esa-sceva/satcom-chunk-collection huggingface dataset:
# Big Data Perspective and Challenges in Next Generation Networks
## 3 Data Analytics Perspective on Next Generation Networks
### Optimization of Next, Generation Cellular Networks
#### 3.2.1 Self Aware Networks  
It is obvious that SON only is not enough for fulfilling 5G network requirements. Thus, in self-aware networking, we combine the benefits of artificial intelligence (machine learning tools)  
Figure 6: Comparison of different types of machine learning.  
and SON for efficient deployment of 5G networks. Such networks may predict changes and demands of the users as well as of networks and make intelligent decision and prediction for future. In this way, a problem might be solved before it occurs in networks. Hence, network subscriber will get best quality of service and quality of experience. Self-aware networks (SAN) will reduce capital expenditures (CAPEX) and operational complexity (OPEX) besides being dynamic to fulfill subscriber demands according to their requirements. The major steps in SAN are: _sense_, _analyze_, _decide_ and _act_. _Sensing_ refers to acquiring the data from network and other users devices such as smart phones, sensor nodes, and touch-pad devices. These input data are fuel of SAN and other functionality of SAN starts from here. In _analyzing_ stage, the gathered data are analyzed to determine what is happening; which events are important and which are not; which user requires resources; and when and where resources are required. _Decision_ and _prediction_ are made after analyzing the input events. Decisions are made from the knowledge of past events while prediction is made for future anticipation. Multi-dimension optimization is performed in this stage. In last stage of SAN, _action_ is taken on the basis of decisions and predictions. Actions can be of different types such as configuration setting, coordination issues or multi-vendor orchestration. The block diagram of SAN is shown in Figure 7.
## 3 Numerical Verification
So, with the same slow bucket detector, the \(PSNR\) value of MIPO-CSGI is better than that of S-CSGI. For the moving \"\(T\)\", we just need to perform the same operation as imaging the jumping \"square\". From Figure 2, we can see that MIPO-CSGI reconstructs four frames of the \"\(T\)\", as well as the jumping \"square\". We can also image each state of the moving \"spacecraft\", although its operationtrajectory is more complex. The reconstruction results of the \"spacecraft\" further confirm the MIPO-CSGI's effectiveness. To further compare the performance of F-CSGI, S-CSGI, and MIPO-CSGI, we take the jumping \"square\" as the target to simulate these schemes with varying compressive sampling ratios \(\beta\). Due to the randomness of the reconstruction results of random speckle patterns in CGI, we present the average _PSNR_ values of the reconstructed images using F-CSGI, S-CSGI, and MIPO-CSGI schemes at 10 times. The results are presented in Figure 3. From Figure 3, it can be seen that the _PSNR_ of S-CSGI under different compression rates \(\beta\) is almost unchanged. The _PSNR_s of F-CSGI and MIPO-CSGI under different compressive sampling ratios \(\beta\) are almost the same, and both increase with the increase in the compressive sampling ratios. Under the same imaging quality, the sampling rate of the bucket detector in F-CSGI is four times that of MIPO-CSGI, and the _PSNR_ of MIPO-CSGI is significantly higher than that of S-CSGI under the same sampling rate of the bucket detector. Figure 3: _PSNR_ curves with varying compressive sampling ratios \(\beta\) using F-CSGI, S-CSGI, and MIPO-CSGI. Figure 2: The numerical simulation results of different periodic moving objects using F-CSGI, S-CSGI, and MIPO-CSGI, where _PSNR_ is presented together. In addition, numerical simulations for the periodic state-changed object with MIPO-CSGI, F-CSGI, and S-CSGI are performed in Figure 4. The original objects are 64 \(\times\) 64-resolution two-grayscale \"running man\" and 64 \(\times\) 64-resolution eight-grayscale \"flying bird\". Taking the \"running man\" as an example, five 64 \(\times\) 64-resolution random speckle patterns with their inverse speckle patterns are modulated in each cycle to obtain five frames of the object. Meanwhile, the bucket detector's sampling rate in F-CSGI is five times that in MIPO-CSGI, and the bucket detector's sampling rate in MIPO-CSGI is the same as that in S-CSGI. With a 60.01% compressive sampling ratio, all schemes will obtain 12,290 differential bucket detector signals. One 12,290 \(\times\) 20,480-resolution random matrix will be programmed in MIPO-CSGI. Figure 4 shows that the _PSNR_ of MIPO-CSGI is almost the same as that of F-CSGI and more than twice that of S-CSGI. According to the reconstruction result of MIPO-CSGI and F-CSGI, we can easily obtain the motion state of the object. The reconstructed \"flying bird\" also verifies that MIPO-CSGI can image eight-grayscale objects with fast periodic state-changes, which further proves the effectiveness of MIPO-CSGI.
## 2 Experiment Setup  
This section describes the subject characteristics, data collection protocol, and system setup used in this study, as illustrated in Figure 1. The equipment was installed in a soundproof booth (IAC Acoustics, North Aurora, IL, USA) and included: (1) a base station and four wireless TrignoTM mini sEMG sensors with a bandwidth of 20 Hz to 450 Hz (Delsys, Natick, MA, USA); (2) a head-worn microphone (AKG, Model C520, Vienna, Austria); (3) an audio interface (Scarlett 2i2, Focusrite, High Wycombe, UK); and (4) a data acquisition device (PowerLab 16/35, ADInstruments, Dunedin, New Zealand), which performed the synchronized sampling of audio and sEMG signals using LabChart v. 8.1.10 for MS Windows. The sampling rate was set to 4 kHz for the sEMG signals and 20 kHz for audio, both with 16-bit quantization. In addition, audio signals were collected using the software Audacity(R)v. 2.1.1 for recording and editing, at a sampling rate of 44.1 kHz and 16-bit quantization. In this study, we focused on classification using only the sEMG data for vowel productions, but the audio signals were important to determine the sEMG ROI's, as explained subsequently.
# Agricultural Evolution: Process, Pattern and Water Resource Effect
## 3 Results
### Spatiotemporal Changes of Cultivated Land
#### 3.1.4 Changes in Gravity Center  
The changes in gravity centers of paddy and dry farmland from 1990 to 2015 are displayed in Table 4. Paddy field transferred from (45.97\({}^{\circ}\) N, 124.01\({}^{\circ}\) E) in 1990 to (46.11\({}^{\circ}\) N, 123.88\({}^{\circ}\) E) in 2015 while dry farmland moved from (45.97\({}^{\circ}\) N, 124.01\({}^{\circ}\) E) to (46.11\({}^{\circ}\) N, 123.88\({}^{\circ}\) E) during 1990-2015. The gravity centers of paddy have moved northwestward while dry farmland has moved southwestward since 1990 in the West Songnen Plain.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **Time** & **PD** & **LPI** & **MA** \\ \hline \multirow{4}{*}{Paddy} & 1990 & 0.0526 & 0.3154 & 54.0416 \\  & 2000 & 0.0061 & 0.673 & 734.314 \\  & 2010 & 0.0069 & 0.6741 & 722.6863 \\  & 2015 & 0.0082 & 0.6691 & 804.7028 \\ \hline \multirow{4}{*}{Dry farmland} & 1990 & 0.1948 & 9.2081 & 215.1724 \\  & 2000 & 0.0331 & 12.9321 & 1419.6731 \\ \cline{1-1}  & 2010 & 0.0332 & 10.4613 & 1450.6182 \\ \cline{1-1}  & 2015 & 0.0367 & 10.3458 & 1282.0991 \\ \hline \end{tabular} Notes: PD and LPI represent patch density and largest patch index, respectively. MA means Mean Area.  
\end{table}
Table 2: The indices (PD, LPI and MA) applied to describe the patch features of paddy and dry farmland.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **Time** & **COHESION** & **SPLIT** & **AI** \\ \hline \multirow{4}{*}{Paddy} & 1990 & 99.3257 & 36,869.8887 & 96.1885 \\  & 2000 & 99.5731 & 12,089.1395 & 97.7839 \\  & 2010 & 99.5598 & 12,070.067 & 97.8907 \\  & 2015 & 99.6665 & 5762.8674 & 97.9792 \\ \hline \multirow{4}{*}{Dry farmland} & 1990 & 99.9077 & 70.1782 & 97.3831 \\  & 2000 & 99.9354 & 44.0645 & 97.9354 \\ \cline{1-1}  & 2010 & 99.93 & 52.7664 & 97.9605 \\ \cline{1-1}  & 2015 & 99.9274 & 55.9848 & 97.9052 \\ \hline \end{tabular} Notes: COHESION and SPLIT represent patch cohesion and splitting, respectively. AI means aggregation.  
\end{table}
Table 3: The indices (COHESION, SPLIT, and AI) applied to describe the aggregation features of paddy and dry farmland.  
The migration distance and speed of gravity center of cultivated land were calculated as shown in Table 5. The migration distance and speed of paddy was much larger than that of dry farmland in different periods. For example, the migration distance was 13.37 km and the speed was 1.34 km/a for paddy during 2000-2010 while that of dry farmland were 4.04 km and 0.40 km/a, respectively. The migration speed for both paddy and dry farmland showed an increasing trend since 1990, especially for paddy.
# On the Use of LoRa Technology for Logic Selectivity in MV Distribution Networks
## 5 LoRa Technology for Logic Selectivity Implementation
The design of a LoRa-based logic selectivity system can be, then, summarized as follows:  
* the type of failure for which the logic selectivity is developed imposes the maximum fault clearing time \(T_{FC}\) and, consequently, the Maximum Blind Waiting time \(T_{MBW}\);
* the possible configurations of the LoRa packet that ensure a transmission time lower than \(T_{MBW}\) can now be examined;
* among all the possible configurations of the packet, the one characterized by the maximum value of SF and the minimum possible BW value should be chosen to ensure the minimum theoretical value of receiver sensitivity and, therefore, maximum reliability of communication;
* finally, it must be verified whether, based on the characteristics of the environment, rural, urban, etc., the values obtained are sufficient to cover the distance between the protections. With regard to the last item, several studies have been conducted that relate the values of SF and BW and, therefore, of the theoretical sensitivity for an effectively achievable communication distance in different scenarios and in different line of sight conditions [37; 38; 39]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & & \\ \hline
**BW** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline \(125\) & \(-123\) & \(-126\) & \(-129\) & \(-132\) & \(-134.5\) & \(-137\) \\ \(250\) & \(-120\) & \(-123\) & \(-126\) & \(-129\) & \(-131.5\) & \(-134\) \\ \(500\) & \(-117\) & \(-120\) & \(-123\) & \(-126\) & \(-128.5\) & \(-131\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Receiver sensitivity in dB. The red color indicates values of the table that are not feasible. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & & \\ \hline
**BW** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline \(125\) & \(32\) & \(53.76\) & \(107.52\) & \(215.04\) & \(348.16\) & \(696.32\) \\ \(250\) & \(16\) & \(26.88\) & \(53.76\) & \(107.52\) & \(174.08\) & \(348.16\) \\ \(500\) & \(8\) & \(13.44\) & \(26.88\) & \(53.76\) & \(87.04\) & \(174.08\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Values of \(T_{packet}\) in ms depending on BW and SF, with CR = 1, CRC disabled, implicit header.
## 5 Conclusions  
This paper presents the experimental and numerical work carried out to compare the heat transfer coefficients, Nusselt number, friction factor, Euler number and comprehensive thermal performance factor of non-Newtonian base fluid and its nanofluids in a helically baffled heat exchanger with elliptic tubes. The main conclusions are summarized as follows:  
1. Experiment results have proven that the accuracy of numerical simulation model is satisfactory. Good agreements exist between experimental and numerical data. The single-phase flow model can simulate the flow and heat transfer characteristic of non-Newtonian nanofluids at low concentration. 2. The heat transfer is significantly enhanced by adding nanoparticle compared to base fluid. The overall and shell-side heat transfer coefficients of nanofluids are higher than those of base fluid at the same Reynolds number. The enhancement in heat transfer coefficient increases with the increasing in the weight fraction of MWCNTs. 3. The \(Nu_{o}.f_{o}\), \(Eu_{o}\) increase with the increasing in the weight fraction of nanoparticle at the same Reynolds number. In the whole range of volume flow rate, the _TPF_ of nanofluids are all higher than 1.0. The changes in flow and heat characteristic of nanofluids can be attributed to the enhanced thermal conductivities and viscosities. 4. Correlations for predicting the shell-side Nusselt number, friction factor and Euler number of non-Newtonian nanofluids in the helically baffled heat exchanger with elliptic tubes were obtained based on experimental results, and fitted the data very well. This work was supported by the National Natural Science Foundation of China (51276066), Guangdong Natural Science Foundation (2014A030312009) and the Fundamental Research Funds for the Central Universities (2015 ZP006). [PERSON] wrote the paper; [PERSON] made the numerical study; [PERSON] performed the experiment; [PERSON] and [PERSON] analyzed the data; and [PERSON] conceived and designed the experiment. Figure 13: Comprehensive thermal performance factor versus volume flow rate. **Conflicts of Interest:** The authors declare no conflict of interest. **Nonenclature**
# Comparison of Damage Indexes for Assessing Seismic Fragility of Bearings in an Offshore Bridge
## 5 Summary and Conclusions  
In this study, numerical analysis was conducted on an offshore bridge to investigate the comparison of damage indexes for assessing the seismic fragility of bearings in an offshore bridge. The study yielded the following conclusions:  
(1) The damage index using the theoretical method is notably larger compared to the one obtained through the empirical method. For example, in terms of the coverage of PGA under minor damage, when using relative displacement as the chosen index, the coverage obtained by the theoretical method is 267% greater than that obtained by the empirical method. Moreover, when using the relative displacement ductility ratio to determine the damage indexes for the four damage states, the corresponding PGA is higher compared to when using relative displacement as the damage index.  
(2) As the service life extends, the failure probabilities for the bearings under various damage indexes tend to increase. The impact of time-dependent effects on the exceedance probability is minimal and subject to minor and medium damage states. As the degree of damage increases, there is a substantial increase in the failure probability with the prolongation of the operational lifespan. Following a service period of 50 years, the exceedance probability for the complete destruction state increases by 7% compared to 0 years of service. Additionally, the sensitivity of the bearing to strong earthquakes also increases with the damage degree. As the level of damage increases, the probability calculated using relative displacement outweighs that calculated using the relative displacement ductility ratio, and the disparity between them amplifies during the service period.  
(3) The relative displacement ductility ratio, which incorporates the shear deformation of the piers, is a preferable option, as it aligns more effectively with the concept of performance-based ductility design and offers cost advantages. In regions prone to seismic activity, it is advisable to employ a damage index focused on safety for evaluating the earthquake resistance for the bearings. However, in less severe environments, an index emphasizing economic factors may be more suitable.  
Conceptualization, J.Y. and A.G.; methodology, J.Y.; software, J.Y.; validation, J.Y. and A.G.; formal analysis, J.Y. and A.G.; investigation, J.Y. and A.G.; resources, J.Y. and A.G.; data curation, J.Y.; writing--original draft preparation, J.Y.; writing--review and editing, A.G.; visualization, J.Y. and A.G.; supervision, A.G.; project administration, A.G.; funding acquisition, A.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of Distinguished Young Scholars of China, grant number 51725801 and the Fundamental Research Funds for the Central Universities, grant number FRFCU5710093320.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Related Work  
### Dataset  
There are several established benchmark datasets for Vehicle ReID task. VeRi [9] (Figure 3(a)) is a relatively small dataset including 40,000 bounding boxes of 619 vehicles. The advantage of VeRi is that the location of cameras is showed on the map and the distance between them is showed in a matrix so researcher can take spatio-temporal relationship into account. VehicleID [10] (Figure 3(b)) is a much larger dataset with 221,763 images of 26,267 vehicles, and the license plate of the vehicles is especially made to be mosaic to better suit the real usage of vehicle ReID task.  
Figure 3: Intra-class variation and inter-class similarity in the vehicle ReID.  
With the rapid development of UAVs, some aerial remote sensing visual datasets were constructed for the research on UAV vision tasks, among which DOTA and VisDrone have received special attention. DOTA [11] is a large-scale remote sensing dataset, part of which comes from UAVs while the other part from satellites. The images from the UAVs aim to serve the object detection task so the flying height is so high that the spatial resolution is not suitable for the ReID task. VisDrone dataset [12] consists of 400 video clips formed by 265,288 frames and 10,209 static images, which is used as the benchmark for the challenge of \"vision meets drone\". VisDrone is used for object detection and tracking, but the images with higher spatial resolution have the probability to evaluate ReID task if further annotation can be labeled.  
Considering that the lack of UAV dataset limits the research of Vehicle ReID task on UAV platform, [PERSON] et al. [4] constructed an aerial dataset called VRAI (Vehicle Re-identification in Aerial Imagery, Figure 4c). VRAI are captured by two DIJ consumer UAVs at different location with a diversified shooting angles and flight altitudes, including 137,613 images of 13,022 vehicles, and 66,113 images among them are published. There is something special about VRAI that the discriminative parts of vehicles are marked by the bounding box, such as luggage racks and personalized stickers. However, high image resolution of the dataset with large memory size is not suitable for practical application, and one single dataset cannot effectively test the robustness of algorithm, so we come up with our own dataset.
# Efficient Distributed Method for NLOS Cooperative Localization in WSNs
## 6 Conclusions  
In this paper, we proposed an efficient distributed algorithm for NLOS cooperative localization. We employed tight convex relaxation for the heuristic model, and approached it in a parallel ADMM framework to offer powerful computation ability. The distributed framework can drastically reduce the computational burden since it decomposes a large-scale problem into numerous small-scale problems, which similarly retains computational time when the number of sensors grows. Furthermore, we derived the accelerated proximal gradient method to improve the convergence rate of local subproblems. Simulation results demonstrate that our method efficiently alleviates the influence of NLOS propagation and has fairly good convergence performance.  
In future works, we plan to study the level of influence that step size and penalty parameters could have on the performance of local subproblems and an ADMM framework. How to establish the mathematic model to find the optimal parameters needs further study as well. Moreover, the lower and upper bounds of range measurements provide a good feasible region, which is nevertheless nonconvex. We are interested in finding better convex relaxation rather than utilizing a heuristic solution as the optimal solution.  
Conceptualization, S.C.; data curation, C.X.; formal analysis, S.C.; investigation, Y.M.; methodology, S.C.; resources, Y.M. and Y.G.; supervision, J.Z.; validation, C.X. and Y.G.; writing--original draft, S.C.  
This research received no external funding.  
No conflict of interest exits in the submission of this manuscript, and the manuscript was approved by all authors for publication. I would like to declare on behalf of my coauthors that the described work was not previously published, and is not under consideration for publication elsewhere, in whole or in part.
## 2 Overview of System Description and Optimization Approach  
In this section, an overview of the problem faced by this paper is provided.  
As it was introduced in Section 1, the issue of the Kantrowitz limit could be improved by adding a compressor to the propulsive system. As it is shown in Figure 1, the outlet of the compressor needs a geometry that readapts the two streams coming out of the compressor in order to conduct the air towards the nozzle at the outlet part of the pod. Therefore, the design of this body--from now on known as the _mixer_ --is the main objective of this paper. For that, Figure 2 reflects the geometry domain that is modelled, making the differentiation between the compressor and the mixer.  
Having the overall problem in mind, a multidimensional geometrical optimization is proposed after exploring the following two approximations:  
* Coupled: Both the compressor and the mixer are simulated simultaneously. The main advantage lays in the fact that the possible effects that the mixer may have upstream on the compressor are completely taken into account. Nevertheless, the computational cost of a problem of these characteristics makes an approach like this impossible,  
Figure 1: Conceptual model of the complete pod with a frontal compressor.  
Figure 2: Geometry of the compressor and the mixer.  
as the number of required cells would increase by more than an order of magnitude. With the independent mesh as established in Section 3.4, the compressor cell has around 80M elements, whereas the mixer is properly solved with only 1M elements.
* Uncoupled: The main hypothesis consists in assuming that the mixer has no relevant upstream impact on the compressor. As a consequence, the mixer can be calculated by just imposing the outlet conditions of a previous simulation of the compressor alone, diminishing considerably the computational cost.  
Considering the extensive optimization numerical campaign of more than 100 simulations conducted in Section 4, the coupled option was not affordable due to the significant increase in computational effort. In consequence, the authors opted for the second option. Indeed, each simulation, adding the time employed in both the mesh of each possible geometry and the calculation of the solution, would cost 40\(\times\) the computational effort of the mixer alone.  
With respect to the chosen approach, the next algorithm flowchart summarized in Figure 3 was developed throughout the following methodology:  
* Base geometry.
* Obtaining the BC values: after a preliminary design is proposed, the boundary conditions from its design together with the compressor are obtained in order to start the process.  
* First optimization (2D): A Taguchi DoE (design of experiments) is implemented for a first optimization of the axisymmetric parts of the mixer.
* Second optimization (3D): by using an adjoint method, an optimization of the 3D complete mixer is carried out.
* Coupling evaluation.  
Figure 3: Flux diagram of the uncoupled process In square, the methodology steps. In oval, the results of the optimization stages.