esa-sceva/satcom-chunk-collection huggingface dataset:
# A Baseband Wireless Spectrum Hypervisor for Multiplexing Concurrent OFDM Signals
## 1 Introduction
The hypervisor layer, which can be seen as a spectrum mapping/allocation layer, abstracts the underlying spectrum bandwidth dynamics and provides the vPHYs with a contiguous or non-contiguous (depending on the application of the proposed architecture) set of frequency subcarriers (i.e., virtual spectrum bandwidth), where the desired spectrum bandwidth can be predefined (by operators for instance) or requested in an online basis by the vPHY itself. Additionally, we present an open-source and free proof of concept prototype that was developed to assess the performance of proposed spectrum hypervisor architecture in real-world experiments. The source code is available at [14] and can be used by researchers and developers to easily run experiments or extend the concept to other applications. The remainder of the work is organized as follows. In Section 2, we present some aspects of hardware virtualization. In Section 3, we describe, mathematically, how we multiplex several OFDM signals. Next, in Section 4, we list and discuss some important use cases where the proposed architecture can be employed. In Section 5 we present and compare some related pieces of work on virtualization. Then, in Section 6, we describe the proof of concept prototype developed to assess the feasibility of the proposed architecture. In Section 7, some simulation results are presented showing the performance of the proposed architecture. Next, in Section 8, we present several experimental results obtained with the prototype. Finally, in Section 9, we conclude our work with some conclusions and future work.
## 2 The Orthodontic Mathematical Model
### The Combined Orthodontic Process  
As mentioned, orthodontic tooth movement is modeled as a sequence of instantaneous tooth transformations and bone remodeling. However, tooth movement is an instant occurrence, while remodeling is a formation and resorption processes of the tissue requiringlonger periods of time. With that said, for each iteration, one should first verify that the equivalent forces applied by the orthodontic appliance and the PDL springs exceed a minimal value that will result in non-negligible tooth movement (Table 2). Otherwise, if the PDL springs' compression/tension strains are above the predefined threshold, remodeling initiates (i.e., remodeling occurs once tooth movement reaches equilibrium). The remodeling process continues reducing the PDL total energy, while increasing the overall energy, since the applied appliance and PDL equivalent forces are in different directions. The process continues until the net generalized force vanishes, and the procedure ends (see Algorithm 1). By trial and error the translation threshold is determined to be \(10^{-4}\) times the PDL thickness, where the angular threshold is \(tan^{-1}\Big{(}\frac{tooth\ mean\ radius}{translation\ threshold}\Big{)}\). The pseudo code is provided below.  
```
While Appliance connected do  If Orthodontic force--PDL springs not in equilibrium (threshold) then  Apply tooth movement  else if PDL springs not in equilibrium(threshold)  Apply remodeling else  break loop  end if  if reached required movement then  Disconnect appliance (loop ends)  end if end while
```  
**Algorithm 1**Tooth orthodontic movement pseudo-code  
The stress distribution over the tooth, and, more specifically, on its root, is a valuable piece of information for clinicians. This is so since, for example, when stresses exceed a certain value, an undesirable side effect called the root resorption process may be triggered. Following the above, the normal stress and shear stress are calculated at each time stamp \(\left\{\left(\sigma_{nt},\tau_{ts},\tau_{nt}\right)\right\}_{i\in I}\); therefore, the \"equivalent\" von Mises stress [47] can be extracted:  
\[\sigma_{i,max}=\sqrt{\sigma_{nt}^{2}+3\big{(}\tau_{ns}^{2}+\tau_{nt}^{2}\big{)}}\]  
It should be noted, though, that the rationale behind von Mises stress is based on the premise that the material fails by shear strain, which, to the authors' knowledge, is a claim that has not yet been examined. Figure 10 depicts the von Mises stress distribution when subjected to an orthodontic force compared to the applied torque. The von Mises stress values presented are low due to the remodeling process.
## 3 Experiment and Analyses
### Analyses of Static Experiments
#### 3.1.2 Results of Pseudolite Differential Pseudorange Positioning  
Based on the analyses of the previous section, the static differential pseudorange positioning experiments of the short baseline are performed, respectively.  
In the outdoor GNSS differential pseudorange positioning, the initial value of the rover station can be given by SPP because the constellations of satellites are changing at all times and the number of satellites is redundant. Thus, a more accurate value can be obtained in a few epochs. Based on the previous analyses, SPP cannot be carried out in indoor pseudolite system. The DPL method is used and the initial value of the rover station is arbitrarily given in this paper. For example, the plane coordinates of the initial value are taken as (0, 0) and (4, 4) in the experiments. Therefore, the rover station does not need to initialize on the known point  
After using the strategies described in the previous chapter, Figure 6 reveals the results of DD pseudorange positioning.  
The plane scatter graph in the lower part of Figure 6 is used to demonstrate the convergence process, which can intuitively reflect the speed and the final convergence accuracy. It can be found that the initial coordinate of decimeter precision can be obtained in a short time by the DPL method. It is not necessary to wait for complete convergence to obtain the coordinates. Thus, the feasibility of indoor pseudolite positioning without using the KPI method can be explained. That is to say, we can obtain the positioning precision at the decimeter level in the first few epochs and the initial value is also arbitrarily given. This is of great significance to the use of AFM in the next chapter. In addition, the short baseline static experiment is a preview of the initialization process in the dynamic test.  
Indoor pseudolite dynamic experiments are generally based on short baselines. However, the multipath and noise have too much of an effect on the pseudorange observations of the rover station under the dynamic condition. If the DPL is used directly, the result will be poor. Therefore,  
Figure 6: The results of DPL in the short baseline static experiment.  
it is impossible to use DPL for dynamic positioning, but it is only employed for static initialization to eliminate the limitation of KPL.
## 2 Methods and Materials
### Experimental Investigations
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Specimen** & **Lateral Imp. L/v\({}_{0}\) [–]** & **Rotational Imp. \(\phi_{0}\)** & **Thickness [mm]** \\ \hline FT\_150\_1 & 782 & 0.012 & 32.84 \\ FT\_150\_2 & 478 & 0.007 & 33.11 \\ FT\_150\_3 & 724 & 0.009 & 32.98 \\ FT\_150\_4 & 508 & 0.008 & 33.15 \\ FT\_150\_5 & 1359 & \(-\)0.003 & 33.11 \\ FT\_150\_6 & 1924 & 0.005 & 33.05 \\ FT\_150\_7 & 17266 & 0.007 & 33.27 \\ \hline FT\_300\_1 & 12144 & 0.001 & 32.99 \\ FT\_300\_2 & 5879 & \(-\)0.001 & 32.80 \\ FT\_300\_3 & 2078 & 0.001 & 32.92 \\ \hline HS\_150\_1 & 532 & 0.012 & 32.95 \\ HS\_150\_2 & 471 & 0.004 & 33.00 \\ HS\_150\_3 & 796 & 0.004 & 32.96 \\ HS\_150\_4 & 1454 & 0.007 & - 2 \\ HS\_150\_5 & 1846 & 0.012 & - 2 \\ \hline HS\_300\_1 & 1563 & 0.005 & 32.74 \\ HS\_300\_2 & 3384 & 0.003 & 32.97 \\ HS\_300\_3 & 1190 & 0.001 & 32.99 \\ \hline Empirical Mean & 1561 1 & 0.005 & 32.99 \\ Emp. 5\%-Quantile & 476 1 & 0.001 & 32.79 \\ Emp. 95\%-Quantile & 4008 1 & 0.012 & 33.18 \\ \hline \hline \end{tabular}  
* Calculated without FT\_150\_7 and FT\_300\_1 (assumed as outliers). 2 No value obtained.  
\end{table} 
Table 2: Measured lateral and rotational imperfections of all test specimens.
## 4 Experiments and Results  
### Dataset  
Ten people were recruited for the experiment. Before the experiment, the approval of the Ethics Committee of the 117 th Hospital of PLA has been obtained and all subjects have signed a consent form. Then, each participator performed the required ten types of gestures one by one, and then repeated this process for ten times. During the experiment, the wristband of the device was fastened to the forearm of each test subject to ensure that the camera position was kept unchanged. With 100 images from each subject, there are totally 1000 images in the final dataset. The ten types of gestures from ten different subjects are shown in Figure 8, which are labeled from zero to nine according to American Sign Language.
## 5 InSAR's Role in Landslide Susceptibility Mapping
### Reported Importance of InSAR in Landslide Susceptibility Mapping
Furthermore, it is crucial to note that the small sample size (nine articles with only five giving the importance ranking) limits the depth and generalizability of this analysis. The limited number of studies incorporating deformation velocity as an evaluation factor might lead to an under-representation of its potential importance. It is worth mentioning that these studies are inherently based on static susceptibility mapping, which relies on factors that can be considered almost time-invariant or averaged over long periods, such as slope, aspect, lithology, etc. InSAR-derived deformation velocity, often used as an annual average, is similarly treated as a static factor. However, landslide processes are inherently dynamic, influenced by natural and anthropogenic forces, and are indicated by temporal variations in factors such as rainfall, land use changes, and vegetation indices. The accelerated deformation generally indicates the occurrence of landslides [147], thus altering the susceptibility level. Evaluating the contribution of InSAR-derived deformation velocity solely based on its static representation may thus underestimate its true importance. Yet, a lack of LSM research focuses on the dynamic processes.
# A Multiple Access Protocol Based on Gray Forecast for Satellite Network
## 6 Simulation Analysis
As such, the three protocols do not remarkably vary. When the number of users in the system increases, the CFDAMA-GF protocol ensures that multiple users access the gray forecast algorithm, and the number of stacking packets in a user queue decreases. Figure 6: Comparison of average delay and number of users.
# Detection of Arc Faults in Transformer Windings via Transient Signal Analysis
## 1 Introduction
conducted an inter-winding fault analysis in the ANSYS environment using the finite element method, considering the mechanical and electromagnetic characteristics of a real oil transformer. In addition to the obtained data, studies were performed to detect faults using vibrations on the transformer casing [19]. [PERSON] et al. proposed a novel method for diagnosing transformer winding faults using polar plots aimed at addressing the current limitations in fault detection. This method utilizes the ratio of excitation to response to identify minor faults and analyzes the distribution of the polar plot, which incorporates both amplitude-frequency and phase-frequency characteristics for a comprehensive diagnosis. Various fault states, including axial displacement, axial deformation, radial displacement, and radial deformation, were examined. The results indicated that these different fault states significantly affect the distribution of sampling points in the polar plot [20]. However, in most of these methods, the transformer must be disconnected from the grid for analysis, and the arcing fault situations between windings were not examined. This paper presents a new approach for the early detection of arcing faults in transformer windings by analyzing the transient currents and voltages measured at the transformer terminals. A nonlinear dynamic arc model, simulated in Matlab (version R2017b), is integrated with the transformer magnetic model in Ansys@Maxwell (version 2023R1). By simultaneously running these tools, transients at the transformer terminals are captured during an internal arc. Time-domain transient signals are transformed into the frequency domain, allowing for the identification of fault-related frequency components that indicate arc occurrence. This method offers the advantage of online detection of transformer arcing faults in their early stages.
## 3 Results  
### Retrieval of Turbidity  
Statistical techniques for the derivation of chlorophyll- a (Chl-a) concentration and turbidity have been a common approach, based on the correlation between insitu data and spectral band values. The derived algorithms can provide an adequate estimate of Chl-a concentration [50] and turbidity [51]. These techniques were also adopted in the Ramganga River, in order to combine in situ data with satellite data to retrieve turbidity. The correlation was pursued between the insitu turbidity data and LSSR (single and ratio bands) for March and November 2014. After testing more than 20 band combinations in this correlation analysis, all single bands showed very poor correlation coefficients. Similar results appeared with different band ratios, except for b2/b3 and b2/b4, which produced higher coefficients of determination. The most significant results are presented in Table 4. Our results agree well with the findings of [52; 53], who used b2/b3 and b2/b4 for the retrieval of turbidity from surface reflectance.  
Using backward linear regression for the March data, all insignificant bands were removed, and the predictive model results were 0.75 and 0.56 for correlation coefficient (\(R\)) and \(R^{2}\) values, respectively--whereas, for November, the value of \(R\) and \(R^{2}\) were 0.852 and 0.726, respectively. However, the absence of autocorrelation in the residuals was indicated by [PERSON]'s statistic (Tables S1 and S2). The description and summary of the final models of water quality parameters are shown in Table 5.  
Figure 5: Flow chart summarizing the approaches followed.
## 3 Methodology
### External Module and Fusion  
To enhance the robustness and applicability of the model, we incorporate key factors that affect passenger travel in rail transit, such as holidays and meteorology. The weekday and holiday information of the time slice \(t\) can be obtained directly. As the meteorological information of the predicted time is unknown in actual situations, we use the meteorological information of the time period \(t-1\) to approximate it. Use \(E_{t}\) to represent the external factors of predicting time slice \(t\), which is then inputted into a neural network comprising of two fully connected layers (see Figure 4). The primary layer is equivalent to an embedding layer for each factor, and the second layer maps the low-dimensional feature matrix to a high-dimensional matrix of the same dimension as \(X_{t}\). Additionally, add a ReLU activation function after each layer and obtain the results \(X_{Ext}\) from the external module.  
Finally, the results of the external module and the spatio-temporal module are merged to obtain the passenger flow prediction value \(Y_{t}\) for the time slice \(t\):  
\[Y_{t}=\text{Tanh}(X_{Res}+X_{Ext}) \tag{11}\]  
where \(\text{Tanh}()\) ensures that resultant values are constrained between \(-1\) and \(1\), which serves to enhance the convergence speed.
## 2 Materials and Methods  
### Field Sites and Cultivation  
The field trials were performed in two fields in Styria (Bad Radkersburg) referred to as \"Styria 1\" (46\({}^{\circ}\)41\({}^{\prime}\)1.9608\({}^{\prime\prime}\) N, 16\({}^{\circ}\)1\({}^{\prime}\)6.7008\({}^{\prime\prime}\) E) and \"Styria 2\" (46\({}^{\circ}\)42\({}^{\prime}\)2.2028\({}^{\prime\prime}\) N, 15\({}^{\circ}\)55\({}^{\prime}\)51.798\({}^{\prime\prime}\) E) and one field in Tyrol (Oberndorf/St. Johann in Tirol; 47\({}^{\circ}\)30\({}^{\prime}\)23.1552\({}^{\prime\prime}\) N, 12\({}^{\circ}\)23\({}^{\prime}\)32.3844\({}^{\prime\prime}\) E) referred to as \"Tyrol\". Both fields in Styria were 2.5 ha in size and 6.6 km apart; the field in Tyrol was 13.5 ha in size. The soil type in all field sites was either a mixture of loamy sand and loamy silt (Styria1, Tyrol) or loamy silt (Styria 2). The region Bad Radkersburg was known for decade-long continuous maize cropping, before an official regulation on crop rotation went into effect in 2019. This region is therefore heavily infested with _D. v. virigifera_ since 2009 [1]. Thus, a natural population of _Diabrotica_ could be expected in all experimental fields and no artificial infestation with any stage of _Diabrotica_ was carried out on either the trial or control fields. Up to the start of this study _D. v. virigifera_ has not infested the region Tyrol. Consequently, preventive biological pest control in Tyrol was also carried out in the absence of _Diabrotica_ infestations.  
While in the field Styria 1 maize and _Cucurbita pepo_ L. _var. styriaca_ were grown in rotation (2012-2014, 2016, 2018 maize; 2015, 2017 pumpkin) in fields Styria 2 and Tyrol maize was grown annually. All the fields were prepared according to common agricultural practice before sowing (i.e., seeding rate was 70,000 seeds ha\({}^{-1}\) in all years. manure and the mineral fertilizer NitramoncalTM (13.5% ammonium and 13.5% nitrate; Borealis L.A.T, Austria) were used as fertilizing elements in April and May each).  
Control field sites in Styria and Tyrol (both approx. 3.5 ha) were in close proximity to the treated field sites (Styria 2 and Tyrol; \(<\)1500 m air distance) and cultivated with maize annually. The following maize seed varieties were sown: Pharaonix RZ 480, Pioneer Hi-Breed Services, in Styria until 2016, thereafter Mexini RZ 450, RAGT Saaten Osterreich; in Tyrol only ATLETICO RZ 280; KWS Austria Saat GmbH. was used. The seeding rate in Tyrol was 80,000 seeds ha\({}^{-1}\) in all years. Manure and DAP 18/46 (EuroChem Agro GmbH, Germany, Mannheim) was used for fertilizing in April each year. The herbicide Laudis(r) + Aspect(r) Pro (Bayer Agrar Austria, Austria, Vienna) was applied at a maize growth stage of 13-15 according to the BBCH scale. More detailed agronomical information on the field sites in Styria can also be found in the full paper of [PERSON] et al. [1]. Weather stations located in the neighborhood of the experimental fields in Styria and Tyrol recorded air temperature, precipitation, relative humidity, daily sunshine duration, and global radiation throughout the whole study.
## 1 Introduction
The main contributions of this paper are divided into four aspects as follows. * We propose a temporally hierarchical deployment architecture with an Enhanced Name Resolution System (ENRS) to provide a deterministic low-latency name resolution service for the 5G-ICN integrated network. ENRS is realized by quantitating the hierarchical and nested areas by transmission latency constraints from the user nodes to the service-providing resolvers in finer grains. * We introduce partitioning-based registration and resolution schemes based on response time requirements to realize local resolution and forwarding locality by constant forwarding hops. We present a tolerable latency-based peer resolver forwarding algorithm to improve the hit ratio. In addition, we discuss the partition imbalance problem. * We design a proactive distribution approach of name binding replicas based on temporal-spatial features to reduce the resolution latency and resolution traffic, which can predict the number and distribution of name replicas individually and cache the replicas of names in resolvers in a hierarchical and directional way. * A typical use case of a video streaming monitoring service in Smart Home is utilized to show the continuity of service guaranteed by ENRS. The remainder of this paper is structured as follows. In Section 2, we introduce the related work from two aspects, i.e., scalability and latency. In Section 3, we describe the system model and basic functions of ENRS. In Section 4, we demonstrate the use case to show the service continuity guaranteed by ENRS. In Section 5, we analyze the name resolution latency of ENRS and evaluate the performance of ENRS. In Section 6, we discuss the problem of partition imbalance, UE provision, and mobility on demand. Finally, we summarize our results and conclusions in Section 7.
## 4 Recurrent Neural Network Modeling
### Data Pre-Processing  
As highlighted in Section 3, analysis of the position and velocity illustrates better position tracking between the master and slave platforms when operated with constant displacement values.  
Thus, we carried out phantom studies for these displacement values to acquire the experimental data for training the neural-based models. To ensure consistency in all data tuples, we fixed the start and end points for all the navigation trials that we performed. Motion commands from the master device emanate from custom code written in Python and executed online via the Raspberry controller. The data acquired are first pre-processed by removing empty and incomplete records that occasionally occur at the slave side. With a total of 188 experimental runs conducted, we obtained 7500 data sequences with exactly 1500 data sequences selected for each 1-5 mm discrete motion. Subsequently,  
Figure 8: Master–slave velocity trajectories based on uniform displacement values of (**A**) 5 mm and (**B**) 10 mm. Inset shows the enlarged view of the master-slave velocity trajectories for a uniform displacement of 5 mm.  
each step's master-slave motion data were divided randomly into training (70%) and test (30%) datasets.  
The displacement and velocity from the master side in addition to the ratio of velocity between the master-slave devices are taken as input variables into the network to estimate the slave robot positions. The training and test datasets for the master-slave positions are shown in Figure 9. The slave robot's response to discrete input commands is shown in Figure 9b. This typifies the occurrence of random values (errors) increasing proportionally with the magnitude of the translational motion. That is, between 1-5 mm, specifically, from 3 mm, there were increasing data points, where the output position of the slave robot were below the input command, thus symbolizing a noisy data. This is a typical response of the slave robot to higher displacement step values without position control or compensation. Furthermore, this also indicates the cause of jerks and vibrations in the slave robot during typical master-slave movements in the absence of position control methods.
## 2 Materials and Methods
### Farm Management System Integration
#### 2.4.1 A Variety of End Users  
Potential end users of MAGDA outputs vary significantly across the different demonstration sites and regions of Europe. From the small clos in Burgundy to the large agricultural fields in Romania, there is a common interest in enhanced extreme weather forecasts and decision-supporting data.  
End users include not only farmers but also agricultural advisors, operating at various scales. The demonstration sites also host a variety of crops: vineyards in France and orchards in Italy, which are perennial crops, and cereals in Romania.  
The impacts of extreme events differ between perennial and annual crops. For instance, frost or hail can affect both the current and subsequent year's yields in vineyards and orchards, while extreme heat can result in permanent damage to trees and vines. Hydric stress primarily affects orchards and cereals, where precise irrigation recommendations based on reliable forecasts can significantly improve water management practices.  
This context led MAGDA's partners to develop both an API and a dashboard to provide access to the data collected and generated by the system, paving the way for seamless integration into Farm Management Systems.