esa-sceva/satcom-chunk-collection huggingface dataset:
# Best Practices for Applying and Interpreting the Total Operating Characteristic
###### Abstract  
The Total Operating Characteristic (TOC) is an improvement on the quantitative method called the Relative Operating Characteristic (ROC), both of which plot the association between a binary variable and a rank variable. TOC curves reveal the sizes of the four entries in the confusion matrix at each threshold, which make TOC curves more easily interpretable than ROC curves. The TOC has become popular, especially to assess the fit of simulation models to predict land change. However, the literature has shown variation in how authors apply and interpret the TOC, creating some misleading conclusions. Our manuscript lists best practices when applying and interpreting the TOC to help scientists learn from TOC curves. An example illustrates these practices by applying the TOC to measure the ability to predict the gain of crop in Western Bahia, Brazil. The application compares four ways to design the rank variable based on the distance to either pixels or patches of either the presence or change of crop. The results show that the gain of crop during the validation time interval is more strongly associated with the distance to patches rather than pixels of crop. The Discussion Section reveals that if authors show the TOC curves, then readers can interpret the results in ways that the authors might have missed. The Conclusion encourages scientists to follow best practices to learn the wealth of information that the TOC reveals.  
chage; land use; land cover; TOC; AUC; ROC
# Sparse Data-Extended Fusion Method for Sea Surface Temperature Prediction on the East China Sea
## 5 Conclusions  
Considering that the whole sea surface temperature has a certain correlation in local areas, this paper first clusters the sea surface temperature data retrieved by satellite remote sensing, then judges which clusters these stations are distributed among according to the location of the observation data, and finally uses the data-assimilation method proposed in this paper to assimilate the remote sensing data with the observation data to obtain the background data with high accuracy, Thus, high prediction accuracy is obtained.  
1. Through the calculation of the correlation relationship between the sea surface temperature retrieved from the remote sensing data, the temperatures with the same law are correlated. It provides a more scientific and accurate way to assimilate the sea surface temperature with the same law for the measured data.
2. The method proposed in this paper has the characteristics of fast convergence speed and stable training process. It can be seen from Figure 4 that the training error of the model tends to converge after 300 iterations.
3. By comparing the calculation results of different methods, the method proposed in this paper shows the best results before and after data assimilation. Especially in the prediction of 3, 7, and 14 days, the method proposed in this paper reduces the error of mmae by about 0.3 compared with other methods.  
Data curation, Z.Z.; Formal analysis, K.C.; Investigation, Y.J.; Methodology, L.W.; Validation, J.L.; Writing--original draft, X.W.; Writing--review & editing, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Tianjin enterprise postdoctoral innovation project merit funding project] grant number [T]QYBSH2018025], Marine Telemetry Technology Innovation Center of the Ministry of Natural Resources, Key Laboratory of Marine Environmental Survey Technology and Application, MNR, [Science and Technology Department of Zhejiang Province] grant number [LGG21F020008].  
This study did not require ethical approval, so we choose to exclude this statement.  
This study did not involve humans, so we also choose to exclude this statement.  
Data Availability Statement:Data sharing not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**LSTM** & **Situation** & **1 Day** & **3 Days** & **7 Days** & **14 Days** \\ \hline \multirow{2}{*}{All points} & Before assimilation & 0.8310 & 1.3930 & 2.0955 & 3.1397 \\  & After assimilation & 0.7473 & 1.1665 & 1.7176 & 2.7794 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Temperature prediction results under different prediction aging after assimilation.
# Nonlinear Metasurface for Structured Light with Tunable Orbital Angular Momentum
## 3 Summary  
In this work, we have experimentally demonstrated an OAM-beam-converting metasurface enabled by the Mie-resonances of the ChG cubes operating at the telecommunication wavelength. We designed and fabricated the beam converter switching an HG mode to an OAM beam. The fabrication of the ChG film requires only single-step lithography. Moreover, ChG possesses relatively large third-order nonlinearity at near-infrared wavelengths. A nonlinear metasurface which can generate reconfigurable OAM beams with opposite topological charges is designed and demonstrated theoretically. With an HG input beam, the output of the metasurface in the linear regime has a helical wavefront and carries an OAM with topological charge plus one. When the input intensity increases to a specific value in the nonlinear regime, the output wavefront is still helical but twisted in the opposite direction than in the linear regime, which leads to a negative charge of the OAM carried by the beam. The experimental realization of the nonlinear tunable metasurface might be enabled with the use of silver-doped As\({}_{2}\)S\({}_{3}\), which is characterized by a nonlinear coefficient two orders of magnitude larger than that of a pure As\({}_{2}\)S\({}_{3}\) film. This direction will be explored in future studies and the results will be presented elsewhere.
# Potential and Limitations of the New European Ground Motion Service in Landslides at a Local Scale
## 2 Materials and Methods
### Sentinel-1 A-DInSAR by Means of European Ground Motion Service
During the multi-interferogram unwrapping step, two families of algorithms are used in the EGMS: network flow and iterative L\({}^{\text{P}}\)-norm optimizations algorithms [54]. The atmospheric filtering differs across processing chains. They are based on the removal of polynomial functions and stratified components after phase unwrapping and kriging of the phase residuals of the identified primary network [51]. With respect to the interpretation of the negative or positive values of the MP, it depends on each dataset. In the case of the LOS MP, the sign indicates a movement towards the satellite if the value is positive; if the value is negative, the movement is away from the satellite. In the case of East-West component, negative values represent a westward movement while positive values represent eastward movements. Negative vertical motion can mean subsidence movement, while positive values can be associated with uplift phenomena. In this research, four datasets from level 2b and level 3 were downloaded and exported to a GIS (Table 1): (1) 001 ascending track from 10 March 2015 to 27 December 2021 with 359 acquisitions and 052 descending track from 6 February 2015 to 31 December 2021 with 345 acquisitions, and (2) horizontal and vertical ground deformation from 5 January 2016 to 16 December 2021, both with 363 acquisitions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Level** & **Time Period** & **Number of Acquisitions** & **Product Code** \\ \hline
001 ascending track & L2b & 01/2015-12/2021 & 359 & L2b\_001\_0255\_WW1\_VV \\ \hline
052 descending track & L2b & 01/2015-12/2021 & 345 & L2b\_052\_0818\_W1\_VV \\ \hline East-West component & L3 & 01/2016-12/2021 & 363 & L3\_E30N23\_100 km\_E \\ \hline Vertical component & L3 & 01/2016-12/2021 & 363 & L3\_E30N23\_100 km\_U \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sentinel-1 A-DInSAR datasets were used in this study. Time period, number of acquisitions, and product code are shown. Figure 1: **(A)** Location of Asturias in the North of Spain. (**B**) Location of the La Miera landslide in Asturias Region and regional geology. (**C**) Geological map of the Central Coal Basin and location of the La Miera landslide and the town of Pola de Lena. (**D**) Geological map of the La Miera landslide taken from [43; 44; 45]. (**E**) La Miera landslide views. Photo taken from Pola de Lena, showing the clear and inferred boundary of the landslide area.
# Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models
## 2 Descriptive Statistics
### Dataset Description  
We use the dataset obtained from a mobile network operator that offers multiple services (applications), such as Internet access, messaging, calls, file transfer, etc. The data flow is bidirectional, with traffic flowing from the base station to the user device in the downlink direction and from the device to the base station in the uplink direction. Each user device is associated with a unique masked mobile station international subscriber directory number (MSISDN). The monitoring system records the upload and download data generated by each device for each application class every hour. The statistics represent the volume of traffic in megabytes (MB) for both the download and upload directions.  
Variable descriptions are provided in Table 2, and dataset records are presented in Table 3. The dataset comprises 41,479,488 records containing information for 15 days, with  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Task** & **Data Source/Application** & **Model** & **Evaluation Metric** \\ \hline \multirow{3}{*}{[41]} & Traffic forecasting and anomaly detection & Traffic from wireless sensor network & ARIMA & Complexity, Accuracy, Intelligence, Independence \\  & Traffic forecasting & Traffic from 191 eNodeB & ARIMA ARIMA ARIMA, FARIMA, SVM, Welevet, ANN & AIC, AICc, BIC MSE, NMSE \\  & Traffic forecasting & Voice and data traffic from 600 cells & Exponential Smoothing, Holt-Winters ARMA, ARMA, ARMA,  
a total of 94,632 users' devices. Table 4 displays the list of application classes along with the corresponding number of records. There are 16 application classes with the largest being \"Web Applications\", which is a client-server application that allows a user to interact with a web server using a browser. The top three most frequently used applications by users are also \"Instant Messages Applications\" and online \"Games\". The least used classes include \"Legacy Protocols\" and \"DB Transactions\", which are responsible for working with database systems and file systems. The\"Others\" category represents non-classified applications not related to user traffic. These may include technical services such as directory services, network management services, automatic network address configuration, and mapping for location determination.
## 3 Improved DBSCAN Algorithm Based on Sparrow Search Algorithm
### Determine the Parameter Range and the Optimal Number of Clusters  
#### 3.2.1 Adaptive Calculation of Parameter Ranges  
In the SSA-DBSCAN algorithm, the parameter Eps depends on how the sample points are distributed within the dataset. The key steps are outlined below:  
Step 1: Start by creating the distance distribution matrix for dataset \(D\) using the following equation:  
\[D_{n\times n}=\{Dist(i,j)|1\leq i\leq n,1\leq j\leq n\} \tag{5}\]  
where \(D_{n\times n}\) represents the matrix is symmetric, \(n\) represents the count of items in dataset \(D\), and \(Dist(i,j)\) is denoted as the distance between the \(i\)-th and \(j\)-th object in dataset \(D\).  
Step 2: For each row in the distance matrix \(D_{n\times n}\), sort the distances in increasing order. The first column will always be zero, since every object has zero distance from itself. By looking at the sorted \(k\)-th column, we can create a distance vector \(D_{k}\) that collects the distance of each object to its \(k\)-th nearest neighbor.  
Step 3: Find the average value in this \(k\)-nearest neighbor distance vector \(D_{k}\), which is the \(k\)-average nearest neighbor distance \(\overline{D_{k}}\), which can also be considered as a possible value for the Eps parameter. By repeating this for different \(k\) values, we can build a list of candidate Eps parameters, which is denoted as follows:  
\[D_{Eps}=\left\{\overline{D_{K}}\middle|1\leq K\leq n\right\} \tag{6}\]Step 4: Obtain each value from the list of Eps parameters, and for each object in the dataset count how many other objects fall within that Eps distance. Then, calculate the mathematical expectation of these counts for all objects. This will become the MinPts parameter, which will be used as the threshold for the neighborhood density in dataset \(D\), which is denoted as follows:  
\[MinPts=\frac{1}{n}{\sum_{i=1}^{n}{P_{i}}} \tag{7}\]  
where \(P_{i}\) represents the number of objects in the Eps neighborhood of the \(i\)-th object, and \(n\) represents the total number of objects in the dataset \(D\). This paper takes the SampleD dataset as an example (the distribution of sample points is shown in Figure 1, which contains a total of 2000 sample points from four categories). Following the above steps, the maximum value of \(D_{Eps}\) is 19.82. Therefore, the Eps parameter range of the SampleD dataset is \([0,\ 19.82]\), and the MinPts parameter range is \([0,\ 515]\).
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 5 Performance Evaluation
### Results and Analysis  
#### 5.2.1 Dynamic vs. Static  
Figure 5 shows that dynamic deployment outperforms static deployment with respect to coverage, defined as the ratio between the nodes covered and the total number of nodes. Comparing dynamic and static deployment over six seconds intervals reveals that dynamic deployment attains superior coverage because PADP considers sensor node mobility. Furthermore, the deployment was determined based on the predicted positions of sensor nodes not just their present locations. This additional consideration preserves higher levels of coverage for more extended periods. The figure also illustrates that for both dynamic and static deployment, the coverage achieved is higher for the experiments with more gateways deployed.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(d\) & 80 m & \(\lambda_{d}\) & 200 m \\ \(N\) & 6 s & \(\gamma\) & 5 \\ \(k_{1}\) & N(\(\pi\),0.1\(\pi\)) & \(k_{2}\) & N(\(\pi\),0.1\(\pi\)) \\ \(k_{3}\) & N(2\(\pi\),0.2\(\pi\)) & \(k_{4}\) & N(1,0.1) \\ \(k_{5}\) & N(1,0.1) & \(\lambda\) & N(3,0.3) \\ \(v\) & N(1,0.1) m/s & \(M\) & 100,000 \\ \(D_{q}\) & 0.1 s & \(D_{c}\) & 0.1 s \\ \(T_{r}\) & 3.1 kbps & \(P_{r}\) & 40 bytes \\ \hline \end{tabular}
\end{table}
Table 1: Simulation Settings.
# A Compact Size Antenna for Extended UWB with WLAN Notch Band Stub
## 3 Results and Discussions
### Group Delay  
Group delay may be used to characterize the degree of distortion in the UWB antenna. In a UWB system, non-uniformity in group delay is desirable [31]. However, keeping the various losses with a small amount of non-uniformity is acceptable. Thus, two identical antennas are placed face-to-face and non-face-to-face at 30 cm to generate a far-field scenario. The group delay of the proposed notch band UWB is illustrated in Figure 9, where, except for the notch band, the group delay is entirely consistent. Except for the notch bands where the value is higher than >4 ns, the group delay varies less than 1 ns for the operational frequency range. Based on these results, the proposed antenna can be utilized for the UWB system, and there will be no distortion between the transmitting and receiving antennas.  
Figure 8: The radiation pattern of proposed notch band extended UWB antenna at various frequencies.
## 3 Results
### Validation of the Degradation Areas and Degradation Level
#### 3.2.2 Cross Validation with High Resolution Data  
We compared the degradation map from MODIS and Landsat product with corresponding high resolution (30 m) degradation map in typical areas in which forest degradation were in a serious situation, as shown in Tables 1 and 2. Specifically, the high resolution (30 m) degradation map was derived by using a decision tree model from Landsat data from 2000 to 2010. In general, high accuracy was achieved of the degradation map from MODIS-Landsat product. The overall accuracy of the classification of forest degradation level from MODIS product was as high as 80.40%, and the Kappa Coefficient was 0.79. And the overall accuracy of the classification of forest degradation level from MODIS-Landsat product was as high as 84.35%, and the Kappa coefficient was 0.82. The high overall accuracy and Kappa coefficient demonstrated that the degradation map from MODIS-Landsat product obtained high accuracy.
## 5 Case Studies
### Case 1: A Proprietary Retrofit  
In this case, we use a radio networked device with a camera and optical character recognition (OCR) to retrofit an electromechanical induction type energy meter with a communication interface, transmitting these values to an energy management system every five minutes. The cyber representation of the counter takes place in an IoT cloud of the camera manufacturer. OCR is a relatively mature and well-researched technology [74] for the digital retrofitting of analog devices, whereby feature extraction and detection without the use of handwriting, while the narrowly defined context and the small character set is not a big challenge. Further work on OCR of energy meters can be followed in [75; 76; 77].  
Despite the advantageous conditions for text recognition with an energy meter, a good result depends on the initial configuration of the software. The following parameters must be set: differentiation between analog and digital meters, the background color, placement of the meter reading in the image, as well as information about possible partially visible digits owing to the rotary movement. We were able to do this configuration with Windows-based software after gluing the camera module to the counter (Figure 2). The power consumption of the camera module is supplied by a 3.6 V 1600 mAh AA lithium-ion battery from an external box. Alternatively, a power supply via USB cable would be possible.  
Figure 2: Mounted camera module on the analog energy meter.  
The camera module uses a proprietary, undocumented radio protocol in the 868 MHz ISM band with a transmission rate of 50 kbit/s and AES 128 encryption according to the manufacturer's specifications. The receiving IP gateway is located in the same IP gateway building, four floors below. The gateway then transmits the measured values to the IoT manufacturer's cloud service, where the data can be retrieved via a documented application programming interface (API) from the energy management system in JavaScript Object Notation (JSON).  
Up to this point, the setup was still easy because we had only moved into the well-documented manufacturer's world. The effort of the non-standardized system becomes clear in the connection to the universal EMS. As access to the IoT cloud is proprietary, a connector had to be implemented in the EMS software first. This connector offers the user sufficient configuration parameters, such as the entry of API access data and interactive selection from the sensors available in the IoT cloud, so that the implementation can be used for all sensors in this cloud. This multiple-use capability relativizes the extra effort required for non-standard interfaces.  
From the link via the connector, the user now generates a measuring point in the EMS and can use it to include the data received from the sensor in the overall energy monitoring. The data transmitted by the sensor are meter readings. The conversion into consumption and costs takes place automatically by the EnM software. Figure 3 shows the result, where the user has created an exemplary dashboard for this sensor.
# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 2 Theoretical Background
### Conventional Long Short-Term Memory (LSTM)
It takes \(h_{t-1}\) and \(x_{t}\) and applies the sigmoid function. Then, the value that takes the hyperbolic tangent (tanh) function and Hadamard product operation is sent from the input gate. Since the range of \(i_{t}\) is from 0 to 1 and the range of \(g_{t}\) is from \(-1\) to 1, each represents the intensity and direction of storing current information. Since the ocean weather has three causalities (i.e., causality with the ocean weather at the previous time, causality between the parameters of ocean weather, and causality between spaces), three types of causalities for a good prediction model should be considered. Using LSTM, time-series causality can be solved. To solve the other two causalities, the ocean weather should be input at all points where training is desired as vector \(x_{t}\). As the size of the area to be trained increases, the input data vector becomes increasingly exponential. In addition, considering the matrix operation required for learning, performing such a task is practically impossible. To address this problem, LSTM requires a spatial element--that is, a method that can be used as 2D data input. Figure 2: Configuration of long short-term memory (LSTM) and cell state “A”.
# Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids
## 3 System Model
In this paper, we introduce the transmission rate of the node into the energy model, because the bandwidth and SNR of the channel have a certain impact on the transmission energy consumption. So the energy consumption of sensor node \(n\) data transmission is as follows:  
\[E_{n,t}=\frac{1}{\eta}(p_{t}+\alpha)*\frac{l*t_{n}}{r_{n}}, \tag{3}\]  
where \(\eta\) is defined as the efficiency of the power amplifier, \(p_{t}\) is the transmission power, \(\alpha\) is the circuit power consumption of data transmission, \(l>0\) is the amount of data acquisition per unit time and \(r_{n}\) is the data transmission rate. The energy consumption of the sensor node receiving acquisition data is expressed as follows:  
\[E_{n,r}=e_{c}*l*t_{n}, \tag{4}\]  
where \(e_{c}\) is the circuit power for data reception. In addition, the CH receives data from all CMs. In order to reduce energy consumption, the received data need to be fused into \(l\cdot t_{n}\) and then transmitted to the BS. The energy consumption per bit of data fusion is denoted as \(E_{DA}\). According to the above energy model, the energy consumption of CHs and CMs in each round can be obtained. \(x_{ij}\) is denoted as \(x_{ij}\in\{0,1\}\) as a binary variable, which represents the association status of CM \(i\), where \(i\in\mathcal{L}\) and \(j\in\mathcal{K}\). If \(x_{ij}=1\), this means that the CM \(i\) chooses to access the CH \(j\), otherwise \(x_{ij}=0\). Then, the following constraints need to be added:  
\[\sum_{j}x_{ij}=1,\quad\forall i\in\mathcal{L}, \tag{5}\] \[\sum_{i}x_{ij}\leq N_{j}^{max},\quad\forall j\in\mathcal{K},\] (6) \[x_{ij}\in\{0,1\},\quad\forall i\in\mathcal{L},j\in\mathcal{K}, \tag{7}\]  
where \(N_{j}^{max}\) is the maximum number of CMs that CH \(j\) can access. Consequently, the energy consumption of CM \(i\) in each round can be expressed as follows:  
\[E_{CM_{i}}=\frac{1}{\eta}(p_{t}+\alpha)*l*t_{i}\sum_{j}\frac{x_{ij}}{r_{ij}} \tag{8}\]where \(r_{ij}\) is the transmission rate from CM \(i\) to CH \(j\). The energy consumption of each CH includes three parts: receiving data from CMs, data fusion and sending data to the BS. The energy consumption of the CH \(j\) in each round can be expressed as follows:  
\[E_{CH_{j}}=e_{c}*\sum_{i}(l*t_{i}*x_{ij})+E_{DA}*\left(\sum_{i}(l*t_{i}*x_{ij})+ l*t_{j}\right)+\frac{1}{\eta}(p_{t}+\alpha)\frac{l*t_{j}}{r_{con}}, \tag{9}\]  
where \(r_{con}\) is the transmission rate between the CHs and the BS. Since the location of the BS is fixed, the CHs have been determined by the CHs selection algorithm, \(r_{con}\) is a constant.