esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
When applying 5G network slicing technology, the operator's network resources in the form of mutually isolated logical network slices provide specific service requirements and quality of service guarantees for smart grid communication services. In the face of the new situation of 5G, which comprises the surge in demand for smart grid communication services and service types, as well as the digital and intelligent development of communication networks, it is even more important to provide a self-intelligent resource allocation and carrying method when slicing resources are allocated. To this end, a carrying method based on a neural network is proposed. The objective is to establish a hierarchical scheduling system for smart grid communication services at the power smart gate-way at the edge, where intelligent classification matching of smart grid communication services to (i) adapt to the characteristics of 5G network slicing and (ii) dynamic prediction of traffic in the slicing network are both realized. This hierarchical scheduling system extracts the data features of the services and encodes the data through a one-dimensional Convolutional Neural Network (1D CNN) in order to achieve intelligent classification and matching of smart grid communication services. This system also combines with Bidirectional Long Short-Term Memory Neural Network (BILSTM) in order to achieve a dynamic prediction of time-series based traffic in the slicing network. The simulation results validate the feasibility of a service classification model based on a 1D CNN and a traffic prediction model based on BILSTM for smart grid communication services.  
CNN; BILSTM; network slicing; edge network; smart grid; service classification; traffic prediction +
Footnote †: journal:  
Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Carrying Method for 5G Network Slicing in Smart Grid Communication Services Based on Neural Network
# First Assessment of GF3-02 SAR Ocean Wind Retrieval
## 2 Materials and Method  
### GF3-02 SAR Data  
The GF3-02 SAR products used in this paper are all produced by the National Satellite Ocean Application Service (NSOAS), which has the production capacity of SAR products for the first time. As shown in Table 1, we have collected 520 GF3-02 SAR products acquired in Quad-Polarization Strip I (QPSI) from January to March 2022. Among them, 478 scenes are for the ocean scenes whose incidence angle is from 20\({}^{\circ}\) to 50\({}^{\circ}\), and there are two sets of data for the Amazon rainforest with orbit ID 1153 and 1427. The map of collected GF3-02 SAR ocean imagery location is shown in Figure 1. The ocean scene locations are mainly concentrated in the waters off the Hawaiian Islands and the waters off the west coast of the United States. Since the radiometric calibration of GF3-02 has not been completed, we used the Amazon rainforest data to obtain the calibration constant of the images. The calibration constants for HH polarization obtained from two sets of Amazon rainforest data with different beam codes are 13.60 dB and 13.52 dB, respectively, and 13.82 dB and 14.00 dB for VV polarization. As the link gain of different beam codes is considered in the radiometric correction process, it shows that the calibration constant of QPSI mode image produced by NSOAS is independent of beam code. We take the scaling constant as 13.56 dB for HH polarization and 13.91 dB for VV polarization. Furthermore, the QPSI imaging mode and capability are as follows: the resolution is 8 m, the swath is 30 km, and HH, HV, VH, and VV polarization data can be obtained simultaneously.
## 2 Smart Cities and Urban Interfaces
### Urban Interfaces  
Since the 1990s, with the commercial growth of the Internet, different public and private organizations have increased the range of their digital offerings to the general public [8]. At first, websites offered the only digital channel through which an individual could interact with a given entity. Later, with the boom in mobile devices, organizations began creating a more ubiquitous and personalized digital presence, mainly through responsive web design or mobile apps, which, as mentioned by [PERSON] [9], facilitated the resolution of certain complex problems specific to how a city needs to go about interacting with its residents.  
[PERSON], [PERSON] and [PERSON] [10] explain that this growth has continued unabated, and it is common to encounter complete digital ecosystems [11] and omnichannel experiences in which a person can consult information, perform transactions and interact with a company or government agency via different channels, be they online or face-to-face. Furthermore, as stated by [PERSON], [PERSON] and [PERSON] [12], more recently many organizations are now not only offering users information and digital tools, but they are also gathering data on user behavior using various Big Data mechanisms. This process helps them to offer more targeted products and services that are of greater value to the individual.  
Some elements in these digital ecosystems operate in the public sphere, e.g., kiosks, sensors, ATMs, or vending machines [13]. As a result of being located in public streets or public spaces they become an integral feature of urban design [14]. [PERSON] [15] explains that an important characteristic of such devices is that they need to have a digital interface that is simple and easy to use so that people can be more efficient and go about their business as fast as possible. In doing so, the machines and devices that are located in the urban setting become natural facilitators in a smart city.  
In accordance with professor [PERSON] [16] from the University of Utrecht, digital urban interfaces include a wide range of technologies, screen types, formats, installations, sensory interfaces and architecture. Said interfaces can be found in public spaces, they are interactive, and they work to connect the individual with their immediate surroundings. What is more, they are important given they are open, shared, intelligent, and play an important role in the overall social well-being of a city.
## 3 Results and Discussion
### Vulnerable Areas for Emergency Medical Services (EMS)
#### 3.1.3 Vulnerable Areas for EMS  
We evaluated and compared the vulnerable areas of EMS, which are defined as areas where a patient from a community center cannot reach the nearest emergency center within 30 min according to the speed limit, and the real speed (Figure 4). When a patient goes directly to the emergency medical center in an emergency situation as in Case 1, the speed limit-based assessment is underestimated using  
Figure 3: Vehicle speeds in the road network measured at a 5-min unit by road type in urban and rural areas. The average speed of roads is shown in blue to red on each road map. The distribution of the actual vehicle speed is indicated by road type (speed limit of 60 km/h and 80 km/h) on the right side in the line graph.  
the current method of assessing emergency medical vulnerability compared to the real speed-based assessment. In particular, the number of vulnerable areas of EMS was 659 villages based on the speed limit (Figure 4a), but 3618 villages based on the real speed (Figure 4b). Rural areas had the most vulnerable areas that increased using the real speed.  
An assessment using a 5-min unit based on the real speed of the road reveals that vulnerable areas occurred mostly during afternoon rush hour. In urban areas, 48 villages were considered vulnerable at 6:35 p.m. In rural areas, 3570 villages were vulnerable at 6:25 p.m. (Figure 5). The trend of real speed of the road by time shows that rural areas are relatively insensitive to traffic congestion during rush hour compared to urban areas, but the trend was still the highest during afternoon rush hour. We think that this tendency refers to locations around emergency medical centers. Since most emergency medical centers are located in cities (397 emergency medical centers, 80.2%), the real traffic flow of roads entering the city from rural areas slowed down when patients were transferred from a rural area to an urban emergency medical center, which ultimately led to increased vulnerability of villages in rural areas.
# A Numerical Simulation of the Underwater Supersonic Gas Jet Evolution and Its Induced Noise
## 3 Results and Discussion  
### Verification with the Experimental Results  
The numerical method was verified using the experimental and simulation results of the underwater supersonic jet used in the present study [23], where the design Mach number of the nozzle was 3.41 and the throat diameter \(D^{\star}\) was 14 mm. The test facility has a nozzle, a combustion engine, and a pressure water tank. The combustion gas comes out of the engine through a Laval nozzle and is produced by the combustion of air and methane. After leaving the nozzle exit, they inject into the surrounding water environment. The inlet of the nozzle was set to a high-temperature gas with a total pressure of 9.6 MPa and a total temperature of 2200 K, the ambient pressure was 0.1 MPa, and the temperature was 300 K. A schematic diagram of the noise measurement points is shown in Figure 2.  
Figure 1: Schematic diagram of the computational domain and grid.  
Figure 2: Schematic diagram of the noise measurement points in reference [23].  
Table 2 compares the overall sound pressure level (OASPL) computed at the measurement points in the present study with the experimental results [23]. Point A is used to monitor the noise in the far field, while points B and C are used to monitor the noise in the near field to observe the jet noise results comprehensively. The OASPL computed by simulation in the current study is slightly larger than the experimental results, with a maximum relative error of 5.1%, indicating the reliability of the calculation method.
# Analysis of Small-Loop Electromagnetic Signals to Detect Subsurface Anomaly Zones
## 5 Detection of Anomaly Zones by Statistical Methods  
### Anomaly Position  
The natural ground in Section 1, where no anomaly zones were present, exhibited a higher ER at shallower depths and a lower ER at deeper depths, as shown in Figure 13. The distributions of the ER of the natural ground were well expressed by a cubic curve according to the depth and were similar at all measurement positions, as shown in Figure 14a. However, the trend of the cubic curve at  
Figure 12: Test result for the drainage pipe (Section No. 8). (**a**) Distribution of the ER. (**b**) ER according to the position at each depth.  
the position where the polystyrene hemispheres were installed in Section 3 was different from that of other positions, as shown in Figure 14b. Therefore, the coefficients of each term (i.e., the first, second, and third terms) of the cubic function obtained at each measurement position were distributed in box plots by section to identify any outliers, as shown in Figure 15. The difference between the first and the third quartile was defined as the interquartile range (IQR). The values smaller than the first quartile minus the IQR and the values larger than the third quartile plus the IQR were defined as outliers.  
Figure 14: _Cont._  
Figure 13: ER according to the depth measured at the natural ground (Section 1).  
Figure 14: Trend lines of the ER according to the depth at each position. (**a**) Absence of anomaly zone (Section No. 1). (**b**) Existence of anomaly zone (Section No. 3).  
Figure 15: _Cont._  
No outliers were identified for the natural ground in Section 1 as no anomaly zones existed there. This can be observed in Figure 15a as the trend of the ER according to the depth was similar at all measurement positions. In Section 3, where cavities were simulated using polystyrene hemispheres, an outlier was identified from each term of the cubic function at the position where the top of the polystyrene hemisphere was located, as shown in Figure 15b. Anomaly zones were simulated at several locations in Sections 5 and 7, while a single large anomaly zone was simulated in Section 2. In these cases, the measurement positions of the anomaly zones occupied 4/11 to 9/11 of the total measurement positions. As a result, the positions where anomaly zones did not exist were plotted on the box plots as outliers, as shown in Figure 15c
## 4 Baseband Modulation Instability
### Computational Approach  
The generation of localized modes resembling rogue waves starting from a chaotic field initial condition was studied in the literature [14]. Such modes can only be generated in parameter regimes with baseband modulation instability [42]. Here we demonstrate the emergence of rogue waves from a plane wave perturbed by one single mode instead of a random noise. More precisely, we consider the initial condition  
\[\begin{array}{l}u(t,0)=[1+0.05\exp(iKt)]u_{0}(t,0),\\ v(t,0)=[1+0.05\exp(iKt)]v_{0}(t,0),\\ w(t,0)=[1+0.05\exp(iKt)]w_{0}(t,0),\end{array} \tag{6}\]  
where \(u_{0}\), \(v_{0}\) and \(w_{0}\) are the plane waves given in Equation (4). Equation (1) is numerically solved with a combination of pseudospectral method and a fourth-order Runge-Kutta scheme [43].  
The result is illustrated with the typical case of \(\sigma=10\), \(\varrho=1\) and \(\delta=5\). The modulation instability gain spectra exhibit multiple bands due to the existence of multiple complex roots of Equation (5) (Figure 2). For small frequency \(K\), patterns resembling rogue waves are observed (Figure 3, only patterns for u are shown, as profiles for the other two waveguides are similar). If there exists a pair of rogue wave modes for the same input parameters, the rogue wave with a higher baseband growth rate would dominate the other mode [39]. However, both modes here share the same baseband growth rate because they correspond to a pair of complex conjugate roots of Equation (3). Similar co-existence of rogue waves in a chaotic wave field was also reported earlier in the literature [40].  
Figure 3: With \(\sigma=10\), \(\varrho=1\) and \(\delta=5\), (**a**) the \(u\)-component of the first rogue wave mode given by Equations (2) and (3); (**b**) the \(u\)-component of the second RW mode; (**c**) the simulated results with the initial condition given by Equation (6) and \(K=0.2\).  
Figure 2: Multiple bands of the modulation instability gain spectra with \(\sigma=10\), \(\varrho=1\) and \(\delta=5\): (**a**) the first band; (**b**) the second band.
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Complete Name** \\ \hline WHO & World Health Organization \\ \hline ECG & Electrocardiogram \\ \hline BAN & Body Area Network \\ \hline IoMT & Internet of Medical Things \\ \hline IoT & Internet of Things \\ \hline HioT & Health Internet of Things \\ \hline DNN & Deep Neural Networks \\ \hline NFC & Near Field Communication \\ \hline UWB & Ultra-Wide Band \\ \hline WAN & Wide Area Network \\ \hline LAN & Local Area Network \\ \hline PPG & Photoplethysmogram \\ \hline EMG & Electromyography \\ \hline IMU & Inertial Measurement Unit \\ \hline RFID & Radio Frequency Identification \\ \hline CMOS & Complementary Metal Oxide Semiconductor \\ \hline ITRS & International Technology Roadmap for \\ Semiconductors \\ \hline FinFET & Fin Field Effect Transistor \\ \hline RISC & Reduced Instruction Set Computer \\ \hline MOPS & Million Operations Per Second \\ \hline CS & Compressive Sensing \\ \hline PRD & Percentage Root-mean square Difference \\ \hline ADC & Analog to Digital Converter \\ \hline LSB & Least Significant Bit \\ \hline MSB & Most Significant Bit \\ \hline SSIM & Structure Similarity Index Measure \\ \hline DRAM & Dynamic Random Access Memory \\ \hline PEH & Piezolectric Energy Harvester \\ \hline TENG & Thermoelectric Nanogenerator \\ \hline TEG & Thermoelectric Generator \\ \hline IPT & Inductive Power Transfer \\ \hline WSN & Wireless Sensor Network \\ \hline MAC & Multiply-And-Accumulate \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of abbreviations.
# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
## 2 Fundamental Concepts and Background  
### Classification of Epilepsy  
The ILAE has updated the classification of epileptic seizures, taking into account their aetiology, symptoms, and underlying physiological mechanisms. The classification adopted by most neurologists and epileptologists was developed by a committee of the ILAE in 1981 [35]. The classification implements types of seizures as simple partial seizure, complex partial seizure, generalised tonic-clonic seizure, absence seizure, secondarily generalised tonic-clonic seizure, and others. The classification, though useful, was limited in classifying several focal motor seizures, such as tonic, clonic, atomic, or myoclonic [36]. Resolving the limitation of this earlier classification, ILAE 2017 introduces a three-step classification system of epilepsy (seizure type, epilepsy type, and epilepsy syndrome) [37] with emphasis on the aetiology and comorbidities at each step of the classification [38]. The starting point for classifying epilepsy is the determination of the seizure type. This implies the identification of the seizure onset, which could be focal, generalised, or unknown. Focal seizures happen when a small network of neurons within one lobe (hemisphere) is hyperexcited and can spread to neighbouring neural regions [39]. In contrast, generalised seizure involves neuronal hyperexcitability of all the hemispheres of the brain from the outset [37]. The types of epilepsy include focal epilepsy resulting from focal seizures, generalised epilepsy, which is characterised by generalised seizures, combined focal and generalised epilepsy, and epilepsy of unknown seizure onset. Seizures can begin in any of the brain lobes, but the most common is the mesial temporal lobe, which holds the amygdala and hippocampus.  
pus [39; 40]. Hence, the most common type of epilepsy is temporal lobe epilepsy (TLE) [13]. The final classification step, epilepsy syndrome, is defined by the ILAE as \"a characteristic cluster of clinical and EEG features, often supported by specific etiological findings (structural, genetic, metabolic, immune, infectious, and unknown)\" [38]. Syndromes frequently exhibit manifestations that vary depending on the age of the individual, and in certain instances, they may wane at specific ages [38].
## 2 RIS-Assisted System and Channel Model
### Channel Model  
The three-dimensional (3D) Saleh-Valenzuela channel model is used in this research for the mmWave propagation [48], which is a statistical channel model in a multipath propagation scenario. The prevalent theory of the channel model for mmWave communication can be represented as follows [49]:  
\[\mathbf{H}=\sqrt{\frac{N}{L}}\sum_{l=1}^{L}\eta_{l}\mathbf{a}(\xi_{l}^{H},\phi_{l} ^{H}), \tag{6}\]  
where the channel vector is represented as \(\mathbf{H}\), the complex gain of the \(l\)-th path is \(\eta_{l}\), and the number of total paths is \(L\). The azimuth angle of departure and the elevation angle of departure are \(\xi_{l}^{H}\) and \(\phi_{l}^{H}\), respectively. The array response vector is defined as \(\mathbf{a}(\xi_{l}^{H},\phi_{l}^{H})\). The array response vector for a standard \(N_{1}\times N_{2}\) is expressed as follows [49]:  
\[\mathbf{a}(\zeta,\phi)=\frac{1}{\sqrt{N}}[e^{-j2\pi d\sin(\zeta)\cos(\phi) \mathbf{n_{1}}/\lambda}]\otimes[e^{-j2\pi d\sin(\phi)\mathbf{n_{2}}/\lambda}], \tag{7}\]  
where \(\mathbf{n_{1}}=[0,1,\ldots,N_{1}-1]\) and \(\mathbf{n_{2}}=[0,1,\ldots,N_{2}-1]\), the carrier wavelength is \(\lambda\), and the antenna spacing is \(d\), the antenna inter-space satisfies the condition \(d=\lambda/2\). Accordingly, the BS to RIS channel \(\mathbf{H}_{br}\) can be presented as follows:  
\[\mathbf{H}_{br}=\sqrt{\frac{MN}{L_{1}}}\sum_{l_{1}=1}^{L_{1}}\eta_{l_{1}}\mathbf{b }(\xi_{l_{1}}^{H_{r}},\phi_{l_{1}}^{H_{r}})\mathbf{a}^{H}(\xi_{l_{1}}^{H_{r}},\phi_{l_{1}}^{H_{r}})^{\mathrm{T}}, \tag{8}\]  
Figure 2: The architecture of RIS-assisted downlink MIMO communication.  
where \(L_{1}\) stands for the pathway numbers among the BS and RIS, \(\eta_{l}\) stands for the complex gain of those paths, \(\mathbf{b}(\zeta_{l}^{H_{l}}\phi_{l}^{H_{l}})\) is the steering vector related to the RIS, and \(\mathbf{a}(\zeta_{l}^{H_{l}},\phi_{l}^{H_{l}})\) is the steering vector connected to the BS for the \(l\)-th path.  
The following definitions can be used to describe the channel \(\mathbf{H}_{2,i}^{H}\) between the RIS and the UE, as follows:  
\[\mathbf{H}_{2,i}^{H}=\sqrt{\frac{N}{L_{2}}}\sum_{l_{2}=1}^{L_{2}}\eta_{l_{2}} \mathbf{a}^{H}(\zeta_{l_{2}}^{H_{l}},\phi_{l_{2}}^{H_{l}}), \tag{9}\]  
where the complex gain of paths is \(\eta_{l_{2}}\), and \(L_{2}\) is the number of pathways among the RIS and the \(i\)-th UE. \(\zeta_{l_{2}}^{H_{l}}\) and \(\phi_{l_{2}}^{H_{l}}\) represent the departure angles in azimuth and elevation, respectively. The array response vector is defined as \(\mathbf{a}(\zeta_{l_{2}}^{H_{l}},\phi_{l_{2}}^{H_{l}})\). In conclusion, according to (4), (8), and (9), the cascaded channel matrix \(\mathbf{H}_{cas}\) from BS to the \(i\)-th UE can be expressed as follows [6]:  
\[\mathbf{H}_{cas}=\sqrt{\frac{MN}{L_{1}L_{2}}}\sum_{l_{1}=1}^{L_{1}} \sum_{l_{2}=2}^{L_{2}}\eta_{l_{1}}\eta_{l_{2}}\] \[\text{diag}(\mathbf{a}^{H}(\zeta_{l_{2}}^{H_{l}},\phi_{l_{2}}^{H_{ l}}))\mathbf{b}(\zeta_{l_{1}}^{H_{l}},\phi_{l_{1}}^{H_{l}})\mathbf{a}^{H}(\zeta_{l_{ 1}}^{H_{l}},\phi_{l_{1}}^{H_{l}})^{T}. \tag{10}\]
## 3 Results and Discussion
### Evolution of VOC Profiles During Successive Kefir Grain Passages
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**No.** & **Compound** & **UKJ** & **Bev1-24 h** & **Bev2-24 h** & **Bev3-24 h** & **Bev4-24 h** & **Bev5-24 h** & **Bev5-24 h** & **Bev5-24 h** & **Bev5-24 h** & **Bev5-24 h** & **Bev5-24 h** & **Bev5-24 h** \\ \hline \multicolumn{11}{c}{Organic acids} \\ \hline
1 & Octanoic acid & – & 0.66 & – & – & – & – & – & – & – & – \\ \hline \multicolumn{11}{c}{Alcohols} \\ \hline
2 & 1-Dodecanol & – & – & – & 18.75 & 35.63 & – & – & – & – & – \\ \hline
3 & 1-Hexadecanol & – & 0.56 & – & – & 0.48 & – & – & 0.17 & 0.17 & 1.04 \\ \hline
4 & 1-Hexanol & 33.93 & – & – & – & – & 28.57 & – & 23.21 & – & – \\ \hline
5 & 1-Octanol & – & – & – & – & – & – & – & – & 1.51 \\ \hline
6 & 1-Pentanol & – & 10.42 & 5.17 & 19.00 & 1.08 & 4.08 & 17.83 & 19.67 & 1.50 & 5.92 \\ \hline
7 & 1-Undecanol & – & 0.50 & 0.07 & 0.27 & 0.31 & – & 0.71 & 0.44 & 0.89 & 0.20 \\ \hline
8 & 2-Heptanol & – & – & – & – & – & – & – & – & – \\ \hline
9 & 2-Hexanol & – & – & – & – & – & – & – & – & – \\ \hline
10 & 2-Methyl-1-propanol & – & – & – & – & – & – & 2.07 & 0.33 & – & – \\ \hline
11 & 2-Nonanol & – & – & – & – & 1.86 & – & – & – & – & – \\ \hline
12 & 2-Phenylethanol & 0.28 & 0.85 & 1.49 & 0.28 & 0.04 & 0.25 & 0.73 & 0.62 & 0.21 & 2.02 \\ \hline
13 & 2-Undecanol & – & – & – & – & 1.22 & – & 6.59 & – & 5.85 & 12.68 \\ \hline
14 & 3-Methyl-1-pentanol & – & – & – & 22.67 & – & – & – & – & – & – \\ \hline
15 & Furfuryl alcohol & 0.22 & 0.03 & 0.02 & – & 0.03 & 0.17 & – & 0.03 & 0.15 & – \\ \hline \multicolumn{11}{c}{Aldehydes} \\ \hline
16 & (E)-2-Hexenal & – & – & – & – & – & – & – & – & – & – & – \\ \hline
17 & (E)-2-Nonneal & – & – & – & – & 203.39 & – & 1694.92 & 237.29 & – & 745.76 \\ \hline
18 & 2-Methylbutanal & – & – & – & – & – & – & – & 40.00 & – \\ \hline
19 & 4-Hydroxy-3-methoxybenzaldehyde & – & 4.72 & 4.15 & 8.49 & 4.15 & – & 14.53 & 5.28 & 3.02 & 6.60 \\ \hline
20 & 5-Hydroymethylfurfurral & – & – & – & – & – & – & – & 0.46 & – \\ \hline
21 & Benzaldehyde & – & 0.33 & 0.25 & 0.67 & 0.37 & – & 1.08 & 0.53 & 0.20 & 0.83 \\ \hline
22 & Furfurral & – & – & – & – & – & – & – & – & 0.01 & – \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Older activity values (OAVs) of the volatile organic compounds detected in the untermented kivioriat juice (UKJ) and in different kiviat-like beverages obtained from the first kefir grain passage (0-24 h).