esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Control Techniques for Interference Management-
## 5 Discussion and Recommendations for Future Study
Even though some of these techniques are generally used in 4G series wireless networks, they can also be enhanced to be deployed in 5G wireless networks. As a common trend in wireless technology, there is always ongoing research to find the most optimal techniques to address interference in wireless networks. These techniques include mathematical programming theory, neural networks, game theory, deep learning, dynamic control theory, machine learning, and network protocol heuristics. There has been significant growth for the \"resource management,\" \"power control\", and \"optimization\" keywords after 2018, indicating their continued relevance in 5G mobile communication. Several factors and trends inform the continued relevance of these keywords in wireless technology. These include the power control and optimization techniques'role in reducing intra-cell and inter-cell interference and optimizing user equipment (UE) power consumption in mobile networks.
# Ecotoxicological Evaluation of Methiocarb Electrochemical Oxidation
###### Abstract  
The ecotoxicity of methiocarb aqueous solutions treated by electrochemical oxidation was evaluated utilizing the model organism _Daphnia magna_. The electrodeposition experiments were performed using a boron-doped diamond anode and the influence of the applied current density and the supporting electrolyte (NaCl or Na\({}_{2}\)SO\({}_{4}\)) on methiocarb degradation and toxicity reduction were assessed. Electrooxidation treatment presented a remarkable efficiency in methiocarb complete degradation and a high potential for reducing the undesirable ecological effects of this priority substance. The reaction rate followed first-order kinetics in both electrolytes, being more favorable in a chloride medium. In fact, the presence of chloride increased the methiocarb removal rate and toxicity reduction and favored nitrogen removal. A 200x reduction in the acute toxicity towards _D. magna_, from 370.9 to 1.6 toxic units, was observed for the solutions prepared with NaCl after 5 h treatment at 100 A m\({}^{-2}\). An increase in the applied current density led to an increase in toxicity towards _D. magna_ of the treated solutions. At optimized experimental conditions, electrooxidation offers a suitable solution for the treatment and elimination of undesirable ecological effects of methiocarb contaminated industrial or agricultural wastewaters, ensuring that this highly hazardous pesticide is not transferred to the aquatic environment.  
acute toxicity; _Daphnia magna_; electrochemical oxidation; emerging contaminants; methiocarb
## 3 The Impact Patterns of SRRV Deviation on Wheel-Rail Dynamic Interaction
### The Impact of SRRV Deviation on the Wheel-Rail Contact Relationship  
When there are varying degrees of positive and negative SRRV deviation, the distribution of vehicle contact points during the vehicle passage through the turnout is shown in Figure 8. From the figure, it can be observed that an increase in positive SRRV deviation gradually shifts the wheel load transition range backward. After the wheel load transition is completed, the position of the wheel contact point is closer to the wheel flange, resulting in an increased wheel-rail contact angle. On the other hand, an increase in negative SRRV deviation shifts the wheel load transition range forward, and the length of the wheel load transition range gradually shortens. These findings demonstrate that SRRV variation significantly influences the position and length of the wheel load transition range, aligning with the conclusions of existing studies [3,6].  
Figure 8: The influence of SRRV deviation on the wheel contact point position during vehicle passage through the turnout: (**a**) positive SRRV deviation; (**b**) negative SRRV deviation.  
Figure 7: The wheel–rail force when the vehicle passes through a turnout with different negative SRRV deviations: (**a**) wheel–rail vertical force; (**b**) wheel–rail lateral force.  
When the wheel contact point changes during the vehicle passage through the turnout, the distribution of wheel-rail force in both vertical and horizontal directions will also change. When the wheel loading transition range is large, after the wheel loading transition is completed, the wheel contact point is close to the wheel flange, and the gauge angle at the rail contact point is large. At this point, the wheel-rail vertical force decreases, and the wheel-rail lateral force increases. On the other hand, when the wheel loading transition range moves forward and shortens, larger wheel-rail impacts will occur, and the impact position will shift forward. From Figure 8, it can be observed that the turnout structural irregularity wavelength under different SRRV deviation is between 2 to 6 m. Therefore, the turnout structural irregularity is a special long-wave irregularity that is slightly different from geometric irregularities.  
According to the analysis results, it can be observed that the SRRV deviation has a noticeable impact on the wheel-rail interaction forces and the turnout structural irregularity wavelength. This finding provides the theoretical foundation for the method of identifying SRRV deviation by extracting the wheel-rail interaction forces' signal features that effectively analyze the turnout structural irregularity.
## 4 Discussion
### Limitations and Future Directions  
Despite the strengths of the current study, including its large and demographically diverse sample, validated instrument, and 100% response rate, several limitations should be acknowledged. First, it is noteworthy that the assessment of self-reported outcomes in dentistry can present several types of bias [34]. The cross-sectional nature of the data restricts causal inferences, and the reliance on self-reported outcomes may introduce bias related to personal attitudes or recall. Moreover, the small number of AI users limited the statistical power to examine differences in this subgroup, warranting caution in generalizing qualitative insights. Finally, the sample of this study was composed of dentists attending a dental congress about digital dentistry and AI, whereas general dental practitioners focused on different dental fields and with less interest or experience in these topics could present different self-reported results. For this reason, longitudinal studies with larger AI-adopting populations are recommended to further explore the causal and temporal relationships between AI integration and clinical practice outcomes.
## 3 Testbed Applications Representing Various Parallel Processing Paradigms
### Dynamic Master-Slave--Numerical Integration  
The concept of this paradigm is to partition input data into independent data packets by a master process, parallelize processing of these packets by slave processes and apply merging by the master process, handling communication using MPI. Partitioning, processing and merging operations are specific to a given application.  
In the case of numerical integration tested in the paper, function \(f(x)=\frac{1}{1+x}\) is to be integrated over the range \([1,100]\). The range is partitioned into a number of subranges which correspond to data packets and are initially distributed one per each slave process. Subsequently, each slave process that has finished processing requests a new data packet from the master having sent a result which is immediately integrated into the final result (sum operation on the master part). In general, this scheme implements dynamic load balancing if processing individual data packets takes different amounts of time or these are processed on CPUs of various performance, provided the number of data packets is considerably larger than the number of slave processes (typically a few times larger or more). On the other hand, generating too many data packets decreases the ratio of compute to communication time (due to communication start-up time) and results in poorer performance [9].  
Other potential applications following this paradigm might include: numerical integration, block-based matrix multiplication [21], filters applied to a set of images [22], computations of similarity measures of large vectors [23].
# Time Modulated Arrays: From their Origin to Their Utilization in Wireless Communication Systems
## 4 Conclusions  
The state of the art of TMA is organized in this work from two perspectives depending on the antenna synthesis criteria: (1) designs from an antenna perspective (exclusively) or (2) designs from a signal processing perspective. Most of the research works about TMA belong to the first group and are characterized by studying the TMA in an isolated manner, or, in other words, exclusively under  
Figure 5: Block diagram of a TMA governed by periodical sum-of-weighted-cosine (SWC) pulses \(g_{n}^{T_{0}}(t)\).  
the antenna designer point of view. The second group consists of those investigations that consider, in addition, the interaction between the TMA technique and the nature of the signals sent or received by such an antenna, i.e., those works with a strong component of array signal processing and, therefore, constituting the hybrid discipline antennas-signal processing. The most recent works belong to the latter group and definitely are marking the future lines of investigation in this promising topic.
## 4 Methodology
### Dream Optimization Algorithm (DOA)  
The Dream Optimization Algorithm (DOA) is a metaheuristic algorithm inspired by the process of human dreaming during REM sleep. It simulates three key cognitive behaviors: memory retention, partial forgetting with supplementation, and dream-sharing. The algorithm operates in two main phases--exploration and exploitation. The key steps in the DOA optimization process are as follows:  
Step 1: Initialization. DOA begins by randomly generating an initial population within the predefined bounds of the search space. Each individual is initialized using the formula:  
\[X_{i}=X_{l}+rand\cdot(X_{u}-X_{l}),i=1,2,...,N \tag{1}\]  
where \(X_{l}\) and \(X_{u}\) are the lower and upper bounds of each dimension and \(rand\) is a uniformly distributed random vector.  
Step 2: Exploration Phase. During the early stage of the optimization (iteration \(t<T_{d}\)), the population is divided into five groups with different memory capacities. Individuals first reset their position to the best solution found in their group:  
\[X_{i}^{t+1}=X_{bestq}^{t} \tag{2}\]  
Then, in selected dimensions \(j\in K_{1},...,K_{k_{q}}\), partial forgetting and supplementation are performed using the cosine-guided strategy:  
\[x_{i,j}^{t+1}=x_{bestq,j}^{t}+\left(x_{l,j}+rand\times\left(x_{u,j}-x_{l,j} \right)\right)\times\frac{1}{2}\times\left(cos\bigg{(}\pi\times\frac{t+T_{max}- T_{d}}{T_{max}}\bigg{)}+1\bigg{)},j=K_{1},K_{2},...,K_{k_{q}} \tag{3}\]  
Additionally, the dream-sharing mechanism allows individuals to acquire dimension-specific information from other members of the population:  
\[X_{i,j}^{t+1}=\begin{cases}x_{m,j}^{t+1},&\text{m}\leq\text{i}\\ x_{m,j}^{t},&\text{i}<\text{m}\leq\text{N}\end{cases} \tag{4}\]  
Step 3: Exploitation Phase. After \(T_{d}\), all individuals follow the globally best solution. The memory retention is updated by:  
\[X_{i}^{t+1}=X_{best}^{t} \tag{5}\]  
For the same selected dimensions, the forgetting and supplementation strategy is reused with a modified cosine schedule:  
\[x_{i,j}^{t+1}=x_{best,j}^{t}+\left(x_{l,j}+rand\times\left(x_{u,j}-x_{l,j} \right)\right)\times\frac{1}{2}\times\left(cos\bigg{(}\pi\times\frac{t}{T_{max }}\bigg{)}+1\bigg{)},j=K_{1},K_{2},...,K_{k_{q}} \tag{6}\]  
\begin{table}
\begin{tabular}{l l l l} \hline
**No.** & **Feature Name** & **No.** & **Feature Name** \\ \hline
1 & src\_ip & 11 & src\_pkts \\
2 & src\_port & 12 & src\_ip\_bytes \\
3 & dst\_ip & 13 & dst\_pkts \\
4 & dst\_port & 14 & dst\_ip\_bytes \\
5 & proto & 15 & dns\_qclass \\
6 & duration & 16 & dns\_qtype \\
7 & src\_bytes & 17 & dns\_rcode \\
8 & dst\_bytes & 18 & http\_request\_body\_len \\
9 & conn\_state & 19 & http\_response\_body\_len \\
10 & missed\_state & 20 & http\_status\_code \\ \hline \end{tabular}
\end{table}
Table 2: List of selected features.  
Step 4: Fitness Evaluation and Iteration. At each iteration, the fitness of all individuals is evaluated. The algorithm iteratively updates the population using the above strategies until a predefined maximum number of iterations \(T_{max}\) is reached.
# Railway Axle and Wheel Assembly Press-Fitting Force Characteristics and Holding Torque Capacity
## 5 Effect of the Interference on the Holding Torque Capacity  
### Maximum Holding Torque Capacity Theoretical Analysis  
Railway wheelsets are generally running under many loading conditions, for example, vertical static forces of the vehicle, wheel and rail contact forces in longitudinal, vertical, and lateral directions, inertial forces, etc. In operation, the contact force, especially in the longitudinal direction, significantly increases as the train is accelerated (traction) or decelerated (braking). This leads to a torsional moment on the wheelset, which may cause slippage at the wheel hub and axle seat. The maximum holding torque capacity, T, is the torque required to predict the slip between the wheel and the axle, as it resists the motion of the wheelset at the contact surface. It is transmitted by frictional forces on the wheelset and can be evaluated using [PERSON]'s equation. It was assumed that the contact pressure in the assembly was uniformly distributed. The radial interference was significant for the holding torque capacity since it was related to the contact pressure and frictional force. The holding torque capacity of the wheelset, T, was thus a function of the frictional coefficient, contact area, and contact pressure [21]  
\[\mathrm{T}=\mathrm{\mu}\frac{\mathrm{D}}{2}\mathrm{\int_{A}PdA} \tag{5}\]  
where \(\mathrm{\mu}\) is the static coefficient of friction, D is the axle diameter, P is the contact surface pressure, and A is the contact surface area. The holding torque capacity derived from Equation (5) was related to the contact length of the press-fit assembly (Figure 17). The maximum holding torque capacities were 71 kNm for the interference of 200 \(\mathrm{\SIUnitSymbolMicro m}\), 85 kNm for 240 \(\mathrm{\SIUnitSymbolMicro m}\), 99 kNm for 280 \(\mathrm{\SIUnitSymbolMicro m}\), and 113 kNm for 320 \(\mathrm{\SIUnitSymbolMicro m}\). Clearly, the holding torque capacity increased with the interference.
## 2 Materials and Methods  
### The Establishment of a Finite Element Model  
In this paper, a healthy male volunteer with a height of 173 cm and a weight of 60 kg was scanned by medical instrument computed tomography (CT) in the range of 10 cm above and below the knee joint center. The correlative parameter was set to 120 kVp and 150 mA, with a scanning interval of 1 mm. The volunteer was scanned with magnetic resonance imaging (MRI). MR (GE Sigma HD1.5T, Boston, MA, USA) was used for proton density weighting (PDWI) and chemical shift fat suppression weighted scanning, and the image coordinates of each layer of different weighted phases were consistent. Flux density is 1.5 T, and slide thickness is 1 mm [14]. Based on the CT and MRI images of the healthy volunteer, a three-dimensional geometric anatomical model including all bone tissues and main soft tissues was established (as shown in Figure 1). A three-dimensional solid model of press fit condylar (PFC) femoral prosthesis (PFC sigma, DePuy orthopaedics, Warsaw, IN, USA.) commonly used in TKA was established. Then the total knee replacement operation was simulated, and the prosthesis was assembled on the three-dimensional geometric anatomical model of the knee joint (as shown in Figure 2a). The model of the knee joint was divided into finite element meshes after TKA. In the finite element model of the knee joint, most of the unit types were hexahedrons (c3d8r, an eight-node linear brick element with reduced integration), and very few were pentahedrons (C3D6, a six-node linear triangular prism element) and tetrahedrons (C3D4, a four-node linear tetrahedron element), with a total unit number of 31,767, and 37,031 nodes (as shown in Figure 2b). The volunteer was provided details of the study and signed an informed consent form.  
Hypermesh software, version 11.0 (Altair Engineering Corp, MI, USA) was used to simulate the release at ends of the pat  
Figure 1: **(a)** Contour tracing of bone from CT; **(b)** MRI image; **(c)** the geometric anatomy model of human knee.  
Figure 2: **(a)** 3D geometric anatomical model of knee joint after TKA; **(b)** finite element model of knee joint after TKA.  
6.84 mm before release (Figure 3a) and 4.788 mm after release (Figure 3b). The height of the patellar tendon at the lower end was 8.09 mm before release (Figure 3c) and 5.633 mm after release (Figure 3d). Figure 3 shows the finite element models before and after release the 30% of the patellar tendon at both ends (clinically, one-third of the central patellar tendon is often transplanted to repair damaged ligaments [15; 16; 17; 18]. Therefore, 30% of the patellar tendon was used as an example). Figure 3a represents the model before release of the patellar tendon at the upper end. Figure 3b represents the model released the patellar tendon at the upper end. Figure 3c represents the model before releasing the patellar tendon at the lower end. Figure 3d represents the model releasing the patellar tendon at the lower end. The establishment of the model was based on the model built by [PERSON], and the validity of the model was verified by in vitro cadaveric experiments [19].
## 6 Results and Discussion
The instrument's effectiveness has been demonstrated through the comparison of results obtained by measuring metalens intensity profiles with those obtained by computational simulation. This validation enables further exploration of the primary functionality of our developed instrument, which is the 3D reconstruction of the transmitted metalens intensity profile surface. Figure 17 illustrates these measured reconstructions, showcasing the ability of the instrument to accurately measure the three-dimensional intensity profiles produced by the metalens. It was necessary to crop 1/4 of the 3D reconstruction in Figure 17 to observe the innermost layers of the fields produced by the metalens. Note that the signal intensity increases as it approaches the focal length of the lens, reaching its maximum at 20 cm, which is the designed focal distance. Figure 16: Measured intensity profiles, considering all the extracted intensity measurements on planes placed at different distances subsequent to the metalens; (**a-i**) show the results on planes that are 12 cm, 14 cm,..., 26 cm, and 28 cm away, respectively. The operating frequency is 30 GHz. We used the previous measurements to characterize the metalens quantitatively with the 3 dB depth-of-focus (DOF) and the full width at half maximum (FWHM), located 20 cm away from receiving antenna. Figure 18 presents the 3D reconstructions of the measured intensity surfaces until the 3 dB decay. Therefore, we measured an 11 cm 3 dB DOF and a 2.17 cm FWHM, as can be seen in Figures 18a and 18b, respectively. Thus, another parameter characterized and extracted from our manufactured metalens was the gain. A measurement cycle without the metalens positioned between the transmitting and receiving antennas was conducted to measure the signal intensity, without modulation from the metalens. The results can be seen in Figure 19a. Figure 17: The 3D reconstruction of the measured intensity fields produced by the metalens. Figure 18: The 3D reconstructions of the measured intensity surfaces until the 3 dB decay: (**a**) 3 dB DOF limits and (**b**) zoomed-in view of the focal plane showing the focal FWHM. It is noticeable that there is a pronounced signal oscillation at the edges of the matrix, caused by the presence of metal bearings, shafts, sensors, and motors at the edges. Therefore, it is appropriate to analyze the central part of the matrix shown in Figure 19b, discarding the edges. To extract the gain of the metalens, it was necessary to compare the intensity measured by the instrument at the focal distance with the intensity measured when without a metalens between the antennas. Figure 16e shows the matrix extracted at the focal distance, reaching a maximum intensity of 6.25 V\({}^{2}\). The average intensity collected in the scan without the metalens was 0.98 V\({}^{2}\), and comparing the two results, a ratio of 6.25/0.98 yields 6.38. Applying the logarithm: 10 log(6.38) results in a gain of 8.05 dB.
## 2 Materials and Methods
### Data Recording and Processing  
Surface EMG signals were bilaterally recorded from the ES, MF, EO, and IO muscles with wireless EMG sensors (FreeEMG 1000; BTS Bioengineering, Milano, Italy) and Ag/AgCl surface electrodes (Covidien Kendall, Minneapolis, MN, USA) placed on the muscle belly, parallel to the muscle fibers, 2 cm apart from each other. Preceding electrode placement, the skin was shaved, gently abraded via sandpaper, and cleaned with an alcoholic solution. Precise electrode locations for each muscle are reported in Appendix A. The raw EMG signals were differentially amplified (933 gain), bandpass filtered from 10 to 500 Hz, digitalized (16-bit resolution, 1 kHz sampling frequency), and transformed into amplitude envelopes through a point-to-point moving root mean square filter (500 ms time interval). The EMG amplitude envelopes recorded from each muscle during the trials were then normalized to the highest 1 s average of the EGM amplitude envelope obtained during the MVIC of the same muscle [30]. Finally, the mean value of each normalized EMG signal was computed for statistical comparison between the task conditions.
# Novel RNA Viruses Discovered in Weeds in Rice Fields
## 3 Results  
To determine the viral diversity of weeds in rice fields, we collected 29 weed samples from four major regions of rice production, including Hangzhou (subtropical region), Xiaogan (subtropical region), Guiyang (plateau region) and Sanya (tropical region) (Table S1).  
These comprised 23 weed species from the families Alismataceae, Solanaceae, Amaranthaceae, Compositae, Scrophulariaceae, Gramineae, Euphorbiaceae, Plantaginaceae, Ranunculaceae, Asteraceae, Cyperaceae, Pontederiaceae, Acanthaceae and Leguminosae, covering all major weed species in rice fields. Total RNA was extracted from the collected samples, and 29 RNA libraries for high-throughput sequencing were constructed from rRNA-depleted RNAs. We performed deep metatranscriptomic sequencing of weed samples. One library contains a single individual from one sample at the same sampling location. In total, 29 libraries were constructed comprising 29 rice weed samples (Table S1). RNA sequencing resulted in 64 million to 116 million reads per pool and a total of 368.29 Gb of clean bases were obtained from 29 libraries. Genomes were then assembled de novo and screened for RNA viruses.  
In total, we identified 304 RNA viral genomes containing RNA-dependent RNA polymerase (RdRp) domain. Using a series of protein sequence similarity-based BLAST searches, 26 contigs matched closely to known viruses, and 278 contigs were novel and distinct from publicly available viruses in GenBank (Table S2). The numbers of viruses identified in each library were significantly different. Inanya, the weed _Leptocthloa chitensis_ harbors up to 99 viruses, while the weeds _Eclipta prostrata_ and _Acalypha australis_ in Hangzhou, _Echinochla crus-galli, Alternanthera philoseroides_ and _Aster indicus_ in Guiyang and _Justicia procumbens_ in Xiaogan harbor only 1 virus (Figure S1).