esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Controller Design
### Nominal Reference Governor  
As explained in Section 2, the valve displacement and pump angle are subject to magnitude and rate saturations. Once the system leaves its linear region, the boundedness of \(\bar{d}\) will not be guaranteed, and the disturbance estimation \(\bar{d}\) will be corrupted due to actuator saturation. This problem can be alleviated by reducing the closed-loop system performance, i.e., by applying a filter to the pressure command. However, it cannot guarantee that the actuator saturation is eliminated. Another approach is to use model predictive control (MPC), which is able to exploit the entire actuator's potential while respecting the actuator constraints, but the computational cost is high, especially for the nonlinear high-order system considered here.  
In this paper, a reference governor is investigated to ensure that actuator limitations are not violated by the system. If a violation of constraints is anticipated, the trajectory will be modified until all constraints are satisfied. The receding horizon principle and the bisectional search algorithm are used here to check for constraint violations, as shown in Figure 2. The command \(x_{d}\) is subjected to a bisectional search until the optimal modification is determined. This is implemented through the optimization of parameter \(\alpha_{k}\):  
\[x_{d}^{rev}(k)=x_{d}^{rev}(k-1)+\alpha_{k}[x_{d}(k)-x_{d}^{rev}(k-1)] \tag{23}\]  
where \(x_{d}\) is the target trajectory, and \(x_{d}^{rev}\) is the modified trajectory. When \(\alpha_{k}=1\), the filter does not affect the input, and conversely, when \(\alpha_{k}=0\), then \(x_{d}^{rev}=x_{d}^{rev}(k-1)\) can be obtained. The optimal value of parameter \(\alpha_{k}\) is obtained during each time interval \(kT_{s}\) by maintaining \(x_{d}^{rev}(\tau)=x_{d}^{rev}(k)\) for \(kT_{s}\leq\tau\leq kT_{s}+T_{p}\), where \(T_{p}\) is the prediction time horizon.  
To determine \(\alpha_{k}\) at each time instant, the system in (11) including the controller dynamics in (16), (18), (21) and (22) are predicted over the interval \([kT_{s},kT_{s}+T_{p}]\). \(\alpha_{k}\) is decreased if constraint violations are anticipated and raised if all constraints are met. The optimal value of \(\alpha_{k}\in[0,1]\) is subsequently determined. The constraints considered in this paper are as follows:  
\[\begin{array}{l}u_{v}^{min}\leq u_{v}\leq u_{v}^{max}\\ u_{\alpha}^{\max}\leq u_{\alpha}\leq u_{\alpha}^{max}\\ \Delta u_{\alpha}^{min}\leq\Delta u_{v}\leq\Delta u_{\alpha}^{max}\\ \Delta u_{\alpha}^{min}\leq\Delta u_{\alpha}\leq\Delta u_{\alpha}^{max}\\ \end{array} \tag{24}\]  
where  
\[\begin{array}{l}\Delta u_{v}(k)=u_{v}(k)-u_{v}(k-1)\\ \Delta u_{\alpha}(k)=u_{\alpha}(k)-u_{\alpha}(k-1)\\ \end{array} \tag{25}\]
## 2 Related Work
### Concept-Based Explainability  
Essentially, in classic supervised learning scenarios, the performance of an ML model is measured by counting the ratio among the correct predictions and the wrong predictions, and by deriving scores, such as accuracy, precision and recall. However, such metrics cannot always give sufficient information about the quality of a model [17] since they only indicate the number of times that a model gives a correct or a wrong answer, but they do not give any insight of what is important for a network to make its prediction. To this end, the literature presents several works aiming at identifying which features are important for a network to make its decision [18; 19; 20]. The basic idea of such approaches is to compute the gradient of the output with respect to the input features. This is suitable for domains such as that of image processing [21; 22], but in other fields (e.g., natural language processing (NLP)), where the object to analyze is a point in a discrete space and it needs a transformation to be used as input for a neural model, it is less effective. The main problem is that, in general, such a transformation from the object to a numeric input vectoris not invertible, and thus operating directly on the input features is not convenient. In the literature, we find an approach to this problem when instances are graphs in [23].  
In this regard, other works operate on _concepts_ rather than on features. The insight is to identify which are the human-understandable concepts that mostly influence the decision process of the network. For instance, in a past work [24], internal neurons of an autoencoder were used as classifiers for patterns not seen during the training phase. More recently, concept activation vectors (CAVs) [2] have proven to be effective to model the internal states of neural networks by means of human-understandable concepts, and similar techniques have been successfully applied in completely different domains, such as those of chess [25] and of software vulnerability detection [3].
# A Comprehensive Signal Quality Assessment for BDS/Galileo/GPS Satellites and Signals
###### Abstract
The combination of BDS-3 with other GNSS systems can provide sufficient observations and huge potential for improving the accuracy, reliability, and availability of PNT services [33; 34; 35], yet the differences in data quality and random characteristics between observations of different types of satellites and signals make it impossible to achieve ideal results in multi-system fusion positioning. In addition, there are currently many brands of receivers for BDS-3 data processing, which also makes the quality and characteristics of the observation data inconsistent. Therefore, conducting a comprehensive evaluation of BDS-3 is of great significance, as compared to other GNSS systems. Utilizing data from globally distributed MGEX stations that can receive BDS-3 satellite signals, this paper first provides a brief description of the BDS-3 signal regime, then provides a systematic and comprehensive assessment of its data quality, analyzing and comparing it with other GNSS systems. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Signal** & **Frequency** & **Bandwidth** & **Code Rate** & **Modulation** & **Broadcast Satellite** & **Compatible Frequency** \\ \hline B1I & 1561.09 & 4.092 & 2.046 & BPSK (2) & All & \\ \hline B3I & 1268.52 & 20.46 & 10.23 & BPSK (10) & All & \\ \hline B1C\_data & & & & & BOC (1,1) & \\ B1C\_pilot & 1575.42 & 32.736 & 1.023 & QMBOC (6,1,4/33) & IGSO MEO & L1, E1 \\ \hline B2a\_data & & & & & \\ B2a\_pilot & 1176.45 & 20.46 & 10.23 & BPSK (10) & IGSO MEO & L5, E5a \\ \hline B2b\_I & & & & & & \\ B2b\_Q & 1207.14 & — & 10.23 & QPSK (10) & IGSO MEO & E5b \\ \hline \hline \end{tabular}
\end{table} 
Table 1: BDS-3 open service signal system.
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 6 Comparison and Evaluation  
### Features Comparison  
In this section, we compare our PPDB with the schemes of [23; 27] in terms of their features; more details are shown in the Table 2.  
In [23], [PERSON] et al. utilized the Paillier cryptosystem and Horner's rule to construct a privacy-preserving dynamic billing scheme, called PPCSB. In their scheme, each SM generates only one type of data; thus, CC can only obtain the total electricity bill of the user. Compared to scheme [23], our PPDB aggregates multi-dimensional data to obtain the electricity bill generated by each type of electrical equipment.  
In PPDB and [27], each SM generates \(l\) types of data for more accurate fine-grained analysis. To reduce the complexity, we use a binary encoding function to construct multi-dimensional data, which can effectively save ciphertext space compared with the [27]. Meanwhile, [PERSON] et al. did not consider dynamic billing based on real-time electricity prices.  
All three schemes meet the security requirements for confidentiality, integrity and authentication. It is worth noting that neither scheme [23] nor scheme [27] has designed an arbitration mechanism. By the above comparison, our PPDB is more diverse in features and is more suitable for practical deployment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Multi-Dimensional Data** & **Confidentiality** & **Integrity** & **Authentication** & **Billing** & **Arbitration** \\ \hline [PERSON] et al.’s scheme [23] & ✗ & ✓ & ✓ & ✓ & ✓ & ✗ \\ [PERSON] et al.’s scheme [27] & ✓ & ✓ & ✓ & ✓ & ✗ & ✗ \\ Our scheme & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Features comparison.
# SGXDump: A Repeatable Code-Reuse Attack for Extracting SGX Enclave Memory
## 4 SGXDump Attack Overview  
Figure 2 depicts the overall architecture of the SGXDump attack. Two main units are the SGXDump and Probe Array Monitor. SGXDump in the form of an ROP payload is injected into the vulnerable code and keeps reading one byte from the enclave memory, accessing one page of the probe array until it reads through the entire enclave memory. The Probe Array Monitor (PAM) module is inserted into the kernel by the attacker beforehand and waits for SGXDump's access to the probe array. The PAM translates the locations of access bits into enclave memory values. Finally, the attacker obtains the enclave data by reading the PAM's memory through _procfs_. In the following subsections, we will explain more about the SGXDump and PAM.
## 4 Implementation and Measurement Results  
Figure 6 shows the measurement setup of the S\({}^{2}\)P chip. For the input signal, we used NI's USB-7845R FPGA and LabVIEW software, and the chip pad and PCB were connected via bond wires for evaluation. The power supply used Agilent's E3631A power supply and the output waveform was measured with a Tektronix's 4 channel oscilloscope.  
Figure 7 shows the operating voltage according to input clock frequency. The operating voltage of S\({}^{2}\)P circuit was measured by 0.91 V at 40 MHz, 0.86 V at 30 MHz, and 0.82 V at 10 MHz and 5 MHz, respectively. As can be seen from the results, the circuit designed with a 1 V supply voltage worked well from 5 MHz to 40 MHz, and the power consumption reduced by 18% to 9%, respectively. The default current was measured by 30.9 \(\mu\)A.  
Figure 8 shows the measured waveform of oscilloscope equipment. The waveforms show only 4 waveforms that can verify operation due to the setup limitations of the equipment. Clock, reset, and enable signal are inputted. Based on the input of the address, the output waveform of address O [1,1] shows a normal output value at a clock frequency of 5 MHz to 40 MHz. The output O [1,1] value changes according to the input data '1001001001001001000'. However, the output results at 20 MHz and 40 MHz do not seem to sufficiently reflect the input data at high operating frequencies, but the designed S\({}^{2}\)P circuit output does not contain a sufficient size drive buffer for the bond-wire, PCB,  
Figure 4: The total chip layout with the S\({}^{2}\)P circuit.  
Figure 5: The post-layout simulation result of the designed S\({}^{2}\)P circuit.  
and measurement probe capacitance. Therefore, it is difficult to represent accurate measurement results. However, the proposed \(\mathsf{S^{2}P}\) circuit is integration with circuit requiring control, and it does not affect the actual operation.  
Figure 8: The measurement results at 1 V supply with 5 MHz to 40 MHz clock frequency.  
Figure 6: The measurement setup and chip-photograph.  
Figure 7: Operating voltage according to clock frequency.
## 3 Results
### Biological Properties  
The survey for the Cention-N alkasite material's functional properties showed that 55 restorations (100%) qualified as clinically excellent at T0, while at T1 (3 months) 53 restorations (96.36%) qualified as clinically excellent, and two (3.63%) as clinically good. At T2 (6 months), 52 restorations (94.55%) qualified as clinically excellent, two as clinically sufficient, and one failure was reported (1.82%). Finally, at T3 (12 months), 51 (92.74%) restorations qualified as clinically excellent restorations, two as clinically sufficient restorations (3.63%), and two failed restorations (3.63%) were reported (Table 5).  
The survey for the Fuji IX GIC functional properties showed that, at T0, 56 restorations (98.25%) qualified as clinically excellent and one (1.75%) as clinically good, whereas at T1 (3 months), 54 restorations (94.73%) qualified as clinically excellent and three (5.26%) as clinically good. At T2 (6 months), 54 restorations (94.73%) qualified as clinically excellent, one (1.75%) as clinically good, and two failures were reported (3.52%). At T3 (12 months), 52 restorations (91.23%) qualified as clinically excellent, three (5.25%) as clinically sufficient, and two failed restorations (3.52%) were reported (Table 5).  
The biological properties of both materials have decreased over time, but no statistically significant difference was observed regarding the decrease in clinically excellent restorations between timepoints considering one material or the other. At different timepoints, no statistically significant differences were observed in terms of functional properties.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Functional Properties**} \\ \cline{2-9}  & \multicolumn{3}{c}{**Cention-N**} & \multicolumn{3}{c}{**GIC**} \\ \cline{2-9}  & \multicolumn{3}{c}{**n (\%)**} & \multicolumn{3}{c}{**n (\%)**} & \multicolumn{3}{c}{**n (\%)**} \\ \hline
**FDI CRITERIA** & **T0** & **T1** & **T2** & **T3** & **T0** & **T1** & **T2** & **T3** \\ \hline
1—CLINICALLY EXCELLENT & 54(98.18) & 48(87.27) & 40(72.73) & 40(72.73) & 53(92.98) & 53(92.98) & 45(78.94) & 36(63.15) \\
2—CLINICALLY GOOD & 1(1.82) & 7(12.73) & 12(21.81) & 11(20.01) & 4(7.02) & 4(7.02) & 9(15.79) & 16(28.07) \\
3—CLINICALLY SUFFICIENT & 0(0) & 0(0) & 1(1.82) & 2(3.63) & 0(0) & 0(0) & 1(1.75) & 3(5.26) \\
4—CLINICALLY & 0(0) & 0(0) & 1(1.82) & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) \\ UNSATISFACTORY & 5—CLINICALLY POOR & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) \\ FAILED & 0(0) & 0(0) & 1(1.82) & 2(3.63) & 0(0) & 0(0) & 2(3.52) & 2(3.52) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of functional properties based on FDI criteria. T1 (3 months), T2 (6 months), T3 (12 months).
# Analytical and Measured Effects of Short and Heavy Rail Cars on Railway Bridges in the USA
## 2 Modeling and Analysis of a Steel Railroad Bridge  
### Current Design of Railroad Bridges in North America  
The equivalent Cooper loading is based on the design loading recommended by the American Railway Engineering and Maintenance of Way Association [8]. It is current practice to design railway bridges for Cooper E80 loads; however, many older railway bridges, which are still in service, were designed for lesser loads, such as Cooper E60.  
In this study, the equivalent Cooper loading was calculated for a large number of bridge spans from 1.5 m to 122 m (5- to 400-ft) long. Figure 2 shows the flexural bending effect on bridge spans less than 18.3 m (60 ft) is lower for a 12.76-m (41 ft, 10.5 in.) AAR railcar (with a bogie (truck) center of 8.3 m (27 ft, 4 in.) than for a 16.2-m (53-ft) coal car (with a truck center of 12.3 m (40 ft, 6 in.). As is also shown in Figure 2, short railcars have greater equivalent Cooper loads compared to common 16.2-m (53-ft) railcars on bridge spans 18.3 m (60 ft) and longer, and the difference is more prominent on bridge spans of 24.4 (80 ft) and longer. A typical six-axle locomotive governs for bridge spans from 16.8 m to 24.4 m (55- to 80-ft) long, and the common 16.2-m (53-ft) long railcar governs for bridge spans less than 16.8 m (55 ft) [1; 2; 3].  
Figure 1: Quantity of short HAL railcars from 2010 to 2015.  
Figure 2: Cooper rating for bridge spans up to 122 m (400 ft) evaluated in flexural bending.
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 4 FAPEC Tailoring for the KMALL Data Format
### SKM Pre-Processing Stage  
The last algorithm we propose is for SKM datagrams. These datagrams do not contain multibeam data as the previous two, although they occupy a relevant percentage of the total size. Hence, it is worth designing a tailored algorithm.  
The proposed algorithm is very similar to that of MRZ datagrams, but instead of exploiting the correlation between soundings, we exploit the correlation between sensor measurements, that is, the correlation between each sample in _SensorSamples_. From the format description [13], we know that every sample has \(N_{B}\) bytes, and hence, every \(N_{B}\) bytes, we find the same field from another sample. As the reader may notice, this approach is very similar to the one proposed in the previous section. In this case, as SKM are less relevant than MRZ datagrams, we do not reorder the samples and we opt for a simpler solution, which gives good enough ratios. Formally, given \(N_{S}\) samples \(S_{i}\) with \(N_{B}\) bytes each:  
\[E_{i}=S_{i}-S_{i-N_{B}},\quad 1\leq i<N_{S} \tag{14}\]  
Note that the first sample \(i=0\) is directly copied to the prediction errors sequence, together with the header and the _InfoPart_structs.
# Channel Estimation for UAV Communication Systems Using Deep Neural Networks
## 1 Introduction
This observation has motivated this work to fill this gap in the literature and propose a framework based on GBRBM that incorporates an autoencoder-based deep neural network to estimate the received signal power of UAVs flying in a range of altitudes connected to a cellular network. Moreover, we propose a novel algorithm that employs an adaptive learning rate alongside an enhanced gradient, which speeds up the learning of hidden neurons, contrary to the traditional gradient decent. The main contributions of this work can be summarized as follows:  
* Apply GBRBM and real measurements to estimate the received signal power at UAV from the cellular network during the flight, where GBRB machines and deep neural network are employed to extract features from the UAV channels as a series set of blocks for channel modeling. * Develop an adaptive learning rate approach and a new enhanced gradient to improve the training performance. Specifically, an autoencoder is used to fine-tune the parameter, while the parameters are trained by using an encoder neural network to model the RSS for the prediction of the ground at different heights. * Verify the effectiveness of the proposed method throughout experimental measurements and comparisons with other benchmark schemes. The numeral results show that the proposed scheme outperforms the conventional autoencoders. The remainder of this paper is organized as follows. Section 2 presents the system model. The proposed method is described in Section 3. Numerical examples and demon  
Figure 1: The network of RBM diagram. strations of various simulation results are given in Section 4. Finally, conclusions are drawn in Section 5.
# Material Characterization for Sustainable Concrete Paving Blocks
## 3 Results and Discussion
### Slip Resistance  
Figure 12 shows that relationships between the blocks' slip resistance and materials' replacement levels. In general, the blocks mixed with the waste materials had a lower slip resistance than the blocks made with natural aggregates.  
The slip resistance of blocks mixed with natural aggregates was 90.38 BPN. For the RCCA, the slip resistance sharply decreased to 79.50 BPN when the replacement level was 20%. The slip resistance increased and was maintained at 84.00 BPN, 83.80 BPN, and 86.00 BPN when the replacement levels of RCCA were 40%, 60%, and 80%, respectively. The value decreased to 80.30 BPN with a 100% replacement level of RCCA. The slip resistance of blocks mixed with RCKA also had a significant reduction when the replacement level was 10%. When the replacement levels increased to 20% and  
Figure 12: Relationship between the different replacement levels of materials and the slip resistance of specimens.  
30%, the corresponding slip resistance increased to 87.92 BPN and 87.11 BPN, respectively. For blocks utilizing CG, the values of slip resistance slightly fluctuated from 85.33 BPN to 86.08 BPN when the replacement levels changed from 10% to 30%. When the replacement level increased to 40%, the slip resistance had a sharp decline to 80.20 BPN. The slip resistance of block mixed with CR did not change too much compared with the block mixed with other recycled materials. The values were 89.17 BPN, 85.75 BPN, and 87.75 BPN when the replacement levels increased from 1% to 3%. A reduction of slip resistance can also be observed when the cement replaced by GGBS in the concrete paving blocks. The value decreased from 87.58 BPN to 81.75 BPN when the replacement level of GGBS increased from 30% to 50%. At the 70% replacement level, the slip resistance increased to 82.92 BPN. However, the standard derivation of the average value at this point was higher than the standard derivations of other results in this group.
# A Computer Simulation Research of Two Types of Cardiac Physiological Pacing
## 2 Materials and Methods
### Simulation of HBP and LBBP  
We conducted all the simulations within CARP. The time step of the simulation was set to 0.005 ms. The bundle block was implemented based on the adjustment of the conductivity of a specific branch to 1/2000 of that in physiological conditions. This assured that the conduction would not be activated through the branch. Two LBBB types were simulated in the ventricular model. In LBBB-1, the block was set in the left bundle branch (LBB) part within the His-bundle. When the excitation was conducted through His-bundle, LBBB appeared, as shown in Figure 2a. In LBBB-2, the block was set in the left main bundle branch, and resulted in a typical LBBB. AVB was also simulated in the ventricular model by setting the block in the distal His-bundle so that atrial excitation could not conduct to the ventricle, as shown in Figure 2b.  
Figure 2: (**a**) LBBB occurs within the His-bundle. Red color represents that the His-bundle and RBB were excited (according to the color bar). (**b**) AVB occurs within the distal His-bundle. The excitation could not be conducted through the His-bundle. (**c**) HBP stimulus initiated at the left side end of the distal His-bundle. (**d**) HBP stimulus initiated at the distal end of His-bundle. (**e**) LBBB occurs in the left main bundle branch, and the LBBP stimulus was performed at the distal end of the left main branch, beyond the block. The transmembrane potential is illustrated by the color bar. LBBB: left bundle branch block. RBB: right bundle branch. HBP: His-bundle pacing. AVB: atrioventricular block. LBBB: left bundle branch pacing. LBB: left bundle branch.  
The simulated physiological excitation initiated from the proximal end of His-bundle started at 30 ms in all simulations. The method of pacing simulation involved the application of a stimulus that lasted 0.5 ms that was strong enough to stimulate the branch and myocardium. The pacing simulation was non-selective HBP (NSHBP) or non-selective LBBP. This meant that both the bundle branch and local myocardium were stimulated.