esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor
## 3 Design and Implementation of Elliptic-Curve Cryptosystem F256 on FPGA
### Point Addition in Jacobian Coordinates  
This section gives the formulas used for implementing point addition. Suppose: \(P_{1}(X_{1},Y_{1},Z_{1})\) and \(P_{2}(X_{2},Y_{2},Z_{2})\) are two points on curve (E) and  
\[\gamma=Y_{1}Z_{2}^{3},\lambda=X_{1}Z_{2}^{2},\,\,\mu=Y_{2}Z_{1}^{3}-Y_{1}Z_{2} ^{3},\xi=X_{2}Z_{1}^{2}-X_{1}Z_{2}^{2}, \tag{6}\]  
The new point P3 on Curve (E) such that: \(P_{3}(X_{3},Y_{3},Z_{3})=P_{1}(X_{1},Y_{1},Z_{1})+P_{2}(X_{2},Y_{2},Z_{2})\) is:  
\[X_{3}=\mu^{2}-\xi^{3}-2\lambda\xi^{2} \tag{7}\]  
\[Y_{3}=\mu(\lambda\xi^{2}-X_{3})-\gammaA modular reduction unit is designed based on an interleaved modular multiplier architecture similar to the one proposed in [33; 34]. Based on the implementation results in [33; 34], an interleaved modular multiplier has more efficient area and timing characteristics. For fast and area-efficient implementation of such a multiplier, we use just one CSA adder and a look-up table. The structure of our design is depicted in Figure 1.  
The look-up table code is given by;  
\[\text{LUT}(0)=0;\] \[\text{LUT}(1)=Y;\] \[\text{LUT}(2)=2*2^{n}\text{ mod }M;\] \[\text{LUT}(3)=(2*2^{n}+Y)\text{ mod }M;\] \[\text{LUT}(4)=4*2^{n}\text{ mod }M;\] \[\text{LUT}(5)=(Y+4*2^{n})\text{ mod }M;\] \[\text{LUT}(6)=6*2^{n}\text{ mod }M;\] \[\text{LUT}(7)=(Y+7*2^{n})\text{ mod }M;\] \[\text{and}\] \[\text{($S$,$C$)}=\text{CSA}(A,B,C)\text{ is}\] \[\text{$S_{i}$}=A_{i}\bigoplus B_{i}\bigoplus C_{i},\] \[\text{$C_{i+1}$}=(A_{i}.B_{i})V(A_{i}.C_{i})V(B_{i}.C_{i}),C_{0}=0\]  
The modular multipliers use one clock cycle to register inputs, 256 (n) clock cycles in the loop, one clock cycle to calculate the CSA addition and one clock to register output, so the calculation is done in just 259 n + 3 clock cycles.  
Figure 1: Interleaved Modular Multiplier (top) and ECC core Diagram (bottom).
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 5 Improved Scheme
### Login  
The login procedure is performed when a user \(U_{i}\) intends to access service from the vehicle sensor network. The login procedure is described as follows:  
1. \(U_{i}\to OBU_{i}\): \(ID_{i}\) and \(PW_{i}\) are input to \(OBU_{i}\) by \(U_{i}\).
2. First, \(OBU_{i}\) retrieves \(A_{i}=h(PW_{i})\oplus B_{i}\). Then, it checks whether \(D_{i}=h(ID_{i}||PW_{i}||A_{i})\) holds. If so, \(OBU_{i}\) launches the general authentication procedure or the trust-extended authentication procedure. Otherwise, the login request will be rejected.
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
###### Abstract
Monitoring the spectral, radiometric, and geometric temporal changes in a sensor is critical for the scientific community, who rely on these data for a wide variety of scientific, environmental, and societal applications. Ground-based techniques are now in a mature state and continue to be used to monitor and characterize any changes that may occur during the operational life of a sensor. The value of these systems is their independence from any onboard calibration systems that may also change over time. Independent radiometric validation has also become more important \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Landsat 9 OLI**} \\ \hline
**Band** & **Center Wavelength (nm)** & **Bandwidth (FWHM, nm)** & **GSD (m)** \\ \hline
1 & 443 & 16 & 30 \\
2 & 483 & 60 & 30 \\
3 & 561 & 57 & 30 \\
4 & 655 & 37 & 30 \\
5 & 865 & 29 & 30 \\
6 & 1609 & 86 & 30 \\
7 & 2201 & 189 & 30 \\
8 (pan) & 592 & 172 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The spectral bands and GSD of the Landsat 9 OLI. The center wavelength defined here is the band average of the relative spectral response (RSR) for each band. with the growing number of operational satellite constellations, many of which consist of CubeSats or nanosats that typically lack onboard calibration systems. Current ground-based techniques include automated instrumented sites [11], in situ measurements by on-site personnel [12; 13; 14; 15; 16; 17; 18], pseudo-invariant calibration sites (PICSs) [19], lunar observations, and mirror-based artificial targets [20; 21]. Cross-calibration with suitable spaceborne and airborne sensors is also widely used for calibration and validation studies. The independent validation of Earth-observing systems ensures that data from sensors with different spectral, spatial, and temporal characteristics can be placed on the same traceable radiometric scale, thereby allowing for harmonized data products with a well-defined uncertainty. Vicarious radiometric calibration is a ground-based technique that has been in use for almost 40 years [22; 23; 24]. It provides an independent analysis that measures and monitors any post-launch changes in the airborne or spaceborne system under test. This work presents the results from the initial nineteen months after the launch of the Landsat 9 OLI and includes measurements using on-site personnel as well as those using an instrumented automated test site. The Landsat 9 OLI results from the tandem flight phase of Landsat 8 and 9 are also presented. Section 2 of this paper presents the reflectance-based and RadCaTS ground-based methodologies. Section 3 describes the field campaigns during the nineteen-month period of this work (November 2021 to June 2023), and Section 4 summarizes the results for each of the ground collects. Section 5 presents a brief description of the ground measurement uncertainty, and Section 6 provides a summary of the results and conclusions of this work.
## 2 Background
We have summarized the methods mentioned above in Table 1 for comparison. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference Notation** & **Experiment Dataset** & **Base Method** & **Machine Learning** \\ \hline
[2] & Crack images collected from simulated & \multirow{2}{*}{Sobel operator edge detection} & \multirow{2}{*}{\(\times\)} \\
[4] & & tunnel environments & \\
[5] & Medical images of mammograms & Region growing & \(\times\) \\
[5] & Selected model dataset & Multi-view region growing & \(\times\) \\
[6] & Concrete crack images from specific reservoir & \multirow{2}{*}{Region growing and} & \multirow{2}{*}{\(\times\)} \\
[7] & Digital images of building structure & & \\
[8] & Field-captured images and public & & \\  & benchmark dataset & & \\
[10] & Six types of tunnel targets including cracks & & \\  & and water leakage & & \\
[12] & 167 different images of cracked ceramics & U-Net & \\
[13] & Electron microscopic stacks, transmitted light & & U-Net & \\
[13] & microscopy images, and DIC-HeLa dataset & & \\
[11] & Concrete crack dataset that was constructed & & \\  & from 840 original images & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detection method comparison. To sum up, the traditional region growth algorithm cannot deal with the interference of dirt and water stains, and its accuracy is low, while the deep learning method is more complex and inefficient. To balance the conflict between efficiency and accuracy, this paper proposes a combined recognition method that integrates machine learning and region growing algorithms. This new combined algorithm ensures high accuracy in concrete crack detection while improving detection efficiency and applicability in construction sites. It is a highly practical algorithm with significant application value.
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 4 Discussion  
Globally, the results allow us to conclude that deep learning techniques achieve promising results. In all cases (close to shore and open-sea) recall is high, more than 75%. We obtained less than 0.14 false alerts per square kilometer in the open sea and close to the sea shore, the false alarm rate is around 1 ship/km2. Although the BL-NDWI baseline could be improved by finding more optimal NDWI thresholds for each image, the performance difference with respect to deep learning approaches seems hard to make up for.  
Networks trained with SSL achieve better results compared with ImageNet pretrained ones. In the few-shot setting, SSL pre-training is usually better and more stable. When sufficient examples are available SSL pretraining is as good as ImageNet or training from scratch. We notice also that performance increases with the size of the pre-training datasets. Since these are not annotated it is easy to build such datasets. The ones chosen here have no relation to the ship detection problem at hand, thus no significant effort is needed to select the images in these datasets.  
The pretext task needs to be chosen according to the downstream task in order to learn the needed invariances. The region based pretext task looks promising probably because it helps the encoder to better cluster together similar elements, such as water, agriculture crops, or residential areas, while a simple pretext task data-augmentation only focuses on  
Figure 10: Ship detection performance according on ship size and location, with a frugal setting (126 training ships).  
color or noise invariances. The benefit of such pretext task can be seen in our case as it lowers the number of false positives over land and near the shore.  
In terms of computational complexity, the difference between all deep learning methods lies in the way we pre-train the weights. Compared to the supervised pipelines that can be used to train a ResNet-50 on ImageNet dataset, the self-supervised pipeline has a similar complexity but requires much larger batch size. This is particularly problematic for multi-spectral images, and a GPU with at least 8 Go of memory is necessary. The pretext task training is time-consuming: it took us nearly two days to train it on SEN12 MS dataset using a multi-GPU machine (4 GPU with a total of 64 Go of memory). Training on a single desktop GPU with at least 8 Go of memory is feasible, but lasts longer.
# Automatic Needle Route Proposal in Preoperative Neck CT for Injection Laryngoplasty
## 3 Experimental Results  
### Data and Evaluation Method  
To evaluate our method, we collected a neck CT dataset from Seoul National University Bundang Hospital (SNUBH), Seongnam, South Korea. A total of 136 neck CT volumes were acquired from 136 different patients during the period of October 2017-August 2021. All the patients were suspected of vocal fold paresis, whereas 29 patients among them were diagnosed with the paresis condition. Among them, 23 patients had unilateral paresis, and 6 patients had bilateral paresis. Table 1 summarizes our data.  
CT scan was performed using 64-row multidetector scanners (Brilliance; Philips Healthcare, Best, The Netherlands). A single-phase bolus injection of 100 cc of iodinated contrast media (Iohexol 300 mg/mL, GE Healthcare) was administered with 2 cc/s of flow rate. Image acquisition began 80 s after the injection, and the scan range was from the frontal sinus to the carina. The following imaging parameters were used: 120 kVp, 250 mAs, 0.835 pitch, 0.5 rotation time, \(128\times 0.625\) collimation, 1-2 mm slice thickness, and 0.5-1 mm increments. The resultant CT volumes had about 160 axial slices on average, where each slice had \(512\times 512\) voxels. The average voxel spacings were 0.44 mm, 0.44 mm, and 0.96 mm for the coronal, sagittal, and axial directions, respectively.  
The final needle route results on the 136 test cases were presented to the expert at the same clinical site for evaluation. The expert evaluator (labeled as _observer 1_) in our study has more than ten years of experience as a TIL surgeon. We also obtained evaluation from a second observer (_observer 2_) who has one and a half years of experience with preoperative neck CT for vocal fold augmentation. Thus, we could determine if the final results are comparable to the inter-observer agreement. For evaluation, results were presented in DICOM using burn-in annotation with maximum intensity, where the needle path was indicated along with the vocal fold and needle insertion locations. The evaluators performed a binary evaluation, where each result was labeled as _accurate_ or _not accurate_. Because the acceptable needle route is not a singular unique path, measuring the distance of the proposed path from the ideal one is not appropriate for the evaluation. Rather, the evaluators conducted a qualitative evaluation by carefully examining the results to determine the acceptability of the proposed route. A result was labeled _accurate_ only if it was a safe and practically plausible route. To be specific, a result was considered to be accurate if the suggested route starts from the cricothyroid membrane and reaches the vocal fold without crossing or touching any critical regions. On the other hand, it was considered to be inaccurate if the route starts from any regions other than the cricothyroid membrane (e.g., thyroid/cricoid, etc.) or passes through the rigid cartilage. The route was also rejected if it came within 2 mm proximity of the paraglottic membrane.
# Slip Estimation for Mars Rover Zhurong Based on Data Drive
###### Abstract
[2] transformed the slip value estimation problem into a classification problem through several machine learning algorithms, and they used signals of the Inertial Measurement Unit to predict the slip level (low, moderate, or high). This method was designed for the wheel-level slip, not the body-level slip. [PERSON] et al. [18] presented slope slip methods, which were established based on data collected in the Jet Propulsion Laboratory Mars Yard. The models were analyzed by data from the Mars rover Curiosity, the results showing a good prediction performance in small rock and bedrock terrains, but could not fully predict the slip of the rover in sand. [PERSON] et al. [19] proposed adjusting the model based on the on-orbit slip data, which significantly improved the prediction performance in sand. These researches indicated that a data-based approach provides adaptability in complex environments without the involvement of detailed mechanical models, overcoming the dependence on images of environmental characteristics. The data-based approach is becoming a common trend in slip research. So far, however, there has been little research considering the difference in environmental conditions between Earth and Mars, especially regarding the reduced gravity on Mars [20]. In this paper, the primary aim is to establish a slip estimation model for the Mars rover Zhuurong, which can be used to assess the risk level of slip, as well as correct the path planning. A data-based approach is selected to establish the models, and we collected data through ground indoor tests. In order to simulate the situation of Zhuurong on Mars as much as possible, the test site was equipped with a low-gravity simulation device and simulated Mars soil. Slip estimation models were built using a back propagation neural network (BP), and an optimized BP based on a genetic algorithm (GA-BP). The obtained models were applied to estimate longitudinal slip (slip_x) and lateral slip (slip_y) using on-orbit data. Then, the path planning points were corrected with the slip prediction values and compared with the actual path points, which verified the feasibility of the model. The rest of this paper is arranged as follows: Section 2 introduces the ground tests and on-orbit validation Section 3 illustrates the training methods, Section 4 displays the training results obtained through ground tests and the estimation results compared to on-orbit real data, and Section 5 presents the conclusion.
# Reduction Potential of Gaseous Emissions in European Ports Using Cold Ironing
## 9 Potential Benefits of Cold Ironing in European Ports  
The analyses carried out highlighted that cold ironing implementation could potentially play a significant role in meeting emissions reduction targets and improving air quality in port cities, in alignment with various previous studies, including those from outside Europe [11].  
The potential reduction of local and global emissions from ships in port docked and connected to the electrical grid with their engines turned off can help European ports to achieve important benefits:  
1. It can significantly reduce emissions of air pollutants and greenhouse gases that have a negative impact on the health of humans and the planet, improving the local air quality and decelerating the global warming process;
2. It can help to save energy and reduce costs for ships: its utilization can decrease fuel utilization by about 50%, which can yield substantial fuel cost reductions for ships, thereby making them more competitive and ecologically sustainable;
3. It can bring economic benefits to ports, increasing revenue and creating jobs, thereby fostering economic development;
4. It can help European ports and countries to meet their emissions reduction targets set by the European Union;
5. It is a key element in the transition towards a low-carbon economy and sustainable shipping;
6. It can also significantly reduce noise pollution as ships turn off their engines while docked;
7. It can reduce the risk of accidents caused by ships' engines and auxiliary systems, such as oil spills and fires.  
Consequently, investments in cold ironing technologies can ameliorate the ecological impact of shipping and advocate for clean and sustainable practices.
## 3 Antenna Single Element
### Cavity-Box  
The multi-layer antenna structure is enclosed within four electric walls in a cavity-box configuration. The inclusion of these walls interrupts the propagation of surface waves, which become relevant in structures composed of several layers with different dielectric permittivities. This way, the coupling between antenna elements is reduced and a higher degree of integration can be achieved, thus maximizing the density of antenna elements.  
The resonant nature of the cavity leads to a bandwidth reduction due to its high quality factor. Considering this cavity-box configuration, the proposed antenna design provides a bandwidth of 150 MHz (around 15%), which still fulfills the bandwidth requirements according to the desired SAR sensor specifications.
## 4 Performance Evaluations
### Metrics  
To compare the localization accuracy of LoMoB and LDB, we define the average location error as follows:  
\[e_{average}=\frac{1}{N}\sum_{k=1}^{N}\|\mathbf{\hat{x}_{k}}-\mathbf{x_{k}}\| \tag{7}\]  
where \(\mathbf{\hat{x}_{k}}=(\hat{x}_{k},\hat{y}_{k})\) is the estimated location of a sensor node that is located at \(\mathbf{x_{k}}=(x_{k},y_{k})\) and \(N\) is the number of sensor nodes.  
Because the average location error may be seriously affected by a small number of large location errors, the ratio of localized sensor nodes with location errors below a given threshold can be used as an additional metric for evaluating the localization accuracy. The ratio of localized sensor nodes below a given threshold allows the localization accuracy to be assessed in greater depth than the average location error alone.
# Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment
## 3 Method
### Hydraulic Model Calibration Framework Using SAR- and DEM-Derived Flood Maps
Floodwater depth maps were compared pixel to pixel to see if there was concordance or discordance between the two paired maps, i.e., the SAR- and DEM-derived map and the water depth map simulated by the hydraulic model, using root square mean errors (_RSME_) and the Nash and Sutcliffe efficiency coefficient (_NSE_), whose formulation is shown below for measuring proper fit [60]. \[RMSE=\sqrt{N^{-1}\sum_{i}^{N}(P_{i}-O_{i})^{2}} \tag{4}\]\[NSE=1-\frac{\sum_{i}^{N}(O_{i}-P_{i})^{2}}{\sum_{i}^{N}\left(O_{i}-\overline{O} \right)^{2}} \tag{5}\]  
where \(P_{i}\) and \(O_{i}\) represent the predicted (simulated by the hydraulic model) and observed SAR values, respectively, and \(\overline{O}\) is the mean of the observed value. The resulting best performance of Manning roughness parameters that were obtained during this calibration process were then used for model validation purposes for the 2-3 December 2013 Bradano flood event scenario to evaluate calibrated model robustness under different operating conditions.
## 2 System Description
The received time-domain waveform \(y(t)\) is given by:  
\[y(t)=h(t)\varox(t)+w_{\text{rx}}(t) \tag{8}\]  
where \(h(t)\) is the channel impulse response, \(w_{\text{rx}}(t)\) is the received white Gaussian noise with variance \(\sigma_{\text{m}_{\text{rx}}}^{2}\), and \(\varox\) denotes a convolution operation. The received signal \(y(t)\) is then converted to a discrete time-domain signal \(y[n]\) by the ADC block followed by serial-to-parallel (S/P) conversion. The received signal is distorted by zero-mean real-valued bipolar additive white Gaussian noise (AWGN) \(w_{\text{rx}}(t)\) which consists of the receiver's shot and thermal noises. The fast Fourier transformation (FFT) is applied on \(y[n]\) which transforms the signal back to the frequency domain, which is given by:  
\[Y[k]=\frac{1}{\sqrt{N}}\sum_{n=0}^{N-1}y[n]\ e^{-2\pi\frac{\text{tr}}{N}}\ ;\ \ \ \ \ \ \ \ 0\leq k\leq N-1 \tag{9}\]  
The obtained frequency-domain symbol \(Y[k]\) can be expressed as:  
\[Y[k]=H[k]\cdot X_{\text{clip}}[k]+W_{\text{rx}}[k] \tag{10}\]  
The AWGN \(W_{\text{rx}}[k]\) noise after the FFT operation can be modelled as the zero-mean complex AWGN with two-sided power spectral density of \(N_{0}/2\) per dimension and a variance of \(\sigma_{\text{m}_{\text{rx}}}^{2}=BN_{0}\)[8]. The embedded pilot sequence is extracted from the received data symbols, then its phase and amplitude are estimated based on the maximum likelihood (ML) algorithm. The estimated pilot phase is used to reverse the phase rotation carried out at the transmitter. The received data symbols are then demodulated before being converted into bit streams followed by calculating the BER of the system based on the decoded binary streams.
# Adaptive Control of Active Magnetic Bearing against Milling Dynamics
## 1 Introduction
### Milling Dynamics  
For milling applications, AMB is employed to counterbalance the cutting force during the milling process. Therefore, the control performance of AMB is highly determined by the modeling accuracy of milling dynamics. However, many parameters are involved in the milling operation; the dynamics of the cutting force are, therefore, complex and hardly predicted. For the purpose of retaining the MRR (Material Removal Rate) and prevent tool breakage for fixed-gain feedback controllers, an experimental approach was reported to build the dynamic milling model under various cut depths and spindle speeds [18]. Since the milling process is a typical time-varying system due to the variations of cutting conditions, a time-domain model was proposed for the analysis of cutting dynamics and chatter stability criterion. The cutting force is estimated by [PERSON]'s predictive machining theory according to the instantaneous un-deformed chip thickness, cutting parameters, material properties of the workpiece, and tool geometry [19]. Similarly, the first-order and second-order mathematical  
Figure 1: Two configuration types of Active Magnetic Bearing (AMB): (**a**) radial design, and (**b**) modified radial design.  
models for milling dynamics were also proposed in 2002 and 2004, respectively [20; 21]. A hybrid adaptive control algorithm is employed to tune the feedrate to regulate the variation of cutting force based on the first-order milling dynamic model [20]. Another second-order model was synthesized in a self-tuning regulator for milling processes under various cut depths [21]. Due to the complexity of milling operations, neural network [22], fuzzy logic algorithms [23], and heuristic methods [24] were all applied to the modeling of milling dynamics.