esa-sceva/satcom-chunk-collection huggingface dataset:
# Instant Center Identification of Single-Loop Multi-DOF Planar Linkage Using Virtual Link
## 4 Mathematical Proof
Note that \(\mathrm{N}_{i}\) are composed of the unknown angles \(\beta\), \(\eta\), \(\alpha\), \(\lambda\) (\(\beta\), \(\eta\) are the interior angles of the link 3', \(\alpha\), \(\lambda\) are the interior angles of the link 1') and the arbitrary link parameters \(a_{11}\), \(a_{12}\), \(a_{6}\), \(a_{31}\), and \(a_{32}\) (\(a_{11}\), \(a_{12}\) are the link parameters of the links 1', \(a_{31}\), and \(a_{32}\) are the link parameters of the links 3'). Contrasted the single-loop five-bar linkage in Figure 7a with the virtual Stephenson linkage in Figure 7b, since the parameters \(\alpha\), \(\lambda\), \(\beta\), \(\eta\), \(a_{11}\), \(a_{12}\), \(a_{31}\), and \(a_{32}\) are random, that is, a lot of virtual Stephenson linkages can be obtained, which correspond to the same single-loop five-bar linkage. However, according to Equations (6) and (13), it is obvious that the angular velocities \(W_{4}\) of Equations (6) and (13) are only related to the input angles \(\theta_{2}\), \(\theta_{5}\) no matter whether the virtual links are added or not. When the single-loop five-bar linkage is in the instantaneous configuration, i.e., the ratio of the inputs (\(W_{2}/W_{5}\)) is specified, the two angular velocities \(W_{4}\) all coming from the loop ABCDE in which the links 4 correspond the same derivative of angle \(\theta_{4}\), are the same. Then the instant centers \(\mathrm{I}_{24}\), which is decided by (\(W_{4}/W_{2}\)), are the same. That is, the virtual links of the virtual Stephenson linkage do not change the motion of the original single-loop five-bar linkage. The virtual Stephenson linkage is actually still the original single-loop five-bar linkage. Virtual link operation is an auxiliary means to change the single-loop linkage into a virtual two-loop linkage which do not affect the motion of the original single-loop linkage. Therefore, the unchanged instant centers \(\mathrm{I}_{24}\) and \(\mathrm{I}_{25}\) in Figure 7a,b, formed by the common part of the original single-loop five-bar linkage and the virtual Stephenson linkage, are the same. In other words, the instant centers \(\mathrm{I}_{24}\) and \(\mathrm{I}_{25}\) obtained from the virtual Stephenson linkage can be used in the instant center identification of the original single-loop five-bar linkage, and in the virtual Stephenson linkage, the instant centers \(\mathrm{I}_{24}\) and \(\mathrm{I}_{25}\) are easy to be obtained. The validity of virtual link operation is demonstrated.
# Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas
###### Abstract  
Air quality (AQ) is one of the most important urban environment indicators for the quality of life. The paper proposes a software solution for predicting and forecasting the air quality index (AQI) in urban areas. The study integrates pollutant factors (CO, NO\({}_{2}\), SO\({}_{2}\), PM\({}_{2.5}\)), meteorological parameters (temperature, humidity, wind speed), and traffic data to determine air quality. For this purpose, 19 predictive models were developed and compared: 12 machine learning algorithms, 7 deep learning, and 1 forecasting model based on structural component analysis. The Random Forest Regression model, customized within the study, achieved the best results, with an R\({}^{2}\) score of 99.59%, an MAE of 0.22%, an MAPE of 0.68%, and an OP (Overall Precision) score of 95.61%. It was subsequently validated on unseen data and recorded a mean deviation of 0.58%. For short-term AQI forecasting (5 days), the AQIF model achieved an R\({}^{2}\) of 71.62%, an MAE of 0.4%, and an MAPE of 0.9%. The proposed solution was integrated into a web application with IoT infrastructure and real-time alert mechanisms. Future directions include expanding the dataset and optimizing hyperparameters for the deep learning models to increase accuracy, as well as integrating PM\({}_{10}\) and O\({}_{3}\) factors, along with the degree of industrialization and demographic level.  
AI methods; machine learning; IoT infrastructure; deep learning; urban area; air quality; pollution control; Azure Communication Service; Azure SQL Server Article
## 4 Results of Performance Evaluation and Industrial Field Application
### Performance Evaluation  
In this study, we used inference files generated through deep learning to input real-world ToF data directly into an AI algorithm without any preprocessing, allowing the algorithm to automatically perform preprocessing and enhance accuracy. As a result, we were able to obtain improved depth data through noise removal and flattening, as shown in Figure 8. Notably, it was confirmed that learning could be conducted in an end-to-end manner without the need for setting parameters such as threshold values, which are typically required in traditional preprocessing steps. However, the accuracy of the data obtained through this method has not yet surpassed the accuracy of data preprocessed by humans. Considering that there were fewer than 50 data samples, securing more data reflecting diverse environmental conditions could lead to improvements in the AI algorithm. Additionally, incorporating specific parameters such as the characteristics of the ToF sensor or material information of the components is expected to yield better results. This research is significant in that it has solved the problem of labeling necessary depth data.  
Additionally, to compare the alignment rate of simple depth data distance information, the difference in depth distances for each pixel between the depth values obtained from the simulation (close to the ground truth) and the actual depth data values was expressed as a percentage. The average alignment rate for all pixels was as shown in Figure 9. In Figure 9, the mobile robot manipulator was used to capture ToF sensor data 10 times by moving along the X-axis from 0\({}^{\circ}\) to 20\({}^{\circ}\) in 2\({}^{\circ}\) increments in the same environment. Depth data was acquired using both a preprocessing algorithm and a trained AI algorithm. When comparing these to the accurate depth data obtained from the simulation, it was observed that the accuracy of the data obtained through the AI algorithm was consistently higher in all 10 instances compared to the depth data processed by the rule-based preprocessing. This can be attributed to the fact that traditional algorithms involve manual adjustment of  
Figure 8: Results of applying deep learning (AI) algorithm to improve ToF depth data accuracy.  
Figure 7: Development environment for acquiring depth data in real and virtual environments.  
parameter values during preprocessing, making it difficult to find the optimal values. In contrast, the AI-based preprocessing method can automatically set the optimal parameter values during the training process to find depth values close to the ground truth. The study demonstrated its value by confirming the potential for achieving better results when additional information, such as temperature data, material properties and characteristics of depth acquisition parts, and environmental information, is provided as additional inputs for training.
# The Optimized Production of 5-(Hydroymethyl)furfural and Related Products from Spent Coffee Grounds
## 4 Discussion
The elemental analysis, TGA, ssNMR and FT-IR demonstrated that cellulose is present in both samples, up to approximately 40% of the phase. A large proportion of nitrogen and sulfur were also observed suggesting that protein was present, although there was no clear indication of high levels of lignin in this phase. As with the other phases, the CEF fraction from the MW separation was heavily contaminated for both biomass sources tested. The CEF fraction can be depolymerized to release glucose for either fermentation or further chemical manufacture. For the manufacture of HMF, the cellulose is too stable, and little HMF was produced from any of the CEF fractions. Indeed, it is only the conversion through multiple enzymatic steps to fructose that yielded reasonable HMF concentrations. While only 8.2% of the original starting material of HMF was produced from the SCG, 8.0% HMF was produced from pistachio hull. This is partly due to low percentage of cellulose in CEF and the thermodynamic equilibrium between glucose and fructose which limits the reaction yields [41]. According to [PERSON] et al. the maximum theoretical yield that can be obtained in the glucose isomerization reaction is approximately 50% [42]. This was the limiting step and reduces amounts of HMF produced per batch. As such, the yields obtained from SCG and pistachio were highly similar to crystalline cellulose when the lower amount of cellulose was taken into account. This suggests that the fractionation works well, and that there are no inhibitory compounds in this phase that limit further processing. Considering the entire process of biomass fractionation (using both pistachio and SCG) and the subsequent conversion of CEF to HMF, it was possible to produce approximately 0.37 g of HMF from 10 g of pistachio hull and approximately 0.35 g from SCG. This value of approximately 4% is close to the theoretical possible, considering that there is 14% and 12% of cellulose in pistachio hull and SCG, respectively, that the conversion of cellulose to glucose is limited to between 75% and 95%, the conversion of glucose to fructose is limited by the kinetic equilibrium between these two sugars, which is translated to a maximum theoretical yield obtained is 50% and maximum HMF yield from fructose is 80%.
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 3 Routing Protocols
### Topology-Based Routing Protocols
#### 3.3.2 Proactive Routing Protocols
In [65], the authors analyzed the adaptability of the DSDV protocol for UAV ad hoc networks and compared the performance with other protocols. In addition to OLSR and DSDV, there are also some relatively new proactive routing protocols. The Better Approach to Mobile Ad hoc Networking (B.A.T.M.A.N.) method proactively maintains the information of all nodes and identifies the best next hop for each destination [66]. [PERSON] et al. [67] have reviewed the comparative studies of B.A.T.M.A.N. The results in [68; 69] show that the B.A.T.M.A.N. method and OLSR behaves similarly for small network sizes and bandwidth-limited networks. But in scenarios of large number of nodes, B.A.T.M.A.N. outperforms OLSR. This may become a new method for routing protocols of the UAV swarm ad hoc network.
## 3 Results and Discussion
### Advanced Numerical Model  
As can be observed, in the first model (Figure 8), the existence of the cavity means a maximum displacement at the surface of about 0.076 m, while this value was nearly 0.115 m at the cavity crest. In the second model, where the ground was considered according to the geotechnical profile seen in Section 3.1 (Figure 6), the most unfavorable position of the cavity was found to be below the embankment. In this model, for the strength properties without any reduction, maximum displacement at the surface was found at around 50 mm, this value being nearly 18 mm at the cavity crest, while the safety factor was 1.2. When the strength reduction factor technique was applied to the model, progressively reducing the strength properties of the geological materials, a minimum safety factor of 1.05 was obtained (Figure 9). Maximum displacement at the surface was around 76 mm, this value being nearly 112 mm at the cavity crest. As the safety factor was greater than 1.0, the cavity was stable against collapse, meaning that the mortar injections and geogrids treatment applied were capable of successfully controlling the sinkhole phenomenon.  
It should be noted that the stability of the area needs the cavity to remain submerged with sufficient internal water pressure. This aspect is expected to be always fulfilled since the cavity is connected to the alluvial aquifer, and the position of its crest with respect to the water table leads the cavity to never empty. However, the natural progressive evolution by the dissolution of the karstic ground and the existence of future summer periods of extremely low water in the Ebro River triggered by climatic change [33], which matches the  
Figure 8: Final phase of the analysis with a homogenous substratum and a fixed position of the cavity in the model.  
Figure 9: Final phase of the analysis with a non-homogenous substratum according to the geotechnical model and the most unfavorable position of a potential cavity.  
cut off of the irrigation of the farms that drain into this basin, may give rise to an important change into the hydrogeological conditions, thus reactivating the sinkhole.
## 2 The ARDU Denoising Model  
Based on the U-Net, the feature extraction unit of the model encoder is composed of atrous convolution and residual dense blocks. The designed ARDU model consists of an input layer, a middle layer, and an output layer. The structure is shown in Figure 1. As shown in this figure, the input to the ARDU model is a seismic signal with noise and the output is the seismic signal after denoising. The red dashed-line box contains the encoder part of the middle layer, and the green dashed-line box contains the decoder part of the middle layer. The ARDU model first uses the input layer to extract the preliminary features of the seismic signal with noise; then extracts the signal feature maps of different scales through the encoder; restores the seismic signal feature maps through the decoder, meanwhile concatenating the feature maps obtained by the encoder and the decoder; and finally performs denoising.
## 2 Materials and Methods
### Research Methods  
Based on previous research [8; 17], this study used the same four indicators of greenness, humidity, dryness, and heat to construct a remote sensing ecological index RESI, but we planned to use different remote sensing image products that extract similar or identical information to represent the above indicators. Because Landsat ETM and Landsat8 OLI_TIRS are data from a single time point, which means that it is greatly affected by cloud cover and other factors, it is difficult to collect ideal annual data, which causes certain limitations when characterizing the ecological environment. The MOD17A2 product and MYD11A2 product of MODIS are the L3 product synthesized by 1 km GPP for 8 days and the average value of 1 km LST for 8 days, respectively. These products offer continuous monitoring data. After preprocessing, the data of a certain period can be obtained directly through the synthetic data, which are less affected by the weather. Therefore, the GPP of the MOD17A2 product represents the greenness, while the LST value of the MYD11A2 represents the heat. In addition, drawing on the methods of [PERSON] [8] and [PERSON] et al. [17], the humidity index and dryness index were extracted from Landsat data to represent humidity and dryness, respectively, to jointly construct a remote sensing ecological index.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Year**} & \begin{tabular}{c} **Landsat7 ETM or Landsat8** \\ **OL\_TIRS** \\ \end{tabular} & \multicolumn{1}{c}{**MODIS**} \\ \hline \multirow{4}{*}{2008} & \begin{tabular}{c} LE71210432008353 EDC00 \\ LE71210442008353 EDC00 \\ LE71200432008346 EDC00 \\ LE71200442008346 EDC00 \\ \end{tabular} & \begin{tabular}{c} MOD17A2 (46 issues in 2008) \\ MYD11A2 (46 issues in 2008) \\ \end{tabular} \\ \hline \multirow{4}{*}{2013} & \begin{tabular}{c} LS8\_C\_20140203120043L4 \\ LS8\_C\_20131201120044L4 \\ LS8\_C\_2013019121043L4 \\ LS8\_C\_20130919121044L4 \\ \end{tabular} & \begin{tabular}{c} MOD17A2 (46 issues in 2013) \\ MYD11A2 (46 issues in 2013) \\ \end{tabular} \\ \hline \multirow{4}{*}{2018} & \
\begin{tabular}{c} LC81200432018013 LGN00 \\ LC81200442017314 LGN00 \\ LC81210432017353 LGN00 \\ LC81210442017305 LGN00 \\ \end{tabular} &
\begin{tabular}{c} MOD17A2 (46 issues in 2018) \\ MYD11A2 (46 issues in 2018) \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Remote sensing image information.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 2 Dynamic Routing Protocols in FANETs
### Reactive Routing Protocols
#### 2.2.3 Time-Slotted On-Demand Routing  
The time-slotted on-demand routing protocol is also recommended for FANETs in the literature [32]. This routing algorithm is basically a time-slotted form of AODV [33]. The time-slotted on-demand protocol sends control packets during dedicated time slots, where only one UAV cantransmit its data packets. This routing method not only utilizes bandwidth efficiently, but also has the ability to avoid packet collisions, and as a result the proportional packet delivery ratio is increased.
# Approximation of the Riesz-Caputo Derivative by Cubic Splines
## 2 Materials and Methods
### The Collocation Method  
To show the effectiveness of the approximations we constructed in Section 2.3, we solve the FBVP (1) by the spline collocation method introduced in [12; 13]. First, we approximate the solution to the differential problem by a spline function as in Equation (8). Then, by collocating the differential problem in the equidistant points  
\[X_{\delta}=\left\{x_{p}=\delta\,p,0\leq p\leq M_{\delta}\right\},\qquad M_{ \delta}=\delta^{-1}L, \tag{12}\]  
we obtain the linear system  
\[\left\{\begin{array}{ll}\sum_{k=0}^{M_{h}}a_{k,h}\frac{d^{\gamma}}{d|x|^{ \gamma}}N_{k,h}(x_{p})+f(x_{p})\,\sum_{k=0}^{M_{h}}a_{k,h}N_{k,h}(x_{p})=g(x_{ p})\,,&1\leq p\leq M_{\delta}-1\,,\\ \\ \rho_{r0}\,\sum_{k=0}^{M_{h}}a_{k,h}N_{k,h}(x_{0})+\zeta_{r0}\,\sum_{k=0}^{M_{ h}}a_{k,h}N_{k,h}(x_{M_{\delta}})=c_{r}\,,&1\leq r\leq m\,,\end{array}\right. \tag{13}\]  
where the coefficients \(\{a_{k,h}\}\) are the \(M_{h}+1\) unknowns. To avoid the linear system being underdetermined, we choose the step \(\delta\) such that \(M_{\delta}-1+m\geq M_{h}+1\).  
Figure 2: The Riesz–Caputo derivative of the cubic B-spline \(B_{3}\) (see Appendix B) for different values of the fractional order \(\gamma\), with \(0<\gamma<2\).
## 4 Experiments and Results
### Utility for Recommendation Tasks  
To complement the _F-score_ metric, we quantified the utility of the _graph-based_ method through a recommendation task algorithm. This section describes the process, the technique, and the main results.  
Figure 4: Utility measures using the F-score for _DP-FSM_ and _Graph-based_ applied to different dataset. Where (**A**) _DP-FSM_ Bank, (**B**) _Graph-based_ Bank, (**C**) _DP-FSM_ German, (**D**) _Graph-based_ German, (**E**) _DP-FSM_ NYC, and (**F**) _Graph-based_ NYC.  
Figure 3: Disclosure risk measures’ comparison between the _graph-based_ and _DP-FSM_ algorithms. Where (**A**) is the Bank dataset, (**B**) is the German dataset, and (**C**) NYC dataset.  
We performed a recommendation using the _EPT_ algorithm from two methods of protecting a sequential pattern dataset as the input: (1) graph-based differentially private publication of sequential patterns and (2) differentially private sequential pattern mining through the _DP-FSM_ algorithm. Both recommendations sets were compared with the results using classical patterns (without noise) to measure the utility for the recommendation of both sanitization techniques. Two metrics were used for this task: Confidence and Coverage values. The first metric allows us to perceive how reliable the prediction is using the constructed EPT, besides the second measure will enable us to realize if the EPT is sufficiently varied to make predictions of different cases. Figure 5 shows the coverage and confidence measures for the recommendations' comparison between the graph-based and the DP-FSM algorithms for the three datasets at our disposal. They show that for \(\varepsilon\) values smaller than one, the _DP-FSM_ algorithm preserves better the metrics than the graph-based algorithm, which equalizes its performance from values \(\varepsilon=1\).
# The Application of Improved YOLO V3 in Multi-Scale Target Detection
## 4 Experiments and Results
### Quantitative and Qualitative Evaluation  
Quantitative evaluation: In Tables 4 and 5, the experimental results on PASCAL VOC dataset and KITTI dataset show that our network achieves better performance than other state-of-the-art detection algorithms. Compared with YOLO V3, our proposed network has improved mAP by 7.47% on the PASCAL VOC dataset and by 1.77% on the KITTI dataset. The performance is much better for the PASCAL VOC dataset than for the KITTI dataset. That is because PASCAL VOC contains 20 categories and every category may have small targets. However, there are 3 categories, namely 'car', 'cyclist', and 'person' in the KITTI dataset. Compared with PASCAL VOC, there are less small targets in the KITTI dataset. By extending the scales from 3 to 4, our proposed network is better than YOLO V3, especially for detecting small targets. What is more, our proposed mathematical derivation method based on IOU can select appropriate size of the anchor boxes for each scale of the output detection layer. The comparative results of model size and the frames per second (FPS) between YOLO V3 and improved YOLO V3 are shown in Table 8. The model size of our network is 249.1 M, which is slightly than that of YOLO V3. The FPS of our network is less than that of YOLO V3. That is because our proposed network extended the scales of the YOLO V3 from 3 to 4.  
Qualitative evaluation: Figures 7 and 8 shows that our proposed network has perfect performance for detecting both big and small targets. A qualitative comparison of our network with YOLOV3 is also provided. The comparative results between YOLO V3 and improved YOLO V3 on KITTI dataset and PASCAL VOC dataset are shown in Figures 9 and 10. The light blue box is the ground truth. The green box is the prediction box. The red box shows the targets with fault. It is obvious that our proposed network achieved a better performance than YOLO V3. In Figures 9 and 10, a lot of targets are missed and detected with fault by YOLO V3, especially some small targets. The improved network can avoid these problems. That is because our network has 4 scales to detect the targets. The feature fusion target detection layer downsampled by 4\(\times\) is helpful to detect small targets. What is more, with the mathematical derivation method based on IOU, targets of different sizes can be accurately assigned to corresponding scales.
## 1 Introduction
The above-mentioned studies for parameter identification mainly focused on distinct features in the mid- and low-frequency bands before and after fluid channel resonance. Only the bulk stiffness of the upper fluid chamber, which is a coupling parameter, and equivalent piston area of the main rubber spring are involved. For the AHM-IT-DM-OCA, a mount with more physical parameters has a conventional fluid channel and an actuator mover whose modal frequency exceeds 200 Hz. Therefore, all physical parameters of the decoupler membrane with an actuator mover must be systematically identified. It is necessary and significant that further investigations on parameter identification by distinct features should be carried out in mid- and high-frequency bands. The discussion focuses on the identification of all key parameters based on a thorough analysis of the distinct features of the AHM-IT-DM-OCA. A nonlinear model involving all key parameters is established. Moreover, the analytical bands are defined by the natural frequency of the fluid channel and actuator mover; thus, methods for simplifying the nonlinear model in different bands are carried out. Subsequently, the distinct features of the dynamics are analyzed, particularly the active dynamics. The relationships between the distinct features and key parameters were simultaneously explored. Experimental research was carried out to validate the consistency of the distinct features between the analysis and test results. Finally, a detailed procedure for all key parameter identification is proposed, which is successively stated from passive dynamics to active dynamics.