esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The purpose of our work is to investigate the performance of fusion wind from multiple remote-sensed data in forcing numeric wave models, and the experiment is described herein. In this study, 0.125\({}^{\circ}\) gridded wind fields at 12 h intervals were fused by using swath products from an advanced scatterometer (ASCAT) (a Haiyang-2B (HY-2B) scatterometer) and a spaceborne polarimetric microwave radiometer (WindSAT) during the period November 2019 to October 2020. The daily average wind speeds were compared with observations from National Data Buoy Center (NDBC) buoys from the National Oceanic and Atmospheric Administration (NOAA), yielding a 1.66 m/s root mean squared error (RMSE) with a 0.81 correlation (COR). This suggests that fusion wind was reliable for our work. The fusion winds were used for hindcasting sea surface waves by using two third-generation numeric wave models, denoted as WAVEWATCH-III (WW3) and Simulation Wave Nearshore (SWAN). The WW3-simulated waves in the North Pacific Ocean and the SWAN-simulated waves in the Gulf of Mexico were validated against the measurements from the NDBC buoys and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) for the period June\(-\)September 2020. The analysis of significant wave heights (SWHs) up to 9 m yielded a \(<\) 0.5 m RMSE with a \(>\) 0.8 COR for the WW3 and SWAN models. Therefore, it was believed that the accuracy of the simulation using the two numeric models was comparable with that forced by a numeric atmospheric model. An error analysis was systematically conducted by comparing the modeled WW3-simulated SWHs with the monthly average products from the HY-2B and a Jason-3 altimeter over global seas. The seasonal analysis showed that the differences in the SWHs (i.e., altimeter minus the WW3) were within \(\pm\)1.5 m in March and June; however, the difference was quite significant in December. It was concluded that remote-sensed fusion wind can serve as a driving force for hindcasting waves using numeric wave models.  
sea surface wave; scatterometer; microwave radiometer; altimeter 2023
## 2 Materials and Methods
### Data
#### 2.2.2 Lvis  
The Laser Vegetation Imaging Sensor (LVIS) is NASA's airborne laser altimeter. LVIS collects data on topography with decimetre accuracy, as well as the vertical height and structure of vegetation from return waveform structures of the radar pulses [29]. Processing of the shapes and amplitudes of waveforms permits the location of the returns from the canopy tops to the ground and derives various products, such as ground heights, canopy top heights, and heights of a specific energy percentage [30]. The LVIS scan angle is about 12\({}^{\circ}\), and the typical diameter of the LVIS footprint on the ground varies from 10 to 25 m, depending on the flight altitude. In this study, LVIS data were used for the evaluation of both canopy height models using heights corresponding to the tops of vegetation and DEMs using bare-earth LVIS measurements.  
LVIS data over Costa Rica were downloaded as the LVIS Classic L1B Geolocated Waveforms and LVIS Classic L2 Geolocated Surface Elevation and Canopy Height Product for three campaigns, in 1998 [31], 2005 [32], and 2019 [33]. The locations and elevations of the point clouds can be found in Figure 2. The resolution of each measurement is 20 metres  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**DEM** & **Mesh Size** & **Mission** & **DTM/DSM** & **Provider** & **Reference** \\ \hline MERIT DEM & 3\({}^{\prime\prime}\approx 90\) m & SRTM & DTM & Univ. of Tokyo [4] & WGS84/EGM96 geoid \\ BEST DEM & 3\({}^{\prime\prime}\approx 90\) m & SRTM & DTM & bris.ac.uk [27] & WGS84/EGM96 geoid \\ NASADEM & 1\({}^{\prime\prime}\approx 30\) m & SRTM & DSM & LP DAAC/USGS [19] & WGS84/EGM96 geoid \\ SRTMGL1 v003 & 1\({}^{\prime\prime}\approx 30\) m & SRTM & DSM & LP DAAC/USGS [18] & WGS84/EGM96 geoid \\ SRTM v4.1 & 3\({}^{\prime\prime}\approx 90\) m & SRTM & DSM & CGIAR-CSI [26] & WGS84/EGM96 geoid \\ SRTM3 v2.1 & 3\({}^{\prime\prime}\approx 90\) m & SRTM & DSM & USGS [25] & WGS84/EGM96 geoid \\ TanDEM-X & 3\({}^{\prime\prime}\approx 90\) m & TDX & DSM & DLR [28] & WGS84/WCS84 ellipsoid \\ ASTER GDEM v3 & 1\({}^{\prime\prime}\approx 30\) m & Terra & DSM & NASA/METI [24] & WGS84/EGM96 geoid \\ AW3D30 v3.1 & 1\({}^{\prime\prime}\approx 30\) m & ALOS & DSM & JAXA [22,23] & WGS84/EGM96 geoid \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the publicly available global Digital Terrain Models (DTM) and Digital Surface Models (DSM) evaluated for Costa Rica in this study. The column “Mission” here indicates the remote sensing mission that was the principal data source used in the DEM in the study area (latitudes below 20\({}^{\circ}\)).  
according to the specifications. Elevations are provided in terms of ellipsoidal heights above the WGS84 ellipsoid. Some characteristics of LVIS products used in this study are listed in Table 2.
# An Aeromagnetic Compensation Algorithm Based on a Temporal Convolutional Network
## 1 Introduction
In the study of high-frequency interference compensation algorithms, [PERSON] used open flight data from the Massachusetts Institute of Technology and the Department of the Air Force Artificial Intelligence Accelerator Program (hereafter DAF-MIT AIA Open Flight Data) [20] and a simple, fully connected neural network to analyze the impact of different inputs on compensation accuracy in 2022 [21]. The results show that the current and voltage data input of some airborne electronic equipment can significantly improve compensation accuracy. In the same year, [PERSON] proposed a compensation method combining wavelet decomposition and a long short-term memory neural network to reduce interference with airborne electronic equipment [22]. In recent years, time convolutional networks have been widely applied in various temporal tasks [23; 24; 25]. In order to establish connections between features and aeromagnetic interference in time series, improve both generalization and compensation accuracy, prevent exploding gradients, and achieve fast convergence, in this article, we propose an aeromagnetic compensation method based on temporal convolutional networks (hereafter MagTCN). In the proposed method, a neural network based on separable convolution with a residual connection is employed to improve the compensation accuracy and convergence stability, and a gradient correction loss function based on the T-L model is used to improve the generalization ability. MagTCN was applied to simulation datasets and DAF-MIT AIA Open Flight Data to verify its effectiveness. The structure of this article is as follows: Section 2 describes the proposed method. Section 3 discusses the experimental details, including dataset preparation, model parameters, and evaluation metrics. Section 4 provides the results and discussion. Section 5 summarizes the conclusions.
# A Review of Blockchain-Based Secure Sharing of Healthcare Data
## 3 Blockchain in Healthcare Data
### Traditional Methods Based on Cryptography  
Data encryption is the traditional method of data protection, and this section lists some ways to protect data privacy using encryption algorithms. Finally, the traditional methods are compared with blockchain-based methods.  
A lightweight encryption algorithm with a shorter secret key computation time has been proposed by [PERSON] et al. [43]. This algorithm solves the problem that traditional encryption algorithms are not applicable to medical image data, and the algorithm obtains a lower signal-to-noise ratio. [PERSON] et al. propose the use of a plaintext encryption method, which embeds private data into medical images [44]. The correlation with the original image is intuitively difficult to see in the plaintext encrypted image, which reduces the chance of being attacked. [PERSON] et al. optimized the traditional homomorphic encryption model [45]. First, edge computing is used to speed up plaintext encryption. Then, avoiding the use of complex centralized encryption algorithms reduces the high computational and communication overhead.  
There are some problems with the traditional approach. For example, the risk of secret key leakage is greater when there are more organizations to share it with, and traditional cryptographic algorithms have no way to achieve fine-grained access control. The blockchain-based approach enables fine-grained access control through smart contracts, and many image data have some distortion after encryption and decryption.
# Dynamic Modeling and Analysis of a Driving Passenger Vehicle
###### Abstract
However, a time-efficient and affordable method is needed for driving tests to quickly respond to consumer demands. Therefore, building a simple mathematical model of a vehicle that can analyze the riding comfort of a vehicle driven by steering input through simulation is desired. However, most of the previous studies investigated by the authors tend to focus on vehicle models for analyzing ride comfort under disturbance conditions, such as uneven road surfaces or irregularities. In addition, several studies have been published to analyze the behavior of the vehicle body by establishing a vehicle model for turning conditions. However, they have used a method to obtain a response generated by inputting an arbitrary lateral acceleration into the vehicle body. Since lateral acceleration is a variable determined by the change in steering angle input by the driver, it is limited as an actual input condition for a turning vehicle. In summary, the limitation of vehicle models in the previous studies is that they cannot analyze the dynamic responses of a turning vehicle according to the vehicle speed and steering angle controlled by a driver. Therefore, this study introduces a full-car model that can analyze the dynamic behavior of a turning vehicle according to the change in steering angle, which is the driver's input condition. The new 7-DOF full-car model proposed in this study is valuable in that the simulation and driving test results for various steering conditions have been comparatively verified. The main contribution of this study is to present a full car model encompassing an anti-roll bar that can analyze the dynamic behavior of the variation of the steering angle controlled by a driver. The remainder of this paper is organized as follows: In Section 2, a dynamic vehicle model that can analyze the behavior of a driving passenger car is developed. A vehicle model with seven degrees of freedom was established, considering the centrifugal force acting on the vehicle during turning and the torsional stiffness of the anti-roll bar. Based on this model, the equations of motion are derived using Lagrange's equation. In Section 3, the established vehicle model is verified by comparing the vertical suspension deformations computed with the vehicle model with the deformations measured through actual vehicle driving tests. For comparison, three driving tests were performed: the slalom, double lane change, and step steer tests. In Section 4, the natural frequencies and mode shapes of the dynamic vehicle model are computed and analyzed using the developed vehicle model. Furthermore, the effects of changes in the design parameters on the dynamic response of the vehicle model were investigated. The considered design parameters were suspension stiffness, suspension damping, and anti-roll bar torsional stiffness. Finally, conclusions and a summary of this study's results are presented in Section 5.
# A Survey on Botnets: Incentives, Evolution, Detection and Current Trends
## 3 Methodology
### Search Strategy  
The PICO (Population, Intervention, Comparison and Outcomes) criteria to identify relevant search queries from the paper's research questions [39]. The criteria for this specific paper are defined as follows:  
* _Population:_ The paper is interested in all research focused on botnet incentives, evolution, detection and mitigation, including other surveys. Malware in general is considered too broad, and only papers focused specifically on botnets are included.
* _Intervention:_ Does not apply as all papers within the research space of botnets are interesting for the purpose of the survey.
* _Comparison:_ Different approaches to the detection of botnets in particular are compared to identify advantages/disadvantages. The frequency distribution of detection and mitigation mechanisms described in papers are also compared.
* _Outcomes:_ Expected results are an overview of botnet progression and mitigation mechanisms as well as an identification of current trends based on the aforementioned overview.  
Two important keywords were identified from these criteria, _Botnet_ and _Security_. As the main goal of the paper is to provide a mitigation-oriented analysis of botnet papers and current trends in botnet security, these two keywords were deemed as the most important.  
Initially, five sources--Google Scholar, DTU FindIt, ACM Digital Library, Scopus and IEEE Explore--were used for database query of botnet papers. The first query of _\"Botnet\"_**in title** produced more than 18,200 papers, too much to realistically process. Additionally, IEEE Explore, DTU FindIt, ACM Digital Library and Scopus found 1455, 5912, 1379 and 3411 papers respectively. Instead, a second query: _\"botnet\"_**in the title and _'security'_**in abstract** was used to both exclude some potentially unrelated papers and to include both the identified keywords.  
For the second query, Google Scholar was removed as it did not provide the option of searching within abstracts. In total, 306, 224, 85 and 399 papers were found on DTU FindIt, IEEE Explore, ACM Digital Library and Scopus, respectively, with the new query. This query did find multiple duplicates between the sources that were removed in the first exclusion step. In total, some \(\sim\)630 papers in total (unique, not including duplicates) were found.
# An Integrated Optimization Method of High-Speed Railway Rescheduling Problem at the Network Level
## 3 Multi-Objective Integrated Optimization Model
### Objectives
#### 3.3.2 Constraints of Train Sets Circulation  
Constraint (21) expresses that a train can be executed by one train set at most simultaneously:  
\[\sum_{i\in I}a_{i}^{c,t}+\sum_{b\in B}h_{b}^{c,t}\leq 1,\forall c\in C,t\in T \tag{21}\]  
In which, \(I\) is the set of train sets in the railway network, train set \(i\in I\). Binary variable \(a_{i}^{c,t}\) is 1 if train set \(i\) executes train \(t\) of circulation \(c\) and 0 otherwise.  
If a standby train set is operated, it must execute one train, as shown in Constrain (22):  
\[\sum_{c\in C^{\prime}}\sum_{t\in T^{\prime}}h_{b}^{c,t}=h_{b},\forall b\in B \tag{22}\]  
In which, binary variables \(h_{b}\) indicate whether the standby train set \(b\) is operated (\(h_{b}=1\)) or not (\(h_{b}=0\)).  
Constraint (23) ensures that a standby train set can only execute one train at most simultaneously:  
\[\sum_{c\in C}\sum_{t\in T}h_{b}^{c,t}\leq 1,\forall b\in B \tag{23}\]  
Similarly, a train set can only execute one train at most simultaneously, as shown in Constraint (24):  
\[\sum_{c\in C}\sum_{t\in T}a_{i}^{c,t}\leq 1,\forall i\in I \tag{24}\]  
The affected trains can be executed by other affected trains and standby train sets, or be cancelled, as Constraint (25) shows:  
\[\begin{array}{l}\sum_{t^{\prime}\in T}tr_{c^{\prime},t^{\prime}}^{c,t}+\sum_ {b\in B}h_{b}^{c,t}+\sum_{t\in T}can^{c,t}=1\\ \forall c\in C^{aff},\forall t\in T^{aff},\forall c^{\prime}\in C,\forall t^{ \prime}\in T\end{array} \tag{25}\]In Constraint (25), binary variables \(tr^{c,t}_{c^{\prime},t^{\prime}}\), are 1 if train \(t\) in circulation \(c\) is executed by train \(t^{\prime}\) in circulation \(c^{\prime}\) and 0 otherwise.  
Constraint (26) ensures that the train sets and standby train sets must execute all subsequent trains when executing the affected circulation.  
\[\left\{\begin{array}{l}a^{c,t}_{i}\leq a^{c,t+1}_{i}\\ h^{c,t}_{b}\leq h^{c,t+1}_{b}\end{array}\right.\forall c\in C^{aff},\forall t \in T^{aff},\forall i\in I,\forall b\in B \tag{26}\]  
Constraint (27) indicates that if the train sets and standby train sets execute the affected trains, the actual departure time of the affected trains cannot be later than the schemed time plus the acceptable maximum associated delay time.  
\[\left\{\begin{array}{l}adt^{c,t}_{j}+EDT_{j}+SVT_{j}\leq(1-a^{c,t}_{i})\times M +PDT^{c,t}_{j}+ADT\\ adt^{b}_{j}+EDT_{j}+SVT_{j}\leq(1-h^{c,t}_{b})\times M+PDT^{c,t}_{j}+ADT\\ \forall c\in C^{aff},\forall t\in T^{aff},\forall i\in I^{aff},\forall b\in B,\forall j\in J\end{array}\right. \tag{27}\]  
In which, \(I^{aff}\) is the set of affected train sets. \(SVT_{j}\) indicates the serving time of train set at station \(j\). \(ADT\) is the acceptable maximum associated delay time. \(PDT^{c,t}_{j}\) is the planned departure time of train \(t\) at station \(j\) in circulation \(c\).
# A Temperature-Robust Envelope Detector Receiving OOK-Modulated Signals for Low-Power Applications
## 4 Implementation Criteria
### Temperature Compensation Validation  
The aim of this section is the validation of the temperature compensation mechanism, i.e., proving its functionality and the performance benefits its use brings to the ED in terms of reduction in the chain sensitivity temperature variation. This has been carried out using the minimal mismatch version, as in Section 4.2.1.  
The temperature compensation mechanism proves to be effective in reducing the variations of \(R_{in}\), which is a very rapidly varying function of temperature without the use of the PTAT, as shown in (9). Figure 6 shows the simulated \(R_{in}\): variations are significantly smaller with the use of the PTAT block than without its use. It is important to note that, even with the use of the PTAT block, \(R_{in}\) is not completely flat across the operating temperature range, most likely due to the fact that the assumptions used, especially that for which the difference in \(V_{DS}\) between the diodes and \(M4\) does not significantly affect \(V_{TH}\), are not entirely met.  
According to [12], at the theoretical sensitivity, the SNR at the output of the amplifier is equal to \(SNR_{req}=4.1\). With reference to Figure 3, the values for \(V_{AV}\) corresponding to the theoretical sensitivity \(V_{sens}\) are estimated through simulations. Figure 7 shows the values for \(P_{RF,ant}\) corresponding to \(V_{sens}\), i.e. \(P_{sens}\), when the PTAT block is used and when it is not used. In both cases, \(NF\) of the subsequent amplifier is considered equal to unity, as mentioned above. The input matching network is a standard LC network with \(Q=80\)[12], whereas \(C_{in}\) is assumed equal to 3 pF, where \(C^{\prime}_{in}=1.68\) pF and \(C_{pad}\) is supposed to beclose to 1.3 pF, which is plausible. ED noise bandwidth \(f_{N}\) is around 1 kHz, i.e., around \(f_{5}\). When the PTAT block is not used, \(V_{B}\) and \(V_{C}\) are set to the values corresponding to those set by the PTAT block at room temperature. Simulations were carried out from \(-40\)\({}^{\circ}\)C to 120 \({}^{\circ}\)C and show that the use of the PTAT block has a positive impact on the stability of sensitivity over temperature, actually yielding a 5 dB reduction in sensitivity temperature variation across the operating range, as reported in Figure 7. As a matter of fact, the worst impact of the lack of compensation appears to be at high temperatures, most likely due to the fact that, here, \(R_{in}\) without PTAT is lower than its nominal designed value, i.e., at 20 \({}^{\circ}\)C, drastically reducing \(A_{v}\) as in (5). This also explains why sensitivity is affected very little at low temperatures.
## 3 Proposed Method
### Student Model That Outputs a Binary Using an Ensemble of Multi-View Teachers  
As introduced in Section 3.1, the student model consists of one model and has only a decoder structure. Also, the student outputs the binary output \(\hat{y}_{s}\in\{0,1\}^{H\times W}\).  
In the student's training of unlabeled data, strong augmentation is applied for input perturbations. Weak augmentations, such as image flipping, cropping, and scaling, are applied for the teacher model, while strong augmentations, such as Gaussian blur, randomized grayscale, and color jitter, are applied for the student model. Among the teachers, one randomly selected is designated as the fixed teacher, and this teacher passes the features obtained by encoding the input image to the student. In the overall loss \(\mathcal{L}_{S}\) of the student introduced in Equation (3), the first loss, supervised segmentation loss \(\mathcal{L}_{sup}^{s}\), is defined as  
\[\mathcal{L}_{sup}^{s}=\frac{1}{|D_{L}|}\sum_{(x_{i}^{l},y_{i}^{l(s)})\in D^{L }}l_{ce}(g_{g}^{\omega}\circ h_{\theta_{h}^{t(fixed)}}(x_{i}^{l}),y_{i}^{l(s)}) \tag{7}\]where \(x_{i}^{l}\) and \(y_{i}^{l(s)}\) represent the \(i\)-th labeled image and corresponding student's label, respectively, and \(h_{\theta_{b}^{l(finol)}}\) and \(g_{\theta_{g}^{s}}\) represent the fixed teacher's encoder and the student's decoder, respectively.  
The student's pseudo-labels for the \(i\)-th unlabeled data at pixel \(j\), based on the hard voting ensemble for all teachers, are defined as follows:  
\[\hat{y}_{ij}^{(s)}=\mathbb{1}(f_{hv}(f_{\theta^{(1)}}(x_{ij}^{u}),f_{\theta^{ (2)}}(x_{ij}^{u}),\dots,f_{\theta^{(C-1)}}(x_{ij}^{u}))>0) \tag{8}\]  
where \(\mathbb{1}(\cdot)\) is the indicator function, \(f_{hv}\) is the hard voting ensemble function, and \(x_{ij}^{u}\) is the \(i\)-th unlabeled data at pixel \(j\).  
The second loss of Equation (3), unsupervised segmentation loss \(\mathcal{L}_{unsup}^{s}\), is defined as  
\[\mathcal{L}_{unsup}^{s}=\frac{1}{|D_{U}|}\sum_{x_{i}^{u}\in D_{U}}l_{ce}(g_{ \theta_{g}^{u}}\circ h_{\theta_{b}^{l(finol)}}(A_{s}(x_{ij}^{u})),\hat{y}_{ij} ^{(s)}) \tag{9}\]  
where \(A_{s}(\cdot)\) represents a strong augmentation function.  
Finally, the student is trained as the weighted sum of Equation (7), \(\mathcal{L}_{sup}^{s}\), based on ground truth labels, and Equation (9), \(\mathcal{L}_{unsup}^{s}\), based on the student's pseudo-labels \(\hat{y}_{ij}^{(s)}\) made by the hard voting ensemble of all teachers.
## 5 Limitations  
First, we could not assess systemic hemodynamics and cannot provide immediate blood gas data, with data only from 2 h after birth. Second, the number of neonates in this study was too small. Thus, we will increase the number of neonates and additionally evaluate not only neonates delivered by normal vaginal delivery, but also by elective cesarean delivery, because we have to consider the possibility that the CBV might be influenced by subcutaneous congestion due to compression of the forehead during vaginal delivery. Third, our TRS system is highly portable and can measure the absolute value of CBV via the transit time of each photon through the tissue of interest. However, the system needs more than 20 min for warmup and calibration. During the immediate transition after birth, we have to adjust the attenuation level of light emission to keep the values stable. Due to neonatal body motion, another team member is required for system fitting.  
Figure 4: Arterial Hb oxygen saturation (\(\text{SpO}_{2}\)) in five neonates during the first 15 min of life. Values are means.