esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Side-Channel Evaluation  
In this section, we provide a side-channel evaluation for the TI Sklansky- and Brent-Kung-Adders using the well-established TVLA [17; 18]. As the authors of [2] already performed a side-channel evaluation of the TI-KSA we studied, we did not repeat this evaluation. Our HPC2-based adder designs rely on AND- and XOR-Gadgets, that are secure in the PINI model. Following this model, the gadgets can be composed arbitrarily in the presence of glitches. Note that the authors of [19] showed, that this composability can break if transitional leakage occurs. However, as our implementations are fully pipelined and successive inputs are assumed to be shared independently, this vulnerability does not apply in this case. The authors of [20; 21] previously demonstrated the side-channel resistance of the HPC2 gadgets using TVLA. Therefore, we did not perform side-channel measurements for the HPC2 adder designs.  
The power side-channel traces were obtained from a Sakura G [22] board specifically designed for side-channel evaluation. The clock rate was set to 4 MHz in order to collect clean traces. The internal amplifier of the board was combined with an external ZFL1000 LN+ low-noise amplifier with a gain of approximately 21 dB. The samples were collected with an Spectrum M4i22 PCIe digitizer at a sample rate of 1.25 GS s\({}^{-1}\). The internal 400 MHz low-pass filter was enabled for the purpose of preventing aliasing of the signal.  
We applied the non-specific fixed-vs-random t-test described in [18] and the confidence-interval-based method proposed in [23] as the evaluation metrics in order to discern if the side-channel is data-dependent. For detailed explanations of these methods the reader is referred to the original publications. The analysis was performed for the first and second orders using 300 million traces at a significance level of \(\alpha=0.01\) adjusted for family-wise error rate. The evaluation results are depicted in Figures 5 and 6.  
As shown in Figure 5d, no first-order leakage can be detected in the SA implementation with 300 million measurements. Following the plot in Figure 5b if any (undetected) first-order leakage is present, it is below 60 mV after amplification. Second order leakage is clearly detectable, which is expected as the TI masking scheme with three shares only provides first-order security. Similar to the SA case, the BKA does not exhibit detectable first order leakage, but is potentially vulnerable against second-order attacks.  
Figure 5: A sample power trace, confidence intervals, and \(t\)-test results for the TI Sklansky Adder using 300 million traces. No first-order leakage is detectable.
# A Numerical Investigation of Laser Beam Welding of Stainless Steel Sheets with a Gap
## 2 Simulation Model
### Discussion on Mesh Density  
In general, the mesh used for the spatial discretization of the computational domain should be dense enough, i.e., the cell size sufficiently small, to reach a mesh-independent solution. The temporal discretization during the solution procedure is determined by the Courant-Friedrichs-Lewy number, CFL, which is defined as [30]  
\[CFL=\frac{u\Delta t}{\Delta x}, \tag{1}\]  
where \(u\) is the maximum velocity, \(\Delta t\) denotes the time step and \(\Delta x\) denotes the smallest cell length. By choosing \(\Delta t\) in such a way that \(CFL<1\), it is ensured that the fluid does not skip a computational cell during the advancement of one time step. It is then clear that a reduction in cell size by a factor of 2 does not only increase the number of cells by a factor 8 (due to three-dimensionality), but also leads to a forced increase in temporal resolution by a factor of 2 in the whole simulation domain. Therefore, a reduction in cell size has great influence on the computational cost of a simulation. For the above-mentioned reasons, the model was carefully calibrated with the aim of reproducing experimental results in a timely manner so as to be able to use it for optimizing process parameters. During the optimization process, a great number of simulation runs had to be completed within a reasonable amount of time. Therefore, a mesh density was chosen that was sufficient for producing accurate results on a larger scale needed for gaining information about the weld bead shape, as this was the main criterion for the optimization process. It was noted that the cell size was not small enough to resolve many smaller scale effects, such as the dynamic waves formation occurring at the leading wall of the keyhole. Figure 4: A comparison of experiment EX1 and simulation S1 (model calibration). Within the scope of this paper, the simulation model was used to optimize results of LBW processes on the one hand (see Section 3), and to investigate highly dynamic small scale processes on the other hand (see Section 4). For the latter, the finite volume density was dynamically increased by a factor of 8 in the vicinity of the keyhole. This, however, increased the penetration depth and showed that it was underestimated by the coarser mesh. One of the possible reasons for the increase in penetration when decreasing the cell size stems from the fact that the resolution of surface waves at the keyhole front slightly changes the local angle of incidence of the incoming laser beam, which has a great influence on absorption. The wave structures on the keyhole wall resolved by using a sufficiently fine computational grid, can be seen in Figure 6. It is clear that with increasing grid coarseness, smaller waves tend to be filtered out and only larger wave structures can be resolved. Figure 5: A comparison of experiment EX2 and simulation S2 (model validation). Figure 6: Surface wave structures on the keyhole wall on a mesh where the characteristic cell length is roughly equal to \(1/8\cdot w_{gap}\).
# Beam Scanning Capabilities of a 3D-Printed Perforated Dielectric Transmitarray
## 5 Conclusions  
In this paper, the beam scanning capabilities of a 3D-printed dielectric transmitarray antenna have been analyzed and experimentally validated. The radiation patterns measured for different scanning angles in the range \(\pm 27^{\circ}\) showed a small variation of the gain, a controlled increase of the SLL and a wideband behavior, confirming the expected antenna properties. While these results prove the suitability of the considered unit-cell for the design of multibeam or beam scanning passive transmitarrays, some further optimization of the antenna would be necessary if similar performances would be obtained on larger scanning coverage. It will be the object of future investigations.  
Conceptualization, A.M. and P.P.; methodology, A.M. and P.P.; validation, G.D.; formal analysis, A.M.; investigation, A.M.; resources, P.P. and G.D.; data curation, A.M.; writing--original draft preparation, A.M. and P.P.; writing--review and editing, P.P.  
This work was supported by the General Directorate for Cultural and Economic Promotion and Innovation of the Ministry of Foreign Affairs and International Cooperation, of the Italian Republic.  
The authors thank the research group of Chilab for the antenna manufacturing.  
The authors declare no conflict of interest.
## 4 Optimization
### Finite Difference  
Calculating the derivatives in a symbolic way may be difficult on certain occasions. Finite Differences [21] can be used to obtain an approximation of the Gradient Vector and the Hessian matrix.  
Mathematically, the Gradient vector \(\
abla\mathbf{f(X)}\) is formed by the first derivatives of the function with respect to all the variables (Equation (44)) and the Hessian matrix \(\
abla^{2}\mathbf{f(X)}\) is composed of the second derivatives (Equation (45)).  
\[\
abla\mathbf{f(X)}=\begin{bmatrix}\frac{\partial f}{\partial x_{1}},&\frac{ \partial f}{\partial x_{2}},&\dots,&\frac{\partial f}{\partial x_{n}}\end{bmatrix} ^{T} \tag{44}\]  
\[\
abla^{2}\mathbf{f(X)}=\begin{bmatrix}\frac{\partial^{2}f}{\partial x_{1}} &\frac{\partial^{2}f}{\partial x_{1}\partial x_{2}}&\dots&\frac{\partial^{2} f}{\partial x_{1}\partial x_{n}}\\ \frac{\partial^{2}f}{\partial x_{2}\partial x_{1}}&\frac{\partial^{2}f}{ \partial x_{2}^{2}}&\dots&\frac{\partial^{2}f}{\partial x_{2}\partial x_{n}} \\ \vdots&\vdots&\ddots&\vdots\\ \frac{\partial^{2}f}{\partial x_{n}\partial x_{1}}&\frac{\partial^{2}f}{ \partial x_{n}\partial x_{2}}&\dots&\frac{\partial^{2}f}{\partial x_{n}^{2}} \end{bmatrix} \tag{45}\]  
To approximate the first partial derivatives, Equation (46) is used. Here, \(\epsilon\) is a scalar constant with a small value (\(\epsilon=1e-5\)), \(\mathbf{e_{i}}\) is a unitary vector with the size of \(\mathbf{X}\) that contains \(0\)'s in all its positions except in the position \(i\) where there is a \(1\).  
\[\frac{\partial f}{\partial X_{i}}=\frac{f(\mathbf{X}+\epsilon\mathbf{e_{i}})- f(\mathbf{X})}{\epsilon} \tag{46}\]  
Since the Hessian matrix will be multiplied by a vector, a finite approximation is used (Equation (47)), where \(V\) is the vector to be multiplied.  
\[\
abla^{2}\mathbf{f(X)}*\mathbf{V}=\frac{\
abla\mathbf{f(X+\epsilon V)}- \
abla\mathbf{f(X)}}{\epsilon} \tag{47}\]
# The Effects of Acidic, Alkaline, and Neutral Anolytes on Electrochemical Seawater Deoxygenation
## 3 Results and Discussion
### Assessment of Anolytes for Industrial Deoxygenation Cell  
In our previous work, seawater was used both as an anolyte and catholyte [2]. The pH of the anolyte and catholyte were the same and, during operations, the anolyte became acidic and the catholyte became alkaline. However, at high flow rates of anolyte, the concentration of hydrogen ion does not increase significantly. Higher flow rates of anolyte and catholyte reduce the pH change in the solution, since the retention time of electrolytes in the anode and cathode chambers decreases with increasing flow rate.  
The catholyte for the tests was 0.5 M NaCl (pH range 6-7); however, during stabilization before experiments, the acidic anolyte made the stagnant catholyte acidic (pH 5), and the alkaline anolyte made the catholyte slightly alkaline (pH 9). In our previous study [1], the pH of the anolyte that was stagnant decreased significantly from pH 8.00 to pH 2.16. Thus, for an industrial-scale deoxygenation cell, it is recommended to avoid keeping the anolyte stagnant. Instead, the anolyte should have a certain flow rate, and the pH should be monitored. Based on the results described herein, an alkaline buffer solution can be recommended as an anolyte because no production of hydrogen peroxide was detected under those conditions. In addition, this solution has a large pH operation range before having a negative effect on the membrane. Monitoring of the pH for the anolyte can ensure the desired performance of the deoxygenation cell.
## 4 Proposed Scheme
### Phase 1: Set up and Key Management  
In the set up step, the network uses the E-DEEC clustering algorithm to cluster the network into \(j\) clusters. In each cluster \(j\), \(n\) sensing nodes and a \(CH\) will be assigned an \(ID\). These nodes can communicate with the CH directly. After that, the AP initializes each node with the necessary set up parameter by sending the following \(\{m_{j},c_{j},\ a_{j},\ H(.,),\ k_{m}\}\), where \(K_{m}\) is the master key of the system that is used by each sensing node in the following function to produce the cluster key \(K_{c}\)  
\[\mathbf{K_{cj}}=\mathbf{k_{m}}\bigoplus\mathbf{CH_{j}}\mathbf{ID}\ \Big{|}\ \mathbf{H}\ (\mathbf{K_{m}}) \tag{4}\]  
where \(m_{j},c_{j}\), and \(a_{j}\) are the parameters that are used for the pseudo-random number generator function. These parameters differ among clusters.
## 3 System Model
### Transmission Model  
When a user \(u\) demands a content \(f\) from its serving SBS \(s\), the desired content is transmitted with a data transmission rate [32] that is evaluated as  
\[R_{\mu}=W\times\log_{2}\bigl{(}1+\Gamma_{\mu}\bigr{)}. \tag{3}\]  
where \(W\) is the bandwidth to user \(u\) and \(\Gamma_{\mu}\) represents the received signal to interference and noise ratio. Here \(\Gamma_{\mu}\) is evaluated as follows:  
\[\Gamma_{\mu}=\frac{P_{s}d_{u,s}}{\sigma^{2}+I_{s}}, \tag{4}\]  
where \(P_{s}\) represents the transmission power of SBS \(s\), \(d_{u,s}\) represents the distance between serving SBS \(s\) and user \(u\). \(\sigma^{2}\) represents the path loss exponent, and \(I_{s}\) represents the inter-cell interference. However, by utilizing interference mitigation mechanisms such as spectrum reuse, power control, and alignment of interference, the inter-cell interference is considered as a constant noise.  
Figure 1: An illustration of data caching at SBSs.
## 5 Noise Robustness and Excitation Adaptability  
This section first quantificationally characterizes the noise robustness of the CWT-SSSM feature-based method using a noise immunity index and then discusses the applicability of the proposed method to excitation.
## 2 Materials and Methods  
### Literature Review and Significance of Research  
#### 2.1.1 GNSS Biases and Errors  
GNSS biases can be classified as dispersive or non-dispersive. The ionospheric effect is a dispersive bias, and the tropospheric effect is a non-dispersive bias. Both ionospheric and tropospheric effects decorrelate to the increase in baseline length. GNSS errors include the satellite clock error, receiver clock errors, multipath errors, and measurement noise/random errors. Sophisticated models of the ionospheric and tropospheric effects and other GNSS biases and errors have recently been developed with the GNSS CORS network data. Multipath errors and measurement noises are site-dependent and receiver-dependent [16]. Differencing cannot eliminate multipath errors and measurement noise but can amplify the errors because of the error propagation of the satellite-receiver pairs in differencing. Multipath occurs when the direct signal from a satellite is mixed with the reflected signal in the vicinity of the rover antenna [15; 17]. Site-dependent multipath errors can be mitigated by sidereal filters [18; 19] and empirical multipath maps [20; 21]. Both sidereal filtering and empirical multipath mapping methods need double-difference carrier-phase residuals at static baselines. In GNSS data processing, especially in kinematic positioning, multipath errors at the reference stations are usually ignored (i.e., multipath errors are not in the functional model) because CORS sites are usually considered \"multipath-free\". In CORS sites, geodetic-grade choke-ring antennas are installed on tall pillars/monuments, and the antennas are usually far from any reflectors in the environments. However, data at CORS stations are still contaminated in practice by near-field multipath errors. IGS uses Translation, Editing, and Quality Checking (TEQC) software [22] to estimate the pseudorange multipath errors in GPS L1 and L2. Examples of the average MP1 and MP2 of five IGS tracking stations are shown in Table 1. The authors of [23] used TEQC software to plot MP1 and MP2 in time series and analyze the possible sources of reflected signals at a site. If the site-dependent multipath errors are not modeled in data processing, we ought to select a CORS station with low multipath errors and measurement noise as the reference station in post-processing/real-time static/kinematic baseline processing or select CORS stations with low multipath errors and measurements for post-processing/RTK network processing. Currently, information on carrier-phase multipath errors and measurement noise is not available for CORS stations. However, high-precision positioning (i.e., baseline and RTK processing) must use carrier-phase measurements. This paper proposes a multi-GNSS and multi-frequency data quality assessment approach for CORS stations with a focus on carrier-phase data quality.
## 3 Simple Model of Bandwidth Consumption Calculation in 5G Fronthaul Interfaces  
The efficiency of the FH and MH links is the basis for a properly functioning C-RAN (in 5G-NG-RAN) structure. Currently, the CPRI format [31] is widely used in the links of fronthaul, the simplicity of which does not require using too large processing power from the signal processing systems, but the resulting streams are characterized by very high bit-rates. The demand for high CPRI bit-rate is primarily due to the need to connect to the gNB-CU/BBU cloud, independently, each antenna  
Figure 5: Example of flexible all-optical network connecting the integrated optical gNB-DU/RRH (with massive-MIMO functions) and O-gNB devices to gNB-CU/BBU/EPC/NGC cloud (presented in proceedings [23]).  
Figure 6: Transport technologies used in the F1 and F2 interfaces of fronthaul/midhaul as a components of the stationary part of NG-RAN (MicrowaveLine is the wireless transport option between gNB-CU/BBU and gNB-DUs/RRHs—wireless communication between gNB-DUs/RRHs is also possible—out of the scope of analysis).  
module (Figure 4) located in the radio-antenna head. In the Spits/Options model (Figure 7), the CPRI interface is at the top, which means that this digital stream will be directed to gNB-DU/RRH with low processing power. The functionality of this network termination will be limited to AAU. Currently, we have defined the fastest optical interface CPRI v.7.0 [31] with 24.3 Gbps. In many situations, especially when connecting the gNB-DU/RRH devices working in massive-MIMO format to fronthaul, the need for a larger number of the fastest CPRI streams is necessary. The introduction of faster bitstreams is pointless due to the emerging dispersal constraints of single-mode telecommunication optical fiber. In a DWDM network, we can concentrate more CPRI streams into one optical fiber, but with the dense location of the gNB-DU/RRH terminals, it is necessary to introduce new solutions in the transmission of radio signals, in conjunction with the introduction of optical nodes that can work more flexibly.
## 4 Results  
### Experimental Results of Method 1  
As the mentioned experimental process in Section 2, the test results of Method 1 with the detection distance and deflection angle as the core fully reproduce our test process, to achieve the purpose of deviation detection.  
Figure 4(a) shows an original image acquired by the camera 1. Before edge detection, we selected a ROI in the original image to reduce the amount of data and calculation time. It is the part of the green rectangular frame in Figure 4(a), also as shown in Figure 4(b), \(c\) is the result of the binarization of the ROI region. Edge detection after binarization can further simplify the grayscale image, make the information in the image pure, and make the changes in edge brightness more obvious.  
Figure 4(d) shows the edge detection results of the Canny algorithm. We performed a Hough transform on the edge detection results to get the straight line in the parameter space shown in Figure 4(e), and the straight lines in image space shown in Figure 4(f). Then we extracted the abscissa of the intersection of the straight line and the x-axis in the image space and the angle in the parameter space. The results are shown in Figure 6.  
We divided the image in Figure 6 into 10 regions to better explain the experimental results, the region 1\(\sim\)4 shows the entire experimental process more completely among them. As mentioned in the experimental setup, our experimental process is divided into four parts: running normally, manufactured running deviation artificially, corrected running deviation artificially, and running normally. The region 1\(\sim\)4 correspond to these four parts one by one.  
Figure 4(b) shows the slope change of the line detected by the algorithm during the experiment. The region 5\(\sim\)10 indicates that there is a \(1^{\circ}\) angle between the detected edge line and the Y-axis (velocity direction).  
The experimental results of method 1 confirmed that the edge recognition and detection algorithm proposed by this paper based on Canny and Hough could effectively extract the parameters (\(d_{x}\) and \(\theta\)) of the edge line to achieve our off-tracking detection.
## 5 Analysis of Numerical-Simulation Results
### The Influence of Particle Shape on the Permeability Coefficient of the Filter Layer  
The conclusion of the previous section indicates that the permeability coefficient of the spherical-particle filter model is lower than that of the ellipsoidal-particle filter model, a conclusion consistent with findings in the literature [28]. This discrepancy is hypothesized to arise from the varying permeability coefficients of filter-layer models based on different particle-shape parameters. Consequently, this section simulates and validates the muddy-water seepage tests of ellipsoidal-filter-layer models under varying particle-shape parameters.  
It has been confirmed that the particle-shape parameter \(S\) exhibits a linear relationship with the ratio of the long axis to the short axis, denoted by \(\lambda\), within a reasonable range. For simplicity, this ratio will be referred to as \(\lambda\) in the following discussion.  
Figure 24 shows the permeability coefficient of the model (without the inclusion of tailings-sand particles) under different particle-shape parameters, calculated using [PERSON]'s formula. With the increase in the ratio of the long axis to the short axis, \(\lambda\), the permeability coefficient of the model initially decreases and then increases. When the \(\lambda\) value is between 1.3 and 1.4, the permeability coefficient of the model is minimized; at \(\lambda\) values of 1.7 to 1.8, the permeability coefficient reaches its maximum, and subsequently, the rate of change in permeability coefficient diminishes. The value of \(\lambda\) = 1.6 can be regarded as a boundary for  
Figure 23: Permeability coefficient by test.  
the permeability coefficient of the model. Prior to this point, the permeability coefficient of each particle shape model is lower than that of spherical particles; beyond this threshold, it becomes greater.  
The difference in the calculation results of the permeability coefficient for different particle-shape parameters is illustrated by Figure 25. When the particle-shape parameter is close to 1, as illustrated on the left side of Figure 25, the pore size of the filter layer decreases with decreasing particle-shape parameters, corresponding to the reduction in the permeability coefficient shown in Figure 24. After reaching a certain critical value, as the particle-shape parameter further decreases, as shown on the right side of Figure 25, the sizes of the pores of the filter layer begin to increase gradually, which corresponds to the subsequent increase in permeability coefficient observed in Figure 24.