esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works and Contribution
### Our Contribution  
The above-discussed state-of-the-art CH rotation methods have important drawbacks that limit their effectiveness in applications to WSNs with smart nodes. First of all, the existing methods are based on an assumption that all sensor nodes in a cluster transmit their sensed data with the same, constant frequency. The possibility of suppressing data transmissions by the smart sensor nodes is not taken into account in those methods. It should be kept in mind that the considered smart nodes perform transmission when recognizing an important event. Each node can detect different number of events and the frequency of events can change in time. It means that the energy consumption of smart node changes dynamically and may differ significantly between nodes. Thus, in case when the smart nodes in a cluster suppress unnecessary transmissions, the existing methods can fail in achieving the balance of energy consumption for all nodes, which leads to reduced network lifetime.  
This paper introduces a new CH rotation method, which takes into account probabilities of detecting events and transmitting data by smart sensor nodes. According to this method, the CH node estimates the probabilities of data transmission for all cluster members. Based on the estimated transmission probabilities, using a lightweight energy consumption model, the CH node decides when the change of CH must take place. The determined time of CH change ensures equal energy consumption for all nodes in the cluster. This leads to maximization of the network lifetime, which is defined as the time to death of the first sensor node.  
Another drawback of the state-of-the-art methods is that they require parameters calibration for each application and each deployment of sensor network. For instance, the most important parameters that have significant impact on effectiveness of those methods, and need to be carefully calibrated, are the time duration of one round (for time-driven approaches) and the energy threshold (for energy-driven algorithms).  
In the proposed method, it is necessary to determine parameters of an energy consumption model. These parameters are related to hardware solutions used to build sensor nodes, and can be determined in advance for a given node architecture. Therefore, the experimental calibration of the parameters after installation of the sensor network is not performed.  
The contribution of this work also includes experimental evaluation of the CH rotation methods with use of real WSN. Comparison of the proposed method with time-driven and energy-driven approaches was conducted for physical prototypes of smart sensor nodes. Results of this study include measurements of WSN lifetime in real-world conditions. In contrast, the results presented in related works were obtained using simulation software.
## 2 Materials and Methods
### Yeast Films Properties
#### 2.4.4 Antimicrobial Activity of YFs  
Microbial suspension of _Bacillus cereus_ ATCC 10876, _Geotrichum candidum_ MIUG M166, and _Rhodotorula glutinis_ MIUG D144 from MIUG collection (Dunarea de Jos University of Galati, Romania), used as test microorganisms, were reactivated from stock cultures stored at \(-80\)\({}^{\circ}\)C. Muller Hinton broth/agar (Merk, Darmstadt, Germany) was used for antibacterial assay, and malt extract broth/agar was used for anti-yeast and fungi growth. Microdilution assay was used to adjust all test cultures to approximately 10\({}^{6}\) CFU/mL. Theminimum inhibitory concentration (MIC) of CLEO was determined by the broth dilution method. Further, the minimum bactericidal concentration (MBC) and minimum fungicidal concentration (MFC) were determined by the subculturing method using concentrations ranging from 1 to 10% [20]. Following the establishment of the MIC and MBC/MFC, CLEO 2% (\(w\)/\(w\)) was used to functionalize YFs [22].  
Antimicrobial activity of YFs was determined by the disk diffusion method. An overnight inoculum was prepared for each test microorganism by cultivation of single colonies at 37 \({}^{\circ}\)C for bacteria or 27 \({}^{\circ}\)C for fungi and yeast. Films with an approximately 15 mm diameter were placed on culture media previously inoculated with approximately 10\({}^{6}\) CFU/mL of test microorganisms. Following incubation at the optimum temperature, inhibition diameters were measured with a digital caliper. Independent duplicate experiments were reported as average inhibition zone \(\pm\) standard deviation.
## 2 System Model
### Calculation Model  
#### 2.3.1 Local Computing Model  
When a task is processed locally in the vehicle, the delay is the processing delay of the task on the CPU. \(f_{i}^{l}\) represents the computing power of the local vehicle. Local computing delay is defined as:  
\[T_{i}^{l}=\frac{c_{i}}{f_{i}^{l}} \tag{4}\]  
where, \(c_{i}\) indicates the number of CPU cycles required to complete task \(i\).  
For local computing, in order to calculate the power consumption of the connected car, the power consumption of the computer CPU per revolution is used. \(\delta\) represents the energy consumption coefficient. Therefore, local energy consumption is defined as:  
\[E_{i}^{l}=\delta(f_{i}^{l})^{2}P_{i} \tag{5}\]  
where \(P_{i}\) represents the equipment power of moving vehicle \(i\).
## 6 Conclusions  
In this paper, the optical system of GeoFWHIS is designed and analyzed. The full spectrum of 0.3-12.5 \(\upmu\)m is divided into five sub-bands--including UVIS, VNIR, SWIR,  
Figure 22: (**a**) Test device for B5 spectrometer, (**b**) spectral lines, (**c**) SRFs containing filter bandwidth, (**d**) \(SRF_{opt}\) and \(SRF_{ins}\) of B5 spectrometer.  
MWIR, and LWIR--which are integrated into the optical system to achieve a 400 km swath, 25-100 m spatial resolution, and 4-200 nm spectral resolution. In each band, four spectrometers are spliced to realize an ultra-long slit, and the total length of the slits is up to 241.3 mm. High-fidelity spectrometers are designed, developed, and tested in this paper. The Offner and Wynne-Offner spectrometers meeting the requirements of long slit and small volume are designed and developed. They have high imaging quality, low spectral distortion, uniform spectral response, and low stray light. The manufactured convex grating has the desired sawtooth groove and high diffraction efficiency; the highest peak efficiency is 86.4%. Test results of the prototypes are consistent with the design and show high fidelity, and all specifications fulfill the requirements. The GeoFWHIS proposed in this paper provides a technical scheme for full-spectrum hyperspectral remote sensing from geostationary orbit. The successful development of full-spectrum high-fidelity spectrometers verifies the feasibility and correctness of the design.  
Writing--original draft: J.Z.; Writing--review and editing, J.Z., Z.Z. and X.C.; Data curation, H.L.; Formal analysis, Z.Z. and H.L.; Investigation, Q.L.; Methodology, Z.Z. and X.C.; Validation, Q.L.; Funding acquisition, J.Z., S.T. and W.S.; Resources, S.T. and W.S.; Project administration, W.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (62105230); National Key Research and Development Program of China (2021 YFD2000101, 2016 YFB0500501); China Postdoctoral Science Foundation (2020M681700); Priority Academic Program Development of Jiangsu Higher Education Institutions.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
Thanks to Collaborative Innovation Center of Suzhou Nano Science and Technology for its support in the fabrication of the convex gratings.  
The authors declare no conflict of interest.
## 3 UAV Power Supply and Charging Mechanisms
### Hybrid Powered UAVs
#### 3.5.1 Fuel Cell-Battery  
There are some limitations associated with the use of fuel cell and battery-powered UAVs. On one hand, the process of electricity generation from fuel cells takes a long time as several components such as compressors for air supply, pumps, and valves are involved, which leads to an increase in response time of the UAV to load changes. On the other hand, battery-powered UAVs have limited flight time and would require frequent recharging, which can negatively affect the effectiveness of their service. Therefore, hybridization of fuel cells and batteries can be used to minimize the delays associated with electricity generation as well as prolonging the service time and effectiveness of UAVs [63].  
Hybrid power supply resources can be used interchangeably to power UAVs. For instance, battery power can be utilized by UAVs during the take-off and ascending process because batteries have more density and power storage as compared to fuel cells, and UAVs require more power when taking off and ascending to higher altitudes [64]. Then, fuel cell can be used in the flight time and descending process. Furthermore, the fuel cell can be used to charge the batteries. In [65], the authors used a technique called hardware in the loop (HIL) in the hybrid UAVs to investigate the power consumption of fuel cells and battery power sources. Several simulations were conducted to determine the level of flight endurance that can be achieved. Another study was conducted by [66], using fuel cells and battery power supply to assess the battery power contribution under different conditions and scenarios.
# Study on Surface Roughness of Sidewall When Micro-Milling LF21 Waveguide Slits
## 3 Results  
### Experimental Results  
The combination of cutting parameters and the experimental results are presented in Table 3. The surface roughness Ra ranged from 0.842 \(\upmu\)m to 1.481 \(\upmu\)m. The experimental value of the surface roughness Ra was fitted with the input variable, using the cubic interpolation method (based on MATLAB), and these results are shown in Figure 6.  
It was observed that when the spindle speed was constant at 50,000 rpm with 0.2 \(\upmu\)m/z, 0.4 \(\upmu\)m/z and 0.6 \(\upmu\)m/z feed per tooth, the surface roughness was almost constant and smaller. Initially, the cutting temperature in the shear zone may have increased significantly along with the increasing spindle speed, which may have resulted in a decrease in the yield strength, chip thickness, and tool-chip contact length of the workpiece material, with a consequent decrease in surface roughness. As the spindle speed increased further, the surface harshness expanded continuously, as can be seen from the increase in the feed rate and the chip cross-section. When the feed per tooth was constant at 0.4 \(\upmu\)m/z with 40,000 rpm, 45,000 rpm, 50,000 rpm, 55,000 rpm, 60,000 rpm and 65,000 rpm spindle speeds,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Spindle Speed**} & \multirow{2}{*}{**Feed per Tooth**} & \multicolumn{2}{c}{**Cutting Depth in the**} & \multicolumn{2}{c}{**Extended Length**} & \multicolumn{1}{c}{**Surface**} \\  & & **(\(\upmu\)m/z)** & & **Axial Direction** & **of Tool** & **Roughness** \\  & & & **(\(\upmu\)m)** & & **(\(\upmu\)m)** \\ \hline
1 & 40,000 & 0.2 & 20 & 20 & 1.271 \\
2 & 40,000 & 0.4 & 20 & 20 & 1.350 \\
3 & 40,000 & 0.6 & 20 & 20 & 1.275 \\
4 & 45,000 & 0.2  
the surface roughness was higher. Theoretically, roughness increases with the increase in feed per tooth. However, in the case of LF21, the sensitivity of the surface roughness to the cutting parameters may have been different at high feed rates. This is because of the problem of chip evacuation, caused by the sticking tendency and thermal softening of LF21. The increase in cutting temperatures with the increase in feeds per tooth may have influenced the surface roughness.  
Based on Table 3, the range analyses were performed to obtain the average response from the surface roughness of the micro-milled LF21 sidewall, as shown in Table 4.  
The parameters impacting the surface roughness from high to low were spindle speed and feed per tooth, based on the average response table, which was consistent with the research results above [5]. Furthermore, the optimal combination of surface roughness was a spindle speed of 50,000 rpm and a feed per tooth of 0.6 \(\upmu\)m/z.
# Energy Optimisation of Industrial Limestone Grinding Using ANN
## 7 Optimisation of the Grinding Process Using Artificial Neural Networks
### Design of a Predictive Model for Evaluation Indicators Using ANN
#### 7.1.1 Model Architecture  
As an additional method for modelling the energy indicators, artificial neural networks were proposed. The developed model performs a regression task, producing a single continuous output representing the selected evaluation indicator [66]. The model was implemented using the TensorFlow library and its high-level API, Keras [67], and was designed with a fully connected, feedforward architecture.  
The input to the model consisted of selected controllable process variables. The architecture is composed of the following layers:  
* Normalization Layer: This layer standardizes the input data to have zero mean and unit variance. It is adapted to the training dataset to ensure consistent feature scaling.
* First Hidden Layer: A dense layer with 64 neurons and a \(tanh\) activation function: \[h_{1}=\tanh(W_{1}x+b_{1})\] (3)
* Second Hidden Layer: A dense layer with 32 neurons and a ReLU activation function: \[h_{2}=\text{ReLU}(W_{2}h_{1}+b_{2})\] (4) where the Rectified Linear Unit (ReLU) is defined as follows: \[\text{ReLU}(x)=\max(0,x)\] (5)
* Output Layer: A single-neuron layer with a linear activation function for continuous output: \[y=W_{3}h_{2}+b_{3}\] (6)
## 1 Introduction
In terms of functions of prestressed anchor cables, they mainly emphasised that prestressed anchor cables improve the stress state of slopes, especially by providing a high pressure on any sliding surfaces, thus increasing the shear resistance of rock and soil masses. Actually, the prestress from prestressed anchor cables acts over a finite transmission depth in slopes meaning it exhibits an insignificant effect on changing the stress state of sliding surface, in addition to improving the stress state of rock and soil masses on the surface of a slope. Therefore, although it is beneficial to optimise the reinforcement scheme of cables from the perspective of improving the stress state, it is not the optimal method. Moreover, in current optimisation methods, different levels of anchoring forces are applied to different parts of slopes. This method is hard to operate and control in practical construction process, bringing a certain difficulty to construction. Numerical simulation is an efficient, convenient and quick method to study slope engineering problems [19; 20; 21; 22; 23; 24]. Based on a reasonable constitutive model and the finite element or finite difference method, the numerical calculation model of slope under arbitrary conditions can be constructed quickly to calculate the deformation of slope under different working conditions. Based on the traditional finite element/finite difference theory, the strength reduction theory of finite element/finite difference is a new method used to simulate the process of slope deformation and instability by reducing the strength parameters of rock and soil mass to obtain the safety factor of a slope [25; 26; 27; 28]. By using the finite element/finite difference strength reduction method, combined with relevant mathematical methods and image processing technology [29; 30], the process of slope deformation and instability can be visually displayed, and the mechanism of slope instability can be explained well. In this paper, a calculation model for high and steep slopes was established by utilising FLAC\({}^{\text{3D}}\) software. According to the strength reduction theory, the deformation rule and the change rule of the internal force of the anchor cable under different prestress are studied in detail. Based on these achievements, the influence law of prestressed anchor cable on the safety factor of slope is obtainedand the deep reinforcement mechanism of prestressed anchor cable for slope stability is explained. Afterwards, the optimization method of prestressed anchor cable arrangement for slope reinforcement is proposed to reduce the difficulty of anchor cable construction and reduce costs.
## 2 Materials and Methods
### Spectral Preprocessing  
Spectral preprocessing is required for accurate spectral analysis [26]. In addition to valid spectral information, original spectra contain irrelevant information, interference, and background noise. Spectral preprocessing was used to reduce or eliminate these unwanted signals from all spectra, and includes steps of normalization (mean normalization treatment), multiplicative scatter correction (MSC), and the Savitzky-Golay smoothing (polynomial order: 3; smoothing points: 15) [27; 28; 29]. All spectral preprocessing methods were performed using Unscrambler v10.4 (CAMO PROCESS AS, Oslo, Norway).
# Research on Non-Contact and Non-Fixed Cable Force Measurement Based on Smartphone
## 2 The Proposed Method for Cable Force Determination
### Vibration Video Data Collection  
In the proposed measurement method, a smartphone is used as a handheld smart terminal to collect video data on cable vibration. The feature points of stay cables need to be predefined so as to eliminate the vibration noise caused by shaking of the handheld smartphone. In this research, specifically designed targets were attached and used for efficient detection. The identified targets will then be compared to the reference points in the video so as to extract the cable vibration displacements. The following five aspects, including the compilation of cable basic information, target design, target placement, smartphone parameters for data acquisition and video recording, will be specified in order to standardize the data collection process.
# _M\({}^{2}\)Tames_: Interaction and Semantic Context Enhanced Pedestrian Trajectory Prediction
## 4 Results
### Ablation Study  
Table 4 illustrates the results of \(M^{2}\)_Tames_ structure's ablation study. The components, \(M^{2}\) for the multi-head mask temporal attention module, \(I^{2}\) for the Interaction Inference Module, and U for the U-Net architecture, are evaluated to demonstrate their individual and combined impacts on the Average Displacement Error (ADE) and Final Displacement Error (FDE). The outcomes indicate that integrating the agent's motion characteristics, interaction among agents, and interactions with the environment produces the best results, and removing any component from \(M^{2}\)_Tames_ leads to a decrease in performance. These results validate the contribution of each module to \(M^{2}\)_Tames_. The effectiveness of the proposed model \(M^{2}\)_Tames_ is thus demonstrated.  
From the sustained performance improvements introduced by the \(M^{2}\)_Tear_ and \(I^{2}\) components, it is evident that the model focuses on deepening the understanding of interactions among agents. The model has targeted and innovative enhancements in this area. Furthermore, the model integrates semantic information from the environmental context, a distinctive combination that enables a more comprehensive analysis and prediction of agents' future trajectories. This approach, which holistically considers agent interactions and environmental factors, not only enhances the accuracy of trajectory prediction but also boosts the model's applicability and robustness in complex scenarios.
## 5 In-Lab System Test
### MPPT Operation  
In this test, the wind turbine operated in Maximum Power Point Tracking (MPPT) mode under variable wind conditions. The airflow incident on the rotor progressively increased over a period of 3 min using a variable-speed fan. The control signal sent to the boost converter was configured to continuously adjust the duty cycle based on real-time voltage and current measurements, following the MPPT algorithm implemented on the Arduino(r) controller. Throughout the experiment, wind speed, DC voltage, current, and output power were acquired and visualized via the MATLAB(r) supervisory interface. Figure 11 shows the evolution of wind speed and delivered power. As expected, power output increased nonlinearly with wind speed, consistent with the aerodynamic behavior of small PMSG-based turbines.  
Figure 12 displays the corresponding voltage and current at the rectifier output. The voltage exhibits a steady increase in response to rising wind speed, while the current adapts to the electrical load as dictated by the MPPT control logic.  
Figure 11: Wind speed and power output plot shown in the monitoring and control app.
# Geometric Similarity Measurement Method for Micro Scene Generalization
## 2 Methodology
### Position Similarity
#### 2.3.2 Micro Scenes Position Similarity  
It is necessary to calculate the similarity of these triangles separately, and the similarity of any two triangles can be calculated according to the fuzzy similarity method. The specific process is as follows:  
First, a pair of similar triangles is taken and marked as \(\Delta ABC\) and \(\Delta A^{\prime}B^{\prime}C^{\prime}\), and the corresponding vertex angle degrees are denoted as \(\theta_{a}\) and \(\theta^{\prime}{}_{a}\). The similarity of the corresponding internal angles of the triangle is denoted as \(Sim_{a}\), \(Sim_{b}\), and \(Sim_{c}\), and the angle similarity equation is:  
\[Sim_{a}=\cos^{3}\Big{(}\frac{\pi}{2}\Big{(}1-e^{d(\epsilon)}\Big{)}\Big{)} \tag{13}\]  
\[d(\epsilon)=e^{-\frac{1}{2\sigma^{2}}\left(\theta_{a}-\theta_{a}\right)^{2}}\]  
\[\sigma=\frac{\theta_{a}}{6}\]  
Then, the similarity of the other two pairs of vertices is obtained using Equation (13), and the similarity of this pair of triangles is:  
\[Sim_{\Delta ABC,\ \Delta A^{\prime}B^{\prime}C}=\left(Sim_{a}+Sim_{b}+Sim_{c} \right)/3 \tag{14}\]  
The similarity of all triangles in the Delaunay triangulation of the two images is calculated using Equations (13) and (14) to construct the following similarity \(M\times N\) matrix:  
\[R=\left[\begin{array}{cccc}S_{11}&S_{12}&\cdots&S_{1n}\\ S_{21}&r_{22}&\cdots&r_{2n}\\ \vdots&\vdots&&\vdots\\ r_{m1}&r_{m2}&\cdots&r_{mn}\end{array}\right] \tag{15}\]  
The mean value is:  
\[\overline{S}=\sum_{i=1}^{n}\sum_{j=1}^{m}S_{ij}/m\times n \tag{16}\]  
Subsequently, the position similarity of each type of triangulation is:  
\[Sim_{type}=\sqrt{\sum_{i=1}^{n}\sum_{j=1}^{m}\left(S_{ij}-\overline{S}\right)^ {2}/\left[\left(m-1\right)\times\left(n-1\right)\right]} \tag{17}\]  
Finally, the position similarity calculation formula is obtained according to the percentage of the area:  
\[Sim_{position}=w_{1}Sim_{Typel}+w_{2}Sim_{Typell}+w_{3}Sim_{Typell} \tag{18}\]  
where \(w_{1}\), \(w_{2}\), and \(w_{3}\) are the percentages of each type of triangulation in the total area, and \(Sim_{position}\) is the position similarity.