esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 1 Introduction  
Implementing 5G technologies has been challenging in the last few years. With the constant increase in the users' bit rate demands, telecom operators require a fast and economical way to provide broadband connectivity to home and business users. Fixed wireless access (FWA) is a viable alternative compared to fiber to provide the needed connectivity with massive growth opportunities for operators due to its reduced cost compared to fiber-to-the-home (FTTH) deployments and faster time to market. It is predicted that up to 60% of geographical regions could be served by FWA, adding up to 230 million connections by 2027 [1, 2].  
To support more than 1 Gbps in the access network, FWA should consider wider bandwidths, typically available in mmWave frequencies. The V-band (including the 60 GHz frequencies) can deliver such bandwidths.  
Since wireless link distances are reduced to a few hundred meters for the 60 GHz band, backhaul fibre connectivity is not feasible due to higher costs and long deployment times. Hence, a wireless backhaul network is a practical solution to install nodes to serve residential users in FWA networks. In these networks, the node that connects the core network (CN) with the serving nodes is the point of presence (PoP), the node that serves the residential users is called an edge node (EN), while the node located on the user's premises is the customer premises equipment (CPE), as depicted in Figure 1. Several challenges arise from implementing such networks, such as faster and cheaper network infrastructure that meets the demand of urban and rural environments.  
The following study investigates diverse aspects needed to plan an FWA network that can support 99% user connectivity for users requesting up to 1 Gbps in urban and rural scenarios. The main focus is to deliver an optimal network that accounts for realistic environments, including buildings, vegetation and rain parameters that highly affect the propagation losses. To this end, we implement an FWA network planner which includes three-dimensional (3D) ray tracing (RT) and a hierarchical backhaul topology under a multi-objective optimisation graph network based on the tool developed in [4; 5; 6]. The main novelties of this work are:  
* A hierarchical FWA network planner with ray tracing capabilities to support future bit rate demands of residential users.
* A multi-objective optimisation algorithm that minimises the infrastructure size and power consumption while maintaining the network service quality requirements.
* A study of environmental parameters such as rain and vegetation that affect the propagation channel in the 60 GHz band and its impact on network performance.
* A survey of edge node locations for telecom operators to provide optimal network performance.
###### Abstract  
The comet assay system is an efficient method used to assess DNA damage and repair; however, it currently provides the average result and, unfortunately, the heterogeneity of DNA damage loses relevance. To take advantage of this heterogeneity, migration groups (MGs) of cell comets can be formed. In this study, genetic damage was quantified in erythrocytes of _Orochromis niloticus_ and _Ambystoma mexicanum_ exposed to ethyl methanesulfonate (ethyl methanesulfonate (EMS) 2.5, 5, and 10 mM over two hours) and ultraviolet C radiation (UV-C) for 5, 10, and 15 min using the tail length, tail moment, and migration group parameters. Additionally, blood cells were exposed to UV-C radiation for 5 min and treated post-treatment at 5, 10, and 15 mM ascorbic acid (AA) for two hours. With the MG parameter, it was possible to observe variations in the magnitude of genetic damage. Our data indicate that MGs help to detect basal and induced genetic damage or damage reduction with approximately the same efficiency of the tail length and tail moment parameters. MGs can be a complementary parameter used to assess DNA integrity in species exposed to mutagens.  
comet assay; migration groups; bioassays; genetic damage; ascorbic acid Article
## 2 Materials and Methods
### Patient Selection  
In each patient with multiple gingival recessions, oral hygiene instructions were given, followed by professional plaque removal. At the subsequent appointments, the correctness in tooth brushing was evaluated.  
The inclusion criteria for the research project were as follows: (1) age group in the region of 18-60 years of age; (2) presence of \(\geq\) 2 adjacent gingival recessions of class I, II and III according to Miller classification [26]; (3) no contraindications for dental surgery; (4) good oral hygiene (API < 15%); (5) BOP index for the entire oral cavity < 10%; (6) clinical attachment level measured mid-buccally on teeth with gingival recessions CAL \(\geq\) 2 mm; (7) Gingival recession depth (GRD) \(\geq\) 1 mm; (8) undamaged cemento-enamel junction (CE) in teeth with gingival recessions.  
The exclusion criteria for the research project were as follows: (1) pregnancy, breastfeeding; (2) use of medication that may affect the structure of periodontal tissue (e.g., anti-convulsants, calcium channel blockers, immunosuppressants); (3) nicotinism (>10 cigarettes a day); (4) dentures or orthodontic braces; (5) systemic diseases (diabetes, autoimmune diseases, diseases with primary and secondary impairment of the immune system, acute infectious diseases); (6) previous coverage of gingival recessions in the past; (7) antibiotic therapy in the last three months before the scheduled surgery.  
Patients meeting the inclusion criteria were divided into two groups depending on the mucous membrane thickness of the hard palate at the potential site for connective tissue graft collection. The palatal gingival thickness (PGT) was measured at points localized 4 mm apical from the marginal gingiva of each tooth from canine to second molar in places with greatest convexities of palatal roots:  
* Control Group--patients not requiring augmentation of the palatal mucous membrane before harvesting the graft (PGT \(\geq\) 2.5 mm in at least 4 out of 5 sites).
* Test Group--patients requiring augmentation (PGT < 2.5 mm in at least 2 out of the 5 examined places).
# Improving Polar-Coded SCMA System by Information Coupling and Parity Check
## 3 Design of Partially Information-Coupled Polar Code based on Parity Check  
PIC codes are constructed by sharing a few systematic information bits between every two consecutive codes blocks. Following [20], the PIC polar code constructed by systematic CA-polar code achieved considerable coding gains over the uncoupled code. In this section, we focus on further improving the performance of the PIC polar code.
## 2 Materials and Methods  
The used sludge was taken from a sewage treatment plant of Wuhan City in Hubei. The sludge parameters are moisture content of 82.98 wt%, natural density of 1.14 kg/m\({}^{3}\), pH of 7.07, the content of organic matter is 42.17 wt%, and the boiling point is 88 \({}^{\circ}\)C. The used microwave oven was self-designed and manufactured by Guangzhou Diwei Microwave Equipment Co., Ltd. (Guangzhou, China), and the output power of microwave ranged from 900 to 6300 W.  
We conducted the single factor experiment to confirm the optimized addition of extraction agent, nanomaterial assistants, microwave-absorbing agents, and activating agents. The typical experimental scheme: Firstly, municipal dehydrated sludge (M\({}_{0}\)) was introduced into a polystyrene cup with the thermal decomposition temperature of over 300 \({}^{\circ}\)C, and then a certain amount of assistant nanomaterials (TiO\({}_{2}\) and ZnO nanoparticles), activating agents (Na\({}_{2}\)SO\({}_{4}\), Ca(OH)\({}_{2}\) and MgCl\({}_{2}\)), and absorbing agents (sludge-based biological carbon) were added into the cup with constant stirring. The total weight of the mixture is M\({}_{1}\). After the microwave treatment was conducted, the sludge was cooled to room temperature and weighted as M\({}_{2}\). The quality reduction rate of the samples (\(\psi_{1}\)) is evaluated by the following Equation (1).  
\[\psi_{1}=((\text{M}_{1}-\text{M}_{2})/\text{M}_{0})\times 100\% \tag{1}\]  
The calculation method for the organic matter content: dry sludge (M\({}_{2}\)) after microwave treatment was put into a ceramic crucible and the total weight is M\({}_{3}\). The crucible was transferred into a muffle furnace for heating treatment at 600 \({}^{\circ}\)C for 6 h, and the resultant weight is M\({}_{4}\). The organic matter content is calculated by Equation (2).  
\[\psi_{2}=((\text{M}_{3}-\text{M}_{4})/\text{M}_{2})\times 100\% \tag{2}\]  
Furthermore, we designed an orthogonal experiment to analyze the influence of activating agents, determining the most suitable addition amount.
## 4 Principle of Characteristic Harmonic Current Differential Protection  
Protection adopts three-phase reclosing in distribution networks, which does not need to determine the fault type. To reduce the amount of data transmitted by communication, this paper prioritizes the sequence component to construct the protection criterion. The CHCs in the short-circuit currents on the IBDG side are decomposed to obtain the positive, negative, and zero sequence components, as shown in Equation (12).  
\[\left[\begin{array}{c}\dot{\ }\ \  
It should be noted that when symmetrical faults occur, NSCFFCs are about 0 within a short time after faults, and the differential protection criterion based on the distortion rate of NSCCHC is no longer applicable. In order to effectively eliminate the dead-zone problem of this criterion in symmetrical faults, the difference in the distortion rate of the A-phase CHCs on both sides of the protected feeder is used as an additional criterion. In summary, the injection CHC differential protection criteria are as follows.  
* In the case of asymmetrical faults: \[\Delta DR_{f}=\left|DR_{fGRID}(h)-DR_{fIBDG}(h)\right|>\Delta DR_{fset}\] (14) where, \(DR_{fGRID}(h)\) and \(DR_{fIBDG}(h)\) are the distortion rates of NSCCHCs on the GRID and IBDG sides of the protected feeder, respectively.
* In the case of symmetrical faults: \[\Delta DR_{A}=\left|DR_{AGRID}(h)-DR_{AIBDG}(h)\right|>\Delta DR_{Aset}\] (15) where, \(DR_{AGRID}(h)\) and \(DR_{AIBDG}(h)\) are the distortion rates of A-phase CHCs on the GRID and IBDG sides of the protected feeder, respectively.  
In Equations (14) and (15), \(\Delta DR_{fset}\) and \(\Delta DR_{Aset}\) are the action thresholds of the CHC differential protection criteria, generally taken as 0.3-0.5 times the total harmonic current distortion rate at the IBDG outlet.
## 4 Analysis
### Analytical Models  
One of the first approximations derived from the RTE is the diffusion equation (DE) [78]. Starting from the RTE, the radiance, the source, and the phase function are decomposed into a spherical harmonic series. Depending on the number \(N\) of terms retained in the calculus, the approximation is called PN. As an example, for P1, the functions are approximated as the sum of isotropic and a slightly anisotropic term:  
\[\left\{\begin{array}{c}L(\mathbf{r},\mathbf{s},t)\simeq\frac{1}{4\pi}\Phi(\mathbf{r},t)+ \frac{3}{4\pi}J(\mathbf{r},t)\cdot\mathbf{s}\\ Q(\mathbf{r},\mathbf{s},t)=\frac{1}{4\pi}\mathbb{Q}_{0}(\mathbf{r},t)+\frac{3}{4\pi} \mathbb{Q}_{\mathbf{1}}(\mathbf{r},t)\cdot\mathbf{s}\\ p(\mathbf{s},\mathbf{s}^{\prime})\simeq\frac{1}{4\pi}+\frac{3}{4\pi}g(\mathbf{s}\cdot\mathbf{s }^{\prime})\end{array}\right. \tag{2}\]  
where g is the anisotropy factor, defined as the average value of \((\mathbf{s}\cdot\mathbf{s}^{\prime})\), which is the cosine of the scattering angle. This approximation, in turns, brings to a coupled equation between \(\Phi(\mathbf{r},t)\) (the fluence) and \(\mathbf{J}(\mathbf{r},t)\) (the flux), whose solution allows one to derive the radiance, which in turn help the derivation of the signal transmitted or reflected. Solutions to the P3 approximation have been recently derived in a semi-infinite medium [79].  
Moreover, by introducing the approximations of (i) \(\mu_{a}\ll\mu_{s}^{\prime}=\mu_{s}(1-g)\), (ii) an isotropic source term, and (iii) a smooth temporal variation of \(\mathbf{J}(\mathbf{r},\mathbf{s},t)\), Equation (2) becomes:  
\[\left\{\begin{array}{c}\Big{(}\frac{1}{v}\frac{\partial}{\partial t}-\
abla \cdot D\
abla+\mu_{a}\Big{)}\Phi=Q_{0}\\ \mathbf{J}=-D\
abla\Phi\end{array}\right., \tag{3}\]  
where \(D=\frac{1}{3\mu_{a}}\), is the diffusion coefficient. The first Equation of (3) is the well-known diffusion equation (DE) and, together with the Fick's Law (second Equation (3)), has been widely used for analytically solving diffusion problems in a variety of geometries, such as: layered laterally infinite media, layered cylinder, sphere, parallelepiped [80; 81; 82; 83; 84; 85]. The diffusion approximation was shown to work quite well in situations in which the diffusion is higher than the absorption and where the source-detector distance allows the detection of photons having undergone a number of scattering events such that the radiance \(L\) becomes almost isotropic [86]. Furthermore, the RTE equation has been recently solved in the time domain for a layered medium by [PERSON] et al. [80], showing a perfect agreement with Monte Carlo simulations, which is considered the gold-standard for model testing.
# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 3 The Proposed AdaAL-MID Algorithm
### Uncertainty Strategy Based on Dual-Margin Dynamic Threshold Matrix
)\times(1+\beta\times margin_{\max}(x))\). 20. endif
```  
**Algorithm 1** Uncertainty Strategy  
(1) If \(y=y_{\text{c1}}\), indicating that the ensemble prediction is correct, the classification difficulty of \(x\) is not high at this time. Therefore, the corresponding elements of the dual-margin threshold matrices \(M_{1}\) and \(M_{2}\) should be reduced to decrease the frequency of labeling requests for such instances, thereby leaving learning opportunities for other challenging instances. Additionally, if \(x\) belongs to the majority class (i.e., \(imb_{y}>1\)), the threshold should be further decreased. This approach aims to reduce the learning opportunities for majority class instances, thereby enhancing the importance of minority classes. 2. Otherwise, when \(y=y_{\text{c2}}\) and \(imb_{y}>1\), the threshold is only lowered once. This means that the ensemble's prediction is close to accurate, and lowering the threshold is also to further reduce the learning opportunities for majority class instances. It should be noted that although the adjustment conditions for \(M_{1}\) and \(M_{2}\) are consistent,the magnitudes of the adjustment are different depending on the corresponding \(margin_{min}(x)\) or \(margin_{max}(x)\), where \(\beta\) is a tuning factor between 0 and 1. 3. Finally, if \(y\
eq y_{\text{c1}}\) and \(y\
eq y_{\text{c2}}\), this indicates that the ensemble's prediction result is very poor, and this class instance is difficult to classify by the current ensemble. Therefore, the corresponding threshold should be increased to ensure that this class instance has more learning opportunities. In summary, the core of the active learning algorithm proposed in this paper is the uncertainty strategy leveraging the dual-margin dynamic threshold matrix, which is used to select instances with the maximum information and minority class instances for labeling from the data stream, and is used for ensemble learning. At the same time, combined with the random strategy, instances selected by the random strategy can well characterize the current class distribution of the data stream, which complements and improves the uncertainty strategy.
## 4 Experimental Results
(b) Transient signals of the gate driver in the case of disconnecting the driver pin from the gate of the transistor: yellow—control signal; blue—driver output gate voltage; red—gate current; green—driver feedback signal. The IGBT driver has an input DC/DC converter, which supplies its circuits. The permitted range of the input voltage is from 9 to 36 V DC. The voltage supply +15 V fault voltage is simulated in the testbench by inserting serial resistor R4 into the supply chain and switching off relay K4 (see Figure 6). Transient processes are shown in Figure 10a,b. The moment at which the voltage supply decreases is shown in Figure 10a. The voltage goes out of the acceptable range, and the driver's feedback signal changes from a low state to a high state, which corresponds to a voltage supply failure. The processes of establishing the voltage supply, +15 V, within the acceptable range of values and resetting the driver fault are shown in Figure 10b. The control circuit supply of the IGBT driver has some energy stored in its filtering capacitors and can withstand the turning off of the input DC/DC converter during the test.
## 2 Electrical Distribution System
### Design Goals  
Despite the fact that power quality monitoring in EDS is becoming more and more important, the performance of existing systems is not sufficient to provide expanded power quality monitoring services. The system has a quite high communication cost of installation and maintenance. In the systems, a major pole transformer periodically gathers data sensed from pole transformers within its coverage using single-hop or multi-hop communication. The major pole transformer sends the gathered data towards the monitoring center through the cellular communication network every 30 minutes. The limitations of the monitoring system are as follows: first, since the data is only transmitted every 30 minutes due to the high communication cost, the power quality data cannot be delivered in a timely fashion to the remote monitoring centers. Second, it is difficult to monitor power quality using only the basic electric quantities such as voltage, current, and temperature of pole transformers. Finally, data collection is severely limited for a specific small area.  
To overcome the limitations, we have designed a communications system to provide data delivery service in a timely and reliable manner. The design goals of our system are as follows:  
* The power quality data is measured every one second.
* The measured data is transmitted to the monitoring center every transmission interval.
* The system is operated over a medium or large-size city. We plan to deploy our system at the area which is covered by a residential division.
* For the usual data, a high data delivery ratio is important within the acceptable transmission delay.  
Figure 2: Electrical distribution system.  
* For the alarm data, the distribution delay of the power quality data is important as well as the data delivery ratio.
* The use of the current off-the-shelf radio interfaces is recommended.
# Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network
## 3 Proposed CNN-Based Fault Diagnosis Method
### Fault Diagnosis Based on CNN  
After condition signals are converted into gray images, a CNN model is built to identify these collected images. For the CNN model, the key is to determine the parameters of the convolutional layers and fully connected layers. In order to obtain the CNN model with the classification capacity, a part of gray images is used as the training dataset, which is applied to train the designed CNN by combining with the feedforward and backpropagation transform algorithm. Furthermore, a validation dataset is collected from these obtained gray images for evaluating the training performance and selecting the model parameters before possible overfitting. After the training process is completed, the generalization fault diagnosis capability of the trained CNN model is tested by the online monitoring signals.
## 3 Performance Evaluation
As a result, the selection of artificial shift frequency when the normalized Doppler is smaller than 0.1 has to be re-optimized. The variation trend of correlation coefficient of interference signals between two  
Figure 4: Bit error rate (BER) performance under two typical normalized Doppler frequency offsets \(F_{D}T\). Figure 5: BER performance comparison under different \(F_{D}T\) and with or without diversity reception. branches when \(F_{D}T<0.1\) is shown in Figure 6. It is believed that when the correlation coefficient is lower than 0.2, two branches produce weak interference correlation and diversity can achieve good gains. The normalized artificial shift frequency range under different \(F_{D}T\) is shown in Table 1. We are surprised that the artificial shift frequency under low normalized Doppler frequency offset has a larger range compared with [0.75, 0.8] which is introduced in previous studies. Consequently, the designed system proposes a looser requirement on parameter stability in practical applications and the system also has better fault tolerance. In short, 1 kHz residual Doppler can achieve better Doppler diversity gain. When the normalized Doppler frequency offset is \(F_{D}T=0.01\), the system has good BER performance and a larger selection range of artificial shift frequency. Under this condition, the normalized Doppler can be obtained 0.01 as long as the symbol period \(T\) is shortened to \(10^{-5}\). In order to discuss the influences of shortening symbol period \(T\), whether a good system performance can be achieved has to be discussed when the residual Doppler frequency offset shortened with \(T=10^{-5}\). BER performance of different \(F_{D}T\) (0.01, 0.025 and 0.05) under \(T=10^{-5}\) versus different normalized artificial shift frequency (0.25, 0.4, 0.5 and 0.75) are shown in Figure 7 under signal-to-noise ratio = 20 dB. The BER curve when \(F_{D}T=0.01\) is close to \(10^{-3}\) and the best BER performance is achieved when the artificial shift frequency is 0.4, which is close to the curve when \(F_{D}T=0.01\) in Figure 5. By observing two curves when \(F_{D}T=0.05\) in Figures 5 and 7, the BER performance when \(F_{D}=5\) kHz is far poorer than that when \(F_{D}=500\) Hz. This reveals that when the actual residual Doppler frequency offset is too large, reducing normalized Doppler frequency offset by shortening the period \(T\) cannot get similar BER performance. Therefore, it is recommended that the Doppler diversity system can be designed as residual Doppler frequency offset no higher than 1 kHz with normalized Doppler frequency offset 0.01, and normalized artificial shift frequency set to be 0.4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & \multicolumn{3}{c}{**Value**} \\ \hline \(F_{D}T\) & 0.01 & 0.05 & 0.1 \\ Selection range of \(f_{x}T\) & 0.1–0.8 & 0.2–0.8 & 0.4–0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Selection range of artificial shift frequency. Figure 6: Correlation coefficient of interference signals between two branches when \(F_{D}T<0.1\).