esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Power EEG Data Encoding for Brain Neurostimulation Implants +
## 2 Materials and Methods
### Design Methodology
#### 2.2.4 Buffer Implementation in VHDL  
The second step of implementation comprises the methods for programming integrated circuits and, in particular, the arrangement of the system's memory. A FIFO buffer was constructed in VHDL for this purpose, utilising the Quartus platform and ModelSim to write and simulate the code. Some principles must be followed while designing a FIFO buffer to fit the transmitting device. Initially, the FIFO buffer entity was built, which contains the memory capacity and the size of each location in the memory, the clock, the reset variable, the variables to permit writing and reading (en_w,en_r), the data array, and the buffer (full, empty) control variables. Then the RAM implementation signals, the record counter (count), the pointers of the writing and reading locations (input, output), and the auxiliary variables for signalling the fullness and emptiness of the buffer (full_i, empty_i) were specified.  
In the architecture, the reset control is conducted to initialise the counter and pointer values. These pointers increase every time data are read or written, and they are controlled to avoid overwriting. This is accomplished by using the signal named \"full\" which, when set to 1, prevents the addition of new values. When a new element is introduced to the buffer, the counting signal is incremented by one. If an element is read, the counter is decremented by one, and new memory space is made available to write a new value. Furthermore, when the reading or writing pointer reaches the end of the buffer, it returns to the start point, creating a circular motion of the pointers. Listing 2 has the above requirements and is a part of the VHDL code.  
```
FIFO_IMPL:process(clk)is begin ifclk'eventandclk='1'andclk'last_value='0' then ifrst='1'then count<=0; input<=0; output<=0; data_read<=(others=>'0'); else --reductionofcountwhenavalueisbeingread if(en_r='1')then count<=count-1; endif; --writtingprocess if(en_w='1'andfull_i='0')then ifinput=RAML-1 then input<=0; else input<=(input+1); endif; ram(input)<=data; count<=count+1; endif; --readingpointerupdate if(en_r='1'andempty_i='0')then ifoutput=RAML-1 then output<=0; else data_read<=ram(output); output<=output+1; endif; endif; endif; endprocessFIFO_IMPL; full_i<='1'whencount=RAMLelse'0'; empty_i<='1'whencount=0 else'0'; full<=full_i; empty<=empty_i;
```
## 4 Future Trends
### First Thoughts towards an Approach for LSTM-Based Path Planning Prediction  
After considering the conditions mentioned above, we propose a novel LSTM approach to predict both lateral displacement and linear velocity in a Frenet coordinate system.  
Many techniques have been proposed to achieve this (as described in Sections 2.5 and 3). Still, they are considered isolated ways to solve the problem as they do not consider integrating the overall pipeline. The reader must recall that vast amounts of data are required to perform this task and acknowledge that a critical concept is often ignored in this phase, i.e., causality. It is essential to consider a high chance of a reckless driver making a poor judgment that could affect the AV, or that a single car's predictions would not change because another care changes the whole driving scenario to poor or unexpected choices. Even deployed systems, such as the Ego Car (described in Section 3), would be part of this causality scenario. Therefore, using the output from the Frenet coordinate system that was taken from object classification via YOLO and boundary classification with instance segmentation, a bidirectional LSTM can be fed with the Frenet data, both for lateral displacements and linear velocities.  
These are four different LSTM models that feed their weights and share weights between lateral displacements and linear velocities. As seen in Figure 18, the LSTM architecture is represented graphically.  
Every Frenet point of each car will be fed to the model, including the Ego Car points. Thus, the prediction will consider every variable in the scenario and change its predictions if the causality demands it. This will derive in a new Frenet prediction point for every neuron in the LSTM architecture, and so the position, orientation, and velocity of every point can be predicted. As a result, the car's volume can be included in every future time instance. This output data can be used for decision-making in an AV, reducing the number of accidents enormously since causality is part now of the overall algorithm deployment and execution.
## 3 Results
### Non-Targeted LC-QTOF-MS Based Metabolomics Data
\begin{table}
\begin{tabular}{c c c c}
**Precursor** & **EC Number** &
\begin{tabular}{c} **Is It Included in** \\ **CannGEM?** \\ \end{tabular} \\ \hline ACAT, atob; acetyl-CoA C-acetyltransferase & 2.3.1.9 & Yes \\ \hline fadA, fadI; acetyl-CoA acyltransferase & 2.3.1.16 & Yes \\ \hline fadB; 3-hydroxyacyl-CoA dehydrogenase/enoyl-CoA hydratase/3-hydroxybutylY-CoA & 1.1.1.35 & Yes \\ \hline fadJ; 3-hydroxyacyl-CoA dehydrogenase/enoyl-CoA hydratase/3-hydroxybutylY-CoA & 1.1.1.35 & Yes \\ \hline pHH; 3-hydroxyacyl-CoA dehydrogenase & 1.1.1.35 & Yes \\ \hline HHAH; 3-hydroxyacyl-CoA dehydrogenase & 1.1.1.35 & Yes \\ \hline HHAH; enoyl-CoA hydratase/long-chain 3-hydroxyacyl-CoA dehydrogenase & 4.2.1.17 & No \\ \hline E1.3.3.6, ACOX1, ACOX3; acyl-CoA oxidase & 1.3.3.6 & No \\ \hline ACAS, bcd; butylypl-CoA dehydrogenase & 1.3.8.1 & No \\ \hline ACAM, acd; acyl-CoA dehydrogenase & 1.3.8.7 & No \\ \hline ACAL; long-chain-acyl-CoA dehydrogenase & 1.3.8.8 & No \\ \hline fadE; acyl-CoA dehydrogenase & 1.3.99.- & No \\ \hline ACASB; short-chain 2-methylacyl-CoA dehydrogenase & 1.3.8.5 & No \\ \hline ACAVL; very long chain acyl-CoA dehydrogenase & 1.3.8.9 & No \\ \hline GCH, gcdH; glutaryl-CoA dehydrogenase & 1.3.8.6 & No \\ \hline ACSL, fad; long-chain acyl-CoA synthetase & 6.2.1.3 & Yes \\ \hline CPT1A; carnitine O-palmitoyltransferase 1, liver isoform & 2.3.1.21 & No \\ \hline EC1, CI; eltta3-elta2-enoyl-CoA isomerase & 5.3.3.8 & No \\ \hline alkB1_2, alkM; alkane 1-monooxygenase & 1.14.15.3 & No \\ \hline hca; 3-phenylpropionate/trans-cinnamate dioxygenase ferredoxin reductase component & 1.18.1.3 & No \\ \hline rubB, alkT; rubredoxin—NA+ reductase & 1.18.1.1 & No \\ \hline AH1\_7; alcohol dehydrogenase 1/7 & 1.1.1.1 & Yes \\ \hline frmA, AH5, adhC; S-(hydroxymethyl)glutathione dehydrogenase/alcohol dehydrogenase & 1.1.1.284 & No \\ \hline AH6; alcohol dehydrogenase 6 & 1.1.1.1 & Yes \\ \hline adhE; acetaldehyde dehydrogenase/alcohol dehydrogenase & 1.2.1.10 & No \\ \hline ALH; aldehyde dehydrogenase (NA+) & 1.2.1.3 & Yes \\ \hline ALH7A1; aldehyde dehydrogenase family 7 member A1 & 1.2.1.31 & No \\ \hline ALH9A1; aldehyde dehydrogenase family 9 member A1 & 1.2.1.47 & No \\ \hline cyp\_E, CYP102A, CYP505; cytochrome P450/NAPH-cytochrome P450 reductase & 1.14.14.1 & No \\ \hline HAB; acetyl-CoA acyltransferase & 2.3.1.16 & Yes \\ \hline HAH; 3-hydroxyacyl-CoA dehydrogenase & 1.1.1.35 & Yes \\ \hline ECHS1; enoyl-CoA hydratase & 4.2.1.17 & No \\ \hline PPT; palmmitoyl-protein thioesterase & 3.1.2.22 & Yes \\ \hline ELOVL1; elongation of very long chain fatty acids protein 1 & 2.3.1.199 & No \\ \hline HS17B12, KAR, IFA38; 17 beta-estradiol 17-dehydrogenase/very-long-chain 3-oxoacyl-CoA & 1.1.1.62 & No \\ \hline HAC, PHS1, PAS2; very-long-chain (3R)-3-hydroxyacyl-CoA dehydratase & 4.2.1.134 & No \\ \hline TER, TSC13, CER10; very-long-chain enoyl-CoA reductase & 1.3.1.93 & No \\ \hline ACOT1\_2,4, acyl-coenzyme A thioesterase 1/2/4 & 3.1.2.2 & Yes \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 3 Dataset Description and Processing
### Cross-Validation and Performance Measures  
In order to minimize the bias introduced by randomly splitting the training and test sets in Section 3.1, the K-fold cross-validation (K-CV) method is utilized. K-CV is a statistical method of splitting a dataset into smaller subsets and effectively eliminating the bias caused by sampling randomness. The original training set is equally divided into \(K\) different subsets, each subset is utilized as a new test set, and the remaining \(K-1\) subsets are utilized as new training sets. The accuracy of the ML model on each subset is evaluated to evaluate the effectiveness of the proposed method. As suggested by [PERSON] and [PERSON] [70], 10-fold or 5-fold cross-validation is recommended. In this study, 10-fold cross-validation is used according to the scale of the dataset and the computational time. The process of 10-fold CV is shown in Figure 4, where P1, P2, \(\ldots\), and P10 represent the prediction results of the corresponding folds, respectively.  
In this section, the correlation coefficient (\(R^{2}\)), mean absolute error (_MAE_), and root mean squared error (_RMSE_) are widely utilized to evaluate the predictive performance and generalization capability of ML models. The basic equations of these evaluation indicators are shown in Equations (1)-(3) [71; 72; 73]. The evaluation indicators between the predicted and measured values are excellent methods to check the predictive performance  
Figure 4: A schematic diagram of K-CV (K = 10).  
and generalization capability of the predictive models [74]. Theoretically, the ML model works best when its evaluation indicators are \(R^{2}=1\), \(MAE=0\), and \(RMSE=0\)[75; 76].  
\[R^{2} =1-\frac{\sum\limits_{i=1}^{N}\left(FoS_{i}^{mea}-FoS_{i}^{pre} \right)^{2}}{\sum\limits_{i=1}^{N}\left(FoS_{i}^{mea}-E[FoS^{mea}]\right)^{2}} \tag{1}\] \[MAE =\frac{1}{N}\sum\limits_{i=1}^{N}\left|FoS_{i}^{mea}-FoS_{i}^{pre} \right|\] (2) \[RMSE =\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left(FoS_{i}^{mea}-FoS_{i} ^{pre}\right)^{2}} \tag{3}\]  
where \(FoS_{i}^{pre}\) and \(FoS_{i}^{mea}\) are the predicted and measured results, respectively; \(E[FoS_{i}^{mea}]\) represents the average of \(FoS_{i}^{mea}\); and \(i=1,2\ldots\), and \(N\) is the number of samples.
# Fragility Assessment and Reinforcement Strategies for Transmission Towers Under Extreme Wind Loads
###### Abstract  
Transmission towers are particularly vulnerable to extreme wind events, which can lead to structural damage or collapse, thereby compromising the stability of power transmission systems. Enhancing the wind-resistant capacity of these towers is therefore critical for improving the reliability and resilience of electrical infrastructure. This study utilizes finite element analysis (FEA) to evaluate the structural response of a 220 kV transmission tower subjected to fluctuating wind loads, effectively capturing the dynamic characteristics of wind-induced forces. A comprehensive dynamic analysis is conducted to account for uncertainties in wind loading and variations in wind direction. Through this approach, this study identifies the most critical wind angle and local structural weaknesses, as well as determines the threshold wind speed that precipitates structural collapse. To improve structural resilience, a concurrent multi-scale modeling strategy is adopted. This allows for localized analysis of vulnerable components while maintaining a holistic understanding of the tower's global behavior. To mitigate failure risks, the traditional perforated plate reinforcement technique is implemented. The reinforcement's effectiveness is evaluated based on its impact on load-bearing capacity, displacement control, and stress redistribution. Results reveal that the critical wind direction is 45\({}^{\circ}\), with failure predominantly initiating from instability in the third section of the tower leg. Post-reinforcement analysis demonstrates a marked improvement in structural performance, evidenced by a significant reduction in top displacement and stress intensity in the critical leg section. Overall, these findings contribute to a deeper understanding of the wind-induced fragility of transmission towers and offer practical reinforcement strategies that can be applied to enhance their structural integrity under extreme wind conditions.  
ref:fig1  
1 POWERCHINA Fujian Electric Power Engineering Co., Ltd., Fuzhou 350001, China; [EMAIL_ADDRESS]  
2 School of Civil and Environmental Engineering, Changsha University of Science and Technology, Changsha 410114, China; 159732791890163.com  
3 School of Civil Engineering, Central South University, Changsha 410075, China  
4 Correspondence: [EMAIL_ADDRESS] (F.C.); [EMAIL_ADDRESS] (Z.S.)
# Smart Sensing of PSC Girders Using a PC Strand with a Built-in Optical Fiber Sensor
## 3 Conclusions  
The present study developed a OSVC strand, a strand incorporating an optical sensor as a core wire, enabling the effective management of the prestressing force in PSC structures. The so-developed OSVC strand was installed in a full-scale precast segmental BH girder bridge that is currently under development to be the railway bridge of the next generation, and the possibility to manage the prestressing force was verified. The prestressing force could be accurately measured longitudinally along the girder since the fabrication of the full-scale prototype as well as during the static and dynamic loading tests of the completed structure. The natural frequency of the prototype could be computed with values in agreement with that obtained from accelerometer data measured during the dynamic test. This allowed the identification of any anomalies in the structure by tracking the change of the natural frequency caused by internal or external variations and damage that might occur throughout the lifetime of the bridge. The OSVC strand made it also possible to acquire reliably prestressing force data from the loading test to the failure of the structure. The future application of the OSVC strand to prestressed structures will provide superior durability compared to the conventional electrical resistance gauge owing to the use of the built-in optical sensor. Moreover, the protection of the fragile sensor optical sensor by a CFRP rod improved its resistance and handling for application to real structures. Consequently, the OSVC strand developed in this study offers an attractive alternative for achieving effective maintenance of prestressed civil structures.  
Conceptualization, S.T.K., Y.-H.P., H.Y. and S.S.; dynamic analysis, S.-S.J.; manufacture of specimen, S.-M.Y.; writing--original draft preparation, S.T.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from a Strategic Research Project (Project No. 20200040-001) funded by the Korean Institute of Civil Engineering and Building Technology.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 4 Simulation Results
### Robustness Analysis  
The well-trained model was valuated at different receiver locations. For convenience, the four PD locations with the root mean square (RMS) delay spread of 7.92, 8.2, 8.3, and 8.9 ns, marked as \(U_{1}\), \(U_{2}\), \(U_{3}\), and \(U_{4}\), were employed in the testing, respectively. The corresponding results are shown in Figure 9. As clearly seen from the figure, the BER performances of these four cases are very similar under the low-SNR region and only have a slight difference for a high SNR. Therefore, the proposed model can still work effectively and can provide robust BER performance even though the testing conditions are not exactly the same as those used in the training stage, showing a good robustness and generalization ability of the proposed scheme.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **FLOPs 1** &
\begin{tabular}{c} **Convergence** \\ **Epochs** \\ \end{tabular} & **Time 2** & **Accuracy** \\ \hline Basic-LSTM & 89.9 M & 2735 & 0.015 s & \(-\)25.3 dB \\ Basic-CNN & 2277.9 M & 3550 & 0.523 s & \(-\)26.8 dB \\ The proposed & 755.6 M & 1750 & 0.268 s & \(-\)28.2 dB \\ \hline \hline \end{tabular}  
* It is also related to the number of input signals. 2 The average time consumed for each training epoch.  
\end{table}
Table 1: Comparison of application complexity.  
Figure 9: BER performance of the proposed scheme at different positions.
## 3 Results and Discussion
### Effect of Steel Fibers and Steel Slag on the Fracture Resistance of Asphalt Mixture  
The fracture energy and post-cracking energy of the four asphalt mixtures were shown in Figure 6. The fracture energy ranking of different mixtures was: SS + SF > SS > SF > BA. Compared with basalt asphalt mixture, the fracture energy of SF, SS and SF + SS increased by 20.9%, 29.1%, and 41.2% respectively. It indicates that the addition of steel fiber and steel slag can improve the fracture resistance of asphalt mixture. Steel slag has relatively high strength characteristics and good adhesion with asphalt binder, increasing the fracture strength of the mixture. Steel fiber increases the maximum fracture strain of the mixture, consuming more energy.  
Figure 6 also shows that the addition of steel fiber can significantly improve the post-cracking energy of asphalt mixture, while adding steel slag decreased the post-cracking energy. It can be seen  
Figure 5: Particle loss resistances of different asphalt mixtures.  
Figure 6: Fracture energy and post-cracking energy of different asphalt mixtures.  
from Figure 6 that the post-cracking energy of SF and SF + SS increased by 31.9% and 14.1% compared with BA. The results demonstrated that the addition of steel fiber improves the toughness of asphalt mixture. The addition of steel slag reduces the toughness of asphalt mixture due to brittle damage of the asphalt mixture. The post-cracking energy of SS decreased by 34.4% compared with BA.
# An Improved Reversible Image Transformation Using K-Means Clustering and Block Patching
## 1 Introduction
In conclusion, the framework of \"VRAE\" used by the cloud manager should be specified together with the receiver, and the framework of \"RRBE\" needs the sender to undertake the algorithm complexity since the original image in the sender should be compressed and reversed for data hiding. In other words, the RDH method used by the cloud manager in these two frameworks is receiver- or sender-related. However, the cloud manager may be semi-honest and should not know the encryption or decryption methods, which concern the sender and receiver in the public cloud environment. Therefore, data embedding in the cloud sever should not affect the encryption and decryption methods, and the cloud manager can utilize arbitrary classic RDH methods to embed secret information into encrypted image which is similar to the other image. The framework is independent of the receiver-related or sender-related frameworks. To achieve this framework, the original image should be encrypted into a meaningful image. Thus, how to transform reversibly the original image to the meaningful image, which is similar to the other image, is a more challenging problem, called \"reversible image transform\" (RIT). In this paper, we propose an improved RIT method, in which the rest image blocks are patched to hide the AI generated by the last (K-T)-classes transformation. The main contribution of this paper is that the stego-image quality is improved by patching image blocks to embed the AI. The rest of this paper is organized as follows. In Section 2, we introduce previous related works. Section 3 introduces our method, which can be divided into an image pairing phase and a proposed algorithm phase. In Section 4, we demonstrate the advantages of the proposed method by presenting our experimental results. Lastly, we give a conclusion in Section 5.
## 2 Materials and Methods
### Methodology
#### 2.3.2 Correlation of GPP with VIs and SIF  
To monitor vegetation productivity, we analyzed the correlation of GPP with VIs and SIF at different time scales. Pearson correlation utilizes statistical theories and methods to quantify the degree of correlation between two variables and has been widely used in ecological and earth science research [57]. Here, we used the coefficient of determination (square of the correlation coefficient, R2) to quantify the correlation.  
We combine SPEI_01 and SSMI, both of which reach the drought level at the same time (SPEI \(\leq-0.5\), SSMI \(\leq 0\)) and last for three months to be considered as a drought event (the year in which it occurs is a drought year) [58]. To analyze the ability of VIs and SIF to capture GPP losses during a drought event, we compared the changes in SIF, VIs, and GPP, expressed as the ratio of the difference (drought year-reference year) to the reference year (average value from 2001 to 2020). Finally, the standardized anomalies of VIs and SIF during drought events in the Loess Plateau were calculated, and the correlation analysis of GPP anomalies with VIs and SIF anomalies was done. The anomaly calculation was similar to the SSMI formula, and the anomalies were categorized into five classes (Table 2) [59].
## 2 Materials and Methods
### Chemical Analysis  
#### 2.4.1 GC/MS Analysis  
Terpenes in the oil phases of pestos were monitored with gas chromatography-mass spectrometry (GC-MS). Each sample was analyzed as three replicants. The oil was separated from the solid materials of the pesto by simply centrifuging at 4000 rpm for 5 min (5415D, Eppendorf, Germany) and pipetting. Then pesto oil samples were diluted with hexane at a 1:10 ratio, the internal standard (99% 1-chlorooctane, Alfa Aesar/Thermo, Thermo Scientific Chemicals, Haverhill, Boston, MA, USA) was added to the samples at a 1:10\({}^{5}\) ratio, and the samples were filtered using Whatman Mini-UnPrep G2 Springeless Filters (GE HealthCare, Amersham, UK). The analysis was performed with a GC-MS device consisting of GCMS-QP2010 Ultra and AOC-5000 Plus injection system (Shimadzu Scientific Instruments, Columbia, MD, USA). A 1 \(\upmu\)L sample was injected into the injector at 280 \({}^{\circ}\)C temperature and split 5. Separation of compounds was performed with BP-5 column (30 m/0.25 mm/0.25 \(\upmu\)m, Trajan Scientific and Medical, RingWood, Australia) using oven temperature 40-280 \({}^{\circ}\)C and helium (N60, Woikoski, Mantyharju, Finland) as a carrier gas with linear velocity 39.5. The temperatures of ion source and interface were 200 and 290 \({}^{\circ}\)C, respectively, and ions were scanned at 33-400 m/z. The compounds were tentatively identified by comparing their mass spectra with data from mass spectral libraries (NIST 11 Mass Spectral library, The National Institute of Standards and Technology, USA Wiley Registry 10 th Edition, John Wiley & Sons, USA and Flavour & Fragrance Natural & Synthetic Compounds GCMS library FFNSC 2, Shimadzu Corp, Kyoto, Japon) and checking their retention indexes with alkane standards (C8-C40 Alkanes Calibration Std, Supelco, Bellefonte, PA, USA). The areas of the GC-MS signal intensities of the terpen were integrated in GCMSsolution software version 4.20 (Shimadzu, Scientific Instruments, Haverhill, Boston, USA) to check if the processes affected the levels of terpen in pesto oils.  
The coefficient of variation of the GC-MS method was monitored with the internal standard, and it was 16.0% as calculated from all the samples in the GC-MS (\(n=18\)). Two replicant samples were excluded from the analysis, since their signal levels varied more than 30% from those of the other two replicants and were therefore considered as outliers.