esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Wirelessly-powered cages for IMD applications are categorized and discussed with their key techniques, such as closed-loop power control, coil design/optimization, scalability for wireless coverage, spatial/angular misalignments, near-field data telemetry, and safety issues. Since the high-performance IMDs mainly focus on long-term recording and stimulation functionalities for central and peripheral nervous systems on freely behaving animal subjects [37; 38], it is important to design a suitable wireless power platform covering a large experimental arena. Different techniques for wirelessly-powered cages that are benchmarked in Tables 2-5 consider various practical issues described in this article. Although most wirelessly-powered cages provide PTE and PDL for specific experimental conditions, the physical constraints of Rx coils, coil separations, and magnetic field homogeneity over the experimental area should be compared for a more suitable choice. As the most recent class of IMDs are millimeter-sized and distributed over a large area in the brain or the rest of the body [43; 44; 45; 46], the importance of wirelessly-powered cages for tiny multiple implants is elevated as introduced in Section 4. Hence, it is important for designers to select suitable wirelessly-powered cages, considering the practical limitations of IMD design related to key aspects, such as PTE, PDL, closed-loop power control, scalability, spatial/angular misalignment, near-field data telemetry, and safety issues against various perturbations during the longitudinal animal experiment. With respect to various IMD applications from conventional implantable devices to recent mm-sized implants, this article contributes the design strategy of the appropriate wirelessly-powered cage designs, which have the ability to create an automated enriched environment inside the experimental arena for long-term electrophysiology experiments.  
Figure 22: (**a**) Rendered view of distributed BCI concept, (**b**) phase distribution among the hex-PSCs in one conductive layer, (**c**) TDM driving techniques to avoid the magnetic interference between three layers, and (**d**) a prototype measurement setup for the three-phase TDM overlapping hex-PSC array in [21].  
**Author Contributions:** Conceptualization: B.L. and Y.J.; methodology: B.L. and Y.J.; validation: B.L. and Y.J.; formal analysis: B.L. and Y.J.; investigation: B.L. and Y.J.; data curation: B.L. and Y.J.; writing--original-draft preparation: B.L. and Y.J.; writing--review and editing: B.L. and Y.J.; visualization: B.L. and Y.J.; supervision, project administration, and funding acquisition: B.L. and Y.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Incheon National University (#2019-0299) research grant in 2019.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Filtering Switch Made by an Improved Coupled Microstrip Line
## 4 Discussion  
On the one hand, the coupling of the microstrip lines is adjusted by the bandpass filter response of the parallel-coupled microstrip lines to gain an accurate bandpass filter effect. The g\({}_{1}\) and g\({}_{2}\) values in Figure 5 decide the bandwidth of the filtering effect. However, the coupling of the brief reduce-load resonator can be adjusted to generate the preferred passband transmission zeros (TZs) to enhance the filter function's passband selectivity. The microstrip line coupling decided via the g\({}_{3}\) and g\({}_{4}\) values in Figure 5 can similarly manipulate the vicinity of the transmission zeros. So, when the filtering switch is operating in the on-state filter state, it has good passband selectivity and out-of-band rejection. When the filtering switch is operating in the off state, the input and output ports are approximately fully reflected, and good off-band rejection is easily obtained by changing the PIN diode's bias.  
Because we chose to design a lower center frequency, the physical size of the prototype shown in Table 1 is inevitably too large; however, this is only at the prototype stage, and we will conduct more research to further reduce the size and production cost while ensuring the operating performance. Due to our limited experimental resources, unavoidable tolerances occurred during the fabrication of the prototype. The prototype in Figure 7b showed differences between the out-of-band simulation and the actual measurement in the off state, but it did not affect the operating condition in the operating bandwidth. In addition to this, the filtering switch prototype operates at a 0.8 Ghz center frequency and 44.5% FBW in the on state. In this case, part of the passband range is within the low frequency band of 700 MHz, and the relative bandwidth of 44.5% can cover most of the band; therefore, the prototype is expected to be applied to the 700 MHz band in 5G communication.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **CF (GHz)** & **IL (dB)** & **FBW (\%)** & **RL (dB)** & **OSS (dB)** & **SIZE (\(\lambda_{g}^{2}\))** & **Feasibility of 5G Band Operation** \\ \hline
[2] & 0.903 & 1.58 & 10.6 & 20 & 37.6 & 0.0355 & Yes \\
[2] & 0.914 & 1.36 & 11.4 & – & \(\times\)40 & 0.0355 & Not \\
[10] & 1.246 & 1.9 & 15.3 & \(<\)15 & 32.8 & 0.0201 & Not \\
[10] & 0.901 & 1.8 & 14.3 & \(<\)15 & 27.8 & 0.0191 & Not \\
[11] & 0.991 & 1.08 & 14.73 & 20 & 48.2 & 0.029874 & Not \\
[12] & 0.994 & 1.72 & 8.96 & – & \(>\)40.6 & 0.033115 & Not \\ This work & 0.8 & 0.65 & 44.5 & 16.3 & 27.3 & 0.1178 & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with other reported works. –: not given.  
Figure 7: Simulated and actual measured filtering switch S-parameters’ (**a**) on state (**b**) off state.  
For the uncontrollable losses that appear above, we will refer to the work on LQG control of linear lossless positive-real systems [20] in our subsequent work to optimize and improve our prototype design, so that it can achieve better operating performance in the future.
## 2 Materials and Methods
### Synthesis and Sample Fabrication  
The synthesis of the perovskite precursor solution was performed by stirring PbI\({}_{2}\) (Lead (II) iodide, Sigma Aldrich 211168, St. Louis, MO, USA) and CH\({}_{3}\)NH\({}_{3}\)I (MAI 98%, TCI M2556) at a molar ratio of 1:1, using a mixture of N,N-Dimethylformamide (DMF 99.8%, Sigma Aldrich 227056) and Dimethyl sulfoxide (DMSO 99.9%, Sigma Aldrich 276855) with a 9:1 (\(v/v\)) ratio at 75 \({}^{\circ}\)C for 50 min and at room temperature overnight [55]. The obtained solution was passed through a 0.22 \(\upmu\)m Polytetrafluoroethylene (PTFE) filter prior to deposition [56]. A volume of 100 \(\upmu\)L of perovskite precursor solution was dispensed on top of glass substrates and then was subjected to a spin rate of 5000 rpm for 30 s. During the spinning, a 3:1 volume ratio of a non-polar solvent (chlorobenzene) was dropped over the substrate, achieving a layer thickness of 300 nm [55], and the RH inside the chamber was maintained between 8-10%, with a constant flow of clean dry air (CDA) [55]. Finally,the sample was annealed at 100 \({}^{\circ}\)C for 10 min inside a furnace to evaporate any residual solvent and to promote crystallization [57]. Samples were stored in a vacuum desiccator to avoid degradation [58; 59].  
The synthesis of the electron transport layer (ETL) was performed by dropwise adding a solution of 175 \(\upmu\)L of Titanium (IV) isopropoxide (TTIP 97%, Sigma Aldrich 205273) in 1.25 mL of EtOH (Ethanol 99.9%, Merck 1.00983.2500, Kenilworth, NJ, USA) into a solution of 17.5 \(\upmu\)L of 2 M HCl (Hydrochloric acid 37%, Labkem CHAC-0 AA-2K5, Dukinfield, UK) in 1.25 mL of EtOH [60]. A 0.22 \(\upmu\)m PTFE filter was used to obtain the solution. Finally, the precursor solution (150 \(\upmu\)L) was dispensed on top of glass substrates, and then the samples were spin-coated at 2000 rpm for 60 s. To obtain a thickness layer of 80 nm, a double ETL was deposited using the spin-coating technique. After the deposition, the first layer was heated to 150 \({}^{\circ}\)C for 30 min, and the second layer was spin-coated and heated to 500 \({}^{\circ}\)C for 60 min.
## 3 Results and Discussion
### Sensitivity Analysis on Features  
Figure 13 shows further evaluation of the top 9 features which overall indicates their superiority over using only the initial features based on all the metrics. The Gradient Boosting model was used to analyse further the performance based on a different model. The results indicate an increase in model accuracy on test data when the top relevant features were selected as input. There was also a reduction in the model's error measurements. The performance of the new features is a testament that wettability, is dependent on the saturation and porosity dependent logs. These logs are quite significant in determining wettability as they resulted in the improvements of the models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Models**} & **Top 10 Features** & **Accuracy** \\ \hline \(\bullet\) & LGBoost & PERM, GR, New feature 1, Sw, NEU, New feature 2, New feature 3, New feature 5, New feature 6, POR & 0.8056 \\ \(\bullet\) & AdaBoost & PERM, New feature 2, New feature 5, New feature 3, NEU, DEN, New feature 1, GR, POR, RES\_SLW & 0.7119 \\ \(\bullet\) & Decision Tree & PERM, New feature 1, POR, New feature 7, New feature 2, New feature 5, RES\_MED, GR, New feature 3, RES\_SLW & 0.6204 \\ \(\bullet\) & Random Forest & PERM, New feature 1, GR, New feature 5, NEU, New feature 7, POR, New feature 3, New feature 2, DEN & 0.8244 \\ \(\bullet\) & Bagging & New feature 1, PERM, GR, POR, NEU, New feature 3, New feature 5, Sw, RES\_DEP, New feature 7 & 0.7991 \\ \(\bullet\) & Extra Tree & PERM, New feature 5, GR, New feature 1, NEU, Sw, RES\_SLW, POR, New feature 4, New feature 7 & 0.807 \\ \(\bullet\) & XGBoost & PERM, New feature 1, New feature 7, GR, New feature 2, RES\_MED, POR, RES\_DEP, New feature 4, New feature 6 & 0.7954 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Computed feature importance and accuracy on test data using different models.  
In selecting the optimal model fitting to the data set, all the features discussed in this research can be compared via error metrics. The selected top 9 features are GR, NEU, Sw, PERM, New feature 1, New feature 2, New feature 3, New feature 5 and New feature 7. This result indicates that to characterise an oil and gas reservoir in terms of NMR T\({}_{2}\)LM, these features should be included as input variables. This inference shows how robust the likelihood of fit the new features is for wettability prediction. As a result, our proposed new features, using Sw, POR and PERM, provide the best likelihood of optimising model fit for NMR T\({}_{2}\)LM predictions.
## 2 Theoretical Analysis  
The energy required for laser amplification comes from the energy stored in the gain medium before signal input. For lasers, the expression of laser pulse amplification gain [15] is  
\[G=\frac{E_{s}}{E_{0}}ln\left\{1+\left[\exp\left(\frac{mE_{0}}{E_{s}}\right)-1 \right]-G_{0}\right\} \tag{1}\]  
where \(E_{s}\) is the saturation energy density; \(E_{0}\) is the energy density of the input pulse; \(G_{0}\) is small signal gain coefficient.  
The relation between small signal gain and pump energy is  
\[G_{0}=\exp(g_{0}l)=\exp(\beta E_{s}l) \tag{2}\]  
where \(g_{0}\) is the gain coefficient of unit length in the gain medium, \(l\) is the length of the gain medium;  
And in a four-level system  
\[E_{s}=\frac{h\
u}{\sigma} \tag{3}\]  
where \(h\) is the Planck constant; \(\
u\) is the laser frequency; \(\sigma\) is Excited interface. Therefore, the output energy of the multistage laser amplification system is \[\mathrm{E}=(\frac{E_{s}}{m})ln\left\{1+\left[\exp(\frac{mE_{0}}{E_{s}})-1\right] \exp(m\sigma N_{0})\right\} \tag{4}\]  
\[N_{0}=\frac{g_{0}l}{\sigma} \tag{5}\]  
where \(m\) is the number of times the laser passes through the gain medium, and \(g_{0}l\) is the one-way small signal gain factor.  
In our experiments, \(h\) = 6.6260755 \(\times\) 10\({}^{-34}\) J-s, \(\
u\) = 500 kHz, \(\sigma\) = 25 \(\times\) 10\({}^{-19}\) cm\({}^{-2}\), \(E_{s}\) = 1.3252 \(\upmu\)J/m\({}^{2}\), \(E_{0}\) = 24.45 \(\upmu\)J/m\({}^{2}\), \(m\) = 2, \(l\) = 20 mm. After theoretical calculation, theoretical output pulse energy is 26.78 \(\upmu\)J was obtained.
# Towards Reliable Baselines for Document-Level Sentiment Analysis in the Czech and Slovak Languages
## 3 Summary of Relevant Experiments and Results Reported in [50]
### Results for Optimum Configurations Using All Features of the Included Feature Types  
**Facebook dataset:** In the ternary classification into positive, negative, and neutral classes, the \"collective winners\" were the preprocessing pipelines that avoided phonetic transcription and lemmatization. In combination with all the available feature types, such configurations systematically achieved the macro-F1 score of 69%. Somewhat surprisingly, the same result was achieved by two specific pre-processing pipelines based on High Precision Stemmer (HPS) with either lower-casing or phonetic transcription even with a reduced feature set comprising just word unigrams, bigrams and POS-related features.  
**Product review dataset (Mal.cz):** In the ternary classification, there were four configurations that managed to cross the 75% level of the macro-F1 measure. The best one (75.30%) was based on Lemmatization by OpenOffice, and the next two on light stemming. Quite surprisingly, all the four achieved this remarkable feat just on the basis of word unigrams and bigrams. In fact, adding the POS-related features into the mix seemed to cause a \"catastrophic\" drop of about 15-20 percentage points in the macro-F1 across the board. This was one of the things that caught our attention as deserving a dedicated replication experiment (which we describe in more detail in Section 4) because other researchers using POS-related features, such as [70,71], did not report any such adverse effects.  
**Movie review dataset (csfd.cz):** In the ternary classification, there were six configurations that crossed the level of 78% in the macro-F1 score, and all of them used some form of stemming. The best configuration (HPS stemming with phonetic transcription) reached the macro-F1 of 78.50%. As with the product reviews, they all managed to do so just on the basis of word unigrams and bigrams, whereas adding the POS-related features again seemed to lead to a drop in the macro-F1 across the board, although this time only by 5 to 10 percentage points.
## 3 Security Weaknesses of Cao et al.'s Scheme  
In this section, we demonstrate that Cao et al.'s scheme is vulnerable to MD and UE impersonation attacks as well as a single point of failure. Further, we also show that Cao et al.'s scheme does not achieve secure mutual authentication and session key security, which is a necessary security requirement for authentication and key agreement scheme.
# A Congestion Control Strategy for Power Scale-Free Communication Network
## 3 Congestion Control Strategy for the Power Scale-Free Network
### Classification of Power Communication Services  
Compared with the public communication network services, the power communication services are more important, and huge losses would be brought to the power grid when congestion occurs. In the power communication network, the following three services are included, as shown in Table 1.  
In Table 1, different communication quality, delays, and so on, are required for different communication services. To ensure the stable operation of the power grid, the real-time requirements of core services should be very high, such as the safety monitoring data and automated dispatch instructions. A slight delay in the delivery process may cause a huge loss, so the core services must reach the destination in a short time interval. On the contrary, the real-time requirements of the administrative telephone are lower. The administrative telephone is mainly responsible for the power grid company administration and daily office management, which has less effect on the normal operation of the power grid. Therefore, according to the importance of services and the requirement of delay, the power communication services are divided into two levels, including key operational services of the I-level and affairs management services of the II-level, as shown in Table 2.  
The amount of key operational services is small, but the requirements of real-time and reliability are very high; and the data flow of affairs management services is large and changes fast, but the requirement of delay is lower than key operational services [18]. Compared with II-level services, the amount of data of I-level services in the power communication network is smaller, but I-level services are more important. In order to make the routing algorithm better suitable for the power communication network and guarantee the key operational services' timely deliver to the destination, integrated routing is proposed. The data packets contain the class information including I-level services and II-level services, and the data packets are put into the appropriate class queue of the root based on the information classifiers. Assuming that the proportion of the I-level packet generation is \(\lambda\) at each time step and the proportion of the II-level packet generation is \(1-\lambda\) and \(\lambda\in[0,1]\), different proportions of different service levels have an impact on the efficiency of data transmission in the power communication network.
## 5 Future Trends, Challenges, and Opportunities
### Database Shortage  
Most of the frameworks reported are supervised approaches, where using labeled data to train a model is a crucial step. ML is a tool for large, complex data processing, where huge numbers of inputs are required [108; 109]. The model performance depends directly on data. Although there exist benchmarks that use channel realizations to generate an input to build DL-based solutions for RIS-assisted wireless communications [56], only a few researchers have considered gathering physical measurements to train ML algorithms [58]. Ensuring data integrity, security and scalability are the main concerns when creating a database for the implementation of learning algorithms. Moreover, for applications on wireless communications, labeling or indexing schemes are crucial for the efficient optimization of systems since most of the data are complex and continuous. It is also important to motivate researchers and engineers to collaborate in the creation, use, and implementation of ML-based frameworks on existing databases to facilitate the knowledge sharing and implementation processes.
## 4 Experimental Results
### Performance Metrics
#### 4.2.3 Detection Efficiency  
SARS-CoV-2 is being updated on a daily basis. To train the data, a self-to-self transfer-learning mechanism is applied. Every month, the data were re-trained. It has been observed that the SARS-CoV-2 data set has more information as time passes. It was difficult to find information for each country. Although various countries have distinct discriminations that cause the disease to spread in different ways. The primary purpose is to establish the detection efficiency of the proposed NMP and competing algorithms: STAMP and STOMP, as well as how they determine the trend of recovered, death, and infected patients. The experiment was carried out using data from January 2020 to October 2020. Figure 6a depicts the infected patients' detection efficiency. The trend in Figure 6a demonstrates that the suggested NMP with infected patient data has an overall detection efficiency of 97.2%, whereas STAMP has an overall detection efficiency of 87.4% and STOMP has an overall detection efficiency of 87.1%.  
The recovered patient data's detection efficiency is displayed in Figure 6b. The suggested NMP yields a 98.4% total detection efficiency. The competing algorithms STAMP and STOMP, on the other hand, yield total detection efficiency of 85.6% and 83.6%, respectively. The proposed NMP's detection efficiency, evaluated at 94.1% in Figure 6c using death patient data, is higher than that of the competing algorithms, which had detection efficiencies of 85.3% and 81.7% for STAMP and STOMP, respectively. According to the findings, the suggested NMP outperforms competing algorithms in terms of total detection efficiency.  
Figure 5: (**a**) Run-time execution in seconds for NMP, STAMP, and STOMP with \(n=\) 135,000, varying the time series length. (**b**) Accuracy of NMP, STAMP, and STOMP with a maximum of 135,000 for the time series data length. (**c**) Accuracy of NMP, STAMP, and STOMP with a maximum of 180,000 for the time series data length.
## 3 Pseudo-TCT and Verification Experiment
### Pseudo-TCT  
#### 3.1.1 Orthogonal Linear Transform  
The TCT is an empirical orthogonal linear transform of multi-band images proposed by [PERSON] and [PERSON] after analyzing the data structure of Landsat MSS images reflecting crop and vegetation growth processes. The TCT relies on the characteristics of the sensor itself, and the TCT coefficients are not universal for different sensors [39]. However, since the parameters of GF-2 and IKONOS were very similar, the TCT coefficients of IKONOS images derived by [PERSON] were directly used as the TCT coefficients of the GF-2 images [40], and the TCT was calculated as follows:  
\[\mathrm{u}=\mathrm{R}^{\mathrm{T}}\mathrm{x}+\mathrm{r} \tag{1}\]  
where u are the bands of the TCT; and R is the TCT coefficients; the superscript T indicates transpose; x is the reflectance of each band; and r is a constant offset to avoid negative values of u after the transformation.  
The TCT is a linear combination of different components of different bands, and we want to verify whether there will be better image enhancements if we change the components of these bands, which was the reason why we propose the pseudo-TCT. Since both R and R\({}^{\mathrm{T}}\) are orthogonal matrices, both can perform an orthogonal linear transformation of the image, so the TCT coefficient matrix without transposition can change the components of different bands and recombine them, so the pseudo-TCT was calculated as follows:  
\[\mathrm{u}=\mathrm{Rx} \tag{2}\]  
where u are the bands of the pseudo-TCT; R is the TCT coefficients (same as R in (1)); and x is the reflectance of each band.