esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In conclusion, the influence of the waveform of circularly polarized laser field on high-order harmonic (HH) generation from atoms is investigated by solving the time-dependent Schrodinger equation (TDSE) and the classical trajectory analysis beyond the transverse-velocity-compensation mechanism. It is found that the HH emissions due to the electron recollision can be switched on or off by modulating the driving laser field with a finite envelope. By a detailed analysis of the classical trajectories of the ionized electron without an initial transverse velocity, we demonstrate that due to the positive temporal gradient, the amplitude of the driving field in the following half cycle is larger than that of the previous half cycle. The larger amplitude of the electric field can pull the previously outgoing electron back to the vicinity of the parent ion. Hence, the electron has the chance to recombine with the parent ion. Moreover, the greater the temporal gradient of the laser field amplitude, the more electron trajectories can return to the vicinity of the parent ion. Correspondingly, the more electrons can recombine with the parent ion and emit the high-order harmonics. As a result, the greater harmonic yield is obtained. This is confirmed by the dependence of the harmonic yield on the pulse duration of the circularly polarized driving field. In addition, the maximum kinetic energy of the return electron, which agrees well with the harmonic cutoff position, is generally gained after multiple revisits. Our results provide a new way of controlling the HH emission in a circularly polarized laser field.  
Conceptualization, Q.L.; Data curation, M.Q.; Formal analysis, M.Q.; Funding acquisition, M.Q.; Investigation, M.Q., Y.Z. and X.Z.; Supervision, Q.L.; Writing - original draft, M.Q.; Writing - review & editing, M.Q., Y.Z., X.Z. and Q.L.  
This work was supported by the National Natural Science Foundation of China under Grants (No.11604248, No. 11674257, No. 11740741), Undergraduate Headmaster Foundation of Wuhan Institute of Technology (No. 2018131), National College Students' innovation and entrepreneurship training program (No. 201810490023), the Program for Distinguished Middle-aged and Young Innovative Research Team in Higher Education of Hubei, China (No. T201806), and the Natural Science Foundation of Hubei Province under Grants No. 2017 CFB150.  
The authors declare no conflict of interest.  
Figure 5: **(a–b)** Pulse-duration dependence of high-order harmonic spectrum for circularly polarized (**a**) and linearly polarized (**b**) sine-squared laser fields. The intensity and wavelength of the laser fields are fixed at \(1.0\times 10^{14}\) W/ cm\({}^{2}\) and 800 nm, respectively. (**c–d**) Pulse-duration dependence of integrated harmonic yields for circularly polarized laser field from 27.19 eV to 37.19 eV (**c**) and linearly polarized laser fields from 56.67 eV to 66.67 eV (**d**).
## 1 Introduction
Nevertheless, the aforementioned methods often fail to adequately capture long\(-\)range temporal dependencies in complex, highly variable traffic patterns, particularly in satellite communication systems characterized by fluctuating user activity, recurring usage patterns, and periodic application demands [17]. In this paper, we address the critical challenge of generating effective handover policies for TTGs in a satellite\(-\)to\(-\)ground communication system, where TTGs remain stationary while LEO satellites continuously orbit the Earth, necessitating robust solutions for seamless connectivity and load balancing in NTNs. We propose the Attention\(-\)Enhanced Rainbow DQN\(-\)based Joint Traffic Prediction and Handover Design Framework (ARTHF), which combines advanced traffic forecasting with intelligent handover decision\(-\)making to enhance the performance of LEO satellite networks. The main contributions in this paper are summarized as follows:  
1. We propose a traffic prediction module based on long short\(-\)term memory (LSTM) networks, which accurately forecasts TTG traffic demands by leveraging historical traffic data, thereby enabling proactive and reliable handover decisions. 2. We model the handover decision problem as a Markov Decision Process (MDP) and solve it using an attention\(-\)enhanced rainbow DQN, which optimizes handover policies by jointly considering satellite switching frequency, communication quality, and load distribution. We validate the proposed framework through extensive simulations using a realistic LEO satellite constellation model. The results show that the proposed ARTHF effectively reduces handover frequency, enhances service quality, and achieves a fair load distribution among the satellites.
# Preliminary Study on Greywater Treatment Using Nonwoven Textile Filters
## 3 Results  
### Start-Up Period Longevities and Filter Hydraulic Capacities  
The start-up period longevities were 12 weeks for set I (from beginning of October till 22 December 2017) and 22 weeks for set II (17 May-25 October 2018). COD\({}_{\rm Cr}\), BOD\({}_{5}\), P\({}_{\rm tot}\), and TSS were collected to observe the removal efficiency on set I and set II from 25 December 2017 to 13 December 2018 (339 days) and from 25 October 2018 to 29 January 2019 (96 days), respectively.  
The average filter hydraulic capacities (flow rates) of both filter sets for operational period (after the end of start-up period) were nearly equal: 1.0 \(\pm\) 0.1 cm d\({}^{-1}\) (\(n\) = 13 + 13) for filter 1 and 1.4 cm d\({}^{-1}\) for filter 2 of set I and 1.0 \(\pm\) 0.1-1.4 \(\pm\) 0.2 cm d\({}^{-1}\) (\(n\) = 21) for filters of set II. Due to the lack of sufficient measurement replications (breakage of ceramic pots) the filter 3 of set II has been excluded from the results analysis related to TSS. The greywater average retention time in the set I was 19.2-48.0 h and in the set II, it was 13.6-28.3 h.
# Large Scale Implementations for Twitter Sentiment Classification
## 4 Sentiment Analysis Classification Framework
### Preprocessing and Features  
We examined both Binary and Ternary Classification on different datasets. On the Binary Classification case, we focus on the way that the dataset size affects the results, while in the Ternary Classification case, the focus is given on the impact of the different features of the feature vector given as an input to the classifier.  
Regarding datasets we used for measuring our proposed algorithms' accuracy, a preprocessing step is utilized so as to discard all irrelevant data. Occurrences of usernames and URLs are replaced by special tags and each tweet is finally represented as a vector that consists of the following features:  
* **Unigrams**, which are frequencies of words occurring in the tweets.
* **Bigrams**, which are frequencies of sequences of two words occurring in the tweets.
* **Trigrams**, which are frequencies of sequences of three words occurring in the tweets.
* **Usename**, which is a binary flag that represents the existence of a user mention in the tweet.
* **Hashtag**, which is a binary flag that represents the existence of a hashtag in the tweet.
* **URL**, which is a binary flag that represents the existence of a URL in the tweet.
* **POS Tags**, where we used the Stanford NLT MaxEnt Tagger [50] to tag the tokenized tweets and the following are counted:  
1. Number of Adjectives,
2. Number of Verbs,
3. Number of Nouns,
4. Number of Adverbs,
5. Number of Interjections.
# A Survey on Change Detection and Time Series Analysis with Applications
## 3 Decomposition Methods into Time-Frequency Domain
### Continuous Wavelet Transform (CWT)
Software packages for CWT are freely available online [47].
# Numerical Study of the Filtration Performance for Electrospun Nanofiber Membranes
## 3 Results and Discussion
### Model Parameter Validation and Results Analysis
#### 3.1.2 Validation of 3D Model Structure  
The three-dimensional structure of the electrospun nanofiber membrane reconstructed by GeoDict software was validated against actual SEM images (Figure 4). The simulated model exhibits consistent characteristics with real membranes, including random fiber arrangement, complex three-dimensional disordered cavity structures, and fiber coverage dependent on electrospinning time (Figure 5). For different membrane thicknesses (0.0150 mm-0.0240 mm), the numerical model maintains similar average fiber diameters to experimental samples, with denser structures corresponding to longer electrospinningmations, which aligns with the actual membrane formation mechanism.
## 3 Results and Discussion
### Dose and Dose-Rate Dependence  
The detector response, as a function of the total dose released by the 62-MeV monochromatic proton beam, was studied in the 2-20 Gy range. The beam dose rate was kept fixed at 16 Gy/min. Considering that the variance associated to each point is not constant in the whole dose range, a weighted least square regression test was used to linearly fit data points. The fit result is reported in Figure 4. The detector response versus the released dose appears linear with an excellent confidence degree. A sensitivity of 0.3912 nC/Gy was derived from the measured data as the slope of the linear fit. Deviations no larger than \(\pm 2\%\) were observed in the whole investigated dose range.  
Figure 3: A patient just before the proton therapy session with the electrode system positioned under the thermoplastic mask.  
The detector sensitivity was also evaluated at six different values of dose-rate, in the range of 10-40 Gy/min. In Figure 5, the percentage discrepancy versus the charge collected at the maximum dose rate (39 Gy/min) is reported. A maximum variation of 3% was found.
# A Power-Gated 8-Transistor Physically Unclonable Function Accelerates Evaluation Speeds
## 2 Power-Gated PUF Architecture and Design Methods  
An example architecture of a power-gated PUF array is illustrated in Figure 2. This architecture consists of some general SRAM function blocks in grey, a 2 kilo-bit power-gated PUF array and two functional blocks, i.e., a Control block and a Data Processing block. The two function blocks will be implemented and evaluated in due course.  
* **Challenge-Response pair (CRP):** The 8-bit address inputs are the PUF _Challenges_, and the 8-bit corresponding data outputs are the PUF _Responses_. Together, they form a Challenge-Response pair (CRP).
* **PUF array:** In the PUF array, there are 128 PUFs in a row gated by a power gating cell. Since the main purpose of power gating is facilitating fast evaluations by switching the power supply, the general term _SLEEP_ for normal power gating is replaced by _SUPPLY_ in this work.
* **Control block:** The switching activity is controlled by the Control block. Apart from passing the decoded higher 4-bit address to choose an 8-bit word from 16:1 multiplexers, it generates _SUPn1_, _SUPn2_ and _WL_ (Word Line) signals from the decoded lower 4-bit address. Once a PUF row is chosen by the lower 4-bit address, the _SUPn1_ signal is discharged to '0' to switch on the power supply. After _Data_ are settled down, the _SUPn2_ will be discharged to '0', and _WL_ will be asserted to '1' in sequence for a reading process. Then, in the Reset stage, both _SUPn1_ and _SUPn2_ signals are asserted to '1'. There will be no power supply to the PUF cells, and the data will be discharged to '0'. The protocol of the two-phase power gating method is shown in Figure 3.  
Figure 3: Protocol of two-phase power gating.  
* **Data Processing block:** The Data Processing block is there to evaluate the raw readout bits and marking out different stability levels of each PUF cell. Using TMV under nominal or various voltage and temperature conditions is to sift the unstable or flipped bits to achieve a lower Bit Error Rate (BER).  
The input vectors and the output vectors of multiple PUF arrays can be concatenated to match the PUF entropy requirement. The parameters of this architecture, e.g., the type of PUF cell, the number of rows or columns, word width, etc., can be altered for various purposes or implemented in different fabrication techniques. It is worth noting that with larger PUF array dimensions, the sizes of _SUPPLY_ transistors need to be evaluated. The driven ability of _SUPn1_ and _SUPn2_ signals needs to be improved.  
The design is implemented with UMC 65 nm technology. The layout of one test circuit is partly illustrated in Figure 4. It includes rows of two-phase power-gated 8T PUFs on the left-hand side and rows of single-phase power-gated 6T-SRAM PUFs on the right-hand side for comparison. Each row has 128 PUF cells.
## 4 Experiment and Analysis
### Contrast Experiments
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Datasets** & **Acc/\%** & **AUC** \\ \hline \multirow{3}{*}{XceptionNet} & DeepFakes-C & 58.25 & 0.5744 \\  & FaceSwap-C & 59.53 & 0.5797 \\  & FaceShifer-C & 82.29 & 0.8721 \\ \hline \multirow{3}{*}{MesoNet} & DeepFakes-C & 75.50 & 0.8360 \\  & FaceSwap-C & 71.35 & 0.7977 \\  & FaceShifer-C & 78.75 & 0.8934 \\ \hline \multirow{3}{*}{EfficientNet-B4} & DeepFakes-C & 60.14 & 0.5783 \\  & FaceSwap-C & 58.98 & 0.5725 \\ \cline{1-1}  & FaceShifer-C & 84.12 & 0.8721 \\ \hline \multirow{3}{*}{F\({}^{3}\)-Net} & DeepFakes-C & 57.78 & 0.5666 \\ \cline{1-1}  & FaceSwap-C & 58.98 & 0.5725 \\ \cline{1-1}  & FaceShifer-C & 84.67 & 0.8885 \\ \hline \multirow{3}{*}{I\_ResNet50} & DeepFakes-C & 90.63 & 0.9657 \\ \cline{1-1}  & FaceSwap-C & 91.61 & 0.9703 \\ \cline{1-1}  & FaceShifer -C & 91.03 & 0.9719 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the metrics Acc and AUC on corrupted datasets between mainstream models and the proposed model. Figure 8 shows that the algorithm proposed in this paper has higher specificity and sensitivity than mainstream models. It performs well in detecting genuine and fake face images after being disturbed. The Mainstream algorithms are usually constructed and verified on standard datasets, using shallow texture and high-level semantic features of images. When the forged images are subjected to unpredictable corruption, the original performance of these models cannot be guaranteed. Although some researchers have used rotation, cropping, translation, and other data augmentation algorithms to enhance the images, covering all the samples encountered in the natural detection environment is still challenging. The proposed algorithm enhances and expands training datasets from two dimensions of diversity and hardness. The generated training samples are more widely distributed, have higher hardness, and are closer to that in the natural detection environment. In the training stage, the model can focus on learning the critical features of forged data by improving the attention mechanism. Finally, we design the multi-branch loss function to make the model thoroughly learn the corrupted data and enhance the correlation between different datasets, significantly improving the model's robustness.
# Overview of FEM-Based Resistance Models for Local Buckling of Welded Steel Box Section Columns
## 4 Verification and Validation of the Numerical Model
### Model Validation and Evaluation of Imperfection and RS Models
#### 4.2.3 Evaluation of Residual Stress Models  
The validation of residual stress distribution patterns considers three different models, which are the most acknowledged and researched models according to the international literature. The model presented by ECCS [44] establishes the maximum compressive residual stress according to the welding type and the ratio between the height of the section and the thickness of the plates. The second model is presented by [PERSON] et al. [20], where the influence of the grade of the steel on the local buckling resistance is studied. As a result, a new distribution of residual stresses is presented, considering the yield strength of the applied material. Third model is presented by [PERSON] et al. [48], where the influence of the number of welding passes during manufacturing process is studied. The plots of residual stress distributions are presented in Figure 5. The results of an initial test of the residual stress distribution models are presented in Tables 5 and 6. These tables show results incorporating the geometric imperfection model presented by [PERSON] and [PERSON] [42] and the new model \"2021_mod\", respectively. From the comparison between residual stress distribution patterns, the model presented by [PERSON] et al. [20] gives the best ultimate resistance ratio.
## 4 Discussion
They show that the energy cost of positioning residues in unfavorable environments can indeed be reduced by the exchanges of polar/hydrophobic properties predicted from the computations but that the reduction of the RHM penalty in the closed state has deleterious effects on ChR2 conductance, selectivity and open state stability. This is not always the case, however and the direction of the effect of RHM reduction depends on the functional mechanism, as illustrated by results for the bacterial transporter LeuT, where removal of RHM by the K288A mutant increases transport function nearly 5-fold [63]. The opposite effect of RHM on ChR2 compared to the LeuT case is consonant with the conservation of the RHM-causing residues in the channelrhodopsins, in contrast to the uniqueness of the K288 which is not conserved among LeuT-fold transporters. Because ChR2 is a conformationally complex channel that is partially regulated by protein-lipid interactions as demonstrated here, it is remarkable that the expression of ChR2 in excitable cells including cardiomyocytes, CA1 pyramidal cells and _Drosophila_ motor neurons covers a variety of cell types with diverse lipid composition across species [64; 65; 66]. This report, covering what we believe to be the first combined analysis of residual hydrophobic mismatch using computational methods, with prospective experimental validation, shows how exposure of ChR2 residues to unfavorable environments affects channel function. Consequently, the consideration of lipid composition and the specific mutant ChR2 being used for optogenetics merit specific consideration. A more targeted approach can be taken for engineering mutants that will favorably express/function depending on the cell line used. Conceptualization, R.R., S.M., H.W. and R.E.D. ; Methodology, R.R., S.M., H.W. and R.E.D. ; Investigation, R.R. and S.M. ; Writing--Original Draft Preparation, R.R., S.M., H.W. and R.E.D. ; Funding Acquisition, H.W. and R.E.D. This work was supported by the National Institutes of Health grants P01 DA012923 and R01 MH054137 (Weinstein lab) and DA026434 and the WPI Research Foundation to [PERSON]. We gratefully acknowledge the allocations of computational resources at (1) the Coffrin Center for Biological Information of the Institute for Computational Biomedicine at Welli Cornell Medical College, (2) the National Energy Research Scientific Computing Center (NERSC, repository m1710) supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02- 05 CH11231 (to H.W.) and (3) NSF Teragrid allocations MCB090132 (to R.E.D). The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results.
# Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud
## 3 Experiment and Analysis  
### Datasets  
This study uses the open-source dataset ApolloScape [36]. Compared to real-world datasets such as KITTI or Cityscapes [37], ApolloScape contains larger and richer scenes and the point clouds generated by LiDAR are more dense, which contains a large number of complete roadside objects such as signboards, street lamps, and utility poles, providing RGB images with a resolution of 3384 \(\times\) 2710 pixels and corresponding depth maps. When training the image segmentation model, 3000 pictures containing utility pole objects are selected as the data set, of which, 2000 are used as training sets, 500 are used as test sets and 500 are used as verification sets.  
When training the pole point cloud segmentation model, 5000 frustums generated by the pole mask and depth map were selected, of which, 3000 were used for training, 1000 for test set and 1000 for verification set, and frustums were annotated by CloudCompare software.
## 5 Analysis of Model Performance
### Analysis of Operation Conditions
#### 5.1.2 Analysis of Operation Using the FHL Strategy  
As can be seen from Figure 7a, under the FHL strategy, electric energy is mainly supplied by the micro-gas turbine and power grid, but a little fluctuation appears in the micro-gas turbine power supply. In terms of power supply economic benefit, it is the same result as that under the FEL strategy. From 7:00 to 16:00, the photovoltaic power generation system participates in the power supply task, but its contribution is limited due to the restriction of economic cost. From Figure 7b, the heat powers are mainly supplied by the micro-gas turbine and the gas boiler. Their output varies with the fluctuation of load. From Figure 7c, the electric chiller can bear more cold load than the adsorption chiller. The system adopts the strategy of FTL, and it ensures itmeets the demand of the heat load. Simultaneously, redundant heat energy is generated for refrigeration. Note that the Hload goes down considerably at 11 o'clock. At this time, the Eload is stable, and Emt thus remains stable. In addition, MT's waste heat recovery device still produces a stable Hre. Under the FHL strategy, the heat balance is preferentially satisfied, and a large amount of heat energy can only supply AC to generate cold power. This results in theQac increasing suddenly.  
Figure 6: Power balance diagram under the FEL strategy: (**a**) Electric power balance; (**b**) Heat power balance; (**c**) Cold load balance.