esa-sceva/satcom-chunk-collection huggingface dataset:
# No-Reference Blurred Image Quality Assessment by Structural Similarity Index
## 3 The Proposed IQA Metric
### Re-Blur  
It is known that sharp images contain more high-frequency components, thus grayscale variations between adjacent pixels in sharp images are more distinct than those in blurred images. The re-blur theory [17] explains that the variation of quality of sharp images would be larger than that of blurred images after blur processing, which is also demonstrated in Figure 1. Considering the input image \(x\), we can take re-blurred image \(y\) as the reference, and the image quality can be assessed by the quantity of high frequency components measured between \(x\) and \(y\).  
The re-blur procedure is shown in Figure 2. Considering Gaussian blur as the distortion type in this paper, we apply a Gaussian kernel \(k_{g}\) (i.e., a Gaussian low-pass filter) to the distorted image \(x\) to obtain a re-blurred image \(y\), which is formulated as  
\[y=x*k_{g} \tag{5}\]  
where \(*\) is the convolution operator, and the Gaussian kernel \(k_{g}\) is sampled from a two-dimensional Gaussian function  
Figure 1: A sharp image (**left**) possesses conspicuous quality decline than a blurred image (**middle**) after blur processing (**right**).  
\[G(u,v)=\frac{1}{2\pi\sigma^{2}}e^{-(u^{2}+v^{2})/(2\sigma^{2})} \tag{6}\]  
It should be noted that \(k_{g}\) is parameterized by the kernel size and the standard deviation \(\sigma\). The impact of these parameters will be discussed in Section 4.  
We decompose an \(M\times N\times 3\) image into the high-frequency part \(I^{H}\) and the low-frequency part \(I^{L}\). \(I^{H}\) represents the drastic part while \(I^{L}\) represents the mild part. Thus the image grayscale variation would be sharper in drastic-change area. In order to focus on the main part that contributes to image quality and reduce the time cost, a down-sampling process for the inputted multi-dimensional image by a simple low-pass filter \(f=max(1,round(min(M,N)/256))\) is applied. If \(f>1\), we define \(f=\frac{1}{f\times f}\times lpf\), where \(lpf=\begin{bmatrix}1&\cdots&1\\ \vdots&\ddots&\vdots\\ 1&\cdots&1\end{bmatrix}_{f\times f}\). Values outside the bounds of the image are computed by mirror-reflecting the image across the border. The filtered image should have the same size of the original image by restricting the points of filtering template in the original image. In addition, we convert both the multi-dimensional original image and the filtered, i.e., the re-blurred image into two-dimensional mode.
## 3 Experiment and Results
### Measurement and Discussion
In addition, in order to better illustrate the performance of the transmission loss in diverse frequency conditions, the transmission loss was analyzed in detail at different frequencies within 0.3 THz, individually (Figure 5e). The results revealed that the transmission loss decreased with a larger gap distance, and a similar trend was also indicated by the simulation results. The above results show that the waveguide performance could be controlled by the gap distance. Thus, the optimum structural parameters of the waveguide should be selected according to the application requirements.
## 2 Materials and Methods
### Procedures
#### 2.2.1 Smartphone Application  
It was specifically designed as a tele-rehabilitation tool, with the following resources: (A) Information about PF: this section was compounded by videos, 3D anatomical models, and information related to anatomy, function, and care of PF. This information was updated every two weeks, as an incentive to use the smartphone application; (B) Communication with health professionals: this section allowed athletes and physiotherapists to keep in contact through a chat; (C) Strengthening exercise program: this section was compounded by exercises with 3D avatars, to be completed three times per week. The use of avatars as models gave participants the option to see the exercises in 3D format. In this way, if an exercise was difficult to understand from a 2D perspective, they could see from a different perspective by clicking the \"3D view\" option (Figure S1). Every two weeks, the exercises were modified. Before starting each session, the athletes were asked to check whether they were able to carry out a correct PF contraction by using an intravaginal probe Periform(r). The entire program is shown in Figure 1 and was designed according to recommendations from previous studies [9].  
Intervention for EG1 athletes consisted of using the smartphone application with access to the following resources: general information about PF, direct communication with physiotherapists, experts in pelvic floor, and exercise program (Figure 2).  
Intervention for EG2 athletes consisted of using the smartphone application with access to the same resources as EG1 but they should wear an IEASE(r) XFT-0010 intra-vaginal biofeedback (Shanghai, China) during the exercise program (Figure 2). The biofeedback from the device helped athletes to monitor their intravaginal pressure (in mmHg) while performing the pelvic floor contractions.  
All athletes from EG1 and EG2 received a rubber band, lubricant gel, and a Neen Periform(r) intra-vaginal probe (Nottinghamshire, UK). Before starting each exercise session, athletes were asked to ensure the correct pelvic floor contraction by checking that this intra-vaginal probe was moving downwards. This was explained with a video. Athletes from EG2 also received the IEASE(r) intravaginal biofeedback device.  
Control intervention consisted of using the smartphone application with access only to general information about PF and direct communication with the physiotherapists. This group did not have access to the exercise program. In the final evaluative session,
## 3 Experimental Analysis
### Proposed Model Results  
#### 3.4.1 Comparative Model Experimental Results  
To evaluate the performance of the subway passenger flow prediction model based on the proposed improved TimesNet, we chose the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) as performance metrics. The processed data from our model were compared with other existing mainstream prediction models. Table 3 presents the performance comparison results of our model and other models in the task of subway passenger flow prediction.  
All the models we use for comparison in this study (including TimesNet) are advanced deep learning models designed specifically for the task of time series forecasting. These models are powerful benchmark models in the field of time series forecasting with architectures optimized to capture complex temporal patterns such as long-term dependencies and chaotic behavior.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Mean** & **Min** & **Max** & **Variance** \\ \hline IMF\({}_{1}\) & \(1.71\times 10^{-17}\) & \(-1.3550\) & \(1.6155\) & \(0.5204\) \\ IMF\({}_{2}\) & \(-2.29\times 10^{-18}\) & \(-0.8613\) & \(1.1168\) & \  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**MODEL** & **MAE** & **RMSE** \\ \hline Informer & 0.7990 & 1.0194 \\ Crossformer & 0.8110 & 1.1263 \\ Autoformer & 0.8139 & 1.1215 \\ Reformer & 0.6873 & 0.9475 \\ PatchTST & 0.3856 & 0.5953 \\ iTransformer & 0.6676 & 0.8793 \\ TimesNet & 0.3399 & 0.4818 \\ FiLM & 0.5180 & 0.7434 \\ Proposed & 0.2253 & 0.3178 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Module performance evaluation index compared to baseline.  
Figure 9: VMD results.  
The comparisons we make in our results focus on models with similar goals, i.e., predicting complex time series data to ensure that baseline model comparisons are reasonable. At the same time, we ensure that the baseline models are relevant and competitive in terms of architecture, input data, and complexity of the tasks addressed.  
To ensure a fair comparison, we used consistent datasets, preprocessing steps, and evaluation metrics for all models. Thus, the performance differences reflect the model's capabilities, not the settings.  
It can be seen from Table 3 that our proposed improved TimesNet model outperforms other models in both the MAE and RMSE performance metrics. This demonstrates the superiority of our model in predicting subway passenger flow. Additionally, in terms of fitting effect, as shown in Figure 10 (the rest of the result in the Supplementary Materials), our work shows more accurate prediction results compared to other baseline models.
## 4 Discussion  
This paper describes results from a unique airborne campaign (FLEX-US 2013) conducted at the Parker Tract Loblolly Pine Plantation (Plymouth, NC, USA) study site [8] as an initial ESA-NASA collaboration to support ESA's FLuorescence Explorer (FLEX) mission [7]. This effort combined capabilities of multiple passive optical, active LiDAR, and thermal measurements, at a well-characterized study site having both an age class chronosequence and an AmeriFlux eddy covariance tower. The results from this airborne campaign demonstrate that the combined spectral information from chlorophyll fluorescence, reflectance spectroscopy, and canopy temperature--enhanced with structure measurements derived from LiDAR--can successfully discriminate canopy age and physiological response categories. Our campaign produced strong evidence in support of the utility of these combined measurement types. This was achieved for a LP forest, even though coniferous forests are known to have relatively low fluorescence emission. We obtained these results during autumn when environmental conditions are associated with lower irradiance and temperatures, with commensurate lower physiological activity, associated with this season in mid-latitude ecosystems. We may have been fortunate to capture the forest when a range of changes in canopy physiological conditions was taking place. Future studies are needed, and it will be important to obtain diurnal observations from different types of forests and other ecosystems over the full growing season, especially to compare the potential for the measurement sets used herein during the fall senescence with similar measurements obtained during summertime in conjunction with peak photosynthetic activity and high canopy chlorophyll content.
## 3 Methods
### Data Processing Flow
#### 3.4.2 Signal Scaling  
An EMG signal represents the combined motor unit action potentials during contraction, recorded at a specific electrode location. The surface EMG voltage potential is greatly influenced by various factors, which differ among individuals and may also change over time within the same individual. Consequently, the amplitude of an EMG signal is ineffective for group comparisons or monitoring over extended durations [25]. Subsequently, several studies have shown that a scaling method such as signal normalization or standardization can effectively reduce the differences between records within and across participants [25; 26; 27]. Several studies have used the z-score normalization method to improve the consistency of EMG signals [8; 25; 28; 29; 30; 31]. This study employed the z-score  
Figure 3: EMG signal-filtering process.  
method for EMG signal scaling. The z-score method scales the signal instances by removing and scaling the mean feature to unit variance.
###### Abstract  
As is well known, multi-hop range-free localization algorithms demonstrate pretty good performance in isotropic networks in which sensor nodes distribute evenly and densely. However, these algorithms are easily affected by network topology, causing a significant decrease in positioning accuracy. To improve the localization performance in anisotropic networks, this paper presents a multi-hop range-free localization algorithm based on Least Square Regularized Regression (LSRR). By building a mapping relationship between hop counts and real distances, we can regard the process of localization as a regularized regression. Firstly, the proximity information of the given network is measured. Then, a mapping model between the geographical distances and the hop distances is constructed by LSRR. Finally, each sensor node finds its own position via this mapping. The Average Localization Error (ALE) metric is used to evaluate the proposed method in our experiments, and results show that, compared with similar methods, our approach can effectively decrease the effect of anisotropy, thus considerably improving the positioning accuracy.  
wireless sensor network; multi-hop localization; regression +
Footnote †: journal:
# The Use of Wireless Networks and Mobile Applications in the Control of Electric Machines
## 4 Model of the System and Simulation Tests  
The main circuit structure of a system is shown in Figure 5. Simulation tests have been conducted in PLECS software to determine exemplary waveforms and frequency spectra of signals which occur under the proper operation of the system. The simulation constitutes also a tool to verify the correct operation of the laboratory system.  
Figure 6 depicts the obtained waveforms whereas their spectra are shown in Figure 7. The results encompass motor current and voltage, as well as the rectifier's input current and voltage.  
Figure 4: The interface of the developed mobile application with sequentially switched screens; from the left: screen 1, establishing a connection (Bluetooth or Wi-Fi); screen 2, enabling control; screen 3, motor rotational speed control (using a slider or entered value) with an emergency stop; screen 4, motor shutdown; screen 5, disconnection.
# Free Vibration of a Taut Cable with Two Discrete Inertial Mass Dampers
## 3 Two Opposite IMDs
### Comparison of Asymptotic and Numerical Solutions  
Figure 2 shows the comparison of asymptotic and numerical complex wavenumbers of a cable with two symmetric identical IMDs for various inertial masses. Two IMDs are assumed to be respectively installed at distances \(x_{1}\) of \(1\%L\) and \(x_{2}\) of \(99\%L\) from the left end of the cable, i.e., \(x=x_{1}=x_{2}^{*}=L-x_{2}=1\%L\). When inertial masses remain constant and damping coefficients of two IMDs increase from zero to infinity, the loci, which nearly trace a semicircular contour, start from the undamped wavenumber and finally attach to the real axis. According to Equation (12), the damping properties result from the imaginary part of the wavenumber. Maximum supplemental modal damping can be obtained at the top point of the semicircle [17]. The diameter of the loci is quite small but increases with the increase of the inertial masses of IMDs, indicating that two symmetric identical IMDs have slight influences on the damped frequency of the cable and can achieve higher supplemental modal damping ratios than traditional VDs. By comparing asymptotic and numerical complex wavenumbers, it is seen that two results coincide well with each other for small or moderate inertial mass (\(\chi\leq 0.6/(n\pi x/L)\)) adopted in the IMDs. Nevertheless, the results deviate significantly from each other when the big inertial mass (\(\chi=0.9/(n\pi x/L)\)) shown in Figure 2d is adopted. Hence, solutions via numerical iteration are used to accurately predict the maximum supplemental modal damping ratio of the cable and corresponding optimal damper size of the IMD in the following discussions.
# A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs
## 2 Background
### AODV Routing Protocol  
The Ad Hoc On-Demand Distance Vector (AODV) protocol [8] is one of the most extensively deployed reactive protocols in wireless ad hoc networks owing to the fact that it has fewer computational requirements since the route is initiated and maintained only when needed. AODV supports unicast, multicast, and broadcast communications. AODV is also loop free because it uses the destination sequence numbers, which are time stamps indicating the freshness of a route. The key mechanisms implemented in AODV are path discovery, reverse-path setup, and route maintenance.  
AODV performs route discovery using two types of messages, namely the Route Request (RREQ) and the Route Reply (RREP). Whenever a source node needs to communicate data to a destination. It broadcasts an RREQ to all its neighboring nodes in an expanding ring lookup procedure until reaching the destination node or an intermediate node with a path to the targeted node. That node delivers unicast response to the source node through the RREP message in the reverse path. When the source node receives the RREP message, it starts the data transmission.  
In AODV route maintenance mechanism, nodes maintain only active routes. Indeed, nodes periodically exchange HELLO messages with their neighbors in order to identify the link's state and inform about the freshness of the path. If any link is broken during the data transmission, a Route Error (RERR) message is sent to all other nodes affected by the broken link, including the source node. When the source node receives the RERR message, it launches a new route discovery process.
## 2 Results and Discussion
### Estimation of Green FPAR
For deciduous broadleaf forests, the mean error of \(FPAR_{PAV}\) was 0.0086, and the error rate was 1.99%, while the mean error was 0.026 and the error rate was 3.76% for evergreen coniferous forests. It seems that the inversion model for deciduous broadleaf forests can produce better results than the model for evergreen coniferous forests.
# Cross-Domain Fake News Detection Through Fusion of Evidence from Multiple Social Media Platforms
## 2 Literature Review
### In-Domain Fake News Detection
Additionally, the concept of generated comments, as proposed by [PERSON] et al. (2024) in 2024, introduces a novel approach to fake news detection. By leveraging large language models (LLMs) to generate diverse comments, this method aims to enrich the dataset and capture a broader range of user interactions. [PERSON] et al. 's model achieved an accuracy of 89%, but the effectiveness of this approach depends heavily on the quality and representation of the generated comments. Although sentiment and emotion analysis are becoming integral to FND, the lack of explainability in many systems poses a barrier to their widespread adoption. The introduction of generated comments represents a promising direction, but further exploration is required to ensure that they can reliably enhance detection capability across different social media platforms. A limitation of comment-based FND is that it is difficult to locate unbiased comments among the rich information on social media. Because each social media platform is distinct, the same fake news on different platforms will have unique users and unique comments. To address this problem, leveraging comments from multiple social media platforms could be a potential solution. Ensuring consistency among comments across platforms can lead to improved detection accuracy. Comments from different platforms capture varied user viewpoints, actions, and language styles, offering complementary insights that can greatly improve fake news detection. Evaluating the consistency of comments across platforms can further improve detection reliability and accuracy. However, one of the primary reasons this approach remains unexplored in the literature is the lack of available datasets containing comments on the same news across multiple social media platforms. Collecting datasets from social media platforms is time-consuming and they often have API restrictions. This absence of multi-platform datasets highlights a critical gap, underscoring the need for research efforts to develop and utilize such datasets to advance cross-platform fake news detection methodologies.
## 3 Materials and Methods
### Data Sources  
#### 3.1.1 Static Data  
Digital elevation models (DEMs) and optical images were acquired from the USGS network. Reference [46] and the disaster management department's reports provided updated lithology data. We acquired the hydrogeology and geomorphology types from the Disaster Management Department of Taskorgan. The 2016 China-Pakistan Economic Corridor Permafrost Dataset and the sixth edition of the Glacier Catalogue provided the glacier and permafrost data [47; 48; 49]. With SAGA-3.0 software ([[https://saga-gis.sourceforge.io/en/index.html](https://saga-gis.sourceforge.io/en/index.html)]([https://saga-gis.sourceforge.io/en/index.html](https://saga-gis.sourceforge.io/en/index.html)), accessed on 18 September 2024), the topographic information (slope, relative relief, aspect, TRI, LS factor, TWI, valley depth, and distance to rivers) was extracted from the DEM. The landslide inventory was derived from the visual interpretation of optical images, the provision of the Disaster Management Department of Taskorgan County, and field investigation. Well-known information on landslides and their surrounding environments extracted from factor data with a higher resolution reflects landslide development better [50]. However, the spatial resolutions of most variables used in LSM are inconsistent [51; 52; 53], which causes uncertainty in the assessment results [54]. To avoid the uncertainty linked to using datasets with different spatial resolutions, we resampled all of the causes and triggers of slides and debris flows and standardized their resolution to 30 m.
## 4 Results  
In a summary, the transferring distance of the quantum state in the fiber can decide the range of constructed quantum information network with a nonclassical state. GAWBS extra noise in the fiber can destroy the quantum property of the entangled state or the squeezed state with the increasing of length of the fiber. With the transferring scheme we proposed, only the LO beam was shaped to a sequence of light pulses and transferred, the quantum property of nonclassical state can be totally reserved. Compared with our previous experiment, the transferring distance of nonclassical state has been enhanced to 20 km, the decreasing of squeezing degree was only caused by the transmission loss in the fiber. Thus the construction of a practical quantum network at the metropolitan scale is possible with this scheme [19].  
Figure 4: The dependence of measured noise variances of the signal beam on the transmission distance (**a**) and the power of the LO beam in BHD2 (**b**) in the fiber. The square is the result when LO beam was continuous and the signal beam was a coherence state. The triangle is the result when LO beam was continuous and the signal beam is a quadrature amplitude squeezed state. The dot is the result when the LO beam was pulsed and the signal beam is squeezed state. The solid traces are the corresponding calculation results, which was in good agreement with the experimental data.  
**Author Contributions:** [PERSON] and [PERSON] conceived the original idea. [PERSON], [PERSON] and [PERSON] designed the experiment. [PERSON], [PERSON], [PERSON] and [PERSON] constructed and performed the experiment. [PERSON], [PERSON] and [PERSON] wrote the paper. All the authors reviewed the manuscript.  
**Funding:** This research was funded by the National Key R&D Program of China (grant no. 2016 YFA0301402), the National Natural Science Foundation of China (grants nos. 11654002, 61775127 and 11834010), the Program for Sanjin Scholars of Shanxi Province and the Fund for Shanxi \"1331\" Project Key Subjects Construction.  
**Conflicts of Interest:** The authors declare no conflict of interest.