esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems
## 5 Channel Estimation Based on Statistical Frames and the Confidence Level
If \(\mathbf{s}_{iM}\) is equal to the statistical frames \(T\), \(\mathbf{K}_{i}(n)\) is one;  
Figure 2: The probability distribution function of the Gaussian distribution of noise under different statistical frames. else, \(\mathbf{K}_{i}(n)\) is zero. For example, when the statistical frame \(T_{1}\) is chosen to be five, the confidence level \(\mathbf{K}_{i_{1}}(n)\) is represented by:  
\[\mathbf{K}_{i_{1}}(n)=\left\{ \begin{array}{ll}1,&\mathbf{s}_{iM_{1}}=5\\ 0,&\mathbf{s}_{iM_{1}}<5\end{array} \right.,\ \ 0\leq n<N. \tag{20}\]  
When the statistical frame \(T_{2}\) is chosen to be seven, the confidence level \(\mathbf{K}_{i_{2}}(n)\) is represented by:  
\[\mathbf{K}_{i_{2}}(n)=\left\{ \begin{array}{ll}1,&\mathbf{s}_{iM_{2}}=7\\ 0,&\mathbf{s}_{iM_{2}}<7\end{array} \right.,\ \ 0\leq n<N. \tag{21}\]  
When the statistical frame \(T_{3}\) is chosen to be nine, the confidence level \(\mathbf{K}_{i_{3}}(n)\) is represented by:  
\[\mathbf{K}_{i_{3}}(n)=\left\{ \begin{array}{ll}1,&\mathbf{s}_{iM_{3}}=9\\ 0,&\mathbf{s}_{iM_{3}}<9\end{array} \right.,\ \ 0\leq n<N. \tag{22}\]  
When the statistical frame \(T_{4}\) is chosen to be eleven, the confidence level \(\mathbf{K}_{i_{4}}(n)\) is represented by:  
\[\mathbf{K}_{i_{4}}(n)=\left\{ \begin{array}{ll}1,&\mathbf{s}_{iM_{4}}=11\\ 0,&\mathbf{s}_{iM_{4}}<11\end{array} \right.,\ \ 0\leq n<N. \tag{23}\]  
When the statistical frame \(T_{5}\) is chosen to be thirteen, the confidence level \(\mathbf{K}_{i_{5}}(n)\) is represented by:  
\[\mathbf{K}_{i_{5}}(n)=\left\{ \begin{array}{ll}1,&\mathbf{s}_{iM_{5}}=13\\ 0,&\mathbf{s}_{iM_{5}}<13\end{array} \right.,\ \ 0\leq n<N. \tag{24}\]  
In the confidence level channel estimation method, channel estimation results after noise reduction \(\hat{\mathbf{h}}_{\text{CL},i}(n)\) can be represented as:  
\[\hat{\mathbf{h}}_{\text{CL},i}(n)=\left\{ \begin{array}{ll}\hat{\mathbf{h}}_{\text{ LS},i}(n)\cdot\mathbf{K}_{i_{1}}(n),&\mathbf{s}_{iM_{1}}=5\\ \hat{\mathbf{h}}_{\text{LS},i}(n)\cdot\mathbf{K}_{i_{2}}(n),&\mathbf{s}_{iM_{2}}=7\\ \hat{\mathbf{h}}_{\text{LS},i}(n)\cdot\mathbf{K}_{i_{3}}(n),&\mathbf{s}_{iM_{3}}=9\\ \hat{\mathbf{h}}_{\text{LS},i}(n)\cdot\mathbf{K}_{i_{4}}(n),&\mathbf{s}_{iM_{4}}=11\\ \hat{\mathbf{h}}_{\text{LS},i}(n)\cdot\mathbf{K}_{i_{5}}(n),&\mathbf{s}_{iM_{5}}=13\end{array}  \right.,\ \ 0\leq n<N. \tag{25}\]  
The IMMSE channel estimation method needs a large number of multiplication and division arithmetic; therefore, its calculation complexity is higher. The threshold value method may suppress the paths as noise, which induces the reduction of channel estimation precision in the dynamic channels since it is not easy to choose the suitable threshold value. The statistical frames and confidence level based channel estimation method only increases the logic judgment and storage resources, and the computational complexity is low, so it is convenient for hardware implementation.
# Recent Advances and Challenges in the Seismo-Electromagnetic Study: A Brief Review
## 5 Possible Mechanisms of Seismo-Electromagnetic Phenomena
Recently, a novel system for monitoring vibrations and perturbations in the lithosphere, lower atmosphere, and upper atmosphere was established in the countryside of Leshan City, Sichuan Province, China, in 2021 [209]. The system combines all kinds of observation instruments collecting data from the lithosphere and atmosphere, to gain abundant measurements across different layers preceding earthquakes. As a novel systematic observing platform, it can provide researchers with more information about the preparation process for forthcoming earthquakes in the future.
###### Abstract  
Soil fertility in smallholder farming areas is known to vary strongly on multiple scales. This study measures the sensitivity of the recorded satellite signal to on-farm soil fertility treatments applied to five crop types, and quantifies this fertilization effect with respect to within-field variation, between-field variation and field position in the catena. Plant growth was assessed in 5-6 plots per field in 48 fields located in the Sudano-Sahelian agro-ecological zone of southeastern Mali. A unique series of Very High Resolution (VHR) satellite and Unmanned Aerial Vehicle (UAV) images were used to calculate the Normalized Difference Vegetation Index (NDVI). In this experiment, for half of the fields at least 50% of the NDVI variance within a field was due to fertilization. Moreover, the sensitivity of NDVI to fertilizer application was crop-dependent and varied through the season, with optima at the end of August for peanut and cotton and early October for sorghum and maize. The influence of fertilizer on NDVI was comparatively small at the landscape scale (up to 35% of total variation), relative to the influence of other components of variation such as field management and catena position. The NDVI response could only partially be benchmarked against a fertilization reference within the field. We conclude that comparisons of the spatial and temporal responses of NDVI, with respect to fertilization and crop management, requires a stratification of soil catena-related crop growth conditions at the landscape scale.  
precision agriculture; fertility; UAV; very high resolution; Digital Globe time series; heterogeneous landscape +
Footnote †: journal: Remote Sensing, 2016
## Abstract  
Thermal conditions in mainland Portugal were evaluated using a new hourly database over the recent period of 2000-2018 (19 years). The number of hours within each different temperature interval was calculated from the long-term means of the hourly temperatures of a network of 63 meteorological stations. A geostatistical approach, using elevation, distance to coastline and latitude, was subsequently applied to provide gridded patterns at a high spatial resolution (1 km grid spacing). Eight fruit species (almond tree, carob tree, chestnut tree, citrus fruits, cork oaks, holm oaks, olive trees, and grapevines) were selected to assess their hourly thermal growing conditions. The results highlight the strong spatial variability of temperature levels in mainland Portugal, providing new insights into their spatial distribution. The number of hours in the year with cool conditions (4-12 \({}^{\circ}\)C) is higher in the northern-central regions, mainly in mountainous areas. Additionally, the number of hours in the year with temperate conditions (12-20 \({}^{\circ}\)C) emphasizes the importance of the distance to the coastline (maritime influence). The warm conditions (20-28 \({}^{\circ}\)C) are most prevalent in the south of the country and in the Douro valley, whereas the very warm conditions (number of hours with temperature between 28-36 \({}^{\circ}\)C) are essentially restricted to inner-southern Portugal and to the upper Douro valley. This study also reveals, with high accuracy, the thermal growing conditions of main fruit species in mainland Portugal, giving particular emphasis to olive trees and grapevines. These findings may help decision support systems providing more reliable and accurate guidelines to stakeholders, decision-makers, and farmers. The main maps are available in a widely used file format (shapefile), thus allowing their application to a wide range of other areas of interest.  
h  
hournal of the American Statistical Association
# Megalos: A Scalable Architecture for the Virtualization of Large Network Scenarios +
## 5 Megalos Networking
### The Megalos Container Network Interface  
Each VD may have several virtual interfaces. In addition to the virtual interfaces required by the user, there is also a mandatory management interface used by Kubernetes (for heartbeat functions, etc.). Kubernetes delegates the management of network interfaces to suitable CNI plug-ins. For the management interface any available CNI plug-in can be used (e.g., [38; 39; 40]), including the Kubernetes default KubeNet CNI. All these CNIs assign to the management interface an IP address chosen in a specific pool which is routable in the cluster. Hence, the management interface belongs to an L3 network. On the other hand, since Megalos offers L2 interfaces to the VDs and enforces traffic segregation among virtual LANs (Section 3.1), no traditional CNI can be used to manage the user-defined interfaces.  
Therefore, we implemented a custom CNI plug-in, called _Megalos CNI_. Megalos CNI is not directly invoked by Kubernetes. Kubernetes, instead, invokes the Multus CNI plug-in  
Figure 3: The Megalos networking Architecture.  
which, in turn, invokes an instance of Megalos CNI for each virtual interface assigned to the VD. When a new Pod representing a VD is created on a worker node, the Multus CNI plug-in, by using the etch [41] distributed key-value store, retrieves from the master node the virtual LANs configuration which contain the VNIs identifying the needed VXLAN Segments. For each VXLAN Segment the worker node is aware of, there exists in the worker node a VTEP and a _VTEP companion bridge._ The Multus CNI plug-in passes to each instance of the Megalos CNI the parameters of its virtual LAN. Using such parameters, the Megalos CNI checks if the VTEP companion bridge already exists in the worker node, and creates it if it doesn't. Then it attaches the virtual interface of the VD to the VTEP companion bridge.
# A Unified Denoising Framework for Restoring the LiDAR Point Cloud Geometry of Reflective Targets
## 2 Materials and Methods
### Methods  
The proposed method uses a unified framework to remove noise points of reflective targets. The workflow consists of three main parts, as illustrated in Figure 2. First, original LiDAR data are preprocessed to remove points caused by the veiling effect, which are usually with a low intensity, thereby identifying potential target points. The intensity threshold is determined by locating the peak probability density center. Second, the MSAC algorithm is applied to extract a plane and classify points as inliers and outliers. Subsequently, noise points with range anomalies are corrected back to the target's surface, followed by the projection of the point cloud onto the YOZ plane and the removal of isolated noise points. Third, boundary extraction is performed using the alpha shape algorithm, followed by point-wise boundary correction based on an adaptive error ellipse to refine the boundary. The parameters of the error ellipse are automatically adjusted according to the target's distance and the normal vector along the original boundary. Finally, noise points caused by the blooming effect are removed using both the original and the newly refined boundaries, ensuring an accurate restoration of the geometry of the target.  
Figure 1: LiDAR data acquisition scenario and the point cloud: (**a**) Livox Tele-15 LiDAR sensor mounted on a tripod; (**b**) roadside traffic sign; (**c**) original point cloud at a specific distance with significant noise points due to the veiling effect, range anomalies, and blooming effect; (**d**) ground truth at the same distance as (**c**); note: (**c**,**d**) are displayed from an oblique perspective at the same scale.
## 4 Experiments and Results  
### Experimental Parameters  
We compared the AWOA with ZF, MMSE, and ML algorithms to evaluate the effectiveness of MIMO signal detection after passing through the MI channel. ZF detects the signal by inverting the received signal, while MMSE solves the signal detection problem by inverting the signal matrix and considering the effect of noise. In ML, all transmitted signals are calculated using the fitness function, and the signal with the minimum value is selected as the output signal. The AWOA performs a random selection in the signal space of the signal composition using the fitness function, and the signal with the minimum value is the output signal. To visually demonstrate the performance of the four algorithms, we created an underwater environment for experimental comparison. We compared the BER and complexity of the four algorithms without signal crosstalk. We also analyzed the  
Figure 5: Comparison of AWOA with other algorithms.  
performance in the same and different numbers of transmit/receive antennas. The main experimental parameters are shown in Table 2.
# Handwritten Signature Verification Method Based on Improved Combined Features
## 5 Conclusions  
This paper proposes a handwritten signature verification method based on improved combined features to meet the requirements of the current high-precision system. We use smart pens to obtain the offline image and online data of the signature at the same time, and perform preprocessing and feature extraction on them respectively. Then we use SVM and DTW for offline images and online data respectively to get verification results and decision scores, and propose the SF-A method to achieve the purpose of combining static and dynamic features by fusing decision scores.  
In order to verify the effectiveness of the proposed method, we conducted experiments on a local data set. By using different numbers of training samples, two classifiers were used to experiment and compare different signature features. Experimental results show that the use of SF-A fusion of static and dynamic features has achieved the greatest accuracy and the lowest FAR and FRR, which can effectively verify handwritten signatures.  
This paper contributes to the score fusion and feature combination. Through SF-A, the complementarity of the results of each classifier is effectively used, and a higher accuracy rate is achieved. At the same time, through smart pen technology, this paper realizes the combination of offline images and online data for the first time. Finally, in the SVM training stage, the real signature of other authors is used as a negative sample for training, which provides a new idea for the small sample training problem. Different from deep learning methods, our proposed method is more interpretable and widely applicable.  
In future work, the data set will be continuously exaggerated to verify the universality of the method. For example, collecting signatures in different languages to compare the verification effect of the proposed method on different languages. We will make the data sets used publicly available for more researchers and institutions to conduct experiments and comparisons. At the same time, we will continue to study signature features in depth and find the most representative signature feature vector. We will also continue to study in depth how to achieve the best verification effect with the minimum training set.  
Conceptualization, Y.Z. and J.Z.; methodology, Y.Z.; software, Y.Z.; validation, Y.Z., H.H. and Y.W.; formal analysis, Y.Z., H.H.; investigation, Y.Z.; resources, H.H., Y.W.; data curation, Y.W.; writing--original draft preparation, Y.Z.; writing--review and editing, H.H.; visualization, Y.Z.; supervision, H.H.; project administration, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Method and Data  
To determine the slip rate of the Doroud Fault, the offsets of tectonic features were analysed using field observations, topographic data, Google Earth satellite images, and OSL dating results [50, 51, 52]. The topographic data were obtained using real-time kinematic measurements (RTK). Given that the horizontal uncertainties for RTK data is \(<\)0.5 m, measurement errors on the displacements mainly arise from the determination of markers and construction of fitting lines [13,53]. We thus chose potential research sites based on the interpretation of a digital elevation model (DEM) and Google Earth satellite images; moreover, before digging trenches, Holocene and Pleistocene rock units were carefully separated, the fault trace and surface geomorphic features around the fault were mapped in the field, and topographic cross-sections from RTK data were drawn (using The Environment for Visualizing Images (ENVI)) software across the fault scarps at the site. To study paleoseismology, we excavated a trench (see its location in Figure 4; TC (Trench)) using both hand and machine tools across the fault scarp and pond, and mainly along the middle segment of the Doroud Fault. We chose the pond to excavate a trench because it is a location of accumulation of recent sediments that certainly record seismic events, and this pond was formed due to the action of the fault in the hanging wall of the Doroud Fault and is the closest location to the epicenter of the 1909 earthquake. A string grid defining 1 m \(\times\) 1 m panels was strung along the length of the trench walls after the walls were scraped and cleaned. Then, each grid panel was photographed with four images that slightly overlapped with one another. The sedimentary layers in each trench were classified as separate units based on color, particle size, sorting, texture, bedding thickness, and other characteristics, such as the colluvium wedge. The sequence of ground-rupturing events was then determined based on strata cross-cutting relationships, sediment thickness variations, soft sediment deformation, and fissures. The radiocarbon (C14) and optically stimulated luminescence (OSL) ages of sediment samples collected from trenches were utilized to constrain the ages of the stratigraphic units and, consequently, the interpreted events.  
Figure 4: The location of the excavated trench marked with a black circle along the Doroud Fault within the Quaternary deposits.
###### Abstract
These two factors are the main reasons for the longer development cycle of MMIC compared to other IC designs. Developing more efficient Electronic Design Automation (EDA) software to meet the growing market demands is one of the research directions that most researchers are focusing on [10]. To overcome the challenges of CPU-intensive electromagnetic (EM) simulation calculations and automated synthesis of microwave circuits, researchers have begun exploring the potential applications of machine learning techniques in design automation and computer-aided design, such as artificial neural networks (ANN) and heuristic optimization algorithms. The main contributions of this paper are as follows:  
1. For the design of hairpin filters, we transform the design process into a continuous reinforcement learning (RL) problem and propose an RL framework based on the PPO algorithm. 2. With guidance from practical design experience, we appropriately design a reward function to stabilize and accelerate the training process of RL. 3. To reduce the time cost of repetitive success or failure in the RL training process, we introduce a forward neural network model trained through supervised learning to replace the CPU-intensive EM simulation. The remaining structure of the article is as follows. Section 2 introduces the related work on neural networks and heuristic algorithms. In Section 3, we present the algorithm framework, design a method for hairpin filter design based on PPO, and provide detailed explanations for the training settings of the forward network model for hairpin filters, as well as the design of RL states, actions, and reward functions, with a specific focus on the reward function design. To validate the effectiveness of the proposed design method, we conducted a series of experiments, and the experimental results are presented and discussed in Section 4. Finally, Section 5 concludes the article, discussing the achievements, limitations, and future research plans.
# Delay Management in Regional Railway Transport
## 4 Discussion  
The application of the proposed method confirmed the expected limitations of the method that were mentioned in the introduction.  
Figure 7: Cut-out of the operational/technological processes graph for solved part of Vysočina Region weekends. Source: the authors.  
A relatively low number of possible disposal measures, which were assessed in the practical application in Section 3.1, leads to a higher number of their combinations. The application was simplified by using only those disposal measures that are relevant in the expected delay values, and which lead to maximization of the transfer waiting times. This rationalization finally led to only one combination of disposal measures, and thus to one graph for operational/technological processes. If this rationalization is not made, the number of graphs would be higher, and the task would become more time-consuming.  
As stated in the introduction, the passenger transportation aspect was not a subject of this research. It is obvious from the achieved results, especially the transfer waiting times of trains on line Os3 in Jihlava (22 min), that the transfer waiting time is too long for passengers who do not transfer from other trains. Such transfer waiting times apply to most of the passengers in the trains of this line. The passenger transportation aspect, using the authors' approach, might be a subject for further research, leading to a rationalization of the transfer waiting time values of passenger trains (which have all the bindings while the other trains are relatively loose).  
On the other hand, the research method satisfactorily addressed all the technological bindings between trains. The method is likely to be successful in its application to other practical cases of determining transfer waiting times.