esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data  
The data employed in this study were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis v5 (ERA5) dataset, a publicly accessible resource. ERA5 offers high-resolution reanalysis data of global weather and climate variables spanning from 1950 to the present, covering atmospheric, terrestrial, and marine climate parameters [29]. This dataset provides comprehensive climatic information, which holds substantial value for climate research and predictive modeling [30; 31; 32; 33]. The temperature data employed in this study were extracted from the ERA5 hourly dataset on single levels, covering the period from 1940 to the present [34]. This dataset features a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\), equivalent to an approximate interval of 25 km. Specifically, the study utilized the 2 m temperature parameter, which denotes the air temperature at 2 m above the surface of land, ocean, or inland water bodies. This parameter is derived through interpolation between the lowest model level and the Earth's surface, incorporating the physical conditions of the atmosphere.  
In the experiment, we conducted downsampling on the original dataset to generate LR data, which was utilized as the input for the model. The temperature data spanning from 1 January 2023 to 31 December 2023 were designated as the training set, while the dataset covering 1 January 2020 to 31 December 2020 was used as the validation set. The region is characterized by a subtropical monsoon climate, with high temperatures typically occurring from June to August and low temperatures prevailing from December to February of the following year. The 2 m temperature data used in the experiment indicate an average annual temperature of 291.49 K; July is the hottest month, averaging 299.47 K, and January is the coldest month, averaging 281.9 K. The dataset features a temporal interval of 1 h and consists of a total of 17,520 data samples. To ensure that the downsampling maintains spatial alignment between the LR data and the original data for subsequent model training, we employed specific downsampling window sizes (2 \(\times\) 2 or 4 \(\times\) 4 pixels) to calculate the value of the data points within each window. During the downsampling process, boundary data points that do not form a complete window were excluded. This method aims to retain key information from the original dataset while reducing its resolution to facilitate model training and analysis. Our ultimate goal is for the model to generate super-resolution reconstruction data for the region spanning latitudes from 16.25\({}^{\circ}\) N to 40\({}^{\circ}\) N and longitudes 100\({}^{\circ}\) E to 123.75\({}^{\circ}\) E, utilizing LR data of varying sizes. The outcomes are illustrated in Figure 2.  
Figure 2: Display of the results of the SR data.
## 3 Materials and Methods
### Clustering Methods Used in the Research
Despite its adaptability, affinity propagation may suffer from high computational complexity, particularly for large datasets, and sensitivity to the choice of preference parameter, which influences the number of exemplars. Nevertheless, the algorithm's ability to automatically determine the number of clusters and its robustness to noise make it a valuable method for various text analysis applications. Spectral clustering is a technique that leverages the spectral properties of the data's similarity matrix to perform clustering in a lower-dimensional space, thus enabling the detection of complex structures and non-linear relationships within the data [69]. The algorithm first constructs a similarity graph, where nodes represent data points, and edges represent pairwise similarities, typically using Gaussian kernel or k-nearest neighbors. It then computes the eigenvalue decomposition of the graph's Laplacian matrix, clustering the eigenvectors corresponding to the k smallest eigenvalues using traditional clustering methods, such as K-means. Spectral clustering has been applied in text analysis tasks to various scenarios, including document clustering. It can efficiently group similar documents based on their feature representations, such as term frequency-inverse document frequency (TF-IDF) vectors [70]. However, Spectral clustering has some limitations, such as sensitivity to the choice of similarity measure and the requirement to specify the number of clusters. The method can also be computationally expensive, especially for large datasets, due to the eigenvalue decomposition step. CBMIDE (clustering based on the modified inversion formula density estimation) [71] and RCBMIDE (reduced clustering based on the modified inversion formula density estimation) [72] are novel clustering methods that leverage the modified inversion density estimation to effectively capture the underlying structure of the data. In contrast to the traditional methods such as the Gaussian mixtures models, CBMIDE focuses on the reciprocal distance between the data points and the cluster centers, thereby providing a more robust estimation of the density structure. By utilizing this alternative approach to density estimation, CBMIDE has the potential to reveal complex relationships and structures within the data that conventional methods may overlook. CBMIDE can be applied in text analysis to various tasks, such as document clustering. It can efficiently group similar documents based on their feature representations, such as the term frequency-inverse document frequency (TF-IDF) vectors. It is also worth noting that this clustering method exhibits robustness, ensuring that only legitimate data clusters are identified and analyzed, thereby minimizing the influence of noise and outliers on the overall results, and enhancing the accuracy and reliability of the findings. In this work, we evaluated all the abovementioned clustering methods and compared these methods' performances based on the different metrics. All results are presented in the results sections.
## 4 Results and Discussion  
### Estimation of Largest Earthquake Magnitudes  
In 1994, American scholars [PERSON] et al. [22] established an empirical relationship between the surface rupture length and the moment magnitude of strike-slip earthquakes based on a large number of samples, which is known briefly as the WC empirical relationship: \(Mw\) = 5.16 + 1.12\(LgL\), where Mw represents the moment magnitude, L represents fault length, and the residual standard deviation is 0.28 [23]. When applying the WC empirical relationship, we encounter the problem of conversion between moment magnitude \(Mw\) and surface wave magnitude \(M\)s. [PERSON] suggested in the 1994 work that when \(M\)s is in the range of 5.7-8.0, there is no systematic difference between them, namely, \(Mw\)\(\approx\)\(M\)s. However, there is a difference between the magnitude determination method and station data adopted in China and the United States; namely, \(M\)s and \(Mw\) are not the same in the continent of China. In 2009, [PERSON] [24] derived a transformation relationship for these two earthquake magnitudes according to seismic data in mainland China during 1973-2008: \(M\)s = 1.412 + 0.845\(Mw\), where \(M\)s represents surface wave magnitude, \(Mw\) represents moment magnitude, and the residual standard deviation is 0.11 [23]. In this paper, we use the aforementioned equation of statistical relationship and combine it with the length of the fault to calculate the maximum earthquake magnitude that could occur in each segment (Table 1). Based on the above analysis, regardless of \(Mw\) or \(M\)s, the earthquake may be induced by the fault belonging to the strong earthquake (\(M\)s \(\geq\) 7.0).
# A Wideband High-Voltage Power Amplifier Post-Linearizer for Medical Ultrasound Transducers
###### Abstract  
The medical ultrasound transducer is a principal component in ultrasound systems, as it significantly influences system performance. The high-voltage power amplifier (HVPA) is the key ultrasound transmitter component and interfaces with the medical ultrasound transducer. Therefore, the performance of the HVPA critically affects the echo signal quality of the ultrasound transducer. As they are inherently non-linear devices, harmonic distortion of echo signals generated by the ultrasound transducers would critically deteriorate the echo signal quality in ultrasound systems. The primary aim of this research is to integrate a novel post-linearizer into the HVPA to suppress harmonic distortion in medical ultrasound transducers. Moreover, this study is based on the assumption that linearizing the HVPA could reduce the harmonic distortion components of the echo signals. The developed HVPA with post-linearizer was tested in an ultrasound microscopy system in order to demonstrate the harmonic suppression capability on the echo signal generated by the ultrasound transducer. When 10 MHz, 5-cycle, 26 dBm input power was sent to the HVPA with and without the post-linearizer circuits, the measured second-, third-, fourth- and fifth- harmonic distortions of a 10 MHz transducer with the post-linearizer (\(-13.11\) dB, \(-10.81\) dB, \(-16.33\) dB, and \(-22.78\) dB) were suppressed to a greater degree than those of the same transducer without the post-linearizer (\(-4.58\) dB, \(-8.87\) dB, \(-10.51\) dB, and \(-15.31\) dB).. Therefore, we conclude that the addition of the post-linearizer to the HVPA is a potentially useful electronic technique for improving echo signal quality in medical ultrasound transducers.  
post-linearizer; medical ultrasound transducer; high-voltage power amplifier +
Footnote †: journal: Applied
## 2 Materials and Methods
### Radiographic Measurements  
Preoperatively, biplane radiographic images were captured in a standing position with the EOS(r) system (EOS imaging, SA, Paris, France) for all patients [11; 12]. An accurate 3D model of the lower extremities was reconstructed for each patient from the lateral and anterior images, as described and validated in previous studies [25]. The 3D model was made using sterEOS(r) (EOS imaging, SA, Paris, France) and was then used for planning the prosthesis (hipEOS(r), EOS imaging, SA, Paris, France). From the 3D reconstruction, five clinical leg parameters, which were described in detail elsewhere [25; 26], were considered to assess the leg alignment (Figure 1).  
* Hip-knee-shaft angle (HKS): This was measured on the frontal plane, considering the femoral mechanical axis (which connects the centers of the femoral head and the trochlea) and the femoral anatomical axis (axis from the center of the trochlea to the center of the distal diaphysis of the femur).
* Femoral offset (FO): The distance defined by the center of the femoral head and the orthogonal projection of this point on the femoral anatomical axis.
* Neck-shaft angle (NSA): The angle measured between the axis going from the center of the femoral head through the femoral neck and the line drawn down the center of the femur's diaphysis.
* Hip-knee angle (HKA): The angle in the frontal femoral plane between the mechanical axes of the femur and the tibia (the line from the center of the tibial plateau to the center of the distal articular surface of the tibia). Valgus \(>180^{\circ}\), varus \(<180^{\circ}\).
* Femoral mechanical angle (FMA): The angle that is defined in the frontal plane between the femoral mechanical axis and the line through the medial and lateral condyles (the two most distal points).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{\
\begin{tabular}{c} **Patients** \\ **(\(n\) = 17)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Healthy** \\ **Controls Gait** \\ **(\(n\) = 18)** \\ \end{tabular} } & \multicolumn{2}{c}{\(p\)**-Values**} & \multicolumn{2}{c}{\(p\)**-Values**} \\  & & & **Patients vs.** & **Healthy Controls Leg** & **Patients vs.** \\  & & & **Healthy** & **Alignment (\(n\) = 53)** & **Healthy Controls** \\  & & & **Controls Gait** & & **Leg Alignment** \\ \hline Age (years) & 60.5 (9.9) & 60.4 (8.0) & 0.963 & 41.8 (8.2) & \textless{}0.001 \\ Height (m) & 1.72 (0.09) & 1.73 (0.09) & 0.684 & 1.69 (0.10) & 0.302 \\ Weight (kg) & 83.3 (15.9) & 72.0 (13.9) & **0.032** & 74.3 (13.7) & **0.028** \\ BMI (kg/m\({}^{2}\)) & 28.1 (4.9) & 23.9 (3.2) & **0.004** & 25.9 (3.5) & **0.047** \\ Speed (m/s) & 1.06 (0.17) & 0.97 (0.07) & 0.054 & - & - \\ Gender (M/F) & 9/8 & 11/7 & 0.625 & 26/27 & 0.780 \\ \hline \multicolumn{5}{l}{Values are represented as mean value (standard deviation). Significant differences are highlighted in bold. Abbreviations: BMI—body mass index.} \\ \end{tabular}
\end{table}
Table 1: Anthropometric data and walking speed.  
Figure 1: Clinical leg parameters overlaid on the frontal EOS image.
# Study on a Landslide Segmentation Algorithm Based on Improved High-Resolution Networks
## 3 Experimental Description
### Evaluation Metrics  
The evaluation metrics employed in this paper included mean intersection over union (MIOU), mean pixel accuracy (MPA), and F1-score.  
1. The _MPA_ computes the percentage of accurately classified pixels for each class, aggregates them, and calculates the average. The calculation formula can be expressed as follows:  
\[MPA=\frac{1}{n}\sum_{i=1}^{n}\frac{TN+TP}{TN+TP+FN+FP}, \tag{7}\]  
where \(n\) is the class, _TN_ (true negative) denotes the count of correctly predicted negative samples, _TP_ (true positive) represents the count of correctly predicted positive samples, _FN_ (false negative) indicates the count of incorrectly predicted negative samples, and _FP_ (false positive) represents the count of incorrectly predicted positive samples.  
2. _MIOU_ tends to provide an intuitive evaluation, which is the average intersection over union between the predicted result and the ground truth. The calculation formula can be expressed as follows:  
\[MIOU=\frac{1}{n}\sum_{i=1}^{n}\frac{TP}{TP+FN+FP}, \tag{8}\]  
3. The calculation of the _F1-score_ depends on two other fundamental metrics, recall and precision, and it is a more comprehensive indicator of the model's performance. The calculation formula is defined as follows:  
\[Precision=\frac{TP}{TP+FP}, \tag{9}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{10}\]  
\[F1-score=\frac{2\times Recall\times Precision}{Recall+Precision}, \tag{11}\]  
where _Precision_ refers to the model's ability to predict correctly, while _Recall_ refers to the model's ability to predict more instances in a given validation set. _Precision_ and _Recall_ are contradictory metrics, so the _F1-score_ is needed to evaluate the comprehensive performance of the network.
# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
## 5 Performance Evaluation
### Simulation Results
#### Detection accuracy
In the pure GRP, a sender continues to forward data packets to a neighbor closest to the BS even though an attacker keeps dropping the packets. This is because the pure GRP has a mechanism to monitor neighbors' packet transmission behaviors. The pure GRP has the least PDR, which means the largest amount of data packets are dropped before reaching the BS. Consequently, the pure GRP has the least amount of energy consumption for packet transmissions and receptions. Likewise, the beta GRP has less \(E_{T}\) than our hybrid GRP. In addition, trust-based routings such as our hybrid GRP and the beta GRP are likely to have longer average hops or a longer average routing distance from a source to the BS than the pure GRP. This is because a trust-based routing avoids an inside packet drop attacker and normal nodes with many transmission errors and then finds another node. The avoided nodes are originally chosen as optimal nodes in terms of minimum hops; we note that the greedy geographic routing approximates the minimum hop routing [8], and thus we assume that the avoided node is likely to be in the shortest path to the BS. Since \(E_{T}\) does not correctly evaluate the energy efficiency of a routing, we use the energy efficiency \(e_{S}\) (mJ) for energy analysis. Second, our hybrid GRP consumes the least energy to deliver a packet to the BS among the three routing algorithms. As shown in Table 4, the energy efficiency \(e_{S}\) of each routing is as follows: \(e_{S}\) of pure GRP \(>e_{S}\) of beta GRP \(>e_{S}\) of hybrid GRP. As shown in Figure 11, when the attack rate is 100% and TH is set to be 0.6, our hybrid GRP can save, at most, 20% more energy than the pure GRP and, at most, 10% more energy than the beta GRP, which corresponds to 6.61 mJ and 3.14 mJ, respectively. Using Equations (17) and (18) as an example, 6.61 mJ is the amount of energy that a node consumes to transmit a 1024-bit packet to another node that is approximately 253 m away.
## 2 Governing Equations and Numerical Methods
### Numerical Scheme  
The dual-time preconditioned system is numerically solved using the alternating direction implicit (ADI) method [39] in structured grids. The convective flux term is approximated using the monotonic upstream-centered scheme for conservation laws (MUSCL) with third-order accuracy [40]. A second-order backward difference is used to discretize the physical time derivative. A first-order backward difference is used to approximate the pseudo-time derivative. At each physical time step, a pseudo-time iterative procedure is applied with a critical residual of \(10^{-4}\).
# A Message Passing-Assisted Iterative Noise Cancellation Method for Clipped OTFS-BFDM Systems
## 6 Conclusions  
In this paper, we propose an MP-AIC method to cancel the clipping noise at the receiver. This method is combined with the OCF at the transmitter to maintain the sparsity of ECM. Therefore, the low complexity MP algorithm can take advantage of the sparsity of ECM to achieve joint interference cancellation and signal decoding. To minimize the influence of clipping noise on MP decoding accuracy, the reference clipping noise is constructed on the receiver. The residual amount of clipping noise in the initial received signal is canceled by iteration. The simulation results show that the MP-AIC method provides better BER performance than MP-A, and the BER curve of the system is close to convergence after two iterations.  
With the increasing pursuit of spectral efficiency in 5G and above communication systems, high-order modulation will also be widely used. However, high-order modulation makes the transmission signal more sensitive to clipping distortion. Therefore, in future work, we will introduce non-distortion techniques, such as signal scrambling or coding techniques, to suppress the PAPR of OTFS-BFDM signals. Moreover, we foresee that how to achieve an optimal performance tradeoff between PAPR and BER in high mobility scenarios will be one of the hot topics of future research.  
Writing--original draft preparation, T.W., H.B. and J.W.; writing-review and editing, T.W.; supervision, H.B.; funding acquisition, H.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (41174158) and (61902211).  
Not applicable.  
Not applicable, the study does not report any data.  
The authors declare no conflict of interest.
###### Abstract
For low-energy area EMPs, which are generated from femtosecond laser air filament-generated plasmas, the focus is more on the mechanism of EMP generation and other laser filament-generated applications [16; 17; 18; 19; 20]. However, to date, no systematic research has been conducted to measure EMPs during the process of hundreds of millijoules, femtosecond laser filaments, and solid targets. Understanding the filament-solid interaction is important for EMPs because their intensity strongly depends on the filament-target coupling. Typically, in the short laser pulse domain, for a given pulse duration, the maximum amplitude of the EMP scales linearly with laser pulse energy [1; 29]. However, this trend may not be true for filament-solid interactions, because higher laser energies lead to the formation of multiple filaments. To date, the process of scaling of magnitude from picosecond to femtosecond pulse durations remains unclear. A thorough investigation of EMPs generated from laser filament-solid target interaction and their consequences has yet to be conducted. Hence, it is important to extend the study of the characteristics of EMPs generated by femtosecond laser air filamentation to that of the characteristics of EMPs generated by femtosecond laser filament-solid interaction, which would provide a unique opportunity to pursue systematic measurements for the comprehension of ultrashort laser-generated EMPs and their effects. To gain more insight into the EMP generation mechanism, this study was performed in the 100 terawatt (TW) laser facility at the Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences [30]. We investigated the characteristics of the EMPs generated from the plasma associated with the laser filaments-excited aluminum (Al) alloy target in air, and confirmed that at the same laser parameters, they are significantly stronger than the EMPs generated from the laser filamentation propagating in air. Additionally, the spatial distribution of the EMPs generated by the interaction between the laser filament and Al alloy target, and the effects of laser pulse duration and laser energy on the EMPs,were also investigated. The results of this study confirmed that the interference effect of the EMPs on electronics, resulting from the interaction of femtosecond air filamentation with solid targets, cannot be ignored; moreover, we discuss the main factors that mitigate the possible malfunction of electronics due to EMPs generated from femtosecond laser-air filament interaction with solid targets.
## 4 Characteristics of Different Time Distribution Systems
### Precision Time Protocol  
The IEEE 1588 Precision Time Protocol (PTP) facilitates accurate synchronization of clocks in packet-based networked systems, accommodating heterogeneous systems with clocks of varying precision, resolution, and stability [82].