esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analyses
### Datasets  
The effectiveness of CGR-NER is evaluated on two datasets from different fields, namely Weibo [37] and OntoNotes 4 [38]. The corpus of the Weibo dataset is collected from Chinese social media site Sina Weibo, and labeled with four entity labels: PER (person), ORG (organization), LOC (location) and GEO (geography). The corpus of the OntoNotes 4 dataset is collected from a news domain, including four entity labels: PER, ORG, LOC and GEO. The statistics of these datasets are shown in Table 1.
## 4 Conclusions  
(i) The analysis of possible slurry fuel components showed good prospects of using different kinds of biomass as additional fuel components in real practice. In terms of the scale of distribution and the main energy performance characteristics, the most relevant kinds of biomass include wood and agricultural waste (sawdust, straw, bagasse, leaves, shell) and oil production waste (oil cake).  
(ii) It was experimentally established that the co-combustion of biomass and coal processing waste can reduce the concentrations of NO\({}_{\text{x}}\) by 25-62% and SO\({}_{\text{x}}\) by 61%-88% compared to dry coal combustion conditions. The presence of water and an additional biomass component in the fuel composition significantly reduces the concentrations of sulfur and nitrogen oxides formed during their combustion.  
(iii) The efficiency of fuel compositions with biomass, when using slurry combustion technologies, exceeds that of coal by 10%-24% in terms of environmental, economic, and energy performance  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & \(A_{\text{n}}\) & & & \\ \cline{2-8}
**Temperature** & & **90\% Filter** & **90\% Filter** & **90\% Filter** & **90\% Filter Cake**, **90\% Filter** & **90\% Filter** & **100\%** \\
**100\% Coal** & **Cake, 10\%** & **Cake, 10\%** & **Cake, 10\%** & **Cake, 10\%** & **10\% Sunflower** & **Cake, 10\%** & **Filter** \\  & & **Sawdust** & **Rapeseed Oil** & **Leaves** & **Cake** & **Straw** & **Cake** \\ \hline
700 \({}^{\circ}\)C & 5.70 & 6.34 & 6.28 & 7.0 & 6.32 & 6.50 & 6.77 \\
800 \({}^{\circ}\)C & 5.73 & 6.57 & 6.79 & 6.81 & 6.46 & 6.62 & 6.44 \\
900 \({}^{\circ}\)C & 5.77 & 6.35 & 6.66 & 6.47 & 6.57 & 6.12 & 6.33 \\ \hline \end{tabular}
\end{table}
Table 6: Resulting efficiency indicators calculated according WSM.  
Figure 5: Comparison of integral efficiency indicator for all the fuels under study.  
indicators. It can vary from 6.12 to 7.0. The temperature conditions of combustion, type of plant component used and its cost have a decisive effect.  
Conceptualization, D.G.; data curation, V.M.; formal analysis, G.N.; funding acquisition, D.G.; investigation, G.N. and K.V.; methodology, V.M.; project administration, K.V.; writing--original draft, G.N. and K.V.; writing--review and editing, D.G.. All authors have read and agreed to the published version of the manuscript.  
The reported study was funded by Russian Foundation for Basic Research, National Council of Brazil for Scientific and Technological Development and Ministry of Science & Technology Government of India according to the research project No 19-53-80019.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### STA Campaigns with Pure Substances  
As part of the fundamental research, NaCl, KCl, CaCl\({}_{2}\cdot\)6H\({}_{2}\)O, MgCl\({}_{2}\cdot\)6H\({}_{2}\)O, AlCl\({}_{3}\cdot\)6H\({}_{2}\)O, and FeCl\({}_{3}\cdot\)6H\({}_{2}\)O were analyzed using STA regarding their behavior for liberating chlorine  
Figure 3: (**a**) Evaporation temperatures of the valuable metals in metallic, chloridic, and oxidic form; (**b**) Delta-G values for reactions of metal oxides with hydrochloric acid.  
or hydrochloric acid. For the STA trials with mixtures, the metals silver, indium, lead, zinc, and nickel were investigated. The following oxides and sulfates in the form of pure chemicals were used for the preparation of the following mixtures: Ag\({}_{2}\)O, Ag\({}_{2}\)SO\({}_{4}\), In\({}_{2}\)O\({}_{3}\), In\({}_{2}\)(SO\({}_{4}\))\({}_{3}\), NiO, PbO, and ZnO. Based on the thermodynamic calculations performed beforehand, the chlorides AlCl\({}_{3}\cdot\)6H\({}_{2}\)O, FeCl\({}_{3}\cdot\)6H\({}_{2}\)O, and MgCl\({}_{2}\cdot\)6H\({}_{2}\)O were selected as chlorination agents, respectively, for each of the metal oxides and sulfates. Furthermore, In\({}_{2}\)O\({}_{3}\) was mixed with NaCl, KCl, and CaCl\({}_{2}\cdot\)6H\({}_{2}\)O to confirm the significantly lower suitability of those chlorides (based on thermodynamic evaluations and the trials with pure chlorides). According to the chlorination reactions described in the thermodynamics section, the metal chlorides AgCl, InCl\({}_{3}\), PbCl\({}_{2}\), ZnCl\({}_{2}\), and NiCl\({}_{2}\) were expected to be formed during chlorination. In reference to the stoichiometry of the reactions, the mixtures were prepared using an excess molar amount of chlorine with a chlorination agent to metal oxide/sulfate ratio of 1.5. The STA was performed using a Netzsch 409 PC analyzer with synthetic air (N\({}_{2}\)/O\({}_{2}\) = 80/20) as purge gas and a flow rate of 0.3 L/min. Sintered Al\({}_{2}\)O\({}_{3}\) crucibles and a sample mass of 100 mg were used; all chemicals were purchased in laboratory grade. All trials were carried out with a constant heating rate of 20 K/min from 20 \({}^{\circ}\)C to a maximum temperature of 1000 \({}^{\circ}\)C, 1200 \({}^{\circ}\)C, or 1400 \({}^{\circ}\)C. The assessment of the STA trials was conducted by evaluating the collected data (mass loss over time, temperature) and comparing the real mass of the solid residual materials in the crucible to the one expected based on theoretical calculations for each mixture.
# Design and Evaluation of Vacuum Central Drum Seed Metering Device
## 3 Key Component Parameter Design and Analysis  
### Determination of Seed Sucking Hole Size  
Analysis of papers that are related to vacuum seed-metering devices shows that the diameter of the seed-suction hole has a direct impact on the performance of seed suction, the diameter of seed suction hole presents a negative cubic relationship with the inlet hole, and the diameter of the inlet hole plays a pivotal role on the performance of the  
Figure 1: The main structure of the rice-metering device. 1. seed box, 2. outer drum, 3. ventilation housing, 4. seeding tubes, and 5. air delivery ports.  
Figure 2: Schematic diagram of the seeding process by the vacuum central drum seed-metering device.  
vacuum-metering device. The diameter of the established suction hole that was measured by experimental methods is:  
\[d=(0.6-0.7)\;b \tag{1}\]  
Rice seeds are slim, long, and pointed with arris, awn, and tomentum, presenting a spindle shape. A suction hole that is too small may easily lead to blockage, while being too big easily leads to resown and stuck seeds. According to the size of the hybrid rice cultivar, Ruanhuayou1179, Shunliangyou1179, Jingliangyou 1212, Teyou338, and Wuyou1179 (Table 1) and Equation (1), the diameter of suction hole can be determined as 1.4\(\sim\)1.8 mm.
## 2 Materials and Methods
### Simulation Design
#### 2.1.1 Transient Fault Signal  
In order to simulate a transmission line with various faults, the Simscape Electrical sub-program of Simulink was used. With this software, it is possible to simulate three-phase transmission lines and a variety of faults at chosen locations. After running the simulation, the fault is introduced at a specific time, and Simscape puts out the transient waveforms in a format readable by MATLAB scripts. To simulate busbars under fault conditions, a three-phase load to ground with a pure capacitive value was used, these loads were rated at 100 VAr. Each transmission line had a length of 100 km and a frequency of 50 Hz. Although in Figure 1 a relay is shown in the single line diagram, for simulation purposes, no operational relay was used to isolate the transmission line. In place of the relay, a three-phase voltage and current measuring device was used to measure the transient signals as they would be experienced by the relay pick-up in practice.  
For the fault, a three-phase fault block was used. This block allows for a combination of all the fault types to be simulated and the reaction of the system monitored. Both the ground resistance (R\({}_{\mathrm{g}}\)) and fault resistance (R\({}_{\mathrm{f}}\)) were left at their default values of 0.01 \(\Omega\) and 0.001 \(\Omega\), respectively. The neural network created in MATLAB, its flow chart, and the model used in Simscape to simulate the three-phase faults on the transmission lines are as shown in Figures 7-9 respectively. Located at each end is a three-phase source with phase-to-phase voltage of 500 kV, with three transmission lines as connections. Between each transmission line is a three-phase load to the ground. These loads represent busbars under fault conditions as they will also conduct any high-frequency components to the ground. Located between lines 1 and 2 at the same location as the relay is a three-phase voltage and current measuring device. From this device, the current is measured and written to the workspace for further processing.
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
## 4 Full-Wave Electromagnetic Simulation Results
### Multibeam Beamforming
#### 4.1.1 Two Independent Signals Transmission in Two DODs  
As an example of two-direction beamforming where two independent signals are to be transmitted in different directions, consider the case where \(\theta_{1}=-26^{\circ}\) and \(\theta_{2}=28^{\circ}\) with corresponding \(\alpha_{1}=-23.67^{\circ}\) and \(\alpha_{2}=25.15^{\circ}\), respectively. The magnitude frequency response \(|H(e^{j\omega_{x}},e^{j\omega_{ct}})|\) of the sum of 2-D FIR filters of order \(14\times 64\), with nonsparse impulse responses and with sparse impulse responses, is shown in Figure 7a and Figure 7b, respectively. Values for the other parameters are \(\mu=0.05\) and \(\epsilon_{th}=0.004997\) for \(\text{DOD}=-26^{\circ}\) filter and \(\mu=0.05\) and \(e_{th}=0.007005\) for \(\text{DOD}=28^{\circ}\) filter. The wide-band transmit beam patterns produced by the nonsparse filter and the proposed sparse filter outputs are shown in Figure 7e and Figure 7f, respectively. Furthermore, Table 1 provides a comparison of the wideband beam pattern and DSP complexity between the proposed 2D sparse and nonsparse FIR filter-based transmit beamformers. The effect of sparsification is minimal on the passband ripple and side-lobe levels, while saving more than 85% in DSP complexity.  
Figure 7: Magnitude frequency responses of the sum of 2D FIR filters \(\left|H(e^{j\omega_{x}},e^{j\omega_{ct}})\right|\) designed for multiple independent signals transmission for DODs (**a**) \((-28^{\circ},26^{\circ})\) (nonsparse filter), (**b**) \((-28^{\circ},26^{\circ})\) (sparse filter), (**c**) \((-45^{\circ},0^{\circ},45^{\circ})\) (nonsparse filter), (**d**) \((-45^{\circ},0^{\circ},45^{\circ})\) (sparse filter). Polar plots of the simulated beam patterns obtained using the designed filters for DODs, (**e**) \((-28^{\circ},26^{\circ})\) (nonsparse filter), (**f**) \((-28^{\circ},26^{\circ})\) (sparse filter), (**g**) \((-45^{\circ},0^{\circ},45^{\circ})\) (nonsparse filter), and (**h**) \((-45^{\circ},0^{\circ},45^{\circ})\) (sparse filter).
## 8 Image Quality Analysis  
For performing the image quality analysis, we considered three elements: the CT acquisition configuration, the significant metrics, and a representative phantom. Usually, most of the work in the literature considers different CT scanners and/or acquisition configurations to research how these elements influence the image quality [46; 47; 48]. However, in our research, we are interested in comprehend the influence of the data formats on the image quality, independently by the CT scanner and acquisition configuration. Therefore for our experiments, we used one scanner with a single configuration for the CT acquisition. Additionally, we used the CATPHAN(r) 500 [15], which is a representative phantom. In fact, this provides the complete characterization for maximizing the image quality.
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 6 Results
### Evaluation of the Applicability of DMD for Channel Estimation in CF-MMIMO Wireless Systems
Figure 18 shows the relative error of the average Spectral Efficiency (SE) derived from the asymptotic closed-form expression compared to the ergodic average SE, plotted against the number of User Equipments (UEs). The simulation parameters include the number of antennas (M) equal to the number of UEs (K), 64 Access Points (APs), a pilot sequence length (\(\tau_{p}\)) equal to half the number of UEs (K/2), and a transmit power (\(p_{k}\)) of 100 mW for each UE. The results indicate superior performance of DMD methods. As seen in Figure 18, the approximated SE per UE becomes more accurate as N and K increase. MMSE is well known for its optimal performance in a noisy and interference-prone environment. In terms of error calculation, its performance is also very good; however, MMSE-PiDMD and MMSE-mpeDMD slightly surpass it. This is because, in addition to the optimal effect of MMSE, DMD is capable of discriminating those signals that are more relevant to the algorithm, ultimately reducing the error. Figure 15: Downlink CDF of the per-user SE for non-coherent transmission with full power with \(f_{d}T_{s}=0.0005\) (X-scale in the graphs are scaled by \(10^{-3}\)). Figure 16: CDF of UL SE for \(M=100\), \(\tau p=5\) and \(K=[10,40]\). (**A**) MR, MR-mpeDMD, and MR-PiDMD is shown for \(K=10\) and \(K=40\). (**B**) MMSE, MMSE-mpeDMD, and MMSE-FiDMD is shown for \(K=10\) and \(K=40\). (**C**) ZF, ZF-mpeDMD, and ZF-FiDMD is shown for \(K=10\) and \(K=40\). (**D**) PZFZ, PZFZ-mpeDMD, and PFZF-PiDMD is shown for \(K=10\) and \(K=40\). Figure 17: The average uplink SE per UE as a function of pilot sequence length, \(\tau_{p}\). We consider \(L=100\), \(N=4\), \(K=40\), and spatially correlated Rayleigh fading with ASD \(\sigma_{\phi}=\sigma_{\theta}=15^{\circ}\). To assess adaptability, a highly dynamic scenario is utilized. PiDMD and mpeDMD are compared with predictors such as Machine Learning (ML) and Kalman Filter (KF) vs. UE speed. The results illustrated in Figure 19 indicate that piDMD demonstrates superior performance due to its computational speed and accuracy. While mpeDMD generally provides better accuracy for system identification compared to piDMD, the significant computational overhead required for its calculation greatly affects its overall performance. Nevertheless, DMD methods outperform traditional ones. Figure 19: Channel prediction algorithm comparison: Machine Learning, Kalman Filter, mpeDMD, and PiDMD. Figure 18: The relative error of the average SE achieved by the asymptotic closed-form expression versus the number of UEs, with \(N=K\), \(L=64\), \(\tau_{p}=K/2\), \(50\leq v\leq 150\), and \(pk=100\) mW for each UE.
# A Feedback Control Method to Maintain the Amplitude of the RF Signal Applied to Ion Traps
## 2 Experimental Environment  
We did not connect the helical resonator to a vacuum chamber equipped with an ion trap. A helical resonator mounted on a vacuum chamber is considerably less vulnerable to the changes in the ambient temperature of the helical resonator and the minute vibration of the optical table compared with the unmounted one. Figure 1 presents the system for the amplification of the RF signal using the helical resonator. We used a 10 pF capacitor instead of a fabricated ion trap. We measured the resonant frequency and resonance width using the helical resonator with the 10 pF capacitor and a voltage divider to find the quality factor of the resonator with the devices [21; 22; 23]. The resonant frequency of the helical resonator was 23.342 MHz, and the resonance width was 149 kHz. Therefore, the helical resonator has a quality factor of 156.7. The helical resonator is fastened by an anodized aluminum breadboard and holder, and stainless-steel posts.  
Direct current (DC) voltage, an output of a feedback controller, and an RF signal generated from a signal generator (Tektronix AFG3021C, Beaverton, OR, USA) were applied to a frequency mixer (Mini-Circuits ZX05-1L-S+, New York, NY, USA). The amplitude of the mixed signal was increased 63 times by an RF amplifier (Mini-Circuits ZX60-100 VH+, New York, NY, USA). In addition, the peak-to-peak voltage was increased to 312 V through the helical resonator. To measure the amplitude of the RF voltage, we mounted a capacitive voltage divider at the output of the helical resonator. The amplitude of the RF signal was reduced to 1/1000 by the voltage divider. Then, the RF signal was converted by the rectifier to the DC voltage and applied to the feedback controller.
## Appendix A Integrating the BBCP as a Design Method  
To ease the adoption of the BBCP, and give users a frame of reference for the development of profiles, we prepared the BBCP Development Index. The BBCP Development Index, available in Table 1, is a table that proposes three levels of development maturity, where level 1 is the least mature and 3 is the most mature. We limited the index to three levels to facilitate its comprehension. Each level consists of the following items:  
* A description of the pre-requirements for each level.
* A description of the process to achieve the target level.
* The expected outcomes of the level.  
Our proposal to incorporate our BBCP into the multiple stages of a GSD method is the following:  
1. Introduce the BBCP to software designers and architects in the design stages of the project, resulting in the following: 1. A description of behavior, expected hardware consumption, and functional dependencies, at the individual function level. 2. The dependencies management among components and an expectation of component hardware consumption, at the component level. These are later passed on to the DevOps (development and operations) team.
2. The DevOps team uses the results of the design stage to achieve the following: 1. Identify and implement the best practices in the code and technology stack, in addition to other tools for testing the prototype's consumption. 2. Swiftly re-adjust the BBCP to categorize the actual hardware consumption of the prototype. 3. Employ our custom interpreter to generate the estimated prospective usage data based on the adjusted software models.
3. The DevOps team uses the prospective usage data of the profiles to achieve the following: 1. Create proactive orchestration algorithms that account for the evolution of hardware usage throughout time using re-deployment simulators like PISCO [43]. 2. Obtain information about the prospective consumption of their stack at the deployment level with tools such as Cloud Carbon [44] or Green Cost Explorer [45]. 3. Re-adjust the configuration of the technology stack for the final deployment.  
Any of the previous steps proposed can be revisited to accommodate new changes that contribute to more frugal software. We envision the BBCP as a tool to model the first-order and third-order effects of the usage stage of the GREENSOFT model [7] during the design stage of the SDLC. The first-order and third-order effects proposed by the GREENSOFT model include, but are not limited to the following:  
* Rebound effects;
* Change of business processes or goals;
* Software-induced hardware consumption;
* Software-induced energy consumption;
* Hardware requirements.
## 3 Three-Stage Development of BCI-Based Language Decoding
### Stage 1: Signal Interpretation, Basic Stage
These implementations underscore the critical importance of controlled experimental conditions in ensuring the reliability and reproducibility of results, particularly given the inherent limitations of first-generation BCI systems, where performance optimization requires careful consideration of signal acquisition protocols, artifact reduction strategies, and the development of robust feature extraction pipelines that can handle the inherent variability in neural signals across different subjects and recording sessions. Despite the promising potential of BCI-based language decoding, first-generation systems faced four major technical limitations that significantly hindered their practical applications in regard to neural signal decoding and interpretation. First, and foremost, these limitations included signal quality dependence, where system performance was heavily influenced by EEG signal quality [87], which significantly impacted the accuracy of linguistic feature extraction and interpretation. Beyond the signal quality issue, the limited generalization ability across users and tasks posed substantial challenges for cross-subject language decoding applications. This challenge was particularly pronounced as traditional methods lacked effective transfer learning techniques, making it difficult to adapt models to new users or tasks [88]. The third major challenge concerned high temporal delay-impeded real-time applications. This delay proved especially problematic for natural language communication scenarios, where immediate feedback and fluent interaction are essential [89]. Finally, and equally significant, are the restricted interaction capabilities hindering system performance in complex environments. This limitation particularly affected the development of practical language-based BCI applications, as natural communication typically occurs in dynamic, multimodal contexts [90]. These constraints emphasized the necessity for advancements in signal processing, generalization techniques, and real-time interaction mechanisms in subsequent BCI system developments, specifically targeting the unique challenges of neural-based language decoding and communication.