esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation of Surface Roughness Model and Process Parameter Optimization
### Establishment of Surface Roughness Model Considering Milling Cutter Vibration
#### 4.1.2 Surface Roughness Model Considering Milling Cutter Vibration
\begin{array}{c}x(j,k,t)=f_{x}(j,k,t)\\ y(j,k,t)=f_{y}(j,k,t)\\ z(j,k,t)=f_{z}(j,k,t)\end{array} \right. \tag{32}\]  
When \(h_{c}(j,k,t)\leq h_{min}\), the actual cutting-edge trajectory can be expressed as:  
\[\left\{ \begin{array}{c}x(j,k,t)=f_{x}(j,k,t)+p_{e}h_{c}(j,k,t)\ \sin\varphi(j,k,t)\\ y(j,k,t)=f_{y}(j,k,t)+p_{e}h_{c}(j,k,t)\cos\varphi(j,k,t)\\ z(j,k,t)=f_{z}(j,k,t)\end{array} \right. \tag{33}\]  
In this study, the evaluation index of surface roughness is the arithmetic mean height of the surface \(S_{a}\). The object of evaluation is the milling side surface, which is \(y_{w}(j,k,t)\). From the relationship between the workpiece coordinate system and the milling cutter coordinate system, when \(z(j,k,t)\leq a_{p}\), the coordinates of any point in the workpiece coordinate system can be expressed as:  
\[\left\{ \begin{array}{c}x_{w}(j,k,t)=x(j,k,t)-R\\ y_{w}(j,k,t)=y(j,k,t)+a_{e}-R\\ z_{w}(j,k,t)=z(j,k,t)\end{array} \right. \tag{34}\]  
where \(a_{e}\) denotes the radial cutting depth of the milling cutter. Eventually, based on the actual trajectory of the milling cutter, the surface morphology model considering the vibration of the milling cutter can be obtained. Based on the sampled points on the sub-oscillation trajectory of the cutting edge, the longitudinal coordinate values at the same position are compared. The point set consisting of the minimum longitudinal coordinate values constitutes the final surface topography of the workpiece generated by the milling process. Figure 26 shows the modeling process of surface roughness. increases. It is worth noting that the theoretical values of surface roughness are all smaller than the experimental values because this model does not consider the effects of surface burrs and tool wear.
# Multi-UAV Mission Allocation under Constraint
## 2 Problem Formulation
### Reconnaissance Reward Function  
Reconnaissance reward means the amount of reward earned by reconnaissance in different target areas. It measures the amount of information acquired and the magnitude of strategic value. According to the different characteristics of the reconnaissance area (including the size of the area, the size of its strategic value, etc.) and the location, our algorithm model will assign the reconnaissance time of the UAV to each area accordingly, so that the reward obtained after the final reconnaissance of all areas will be maximized. This allocation process is also affected by the nature of the performance of the UAV itself, which is actually a compromise maximum after reconciling all contradictions.  
It is used to obtain the reconnaissance ratio and reconnaissance reward by the UAV after reconnoitering a certain target for a certain period of time. The function is as follows:  
\[R(t)=c\cdot\left(R_{p}+R_{q}\Big{(}1-e^{(-\beta t)}\Big{)}\right) \tag{1}\]  
\[\beta=\frac{w\cdot v}{S} \tag{2}\]Equation (1) is the reconnaissance reward function, where \(R(t)\) represents the reconnaissance reward that can be obtained when the reconnaissance time is \(t\). Here \(c\) is the value coefficient, which is a value between [0, 1] determined by the strategic value of the target point. The larger the value of \(c\), the higher the strategic value of the target area and the higher the reconnaissance reward obtained by searching within the area per unit time. \(R_{p}\) is the prior information about the target before reconnoitering, \(R_{q}\) is the information uncertainty need to be reconnoitered, where \(0<R_{p}<1\), \(0<R_{q}<1\), \(R_{p}+R_{q}=1\). \(\beta\) is a coefficient representing the efficiency of sensor reconnaissance. In Equation (2) are some parameters that affect \(\beta\), where \(w\) represents the scan width of the reconnaissance sensor carried by UAV, and \(v\) is the flight speed of UAV. \(S\) is the area to be reconnoitered in the reconnaissance target area.
###### Abstract  
This study proposes a non-contact engine speed measurement system using vehicle electrical characterization to address the limitations of traditional contact-type methods and optical methods. The developed system collects coupled AC signals through a cigarette lighter interface and extracts the AC features for frequency analysis through a signal conditioning circuit. The system employs a hybrid algorithm combining Fast Fourier Transform (FFT) and phase difference compensation to estimate the coarse frequency in the 1-second FFT analysis via sinusoidal least squares fitting and phase difference calculation. The STM32F4-based hardware integrates dual-channel acquisition and adaptive signal conditioning. The experimental results demonstrate high measurement accuracy with errors below 0.4%, real-time performance (1 Hz update rate), and operational portability. Validation tests show a 33-fold improvement in accuracy over the pure FFT method under transient conditions. Key innovations include (1) phase-difference-enhanced frequency resolution (0.1% error), and (2) optimized computational efficiency for embedded deployments. The system's portability and robustness make it suitable for on-site diagnostics, meeting the automotive industry's need for non-intrusive, high-precision speed measurements.  
engine rotation speed; spectral analysis; phase-difference compensation algorithm; sinusoidal least-squares fitting Article
## 3 TVWS-DTV Coexistence: Field Measurements
### Field Tests Considering Long Distances  
The long-distance field tests were conducted to collect information to complement the coexistence analyses of TVWS services with DTV systems, as well as to estimate the impact of the current power limitation on the maximum coverage of TVWS systems [33]. This section provides details about the methodology, setup, and key results obtained from the execution of the field tests. First of all, it is worth noting that several challenges had to be overcome in order to carry out the field tests. Among these, we can highlight the definition of the measurements performed, the logistics involved in installing the BS in a location with good coverage and preparing a mobile station, evaluating system coverage, and defining strategic locations for conducting field measurements. These challenges were addressed through the establishment of a clear methodology for conducting the tests, the generous provision of space in a shelter for BS installation, and the use of a simulation tool for coverage testing and selecting the measurement locations in the field.
## 4 Results and Discussion
### Studying the Effect of Pressure on the Performance of the Absorber  
The performance of the absorber for CO\({}_{2}\) capture was measured by estimation of the absorbed rate of CO\({}_{2}\) per unit Nm\({}^{3}\)/h of the inlet flow rate of CO\({}_{2}\). The absorbed rate \(N_{CO2}\) of CO\({}_{2}\) per unit Nm\({}^{3}\)/h of inlet CO\({}_{2}\) was calculated using the following equation:  
\[N_{CO_{2}}\Big{[}\text{Nm}^{3}/\text{Nm}^{3}\Big{]}=\Big{[}\big{(}y_{CO_{2},in}- y_{CO_{2},out}\big{)}F_{gas,in}\Big{[}\text{Nm}^{3}/\text{h}\Big{]}\Big{]}/F_{ CO_{2},\text{ in}}\Big{[}\text{Nm}^{3}/\text{h}\Big{]} \tag{27}\]  
where \(F_{gas,in}\) is the inlet gas flow rate, \(F_{CO2,in}\) is the inlet CO\({}_{2}\) flow rate, \(y_{CO2,out}\) is the outlet volumetric fraction of CO\({}_{2}\) that was measured by the gas analysis unit, and \(y_{CO2,in}\) is the inlet volumetric fraction of CO\({}_{2}\), which was calculated as follows:  
\[y_{CO_{2},in}=\frac{F_{CO_{2},in}\Big{[}\text{Nm}^{3}/\text{h}\Big{]}}{F_{gas, in}\Big{[}\text{Nm}^{3}/\text{h}\Big{]}}=\frac{F_{CO_{2},in}\Big{[}\text{Nm}^{3}/ \text{h}\Big{]}}{F_{CO_{2},in}\Big{[}\text{Nm}^{3}/\text{h}\Big{]}+F_{air,in} \Big{[}\text{Nm}^{3}/\text{h}\Big{]}} \tag{28}\]  
\(F_{air,in}\) is the inlet air flow rate.  
Figure 7 illustrates the effect of the pressure on the absorbed rate of CO\({}_{2}\) at 15 and 20 Nm\({}^{3}\)/h of inlet gas flow rates. It is clear that at the inlet gas flow rate of 15 Nm\({}^{3}\)/h, increasing the pressure has a significant effect on the absorbed rate of CO\({}_{2}\). The absorbed rate of CO\({}_{2}\) increased significantly by increasing the pressure at 15 Nm\({}^{3}\)/h of the inlet gas flow rate, whereas at the inlet gas flow rate of 20 Nm\({  
As explained above, one can conclude that the pressure may have an obvious effect on the absorbed rate of CO\({}_{2}\) at a high inlet gas flow rate since it decreases the gas/liquid interfacial area, which reflects at the end of the absorbed rate of CO\({}_{2}\).
## 5 Modeling Results and Discussion
### Model Transferability  
The goodness-of-fit and prediction accuracy of the SRP-logit model are optimal compared to other models developed in this study; hence, the transferability of the SRP-logit model across different vehicle types was investigated. The calculation results of TI values are shown in Table 6.  
The diagonals in Table 6 have a value of 1, as they represent the respective local conditions. In addition, most of the transfer indices on both sides of the diagonal are negative, suggesting that the local constant-only model outperforms the transfer model. The exception is that the transfer indices between passenger car and pickup show a positive relationship. This may be related to the similar structure of the passenger car and pickup.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Application Data**} & \multicolumn{4}{c}{**Established Model**} \\ \cline{2-5}  & **Passenger Car** & **Motorcycle** & **Pickup** & **Truck** \\ \hline Passenger car & \(1\) & \(-1.260\) & \(0.329\) & \(-0.895\) \\ Motorcycle & \(-0.378\) & \(1\) & \(-0.437\) & \(-1.139\) \\ Pickup & \(0.417\) & \(-0.917\) & \(1\) & \(-0.743\) \\ Truck & \(-0.129\) & \(-2.031\) & \(-0.154\) & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: The calculation results of model transferability.  
Nonetheless, the transferability indices indicate that the SPFs of passenger car and pickup cannot be transferred directly in traffic safety analysis (the transfer indices are 0.417 and 0.329, respectively, which are much smaller than 1) [62]. Therefore, a separate SPF for each vehicle type is needed in rural safety diagnosis.
## 4 Materials and Methods
### Antimicrobial Activities  
#### 4.3.1 Test Microorganisms  
The obtained methanolic extracts of _S. nigrun_ were tested against a wide range of microorganisms, including Gram-positive cocci: _Microoccus luteus_ PCM 1944 (ATCC 10240), _Staphylococcus aureus_ PCM 2054 (ATCC 25923), _Staphylococcus pseudotimerdius_ PCM 2741, _Streptococcus agalaciae_ PCM 2683, _Streptococcus_ canis_ KW-Sac1, _Bacillus subtilis_ PCM 1949 (ATCC 6633); Gram-positive endospore-forming rods: _Bacillus cereus_ PCM 2019(ATCC 11778); Gram-negative rods: _Escherichia coli_ PCM 2057 (ATCC 25922), _Pseudomonas aeruginosa_ PCM 2058, _Serratia marcescens_ PCM 501; as well as plant pathogens: _Pectobacterium carovora_ spp. _carovora_ IOR strains 1815 and 1822, _Pectobacterium atroseticum_ IOR 1825, _Erwinia chrysantemi_ IOR 1452; yeasts: _Candida albicans_ ATCC 10231, _Candida parapsilosis_ KW-Cp1; and filamentous fungi: _Alternaria alternata_, _A Aspergillus flavus_, _A niger_, _A. versicolor_, _Cheatomium globosum_, _Cladosporium herbarium_, _Epicoccum nigrun_, _Penicillium notatum_, _Bipolaris sorkiniana_, _Fusarium oksyporum_, _Trichoderma harzianum_ spp. _aggressivum_ KW-Tha1. These strains came from the following collections: PCM--Polish Collection of Microorganisms (Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland), IOR--Culture Collection of Plant Pathogens at Institute of Plant Protection (Poznan, Poland), KW--own collection (Division of Biogeochemistry and Environmental Microbiology, University of Environmental and Life Sciences, Wroclaw, Poland). Filamentous fungi were from the Division of Phytopathology and Mycology (the University of Environmental and Life Sciences, Wroclaw, Poland). Cultures of bacteria were maintained on TSA (Tryptive Soy Agar, Sigma-Aldrich, St. Louis, MO, USA), and yeast and filamentous fungi were maintained on PDA (Potato Dextrose Agar, Biocrop, Warsaw, Poland). Strains were stored on slants at 4 \({}^{\circ}\)C.
## 4 Experiments and Results
### Evaluation Experiments Using Representations Extracted from the Fine-Tuned Text Encoder
#### 4.3.1 Pearson Correlation Coefficient Calculation on the HCN and CN Datasets  
Correlation coefficients were used to measure the strength of a relationship between two vectors.  
The points in Figure 15 represent the vector visualization of the dimension reduction to two dimensions. The pink line indicates the distribution of the vector under different correlation values. We extracted the color name description pairs from HCN and CN under the corresponding colors and obtained embeddings from the learned model to calculate the correlation coefficients between the two datasets. Since CN was not used in the training process, it can be regarded as an evaluation dataset.  
Table 6 shows that the correlation coefficients between the two datasets increase, proving that the model has learned more correspondence between the color information and the text description.  
3.2 Example of Color-Based Similar Description Retrieval by Using Features Extracted from Pre-Trained Language Models  
In this section, we show an example of color-based similar abstract description retrieval by extracting text description embeddings from different pre-trained language models. Data collected from colormames.org were used for evaluation, and some examples are shown in Table 7. Table 8 shows part of the top three results and their corresponding colors. In Table 7, we show some examples of color names collected from colormames.org (accessed on 4 July 2022).  
As shown in Table 8, some users named some colors with character names such as 'Luigi Yellow,' and the uncased-based model has difficulty in processing color names, as in this case. Compared with the BERT [27] model trained with a single language, the model trained with a multilingual corpus shows a higher color name information processing ability. This provides reference information for our future work to expand the text encoder to a multilingual space. Figure 16 shows a visualization of the extracted text embedding in each pre-trained model linguistic space. It can be seen that the fine-tuned CLIP has a better performance in retrieving color information in a linguistic space.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pre-Trained Model** & **Pearson Correlation Coefficient Score** \\ \hline CLIP (ViT-B/32) & 0.7990 \\ \hline Fine-tunned CLIP (ours) & 0.7996 \(\uparrow\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: The Pearson correlation coefficient score between HCN and CN before and after fine-tuning at 500 epochs.  
Figure 15: Example of correlation scores: \(-\)0.3, 0, and \(+\)0.3.
# Heavy Metals in Post-Exploitation Reservoirs--The Bagry Lake Case Study (Poland)
## 6 Equilibrium between the Metal Concentration in Supernatant and in Sediment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Metal**} & \multirow{2}{*}{\(k_{1}\)} & \multirow{2}{*}{\(\alpha_{1}\)} & \multirow{2}{*}{\(k_{2}\)} & \multirow{2}{*}{\(\alpha_{2}\)} & \multirow{2}{*}{**R**} & \multicolumn{2}{c}{**Average Value**} \\ \cline{5-8}  & & & & & & \([Me_{s}]\) & \([Me_{s}]\) \\  & & & & & & **[mg/kg d.m. m]** & **[mg/kg d.m. v]** \\ \hline Fe & 2.510 & 0.000 & \(1.611\times 10^{2}\) & 0.000 & 0.783 & 2.510 g/kg d.m. m & 161.1 g/kg d.m. v \\ \hline Mn & 62.57 & 0.000 & \(2.931\times 10^{3}\) & 0.000 & 0.799 & 62.57 & 2930.9 \\ \hline Cu & 32.77 & 0.2410 & \(6.290\times 10^{2}\) & \(9.552\times 10^{-3}\) & 0.796 & 9.583 & 597.7 \\ \hline Pb & 0.000 & 0.000 & \(3.028\times 10^{2}\) & 0.000 & 0.878 & 0.000 & 302.8 \\ \hline Zn & \(8.956\times 10^{2}\) & 3.277 & \(3.092\times 10^{3}\) & 0.2610 & 0.884 & 0.1176 & 1279.3 \\ \hline Cd & \(1.144\times 10^{4}\) & 4.938 & \(3.509\times 10^{3}\) & 0.5627 & 0.847 & \(9.052\times 10^{-17}\) & 12.788 \\ \hline \hline \end{tabular}  
* R—correlation coefficient.  
\end{table} 
Table 6: Coefficients for Equation (6). Lead present in the sediment volatile fraction affects 100% of the concentration of this metal in the supernatant (Table 7). Zinc in the sediment volatile fraction affects about 99% of the concentration of this metal in the supernatant; the mineral fraction affects the aquatic environment only in 1%. The relative sensitivity of the aquatic environment to changes of the Zn concentration in the mineral and volatile fractions of the sediment remains low (Table 7, \(Q_{m}=0.3051\)\(\%Me_{w}/\%Me_{m}\), \(Q_{v}=3.832\)\(\%Me_{w}/\%Me_{v}\), respectively). Cadmium in the sediment volatile fraction affects almost 100% of its concentration in the supernatant. The sensitivity of the aquatic environment to changes in the Cd concentration in the mineral and volatile fractions of the sediments remains low (Table 7, \(Q_{m}=0.2025\)\(\%Me_{w}/\%Me_{m}\), \(Q_{v}=1.777\)\(\%Me_{w}/\%Me_{v}\)). where:  
\(P_{m}\)--degree of the influence of the metal in the mineral fraction on its concentration in the supernatant (Equation (13));  
\(P_{v}\)--degree of the influence of the metal in the volatile fraction on its concentration in the supernatant (Equation (14));  
\(Q_{m}\)--relative sensitivity of the aquatic environment to relative changes in the metal concentration in the mineral fraction (Equation (15));  
\(Q_{v}\)--relative sensitivity of the aquatic environment to relative changes in the metal concentration in the volatile fraction (Equation (16)).
# Heat Sink Equivalent Thermal Test Method and Its Application in Low-Orbit Satellites
## 4 Conclusions  
In this paper, we propose a heat sink equivalent thermal test method suitable for SSO satellites. The test method involves simulating the space heating flow by setting the temperature of the heat sink of the vacuum vessel reasonably. This method can shorten the thermal test cycle by one-third and reduce the cost of operating thermal test equipment by more than 70% without cutting tests. Since the heat flow simulation device is no longer used in the test, the time and cost of the design and manufacture of the infrared heating cage can be reduced, and the safety of the test is improved. The principle of using the heat sink to simulate the heat flow is described, the formula to calculate the heat sink temperature in the test is given, and an error analysis is also carried out. It is shown that when the ratio of the heat sink surface area to the satellite surface area is greater than 10, and the heat sink surface emissivity is higher than 0.9, the error of the heat sink equivalent thermal test should be less than 1 \({}^{\circ}\)C. The heat sink equivalent thermal test method has been successfully applied to the Jilin\(-\)1 GF02 and GF03 series satellites. The on-orbit temperature data of the GF02F satellite demonstrate that the thermal design is great, the TCS performs well, and the error of the heat sink equivalent thermal test method is 0.9 \({}^{\circ}\)C, which is in line with theoretical expectations and meets the requirements of satellite development.  
Conceptualization, L.K. and T.B.; methodology, L.K. and T.B.; validation, T.B., H.A., and F.J.; formal analysis, T.B.; data curation, T.B. and F.J.; writing--original draft preparation, T.B.; writing--review and editing, T.B.; visualization, T.B.; supervision, L.K. and H.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are available on request from the authors.  
Thanks to the Jilin\(-\)1 project team for their support during the tests.  
Author [PERSON], [PERSON] and [PERSON] were employed by the company Chang Guang Satellite Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
Figure 7: Comparison of time and liquid nitrogen consumption of different test methods.
# Situationally Sensitive Path Planning
## 3 Implementation Considerations  
Our aim is to develop an algorithm that is faithful to path planning heuristics but which allows for more detailed representations of hyper-localized movement around graph nodes and during traversal of graph edges. We aim for sensitivity to behavioral factors of embodiment and movement, but not full behavioral replication. Consider the example in Figure 2. It is obvious, even visually, that the shortest path movement instructions being delivered to a model pedestrian are much more straight and more robotic than one would expect of real human movement. This linear artifact of path planning will always persist, even across finer and finer resolutions of graph mesh. Particle-type motion, by contrast, is much more sinuous, but overly so. A compromise between the two would therefore be desirable in reflecting real-world patterns of movement. At the same time, the behaviors by which real pedestrians traverse enclosed spaces include distinct phases of planning, steering, and collision avoidance [165] that are substantively different in the decision-making that they invoke [166; 159; 167], in the patterns of motion that they produce [168], and in the information that pedestrians draw from their embodied surroundings to animate them in space and time [169; 170; 140; 168; 171; 172; 173; 174; 175]. Our aim of faithfulness to path planning heuristics also involves the preservation of computational economy, particularly as the cost of running companion motion algorithms in popularly used design software is always a feature that could be decreased in favor of other resources that are more mission-focused for design tasks.  
Our objectives relative to these twin aims, then, are as follows. First, we wish to instantiate an extensible path planning scheme in popularly used design software. We will show that this can be achieved in Unity 3D through implementation of a base planner using [PERSON]'s formulation for greedy shortest paths. Second, we will flex this implementation to add hyper-localized deviations to the graph that account for situational factors of the enclosed space and situational awareness of model pedestrians. At the graph edge, we will adapt the planner to accommodate specifications of hyper-specific and movement-sensitive peculiarities of the environment, some of which are fixed over the term of movement and others that are dynamic. Then, at the graph node, we will instantiate a set of agent preferences that map to situation factors, as well as sub-models that run hyper-localized steering algorithms. The combined result will be (1) a typical site-based path planner that (2) accommodates CAD-compatible specifications of situational factors and (3) agent pedestrian models that are capable of generating site-aware and situationally sensitive movement behaviors, (4) running (efficiently) on popular commercial software (Unity 3D) (5) in run time.