esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Reputation in the Sharing Economy with Twitter Social Data
## 3 Case Study and Methodology
### Data Collection and Feature Generation
#### 3.2.1 Data Collection  
Downloading Twitter public data is a straightforward process thanks to the existing API that allows us to download structured data for each one of the paired identities. The different characteristics available through different endpoints should be included to build a full Twitter profile:  
* _users/show_: profile characteristics as name, picture, or description.
* _statuses/user_timeline_: list of tweets posted by the user.
* _followers/list_: list of followers.
* _friends/list_: list of friends.  
In contrast with Twitter, Wallapop does not offer a documented API to access its service. One alternative could be to use scraping techniques to extract data from the website, but this technique has the downside of not being resilient to changes in the web interface. Given that we wanted to obtain data for an extended period, we opted for a different option focused on accessing the data through the internal API that the official mobile applications use to communicate with the servers.  
Wallapop mobile applications follow a typical pattern of server-client communication: a set of servers that perform most of the business logic feed applications with structured data to be rendered to users. Both parties communicate with each other through the use of an API that is usually stable for a long time, which makes it ideal for gathering data for an extensive period. To download data from the internal API, the first step was to identify which endpoints the mobile apps were connecting to and how they exchanged the information. The process started by configuring and deploying a transparent web proxy and an HTTP interceptor that logged all the data sent through it. Then, a smartphone was configured to use the proxy; the official Wallapop application was installed and used for some time. The output of the process was the full set of requests exchanged between the server and the application. By studying these logs, it was possible to get all the endpoints we needed:  
* _Search_: a list of items available near a given location.  
* _Item_: description of an item (including price and seller profile).
* _User_: user public profile, including data such as the number of reviews and verifications.
* _User reviews_: full set of reviews given or received by a user.  
The output is a dataset of size 35,706 samples. As user reputation is based on ratings left by others, the only samples that are useful for the experiments are the ones who have received at least one review (\(n=19\),325). Users are rated from 1 to 5 stars. The average reviews rating distribution for each user is highly skewed to the right, due to the majority of positive reviews, with a big mass around integer values, as shown in Figure 2(a). If we consider a review as negative to the ones with ratings below three stars out of five, the distribution of the count of negative reviews for each user has a mean value of 0.099 and a variance of 0.131, as shown in Figure 2(b).
# Co-Efficient Vector Based Differential Distributed Quasi-Orthogonal Space Time Frequency Coding
## 2 Distributed Space-Time-Frequency Coding  
### System Model  
The cooperative network consists of a source node, a destination node, and \(P\) relay nodes as shown in Figure 1. Each node is equipped with a single antenna, which is used for both transmission and reception. The transmission from the source node to the destination is divided into the 'transmit' and 'cooperate' stages. In the 'transmit' stage, the source node sends information signals to the cooperating relay nodes while in the 'cooperate' stage, the source node keeps silent and the cooperating relay nodes decode and forward the information signals to the destination. We address the problem of differential encoding and decoding where the relay nodes and the destination are unable to acquire CSI. Our investigation in this work is carried out under the assumption of perfect inter-relay synchronization. It is noteworthy that the use of cyclic prefix can provide robustness against synchronization errors at the relays. This benefit, which is owed to the employment of OFDM transmission, is applicable to our proposed DQSFC scheme. This assumption is, however, critical in practice due to the distributive nature of relays in space. Asynchronous transmission of the relays may result in degradation in diversity gain. Specifically, the impact of synchronization errors have been studied in [26] based on analytical and simulation results. Unlike STF coding in multiple-antenna systems, STF coding in cooperative networks must be implemented in two distinct stages, namely coding at the source node and coding at the relay nodes. We first describe how the coded data is designed at the source node.
## 3 Experimental Setup
The typical signals measured at the input and output of the MZI are shown in the left and right insets of Figure 5, respectively. We can see from the insets that additional amplitude modulation appears at the MZI output, which contains information about positions and amplitudes of reflectors/scatters in the LUT. Thus, the main task of the signal processing procedure was an accurate extraction of the interferogram from the measured signals with the highest possible signal-to-noise ratio (SNR). Analyzing zoomed views of the signals shown in the bottom of Figure 5, we noted that the signal levels observed during generation of individual mode pulses are not quite constant. The observed slight signal increase was related to the PDs inertia. In spite of the large bandwidth (MHz level) of the PDs suitable for measuring sub-microsecond signals, we observed slow residual growth of the signals' amplitudes at millisecond time scale. To reduce the influence of this slow growth of the measured signals levels on the interferogram extraction result, we used an additional variable resistor to increase load impedance for the reference PD and make this growth identical for both PDs. In our case, this approach corresponded to the reference PD load impedance of about 2 kOhm. As a result, the bandwidths of the reference and signal PDs were estimated to be 25 and 1.5 MHz, respectively. Figure 4: Probe laser frequency: (**a**) mode intensity; (**b**) and output intensity; (**c**) dynamics.
# Lossless Compression of Large Aperture Static Imaging Spectrometer Data +
## 2 Instrument and Data Characteristics  
Conventional hyperspectral imagers, both interference and dispersive spectrometers, are all equipped with a slit that greatly decreases energy utilization. By adding a lateral shearing interferometer to a conventional imaging system, the energy utilization ratio of the LASIS is nearly equivalent to that of ordinary cameras and almost a hundred times greater than that of traditional hyperspectral imagers [8]. The imaging principle of LASIS is presented in Figure 1.  
The original data collected by the LASIS instrument are called LASIS data. A frame of a LASIS cube, as depicted in Figure 2, is not a regular image, as it is a combination of both two-dimensional spatial information and interference fringes with uniform thickness. In the pushbroom scanning approach, each row of detectors continuously observes the same points in object space. Consequently, the interferogram of a spatial location can be extracted only after performing pushbroom scanning of the full field of view. The extracted interferogram data cube is defined as LAMIS, a frame of which contains one-dimensional spatial information and one-dimensional interference information. Figure 2 illustrates the interferogram and LAMIS data extraction process for the LASIS.  
Figure 1: Image-forming principle of LASIS.  
Figure 2: Interferogram and LAMIS data extraction process for the LASIS.
## 5 Conclusions  
In the paper the investigations of how the amount of Si-OH groups governs the intrinsic losses and the radiation-induced attenuation (RIA) of multi-mode pure silica core (MM PSC) optical fibers in the vis-near IR spectral range are presented. For the study, three optical fibers have been drawn from three different preforms with different OH concentrations: the _low_-OH (0.41 ppm at 1385 nm), the _medium_-OH (27 ppm at 1240 nm) and the _high_-OH (873 ppm at 1240 nm). In addition, the effect of a combined H\({}_{2}\) loading added to a thermal-treatment before the irradiation has been studied on the _medium_-OH sample, in order to eliminate its drawing-induced defects (DID), the main cause of the signal degradation in the considered spectral region.  
The X-ray irradiation at 6 Gy(SiO\({}_{2}\)) s\({}^{-1}\) up to 300 kGy(SiO\({}_{2}\)) shows a monotonic increase of the vis-near IR RIA for the _low_- and _high_-OH samples, while a different behavior was observed for the _medium_-OH, depending on the spectral range of interest: a monotonic increase of the RIA for wavelengths below \(\sim\)600 nm and a non-monotonic behavior for wavelengths above \(\sim\)600 nm.  
The spectral decomposition of the RIA highlighted the various point defects that contribute to the spectral absorption. In particular, the absorption band peaked at 1.97 eV (\(\sim\)630 nm), attributed to non-bridging oxygen hole centers (NBOHCs), increases according to the OH amount present in the fiber. Moreover, due to its asymmetrical shape, the absorption band associated with the NBOHC has been fitted with two models: a 3-Gaussian model already presented in the literature [39] and the \"standard\" 1-Gaussian model [14], this latter revealing the necessity to include one or more yet unknown contributions in order to match the experimental data.  
The non-monotonic trend with the dose of the RIA around 650 nm for the _medium_-OH sample has been studied by the decomposition of the RIA spectrum at 10 kGy(SiO\({}_{2}\)) dose at which the maximum value in the kinetic has been measured. Here, the strain-assisted self-trapped holes (s-a- STH) contribution is introduced and shown to be the most adapted one to reproduce the experimental data.  
Conceptualization, C.C., A.M., G.M., T.R., Y.O., A.B. and S.G.; Data curation, C.C., V.D.M., A.M. and E.M.; Formal analysis, C.C., V.D.M., A.M. and S.G.; Resources, T.R.; Writing - original draft, C.C., A.M., S.G.; Writing - review & editing, V.D.M., G.M., T.R., E.M., Y.O., A.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on reasonable request from the corresponding author. Boca Raton.  
The authors declare no conflict of interest.
## 1 Introduction
The ToA and TDoA schemes have better accuracy, but require additional hardware such as a highly accurate timer or extra equipment for sending and receiving signals that has a lower propagation speed than radio [24]. When accurate distances between neighbor nodes are obtained, the MDS-based[23] localization algorithm can have accurate position estimation. Multidimensional scaling (MDS) is an efficient data analysis technique originating in psychometrics and psychophysics. MDS-MAP(C) [25] is a centralized algorithm. It requires the distances between all pairs of nodes to form the distance matrix for MDS, and then apply MDS to the distance matrix to calculate the relative positions of these nodes. The relative positions can be converted into global positions if there are absolute positions for more than three or four anchor nodes. The computational complexity is \(O(n^{3})\). When the number of nodes in the network grows larger, the computational complexity is much larger. MDS-MAP(P) [26] is an improved algorithm for MDS-MAP(C). It is a distributed algorithm. Each node in the network builds a local map based on MDS-MAP(C). Then these maps are patched together to form a global map. The computational complexity is \(O(n)\) and the total communication cost is \(O(n\log n)\) in this step [26]. In a large scale sensor network containing thousands of sensor nodes, it is very sensitive to the complexity of network, such as computational complexity, time complexity and communication complexity. In this paper, we propose a low complexity localization algorithm based on spring model which is suitable for large scale networks. It has a complexity of \(O(1)\) for each sensor nodes. In the spring model, the sensor nodes are represented by particles with masses. The connection information between each pair of neighbor nodes is represented by a spring connecting this pair of nodes. The distance between this pair of neighbor nodes is represented by the original length of the spring. To localize the positions of blind particles, we imagine that these blind particles are first drawn to random virtual positions by extra forces, and then these particles will go back to their stable positions virtually by the forces of springs. Three patches are also used to avoid local optimization, kick out bad nodes and deal with node variation. The proposed LASM localization algorithm has the following advantages: First of all, LASM has a low computational, time, and communication complexity of \(O(1)\) for each sensor node in distributed scheme, which is suitable for the large scale sensor networks. Second, LASM is optimal under the aspect of minimizing error square sum in the network. This will be proven in Section 2. In addition, LASM is simple to implement. Each sensor node just needs to use some simple addition, subtraction, multiplication and division to calculate its acceleration, velocity, position and so on. This paper is organized as follows: Both the basic LASM localization algorithm and the patches of LASM are described in Section 2. Following which, the simulation results are shown in Section 3. Conclusions are given in Section 4.
## 2 Mathematical Models  
Multipath effects are due to the reflections of satellite signals from nearby objects, such as trees, walls, and water surfaces. Although multipath effects are usually considered to occur at the receiver end, it is worth noting that the \"nearby objects\" here can also refer to the possible reflectors at the satellite transmitters, such as the solar panels or so. The multipath effects on code measurements could be investigated through the so-called multipath combination [15; 16; 17], which will be discussed below. To assess the errors of carrier phases, the raw observations of carrier phases on three different frequencies are usually employed to form the GFIF combination [13].
# A Tale of Many Networks: Splitting and Merging of Chord-like Overlays in Partitioned Networks
## 3 Our Merging Approach: Ring Reunion Algorithm
### Merging Algorithm  
If a contact node is found and the probability condition is fulfilled, then the merging protocol, as depicted in Figure 5, is started. The initiator starts the merging algorithm by sending the contact details of its successor to its alternative successor (if \(altSucc\in(n,\,succ)\), line 10, Figure 5), or by sending the contact details of the alternative successor to its successor (if \(altSucc\
otin(n,\,succ)\), line 13, Figure 5).  
The receiver of this merge message considers the contact information, which have been sent with, as its own alternative successor. The sender is seen as possible predecessor of the receiver and is used as new predecessor, if it fits better than the current predecessor. By this, the active node (receiver) is able to combine the two overlay constructs locally by deciding which contacts are best suited to be kept in its routing table. This active role of combining two rings in this specific point of the id space is termed as having the merger token. Next, the successor with the next closest ID (to the active node) is informed about potential new candidate nodes and, thus, the merger token is passed on to it, so that it can update its successor and predecessor. By this process, a node for a node and a successor for a successor, the two overlay constructs are merged.  
In contrast to Chord-Zip, the Ring Reunion Algorithm does not combine finger entries to prevent current routing behavior. For the Ring Reunion Algorithm, an initial lookup is needed to start the merger. After that, the merger token is passed through the ring, once for each node, until the algorithm terminates. Consequently, the time complexity for merging is given by \(O(N+log\ N)\), where \(N\) denotes the number of nodes participating in the overlay.
## 4 Discussion
### Effects of Reference Value  
Since precipitation is a non-normal distributed variable, especially at an hourly scale, the selected reference value may have a certain impact on the evaluation results. This study involved the reference value of 0.1 mm/h for IMERG data, to ensure the consistency with the minimum value that the rain gauge could detect. Some studies of extreme weather indices focused on a reference value of 1 mm/day, which excluded the days with very light precipitation [77; 78]. To detect the uncertainty caused by different reference value, the statistics were recalculated using the reference value of 0.5 mm/h. The regional mean POD is 40.2%, 40.0%, 47.1%, 49.4%, and FAR is 66.7%, 60.8%, 63.6%, 60.3% for North America, Europe, North Asia and South Asia, respectively. It shows that these metrics are insensitive to the reference value. However, the MAE and RMSE are much larger for the case of 0.5 mm/h than for 0.1 mm/h, due to the large magnitude and low frequency of precipitation in both gauge observation and IMERG data under the large truncation.  
Actually, precipitation is a discontinuous variable largely affected by many thermal and dynamic factors, which cause its intermittency and fluctuation at nearly all temporal and spatial scales. Although the evaluation conducted in this study was based on an hourly time scale, it essentially adopted the equal-time-interval method. Alternatively, the event-based evaluation method would provide more information, reflecting the discontinuity of precipitation [16; 48; 79]. In the future research, we plan to use more precipitation data and other assessment methods to reveal water vapor sources and the mechanisms regulating global precipitation. Moreover, we will investigate the error associated with satellite precipitation products in different climatic regions than the geographical zone examined in this study.
# Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks +
###### Abstract  
Wireless and mobile health applications promote the development of smart healthcare. Effective diagnosis and feedbacks of remote health data pose significant challenges due to streaming data, high noise, network latency and user privacy. Therefore, we explore efficient edge and cloud design to maintain electrocardiogram classification performance while reducing the communication cost. These contributions include: (1) We introduce a hybrid smart medical architecture named edge convolutional neural networks (EdgeCNN) that balances the capability of edge and cloud computing to address the issue for agile learning of healthcare data from IoT devices. (2) We present an effective deep learning model for electrocardiogram (ECG) inference, which can be deployed to run on edge smart devices for low-latency diagnosis. (3) We design a data enhancement method for ECG based on deep convolutional generative adversarial network to expand ECG data volume. (4) We carried out experiments on two representative datasets to evaluate the effectiveness of the deep learning model of ECG classification based on EdgeCNN. EdgeCNN shows superior to traditional cloud medical systems in terms of network Input/Output (I/O) pressure, architecture cost and system high availability. The deep learning model not only ensures high diagnostic accuracy, but also has advantages in aspect of inference time, storage, running memory and power consumption.  
deep convolutional generative adversarial network; deep learning; edge computing; electrocardiograms +
Footnote †: journal: Applied Physics Letters B
## 6 Results and Discussion
### Statistical Analysis  
As we used several approaches for spelling correction, including some proposed neural network models, we performed the statistical analysis comparing the method with the best result against the traditional one, performed by [PERSON]. In order to define our candidate for analysis, we considered the lowest average error rate in the metrics CER, WER, and SER. Thus, to compose this average, we take into account all the results among all datasets, optical models, and executions.  
Furthermore, we take as baseline the output of the three optical models (Bluche, Puigcerver, and Flor) over the three metrics (CER, WER, and SER). This baseline refers to the standalone application of the optical model, that is, no post-processing is applied.  
In this context, our proposed model with Luong 1024 had the best results with the average character, word, and sentence error rates of 3.2% (\(\pm\)0.0632), 7.7% (\(\pm\)0.1095) and 35.3% (\(\pm\)0.3898), respectively. On the other hand, [PERSON] obtained 6.5% (\(\pm\)0.0632), 19.0% (\(\pm\) 0.1612), and 76.7% (\(\pm\)0.4000). This means that the model with Luong 1024 reached around 65% corrected sentences on average, while Kaldi, 23%. Figure 10 shows in detail the average performance of each technique on the error rate metrics (baseline included).  
Thus, for confidence interval of 95%, we need to validate if the \(p\)-value is below the \(\alpha=0.05\) threshold (\(p<0.05\)) to reject the null hypothesis (\(H_{0}\)) or if the \(p\)-value is above (\(p>0.05\)) to reject the alternative hypothesis (\(H_{1}\)). In addition, an alternative hypothesis to be tested is formulated for each metric. Table 7 details the alternative hypotheses and results obtained, providing statistical evidence.  
Therefore, in all three alternative hypotheses, we compute the \(p\)-value lower of \(\alpha=0.05\) threshold and, thus, we reject the null hypothesis. In other words, the rejection indicates a statistically significant  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Hypothesis Test** & \(p\)**-Value** \\ \hline Proposed model presents lower CER results than traditional decoding & \(6\times 10^{-7}\) (\(H_{0}\) rejected) \\ Proposed model presents lower WER results than traditional decoding & \(5\times 10^{-7}\) (\(H_{0}\) rejected) \\ Proposed model presents lower SER results than traditional decoding & \(5\times 10^{-7}\) (\(H_{0}\) rejected) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistical analysis (\(\alpha=0.05\)).  
Figure 10: Error rates summary (lower is better), (a) Character Error Rate (CER); (b) Word Error Rate (WER); (c) Sequence Error Rate (SER).  
superiority of our proposal when using spelling correction as post-processing of text over the traditional decoding method.