esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Application and Methodology
### Cropping Pattern Optimization  
Defining a correct objective function is the main requirement when developing a robust optimization model. Equation (4) displays the initial objective function, where FNC is the optimal nitrate concentration, and \(\text{ONC}_{\text{t}}\) is the optimized nitrate concentration. T is the time horizon or total number of simulated months. The outputs of the SWAT were used to compute \(\text{ONC}_{\text{t}}\) for each time step.  
\[\text{OF}=\sum_{\text{t}=1}^{\text{T}}\left(\frac{\text{FNC}-\text{ONC}_{\text {t}}}{\text{FNC}_{\text{t}}}\right)^{2} \tag{4}\]  
Each optimization model might need some constraints. One required constraint in the proposed framework is the total cultivated area, which should be the same as the total available area in the catchment. We applied metaheuristic optimization in this study because of the robustness of this optimization algorithm. Thus, a solution was required to insert the constraint in the optimization algorithm; the penalty function method has been used for this in some previous studies [22]. Two penalty functions were required in the optimization model to increase the penalties of the system when the total cultivated area is more or less than the available area in the catchment, as displayed in Equations (5) and (6), where A is total area. C1 and C2 are constant coefficients that can be assessed based on the initial sensitivity analysis. Equation (7) displays final form of the objective function in the optimization model.  
\[\text{ifA}_{\text{t}}>\text{A}_{\text{available}}\rightarrow\text{P1}=\text{c1} \left(\frac{\text{A}_{\text{t}}-\text{A}_{\text{available}}}{\text{A}_{\text{ available}}}\right)^{2} \tag{5}\]  
\[\text{ifA}_{\text{t}}<\text{A}_{\text{available}}\rightarrow\text{P2}=\text{c2} \left(\frac{\text{A}_{\text{available}}-\text{A}_{\text{t}}}{\text{A}_{\text{ available}}}\right)^{2} \tag{6}\]  
\[\text{OF}=\sum_{\text{t}=1}^{\text{T}}\left(\frac{\text{FNC}-\text{ONC}_{\text {t}}}{\text{FNC}_{\text{t}}}\right)^{2}+\text{P1}_{\text{t}}+\text{P2}_{\text {t}} \tag{7}\]  
The vulnerability index was used to measure the performance of the system in terms of nitrate concentration and total cultivated area, as displayed in Equations (8) and (9).  
\[\text{ifONC}_{\text{t}}>\text{FNC}\rightarrow\text{VI}=\text{MAX}_{\text{t}= 1}^{\text{T}}\text{abs}\left(\frac{\text{ONC}_{\text{t}}-\text{FNC}}{\text{FNC }}\right) \tag{8}\]  
\[\text{VI}=\text{MAX}_{\text{t}=1}^{\text{T}}\text{abs}\left(\frac{\text{A}_{ \text{t}}-\text{A}_{\text{available}}}{\text{A}_{\text{available}}}\right) \tag{9}\]
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 6 Discussion and Prospect  
In this section, we discuss the benefits and limitations of existing DIFT works. We also present some promising future directions for more efficient DIFT frameworks.
## 4 Discussion
[47] found that nano-HA-SiO\({}_{2}\)-GIC extracts promote an early odontogenic differentiation of human dental pulp stem cells (DPSCs). This supports the idea that nano-HA-SiO\({}_{2}\)-GIC may have a better inductive influence on early differentiation and ultimately deposition of tertiary dentin from ecto-mesenchymal cells of the dental pulp. A study performed by [PERSON] et al. [48] observed the effects of the incorporation of nano-hydroxapatite (nano-HAp) in GIC and found it to have better mechanical properties. Moreover, nano-HAp crystals enhanced bonding strength to dentin [49]. Also, Nano-silica has been reported to have high mechanical strength and good thermostability [18] which may also have some positive influence on our study results related to nano-HA-SiO\({}_{2}\)-GIC which is a merger of nano HAp and nano silica in GIC. The nano-HA-SiO\({}_{2}\)-GIC was found to have improved hardness when combined with c-GIC due the higher content of nano-silica, which results in denser cement to produce stronger GIC [50]. When tested for genotoxicity, [PERSON] et al. [51] reported that there was no notable rise in the number of revert colonies related to the upsurge in the concentrations of nano-HA-SiO\({}_{2}\), and hence was concluded to be non-genotoxic material and had no mutagenic potential. [PERSON] et al. [52] recommended the integration of nanohydroxyapatite into c-GIC, which exhibited promising enhancements in the mechanical features, flexural strength, antibacterial properties, and a decrease in the microleakage of the c-GIC, therefore making it one of the most appropriate additives to be utilized. However, further research needs to be performed to authenticate the prospective use of nano HA SiO\({}_{2}\) GIC in clinical human trials.
## 5 Recent Developments in RF Energy Harvesting
### Impedance Matching and Power Management Circuits  
Impedance matching is critical for maximizing RF energy transfer from the antenna to the rectifier. At a fundamental level, proper matching ensures that the antenna's complex impedance is the complex conjugate of the rectifier input, thereby minimizing reflection (\(\,\Gamma\,\mid\,\rightarrow\,0\)) and maximizing power delivered to the load. Recent studies have introduced adaptive impedance matching techniques that adjust in real time to variations in RF power levels and frequency shifts. A dynamic impedance matching circuit based on varactor diodes was proposed to enhance energy transfer across varying RF environments. Itemploys a three-state tuning scheme that adapts to variations in the RF-to-DC converter's input impedance, resulting in enhanced power extraction efficiency [74]. The varactor approach exploits the voltage-dependent capacitance of the diode to shift the network's resonant frequency on the fly; its main strength is rapid, continuous tuning, but drawbacks include added insertion loss and potential non-linearity at high RF power. The circuit adaptively adjusts the impedance based on the incident RF power, resulting in an efficiency improvement of up to 30% compared to traditional fixed impedance matching designs. Another approach involves integrating AI-based adaptive impedance matching circuits, which utilize machine learning models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to predict optimal impedance settings for different ambient RF conditions [75]. These data-driven techniques excel in highly dynamic, multi-band environments where analytical tuning rules break down; however, they require on-chip processing or periodic cloud computation, adding silicon area and quiescent power overhead. In power management, the latest RF harvesting circuits incorporate ultra-low-power DC-DC converters to optimize the voltage levels required for energy storage and utilization. Synchronous buck/boost topologies with sub-\(\upmu\)A quiescent currents are now common, ensuring that the converter's consumption does not negate the harvested energy. A novel maximum power point tracking (MPPT) circuit was introduced, which dynamically adjusts the rectifier's load resistance to extract the maximum available power from the ambient RF signals [76]. While MPPT boosts net energy by 10-25% in fluctuating fields, it introduces control-loop latency and demands the precise sensing of rectifier voltage/current, which can be challenging at \(\upmu\)W power levels. An equivalent circuit model has been developed that closely aligns with the mechanical model of piezoelectric energy harvesters, facilitating improved impedance matching and power management [77]. This cross-domain modelling unifies electrical and mechanical perspectives, but its practical adoption hinges on the accurate parameter extraction of real-world piezoelectric materials, which can vary with age and temperature. These improvements enhance the overall efficiency of RFEH systems in dynamic environments.
## 3 Experimental Results and Analysis
### Ablation Experiments  
#### 3.3.1 Detection Performance  
Because the detection performance largely determines the performance of the tracking algorithm, in order to verify the effectiveness of the improved CenterNet detection network proposed in this paper, first, we conducted two sets of ablation experiments for the two parts of the improvement and then selected four current mainstream detection networks to compare with the improved CenterNet. The first set of experiments tested the effectiveness of ablation of the feature pyramid network (FPN) and high-resolution feature map (HR) introduced in the neck part of the CenterNet The data in Table 2 show that the introduction of both the FPN and the HR can effectively improve the detection capability of the network, and the combination of the two results in a more significant improvement in detection capability.  
The second group of experiments compared the effect of the improved channel attention (CA) mechanism in the backbone network on the detection capability of CenterNet by using the same dataset. As shown in Table 3, the improved channel attention mechanism significantly improves the performance of the detection network by adaptively improving the differentiation of pedestrian features extracted by the backbone network with almost no increase in computation and inference time.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **mAP(\%)** \\ \hline CenterNet & 68.5 \\ CenterNet+FPN & 68.9 \\ CenterNet+HR & 68.6 \\ CenterNet+FPN+HR & 70.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Impact of improvements to neck part of CenterNet on detection performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **mAP(\%)** \\ \hline CenterNet & 68.5 \\ CenterNet+FPN+HR & 70.3 \\ CenterNet+FPN+HR+CA\_ours & 70.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Impact of improved spatial attention mechanism on CenterNet detection capability.  
The third group of experiments was conducted to verify the performance of the proposed improved CenterNet detection network compared with four mainstream detection networks, among which SSD [39] and YOLOv4 [40] are single-stage detection networks, and Faster RCNN [41] and Mask RCNN are two-stage detection networks. CenterNet_ours is the improved network in this paper. The results in Table 4 show that the improved CenterNet proposed in this paper is significantly better than the mainstream networks in common use today, with a 4.2 percentage point improvement over YOLOv4, and a 13.1 percentage point improvement compared to Faster RCNN, the classical two-stage detection network.
# A Novel Methodology for Planning Urban Road Safety Interventions
## 2 Literature Review
### Reactive Approaches  
Reactive approaches focus on analyzing past accidents to identify high-risk areas (i.e., black spots) in existing road networks, particularly in regions with comprehensive accident data and implement targeted interventions. These methods are less effective in areas with incomplete data, such as developing countries. Reactive approaches can be categorized into aggregate and disaggregate analyses.  
* Aggregate analysis provides an overview of road safety by using indicators such as accident frequency, rates, and severity [7] to prioritize interventions. Tools like GIS enhance this process by mapping accident data spatially [8; 9], making it easier to identify black spots and plan targeted improvements;
* Disaggregate analysis focuses on specific accident details, such as timing, location, and conditions, to tailor corrective measures. Techniques include collision diagrams, scenario-based analyses, and scenario-class grouping [10], which help identify trends and recommend engineering solutions.  
Research shows these methods effectively identify accident patterns and high-risk zones [11]. Tools like the Collision Diagram Builder (CDB) enhance the visualization of accident data over corridors, aiding in the development of strategies to improve road safety [12]. The downside of this method is that it cannot identify risky spots caused by design or infrastructure issues if no accidents have been recorded in those locations.
## 2 Video Camera Imaging Based Method
### Basic Framework
#### 2.1.3 Raw Signal Extraction  
Then, the raw signals are extracted from the selected ROI by calculating the spatial average of pixel value in the ROI for each frame using Equation (1). This describing an intensity-based method that uses spatial averaging. The purpose of this is to average out camera noise contained in each single pixel, thus improving signal to noise ratio.  
\[i_{R}(t),i_{G}(t),i_{B}(t)\ =\ \frac{\sum_{x,y\in ROI}I(x,y,t)}{|ROI|} \tag{1}\]  
where \(i_{R}(t),i_{G}(t),i_{B}(t)\) are three source signals from red, green and blue components, respectively, \(I(x,y,t)\) is the brightness pixel value at image location (\(x\),\(y\)) at time \(t\), and \(|ROI|\) is the size of the selected ROI.  
Figure 3: Different sensors used for research surveyed in this study. (**a**) Digital camera, (**b**) webcam, (**c**) Microsoft Kinect, (**d**) smart phone, (**e**) laptop, (**f**) unmanned aerial vehicle (UAV).  
Figure 2: Block diagram of contactless monitoring system.
# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 3 Methodology
### Transformation and Weighting  
The direct georeferencing or the lidar equation  
\[\left[\begin{array}{c}p_{x}\\ p_{y}\\ p_{z}\end{array}\right]=\underbrace{\left[\begin{array}{c}t_{x}\\ t_{y}\\ t_{z}\end{array}\right]}_{\text{trajectory}}+\mathbf{R}(r,\,p,\,y)\cdot \underbrace{\left(\left[\begin{array}{c}\Delta_{x}\\ \Delta_{y}\\ \Delta_{z}\end{array}\right]+\mathbf{R}(\alpha,\beta,\gamma)\cdot\left[ \begin{array}{c}s_{x}\\ s_{y}\\ s_{z}\end{array}\right]\right)}_{\text{calibration, sensing}} \tag{8}\]  
can be expressed more simply as  
\[\mathbf{p}=\mathbf{t}+\mathbf{R}(r,\,p,\,y)\cdot\mathbf{p}_{IMU}. \tag{9}\]  
It establishes the functional relationship between georeferenced coordinates and sensor data of the navigation and scanning system. The MLS data service provider delivers coordinates \(\mathbf{p}\) in a predefined mapping frame (e.g., UTM). With trajectory information, i.e., the vehicle's position \(\mathbf{t}\) and its attitude \(r,p,y\) (roll, pitch, and yaw), we can transform \(\mathbf{p}\) into the IMU's system by rearranging Equation (9) to  
\[\mathbf{R}^{T}(r,p,y)(\mathbf{p}-\mathbf{t})=\mathbf{p}_{IMU}. \tag{10}\]The proposed data transformation is motivated by two factors.  
**Multiple scanner data**: We may use data from multiple laser scanners as reference and query sets \(R\), \(M\) to validate their co-registration in the IMU frame (cf. Figure 6 left).
**Weighting**: We can reconstruct the sampling of the mobile laser scanners once we know the position and orientation of the vehicle. Using the local coordinates in the IMU system, we determine the distance and the angle of incidence of the laser beams  
\[\delta_{i}=\big{|}|\mathbf{p}_{IMU_{i}}|\big{|}^{2}\qquad\varphi_{i}=\arccos \frac{\big{\langle}-\mathbf{p}_{IMU_{i}},\mathbf{n}_{i}\big{\rangle}}{\delta_{ i}}\;. \tag{11}\]  
According to many studies [10; 11; 42], short distances and scans orthogonal to the surface produce the most precise results. Therefore, we combine both information in a weight function  
\[\omega_{i}=e^{-\frac{(\delta_{i}-\mu)^{2}}{2\sigma^{2}}}\cdot\cos\varphi_{i}\;, \tag{12}\]  
and adjust \(\mu\) and \(\sigma\) according to the ranging uncertainty provided by the manufacturers (see Figure 6 right). For the Z + F profiler 9012 [35], for example, we set \(\mu=10\,\mathrm{m}\), \(\sigma=15\,\mathrm{m}\).
# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 4 Classification of ML Models Applied in the Optimization of Wastewater Treatment Processes
### ML Models Applied in the Optimization of Wastewater Treatment Processes
#### 4.2.5 Hybrid Deep Learning (HDL) Approaches
\begin{table}
\begin{tabular}{c c c c} \hline
**DL Technique** & **Advantages** & **Disadvantages** & **Applications** \\ \hline \multirow{8}{*}{ANNs} & High predictive power & Complex training & \begin{tabular}{c} Trends and pollution \\ prediction \\ \end{tabular} \\ \cline{2-4}  & Flexible architectures & Risk of overfitting & Process modeling \& control \\ \cline{2-4}  & Nonlinear modeling (e.g.,  ReLU) & Time-consuming tuning & \begin{tabular}{c} Optimization in treatment \\ processes \\ \end{tabular} \\ \cline{2-4}  & Hybrid adaptability (e.g.,  GA-ANN) & Computationally intensive & \begin{tabular}{c} Forecasting wastewater inflow \\ dynamics \\ \end{tabular} \\ \hline \multirow{8}{*}{RNNNs} & Effective time-series modeling & Training complexity & Water quality prediction \\ \cline{2-4}  & Real-time monitoring & Overfitting (esp. LSTM) & N\({}_{2}\)O/COD prediction \\ \cline{2-4}  & Handles complex temporal patterns & Data quality challenges & \begin{tabular}{c} IoT-based real-time \\ monitoring \\ \end{tabular} \\ \cline{2-4}  & Suitable for IoT integration & High resource demands & Predictive maintenance \\ \hline \multirow{8}{*}{CNNs} & Accurate feature extraction & Computationally intensive & Contamination detection \\ \cline{2-4}  & Robust to noise & Needs large datasets & Water quality monitoring \\ \cline{2-4}  & Dynamic system predictions & Overfitting risk & \begin{tabular}{c} Dynamic wastewater system \\ analysis \\ \end{tabular} \\ \cline{2-4}  & Real-time anomaly detection & \
\begin{tabular}{c} Limited interpretability in \\ deep layers \\ \end{tabular} &
\begin{tabular}{c} Fault detection in water \\ filtration systems \\ \end{tabular} \\ \hline \multirow{8}{*}{Autoencoders and DBNs} & Dimensionality reduction & Training complexity & Sensor data compression \\ \cline{2-4}  & Hierarchical feature learning & Overfitting challenges & Key feature extraction \\ \cline{1-1} \cline{2-4}  & Effective on complex data & Data preparation challenges & Real-time quality monitoring \\ \cline{1-1} \cline{2-4}  & Noise reduction & Limited interpretability & Environmental surveillance \\ \hline \end{tabular}
\end{table} 
Table 4: DL models’ advantages, disadvantages, and applications in the optimization of wastewater treatment processes.
## 1 Introduction  
During the displacement measurement based on the principle of laser interferometry, the refractive index of air in the optical path is often changed because of the change of ambient atmosphere, but it is difficult to measure the refractive index of air rapidly and accurately because of the limitation of the conditions or measurement schemes [1]. According to the principle of laser interference:  
\[L=\frac{K\,\lambda_{0}}{2n} \tag{1}\]  
where \(L\) is the length of the measurement, \(K\) is the interference level, \(n\) is the refractive index of air, and \(\lambda_{0}\) is the vacuum wavelength.  
It is known that the variation (\(\Delta L\)) of the displacement measurement \(L\), caused by the variation (\(\Delta n\)) of air refractive index \(n\), is as follows:  
\[\Delta L=\frac{K\,\lambda_{0}}{2\,n^{2}}\Delta n \tag{2}\]  
Therefore, for precise measurement, the change in the air refractive index \(n\), caused by the change in environmental factors and the inaccuracy of its measurement, become one of the main reasons for the limitation on the improvement of the accuracy of the measurement results. How to measure the air refractive index accurately and quickly in order to compensate for the limitation is the primary consideration in the scheme design of measurement.  
At present, there are two ways to obtain a measurement of the air refractive index: The direct method and the indirect method. The direct method is to calculate the air refractive index by directly comparing the optical path difference between the air cavity and the same-length vacuum cavity with the refractive index measuring instrument or laser interference technology [2; 3], which can reach a \(10^{-8}\) level of measurement accuracy. However, its design and fabrication is complex and the vacuum required by the experiment is very high. At the same time, the atmospheric pressure makes the vacuum system easy to deform and re-introduce the error source, so it is seldom used in engineering. The indirect method is to measure the parameters of the air composition through various sensors, such as ambient temperature, humidity, air pressure and so on. The refractive index value is calculated in order to obtain the air refractive index formula [4; 5; 6; 7; 8; 9; 10; 11; 12]. The important characteristic of the indirect measurement method is that it is convenient and easy to use. The measurement accuracy is \(10^{-7}\) level but because of the delay of the sensor itself, the air refractive index value calculated after the measurement is not the true value at that time. The measurement system accuracy is also low and is prone to produce errors in fact. Some errors are up to a \(10^{-6}\) level in some measurement systems.  
In order to synthesize the advantages of the above two methods and avoid their disadvantages, this paper presents a new measurement method for the refractive index of air based on a special material etalon.
## 2 The Proposed Method
### Residual Time-Frequency Mixed Attention Module Network  
The convolutional neural network has shown excellent performance in image feature extraction. However, as the complexity of the network model increases, the phenomenon of gradient explosion or disappearance is easy to occurs, and the model performance is affected. Residual network structure [43] is widely used in various network models due to its special jump connection mode that can effectively alleviate gradient explosion or disappearance. The deep convolutional neural network has many parameters and performs image classification tasks well under the condition of sufficient sample size, whereas, in practical engineering, the insufficient sample size is a common problem. The sample size of vibration signals of variable speed rolling bearings used in this article is small, with only 1200 for each health condition. It is easy to overfit when directly input into the network for learning, resulting in a poor sample effect of the test set. Moreover, in the classification task, only a few important contents in the image contribute to the recognition result. Other redundant information quickly interferes with network learning and reduces network performance. Therefore, the RTFANet model is proposed. The residual time-frequency mixed attention module (RTFA) is designed and embedded into the convolutional neural network to fully extract important time-frequency features and improve the model's classification accuracy.