esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Analytical-Numerical Procedure for Ultrasonic Sludge Treatment for Agricultural Use
## 5 Temperature-Humidity Coupled Model for Sludge Treatment
### On the Existence, Uniqueness, and Regularity of the Solution: Variational Formulation
#### 5.7.2 Regularity  
The regularity of the solutions \(T(\mathbf{x},t)\) and \(W(\mathbf{x},t)\) can be analyzed by exploiting the coupled structure of the system. In particular, the time derivative \(\frac{\partial T(\mathbf{x},t)}{\partial t}\) belongs to \(L^{2}(0,T;H^{1}(\Omega)^{\prime})\). This result is ensured by the regularity of \(W(\mathbf{x},t)\), which belongs to \(L^{2}(0,T;H^{1}(\Omega))\), and by the fact that the coupling term \(\lambda\frac{\partial W(\mathbf{x},t)}{\partial t}\) is sufficiently regular. Consequently, \(T(\mathbf{x},t)\) evolves in time regularly within a weak functional space.  
The elliptic structure of the term \(-\
abla\cdot(k\
abla T(\mathbf{x},t))\) in the first equation also guarantees that \(T(\mathbf{x},t)\in H^{1}(\Omega)\) at each instant \(t\), provided that \(k\), \(\mathbf{u}\), \(Q\) are regular. If \(k\) is sufficiently regular, e.g., \(k\in C^{2}(\Omega)\), we can conclude that \(T(\mathbf{x},t)\in H^{2}(\Omega)\) for each instant \(t\).  
The regularity of the solution \(W(\mathbf{x},t)\) depends directly on the regularity of \(T(\mathbf{x},t)\) through the nonlinear term \(J_{e}=k_{e}(T)\big{(}p_{v}(T)-p_{v,\text{amb}}big)\). If \(T(T(\mathbf{x},t)\in H^{1}(\Omega)\), then \(W(\mathbf{x},t)\in H^{1}(\Omega)\). If moreover \(T(\mathbf{x},t)\in H^{2}(\Omega)\) and the coefficients \(k_{e}(T)\) and \(p_{v}(T)\) are regular, e.g., \(C^{2}\), then we can conclude that \(W\) also belongs to \(H^{2}(\Omega)\).
## 4 Results
### Effect of Bayesian Hyperparameter Optimization  
The effect of Bayesian hyperparameter optimization is demonstrated through a comparison between a manually tuned temporal attention-LSTM (TA-LSTM) model and a model tuned by Bayesian optimization. Tables 9 and 10 show the effectiveness of Bayesian hyperparameter optimization on the R\({}^{2}\) and RMSE metrics. Overall, the BOTA-LSTM model outperforms the manually tuned model in predicting all five shield construction parameters, which indicates that the BOTA-LSTM model can achieve optimal prediction results in all five shield construction parameters. Specifically, in terms of the R\({}^{2}\) evaluation metric (Table 8), the BOTA-LSTM model shows a modest improvement in prediction performance for HDSH (from 0.84 to 0.94) and total thrust (from 0.76 to 0.83), and a significant improvement for VDSH (from 0.53 to 0.76). This variation indicates that Bayesian optimization has varying effects on different tasks, but it always improves the prediction  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation Metric** & **HDSH** & **HDST** & **VDSH** & **VDST** & **Total Thrust** \\ \hline R\({}^{2}\) & 0.88 & 0.94 & 0.95 & 0.91 & 0.93 \\ RMSE & 5.54 (mm) & 8.43 (mm) & 1.44 (mm) & 6.49 (mm) & 2480 (kN) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction results of BOTA-LSTM model (testing set).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **2-Ring** & **4-Ring** & **6-Ring** & **8-Ring** & **10-Ring** \\ \hline BO-LSTM & 0.637 & 0.625 & 0.442 & 0.213 & 0.114 \\ BOTA-LSTM & 0.921 & 0.862 & 0.704 & 0.547 & 0.400 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of multi-ring HDSH predictions between BOTA-LSTM and BO-LSTM models in terms of R\({}^{2}\).  
Figure 11: Multi-ring prediction of the BOTA-LSTM model: (**a**) HDSH and (**b**) HDST.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **2-Ring** & **4-Ring** & **6-Ring** & **8-Ring** & **10-Ring** \\ \hline BO-LSTM & 0.770 & 0.561 & 0.382 & 0.154 & \(-\)0.110 \\ BOTA-LSTM & 0.900 & 0.822 & 0.714 & 0.597 & 0.360 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of multi-ring HDST predictions between BOTA-LSTM and BO-LSTM models in terms of R\({}^{2}\).  
performance. In addition, compared to manual tuning, Bayesian hyperparameter optimization tends to be more efficient in finding good hyperparameter combinations, thus saving time and resources. Manual tuning usually takes a lot of time and relies heavily on the experience and intuition of the researcher. Although experienced researchers may obtain acceptable prediction results in a relatively short time, this method usually possesses large uncertainties and may only find locally optimal solutions.
# MVACLNet: A Multimodal Virtual Augmentation Contrastive Learning Network for Rumor Detection
## 2 Related Work
### Unimodal Rumor Detection Methods  
Existing unimodal rumor detection methods utilize features extracted from either texts or images to detect rumors, where the former accounts for the vast majority. Traditional machine learning-based models often rely on hand-crafted textual features to detect rumors [3; 4; 5; 6]. For example, [PERSON] et al. [3] manually constructed more than 80 statistical features, such as punctuation marks, emoticons, and link counts. [PERSON] et al. [4] designed a set of regular expressions to select signal tweets containing skeptical enquiries. However, these feature engineering-based methods are time-consuming and labor-consuming, which promoted the emergence and rapid development of deep learning-based models. The deep learning-based models show superior performance due to their ability to capture high-level semantic features automatically [7; 8; 9; 10; 11; 31]. For instance, [PERSON] et al. [7] proposed a recurrent neural network-based model to capture hidden temporal and textual features from relevant posts modeled as variable-length time series. [PERSON] et al. [8] designed a convolutional neural network-based model to extract crucial textual features from the input sequence and model high-level interactions. [PERSON] et al. [9] used the multi-task learning idea to jointly learn rumor detection and stance classification tasks to obtain better feature representation. [PERSON] et al. [31] only utilized visual information from posts. They extracted visual features from the frequency and pixel domains and fused them to obtain feature representations of posts. In addition, researchers have also leveraged propagation structure features during the spread of rumors [12; 13; 14; 15; 16]. Concretely, [PERSON] et al. [12] proposed a tree-structured recursive neural network to simultaneously capture textual semantic features and propagation structure features. [PERSON] et al. [13] modeled the propagation and dispersion of rumors as top-down and bottom-up directed graphs, respectively, and leveraged graph convolutional networks to capture corresponding patterns and structure features. [PERSON] et al. [16] modeled the dynamics of rumor propagation and background knowledge structures. Then, they utilized graph convolutional networks to capture these structure features at different time stages and incrementally combined them using a time fusion unit. However, such propagation structure features are generally unavailable at the early stage of rumor dissemination.  
Although these unimodal methods have achieved good performance in rumor detection, they can only utilize features extracted from a single modality. The growing prevalence of multimodal posts on social platforms has amplified their limitations in effectively detecting multimodal rumors.
## Appendix C LLM Implementation Details
### _Query Generation_
Some believe that higher doses are necessary to see any significant effects, while others argue that lower doses, when taken consistently over a longer period of time, can be more beneficial. Additionally, there is debate about whether resveratrol should be taken in a fasting state or with food to maximize its absorption and potential benefits. Query: (resveratrol OR \"trans-3,5,4\"-trihydroxystilbene\") AND dose User: What are the scientifically proven benefits of taking ginseng supplements? AI: * Point of Contention 1: Efficacy of Ginseng in Cognitive Function Summary: The debate revolves around the effectiveness of ginseng supplements in enhancing cognitive function. Some studies suggest that ginseng may have various cognitive benefits, such as improving memory and concentration, while others argue that the evidence is inconclusive or insufficient. Query: (ginseng OR \"Panax ginseng\") AND cognition * Point of Contention 2: Ginseng for Immune System Enhancement Summary: This point of contention focuses on the role of ginseng in immune system enhancement. Some believe that ginseng can significantly boost immune system function, while others argue that the evidence is not strong enough to make such claims. Query: (ginseng OR \"Panax ginseng\") AND immune * Point of Contention 3: Ginseng for Energy and Stamina Summary: The efficacy of ginseng in increasing energy and stamina is a common point of debate. While some studies suggest that ginseng can help to combat fatigue and increase physical performance, others argue that these effects are not consistently observed across studies. Query: (ginseng OR \"Panax ginseng\") AND energy * Point of Contention 4: Safety and side effects of Gingko supplements Summary: The safety of Gingko supplements is a point of contention, with some concerns raised about potential side effects such as dizziness, upset stomach, and increased bleeding risk. While some studies suggest that Gingko supplements are generally safe, others argue that caution should be exercised, especially when combined with certain medications or in individuals with specific health conditions. Query: (Gingko OR \"Gingko Biloba\") AND (safety OR \"side effects\")
## 3 Experimental Setup  
### Dataset Preprocessing and Acoustic Features  
We performed experiments on the TIMIT corpus [26]. It is a reading speech corpus developed to provide speech data for acoustic-phonetic research studies and the evaluation of automatic speech recognition systems. It was produced by people from the eight major dialect regions of the United States. All data are manually divided and annotated at the phoneme level.  
The phoneme sequences in the data annotation are preprocessed. As described in the TIMIT corpus documentation, /ix/ /ax/ are used to represent schwas produced by vowel reduction. /ix/ is usually used for schwas between two alveolars (\"roses\" / r ow z ix z/), otherwise /ax/ is used (\"ahead\" / ax hh eh d/). Therefore, in the experiments, we still distinguish these two kinds of schwa. /ih/ and /ah/ are the phonemes that most similar to /ix/ /ax/. Generally speaking, when there is no stress on /ih/ and /ah/, they are tend to be reduced to schwa. In our experiments, /ih/ /ah/ /ix/ /ax/ are marked as _ihnormal_, _ahnormal_, _ihschwa_, _ihschwa_.  
Data set division and acoustic feature extraction are performed in the usual way. We have maintained the TIMIT suggested training/test subdivision, which follows criteria containing restrictions on speaker, gender, text overlap, and occurrence times of phonemes. The development set is divided from the training set with reference to the TIMIT recipe in the ESPNET toolkit [27]. The division of the data set and the number of phonemes related to vowel reduction in each subset are shown in Table 2. We also use the TIMIT recipe in the ESPNET toolkit to extract 23-dimensional filter bank features and 3-dimensional fundamental frequency features to form a total of 26-dimensional acoustic features for each experiment.  
Figure 2: Multi-stream expansion based on location-aware attention mechanism.
## 3 Results and Discussion
### Determination of Hardly Grinding Phases (HGP) in Converter Steel Slag
#### 3.2.3 Chemical Compositions of HGP in Converter Steel Slag  
The chemical compositions of oversized substances passed a 0.9 mm square-hole screen under different grinding times, which were determined by X-ray fluorescence analysis (XRF) and chemical titration, are shown in Table 3. The results show that the contents of CaO and SiO\({}_{2}\) in HGP gradually decrease with the increase in grinding time, while the contents of metallic iron show a reverse trend, and the contents of Fe\({}_{2}\)O\({}_{3}\) and MgO are firstly increased and then decreased with the increasing of grinding time. In the initial grinding period (10-20 min grinding), the chemical compositions of HGP mainly consist of CaO, SiO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\), and MgO; in the middle grinding period (40-50 min grinding), it mainly consists of Fe\({}_{2}\)O\({}_{3}\) and metallic iron; in the later grinding period (60-70 min grinding), metallic iron takes up the most component. So the HGP have different chemical compositions during different grinding period.
# SPL-YOLOv8: A Lightweight Method for Rape Flower Cluster Detection and Counting Based on YOLOv8n
## 4 Results and Analysis
### Module Ablation Experiments  
The lightweight SPL-YOLOv8 model for rapeseed flower detection and counting consists of three improved modules: StarNet, C2f-Star, and PGCD. To verify the individual functionality of each module and the synergistic effects of the modules, this paper uses a split dataset of 73 rapeseed flower images as the test set for ablation experiments. The corresponding results are shown in Table 2. And each module ablation experiment is visualized, as shown in the Figure 9.  
The experimental results show that replacing the backbone feature extraction network of YOLOv8n with StarNet can reduce the computational cost and number of parameters of the network without decreasing the mAP, while also reducing the size of the model weights. This is because the StarNet network structure uses only one layer per stage to build the network, significantly reducing the complexity of the model while preserving feature mapping information, allowing the network to achieve high recognition accuracy while being lightweight. The addition of C2f-Star further enhances the lightweight nature of the model compared to the original YOLOv8n network, with a slight improvement in accuracy. When all three modules are integrated into the model to form our lightweight SPL-YOLOv8 model, the AP\({}_{50}\) reaches 92.4%, with GFLOPs and parameters of 3.4 G and 1.240 M, respectively, representing reductions of 58% and 56% compared to the baseline model. This is attributed to the synergistic collaboration between the modules, which enhances the detection of small targets while significantly reducing the model's parameter count and computational cost.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Backbone** & **Precision P/\%** & **Recall R/\%** & **AP\({}_{50}\)/\%** & **Parameters/M** & **GFLOPs/G** & **Model Size/MB** \\ \hline \multirow{4}{*}{CNN} & baseline & 90.3 & 84.5 & 92.2 & 2.86 & 8.1 & 6.3 MB \\  & convnextv2 & 90.0 & 85.4 & 92.4 & 5.40 & 14.1 & 11.6 MB \\  & Fasternet & 89.0 & 85.7 & 92.6 & 3.98 & 10.7 & 8.6 MB \\  & StarNet & **90.5** & 84.6 & 92.4 & **2.11** & **6.5** & **4.7 MB** \\ \hline \multirow{4}{*}{ViT} & MobileNet & 89.7 & 84.8 & 92.2 & 5.78 & 17.5 & 12.4 MB \\  & EfficientFormerV2 & 89.8 & 86.1 & 92.8 & 4.87 & 11.7 & 41.4 MB \\  & efficientViT & 90.1 & 84.2 & 92.1 & 3.82 & 9.4 & 8.8 MB \\  & Biformer & 90.2 & 86.0 & 92.7 & 4.52 & 11.4 & 43.5 MB \\  & Swintransformer & 89.8 & **86.8** & **93.0** & 38.54 & 45.1 & 58.6 MB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of model training results with different backbone networks.
## 1 Introduction
Compared to traditional methods, this approach offers higher accuracy, applicability, and practicality, enabling the real-time monitoring and prediction of optic damage to enhance operational efficiency and longevity in large laser facilities.
## 3 Original DE Algorithm
### Experiment Results and Analysis  
#### 4.3.1 Evaluation Criterion  
SSIM and PSNR are used in our experiment to evaluate the effectiveness of the segmentation algorithm. Both of those criteria follow the rule that the higher the value, the better the image quality. As it is easy to extract structural information from images with human vision, then the structural similarity is practicably computed to appraise the quality of the image after being segmented. The range of SSIM is 0 to 1, and if the same two images are measured, the SSIM value of those two images equals 1. The formula is as follows:  
\[SSIM=\frac{(2\mu_{x}\mu_{y}+C_{1})(\sigma_{xy}+C_{2})}{(\mu_{x}^{2}+\mu_{y}^{2 }+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})}, \tag{21}\]  
where \(\mu_{x}\) and \(\mu_{y}\) is the mean value of x and y respectively, \(\mu_{x}^{2}\) and \(\mu_{y}^{2}\) are the variance of x and y respectively, and \(\sigma_{xy}\) represents the covariance between x and y, \(C_{1}\) and \(C_{2}\) are two constants for stabilization denoted as \(C_{1}=\left(k_{1}L\right)^{2}\), \(C_{2}=\left(k_{2}L\right)^{2}\), among which \(k_{1}=0.01\), \(k_{2}=~{}0.03\), and \(L\) refers to the dynamic range of the pixel value and is usually equal to 255.  
PSNR is Peak-Signal-to-Noise-Ratio, which is also the image objective evaluation index. It is the most commonly and widely used criteria and the unit of the measurement of PSNR is decibels (DB). The formula is as follows:  
\[PSNR\ =\ 10 log_{10}\frac{255^{2}}{MSE} \tag{22}\]  
\[MSE=\frac{\sum_{i=1}^{M}\sum_{j=1}^{N}\left(f^{\prime}(i,\ j)-f(i,\ j)\right)^{ 2}}{M\ \times\ N}, \tag{23}\]  
where \(f^{\prime}(i,\ j)\) and \(f(i,\ j)\) refer to the image to be evaluated and the original image respectively, \(M\) and \(N\) represent the length and width of the image. \(MSE\) in image segmentation indicates the mean value of the sum of squares of the difference of pixel values between the segmented image and the original one.
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 4 Handover Decision Techniques
### RSRP-Based HO Decision Approaches
In order to reduce unnecessary HOs in HetNets, the authors suggested a frequent HO mitigation technique based on a threshold control parameter. This is achieved by identifying users as fast-movers or HOPP based on the frequency with which they encounter HOs. The suggested system tracks users' dwelling times and their serving cell histories to identify intrusive HOs. The algorithm labels a situation as \"ping-pong\" if the HO history data shows a pattern of repetition within a relatively small amount of time spent at each location. It is assumed that users are always on the move if there is no discernible trend in the HO history data over a very short dwelling time. The problem of superfluous HOs has been effectively addressed by the suggested approach [108]. A distributed optimization algorithm for MRO was proposed in [95] to minimize the number of RLFs in LTE small cell scenarios. They employed RSRP for HO decisions and adjusted the TTT, HO offset and CIO value for HCP tunings. Here TTT and CIO act as temporal and spatial parameters for joint optimization. The simulation results are compared on the basis of the number of RLFs, and HOPPs. They also highlighted the impact of UE speed on RLFs. The inclusion of HOM value along with included parameters can further enhance the efficiency of the algorithm. Table 6 presents the studies discussed in this section, along with their selection parameters, HCPs considered for optimization, key performance indicators, the simulator utilized, and the achievements and limitations of each.
## 5 Suites of Earthquakes Consistent with the Lebanese DRS
### Step 2. Obtaining Suites of Spectrum-Consistent Earthqu  
Codes recommend using a suite of SCEs when carrying out time-history analyses. A minimum number of three earthquakes is suggested by EC8 as well as by ASCE 7-16 [40], to which IBC refers on this matter. Should a suite of seven earthquakes be considered, both codes allow the averaging of the results from the seven analyses. Different methods can be used to find the most appropriate set of ground motions, ranging from just a selection of real records based on their seismological characteristics to more powerful matching procedures that are carried out in the frequency domain through wavelet adjustments over one or multiple target spectra [41]. The number of records required to obtain a stable and unbiased estimate of the inelastic response of a structure was found to significantly depend on the method adopted [42]. When simpler methods like those that scale the accelerograms to the target spectral acceleration are applied, a higher number of accelerograms is needed. This number can be reduced when the Spectral Matching (SM) method is applied instead. The scarcity of recorded ground motions in a country may thus be a reason to adopt the SM method [43]. The SM changes both the amplitude and the frequency content of the earthquake to make its response spectrum match the target design one, this procedure generally leading to more stable results [42]. The addition of wavelets has the same advantages provided by the Fourier adjustment with the extra benefit of introducing less energy into the ground motion and preserving the non-stationary characteristics of the original ground motion [41]. Based on previous studies [44; 45; 46], an effective SM algorithm was developed by [PERSON] [47], and subsequently refined by [PERSON] et al. [41]. Due to the rarity of strong recorded earthquakes in the Lebanese area and to the higher effectiveness of the SM method with respect to other methods, the latter is adopted in the present study. Based on the Abrahamson-Hancock algorithm, the SeismoMatch [48] application is used to obtain suites of earthquakes consistent with the IBC design spectra (Figure 3b) and with the EC8 design spectra (Figure 4). For this purpose, the values of the matching parameters listed in Table 9 and the earthquakes listed in Table 8 were considered in SeismoMatch. In Table 9, the mismatch tolerance denotes the maximum allowable discrepancy between the matched earthquake spectrum and the target one. It is noted that SeismoMatch allows setting one single value for the mismatch tolerance, which means giving the same value to the upper and lower bounds. EC8 suggests a 10% lower bound, but it does not give an upper bound. On the other hand, IBC does not provide specific