esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Energy-Efficient Mobile Ad Optimization: An App Developer Perspective
## 5 Evaluation and Experiments  
In this section, we describe and evaluate the experimentation of the proposed gamma correction algorithm as presented in Algorithm 1. In particular, we categorize the experiment to address the following research questions.  
* **RQ1:** Can image-compression technique (gamma correction) reduce the energy consumption of the mobile app?
* **RQ2:** Does gamma correction efficiently increase battery lifetime and performance the of a mobile device?  
```
Initially;  Image \(\leftarrow\) get-image(URL);  Bit-image \(\leftarrow\) Bitmap-cover(Image);  Gamma correction (Bit-image)  W \(\leftarrow\) Bit-image.Width;  H \(\leftarrow\) Bit-image.Height; if\(W\geq\)threshold value & \(H\geq\)threshold valuethen  SW \(\leftarrow\) W/half;  SH \(\leftarrow\) H/half;  Goto \(IF(W\&H\geq threshold-value)\)  D-image \(\leftarrow\) decode(Bit-image); foreach\(Pixels\in Bit-image\)do  C-Image \(\leftarrow\) Compress-matrix(Bit-image);  E-image \(\leftarrow\) encode(C-Image);  B-image \(\leftarrow\) Bitmap-covert(E-image)  return Gamma correction(\(B-image\));
```  
**Algorithm 1**Gamma correction on mobile ads.
# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
## 2 Related Work
### Reblur to Deblur and Degradation Estimation  
Aside from deep auto-encoders (DAE), generative adversarial networks (GAN) and multi-scale networks, reblurring networks have been widely studied in recent years due to their ability to generate additional blurred images for learning [30; 31; 32]. [PERSON] et al. [31] propose a novel network combining two GAN-based models, learning-to-Blur GAN (BGAN) and learning-to-DeBlur GAN (DBGAN). The BGAN learns to convert a sharp image to a reblurred image, and DBGAN learns to recover the latent image from BGAN. Such multi-GAN structures are very innovative, but due to the inherent limitations of GAN-based networks, their performance on traditional deblurring metrics such as PSNR and SSIM is not very good. Moreover, the final deblurring performance of the network proposed by [PERSON] et al. [31] depends more on the generative adversarial structure, i.e., whether the discriminator \(D\) of DBGAN can distinguish between (real) sharp and deblurred (fake sharp) images, and does not explicitly extract the blurring features of blurred images themselves.  
Some recent deblurring works treat image blurring as a kind of degradation and achieve deblurring by extracting the degradation representations of the blurred images [24; 25; 26; 33]. [PERSON] et al. [24] proposed a novel CNN-based iterative network, and incorporated a gradient descent algorithm in the design of the deep network, resulting in state-of-the-art results. [PERSON] et al. [25] instead designed multiple modules to extract and utilize degradation representations in a multi-scale manner, including residual blocks, a feature fusion module, skip connections, and attention, so that the obtained degradation representations can reflect the nature of the blurred image itself more comprehensively.  
Inspired by the above works [24; 25; 26; 33], we propose a deblurring method based on implicit degradation representations and reblur estimation. It can effectively combine the advantages of the above-mentioned reblurring estimation and degradation extraction, which not only effectively extracts and utilizes the degradation representations of the blurred image itself, but also allows the network to learn the degradation representations better through the reblurring process, thus making the deblurring results more stable and improving their quality.
## 3 Proposed Ku-Band Sharp Bent Right-Hand/Left-Hand Transmission Line-Based LWA  
### Dispersion Analysis  
The proposed unit cells with a sharp bend have been analyzed to study the dispersion behavior of different modes. Figure 2 shows the geometry of the unit element and its field distribution. As can be seen from the field distribution, the radiation was mostly leaking out from the edges. The sharply-bent unit cell on a thin Rogers Duroid 5880 substrate has been simulated using the electromagnetic eigen mode solver CST. The substrate has a thickness of 0.5 mm and a dielectric constant of 2.2. The period of the unit element/cell was 10.6 mm in the horizontal x-direction and 6 mm in length towards the vertical y-direction.  
The angle of each bend in each of the unit cells was 56 degrees. The dispersion graph showing different modes of the proposed unit element is depicted in Figure 3. The dispersion analysis presents three modes viz. the dominant mode as quasi-TEMmode, the first higher order mode, and the second higher order mode. The slow wave modes and fast wave modes have been separated by the air line, which served as a reference for the dispersion analysis. The slow wave mode was the quasi-TEM mode, which was below the air line, as can be seen in the figure, and is termed as a slow wave mode due to its lower phase velocity than air. The fast wave modes are shown in the graph above the air line, which had a phase velocity higher than the speed of light. The fast wave modes were generally responsible for scanning features. The air line will divide the dispersion graph into the region of operation in the left-hand backward quadrant and in the right-hand forward quadrant. As shown in Figure 3, at  
Figure 2: **(a)** Unit element of the proposed LWA antenna and **(b)** field distribution.  
frequencies below 14.5 GHz, the antenna exhibited a left-hand quadrant behavior and, at frequencies above 15 GHz, the antenna exhibited a right-hand quadrant behavior. Furthermore, the upper and lower frequency limits can be defined by this dispersion diagram. This analysis can be further verified by analyzing the reflection coefficient graph, i.e., S-parameter graph.  
Figure 4 depicts the propagation constant graph that shows the variation of the phase velocity (Vp) with the frequency and curve for the normalized attenuation constant. Furthermore, the curve plotted is Vp/cas a function of frequency, where 'c' is the speed of light. This graph shows the inverse normalized propagation constant. The frequency region below the air line (see Reference Point 1) is the slow wave region and above the air line is the fast wave region. This graph further verifies the frequency of operation of the proposed unit element in fast wave region. The left-hand frequency region shows the backward scanning and the right-hand frequency region the forward scanning.  
Figure 4: Phase velocity plot of the unit cell (inverse propagation constant) and attenuation constant (leakage).  
Figure 3: Dispersion curve for unit element: The slow wave and fast wave regions separated by an air line.
# The Effects of Fertilization on the Growth and Physiological Characteristics of _Ginkgo biloba_ L.
## Materials and Methods
### Materials and Experimental Design  
This study was carried out in 2014 and 2015. The test forest was pure _Ginkgo_ forest planted in 2005, with a vine space and row space of 2 m \(\times\) 8 m. The trees came from the same variety. The experiment with different fertilizer treatments was designed based on a single factor design (EXP.1) and an orthogonal design (EXP.2). Fertilizer was applied to a depth of approximately 10-15 cm (to prevent losses to rain or wind) in several spots in a 70 cm radius circle around each tree in early April 2014 and early April 2015.  
Single factor designs (unit: g\(\cdot\)tree\({}^{-1}\)): EXP.1 was designed as single-factor experiment. The dosage of four treatments of single N fertilizer was 600, 400, 200, and 100, respectively. The dosage of four treatments of single P fertilizer was 800, 600, 400, and 200, respectively. The dosage of four treatments of single K fertilizer was 200, 90, 40, and 15, respectively. The amounts of fertilizers were in descending order. The fertilizers used in the experiments were urea (N), superphosphate (P), and potassium sulfate (K). There were a total of 13 treatments, and one of them was CK\({}_{1}\) (control, without fertilizer application). Each treatment had 10 _Ginkgo_ trees with three replications in the trial. There were a total of 39 plots and 390 _Ginkgo_ trees (average diameter at breast height was 13.37 cm, average height was 8.04 m, and their growth was uniform.) in the single factor tests. Additionally, there were two guard rows around the test plots.  
Orthogonal designs (unit: g\(\cdot\)tree\({}^{-1}\)): Second, the experiment with different fertilizer treatments (EXP.2) was designed based on orthogonal designs with three levels of urea, superphosphate, or potassium sulfate, for a total of 10 treatments one of them was CK\({}_{2}\) (control), without fertilizer application, which differed from the CK\({}_{1}\) above. Treatments were replicated three times (Table 1). There were a total of 30 plots and 300 _Ginkgo_ trees (average diameter at breast height was 11.62 cm, average height was 5.78 m, and their growth was uniform) in the orthogonal tests. Orthogonal tests plots were in close proximity to the single-factor test spots.
## 3 Background
### Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) Model  
Dec-POMDP generalizes POMDP to a multi-agent version where rewards are common and based on joint actions, but observations are individualistic [23; 24]. The detail definition is as follows. **Definition 1**. : A decentralized partially observable Markov decision process is defined as a 9-tuple <\(I\), \(S,A,T,\mathcal{O},O,R\), \(b^{0}\), \(t\)>, where  
* \(I=\{1,\ldots,n\}\) is the finite set of agents;
* \(S=\left\{s_{0},\ldots,s_{|S|-1}\right\}\) is the finite set of states, where \(s_{0}\) is the initial state;
* \(A=\times_{i=1}^{n}A_{i}\) is the set of joint actions where \(\overset{\rightarrow}{a}=<a_{1},a_{2},\ldots,a_{n}>\), and \(A_{i}\) is the finite set of individual actions of agent \(i\). The component \(a_{i}\) in the joint action \(\overset{\rightarrow}{a}\) belongs to \(A_{i}\);
* \(T\) is the transition function where \(P(s^{\prime}\big{|}s,\overset{\rightarrow}{a})\) denotes the probability of transiting to the new state \(s^{\prime}\) when taking the joint action \(\overset{\rightarrow}{a}\) in state \(s\);
* \(\mathcal{O}=\times_{i=1}^{n}\mathcal{O}_{i}\) is the set of joint observations where \(\overset{\rightarrow}{o}=<\)\(o_{1}\),..., \(o_{n}\)\(\in\mathcal{O}\), and \(\mathcal{O}_{i}\) is the finite set of individual observations of agent \(i\). The component \(o_{i}\) in the joint observation \(\overset{\rightarrow}{o}\) belongs to \(\mathcal{O}^{i}\);
* \(\mathcal{O}\) is the observation function where \(P(\overset{\rightarrow}{o}\big{|}\overset{\rightarrow}{a},s^{\prime})\) denotes the probability of observing \(\overset{\rightarrow}{o}\) when the system state transfer to \(s^{\prime}\) taking the joint action \(\overset{\rightarrow}{a}\);
* \(R\) is the reward function, where \(R(s,\overset{\rightarrow}{a})\) denotes the immediate reward when taking the joint action \(\overset{\rightarrow}{a}\) in state \(s\);
* \(b^{0}\) is the initial state distribution;
* \(h\) is the finite horizon. At every step \(t\), the environment is in a particular state \(s\), which emits a joint observation \(\overset{\rightarrow}{o}^{t}\) according to the observation model. Each agent obtains its individual observation component \(o_{i}^{t}\). Each agent selects its individual action \(a_{i}^{t}\), forming a joint action \(\overset{\rightarrow}{a}^{t}\), which leads to a state \(s^{\prime}\) according to the transition model. Every agent receives an immediate reward \(r\). The action-observation history \(\theta_{i}^{t}\) for agent \(i\) is the sequence of actions taken and observations received, which is denoted as a sequence \((o_{i}^{0},a_{i}^{0},o_{i}^{1},\ldots,a_{i}^{t-1},o_{i}^{t})\). Thus, the joint action-observation history \(\overset{\rightarrow}{\theta}^{t}\) is \(<\theta_{1}^{t},\theta_{2}^{t},\ldots,\theta_{n}^{t}>\). A policy for agent \(i\) is a mapping from history to actions \(\pi_{i}:\theta_{i}\to A_{i}\)and a joint policy is denoted as \(\pi=<\pi_{1},\pi_{2},\ldots,\pi_{n}>\) which specifies a policy for each agent.
## Materials and Methods
### Verifications of FE Model  
The stress-strain curve of the standard hexagonal honeycomb during the simulation is compared with the experiment results and the theoretical calculations, in order to validate the accuracy of the FE simulation. Figure 3 shows two experiment results and one FE prediction curve. There is negligible difference between them. The horizontal dashed line in Figure 3 represents the theoretical value of plateau stress for hexagonal honeycomb with the same size. The plateau stress of the experiment results and the FE prediction is further measured by averaging stress value when the strain is in the range of 0.2-0.4. The relative error is calculated as shown in Table 3, with the value of approximately 5.04%, demonstrating the effectiveness and accuracy of the FE simulation settings.
# A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model
## 1 Introduction
### Structure  
Based on the above research and fully considering the influence of various parameters and meteorological factors, this paper proposes an AE-GRU heat load prediction model optimized by the GWO algorithm, using a DHS as a case study. Firstly, to address the issue of short system running time and insufficient training data, AE improves the model's generalization performance by increasing the amount of effective data. Secondly, the GWO algorithm is utilized to optimize the GRU network's model parameters. The GRU model is then trained with the optimal parameter combination to predict the heat load of the DHS.
## 3 Validation of the Proposed Numerical Methods
### Experimental Results of the Flow around a Vertical Plane
#### 3.2.1 Comparison of the Mean Forces and Moments  
Figure 12 shows a comparison between numerical calculations and experimental results for longitudinal force (\(F_{x}\)), vertical force (\(F_{z}\)), and pitching moment (\(M_{y}\)). The detailed statistical results are also presented in Table 4. It can be seen that the CFD calculations are generally in reasonably good agreement with the experimental results. At velocities below 1 m/s, the CFD calculations exhibit slightly larger discrepancies compared to the experimental results, while the discrepancies notably lessen at velocities above 1 m/s. The errors for \(F_{x}\), \(F_{z}\), and \(M_{y}\), in order, range from 4.0% to 12.6%, 3.4% to 14.0%, and 3.0% to 13.8%, respectively. As a result, it can be concluded that the numerical calculation method using the SST
## 4 Discussion  
### Research Conclusions  
This study, through the integration of sentiment analysis and spatial analysis, reveals the regional distribution characteristics and spatial clustering patterns of public sentiment towards AI education applications in China. Specifically, the findings are as follows:  
* Dominance of Positive Sentiment: In the 31 provincial-level administrative regions, positive sentiment significantly outweighs negative sentiment, maintaining a roughly three-fold difference. This indicates that the public generally holds an optimistic and supportive attitude towards the functionality and application of AI education technologies.
* Regional Variations in Sentiment: The imbalance index (S) and spatial distribution maps reveal significant regional differences in sentiment towards AI education. The eastern coastal regions (e.g., Beijing, Shanghai, Guangdong) show significantly higher positive sentiment scores compared to the central and western regions, while the western areas (e.g., Tibet, Gansu, Qinghai) exhibit lower sentiment scores.
* Spatial Diffusion of Sentiment: Moran's I and Getis-Ord Gi* indices show that sentiment attitudes display a high-value clustering characteristic in the eastern coastal regions. These areas, with higher urban development levels, concentrate positive sentiment and spread to neighboring regions. Negative sentiment, in particular, exhibits stronger significance in terms of spatial diffusion.
* Influence of Regional Comprehensive Resources and Practical Experience: The comprehensive development level of a region serves as a fundamental factor influencing sentiment towards AI education. In economically developed and technology-rich regions, where practical applications of AI are more frequent, the public is more likely to develop trust in the technology and provide positive feedback. In contrast, areas with fewer resources and insufficient understanding tend to foster more negative sentiment due to the lack of adequate exposure and knowledge.
# Measurement and Analysis of Industrial Forklifts Vibration Levels for Unit Load Testing Purposes
## 3 Results and Discussion
### Effect of Forklift Type on the PSD  
To determine how vibration levels varied due to the type of forklift, PSD profiles were collected and analyzed for all three forklifts (Figure 10). The comparison was conducted using vibration data measured at the fork time tips, using the parameters that resulted in the highest vibration intensity, which was a speed of 4.82 km/h and a weight of 680 kg. The concrete road condition was chosen for the comparison because the reach truck was not driven on the asphalt during this study. The gas forklift presented the highest vibration intensity (Figure 10). The electric forklift presented the second highest vibration intensity, while the reach truck showed the least intensity. The highest peak was observed at around 3-4 Hz, while the presence of a second peak was observed in the 5-8 Hz region for both the gas and the electric forklifts. Meanwhile, for the reach truck, the peaks were shifted to 4-5 Hz and 8-11 Hz, indicating that the reach truck had a stiffer suspension and/or tires. Similar results were observed in a study conducted in the United States that measured truck vibration for both leaf spring and air ride suspensions [15]. It was observed that, for trucks with air ride suspensions, peaks occurred at lower frequencies in comparison to trucks with leaf spring suspensions.  
In addition, both the gas and the electric forklift have wheels under the mast, while the reach truck does not (Figure 1). The wheels of the outrigger arms of the reach truck are also smaller, which could have contributed to the shifting of the peaks to the higher frequency region.  
Figure 9: PSD plots of the three investigated forklifts as a function of road roughness (concrete and asphalt).
## 2 Data and Methods
### Study Area and Data
#### 2.1.1 Mean Sea-Surface Model  
The mean sea-surface model is a key element for modeling MDT. DTU15 MSS and DTU18 MSS, published by the Danish Technical University (DTU), were chosen to construct MDT in the paper. DTU15 MSS uses the last 4 years of Cryostat-2 data to improve the accuracy of the MSS model. The resolution of DTU15 MSS is 1\({}^{\prime}\)\(\times\) 1\({}^{\prime}\). DTU18 MSS is based on the previous generation model DTU15 MSS, incorporating 3 years of Sentinel-3A and 7 years of Cryostat-2 altimetry data from two satellites that provide data in SAR mode, and using the new tide model FES2014, which allows for a more accurate MSS [11]. DTU18 MSS has a spatial resolution of 1\({}^{\prime}\)\(\times\) 1\({}^{\prime}\) and a reference time of 1993-2012.
# A Hybrid DenseNet-LSTM Model for Epileptic Seizure Prediction
## 3 Proposed Method: DenseNet-LSTM  
### System Model  
Figure 2 shows the overall system model of the proposed method. First, it goes through a preprocessing method to use the EEG signal as input data to the deep learning model. The preprocessing divides the raw EEG signal by channel and then segments it by the window size and applies the mother function db4 of the DWT to convert it into a time-frequency type 2D image. The db4 is a transform of Daubechies wavelet, it encodes polynomials with two coefficients, which has a relatively fast calculation speed processing time. Next, the preprocessing data are used as the input data of DenseNet, and the resulting feature map is used as the input data of LSTM. As a result, the proposed model trains the difference between interictal and preictal states and then predicts seizures by detecting the preictal state before the onset of seizures.
## 3 Results and Discussion
### Humidity Effect  
The discharge setup was placed in an acryl chamber with controlled humidity to check the effect of humidity on RONS concentrations in the treated liquid. Generator and pumps were placed outside the chamber and connected to the discharge setup through connectors mounted in the chamber. The chamber (volume about 10 L) was sealed and attached to the air dryer equipped with pump to achieve circulation of the air between chamber and dryer. Additionally, a small flow of dry air was supplied to the chamber to provide higher pressure inside the chamber and ensure that ambient air did not enter the chamber. Air circulation was controlled using a valve allowing to regulate the rate of drying that, together with evaporation of the liquid by the plasma, allowed to control the relative humidity in a range from 30% to 90%. The flow of DI water was treated with AAGD at the same condition at varied humidity, and samples were collected 5 min after discharge initiation to ensure plasma stability and RONS concentration. DUV absorption spectra of the collected samples and absorption peak intensity at 200 nm are presented in Figure 5.  
Some variation in the mean of intensity of absorption spectra could be observed with the change of humidity; however, the shape of the absorption peak remained the same (Figure 5a). As was discussed above, considering the error bars represented in Figure 5b, humidity did not have an effect on absorption intensity within the experiment uncertainty. Additionally, the shape of the absorption peak remained the same, suggesting the same ratio among concentrations of produced RONS.  
Humidity therefore did not have a significant effect on concentrations of produced RONS. The observed effect could be explained by the local evaporation of the liquid in the area contacting with the plasma (about 0.25 mL of liquid was evaporated per minute) [19]. Considering the relatively small gap (8.5 mm) and fast evaporation, the plasma volume and the surrounding area could be saturated with evaporated water, eliminating the effect of humidity in ambient air.  
If deionized water was replaced on another liquid with different conductivity, it would mostly affect the duration of the unstable phase in discharge after initiation. In general, solutions of organic compounds (such as 80 mM L-Phe) have much higher conductivity compared to DI water, resulting in much faster discharge stabilization. Therefore, the use of plasma-treated solutions of organic compounds collected 5 min or later after initiation of the discharge ensures stable concentrations of RONS. Moreover, no effect of humidity should be observed in the case of the liquid containing water to compensate variation of humidity by the evaporation of liquid. The effect may not work for liquids not containing water; however, it would work in the case of 80 mM L-Phe.  
The flow of liquid containing water could be reproducibly treated with AAGD irrespective of humidity if the treated liquid is collected 5 min or later after the initiation of discharge.