furthermore, the phase shift range and phase error in [10; 16] do not match those of the current phase shifter. While the insertion loss, return loss, and relative bandwidth in [10] are better, the complexity of its structure makes processing and fabrication more challenging than for the current phase shifter.  
Figure 8: Frequency response of theoretical, simulated, and measured results.
## 5 Experimental Results
### Temperature Dependence
For example, using the previously determined value of \(V_{R}=10\) mV, and assuming a possible temperature increase of 10 \({}^{\circ}\)C, Equation (24) indicates that \(V_{R}^{\prime}=25\) mV. This increases the fraction of unreliable bits to \(p_{U}=0.41\). This method could be employed by determining the parameters \(a\) and \(\sigma\) of Equation (24) once for a particular manufacturing process. The characterization of individual chips would then not require any extra measurements beyond the two already required by the characterization scheme, and these measurements do not have to be taken at a particular temperature. This method is, however, inefficient due to the constraint imposed by Equation (23), which assumes the worst case temperature dependence. This is highly unlikely for any given ID cell; a large fraction of cells characterized in this way as unreliable would in fact behave reliably. An obvious alternative would be simply to characterize each cell twice, once at each extreme of the expected operating temperature range, and only select cells that have the same classification at both extremes as reliable. This is more efficient, in the sense that the fraction of unreliable bits will be smaller, but requires a temperature controlled environment for characterizing each chip, which is frequently expensive and time-consuming to employ.
## 2 Vehicular Visible Light Communication Issues, Related Work, and Specificity of this Work
### Research Trends in Automotive VLC Applications  
Recent years have shown important progress in the area of automotive VLC applications. Still, there are few steps ahead before having a VLC system that is fully compatible with automotive applications. Simulation results [49; 50; 51; 52] indicate that VLC systems could be capable of fulfilling these requirements, whereas, in favorable cases, such systems could provide 50 Mb/s data rates and communication ranges of up to 70 m [53]. However, existing systems are not able to experimentally demonstrate such performances so far. Thus, several challenges still need to be addressed. Accordingly, from the physical point of view, automotive VLC systems must be able to further enhance their (i) resilience to ambient light sources, (ii) communication range, (iii) data rate, and (iv) ensure their compatibility to the mobility of vehicular applications [17]. Nevertheless, high performances on a specific feature will not be able to provide a ready-to-deployment vehicular VLC system. Hence, future automotive VLC systems must be able to ensure decent overall performances, while achieving an optimal tradeoff between different characteristics (i.e., noise resilience, communication distance, mobility, and data rate). This could be achieved by developing environment and context adaptive VLC systems [34]. This concept envisions that the system is analyzing the specific context and adjusts the configuration in order to maximize the performances and achieve the most optimal trade-off between the different parameters.
## 3 Results
### Feature Selection  
By comparing the Pearson correlation coefficients of the different features with the target emission values and the logarithm of the emissions we found that considering the logarithm of the emissions (as it is the common procedure for example in the case of ammonia) did not considerably improve predictions when modeling methane emissions. From Table 1 it can be seen that the correlation in most cases was very low and even decreased when considering the logarithm. On the other hand, we found that using the \(T^{2}\) feature instead of \(T\) considerably increased the obtained correlation coefficient. The highest absolute value of the correlation coefficient, i.e., the highest expected predictive performance of individual features, was obtained from the day-time components followed by the squared temperature. The obtained correlation coefficients highlighted that for the studied phenomena there was only very little linear relationship. The existence of a non-linear relationship in the case of day-time and temperature can be seen from the scatter plots in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline  & \(\mathbf{E_{n}}\) & \(\mathbf{In(E_{n})}\) \\ \hline \(T\) & 0.082 & 0.074 \\ \(T^{2}\) & 0.197 & 0.183 \\ \hline wind speed & 0.071 & 0.068 \\ wind direction (sin) & 0.037 & 0.022 \\ wind direction (cos) & 0.045 & 0.042 \\ \hline day-time component (sin) & \(-\)0.600 & \(-\)0.577 \\ day-time component (cos) & \(-\)0.496 & \(-\)0.475 \\ \hline year time component (sin) & 0.023 & 0.029 \\ year time component (cos) & 0.001 & 0.002 \\ \hline \end{tabular}
\end{table}
Table 1: Pearson correlation coefficients between all features and the methane emission \(\mathbf{E_{n}}\), respectively the logarithm of the emission \(\ln(\mathbf{E_{n}})\), as target. Here, \(T\) refers to the ambient air temperature close to the building. Wind speed and wind direction were monitored by an anemometer on the roof.
## 6 Discussion
As observed in the temporal consistency, the use of QFLAGS removes most of the valid snow retrievals in the case of PROBA-V. Then, this exercise is almost equivalent to snow-free conditions, which is more convenient for assessing the uncertainty of satellite EO products, since it is expected that the spatial representativeness of the pixels dropped during the fall and winter months as a consequence of the increased sub-pixel heterogeneity, due to processes, such as non-uniform patterns of snowmelt [77]. The positive bias of C3S PROBA-V SA v1.0 is consistent with previous studies performed on the CGLS, where a positive bias of approximately 22% was found compared to ground measurements over 17 stations [27], and a positive bias of approximately 14% was found compared to National Ecological Observatory Network (NEON) ground data [78]. The accuracy assessment of MCD43A3 C6 was also consistent with that previously reported when comparing the combined TERRA+AQUA albedo product with eight field stations during the spring and summer months of 2003 and 2004 (i.e., equivalent to snow-free conditions), where an accuracy of 0.013 was reported in terms of the absolute bias [79]. It should be noted that the estimation of the uncertainty of ground reference data is around 5% under ideal conditions [58]. Thus, fiducial reference measurements must be characterized with highly calibrated instrumentation at dedicated cal/val sites to better estimate the satellite product uncertainty budget. The compliance of satellite EO products versus ground data with user requirements showed a low percentage of pixels within GCOS (only \(<\)25%) and C3S (\(<\)50%) requirements, which indicates the difficulties of achieving these requirements using current products. To further improve the compliance with requirements, it is recommended that EO programs provide the uncertainties associated with the processing chain, mainly related to the sensor calibration and atmospheric correction. In that way, the steps providing higher error can be improved. However, the comparison between satellite C3S SA v1.0 products showed an overall good spatio-temporal consistency in the comparison of SPOT/VGT versus PROBA-V during the 6-month overlapping period (December 2013-May 2014), with typically more than 60% of the samples within the C3S target requirements for the visible domain, and more than 75% of the samples within the requirements for the NIR and total shortwave.
# Recurrence Quantification Analysis as a Form of Postural Control Assessment: A Systematic Review
## 4 Discussion
### RQA Results across Age Groups  
Several studies [3; 71] report that the ability to control standing balance develops during childhood into early adulthood and deteriorates from age 40 to 59. Of course, this is related, among other things, to the fact that postural control is closely linked to the functionality of the central nervous system and consequently to cognitive and perceptual processes [72]. [PERSON] et al. [20] and [PERSON] et al. [50] showed that RQA measures in the anterior-posterior direction are sufficient to distinguish the young and elderly groups and even distinguish non-fallers from fallers. [PERSON] et al. [44] addressed the description of changes in standing balance in preschoolers. They found that 5-year-old children showed increased CoP sway's regularity (higher %DET) and intermittency (higher %LAM) in the AP direction than 3- and 4-year-old children. The intermittent behavior of CoP movement reflects the interrupted control mechanism of standing balance, which manifests as changes in CoP dynamics from fluctuating to quasi-stationary [20]. [PERSON] et al. [20] observed a decrease in %LAM with age and for older fallers compared to older non-fallers. These results indicate that intermittent CoP movement increases with age in preschoolers and decreases in older adults, suggesting that this may be related to physiological changes in development or aging. Similar conclusions came from [PERSON] et al. [54]. They concluded that %LAM is the most sensitive factor in determining differences in CoP position between different age groups, as it describes motion fluidity.
# Structural Information Reconstruction of Distorted Underwater Images Using Image Registration
## 4 Discussion
### Compared with Nonregistration Method  
The above discussions have proven that the proposed method has faster convergence speed and better behavior than other registration strategies. In order to verify the performance more objectively, the proposed method was also compared with state-of-the-art technology without registration. Considering the reproducibility, the James method [24], whose resource code is available online, it was selected as a reference method. The same data set \"Large fonts\" with 61 frames were applied to the proposed method and the James method simultaneously. Figure 8 describes one frame of data set and the results of two methods. We found that the James method failed to deal with details under strong disturbance, while the proposed method was able to reconstruct better boundaries and more details. The comparison illustrates that the periodicity hypothesis may fail in severely disturbed environments. Instead, we intend to treat all distortions as local deformation that can be corrected through registration, and achieve better recovery in a strongly disturbed situation.
## 2 Material and Methods
### Mathematical-Formulation Part
#### 2.2.2 Modeling of the Transcritical Rankine Cycle  
The transcritical Rankine cycle can be modeled by applying the equations of the present section. These formulas regard mainly energy balances and efficiency indexes definitions.  
The heat input in the ORC (\(Q_{\text{in}}\)) is the same as the useful heat product in the solar collector:  
\[Q_{\text{in}}=Q_{\text{u}} \tag{28}\]  
The turbine's work production (\(W_{t}\)) is calculated as follows:  
\[W_{t}=m\cdot(h_{4}-h_{5}) \tag{29}\]  
The pump's work consumption in (\(W_{p}\)) is calculated as follows:  
\[W_{p}=m\cdot(h_{2}-h_{1}) \tag{30}\]  
The net electricity production of the unit (\(P_{el}\)) is calculated according to the next expression:  
\[P_{el}=\eta_{\text{mg}}\cdot W_{t}-\frac{W_{p}}{\eta_{\text{motor}}} \tag{31}\]  
The expansion in the turbine is modeled with the turbine's isentropic efficiency (\(\eta_{\text{is,t}}\)):  
\[\eta_{\text{is,t}}=\frac{h_{4}-h_{5}}{h_{4}-h_{5,\text{is}}} \tag{32}\]  
The compression in the pump is modeled with the pump's isentropic efficiency (\(\eta_{\text{is,p}}\)):  
\[\eta_{\text{is,p}}=\frac{h_{2,\text{is}}-h_{1}}{h_{2}-h_{1}} \tag{33}\]  
The heat rejection in the ambient (\(Q_{\text{out}}\)) can be calculated as:  
\[Q_{\text{out}}=m\cdot(h_{1}-h_{6}) \tag{34}\]  
The conduction of the energy balance in the recuperator device leads to the next expression:  
\[h_{3}-h_{2}=h_{5}-h_{6} \tag{35}\]  
The minimum recuperator temperature difference (\(\Delta T_{\text{rec}}\)) is given as:  
\[\Delta T_{\text{rec}}=T_{6}-T_{2} \tag{36}\]  
The efficiency of the thermodynamic cycle (\(\eta_{\text{cycle}}\)) is defined as:  
\[\eta_{\text{cycle}}=\frac{P_{el}}{Q_{\text{in}}} \tag{37}\]  
The overall unit energy efficiency (\(\eta_{\text{sys}}\)) can be found by the next equation:  
\[\eta_{\text{sys}}=\frac{P_{el}}{Q_{\text{sol}}} \tag{38}\]The overall unit exergy efficiency (\(\eta_{\text{ex,sys}}\)) is defined as:  
\[\eta_{\text{ex,sys}}=\frac{\text{P}_{\text{el}}}{\text{Ex}_{\text{sol}}} \tag{39}\]  
The data of the thermodynamic cycle analysis are included in Table 2.
## 2 Massive MIMO 5G Networks
### Pilot Contamination  
In a multi-cell environment where all users from different cells simultaneously send the \(\tau_{p}\) uplink pilots sequences to their respective BSs, the transmissions of some users could be contaminated (Figure 1) by the transmissions of the other users belonging to the neighboring cells. One way to mitigate this effect is to set apart the cells with the users sharing the same pilot sequences through a reasonable pilot reuse pattern. A pilot reuse factor of 3 [22] has been considered for this study since it provides a better compromise between the lower level of inter-cell interference and the spectral efficiency offered which scales with the number of the uplink pilots [23; 24].  
Assuming the favorable propagation conditions for the operation of the massive MIMO that make the users' channels directions almost orthogonal when the number of BS antenna elements grow large [18; 25], the received signal vector \(y_{lk}\) observed at the \(k\)th user in the \(l\)th cell is modeled as follows [18; 26]:  
\[y_{lk}=\underbrace{M\alpha_{MRT}\sqrt{p_{p}}\sqrt{p_{dk}}\beta_{lk}s_{lk}}_{ desired\ signal}+\underbrace{M\alpha_{MRT}\sqrt{p_{p}}\sum_{l^{\prime}\
eq l}^{L} \sqrt{p_{d^{\prime}k}}\beta_{l^{\prime}k}s_{l^{\prime}k}}_{contaminated\ signal}+ \underbrace{n_{dI}}_{noise} \tag{1}\]  
Figure 1: Multi-cell massive Multiple Input Multiple Output (MIMO) network system model.  
With \(M\) the number of BS antenna elements, \(\alpha_{MRT}\) the normalization constant considered to satisfy the transmit power constraint at the BS [26]; \(p_{p}\) is the power of the pilots (in W) and \(p_{dIk}\) is the DL power of the serving base station (in W) received by the \(k\)th user in cell \(l\); \(p_{d^{\prime}k}\) is the DL power of the interfering BS (in W) in cell \(l^{\prime}\) received by the \(k\)th user and \(n_{dI}\) is the noise level with variance \(\sigma_{n}^{2}\); \(\beta_{lk}\) and \(\beta_{l^{\prime}k}\) are the large-scale fadings which are almost constant during the coherence interval and model the effects of the path loss, geometric attenuation and shadowing in cell \(l\) and \(l^{\prime}\), respectively [18; 27].  
The Signal to Interference Noise Ratio (SINR) at the receiver side and the sum capacity \(C_{l}\) offered by the \(l\)th cell can be obtained as follows, respectively [27; 28]:  
\[SINR_{lk}=\frac{M\alpha_{MRT}p_{p}p_{dIk}\beta_{lk}^{2}}{M\alpha_{MRT}p_{p} \sum_{l^{\prime}\
eq l}^{L}p_{dI^{\prime}k}\beta_{l^{\prime}k}^{2}+\sigma_{n} ^{2}} \tag{2}\]  
\[C_{l}=B\cdot\sum_{k=1}^{K}\gamma^{DL}(1-\frac{\tau_{p}}{\tau_{c}})\log_{2}(1+ SINR_{lk})(bits/s) \tag{3}\]  
where \(\gamma^{DL}\) is the data symbol portions (out of the coherence interval) reserved for DL transmission (\(\gamma^{DL}=50\%\)[18]); \(B\) is the bandwidth of the channel (in Hz) and \(K\) is the maximum number of users simultaneously served by the cell.