esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey on Real-Time Applications of WSN
## 3 Applications of WSN
### Industrial and Business Uses  
This is very helpful in an industrial application. These sensors are also used for monitoring, controlling, and processing the data, such as pressure, vibration, temperature, and viscosity. Sensors collect the data or information sent to the control system management. They also play a pivotal role in establishing the business process. A better routing algorithm provides us with a long time, an easy to use system, and low cost devices which are very helpful in business applications [35]. The WSN uses the building decision support systems, avoiding various problems in the real world. The decision support system is also being employed increasing widely in agriculture to solve the most complex problems of agriculture related to the monitoring of land. This approach will be valid for helping the farmers make the right decisions. The rapid and progressive development of WSN applications in precision agriculture makes it possible to enhance the efficiency and productivity in various agriculture production systems. Additionally, farmers get an insight and detailed information pertaining to their fields and find their best solution via the usage of WSN. Table 3, represents the examples of real-time applications for industrial and business uses that utilize WSNs.  
[PERSON] et al., 2009 investigated the gas meter sensor system. They developed a network hierarchy, such as receiving, transmitting, auto-reconfiguration, scheduling, and deep sleeping strict requirement of energy utilization.  
[PERSON] et al., 2011 studied the reduction of air conditioning energy utilization. They gave an insight into the mechanism of how to reduce the utilization by predicting a mean vote and computational fluid dynamics approach.  
[PERSON] et al., 2012 investigated a coffee factory in Madrid, Spain. They installed the sensors in the industry and it is a very daunting task to deploy sensors. They installed them everywhere in the production unit from raw coffee to the final product. They installed the hardware system along with the software for better sensing the pollutants in the industry.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Application** & **Sensors** & **City Deployed in** \\ \hline [PERSON] et al. (2013) [33] & Climate Control & \
\begin{tabular}{c} Vapor pressure deficit (VPD), \\ Humidity, Temperature \\ \end{tabular} & Punjab, India \\ \hline [PERSON] et al. (2016) [34] & Intelligent Environment &
\begin{tabular}{c} Multiple Sensors, illumination, \\ Soil Moisture, CO\({}_{2}\) \\ \end{tabular} & Jiangsu, China \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examples of prototyped applications for greenhouse monitoring.
## 3 Results and Discussion  
### Therapeutic Intervention and Symptom Change during the Proposed Program  
To analyze the effectiveness of the therapeutic intervention on the symptoms of the subject in every session, the observation notes (dramatic changes in positive and negative emotions of the subject, self-reporting of his traumatic events, treatment with stabilization techniques) and the transcripts were analyzed qualitatively. The qualitative analysis was compared with the calculated HRV in each session. The comparison is outlined below.  
1.1 Session One: Exploring Traumatic Events and CPTSD Symptoms, Creating a Safety Zone, and Training Stabilization  
In the first session, traumatic events and CPTSD symptoms were first explored. After this, the stabilization treatment to establish a safety zone for relieving and self-managing the subject's symptoms was implemented. Figure 1 shows the 5 min RMSSD and the SDNN for the first session. At 15 min from the start of the first session, the subject self-reported his core traumatic event (the \"T\" remark). The abrupt negative emotional change (the \"N\" remark) and the abrupt drop in the RMSSD near the 15 min point appear to have been related to this self-reporting. However, the SDNN near the 15 min point increased in contrast to the RMSSD.  
In the first session, the subject self-narrated his traumatic events at approximately minute 14 and minute 51 from the session start: \"Hits for no reason...all my friends know.\" \"I'm still scared when I run into kids who look like mutts on the way. When I think of their faces, I get angry...scared.\" The subject expressed negative emotions, such as shame and neglect, while recalling the above key traumatic events. The subject self-reported symptoms of CPTSD caused by these events, such as re-experience, avoidance, negative self-concept, and interpersonal difficulties. To address the core symptoms, four sets of safety-zone-making and stabilization techniques were performed from minute 35 to minute 52. When the subject recalled the traumatic event, he self-reported discomfort as \"8\" on the scale before the stabilization technique was implemented. After the stabilization technique was performed, he self-reported \"4\" for his discomfort level.
# Vector Approximate Message Passing Based OFDM-IM Detection for Underwater Acoustic Communications
## 3 Proposed Method  
OFDM-IM symbols with different non-zero supports exhibit sparse structure, which is different from the OFDM frame. In the OFDM frame, almost all subcarriers are required for data transmission. To solve the problem of data detection in OFDM-IM, VAMP based framework is incorporated by considering the symbol sparsity generated from the SAPs. In this section, the concept of VAMP detection and detailed message scheduling is first introduced. Then the VAMP-based data detection with the designed prior aided shrinkage functions is presented.
## 2 Key Steps in a FRB Search Pipeline
### Matched Filtering  
In time-domain signal processing, matched filtering is an effective way of unearthing a non-Gaussian signal from noisy data if the shape of the expected signal is known a priori. In reality, any time series  
\[X[t]=a[t]+n[t], \tag{14}\]  
where \(a[t]\) is the signal and \(n[t]\) is the noise at any given time \(t\). Matched filtering involves applying a filter to the time series such that maximum S/N will occur when the impulse response of the filter is equal to the shape of the signal in the time series. Mathematically, this means that the filter is convolved with the time series to detect the signal and determine its physical properties. In time-domain astronomy, this filtering technique is used widely to detect FRBs and single pulses in noisy data, in real-time. In the simplest case, a matched filter is a set of box cars with varying widths which approximates the shape of a single pulse. For any time series produced after dedispersing the data at a given DM, these boxcars of varying widths are convolved with the time series and the result is searched for any points above the signal-to-noise threshold. In reality, any real pulse can be best approximated by a Gaussian pulse. Hence, for a time series \(X[t]\), the maximum S/N is  
\[S/N_{\text{max}}=\max[X[t]*B[t,w]], \tag{15}\]  
where \(B[t]\) is the boxcar function and \(*\) denotes a convolution operation. \(S/N_{\text{max}}\) will maximise when the width \(w\) is closest to the true FRB width. Since this width is unknown a priori, the time series has to be convolved with a large number of boxcar trials, thus increasing the complexity of the search. To optimise this process, boxcar widths are geometrically spaced in powers of 2 to convolve the data. The data themselves are also downsampled for larger widths to reduce the data volume/number of time samples and hence, the time of processing. Typical widths of FRB range from a millisecond to a few tens of ms [26]. Hence, real-time FRB surveys generally only search for transients spanning between an order 0.1 and 100 ms--although recent discoveries have shown the importance of going to larger widths in the search [27; 28; 29].
## 5 Empirical Study Design
### Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Repository** & **Project Key** & **Project** & **\#Issues** & **Mean** & **Std** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline Apache & MESOS & Mesos & 1513 & 3.15 & 2.14 & 0 & 2 & 3 & 5 & 13 \\ \hline Apcelerator & ALOY & Alloy & 251 & 3.71 & 2.32 & 0 & 3 & 3 & 5 & 13 \\ \hline Apcelerator & TISTUD & Appcelerator Studio & 2794 & 5.48 & 3.23 & 0 & 3 & 5 & 8 & 40 \\ \hline Apcelerator & APSTUD & Aptana Studio & 476 & 7.93 & 7.19 & 0 & 5 & 8 & 8 & 100 \\ \hline Apcelerator & CLI & Command–Line Interface & 293 & 3.18 & 2.30 & 0 & 1 & 3 & 5 & 13 \\ \hline Apcelerator & DAEMON & Daemon & 205 & 5.58 & 3.76 & 1 & 3 & 5 & 8 & 13 \\ \hline Apcelerator & TIDOC & Documentation & 1005 & 3.58 & 3.68 & 0 & 1 & 2 & 5 & 40 \\ \hline Apcelerator & TIMOB & Titanium & 3915 & 4.68 & 3.32 & 0 & 3 & 5 & 5 & 20 \\ \hline Atlassian & CLOV & Clover & 336 & 5.33 & 11.03 & 0 & 1 & 2 & 5 & 100 \\ \hline Atlassian & CONFCLOUD & Confluence Cloud & 234 & 2.91 & 2.24 & 0 & 2 & 2 & 3 & 13 \\ \hline Atlassian & CONFSERVER & Confluence Server and Data Center & 456 & 3.12 & 1.93 & 0 & 2 & 3 & 3 & 13 \\ \hline DNNSoftware & DNN & DotNetNuke Platform & 234 & 2.91 & 2.24 & 0 & 2 & 2 & 3 & 13 \\ \hline DuraSpace & DURACLOUD & Duracloud & 310 & 1.72 & 1.7 & 0 & 1 & 1 & 2 & 20 \\ \hline Hyperledger & FAB & Fabric & 303 & 2.69 & 3.2 & 0 & 1 & 2 & 3 & 40 \\ \hline Hyperledger & STL & Sawtooth & 206 & 2.09 & 1.19 & 0 & 1 & 2 & 3 & 5 \\ \hline Lsstcorp & DM & Data Management & 5381 & 3.05 & 6.87 & 0 & 1 & 2 & 2 & 100 \\ \hline MongoDB & COMPASS & Compass & 260 & 3.55 & 1.85 & 1 & 2.75 & 3 & 5 & 8 \\ \hline MongoDB & SERVER & Server & 519 & 2.58 & 2.4 & 0 & 1 & 2 & 3 & 20 \\ \hline MongoDB & EVG & Evergreen & 2824 & 1.43 & 0.86 & 0 & 1 & 1 & 2 & 8 \\ \hline Moodle & MDL & Moodle & 1394 & 11.8 & 18.81 & 0 & 2 & 5 & 13 & 100 \\ \hline MuleSoft & MULE & Mule & 2935 & 3.88 & 3.46 & 0 & 1 & 3 & 8 & 13 \\ \hline Sonatype & Sonatype’s & Nexus & NEXUS & 1425 & 1.7 & 1.82 & 0 & 1 & 1 & 2 & 40 \\ \hline Spring & XD & SpringXD & 2811 & 3.16 & 2.56 & 0 & 1 & 3 & 5 & 20 \\ \hline Talendororge & TDP & Talend Data Preparation & 471 & 2.31 & 1.84 & 0 & 1 & 2 & 3 & 13 \\ \hline Talendororge & TDQ & Talend Data Quality & 859 & 6.01 & 4.66 & 0 & 3 & 5 & 8 & 40 \\ \hline Talendororge & TESB & Talend ESB & 730 & 2.13 & 1.45 & 0 & 1 & 2 & 3 & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Story point statistics for various projects.
## 4 Performance Evaluation
### Classification Prediction Accuracy
The NB provided overall accuracy of 86.94%, whereas DA, SVM and DT algorithms performed in a somewhat similar fashion, delivering overall classification accuracy of more than 97.00%. Finally, KNN classifier in the Energy-Aware Framework performed better than the other five classifiers with overall classification accuracy of 98.82%. On obtaining the best performing model for daily bus ridership travel times-based scenario, its performance was evaluated against advance evaluation metrics, as presented in Table 2. It can be seen that the overall daily bus ridership travel data show the highest precision, recall and f-measure, as 0.97, 0.96 and 0.97, respectively. The Receiver Operating Characteristic (ROC) curve is presented in Figure 5 for the best performing model (KNN), illustrating its ability of classification against No-Skill metric. The overfitting occurs when bias (train loss) is reduced and variance is increased in relation to model complexity. As more parameters are added to a model, the complexity of the model rises, and variance becomes our primary concern while bias (train loss) steadily falls. Our model attempted to learn the relationship on the training data and was evaluated on the test data with 70% of data as training, 20% as test and 10% as validation. On the other hand, instead of training on fixed numbers of epochs, we used early stopping process to stop the training because the validation loss was raised. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Machine Learning Algorithm** & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline K-Nearest Neighbour (KNN) & 98.82\% & 0.97 & 0.96 & 0.97 \\ Discriminant Analysis (DA) & 98.75\% & 0.96 & 0.96 & 0.96 \\ Support Vector Machine (SVM) & 98.75\% & 0.97 & 0.95 & 0.95 \\ Decision Tree (DT) & 97.78\% & 0.97 & 0.96 & 0.97 \\ Naive Bayes (NB) & 86.94\% & 0.86 & 0.85 & 0.86 \\ Artificial Neural Network (ANN) & 73.08\% & 0.73 & 0.72 & 0.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification of mobility prediction accuracies. Figure 5: KNN Receiver Operating Characteristic (ROC) Curve illustrating the diagnostic ability of a KNN classifier against No-Skill threshold.
# Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm
## 1 Introduction  
Traffic problems are common problems faced by cities all over the world. Common traffic problems include traffic congestion, exhaust pollution, traffic accidents, parking difficulties and so on [1]. At present, the main traffic problems in urban traffic are traffic congestion, the increase of energy consumption and the aggravation of environmental pollution. Traffic jams not only cause a huge waste of time and environmental damage, but also hinder the development of urbanization and become the blocking factor to economic development. In the face of these severe urban traffic problems, how to solve the traffic congestion problem better under the condition of limited traffic resources has become a hot topic.  
Researchers are beginning to try to use modern science and technology to solve traffic problems, integrating sensing, information, communication, computer technology, and intelligent transportation systems [2]. Intelligent transportation system technology, represented by the car network, can help users plan the optimal route and reduce vehicle travel time by strengthening the traffic distribution of the urban road network and conducting traffic guidance in real-time. It is an effective method to optimize the capacity of the urban road network and to reduce traffic congestion and traffic accidents.  
As the core technology of intelligent transportation system, the development of dynamic path planning technology is becoming more and more mature and perfect [3]. In this paper, we present the Spatial Optimized Dynamic Path Planning (SODPP) algorithm which is based on the road-map loading mechanism and search direction optimization.  
Our contributions are as follows:  
1. We enhance the effect of the loading mechanism for road maps by dividing the connected sub-net and building an spatial index;
2. We enhance the effect of dynamic path planning by optimizing the search direction.  
The following is organized as follows: In Section 2, we discuss related works, while in Section 3, we discuss the motive behind our algorithm; the whole algorithm is discussed in Section 4; Section 5 gives experiment results and discussions; the conclusions are given in Section 6.
## 2 Results
### Environmental Responses of SIF and GPP  
Figure 4 shows the correlation between daily SIF, GPP and environmental variables. The regression analysis showed that the influence of Ta on SIF reached 20%, while the influence of PAR on SIF was 15%, which was slightly lower than that of Ta. Both Ta and PAR had strong correlations with GPP. Ta had an impact on GPP of 67%, while the influence of PAR on GPP increased to 79%. However, GPP appeared to be saturated when PAR was much higher. In addition, SWC had no significant impact on either SIF and GPP.  
Figure 3: Correlation of SIF-GPP at different time scales: (a) daily; (b) 8-day; (c) monthly.  
In order to explore the impact of different environmental changes on the relationship between SIF and GPP, in this study, we obtained the changes in the relationship between SIF and GPP under different environmental which were divided into groups with 30 data. Figure 5 shows that when Ta was low, SIF had a strong relationship with GPP. When Ta was between 8.8 and 13.6 \({}^{\circ}\)C, the performance was the strongest. After that, the relationship between SIF on GPP decreased. When Ta attained 22.1-28.2 \({}^{\circ}\)C and 29.7-32.5 \({}^{\circ}\)C, no significant linear correlation was detected.  
The impact of PAR on the correlation between SIF and GPP also showed a trend of decreasing linear correlation between SIF and GPP with the increase of PAR. When PAR was at 21.29-79.78 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\), SIF appeared the strongest indication of GPP. When PAR was greater than 362.84 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\), there was no significant linear correlation between SIF and GPP.  
When SWC was 0.20-0.30 m\({}^{-3}\), SIF had no significant linear correlation with GPP. When SWC was 0.30-0.35 m\({}^{3}\) m\({}^{-3}\), SIF had the strongest indication effect on GPP, and then, with the increase in SWC, such effect decreased dramatically. This reduction may have been related to the increase in precipitation during this period, which led to the lower radiation and affected GPP.  
It can be seen that Ta and PAR had a significant impact on the changes in SIF and GPP, while SWC did not have a significant impact on either. Regarding the correlation between SIF and GPP, except when SWC was 0.20-0.30 m\({}^{-3}\) m\({}^{-3}\), SIF had no significant linear correlation with GPP, and the linear correlation between SIF and GPP gradually decreased with the increase in Ta, PAR or SWC. When Ta or PAR was much higher, no significant linear correlation appeared between SIF and GPP.  
Figure 4: Correlation between SIF, GPP, and environmental variables on daily scale: Each point represents the daily average value.
## 3 Proposed Beam Learning with MARL
### Data Preprocessing  
The SRS provides the BS with comprehensive channel information across the entire bandwidth. Utilizing this information, the BS optimizes resource allocation, giving preference to areas with superior channel quality over other bandwidth segments. In this proposed work, emphasis was placed on a central cluster consisting of four resource blocks (RBs), each encompassing a bandwidth of 180 kHz. Within each RB, 12 subcarriers are positioned at 15 kHz intervals, resulting in a combined bandwidth of 720 kHz. A frequency-domain vector comprising 48 complex numbers is derived through channel estimation across this contiguous frequency range. Given that only a narrow band of the entire spec trum is required for the proposed algorithm, achieving high SNR for SRS transmission is feasible.  
For further analysis, this complex vector is transformed into its magnitude and then downscaled by a factor of 2, resulting in a real-valued vector comprising 24 elements. To ensure consistency, in the subsequent preprocessing stage, this 24-element vector is normalized by dividing it by its maximum value. This procedure is iterated for each of the four simultaneous users, producing four channel vectors.  
The other part of the state vector input consists of the phases of the phase shifter network for a particular RF chain of length \(N_{BS}\) which is 32 in this case. This is also normalized by the maximum absolute value of the phase vector. Here, four such phase shifter vectors are obtained for four RF chains.
## 1 Introduction
The IGS RTS combinations, individual analysis center streams and NTRIP mountpoint designations are listed in Table 2 ([[https://igs.bkg.bund.de/ntrip/ppp](https://igs.bkg.bund.de/ntrip/ppp)]([https://igs.bkg.bund.de/ntrip/ppp](https://igs.bkg.bund.de/ntrip/ppp))), (accessed on 25 July 2022). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**System** & **Type** & **Signals** & **Sats** \\ \hline \multirow{4}{*}{GPS} & Block IIR & L1 C/A, L1/L2 P(Y) & 8 \\  & Block IIR-M & L1 C/A, L1/L2 P(Y), L2C, L1/L2 & 7 \\  & Block IIF & L1 C/A, L1/L2 P(Y), L1/L2 M, & 12 \\  & Block IIF & L2C, L5 & 12 \\  & Block IIIA & L1 C/A, L1/L2 P(Y), L1/L2 M, & 5 \\  & L2C, L5 & & \\ \hline \multirow{2}{*}{GLONASS} & GLONASS-M & L1/L2 C/A and P, L3, FDMA & 18 \\  & GLONASS-K1 & L1/L2 C/A and P, L3, & 3 \\  & & FDMA+CDMA & \\ \hline \multirow{2}{*}{BDS-2} & GEO & \multirow{2}{*}{B1I, B2I, B3I} & 5 \\  & IGSO & B1I, B2I, B3I & 7 \\  & MEO & & 3 \\ \hline \multirow{2}{*}{BDS-3} & GEO & \multirow{2}{*}{B1C, B2a, B2b, B3C, B1I, B3I} & 3 \\  & IGSO & & 24 \\ \hline \multirow{2}{*}{Galileo} & IOV & \multirow{2}{*}{E1, (E6), E5a/b/ab} & 4 \\  & FOC & & 24 \\ \hline \multirow{2}{*}{QZSS} & GEO & L1 C/A (L1 C/B), L1-SAIF, L1C, & 1 \\  & IGSO & L2C, L5, L6-LEX & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Operational status of the GNSS constellations as of July 2022. APM (which was former called Institute of Geodesy and Geophysics, Chinese Academy of Sciences, CAS) has provided real-time orbit and clock streams to IGS RTS since 2020 with the mountpoint of SSRA00 CAS0. In this contribution, the hourly updated ultrarapid orbit prediction strategy is described in detail. The contribution is structured as follows. The ground tracking network used to estimate the hourly updated ultrarapid multi-GNSS orbits and the POD processing strategy at APM are summarized in Section 2. Orbits are assessed by comparing with GFZ final multi-GNSS orbits, orbit boundary overlapping comparison and SLR validation, and the corresponding results are addressed and analyzed in Section 3. In Section 4, the results are further analyzed, and some key issues are discussed. Finally, a summary is made in Section 5.
## 4 Smart5 Grid NetApp Use Cases
### Uci--Automatic Power Distribution Grid Fault Detection
#### 4.1.4 Site Architecture  
The pilot site of UC1 consists of several remote, medium voltage (MV) power substations along two contiguous feeders in which the smart fault selection automation framework will be implemented. The site architecture is presented in Figure 8.  
Figure 7: Software component architecture of the NetApp in UC1.  
The MV protection devices and remote terminal units (RTUs) forward the collected data to the local gateway. Its purpose is to send them through the DSO telecommunication infrastructure to the central systems in order to ensure the remote control of the electric grid and, in the meantime, also ensure the interaction with other MV protection devices in the field, with the aim of automatically finding and isolating a fault on the grid when it occurs. The MV protection devices use standard communication protocols, such as IEC 61850-MMS/GOOSE [37] and IEC60870-5-104 [38].  
The 5G network enables the gateway to send and receive, in a very fast and reliable manner, the information coming from and destined for the local MV protection devices. In addition, this kind of mobile communication network enables innovative edge computing functions provided by the telco operators.  
In this demonstration, we will use the computing and data storage resources provided by the local telco to implement a NetApp that collects information concerning the state of the 5G radio access network versus each gateway of all substations involved in this pilot.  
The objective is to have a clear view of the communication network layer so as to ensure better electric service levels and to dispatch the DSO technicians to the field only when it is strictly required.