esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Crash fault tolerance describes the capability of a distributed system to maintain its proper function despite the occurrence of crashes or failures in one or more of its components. When a distributed system possesses crash fault tolerance, it can be further fortified to achieve Byzantine fault tolerance. Byzantine fault tolerance empowers a distributed system to establish consensus among participants, even when faced with faulty or malicious behavior. Consensus plays a critical role in various tasks, including determining the accurate value of a shared variable, electing a leader, or validating the integrity of a business transaction. Compared to crash fault tolerance, Byzantine fault tolerance instills greater trust because it enables consensus even in the presence of malicious entities. This paper focuses on the performance evaluation of two blockchain solutions that exhibit Byzantine fault tolerance, in contrast to a blockchain solution that demonstrates crash fault tolerance. Specifically, the paper investigates the additional performance requirements associated with the enhanced trust resulting from Byzantine fault tolerance in e-business trading on both national and transnational scales. We analyze the resources needed to operate a business-to-business/business-to-government (B2B/B2G) compliance framework in two distinct geographic scenarios. The first examines the national scale, using Denmark as an example, which is the eleventh largest European country by GDP. The second scenario considers the scale of the European Union (EU) with its 27 member states (plus the United Kingdom).  
distributed ledger; blockchain; national and transnational e-business; distributed network trust; crash fault tolerance; Byzantine fault tolerance; business system scalability
# A Dual-Band Modified Franklin mm-Wave Antenna for 5G Wireless Applications
## 3 Parametric Analysis
### Effect of Length of Patch Element P\({}_{\text{L}}\)  
The effect of the length of the patch element on the antenna is studied. The length of the element is increased to analyze its effect. The increase in the length causes a variation in the resonant frequency as well as the return loss of the antenna. The variation of the length of patch P\({}_{\text{L}}\) is shown in Figure 7.  
Figure 5: (**a**). The top view of the antenna showing the antenna’s patch array structure, (**b**) the bottom view of the antenna showing the ground plane with the third set of three cuts.  
Figure 6: Variation in reflection coefficient with frequency for different antenna configurations.
# A Phase Correlation Anti-Noise Displacement Estimation Method Based on PSD Phase Screening
###### Abstract
In order to further improve the anti-noise performance of phase correlation, some scholars have proposed the use of the SVD decomposition method [40; 41] or the random sampling consistency method (RANSAC) [42; 43] to further solve the noise problem in phase correlation displacement estimation. These methods are indeed helpful for further improving the anti-noise performance of phase correlation, but there are still some limitations. For example, when the proportion of the noise is much larger than the proportion of the true phase, SVD decomposition cannot accurately restore the true phase; in addition, the RANSAC method may not work when noise interference is particularly severe. In general, the existing phase correlation methods can achieve higher accuracy when the noise is not particularly severe. However, when the noise interference is particularly severe, the accuracy will decrease to a certain extent. Aiming at the situation of particularly severe noise, this paper proposes a phase correlation improvement method based on PSD [44] phase screening, to accurately estimate the displacement of the scene under severe noise conditions. Firstly, PSD is used to remove the cross-power spectrum matrix area that is severely disturbed by noise, then SVD is used to convert the phase into one dimension, which reduces the difficulty of phase unwrapping, and, finally, a least squares straight-line fitting method is used to obtain the displacement of the subject scene. The remainder of this paper is organized as follows. Section 2 describes the specific approach of the method in this paper, the experimental validation part of this paper is discussed Section 3, including simulated data experiments and real data validation experiments, and the conclusions and future work are in Section 4.
# A Spectrum Correction Method Based on Optimizing Turbulence Intensity
## 2 Materials and Methods  
### Turbulent Wind Field Model  
In a turbulent wind field, the wind speed can be decomposed into average wind speed and fluctuating wind speed. Thus, the simulated three-dimensional wind speed at any simulation point is the linear superposition of the average wind speed and fluctuating wind speed in longitudinal, transverse, and vertical directions [2], which can be calculated by:  
\[\bar{V}(z,t)=\overline{U}(z)+v(z,t) \tag{1}\]  
where, \(\overline{U}(z)\) denotes the average wind speed, \(v(z,t)\) denotes the fluctuating wind speed of a simulated point, z denotes simulated height, and t denotes time.  
When setting the average wind speed, the influence of wind shear must be considered for large wind turbines whose installation height is usually higher than 100 m. Commonly used wind shear models include exponential model and logarithmic model, and the stable modified logarithmic model was used in this paper [8], which can be calculated by:  
\[u(z)=\frac{u^{*}}{\kappa}\bigg{(}ln\frac{z}{z_{0}}-\psi\bigg{)} \tag{2}\]  
where, \(u^{*}\) denotes friction velocity, it can be represented as \(u^{*}=0.045V_{ref}-0.012\), \(V_{ref}\), which means the reference mean wind speed; \(\kappa\) denotes Von Karman constant, under neutral atmospheric conditions, \(\kappa=0.4\); \(z_{0}\) denotes terrain roughness parameter, which can be calculated as \(z_{0}=A_{c}\frac{(u^{*})^{2}}{g}\), where \(A_{c}=0.034\) when the terrain type is an offshore area, g denotes the acceleration of gravity; and \(\psi\) denotes the stability function when in the neutral condition, its value is 0.  
Pulsating wind speed is regarded as a stationary Gaussian random process. According to the stochastic process theory, the power spectral density function combines with the coherence function, which is set to describe the wind speed correlation between two different points that may generate different wind speeds during a period in a stochastic wind field while a smaller separation distance of any two points will bring a greater correlation, is used to simulate the pulsating wind speed time history [15].
## 2 NB-IoT System Detailed Design and Implementation
### NPUSCH Transmitter Chain Implementation
#### 2.1.5 Scrambler  
The scrambler block adds randomness to the data stream before applying the modulation techniques to it. Therefore, the scrambler can eliminate the long run of zeros or ones, which cause idle and reset cases. It also helps in clock recovery and having no DC component. In addition, changing the scrambling codes helps in security.  
Scrambling/descrambling implementation is based on a pseudo random sequence generated from two 31-bit linear feedback shift registers (LFSRs). The two sequences of the two LFSR get XOR-ed, and the output is called a golden sequence. The sequence of the first LFSR is configured by the following polynomial [9]:  
\[x_{1}(n)=1+D^{3}+D^{0} \tag{14}\]  
Figure 4: Block diagram of the channel interleaver unit.  
where \(x_{1}\) is initialized as follows:  
\[x_{1}(0)=1,x_{1}(n)=0,n=1,2,\ldots,30 \tag{15}\]  
and the sequence of second LFSR is configured as follows:  
\[x_{2}(n)=1+D^{3}+D^{2}+D^{1}+D^{0} \tag{16}\]  
where \(x_{2}\) is initialized for each codeword, \(q\) as follows:  
\[C_{init}=n_{RNTI}.2^{14}+q.2^{13}+(\frac{n_{s}}{2}).2^{9}+N_{ID}^{cell} \tag{17}\]  
where \(n_{RNTI}\) corresponds to the radio network temporary identifier (RNTI) associated with the NPUSCH transmission, \(n_{s}\) is the number of the time slot, and \(N_{ID}^{cell}\) is the physical layer cell identity.  
Then, the two sequences get XOR-ed to have the golden sequence \(c_{n}\). Afterward, the golden sequence gets XORed with the input data. Due to the golden sequence's initialization taking a 1600 clock cycles, it is initialized simultaneously when the subframe gets ready.
# Design and Development of a Flexible 3D-Printed Endoscopic Grasping Instrument
## 2 Materials and Methods
### Instrument Design
#### 2.1.2 Bending Section  
To enable the distal tip of the instrument to be steered, a bending section composed of multiple linked segments was developed. The main goal of the bending section was to achieve a high bending angle that would enable the effective manipulation of the instrument and to be directly controlled by a physician. Additionally, the bending radius of the instrument had to be small to ensure that it could be effectively used in the narrow space of the GI tract. A bending radius of 6 mm was adopted.  
To achieve the desired performance, the bending section was composed of seven serially assembled 3D-printed segments. Several forms of segmented bending sections driven by tendon wires have been developed and analyzed by previous works [35; 36; 37]. The design of the bending section allows for a maximum bending angle of 216\({}^{\circ}\) in both directions, which provides physicians with a high degree of flexibility when manipulating the instrument during endoscopic procedures. Bending in a single plane was accepted for the instrument since any additional rotation or translation could be achieved by twisting or pushing the flexible shaft of the instrument.  
Overall, the design of the bending section of the instrument was carefully balanced to ensure that it could achieve a high bending angle while still being effective in the narrow passages of the GI tract. By using multiple linked segments, a high degree of flexibility was achieved, while the small bending radius and the ability to manipulate the instrument in a single plane ensure effective use during endoscopic procedures (Figure 1).
# Estimation of the Block Adjustment Error in UAV Photogrammetric Flights in Flat Areas
## 3 Results
### Photogrammetric Flight Results
#### 3.2.4 Block 4 Results  
The block measures an approximate area of 18.8 ha. The GCPs continued to be distributed at the four corners; points 7 and 6 were the same as in Block 3. Figure 9 shows the results of the RMSE for the GCPs and ChPs. The GSD was 6.12 cm for Scenario A, 6.11 cm for Scenario B, 5.53 cm for Scenario C, and 4.47 cm for scenario D.
# Is Malware Detection Needed for Android TV?
## 2 Related Work
They exposed that 2997 Android TV applications contained critical security bugs, and 1673 Android TV applications were found to have privacy leaks. They labeled 89 Android TV applications as malicious on the basis of at least one antivirus scanner on VirusTotal considering it to be malicious. [PERSON] et al. [25] tested a remote access Trojan attack on Android smartphones using the Meterpreter reverse TCP payload. They obtained critical information such as the system information, contacts, call logs, and messages and access to the system directory of the victim's smartphone via the backdoor of the injected Android application. Their Trojan was successfully installed on a test device running Android 11, but Google's Play Protect detected it as dangerous on Android 12. They recommend that users update their devices' operating systems regularly. None of the existing approaches have extracted features from Android TV applications for malware detection. In our study, we extracted features from the Android Manifest and DEX files at the bytecode level without applying reverse engineering to the source code. Using the extracted features, we trained classifiers to detect malware and compared the performance results on the collected Android TV applications. Our findings show that a dataset containing benign and malicious TV applications is needed to increase the performance of malware detectors. Therefore, we publicly shared our dataset containing 1000 features from 1305 Android TV applications.
## 2 Sampling and Recording of Revealing Emission Signals
The experience of the EMC Laboratory of MCI-SRI shows that in this case the value should be determined with an accuracy of at least \(10^{-4}\). Figure 6 shows a comparison of the effect of summing images (30 times) with an accuracy of 0.001. The image signal was obtained \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Operating Mode** & **Total Number** & **Total Number** & **Fixed** & **Horizontal Sync** \\
**of Display** & **of Image Lines** & **of Pixels in Line** & **Frequency** & **Frequency** \\ \hline
640 \(\times\) 480/60 Hz & 525 & 800 & 25.175 & 31.500 \\
800 \(\times\) 600/60 Hz & 628 & 1056 & 40.000 & 37.789 \\
1024 \(\times\) 768/60 Hz & 806 & 1344 & 65.000 & 48.363 \\
1280 \(\times\) 1024/60 Hz & 1066 & 1688 & 108.000 & 63.981 \\
1366 \(\times\) 768/60 Hz & 798 & 1792 & 85.500 & 47.712 \\
1440 \(\times\) 900/60 Hz & 934 & 1904 & 106.500 & 55.935 \\
1600 \(\times\) 900/60 Hz & 1000 & 1800 & 108.000 & 60.000 \\
1600 \(\times\) 1200/60 Hz & 1250 & 2160 & 162.000 & 75.000 \\
1920 \(\times\) 1080/60 Hz & 1125 & 2200 & 148.500 & 67.500 \\
2048 \(\times\) 1152/60 Hz & 1200 & 2250 & 162.000 & 72.000 \\
4096 \(\times\) 2160/60 Hz & 2222 & 4176 & 556.744 & 133.320 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Nominal parameters for selected graphic modes according to VESA [50]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Operating Mode** & **Pixel** & **Sampling Rate** & **Vertical Synchronization Frequency** & **Estimated Number of Pixels on the Line** \\
**of Display** & **[MHz]** & **[MHz]** & **[kHz]** & **Line** \\ \hline
640 \(\times\) 480/60 Hz & 25.175 & 31.25 & 31.500 & 992.063492 \\
800 \(\times\) 600/60 Hz & 40.000 & 62.50 & 37.789 & 1653.920453 \\
1024 \(\times\) 768/60 Hz & 65.000 & 125.00 & 48.363 & 2584.620474 \\
1280 \(\times\) 1024/60 Hz & 108.000 & 125.00 & 63.981 & 1953.705006 \\
1366 \(\times\) 768/60 Hz & 85.500 & 125.00 & 47.712 & 2619.885983 \\
1440 \(\times\) 900/60 Hz & 106.500 & 125.00 & 55.935 & 2234.736748 \\
1600 \(\times\) 900/60 Hz & 108.000 & 125.00 & 60.000 & 2083.3333333 \\
1600 \(\times\) 1200/60 Hz & 162.000 & 250.00 & 75.000 & 3333.333333333 \\
1920 \(\times\) 1080/60 Hz & 148.500 & 250.00 & 67.500 & 3703.703704 \\
2048 \(\times\) 1152/60 Hz & 162.000 & 250.00 & 72.000 & 3472.222222 \\
4096 \(\times\) 2160/60 Hz & 556.744 & 1000.00 & 133.320 & 7500.750075 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Nominal raster parameters for sampling signals. by reading the luminance values from the *.bmp file mapping the so-called Word editor screenshot for the 1920\(\times\)1080 monitor; the correct image line length was 1920 pixels. Observing the effect of the summation operation, one can notice a clear effect of desynchronization of the replicas of the image caused by the insufficient accuracy of reproducing the length of its lines (image width).
# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 2 Background
### Physiological Factors Influencing a PPG Signal  
In addition to measurement perturbations, several studies have highlighted the influence of physiological factors (e.g., skin tone, skin thickness, arterial thickness, venous return) and states of the patient (such as illness, age, weight, blood pressure) on the shape of a PPG signal (see Table 2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measurement Factor** & **cPPG** & **iPPG** \\ \hline \multirow{8}{*}{Means} & Low resolution of the sensor \\  & & Inadequate sampling frequency of the sensor \\  & & Clipping \\  & & Sensor’s noise \\ \cline{2-3}  & Power source interference [1] & Automatic exposition \\ \cline{2-3}  & Contact pressure [20] & Rolling shutter [21] \\ \cline{2-3}  & & Irregular frame rate [21] \\ \hline \multirow{8}{*}{Environment} & Ambient light \\  & & Temperature [22] \\ \cline{2-3}  & & Low luminosity \\ \cline{2-3}  & & Nonstable environment \\  & & (varying luminosity) \\ \hline \multirow{8}{*}{Material} & Motion artifacts \\ \cline{2-3}  & & Presence of makeup [23] \\ \cline{1-1} \cline{2-3}  & Ballistocardiographic artifacts [24] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Non-exhaustive perturbations affecting a PPG measurement.  
Figure 1: Ishikawa diagram presenting the factors influencing a photoplethysmographic measurement.
## 5 Simulation and Discussion
### Pavement Recognition Simulation  
In order to verify the effectiveness and accuracy of the road recognition method, two kinds of road surfaces, dry cement and snow, were selected for recognition verification, and the simulation condition was that the braking started at a wheel speed of 20 m/s.  
Figure 6a shows the speed curve on a dry cement road; it can be seen that from the beginning of braking, the accuracy error of the observer slowly increases, and finally stabilizes at about 0.4 m/s. Figure 6b shows the speed curve on a snowy road; here, the observer's observation error slowly increases with the observation time, and finally stabilizes at 0.6 m/s. When the actual speed is lower than 2 m/s, the sensor receives weakened signal strength and the ambient noise becomes relatively more significant, resulting in a decrease in the signal-to-noise ratio, making it more difficult to extract accurate speed information from the signal; at the same time, the speed is too low, which makes the number of pulses generated by the wheel speed sensor per unit of time decrease, which can lead to insufficient resolution and affect the observations. In addition, the increase in error is due to the fact that the observer is always running during the braking process, resulting in there being some cumulative error. Figure 6c,d show the recognition results of dry concrete and snow pavement, respectively, where the pavement is recognized near 0.2 s and the optimal rate and maximum adhesion coefficient of the current pavement are outputted; meanwhile, at 0.2 s, the speed observation error is so small that it can be ignored.
## 3 Methods  
The spatiotemporal joint reasoning process of driving intention is a solution to the sequence image classification problem. In our work, we propose a novel driving intention inference framework, Spatial-Temporal Joint Attention Network (STA-NET), which simultaneously utilizes two input sources: internal and external videos, as shown in Figure 1. One branch learns spatial semantic features in traffic videos. In contrast, the other branch learns spatial semantic features in driver videos, thereby addressing the deficiency of relying solely on in-vehicle driver spatiotemporal features for driving intention inference. As illustrated in Figure 1, the backbone network is a parallel dual-branch network. The basic structure of the backbone network is mainly composed of Spatial-Temporal Joint Attention Block (STA Block) and Cross-Spatial Attention Module (CSAM), where the STA Block consists of Multi-Scale Transposed Attention (MSTA) and Multi-Scale Feedforward Network (MSFN). The STA Block adopts a joint CNN and transformer approach to simultaneously extract driver maneuver features and spatiotemporal features of the traffic scene. MSTA and MSFN alleviate the insufficient receptive field at different levels and enhance the richness of spatiotemporal feature information. At each stage, features extracted by the STA Block at the same scale are aggregated through CSAM. The MCFM (Multi-CSAM Fusion Module) aggregates different scales of in-vehicle driver features and traffic scene features at different stages for driving intention inference.  
More specifically, we hierarchically stack STA Block units to construct our network for spatiotemporal learning. As shown in Figure 1, our network comprises four stages with channel numbers 64, 128, 256, and 512, respectively. We build the backbone network of the STA framework based on the quantities of STA Block units in each stage, which are [5,7,8,20]. We employ MSTA (Equation (1)) at each STA Block to reduce spatiotemporal redundancy. We normalize the data using LN [29]. Before the first stage, we apply a 3 \(\times\) 4 \(\times\) 4 convolution with a stride of 2 \(\times\) 4 \(\times\) 4, meaning both spatial and temporal dimensions are downsampled. Before the other stages, we use a \(1\times 1\times 2\) convolution with a stride of \(1\times 1\times 2\). Finally, the spatiotemporal average pooling and fully connected layers are employed for the ultimate prediction. In this way, our STA-Net, with an insightful unified framework, addresses video redundancy and dependencies. Each module is detailed as follows.
## 3 Results
### Annual Analysis between MODIS Products and Flux Tower GPP
#### 3.2.2 LAI and FPAR _vs._ Annual Mean GPP  
LAI had the highest correlation with annual GPP, regardless of the compositing period length (Table 4 and Figure 5(a)). LAI showed high correlation with tower GPP both for non-tropical sites (\(R^{2}\) 0.43 to 0.68) and tropical sites (\(R^{2}\) 0.66 to 0.70). Increased compositing length tended to increase \(R^{2}\) for non-tropical sites and decrease \(R^{2}\) in tropical sites. The \(R^{2}\) values for the comparisons between FPAR and annual GPP were similar, but slightly lower than those for LAI (Table 4). At FPAR greater than 0.7, flux tower GPP was generally underpredicted by the regression at non-tropical sites (Figure 5(b)). As for LAI, when compositing to longer periods, FPAR correlations increased at non-tropical sites and tended to decrease at tropical sites (Table 4).  
While several metrics showed promise in estimating annual flux tower GPP, annual mean LAI derived from 32-day composites had the highest correlation, explaining 88% of measured variance. The simple model is:  
\[GPP=615*annual\ mean\ LAI-376 \tag{4}\]  
where annual GPP is in g C/m2/year. It is noteworthy that we tried the same analysis with max LAI, but max LAI was poorly correlated with annual GPP, as previously shown by other studies [10, 13].  
Figure 4: Scatterplot of annual mean MOD13 NDVI (**a**) and EVI (**b**) against flux tower annual GPP. NDVI and EVI data were processed to a 32-day maximum value composite (MVC) before calculating the annual mean value. The solid line represents the reduced major axis regression between the variables. The dashed lines are 95% confidence interval lines. Multiple points for a given site correspond to data from different years.