esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Outlet Shrinkage on Hydraulics in Hyper-Concentrated Sediment-Laden Flow
## 3 Experimental Results
### Flow Regime
#### 3.2.2 Jet Trajectory  
The length of the jet trajectory was analyzed for different contraction ratios, as shown in Figure 6. The experimental results indicate that the trajectory increased with a decrease in contraction ratio. The outlet of shape 3 was narrower and the contraction ratio decided the flow regime of the water nappe. The enhanced air entrainment enlarged the trajectory. Therefore, the trajectories of shape 1 and 2 were two parallel lines, while the trajectory of shape 3 sharply increased.  
Figure 5: Water nappe regime at upstream water head of 2.20 m and downstream water head of 0.15 m: (**a**) fresh water at \(\varepsilon\) of 0.5; (**b**) fresh water at \(\varepsilon\) of 0.4; (**c**) fresh water at \(\varepsilon\) of 0.3; (**d**) sediment-laden flow with a concentration of 250 kg/m\({}^{3}\) at \(\varepsilon\) of 0.5; (**e**) sediment-laden flow with a concentration of 250 kg/m\({}^{3}\) at \(\varepsilon\) of 0.4; (**f**) sediment-laden flow with a concentration of 250 kg/m\({}^{3}\) at \(\varepsilon\) of 0.3.  
Figure 6: Relationship between lengths of jet trajectory and sediment concentrations for three shapes of overflow at three water heads.  
In fresh water, the jet trajectory was mainly affected by factors such as the shape of overflow and the upstream and downstream water head. With an increase of sediment concentration, the length of the jet trajectory decreased. The length was slightly reduced for shapes 1 and 2. The water nappe had slight air entrainment and the influence of sediment concentration was relatively small. For shape 3, though using a narrower outlet, the water nappe was still heavily aerated at a low-sediment concentration, the length of the jet trajectory decreased slightly, and the contracted water nappe diffused significantly along the longitudinal direction, obviously reducing the kinetic energy carried in the water. However, while the sediment concentration increased to a critical level, the length of jet trajectory decreased abruptly and sharply. The critical concentration was relative to the upper water head.  
The water nappe exhibited different flow regimes with different sediment concentrations. In general, the distance between the inner and outer edges (i.e., the length of the jet trajectory) decreased with the increase of the sediment concentration. The length change of the jet trajectory was affected by air entrainment and gravity, but the increase of viscosity reduced the air entrainment of the water jet in the air. This phenomenon was attributed to the higher viscosity of the sediment-laden flow [21; 29]. Additionally, the influence of the outlet shrinkage on the water nappe became smaller. This was due to the increase in effective gravity of the hyper-concentrated sediment-laden flow. Velocity component in the direction of gravity of the water nappe with higher sediment concentrations was greater than that with lower sediment concentrations.
## 2 Background of the Proposed Wireless VR System  
### System Specification  
Figure 1 conceptually illustrates the proposed wireless VR system connecting the host system and a VR HMD via 60 GHz wireless technique. In addition to the previous 60 GHz RFIC [14], we newly develop in this work a compact baseband transceiver achieving the transmission latency of 1 ms, which is regarded as the zero-delay transmission of VR applications. Note that the proposed baseband system consists of three parts: two high-speed analog interface parts for performing as the de-serializer and the serializer, and the compact low-latency digital baseband unit. More precisely, for transmitting the high-quality video stream, the de-serializer shown in Figure 1 receives a high-speed serialized video stream from the host device and parallelizes the received data into the low-speed 16b-parallel stream, which can be managed by the following baseband architecture in transmitting (TX) mode. The baseband processing then generates the compact ECC and rearranges a number of codewords by utilizing the block-level interleaving scheme, constructing the 18b-parallel stream. The following analog-based serializer unit finally converts this low-speed 18b-parallel data stream into the high-speed serial data, which are delivered to a single lane of the previously-designed 60 GHz RFIC in TX mode.  
By reversing the order of data processing, as shown in Figure 1, the receiving (RX) mode of the proposed baseband architecture can efficiently accept the transmitted data through the wireless channel. More precisely, similar to the operations in TX mode, the de-serializer firstly obtains the high-speed bit stream from a single lane of the 60 GHz RF module, and parallelizes the input data into 18 bits, lowering the operating frequency to be applied at the digital baseband architecture in RX mode. With the block-level de-interleaving followed by the high-throughput massive-parallel ECC decoder, the digital baseband processing successfully corrects the erroneous bits whose patterns are mainly occurred at the practical channel conditions. The recovered 16b-parallel data stream is then fed into the serializer to generate the error-free bit stream delivered to the display controller of HMD.  
In this work, for providing the immersive experience, we target a data rate of over 12 Gbps from the host device, which can successfully transfer the 4 K/60 Hz full high-definition (FHD) videos [1; 5]. To support such a high data-rate, we utilize two transmission lanes at the baseband architecture, realizing two identical sets of baseband processing units each of which achieves over 6 Gbps throughput in 65 nm CMOS technology. Note that the 60 GHz RF module supports both BPSK and QPSK modulations, and therefore the proposed wireless VR solution can support two transmission rates, i.e., 6.4 Gbps and 12.8 Gbps, allowing the trade-offs between the video quality and the power consumption.
# Ultrawideband Cross-Polarization Converter Using Anisotropic Reflective Metasurface
Due to the difference in refractive indexes, the orthogonal components of EM waves travel with different speeds, which causes phase difference and results in polarization conversion. The polarization conversion of EM waves can be converted through a half\(-\)wave plate and quarter\(-\)wave plate. Using Faraday's effects, polarization can also be achieved; however, such devices often have large volumes and a narrow bandwidth, and these attributes severely limit their application. As a result, researchers have been striving to develop polarization converters that are more efficient, compact, have a large bandwidth, and are easily fabricable [27]. Even though various polarization converters have been documented, they all have substantial disadvantages, such as a limited bandwidth and larger unit cell thickness than the operating wavelength. To overcome these flaws, a variety of strategies have been employed. Recently, multilayer and multiple resonators have been used to achieve a wide bandwidth in polarization conversion metasurfaces [28; 29; 30]. However, these techniques limit their implementation in practical applications due to a large thickness and costly fabrication. In our proposed metasurface, we address these issues by designing an ultrathin, simple, and single\(-\)layer meta\(-\)structure. Recently, various operating frequency spectrums have reported reflective metasurface\(-\)based polarization converters. For a perfect polarization converter, a high anisotropic or multiple plasmon resonance\(-\)based metasurfaces were exploited to attain wideband features. Resultantly, there are different types of metasurface\(-\)based polarization converters, such as double arrowhead [31], V\(-\)shaped [32], U\(-\)shaped [33], and L-shaped [34]. This paper suggests an ultra-broadband and wide incident angle polarization converter reflective metasurface with simple, highly efficient anisotropic unit cell geometry. The proposed structure is single layer and easy to fabricate compared to multilayer structures because broadband multilayer structures need special and complicated techniques for fabrication. We utilized a FR\(-\)4 dielectric substrate, which is more readily accessible than F4B\(-\)2 and has a thinner thickness of 1.6 mm compared to 3 mm for F4B\(-\)2. The operating frequency range is considered from 11 to 27 GHz to examine its reflection characteristics. The polarization conversion ratio (_PCR_) is above 90% in the 13 to 26 GHz frequency band. The proposed broadband and highly efficient metasurface could be very effective for the applications of radar cross\(-\)section reduction and MIMO antennas, etc.
# Equatorial Plasma Bubbles: A Review
## 2 Observations of EPBs and Associated Irregularities
### Ionospheric Scintillation Measurements
#### 2.3.1 Dependence of Scintillation Parameters on Irregularity Characteristics
Since the distance between the two receivers, \(x_{0}\), is known and \(V_{0}\), \(V_{C}\), and \(C_{I}(x_{0},\ t_{m})\) are computed for each sub-interval from scintillation event data, it is possible to make scatter plots, such as the one in Figure 5, to obtain a picture of how the dominant spatial scale \(d_{I}\) in the ground scintillation pattern of intensity variations evolves with local time, which is related to the development of the irregularities encountered by the radio signal [49]. Evolution of the EPB irregularities with time is dependent on the season and also on the level of ionospheric disturbance  
Figure 4: Local time (LT) variation of the parameters \(S_{4}\), \(V_{0}\), \(V_{C}\), and \(C_{I}(x_{0},\ t_{m})\) as computed for each 3-min interval during a scintillation event for a 251 MHz radio signal transmitted from a geostationary satellite and recorded by spaced receivers at equatorial station Tirunelveli on a magnetically quiet night. (After [46]). due to magnetic storms. Parameters estimated from records of amplitude scintillations on a 251 MHz signal transmitted from a geostationary satellite and recorded by spaced receivers at the equatorial station Tirunelveli during magnetically quiet days of September 2002 have been used in Figure 5. The scatter plot shows that the dominant spatial scale in the intensity variations tends to be shortest around midnight, which has implications for how the intermediate-scale irregularity spectrum develops near the equatorial F region peak [49]. Observations of irregularities associated with EPBs using ionosonde, radar, or the scintillation technique utilize radio waves. As has been mentioned earlier, these observations give information about EPB irregularities of scale lengths ranging from about a meter to a few kilometers, depending on the signal wavelength and the scattering process involved. Further, this information pertains to the irregularities that are intercepted by the signal. Thus, a picture of the large-scale features of an EPB are not captured by these observations. Airglow observations have bridged this gap and proved their great utility in studies of the large-scale features of EPBs [50; 51; 52; 53]. This important technique is not discussed further here. However, it is to be noted that airglow measurements make it possible to produce geomagnetically conjugate observations of the field-aligned EPBs at a particular time and to determine the zonal drift of EPBs [51; 53].
# Microbial Depolymerization of Epoxy Resins: A Novel Approach to a Complex Challenge
## 2 Materials and Methods
### Analytical Techniques
#### 2.3.6 Fourier Transformed Infrared Spectroscopy (FTIR)  
Aiming to study the impact of our experiment on the surface chemical structure of epoxy, FTIR analyses were performed with a spectrometer (type Tensor 27, Bruker, Billerica, MA, USA). Spectra were obtained with a diamond attenuated total reflectance (ATR) crystal plate. For each measurement, 32 scans were measured in the range of 4000-650 cm\({}^{-1}\). The scans were recorded with a spectral resolution of 4 cm\({}^{-1}\), and the resulting spectra averaged. OPUS software (Winterthur, Switzerland) was used to collect and correct the data (baseline correction). As a standard, 6 measurements per replicate were performed--3 for each side. To evaluate the degrees of oxidation of the tested material, carbonyl and hydroxyl indexes (Equations (1) and (2)) were calculated. The indices were calculated with the OPUS software (Winterthur, Switzerland) using the intensity (I) of the peaks (K algorithm).  
\[\text{Carbonyl Index}=\text{I}_{\text{abs at 1745}}/\text{I}_{\text{abs at 2861}} \tag{1}\]  
\[\text{Hydroxyl Index}=\text{I}_{\text{abs at 3400}}/\text{I}_{\text{abs at 2861}} \tag{2}\]  
For the carbonyl index of the samples from the experiment with the microbiological water, the difference from before and after the experiment was calculated and compared for each condition.
## 2 Related Works
SATIRE [15] is designed to identify user activity based on accelerometer and global positioning system (GPS) readings. The system uses SHIMMER motes and accelerometers to sense and record data to a local memory mechanism. These data are then opportunely transmitted using a low-power radio device when the SHIMMER node is within communication range of the base station, thus extending the duty cycle of the node by increasing battery life. The data is then processed offline to characterize the user's activity patterns. In the medical application field, there have been some projects focusing directly on ECG measurement and data processing. A variable control system is proposed in [16] to optimize the measurement resolution of ECG readings to save power. This system allows users to set the ECG Signal-to-Noise-Ratio, thus permitting them to select the exact resolution to meet their needs. It is important to note that the higher the resolution selected, the greater the amount of energy required to save and transmit information. Therefore, by selecting the exact resolution, users avoid registering and transmitting more information than is actually required by the health care professional. CodeBlue [17] is a hardware and software platform developed at Harvard University. The network architecture is based on the publish/subscribe routing framework. The sensors do not publish data at an arbitrary rate because the platform filters the data locally. A multi-hop routing protocol can be used when the subscribers and publishers are not within a single hop radio range. The publishers and subscribers are mobile, so position information has to be available to define routing paths. This information is obtained using a localization system called \"MoteTrack\" [18]. With the increasing popularity of mobile ECG measurement, data transmission to a remote place for processing and diagnosis (such as a medical center) gains importance. In [19] the authors describe a cardiac healthcare system that can use WLAN and CDMA technologies to transmit data. When the ECG sensor detects a WLAN, it transmits data using that path; otherwise, a cell phone with a prototype wireless dongle performs a simple electrocardiogram diagnosis algorithm. Again, in order to save energy, the data is transmitted only when an abnormality is detected. Mobile ECGs [20] measure and analyze the user heart rates by means of a smart mobile phone that functions as a base station. The ECG is then transmitted via Bluetooth back to the mobile phone for the patient to view and take the appropriate actions. However, if an abnormality is discovered, the mobile phone analyzes the received data and sends the ECG data to a server for further analysis by healthcare professionals.
# Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy
###### Abstract  
Accurate wind speed forecasting is of great significance for a reliable and secure power generation system. In order to improve forecasting accuracy, this paper introduces the LSTM neural network and proposes a wind speed statistical forecasting method based on the EEMD-FuzzyEn-LSTMNN model. Moreover, the MIC is used to analyze the autocorrelation of wind speed series, and the predictable time of wind speed statistical forecasting method for direct multistep forecasting is taken as four hours. In the EEMD-FuzzyEn-LSTMNN model, the original wind speed series is firstly decomposed into a series of components by using EEMD. Then, the FuzzyEn is used to calculate the complexity of each component, and the components with similar FuzzyEn values are classified into one group. Finally, the LSTMNN model is used to forecast each subsequence after classification. The forecasting result of the original wind speed series is obtained by aggregating the forecasting result of each subsequence. Three forecasting cases under different terrain conditions were selected to validate the proposed model, and the BPNN model, the SVM model and the LSTMNN model were used for comparison. The experimental results show that the forecasting accuracy of the EEMD-FuzzyEn-LSTMNN model is much higher than that of the other three models.  
wind speed forecasting; long short-term memory; neural network; ensemble empirical mode decomposition; fuzzy entropy; maximal information coefficient; autocorrelation +
Footnote †: journal: Applied Sciences
## 1 Introduction
However, this scheme requires that all client data be periodically centralized on the server for centralized training without considering the additional communication burden caused by the transmission of a large amount of training data, which may have a negative impact on the quality of video transmission. In this paper, we aim to design a fair bandwidth allocation based on congestion control, which can maximize the QoE fairness among video users while maintaining the overall  
Figure 1: The perceived quality of different videos at each bitrate. QoE efficiency and network bandwidth utilization of the video system. In general, the contributions of this paper are as follows:  
1. An active bandwidth allocation framework based on congestion control is developed, which can ensure the utilization of network links while allocating bandwidth;
2. Combined with the characteristics of the video on demand system, a parameter containing a global state is constructed as the input of the neural network, which effectively improves the performance of the distributed DRL algorithm in multiple video stream scenes;
3. The combination of deep reinforcement learning and federated learning [4] is used to optimize the QoE fairness of multiple video streams. A video system is constructed in the real network to complete the deployment and evaluation of the algorithm.
# Performance of 5G Trials for Industrial Automation
## 3 Trial Systems in the 5G-SMART Project
We have conducted extensive testing on the 5G trial networks in Aachen to validate the performance of the 5G networks. Tests on the trial systems in Kista and Reutlingen are ongoing and will intensify as the industrial equipment for the use cases has been installed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Trial Site** & **Trial System** & **5G Architecture** & **5G Frequency** & **4G Frequency** & **5G Bandwidth** \\  & & **Band** & **Band** & **[MHz]** \\ \hline Aachen & MB NSA & NSA & 3.6 GHz (n78) & 2300 MHz (B40) & 100 \\ Aachen & MB SA & SA & 3.6 GHz (n78) & NA & 100 \\ Aachen & URLLC & SA & 28 GHz (n261) & NA & 100 \\ Kista & HB NSA & NSA & 28 GHz (n257) & 1800 MHz (B3) & 200 \\ Vasteras & MB NSA & NSA & 3.6 GHz (n78) & 1800 MHz (B3) & 80 \\ Reutlingen & MB SA & SA & 3.6 GHz (n78) & NA & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: 5G-SMART trial system summary.
## 3 SDN-Enabled Edge Cloud Architecture
### Fixed Edge Cloud  
As we mentioned before, the early vision of edge computing was to enhance mobile devices' limited local resources by neighboring edge servers to run low-latency, high bandwidth, and data-intensive applications. However, today's mission and safety-critical industrial applications and video analytics need onsite edge deployment called edge-site or fixed-edge deployment. For example, offshore oil rigs need to analyze live video to identify safety hazards; restaurants need to forecast the amount of food to prepare based on  
Figure 4: Mobile edge cloud architecture.  
Figure 3: A comparison between mobile edge cloud and fixed edge cloud.  
the number of customers and vehicles entering the location; railways use high-definition cameras for detecting cracks in the wheels of a train, etc. The dominant reasons to adopt fixed edge cloud for all of the above industrial applications are the scarcity of bandwidth and outage of cloud due to unreliable network links [19]. Figure 5 presents the simplified architecture of a fixed edge cloud in a enterprise environment.  
Fixed edge cloud (FEC) is a set of dedicated edge devices which are used as a primary place of storage and computation [19, 26]. It consists of input devices like sensors, cameras, robots, etc., and edge clusters. Large-scale data (MBs-PBs) will be generated from the input devices, so high bandwidth links with reliable connectivity and low latency are required. A limited bandwidth in the out links can be a problem, especially in remote or rural areas.  
Edge clusters collect large-scale data from the input devices and transfer those to a traditional central cloud through different transmission mechanisms like WAN, cellular, satellite, etc. But these connections have a limited bandwidth, high latency, and unreliable connectivity. Here, satellite and cellular connectivity can be used as a backup, having lower bandwidth capacity.
## 1 Introduction
However, augmented data are expected to be representative of the real experimental data; hence, they require careful creation. A few of the various approaches used for creating augmented data include using Gaussian noise, local averaging, rotation, and time warping. Additionally, both the Morlet and the Morse mother wavelets were extensively experimented on to evaluate the performance of the augmented CWT features. The generalized Morse and Morlet wavelets were chosen so as to capture the general performance of limiting special forms of wavelets in both families. The key contributions of this paper are therefore summarized as follows:  
* A dataset based on CWT scalogram image data. The CWT images were augmented with CWT scalograms derived from recorded RSSI signals complemented with additional white Gaussian noise (WGN). A numerical CWT PSD dataset derived from CWT scalogram images. The numerical CWT PSD dataset was augmented with numerical PSD's of CWT scalograms derived from recorded RSSI signals complemented with additional WGN. * A performance analysis of the proposed datasets together with conventional feature set formulations. All datasets were trained using CNN and ANN to estimate the room a human subject is in and also estimate the exact position of the human subject in a room. The rest of this article is organized as follows: Section 2 discusses the related works in indoor localization. Section 3 presents the preliminaries. Section 4 presents the proposed feature extraction algorithm and augmentation method. Section 5 presents the experimental setup. Section 6 presents the results and discussions. Finally, Section 7 concludes this article.