esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation Method for Ice Shedding
### Finite Element Calculation Procedure  
The ice shedding problem is a complex nonlinear dynamics problem, and researchers have used a variety of methods to determine the ice shedding jump mechanical model for static loads, transient loads, simple harmonic loads, and any combination of time-varying stresses, strains, and displacements.  
As shown in Figure 3, the simulation of the bouncing process induced by ice shedding on overhead conductors under strong wind conditions contains the knowledge of nonlinear structural statics as well as structural dynamics, and the simulation process is generally divided into several load steps:  
Step 1: Calculate the resistance and lift based on the hydrodynamic method for the bare and iced conductors.  
Step 2: Static analysis of a conductor under its self-weight. It is determined by calculating whether the deformation of the conductor under its self-weight is equal to the known shape.  
Step 3: On the basis of Step 1, apply the icing load and wind load and carry out static analysis without considering the ice shedding and the fluctuations of wind load.  
Step 4: Simultaneously release the icing load and wind load, where the wind load is updated from the value before ice shedding to the bare conductor.  
Step 5: Use the transient dynamics method to determine the final stabilization time.  
During nonlinear finite element numerical simulation, pole elements were used to simulate the transmission conductors. The accuracy of the finite element simulation results is closely related to the number of elements used for discretization. Generally, higher numbers of elements lead to greater accuracy but also require longer computational time. To validate the convergence of the unit mesh, the 400 m transmission conductor span was discretized into 100, 200, 400, and 1000 elements, and the numerical values of sag and maximum bounce height were calculated under the same conditions. The results show that when the line is divided into 400 elements, the error between the sag and maximum bounce height values and those obtained with 1000 elements is less than 0.5%. Therefore, in this study, each meter of the conductor is divided into one element.
## 3 Background
### Overview of Target Algorithms
#### Aes  
The AES block cipher algorithm is based on the SPN structure and has a block length of 128 bits. The key lengths used for AES are 128, 196, and 256 bits, and the number of rounds according to the key length is 10, 12, and 14 rounds. Each round consists of AddRoundKey, SubBytes, ShiftRows, and MixColumns functions. In the last round, there is no MixColumns function involved. The result of grouping SubBytes, ShiftRows, and MixColumns and tabulating the results is called T-table and has shown significant performance improvements [16]. The T-table consists of four tables and each table consists of 256 32-bit values. Encryption is performed while referring to the table value 16 times in a round. In this paper, we implement the AES version using T-table. The creation of T-table and encryption operation using T-table are as follows.  
\[T_{0}[a]=\begin{bmatrix}Sbox[a]*02\\ Sbox[a]\\ Sbox[a]\\ Sbox[a]*03\end{bmatrix}T_{1}[a]=\begin{bmatrix}Sbox[a]*03\\ Sbox[a]*02\\ Sbox[a]\end{bmatrix}T_{2}[a]=\begin{bmatrix}Sbox[a]\\ Sbox[a]*03\\ Sbox[a]*02\\ Sbox[a]\end{bmatrix}T_{3}[a]=\begin{bmatrix}Sbox[a]\\ Sbox[a]\\ Sbox[a]*03\\ Sbox[a]*02\\ \end{bmatrix} \tag{1}\]  
\[e_{j}=T_{0}[a_{0,j}]\oplus T_{1}[a_{1,j-C_{1}}]\oplus T_{2}[a_{2,j-C_{2}}] \oplus T_{3}[a_{3,j-C_{3}}] \tag{2}\]
# Effect of Seawater Irrigation on the Sugars, Organic Acids, and Volatiles in 'Reliance' Grape
## 3 Results
### Relative Expression Levels of Key Genes Responsible for Volatiles in the Fatty Acid Pathway  
C6 volatiles, especially C6 esters synthesized from the lipoxygenase-hydroperoxides lyase (LOX-HPL) pathway, produce the primary aroma, contributing to the fruity scent in 'Reliance' grapes. The present study conducted RT-qPCR analysis of _VvLOXA_, _VvHPL_, _VvADH_, and _VvAAT_ to describe the relative expression gene profiles of these volatiles. The results revealed similar expression trends during the grape growing period among the four genes (Figure 6). For _VvLOXA_, _VvHPL_, and _VvADH_, the highest expression peaks appeared at either EL 33, EL 35, or EL 36, and then gradually declined until a slight upward trend at EL 38. The expression of the four genes responded most significantly to S1 treatment, which concurs with the trends of C6 volatiles. As the first rate-limiting enzyme in the LOX-HPL pathway, and crucial for fatty acid oxidation and provision of abundant substrates for the synthesis of downstream volatiles, the expression level of _VvLOXA_ in the S1 berries was 10.42-fold greater than the control at EL 35. _VvHPL_ catalyzes the formation of C6 aldehydes, and the highest expression peak in the S1 berries appeared at EL 33. The _VvAAT_ can convert alcohol into C6 esters, and its expression pattern is different from that of the above three genes. Its transcript abundance was relatively low at the early periods, gradually increased from EL 35, and peaked at EL 38. At EL 38, the expression of _VvAAT_ in the S1, S2, and S3 berries reached 1.20, 1.99, and 1.68 folds in the control berries, respectively.  
Figure 5: Principal component analysis of the effect of seawater irrigation on the volatiles in ‘Reliance’ grape during the berry development period. (**a**) Score plot of volatiles of berries in different treatment during the grape growing stage. (**b**) Loading plot of the component in a different pathway. (**c**) Specific volatiles of the dotted portion of (**b**).
## 3 Results and Discussion
### System Application Effectiveness  
The plate recognition system developed in this study has been successfully implemented in a campus cafeteria environment, demonstrating significant effectiveness. Over one year of operation, the system covered a total of 12,000 users, with a notable increase in the proportion of users reducing food waste through point-based incentives. As a result, overall food waste in the cafeteria decreased by 13%. User feedback data indicate that over 85% of users were satisfied with the point redemption mechanism, and more than 75% actively participated in data uploads and provided improvement suggestions, highlighting broad acceptance of the system's incentive strategy. Furthermore, the proposed approach was recognized as one of the \"Top 10 Best Practices in Energy and Resource Conservation for Public Institutions (2021-2022)\" by the China National Government Offices Administration, further validating its feasibility and effectiveness in promoting green campus initiatives and reducing food waste.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SNR & signal-to-noise ratio \\ CRC & cyclic redundancy check \\ BLER & block error rate \\ IoT & Internet of Things \\ PBCH & Physical Broadcast Channel \\ SC & Successive Cancellation Decoding \\ SCL & Successive Cancellation List Decoding \\ BP & Belief Propagation Decoding \\ SCC-SCL & Segmented CRC Error Correction-Assisted SCL Decoding \\ BER & bit error rate \\ PCC & Parity Check Concatenated \\ LDPC & Low-Density Parity Check \\ B-DMC & Binary-Input Discrete Memoryless Channel \\ LRs & likelihood ratios \\ UBA & Unrepeated Bits Area \\ RBA & Repeated Bits Area \\ EOP & Error Occur Probability \\ BRP & Bit Reliable Probability \\ SC+ & new Successive Cancellation Decoding algorithm \\ \end{tabular}
# Mdpj _sciences_
## 4 Experimental Detail
### Sentiment Classification  
This part of our study involves three crucial stages: Preprocessing, Sentiment Modeling, and Evaluation.  
**Stage 1:Preprocessing**  
The preprocessing stage for sentiment classification is different from preprocessing for review generation. This process includes a few essential actions: first, content cleaning where we removed non-English sentences, duplicates, and unnecessary elements from the content, like URLs, user handles, and hashtags that do not significantly contribute to sentiment analysis; second, text normalization, which involved removing special characters, numbers, and multiple spaces to ensure uniformity in the text; and lastly, language processing that further refines the text data by excluding stop words, emojis, and sentences that only consist of a single word.  
**Stage 2:Sentiment Modeling**  
In this study, two sentiment classification scenarios were undertaken: one using the original imbalanced dataset, and the other using a balanced dataset supplementedwith synthetic data generated by GPT-3. Both scenarios employed the same testing data, ensuring a consistent basis for comparison.  
To begin with, the original imbalanced dataset was systematically split into two primary subsets: training and testing, following an 80:20 ratio. It is important to highlight that the testing dataset preserved the inherent imbalanced nature of the primary dataset. To guarantee a broad representation, this testing dataset was proportional according to sentiment categories, namely, positive, negative, and neutral. Moreover, to capture the nuances of each category _aspect_ variable, the testing data were proportional to mirror the distribution of these _aspects_. This detailed strategy ensured that the testing data faithfully represented the original dataset, capturing its intrinsic imbalanced distribution across classes. After this, the creation of training and validation datasets for both imbalanced and balanced datasets took place. For the imbalanced dataset, the training data derived from the initial step were further divided into Train and Validation sets at an 80:20 ratio. For the balanced dataset, the procedure entailed two phases: (1) The training data from the first stage were augmented with synthetic data to achieve a balanced state. (2) Upon reaching this balance, the synthetic data were split into Train and Validation sets, again with the 80:20 ratio. A detailed representation of the data splitting and sentiment classification process can be seen in Figure 4.  
For both the balanced and imbalanced datasets, we utilized a total of nine models, which spanned both traditional machine learning and deep learning techniques, for sentiment classification. Within traditional machine learning, methods such as the Support Vector Machine, Decision Tree, Naive Bayes, and AdaBoost were used. Conversely, the deep learning domain was explored using architectures like RNN, CNN, and their advanced variants like LSTM, GRU, and BiLSTM. Except for RNN, these deep learning models incorporated GloVe embeddings.
# Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia
## 2 Methods
### Comparison Methods
This model was originally fitted using historic Landsat data, using surface reflectance as input (13,14), and was used with the adjustment equations derived by Flood (7) to account for the differences between Landsat-7 and Landsat-8. A simpler, but more commonly used indicator of green vegetation cover is the Normalized Difference Vegetation Index (NDVI) (reviewed by [PERSON] et al. (15)). NDVI is calculated using the NIR and Red bands, as shown in Equation (2)  
\[\text{NDVI}=\frac{(\rho_{\text{NIR}}-\rho_{\text{R}})}{(\rho_{\text{NIR}}+ \rho_{\text{R}})}, \tag{2}\]  
where \(\rho_{\text{NIR}}\) and \(\rho_{\text{R}}\) are the reflectances in the NIR and Red bands, respectively. Comparisons were performed for both the fractional cover model and the NDVI. Results are presented for the case when using the unmodified reflectances, and for using the reflectances when adjusted using Equation (1).
# Progress in THz Rectifier Technology: Research and Perspectives
### THz Diode Technology: I-V Characteristics and Figures of Merit (FOM) for MIM Diode
#### 2.2.3 Turn-On Voltage (TOV)  
The turn-on voltage (TOV) represents a positive voltage applied to the diode to \"turn it on\" and conduct current in the forward direction. In general, the TOV of these diodes has to be equal to the open circuit voltage of the antenna, which is in the range of a few picovolts. This small value could be insufficient for the rectification process. An increase in the TOV is possible if the insulator layer is thinner [7; 23; 27; 28; 29].
## 1 Introduction
There was no detailed study on the operation control strategy of FMSS, and therefore, real-time regulation of DG was not possible. There are relatively few studies on the operation control strategy of DG based on FMSS. Further studies [15; 16] provided a detailed comparative analysis of network reconfiguration with SNOP, verified the advantages and potential benefits of SNOP in real-time adjustment, and put forward a simulated annealing (SA) and conic programming (CP) of hybrid optimization algorithms. This was to achieve the purpose of the distribution network operation sequence optimization. However, its objective function is the minimum value of energy loss in a period of an operation cycle, that is, 24 h to obtain a set of optimization results, which obviously does not meet the control requirements of real-time operation of the system. Another study [17] established a two-stage tunable robust optimization model, which improved the calculation accuracy and efficiency of the robust optimization model, generated the robust operation strategy of the photovoltaic system, and improved the consumption capacity of the distribution network to photovoltaic. However, the voltage index in the objective function only works when the voltage exceeds the limit. Therefore, in the process of real-time adjustment of the system, the over-limit voltage may occur. In a further study [18], a sensitivity method based on a Jacobian matrix was proposed to determine the adjustable capacity boundary of SNOP when the grid/feeder at both ends of SNOP had different loads and power generation conditions to obtain its control strategy. However, this research is still focused on the level of distribution network planning, and its method has the potential of real-time operation control, but still needs to be optimized and improved. In this paper, a strategy regulation of FMSS is proposed to maximize the consumption of DG in the complex distribution network. Firstly, the principle of FMSS promoting the consumption of DG is analyzed. Secondly, the related indexes and constraints of the distribution network's consumption of DG are analyzed. The supply system operation constraints for the distributed power's given situation and voltage fluctuation index constructs the objective function using the improved particle swarm optimization algorithm. From this, the instruction value of each port of the flexible multi-state switch and the maximum output value of DG are obtained. Finally, a complex network distribution system based on three IEEE-33 node networks is used to verify the correctness and superiority of the real-time control strategy of FMSS for maximum consumption of DG.
## 4 Circuit Description
### Hardware Specifications  
From the sampling theory above, we can see that the SCMWC is a flexible system with several parameters. Table 1 shows the specifications we choose for circuit level implementation.  
To begin with, we should specify the multiband signal model, \(N=4\), \(B=15\) MHz, \(f_{nyq}=2\) GHz. That is to say we can sense two bands at the same time and each band is no more than 15 MHz. The alternation rate of the periodic sign is 2 GHz, which is generated by Gigabit Transceiver X (GTX) on Xilinx FPGA. Based on the theory in [11], the basic parameter choice is \(m\geq 4N\) and \(f_{s}=f_{p}\geq B\). In order to reduce sensing time, we choose the minimum number \(m=4N=16\). For convenience, we set the cut-off frequency of LPF at 33 MHz, owing to the difficulty in implementing a much lower cut-off frequency.  
Figure 4: Block diagram of the proposed circuit board based on SCMWC.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 3 Proposed Framework
### Phase 2: FHE Setup and SIMD Encryption
```
0: Public params \(\Pi_{pub}\), client identifier \(\mathcal{C}_{ID}\)
0: Client context \(\mathcal{C}_{client}\), keys \((pk,sk)\), eval keys \(evk\) or \(\perp\)
1: Context Initialization:
2:\(\mathcal{C}_{client}\leftarrow\textsc{InitializeCKKS}(\Pi_{pub})\)
3: Key Pair Generation:
4:\((pk,sk)\leftarrow\textsc{KeyGen}(\mathcal{C}_{client})\)
5: Evaluation Key Generation:
6:\(\mathcal{S}\leftarrow\{\pm 1,\pm 2,\pm 4,\pm 8,\pm 16,\pm 32,n/2\}\) {Comprehensive rotation set}
7:\(evk_{rot}\leftarrow\textsc{RotKeyGen}(sk,\mathcal{S})\)
8:\(evk_{retin}\leftarrow\textsc{RelNK}\textsc{KeyGen}(sk)\)
9:\(evk\leftarrow(evk_{rot},evk_{retin})\)
10: Key Validation:
11:\(\mathbf{m}_{test}\leftarrow(1.0,2.0,3.0)^{T}\) {Test vector}
12:\(ct_{test}\leftarrow\textsc{Enc}_{pk}(\mathbf{m}_{test})\)
13:\(\mathbf{m}^{\prime}\leftarrow\textsc{Dec}_{sk}(ct_{test})\)
14:if\(\|\mathbf{m}^{\prime}-\mathbf{m}_{test}\|_{2}>\epsilon\) where \(\epsilon=10^{-3}\)then
15:return\(\perp\) [Key validation failed]
16:endif
17:return\((\mathcal{C}_{client},pk,sk,evk)\)
```  
**Algorithm 5** Client-Side FHE Key Generation Protocol
# Optimized Architecture for Efficient OFDMA Network Design
## 5 Simulation Results
### Validation Study
#### 5.1.2 Throttling Regime  
Below are the results from the simulations conducted in the validation study, alongside the outcomes from the analytical model for the bottleneck regime. Figures 8 and 9 illustrate the average number of active users \(\overline{Q}\) and the average resource utilization \(\overline{U}\) for the throttling regime, respectively.  
Figure 6: The average resource utilization for conventional scheduling algorithms (\(N=10\), \(x_{on}=3\), _Mbits_, and _toff_ = 3 s).  
Figure 7: The stationary state probability for conventional scheduling algorithms (\(N=10\), \(x_{on}=3\), _Mbits_, and _toff_ = 3 s).  
Figure 10 shows the stationary state probability \(\pi(n)\) for the Throttling regime.  
Figure 8: The average number of active users for the Throttling regime (\(N=10\), \(x_{on}=3\) Mbits, \(t_{off}=3\) s, and \(MSTR=512\) Kbps).  
Figure 10: The stationary state probability for the Throttling regime (\(N=10\), \(x_{on}=3\) Mbits, \(t_{off}=3\) s, and \(MSTR=512\) Kbps).  
Figure 9: The average resource utilization for the Throttling regime (\(N=10\), \(x_{on}=3\) Mbits, \(t_{off}=3\) s and \(MSTR=512\) Kbps).  
Even for the Throttling regime, the performance parameter results obtained from the analytical model closely resemble those from the validation study.
###### Abstract  
Repairing bone defects in oral surgery often requires the use of bone regeneration techniques. Silicon is an element that has been employed as regeneration material in several studies. In our study, silicon was combined with autologous bone and platelet-rich fibrin (PRF) membranes to analyse the behaviour of this element in bone regeneration. Four circumferential defects were created in the cranial vault of five New Zealand rabbits. The following elements were applied to the regeneration of the defects: (P): PRF; (S): silicon and (B): autologous bone, with the following distribution of study groups: Group 1 (PSB); Group 2 (PS); Group 3 (SB) and Group 4 (CONTROL): unregenerate group. The animals were sacrificed after 3 weeks. Computed microtomography studies (u-CT) were carried out, as well as histomorphometric ones. The ANOVA statistical test was used with a Bonferroni post-hoc test to compare the results (\(p\leq 0.05\)). Radiologically, groups PSB and SB were better as far as quantity and percentage of healthy bone observed, but not significantly compared to the control group. The PS group was significantly worse. The histological test revealed that the PSB group was the one to present the largest area, percentage and perimeter of mineralised bone. On evaluating the forming bone (osteoid), no difference was observed across the groups with the exception of the bone perimeter, where the SB group was significantly better. The bone height variable showed no significant differences. In conclusion we can state that the combination of PRF, autologous bone and silicon provides good results at 3 weeks whilst the PS group shows the worst results. This highlights the importance of autologous bone forming part of the graft material in order for the bone to mineralise.  
bone regeneration; silicon; platelet-rich fibrin; PRF; autologous bone +
Footnote †: journal: Applied Sciences
# Preprocessing for Lessening the Influence of Eye Artifacts in EEG Analysis
## 4 Artifact Processing Methods
### Partial Rejection of ICA Components  
The partial rejection (PR) of artifactual components is achieved by attenuating the signal excerpts related to eye artifacts but leaving non-artifact samples untouched. The samples related to artifacts are identified by the membership function (MSF) obtained as described in Section 3.3, which is later converted into a Windowed Membership Function (WMSF) by employing smooth transitions adding Blackman's window slopes to the edges of the steps in every marked excerpt. Figure 6 illustrates such a transformation.  
Figure 4: Example of cumulative cross-correlation coefficients between every IC and EOG, summed across trials, for subject \(S_{12}\).  
Figure 5: Scheme of the complete removal (CR) approach.  
Figure 6: A piece of a MSF and its corresponding WMSE.  
A MSF can be tuned following this approach by using an attenuation factor, \(\alpha\), with a value between 0 and 1 to adjust the desired attenuation strength, with 0 for no attenuation, employing the following expression:  
\[s_{\alpha,n}(t)=s_{n}(t)\cdot(1-\alpha WMSF_{k}(t)) \tag{5}\]  
In this paper \(\alpha\) is fixed to 1 to better analyze the effect of the rejection scheme.  
Partial artifact rejection is then performed for each selected component. A graphic representation of the partial rejection produced by the utilization of the WMSF is shown in Figure 7.  
A simple scheme of the process described to perform PR of independent components is drawn in Figure 8.