esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In the network control system, uncertain factors, such as network delays, can usually deteriorate the control performance and stability of networked control systems. Therefore, a time-delay compensation method based on time-delay prediction and improved implicit GPC is proposed. First, the GA-optimized LS-SVM prediction model is used to estimate the time delay in the networked control system. Then, an improved implicit GPC with predictive time delay is used to obtain the system future control value, to compensate for the time delay of the networked control system. Finally, through the simulation results, the method presented in the paper has a better compensation effect and a smaller calculation time.  
In this paper, the parameters of the LS-SVM prediction model are optimized by the genetic algorithm off-line. Additionally, if the time-delay sequence appears to have substantial changes, the prediction accuracy will be reduced. Therefore, the future work is to increase the threshold value; when the cumulative error is larger than the threshold, the optimization process of the parameters is repeated, and more appropriate prediction parameters can be obtained.
## 3 Simulation and Analysis of Structural Behavior
### Deformation Mechanism  
Based on the proposed MBS model, a hybrid analysis method is proposed to analyze the deformation contributions of different components. As shown in Figure 14, unit loads are applied on the top, bottom, and waists to get virtual states of the structure. According to the principle of virtual work, structural convergence deformations can be expressed as  
\[\Delta=\oint\frac{M(\varphi)M^{\prime}(\varphi)}{EI(x)}R(\varphi)d\varphi+\sum \limits_{i=1}^{10}\frac{M(\varphi_{i})M^{\prime}(\varphi_{i})}{k_{i}}. \tag{4}\]  
\[\Delta=\oint\frac{M(\varphi)M^{\prime}(\varphi)}{EI(x)}R(\varphi)d\varphi+\sum \limits_{i=1}^{10}\theta_{i}M^{\prime}(\varphi_{i}). \tag{5}\]  
The convergence deformation of the short and long axes of the structure is calculated using Equation (4). The convergence deformation mainly comprises two parts: the deformation contribution induced by the segment (first term of Equation (4)) and the contribution induced by joints (second term of Equation (4)). In Equation (4), \(M(\varphi)\) and \(M(\varphi_{i})\) represent the bending moments of the segment and the joints of the actual test structure, respectively. \(M^{\prime}(\varphi)\) and \(M^{\prime}(\varphi_{i})\) represent those of the virtual structure subjected to unit loads. \(k_{i}\) is the bending stiffness of Joint No. \(i\) (ranging from 1 to 10). Hence, the actual joint bending moment \(M(\varphi_{i})\) divided by the joint bending stiffness \(k_{i}\) yields the actual joint rotation angle \(\theta_{i}\) obtained from the test. Therefore, Equation (4) can be transformed into Equation (5).  
The analysis of deformation contributions is summarized in Table 5. The deformation of the short axis under three different conditions during the service stage is calculated and compared with test results, showing good agreement. According to the joint contributions shown in parentheses, the deformation contributed by joints was 65-70%. These results also suggest the optimization of joints to reduce structural deformation.  
Figure 14: Illustration of the structure under virtual unit loads used for the calculation of convergence deformations.
## 3 Materials and Methods  
### Database  
Voice samples of sustained vowels /a/, /i/, and /u/ were digitally recorded and published online in the SVD, created by the Institute of Phonetics of the University of Saarland [24]. The SVD consists of voices recorded by more than 2000 people. The patient's voices were recorded for vowels /a/, /i/, and /u/ at high, low, low-high, and normal pitches, respectively. The pitches are described and can be allocated into four categories. The length of the recordings ranges from 1 to 4 s. The audio format is a waveform (16-bit sample), sampled at 50 kHz. The entire database contains recordings of 71 different and well-defined voice pathologies and healthy patients. This paper used 259 healthy and 259 pathological female recordings of the /a/, /i/, and /u/vowels at normal pitch (97 suffered from hyperfunctional dysphonia, 51 had functional dysphonia, 30 suffered from laryngitis, and 81 suffered from other pathologies listed in the database [24]) and 259 healthy and 259 pathological male recordings of the same vowels at normal pitch (62 men suffered from laryngitis, 44 had hyperfunctional dysphonia, 25 suffered from a vocal fold polyp, and 128 suffered from other pathologies listed in the database [24]). The details are described in Table 1. Due to the essential differences in voice behavior between men and women, the parameters were statistically analyzed for men and women separately.
# Determining Signatures for Energy Mix Produced by Photovoltaic Systems and Wind Turbines
###### Abstract  
The article contains an analysis of power generation by a photovoltaic system with a peak power of 3 MWp and a wind turbine with a power of 3.45 MW. The acquired time series of generated power was analyzed using traditional and modern analytical methods. The power generated by these two Renewable Energy Sources was characterized separately and then by their mix. In this article, the power signature was defined as the power generated by the photovoltaic system and the wind turbine in the state space over a period of one month. The state space was extracted from the results of cluster analysis. The experiment with clustering was carried out into 10 classes. The K-Means clustering algorithm was used to determine the clusters in a variant without prior labeling of classes with the method of learning without the participation of the teacher. In this way, the trajectories of the power generation process from two Renewable Energy Sources were determined in the 10-state space. Knowing which class each data record belongs to, the frequencies of staying in each state were determined. The computational algorithm presented in the article may have great practical application in balancing the power grid powered by energy produced from renewable sources.  
wind measurement techniques; renewable energy source; photovoltaic systems; onshore wind turbines; energy mix; power signatures; smart grid; energy grid balancing Article
## 5 Analysis of the Transmission Delay
### One-Hop Delay/Distance for Intra-/Inter-Lane Scenario
#### 5.2.2 One-Hop Transmission Distance for the Intra-Lane Scenario  
It is the vehicle that can achieve the minimum timer value \(T^{(1,1)}=\text{min}\{T_{1},\ldots,T_{N}\}\) that will be selected as the elected backbone vehicle node. The random variables \(T^{(1,1)}\) and \(D^{(1,1)}\) are directly related by Equation (11). Therefore, we have:  
\[D^{(1,1)}=R\left(1-\frac{T^{(1,1)}-t_{\text{min}}}{t_{\Delta}(1-\epsilon)} \right). \tag{18}\]  
Then, the CDF of the random variable \(D^{(1,1)}\), conditional on there being at least one vehicle in the range \(R\), i.e., \(N>0\), is given by:  
\[\begin{split}\Pr\{D^{(1,1)}\leq x|N>0\}=&\Pr\left\{ T^{(1,1)}\geq t_{\text{min}}+t_{\Delta}(1-\epsilon)\left(1-\frac{x}{R} \right)|N>0\right\}\\ =&\frac{\exp(\lambda_{1}x/R)-1}{\exp(\lambda_{1})-1 },\qquad x\in[0,R].\end{split} \tag{19}\]  
Let \(d^{(1,1)}\) denote the expected value of \(D^{(1,1)}\). Then, we have:  
\[d^{(1,1)}=R\left(\frac{1}{1-\exp(-\lambda_{1})}-\frac{1}{\lambda_{1}}\right). \tag{20}\]
## 2 Literature Review
### Research Gap  
In summary, while the existing studies provide valuable insights, notable research gaps persist in terms of cost contingency estimation. First, most studies employ a limited selection of ML algorithms, mainly ANNs and linear regression. This may restrict the potential for increasing the accuracy and robustness of predictions. Furthermore, few ML models for contingency estimation incorporate project-specific characteristics, which are essential for capturing unique project dynamics and improving estimation precision. Another key limitation is the absence of interval estimation techniques in contingency models, although these would define a range of probable values and offer project managers a clearer understanding of potential cost variability. Finally, project complexity, although it is a critical factor affecting estimation accuracy, is rarely integrated into current models. This may limit the ability to account for the nuances affecting cost performance, especially for relatively complex infrastructure projects. Therefore, addressing these gaps could significantly advance contingency estimation methods and produce more reliable and adaptable tools for infrastructure project cost management.
## 3 Results and Discussion
### Adsorption Performances of DE and DE-LDH
#### 3.2.2 Effect of Adsorbent Dosage  
The influence of adsorbent dosage on dye removal experiments were carried out by adjusting the adsorbent dosage from 10 to 60 mg at optimized pH (Figure 5b). From the results, the CR removal efficiency was improved from 6% to 15.5% for DE and 71% to 98.6% for DE-LDH when the dosage was increased from 10 to 60 mg. However, there was no observable change in the removal efficiency  
Figure 5: The CR removal efficiency with respect to effect of (**a**) pH, (**b**) adsorbent dosage (inset: photograph of CR supernatant solutions before and after adsorption at pH 7), (**c**) dye concentration and (**d**) contact time.  
after 40 mg of adsorbent dosage, suggesting that it had reached its saturation level as there were no dye molecules available to be adsorbed at higher dosages [30]. Thus, the 40 mg of adsorbent dosage was optimized and selected for further experiments. The dye removal efficiency of DE-LDH is higher than that of DE due to the larger surface area and higher protonation. DE has less surface area and less hydroxyl groups (low protonation) which leads to much lower dye adsorption. The supernatant solution of CR before and after dye adsorption confirms that 40 mg of DE-LDH is a reasonable dosage for the removal of CR (Figure 5b, inset). However, it is obvious that the dye removal efficiency increases with the increase in adsorbent dosage due to the availability of active adsorbent sites. In addition, the removal efficiency also depends on the surface area and surface functional groups of adsorbents, which play important roles in the removal of dye.
# Coal Mine Water Inflow Prediction Model Based on Multi-Factor Pearson Correlation Analysis
###### Abstract  
Since geological structures around coal mines are complex, sudden coal mine water inflow is seriously threatening coal mining safety. To improve the accuracy of predicting coal mine water inflow, a multi-source dataset is collected to develop a coal mine water inflow prediction model based on multi-factor Pearson correlation analysis, where a convolutional neural network and bidirectional long short-term memory neural network are adopted to extract features from time-series data. To validate the performance of the present prediction model, a case study is conducted, where the predicted coal mine water inflow is close to the collected coal mine water inflow. Meanwhile, compared to other prediction models, the present prediction model can predict the magnitude and development trend of coal mine water inflow in the next 8 h more accurately, where the mean absolute percentage error is 5.76% and the correlation coefficient is 0.922.  
Keywords:coal mine water inflow; Pearson correlation analysis; convolutional neural network; bidirectional long short-term memory neural network Article
# Review and Categorization of Digital Applications in the Energy Sector
## 4 Categorization of Digital Applications  
As discussed in Section 2, potential digital applications in the energy sector are numerous and extremely diverse in their area of application, intended benefit, and functionality. Subsequently, there are many ways to categorize these digital applications. Based on the findings of the present literature review the applications are clustered in three categories and seven subcategories as depicted in Figure 1, based on the area where they cause the highest impact. The three categories are:  
* System balance--These applications help to level energy generation, demand, and grid capacity;
* Process optimization--These applications improve internal processes and raise efficiency and effectiveness;
* Customer orientation--These applications offer additional benefits to the user and increase revenues.  
These three categories and seven subcategories allow a mostly unambiguous allocation of applications into the different clusters. Nevertheless, some applications exist which could be allocated in more than one category or subcategory. This is mainly the case for \"Process optimization\" applications, such as the \"digital twin\" which, depending on its area of utilization, can also help to improve the balance of the overall system.  
Figure 3: Digital applications and affected stakeholders.  
Furthermore, an indication of how often the applications of the subcategory are mentioned is given (i.e., from 0 out of 10 to 10 out of 10). It becomes apparent that the discussion about digital applications in the energy sector are mainly focused on applications which support the balancing mechanisms of the system, i.e., \"smart grid\", \"smart markets\", and integration of flexibilities. This coincides with one of the main current challenges of Germany's electricity system--to cope with the rising integration of volatile generation. The applications in the subcategory \"anomaly detection and predations\" are mentioned in the context of both application categories \"System balance\" and \"Process optimization\" and, based on their frequent reference, are identified as important parts of the digitalization of the energy sector. Contrastingly, the process efficiency applications based on process automation are less frequently mentioned. These are the least energy-specific applications, which might be a reason why they are discussed less in energy-specific literature. Customer orientation applications of all three subcategories are frequently mentioned in the analyzed literature, however not as frequently as the \"System balance\" applications.  
In the following, the identified digital applications are described. A visual overview of which benefit is generated by which of the seven subcategories is given in Figure 2. Finally, an allocation of affected stakeholders to the according application subcategory is performed and depicted in Figure 3.
## 2 Related Literature
In addition to neural network approaches, other methods such as CNNs [39], XGBoost decision tree algorithms [40], K-Nearest Neighbors (KNN), Fuzzy K-Nearest Neighbors (Fuzzy KNN) [41], Bayes Net, Naive Bayes, SGD, and LogitBoost [42] have also been reported in research. These reported algorithms have certain limitations when compared to artificial neural networks (ANNs). In this study, a feed-forward artificial neural network (ANN) was selected over the referenced machine learning models, such as XGBoost, K-Nearest Neighbors, Naive Bayes, and decision-tree-based methods, due to the specific challenges posed by PCG signal classification. Although models like decision trees and KNN are effective for structured and low-dimensional data, they often struggle to accurately process high-dimensional and non-linear data such as PCG signals, where the relationships between features are complex and interdependent [40]. With its layered architecture, an ANN captures these detailed patterns more effectively based on its ability to introduce non-linearity through activation functions and process multiple layers of features simultaneously. This makes the ANN highly robust against the noise and variability inherent in PCG data, resulting in a more reliable classification performance. Additionally, the ANN offers significant computational advantages, making it suitable for real-time applications. When supported by an appropriate hardware device, its parallel processing capabilities and scalability are critical for deploying PCG classification models in clinical settings, where rapid and efficient data processing is essential. This approach will further emphasize the deep learning model's strengths and the added value that an ANN brings to PCG signal analysis.
# A Lightweight Small Target Detection Algorithm for UAV Platforms
###### Abstract  
The targets in the aerial view of UAVs are small, scenes are complex, and background noise is strong. Additionally, the low computational capability of UAVs is challenged when trying to meet the requirements of large neural networks. Therefore, a lightweight object detection algorithm tailored for UAV platforms, called RSG-YOLO, is proposed. The algorithm introduces an attention module constructed with receptive field attention and coordinate attention, which helps reduce background noise interference while improving long-range information dependency. It also introduces and refines a fine-grained downsampling structure to minimize the loss of target information during the downsampling process. A general efficient layer aggregation network enhances the base feature extraction module, improving gradient flow information. Additionally, a detection layer rich in small target information is added, while redundant large object detection layers are removed, achieving a lightweight design while enhancing detection accuracy. Experimental results show that, compared to the baseline algorithm, the improved algorithm increases the P, R, [EMAIL_ADDRESS], and [EMAIL_ADDRESS] by 6.9%, 7.2%, 8.4%, 5.8%, respectively, on the VisDrone 2019 dataset, and by 5.7%, 9%, 9.3%, 3.6%, respectively, on the TinyPerson dataset, while reducing the number of parameters by 23.3%. This significantly enhances the model's detection performance and robustness, making it highly suitable for object detection tasks on low-computing-power UAV platforms.  
unmanned aerial vehicle; small target detection; YOLOv8; receptive field coordinated attention; fine-grained downsampling Article
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 7 Wireless Technologies in IoV
### Computational Complexity  
Communication protocols must be strong and should be able to support high-speed data transmission. Additionally, secure and private data processing of IoV systems between computation tasks requires encryption methods, and more privacy-preserving techniques. It raises some other facts like strategies for 6G-IoT, edge computing, distributed computing architecture, and adaptive resource allocation algorithms to improve the performance of computational resources towards improving the overall performance of the systems in the smart city [113]. Additionally, the challenges faced by multicarrier technologies like 802.11p under high Doppler conditions such as degraded channel estimation and equalization performance. Moreover, emerging physical layer alternatives like orthogonal time frequency space (OTFS) modulation offer improved resilience in high-mobility environments, thereby contributing to more reliable and efficient V2X communications [114]. The potential of emerging technologies like blockchain and quantum computing in solving computational complexity issues brings up new possibilities in the field of IoV applications in smart cities.  
To address these challenges, the future directions focus on key research priorities in the domain of IoV in smart cities. Key focus areas include enhancing security and privacy, ensuring interoperability and standardization, managing dynamic traffic flows, and addressing computational complexity. This research identifies the need for robust encryption, resilient cyber-physical frameworks, and scalable communication protocols. The emphasis should be on the integration of AI and ML for adaptive systems, the adoption of green and sustainable technologies, and the application of emerging technologies such as edge computing, blockchain, and quantum computing. Additionally, the formulation of ethical policy frameworks and unified infrastructure standards is essential for realizing a secure, intelligent, and user-centric urban mobility environment.
# Zero-Inflated Patent Data Analysis Using Generating Synthetic Samples
## 4 Results
### Simulation Study with Zero-Inflated Data

Table 4: Descriptive statistics of simulation data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Parameter** & **X2** & **X3** & **X4** & **AIC** \\ \hline \multirow{2}{*}{Original} & b & 0.1442 & 0.0874 & 0.1414 & \multirow{2}{*}{52,133.6} \\  & SE (b) & 0.0039 & 0.0033 & 0.0049 & \\ \hline \multirow{2}{*}{\begin{tabular}{c} With noise \\ (sigma = 0.1) \\ \end{tabular} } & b & 0.1444 & 0.0873 & 0.1412 & \multirow{2}{*}{52,340.2} \\  & SE (b) & 0.0039 & 0.0033 & 0.0049 & \\ \hline \multirow{2}{*}{\begin{tabular}{c} With noise \\ (sigma = 0.5) \\ \end{tabular} } & b & 0.1356 & 0.0869 & 0.1272 & \multirow{2}{*}{57,671.4} \\  & SE (b) & 0.0042 & 0.0036 & 0.0052 & \\ \hline \multirow{2}{*}{\begin{tabular}{c} With noise \\ (sigma = 1) \\ \end{tabular} } & b & 0.1163 & 0.0814 & 0.0983 & \multirow{2}{*}{68,623.9} \\  & SE (b) & 0.0049 & 0.0043 & 0.0057 & \\ \hline \multirow{2}{*}{\begin{tabular}{c} Zero-inflated \\ Negative Binomial \\ \end{tabular} } & b & 0.1204 & 0.0776 & 0.1111 & \multirow{2}{*}{48,333.9} \\  & SE (b) & 0.0044 & 0.0037 & 0.0054 & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Zero-inflated \\ Poisson \\ \end{tabular} } & b & 0.1204 & 0.0776 & 0.1111 & \multirow{2}{*}{48,331.9} \\  & SE (b) & 0.0044 & 0.0037 & 0.0054 & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Synthetic sample \\ \end{tabular} } & b & 0.0077 & \(-\)0.0005 & \(-\)0.0221 & \multirow{2}{*}{2907.6} \\  & SE (b) & 0.0190 & 0.0161 & 0.0235 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance evaluation between comparative methods (simulation data 1 with high correlation). in all zero-inflated text data analyses by the simulations. Table 6 represents the performance evaluation result of simulation data 2 with the lower correlations. We also found that the AIC of the synthetic sample is the smallest in all comparative methods. Therefore, we could confirm the good performance of the proposed method using synthetic samples.