esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Artificial Neural Networks (ANNs) were considered as particularly interesting candidates given their flexible way of approximating highly non-linear relationships between variables [9] and the fact that no a priori assumptions need to be made about signal stationarity nor the connectivity pattern (for a clear overview, see [10]). Temporal convolutional networks (TCNs), as well as recurrent neural networks (RNNs), are usually well-suited architectures for time series [11; 12; 13; 14; 15]. While RNNs are often seen as the gold standard for sequence modeling, TCNs have also proven their suitability, for instance, in financial forecasting [14], electric power forecasting [16] and language modeling [17]. However, it remains unclear whether ANNs can signal the presence or absence of connections and their strength. Although some authors already used ANNs to derive directed brain connectivity with multilayer perceptrons and recurrent networks [15; 18], these approaches did not include source-reconstructed EEG data. As stated before, unlike EEG source reconstruction, analyses based on EEG electrode levels do not allow for trustworthy inferences about interacting regions [19]. Hence, the suitability of ANNs in deriving directed connectivity between reconstructed EEG sources remains unknown. Our motivation to assess ANNs for directed connectivity estimation between reconstructed EEG sources was two-fold. First, although many connectivity estimators exist, it is not yet known which current ANNs architectures can cope better with source-reconstructed EEG activity and under various circumstances. The authors of [1] were the first to conduct a comprehensive simulation study on the influence of dipole location, noise level, inverse solution and connectivity estimation, as well as the interactions between these factors. It was shown that different circumstances call for different analysis pipelines and that under advanced noise levels and for particular dipole configurations, even well-established methods such as TRGC can return aberrant connectivity estimates. Second, ANNs boast several appealing modeling properties that are potentially relevant to EEG modelers, such as the ability to deal with non-stationarity, non-linearity and, depending on the ANN architecture, to dispense with the prior specification of model order. In order to assess the ability of ANNs to correctly signal the presence or absence of directed connectivity as well as connectivity strength, we compared several ANN models,including Conv2D, a novel ANN model we propose, with TRGC. We compared their performance for different dipole locations (i.e., Far-Deep/Far-Superficial) as this can inform us whether there is a future for ANN models in brain connectivity estimation. In addition, we evaluated the ANN models relevant for directed connectivity estimation. We investigated these issues by means of a simulation study, thereby making use of a slightly adapted version of the simulation framework developed by [1] in which we manipulated the location of the dipoles and their connectivity while keeping noise level and the choice of the inverse solution constant.
## 2 Experiment Details  
The complete experimental procedure of noninvasive sensing and a comparison with the conventional method of glucose level prediction have been depicted in Figure 1. With the proposed noninvasive technique, a commercial smartphone camera can be used for the recording of a short video (20 s-50 s) of the subject's fingertip, which is then converted into frames containing RGB channel information of different wavelength. Red light has a longer wavelength than green or blue, which consequently allows it to penetrate deeper into tissue. Consequently, data from the red channel can be used for generating the respective PPG signal from the video frames. Regression models can then be trained with the acquired smartphone PPG signals, and corresponding glucose levels are acquired with a commercially available glucometer.  
Fifty-two subjects aged between 17 and 61 years, with a male to female ratio of 75:25, and with blood glucose levels ranging between 68 and 211 mg/dL participated in this experiment. The participants were as adequately briefed about the procedure before it began. A minimum of three trials were taken from each subject, resulting in one-hundred and ninety-one trials altogether. Each video recorded during the trials was 60 s long. All the subjects provided their informed consent paper and proper hygiene was maintained during the collection of reference values using a commercially approved glucometer (Accu-Check Active) which has an accuracy that meets the DIN EN ISO 15197:2013 requirements of a measuring interval that is 10-600 mg/dL. The subjects' fingers and smartphone camera lenses were also cleaned using alcohol wipes before acquiring video as part of this study.
## 3 Prediction of AWTT
### Factor Analysis Feature Dimension Reduction  
A notable correlation among ambient temperature, load current, and top oil temperature is observed as mentioned above. If these three highly correlated parameter data are directly used to predict the AWTT, there will be an abundance of superfluous information, potentially leading to the problem of over-fitting and the complexity of prediction models. The factor analysis algorithm can effectively decouple and improve the efficiency of the network model. Based on retaining the main original data information, the correlation between the data is removed, and the dimensionality of the data is reduced.  
Ambient temperature \(x_{1}\), load current \(x_{2}\), and top oil temperature \(x_{3}\) are selected as three evaluation indexes in this paper. When there are \(n\) samples, a data matrix of \(n\times 3\) can be expressed as  
\[x=\begin{pmatrix}x_{11}&x_{12}&x_{13}\\ x_{21}&x_{22}&x_{23}\\ \vdots&\vdots&\vdots\\ x_{n1}&x_{n2}&x_{n3}\end{pmatrix}=(x_{1},x_{2},x_{3}) \tag{5}\]  
The ambient temperature, load current, and top oil temperature are represented as a linear combination of common factors and special factors, so as to establish the factor analysis model. Then, the Kaiser-Meyer-Olkin (KMO) test and Bartlett test are performed on the data. Following the confirmation of significant correlations, the quantity of common factors is determined. Factor rotation is executed to minimize bias and enhance the explication of the practical implications. Finally, the Anderson-Rubin method is used to compute the factor scores. According to the component score coefficient matrix, the coefficients of the factor score function are obtained, resulting in the acquisition of all factor scores. That is, the common factor is represented as a linear combination of the original variables.  
\[\left\{\begin{array}{l}f_{1}=b_{11}x_{1}+b_{12}x_{2}+b_{13}x_{3}\\ f_{2}=b_{21}x_{1}+b_{22}x_{2}+b_{23}x_{3}\\ \cdots\\ f_{m}=b_{m1}x_{1}+b_{m2}x_{2}+b_{m3}x_{3}\end{array}\right. \tag{6}\]  
where \(f_{1},f_{2},\cdots,f_{m}(m<3)\) is the common factor, and \(b_{ij}\) is the coefficient of the \(i\)th factor corresponding to the \(j\)th variable.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Ambient Temperature** & **Load Current** & **Top Oil Temperature** \\ \hline Ambient temperature & 1 & 0.657 & 0.793 \\ Load current & 0.657 & 1 & 0.724 \\ Top oil temperature & 0.793 & 0.724 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation coefficient of ambient temperature, load current, and top oil temperature.
## 3 The Concept of Area-Based Vehicle Speed Control
### Selection of the Area for the Presentation of the Concept  
For the presentation of the concept of area-based vehicle speed control, the Stare Babice municipality region, located within the Warsaw West District, was selected. This choice is not accidental, as the commune is characterized by a very high traffic volume, being a synthesis of local and transit traffic. The municipality is located in the central part of the Mazovia Province. Its area lies near Warsaw, at its western border, near the Kampinos Forest, between two critical international routes (the Poznari route and the Gdarisk route). Running through the eastern part of the municipality is the S8 expressway, a part of the Warsaw ring road connector. The city borders the municipalities of Ozarow Mazowiecki, Leszno, and Izabelin, as well as the Warsaw districts of Bemwoo and Bielany. The city spans an area of approximately 63 km\({}^{2}\), excluding the part devoid of roads and located within the Kampinos Forest; it can be described as a rectangular area measuring about 14 km by 6 km. The area of the municipality, as shown in Figure 2, passes through a transportation route that consists of Provincial Road No. 580, which leaves Warsaw in the direction of Zelazowa Wola and Sochaczew. Provincial roads 718 and 898 are also connected to the 580 road. The proximity to Warsaw, the large and growing population density, as well as the extensive road network and proximity to other transportation routes (92, S7, S8) translate into a very high traffic volume, which is usually not conducive to road safety. This traffic is superimposed on local traffic, further supplemented by the presence of many vulnerable participants, including a significant proportion of children.  
The concept is to deploy a network of speed sensors at regular intervals on the layout of local and transit roads. These sensors record the speed of passing vehicles and, in real  
Figure 2: Stare Babice municipality area, road layout, and distribution of speed sensors (source: own elaboration using QGIS 3.34.14-Prizren).  
time, transmit the recorded data via the LoRaWAN network to a system cloud application. There, they are processed, pre-visualized, and prepared for further analysis.
## 3 Methodology
### Design of Workspace
#### 3.2.1 Configuration of Data-Processing Workflow  
When there are many data services distributed in the network space, how to find and use them in time is the concern of data users. Therefore, the registration mechanism of the data service is proposed in this paper. After a data provider deploys a data service container, he/she can decide whether the container provides data services on the Intranet or in an open network environment. When a data service container is authorized to be shared in an open network, it will be registered in the workspace. Any data service information updated in this service container in the future will be synchronized and updated to the workspace. In this way, the data service resources in the open network environment are recorded in the workspace in real time.  
When users need to customize their data requirements, they need to create a workspace instance in the workspace. A workspace instance contains all the resources and tools to customize the current data requirements, including data resources, data-processing methods, and data configuration tools (such as workflow canvas, service-binding tool, etc.). Then, according to the requirements of data processing, users can visualize the data-processing logic through workflow canvas. For example, when users need to obtain DEM data of a target research area, they need to perform image mosaic, reprojection, and clipping operations on the original DEM data (which has been published as data services). Then, drag and drop three workflow nodes in the workflow canvas to represent the above three data-processing operations, respectively, and use arrows to connect these three nodes in sequence. At this time, the workflow construction of DEM data extraction in the target research area is completed. Moreover, when there are special data requirements, for example, missing values in DEM data need to be filled before DEM data clipping, the user can still drag and drop a node to the workflow canvas to represent data filling operations. Then, break the arrow line between the \"reprojection\" and \"clipping\" nodes, and reconnect the \"reprojection\", \"data filling\", and \"clipping\" nodes. Thus, a data-processing workflow that meets the specific requirements is configured.  
Each node in the workflow represents an abstract data-processing process that is not capable of execution. Users can customize any data-processing nodes and connect them according to the sequence of data-processing logic to form an abstract data-processing logic workflow. Finally, a data-processing workflow that meets the user's data customization needs is built. However, the workflow cannot perform real data-processing operations until the corresponding data service is bound to each node through the service-binding tool.  
Figure 5: Design of workspace.
## 2 Related Work
Therefore, a method is needed to compress and enhance original remote sensing images. An auto-encoder is the most appropriate tool for this purpose. CAE's application in remote sensing classification: An auto-encoder can learn important features from sample data to better complete classification and regression tasks. [PERSON] and [PERSON] first proposed the application of an auto-encoder network to data dimension reduction and concluded that it yielded improved results over the mainstream principal component analysis method [23]. The CAE is a type of auto-encoder suitable for processing images. The CAE uses traditional self-supervised learning methods and is combined with the convolution and pooling operation of CNNs to achieve feature extraction. Compared to the traditional auto-encoder method, CAE can greatly reduce the parameters and improve efficiency using local receptive fields and weight sharing [24]. At present, the CAE involves two primary classification procedures. The first is to pre-train the CNN weight determination to prevent the network from falling to the local minimum. Then, the weights are adjusted by adding labels to the samples to classify them [24; 25; 26]. [PERSON] et al. 's research also involved pre-training weight determination for CNNs [11]. The second procedure uses a traditional classifier to classify the features extracted by the CAE [27; 28; 29]. In reference to the first procedure, some scholars find that good initialization strategies and the use of batch normalization, as well as residual learning, are more effective than pre-training the weight using a CAE, particularly when training a deep network [15; 30]. In reference to the second procedure, some scholars have demonstrated that traditional classifiers do not necessarily effectively classify the data after the CAE's feature extraction [31]. The features extracted by a CAE are determined using pixel-level reconstruction, not a relatively abstract classification feature, thus, direct classification cannot achieve good classification results. This suggests that other models need to be used to further extract classification features.
## 4 Results
### Software Defined Radio Experimental Results
#### 4.2.2 OFDM Experiments  
In the experiments, an OFDM transmitter-receiver is configured with a GNU-Radio system and one SU is sending data to another SU. The MCBS is configured with a basic cognitive protocol: when the signal is not detected, it will allow two SU to transmit and receive information, but if the system detects a signal with the Renyi-entropy detector, then the MCBS must change the carrier frequency. If there is no other carrier frequency available, it must cease transmission. The parameters of SDR experiments are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Number of samples & 600 \\ Encoded polynomial & [171; 172; 173; 174; 175; 176; 177; 178; 179; 180; 181; 182; 183; 184; 185; 186; 187; 188; 189; 190; 191; 192; 193; 194; 195; 196; 197; 198; 1  
The threshold selected for noise is the same as for GMSK with a value of 2.161. Values surpassing this threshold lead to classifying the signal as noise. The averaged Renyi-entropy for the OFDM signal was 1.52. According to tests, if the Renyi-entropy of received signal is higher than 2.161, the signal is labeled as noise, but in order to be classified as a PU signal, its value must remain between 1 and 2.161. The number of detections are compared when a PU is present against the total number of samples and the \(\mathrm{P_{D}}\) is also estimated.  
The results are compared with the simulation results as shown in Figure 11. 600 samples were taken for each SNR value. The experimental results are similar to the AWGN model for an urban indoor environment with no obstacles. For example, for a Renyi-entropy detector with SNR = \(-\)6 dB, the \(\mathrm{P_{D}}\) is above 92% (556/600 samples) and the difference with the AWGN model is \(-\)3 dB because of fading.  
The results show a difference of \(-\)6 dB between the GMSK and OFDM experiments. The entropy values change from 1.26 to 1.52, increasing the Renyi-entropy value by 20%. The proposed technique experimental results show a PFA below 10% and PD higher than 90% compared with energy detector in [11].
# Adaptive Emergency Call Service for Disaster Management
## 5 Simulation Results
### Performance Analysis  
In this section, the performance of 5G-SOS is studied and compared with M-HELP [9] and FINDER [10] under multiple disaster configurations while 75% of the gNBs in the network area are out of operation. The emergency calls are randomly generated in a uniform distribution, over an emergency occurring time interval (ETI) of 30 min. Further, the performance is assessed by considering that all nodes are stationary. Moreover, for the network protocols considered in this work, if the network nodes do not form a connected graph, emergency calls cannot reach the gNBs during a single emergency call initiation. Hence, the D2D link connection range is set in order to maintain such a connected network. Furthermore, the number of UEs (NoU) and number of emergency calls (NEC) satisfy the condition, NEC < NoU. The provided results are all averaged over 100 random executions, which differ by the source devices at which the emergency occurs and emergency arrival times. Table 5 summarizes the parameters used in the simulation scenarios. The emergency data transfer takes \(T_{d2d}\) seconds. During the emergency call transmission (D2D transmission or ordinary transmission) the UE cannot transmit other emergency requests.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Total network area & 16.2 \(\times\) 21 km\({}^{2}\) \\ Amount of working gNBs & 7 \\ Total UE spread & RAND (16.2,21) \\ Initial \(T_{\text{max}}\) & 120 s \\ Initial \(T_{0}\) & 120 s \\ UE’s GPS localization accuracy & RAND (0.001,1) \\ Initial UE’s SoC & RAND (0,100) J \\ BS/gNB Link distance (d) & 1.5 km \\ D2D link connection range & 1.5 km \\ Data transfer delay by D2D mode, \(T_{\text{d2d}}\) & 10 s \\ Data transfer delay by classical PUSCH mode & 1 s \\ Maximum limit for \(RS_{\text{th}}\), \(RS_{\text{th}}^{\text{max}}\) & 5 \\ Upper bound of waiting time, \(u_{b}\) & 120 s \\ Upper limit of re-transmissions, \(n_{0}\) & 4 \\ Maximum number of messages in \(\eta\) & 3 \\ Transmit power of gNB, (\(P_{g}\)) & 300.0 dBm \\ Signal transmission frequency of gNBs (\(f\)) & 885 MHz \\ BS/gNB antenna effective height (\(h_{g}\)) & 100 m \\ UE antenna effective height (\(h_{m}\)) & 1.5 m \\ Constant offset of Cost231 Hata model (\(C_{m}\)) & 0 dB \\ Number of UEs, NoU & variable \\ Number of emergency calls, NEC & variable \\ Emergency calls occurring interval, ETI & variable \\ Energy to transmit & 0.08 mJ [10] \\ Energy to receive & 0.05 mJ [10] \\ Total Simulation running time & 30 min \\ \hline \hline \end{tabular}
\end{table}
Table 5: Traverse city area emergency simulation parameters.
# A Stable and High-Precision Downward Continuation Method of Magnetic Data
## 5 Conclusions  
In this paper we introduce a constrained equivalent source downward continuation scheme to achieve the long-distance stable downward continuation, which overcomes the amplitude attenuation effect and recovers the lost deep high-frequency signal. We obtain the solution by minimizing a specific objective function of the model that integrates the lower space measured data. When the downward continuation distance is too large, the high frequency potential field information generated by the magnetization anomaly in the underground space is seriously attenuated and difficult to reflect on the observation surface. Therefore, whether it is to use the filter operator to amplify signal or the equivalent source to construct the forward model, the result would deviate from the true value. The method proposed in this paper takes the actual measured value of the underground space as the constraint data and constrains it during the process of calculating the equivalent source model parameters, so that the final continuation result is close to the true value, and it is used in the model test and actual data processing, both obtain a good fitting performance.  
Figure 15: Comparison of different downward continuation method results along with survey line 1 (**a**), line 2 (**b**), line 3 (**c**) and with line 4 (**d**). In each panel, the blue line shows the underwater measured data, the black line is the result of the wave number domain iteration method, the green line represents the result of the equivalent source method, and the red line is for the result of the equivalent source constrain method.  
However, the number of subdivision cells in the underground space will also directly affect the continuation accuracy. Theoretically, the finer the subdivision cells are, the higher the accuracy is, but this would increase the amount of calculation. Subsequent studies will be focused on how to refine the division as much as possible. Researching a balance between more equivalent cells and satisfying the calculation speed makes this method more accurate and efficient.  
Conceptualization, J.W. and X.M.; investigation, Y.F.; methodology, Z.Z.; software, Y.F.; visualization, Z.Z. and Y.F.; writing--original draft, Z.Z.; writing--review and editing, J.W. and X.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by (1) The National Natural Science Foundation of China (Grant number: 41974161), (2) The National Natural Science Foundation of China (Grant number: 41804099) and (3) 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (Grant number: ZD2021 YC029).  
The authors declare no conflict of interest.