esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper aims to evaluate the accessibility to urban mobility services with high spatial resolution. The methodology identifies the percentage of the population living within walking distance of the different public transport and shared mobility systems. The motivation for this study stems from the importance of understanding and improving sustainable urban mobility, as well as the need to evaluate the effectiveness of transportation infrastructure. The study involved creating buffers from each station following the road network according to urban design to identify areas within a 5-min walk, then calculating the affected population by age range within these zones to ensure equity. This method determined the percentage of the population with access to available mobility services: bike renting, urban bus, intercity bus, metro, taxi, tram and train. The application of the methodology to the Valencia case study underscores the overall strong accessibility of mobility services. The main results indicate access to mobility services for 99.98% of the total population of the city. The accessibility percentages for specific mobility services vary, with urban bus and bike renting showing high accessibility, while train and tram services exhibit lower accessibility rates. Additionally, the study extends its assessment to the peri-urban area, revealing a global access percentage of 46.33%. In conclusion, this study allows us to identify areas with lower accessibility and help to improve the sustainability of urban mobility. These findings can aid urban planners and policymakers by providing valuable insights into the city's mobility network's strengths and areas for improvement.  
accesibility; urban mobility; Valencia; sustainability; population; peri-urban area; transportation infrastructure; mobility systems Article
## 3 Results
### Temperature Variation and \(\Delta\)AIRT  
#### 3.3.1 Results in 'EM1'  
The sequences of thermal images for each sample during the loading process were recorded and the \(\Delta\)AIRT in the black area was calculated. Figure 4a,b illustrates the measured temperature and \(\Delta\)AIRT results for HG-1. The ambient temperature decreased monotonically throughout the cyclic loading process from 20.35 \({}^{\circ}\)C to 20.02 \({}^{\circ}\)C at nighttime, with an approximate value of 0.33 \({}^{\circ}\)C. The \(\Delta\)AIRT decreased with fluctuations at a value of approximately 0.27 K as well (in Figure 4b). It was found that there was an approximate cyclic trend on the \(\Delta\)AIRT curve. In order to extract the cyclic variation trend clearly, the decomposed results denote the \(\Delta\)AIRT\({}_{\text{low}}\), \(\Delta\)AIRT\({}_{\text{mid}}\), and \(\Delta\)AIRT\({}_{\text{high}}\) plotted in Figure 5a-c, respectively.  
Figure 5a shows that the \(\Delta\)AIRT\({}_{\text{low}}\) decreased monotonically with a value of 0.27 K, which was consistent with the change in the ambient temperature (Figure 4a). The \(\Delta\)AIRT\({}_{\text{low}}\) could be used to represent the change in the ambient temperature. Figure 5b shows that the \(\Delta\)AIRT\({}_{\text{mid}}\) was consistent with the stress curve with temporal cyclic changes. The maximum amplitude of \(\Delta\)AIRT\({}_{\text{mid}}\) was 0.05 K, which was only approximately 15% of the change amplitude in the ambient temperature. Figure 5c shows that the \(\Delta\)AIRT\({}_{\text{high}}\) changed drastically with large fluctuations. Apart from the influence of the background and stress, the fluctuations of \(\Delta\)AIRT\({}_{\text{high}}\) were mainly due to instrumental noise and wind in the range of 0.05 K. The standard error of \(\Delta\)AIRT\({}_{\text{high}}\) is about 8 mK, indicating that the experimental condition was relatively stable in 'EM1'.  
Figure 4: The measured results of sample HG-1 in the cyclic loading process: (**a**) The ambient temperature change; (**b**) \(\Delta\)AIRT change.  
Figure 5: \(Cont.\)
# Demand Analysis of Lubricating Oil in Spur Gear Pairs
## 2 Theory
### Oil for Cooling  
The tooth surface velocity of a pinion is the same as that of a gear in the normal direction of the contact surface to ensure smooth transmission. However, the velocity of two gears in the tangential direction of a tooth surface is different. Relative sliding occurs between two gears, and the relative slide velocity and rate can be described using Equations (28) and (29), as follows:  
\[\mathrm{u_{R}}=\mathrm{u_{1}}-\mathrm{u_{2}}, \tag{28}\]  
\[\mathrm{SR}=\frac{\mathrm{u_{R}}}{u_{E}}=\frac{2(u_{1}-u_{2})}{u_{1}+u_{2}}. \tag{29}\]  
Tooth surfaces are completely separated by the lubricating oil film in EHL, and the friction coefficient between two teeth can be written as [25]  
\[\mathrm{\mu}=\mathrm{e}^{\mathrm{f}(\mathrm{SR},\mathrm{P_{H}},\mathrm{u_{0}},\mathrm{S})}\mathrm{p_{H}^{1.03}}\mathrm{[SR]}^{1.03}\mathrm{u_{E}^{-0.1}} \mathrm{u_{0}^{0.75}}\mathrm{R^{-0.39}} \tag{30}\]  
where  
\[\mathrm{f}(\mathrm{SR},\mathrm{P_{H}},\mathrm{u_{0}},\mathrm{S})=-8.91-0.35 \mathrm{[SR]}\mathrm{P_{H}}\mathrm{I}\mathrm{g}\mathrm{n_{0}}+2.81\mathrm{e} ^{-\mathrm{[SR]}\mathrm{P_{H}}\mathrm{I}\mathrm{g}\mathrm{n_{0}}}+0.62. \tag{31}\]  
The assumption is that the frictional losses of a gear are all converted into heat, and heat is removed by lubricating oil. The heat generated by friction and removed by oil per tooth width and unit time can be expressed as  
\[\mathrm{P_{f}}=\mathrm{u}\mathrm{w}\mathrm{u_{R}}, \tag{32}\]  
\[\mathrm{P_{f}}=\mathrm{q_{sc}}\mathrm{p_{0}}\mathrm{c}\mathrm{\Delta T}, \tag{33}\]  
where \(\mathrm{q_{sc}}\) is the oil volume for the cooling of a tooth, \(\mathrm{c}\) is the specific heat capacity of lubricating oil and \(\mathrm{\Delta T}\) is the maximum temperature increase in lubricating oil. The oil volume for the cooling of a tooth can be described using the function of load per unit tooth width and meshing point coordinates, which is expressed as  
\[\mathrm{q_{sc}}=\frac{\mu wu_{R}}{\rho_{0}\mathrm{c}\mathrm{\Delta T}}, \tag{34}\]  
\[\mathrm{q_{sc}}=F_{2}(w,s). \tag{35}\]  
In a meshing circle, the oil volume for the cooling of a gear can be expressed as  
\[\mathrm{q_{c}}=\left\{\begin{array}{c}F_{2}(zw,s)+F_{2}((1-z)w,s+p_{b})\ (-PB_{1}\leq s\leq-PC)\\ F_{2}(w,s)\ (-PC<s<PD)\\ F_{2}(zw,s)+F_{2}((1-z)w,s-p_{b})\ (PD\leq s\leq PB_{2})\end{array}\right.. \tag{36}\]
# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Multi-Robot Task Allocation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Centralized Distribution** & **Distributed Distribution** \\ \hline \multirow{4}{*}{Advantages} & \multirow{4}{*}{Algorithm design is simpler;} & Communication is more dispersed, which can avoid the situation that affects the efficiency of the algorithm due to communication congestion; \\  & & It has the potential to generate globally optimal solution; & Good real-time performance; \\ \hline \multirow{4}{*}{Disadvantages} & The communication is concentrated and susceptible to congestion, affecting the algorithm efficiency; & The solution result can easily fall into a local optimum; \\  & Poor real-time performance; & Consultation increases the communication burden of the system; \\ \hline \multirow{4}{*}{Scope of the application} & The task is known and determined in a static environment; & The task is unknown and uncertainly determined in the dynamic environment; \\  & The environment model is fully known; & The environmental model is partially known or unknown; \\  & The scale of this system is relatively small; & The scale of this system is medium or large \\ \hline \multirow{4}{*}{Allocation method} & Mixed integer programming & A method based on behavioral motivation; \\  & (branch-and-bound); & Heuristic search algorithm & A market-based approach; \\  & (reinforcement learning); & Group intelligence algorithm (greedy algorithm, ant colony algorithm [84]); \\ \hline \multirow{4}{*}{Application} & Sprayed and weeding in an orchard & \multirow{2}{*}{Weeded farmland [85];} \\  & (manned) [14]; & \\ \cline{1-1}  & Seeded in a field [58]; & Agricultural monitored in vineyards [28]; \\ \cline{1-1}  & Sprayed in the Orchard [66] & \\ \cline{1-1}  & Refill scheduling [78] & Harvested fields (simulation) [88]; \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Task allocation classification comparison of agricultural multi-robots [86; 87]. increases in the number of tasks and robots. In general, it is often difficult to meet real-time requirements in task allocation issues. Distributed task allocation mainly uses a method based on behavioral motivation and a market-based approach. The former applies to multi-robot systems with strong autonomy, but the method has low system allocation efficiency; the latter has a wide range of applications based on the resource optimization configuration idea of economics, but the difficulty of this research method is how to design the negotiation mechanism and reasonably determine the cost-income models of the task [44].
# A Fast Particle-Locating Method for the Arbitrary Polyhedral Mesh
## 2 PLUG Method
### Background Grid  
A uniform Cartesian grid is adopted in the present study to be the background grid. The background grid should cover the whole computational domain as  
\[D=[x_{\min}-\delta,x_{\max}+\delta]\times[y_{\min}-\delta,y_{\max}+\delta] \times[z_{\min}-\delta,z_{\max}+\delta], \tag{1}\]where \(x_{min}\), \(y_{min}\), and \(z_{min}\) are minimal values of \(x\), \(y\), and \(z\) coordinates of the computational domain, \(x_{max}\), \(y_{max}\), and \(z_{max}\) are corresponding maximal values, and \(\delta\) is a small positive number to assure that the background grid completely covers the whole computational domain.  
A uniform grid is constructed by prescribing the number of elements in three directions, i.e., \(n_{x}\), \(n_{y}\), and \(n_{z}\). As a result, the grid spacings in different directions are  
\[\Delta x=\frac{x_{\max}-x_{\min}}{n_{x}},\Delta y=\frac{y_{\max}-y_{\min}}{n_ {y}}\;and\;\Delta z=\frac{z_{\max}-z_{\min}}{n_{z}} \tag{2}\]  
For a particle centered at point \(P\) (\(x_{p}\), \(y_{p}\), \(z_{p}\)), the index of background cell containing the point \(P\) (\(\overline{C}_{ijk}\), the host cell on the background grid) is easily determined as  
\[(i,j,k)=\Big{[}\text{int}\Big{(}x_{p}-x_{\min},\Delta x\Big{)},\text{int} \Big{(}y_{p}-y_{\min},\Delta y\Big{)},\text{int}\Big{(}z_{p}-z_{\min},\Delta z \Big{)}\Big{]}, \tag{3}\]  
where the function \(\text{int}\) (\(x\), \(y\)) gives the maximal integer smaller than \(\chi y\).  
For sake of brevity, this three-dimensional index (\(i,j,k\)) can be replaced by a one-dimensional index \(I\) as  
\[I=n_{y}n_{z}i+n_{z}j+k, \tag{4}\]  
and the inverse transformation is accomplished by  
\[i=\text{int}\{[\text{int}(I,n_{z}),n_{y}],j=\text{mod}\Big{[}\text{int}(I,n_{z }),n_{y}\Big{]}\;\text{and}\;z=\text{mod}(I,n_{z}), \tag{5}\]  
where the function mod (\(a\), \(b\)) is the remainder operator for two integers \(a\) and \(b\).
## 3 Experimental Results and Analysis
### Validation of the Effectiveness of the Rail Surface Condition Identification Algorithm
#### 3.3.3 Validation of the Effectiveness of Mixed-Precision Training  
To evaluate whether mixed-precision training enhances the computational efficiency of the model, a third group of ablation experiments was conducted. In this experiment, mixed-precision training was applied to the MobileNet-Rail model trained with knowledge distillation. The objective was to compare the model inference time and classification accuracy between full-precision training and mixed-precision training, with the results presented in Table 5. The experimental findings indicate that mixed-precision training significantly improves computational efficiency, reducing model inference time while maintaining the classification performance of the original model. This demonstrates that mixed-precision training is an effective technique for optimizing deep learning models, enabling faster inference without compromising accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**T** & **Accuracy/\%** & **Precision/\%** & **Recall/\%** & **F1/\%** & **Model Inference Time/s** \\ \hline
1 & 94.78 & 95.02 & 94.78 & 94.76 & 0.0385 \\
2 & 95.35 & 95.49 & 95.35 & 95.35 & 0.0384 \\
3 & 95.30 & 95.53 & 95.30 & 95.28 & 0.0383 \\
4 & 95.97 & 96.12 & 95.97 & 95.96 & 0.0382 \\
5 & 97.10 & 97.16 & 97.10 & 97.10 & 0.0380 \\
10 & 97.11 & 97.17 & 97.11 & 97.12 & 0.0379 \\
20 & 97.08 & 97.17 & 97.08 & 97.07 & 0.0381 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results for different distillation temperatures in the student model MobileNet-Rail.  
Figure 10: Effect of knowledge distillation on the MobileNet-Rail model: (**a**) change curves of MobileNet-Rail loss before and after knowledge distillation; (**b**) change curve of MobileNet-Rail accuracy before and after knowledge distillation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mixed-Precision Training** & **Accuracy/\%** & **Model Inference Time/s** \\ \hline \(\times\) & 97.11 & 0.0379 \\ \(\surd\) & 97.38 & 0.0371 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mixed-precision training test results.
## 2 Materials and Methods
### Experimental Setup  
Ultrasonic measurements were performed using a custom-built testing system (cf. Figure 1a) based on an experimental workflow (cf. Figure 1b). The lookup table (LUT) contains the desired waveform sampled with 2048 datapoints and is the input of the digital system. To transmit the waveform while matching the center frequency of the transducer, the number of datapoints in the LUT must be reduced within the field programmable gate array (FPGA). Next, the signal is discretized in three levels that the output stage is able to replicate and transmit to the ultrasonic transducer (1506.301, [PERSON], Germany) with a center frequency of 4 MHz and a 6 dB bandwidth of 3 MHz. All measurements mentioned in Sections 3.1-3.4 were conducted on a steel reference with a thickness of 60 mm. The validation measurements in Section 3.5 were conducted on samples of the same steel with thicknesses of 30 mm and 15 mm. The ultrasonic echoes are recorded with the transducer connected to an electronic receiver and converted to a digital signal that can be further processed. The measurement system cannot record a complete impulse response because the receiver is highly overdriven during activity of the transmitter. Through electrical coupling between transmitter and receiver, the voltage at the input of the measurement electronics exceeds its limits, which prevents recording useful signals during transmission. Therefore, the recorded signal is delayed in time compared to the emission of the stimulation sequence data frame to include at least one backwall echo.  
The electronic components, such as the transmitter, receiver and controller board are custom-built. The AD and DA conversions are performed with 122.88 MHz. The hardware is capable of transmitting signals up to 20 MHz and the receiver has its \(-3\) dB attenuation points at 800 kHz and 11 MHz.
# A Research Platform for Autonomous Vehicles Technologies Research in the Insurance Sector
## 3 Software Architecture
### Mapping  
Mapping is the process of modeling the vehicle surroundings, allowing the vehicle to interact with the environment. The maps are used by the vehicle to generate routes and to navigate through the environment. Mapping techniques can be categorized into global and local mapping.  
In order to plan and generate routes, a global map of the driving area is required. This map is also employed as a reference for global localization, as stated in the localization section. The global map of an area is usually generated offline by integrating multiple measurements from previous (manually-driven) sequences around the area to be mapped. Some high efficiency SLAM methods such as LOAM [7] offer the possibility of creating accurate maps in real time, although these are subject to drift errors. Additionally, the global map can also be obtained from digital map providers such as OpenStreetMaps [10] as shown in Figure 3.  
The main disadvantage of global maps is that they are static. Therefore, although they are used for global route planning, they do not contemplate dynamic obstacles, such as other vehicles or pedestrians, which makes them insufficient for autonomous navigation. In order to compensate for this absence of information, local maps are included in the system. Local maps are generated online and only cover the nearest surroundings of the vehicle, integrating the latest readings from the sensors. In the presented platform, the information from the three LiDAR sensors is processed in real time using the approach presented in [11]. This method generates an occupancy grid map (OGM), which is used later on by the planning and navigation modules in order to handle the existing obstacles around the vehicle. An example of the LiDAR point cloud and the local map output is presented in Figure 4.  
Figure 3: Digital map built from OpenStreetMap data.
###### Abstract  
We present a novel, noninvasive, super-continuum infrared spectroscopy of cytochrome c oxidase (SCISCCO) system for simultaneously measuring hemodynamic and metabolic parameters, and we demonstrate its utility by applying it to lab calibration tests, human studies, and swine small studies. The system optically assays the redox state of cytochrome c oxidase (CCO), as well as traditional markers including oxygenated (HbO) and deoxygenated (HbR) hemoglobin. To demonstrate in vivo feasibility, the measured responses of oxygenation and CCO responses to acute ischemia on the arm and forehead in human participants are compared to data from the literature. The validated SCISCCO system is then applied in human studies to measure cerebral oxygenation and the redox state of CCO in participants during an attention test protocol. We show that the redox state of CCO and hemodynamics measured by the SCISCCO system are consistent with the physiological hypothesis established in prior studies. To enable use of the SCISCCO system in laboratory and hospital settings as well as transportation to remote locations, a cart-based SCISCCO prototype system has also been developed. The cart-based SCISCCO prototype is applied to swine animal models undergoing induction of hemorrhagic shock followed by partial resuscitative endovascular balloon occlusion of the aorta (pREBOA). The pilot study demonstrates the feasibility of using the SCISCCO instrument within the context of existing protocols and validates the instrument's measurements against the physiological and hemodynamic parameters measured by other conventional devices.  
super-continuum laser; near-infrared spectroscopy; noninvasive monitoring; metabolism monitoring; cytochrome c oxidase +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Geostatistical Methods
#### 3.2.1 Ordinary Kriging (OK)  
Ordinary kriging is the most popular kriging interpolator. It provides an estimate of the value of the random variable \(Z(x)\) at an unsampled point \(x_{0}\) based on the weighted average of observed neighbouring points \(Z(x_{\alpha})\) within a specific area [56]:  
\[Z^{*}_{OK}(x_{0})=\sum_{\alpha=1}^{n(x_{0})}\lambda_{\alpha}^{OK}z(x_{\alpha}), \tag{2}\]  
where \(\lambda_{\alpha}\) are the ordinary kriging weights and \(n(x_{0})\) is the number of measured values of pairs of points multiplied by their spatial distance \(h\). The weights are obtained so that the estimate is unbiased and the variance is minimised.
## 4 Discussion
### Factors That May Influence Early Healing
#### 4.2.2 Diabetes  
An increasing number of patients with diabetes are receiving dental implants and therefore are becoming even more susceptible to infection and poor wound healing than they already are. Thus, as reported in the work of [PERSON] et al. [21] on the effects of diabetes on soft tissue healing, hyperglyaemia will have a negative effect on both implant osseointegration and soft tissue healing. Poor glycaemic control seems to be a predisposing factor for infections, and microangiopathy can compromise the vascularization of the flap.  
[PERSON] et al. [22] conducted a 7-year comparative study of implant treatment in 48 patients, who were divided into two groups of 24, one consisting of patients with diabetes and one with relatively healthy patients. A total of 96 implants were placed, two of which were lost in two patients. There were no significant differences in implant success between the two groups.  
In several studies, such as the one carried out by [PERSON] et al. (2018), and the one conducted by [PERSON] et al. (2018), animal models of diabetes demonstrated a greater alteration in the bone remodelling process and a decrease in the osseointegration of the implant.  
In our study, we did not find a relationship between the healing of the surgical wound at 1 week and implant success at 2 months with respect to diabetes status. A possible explanation is that only four patients had diabetes, but it was controlled in all cases, with glycosylated haemoglobin levels lower than 7%, and none of these patients experienced implant failure. We believe that this lack of a relationship is due to the small number of diabetic patients, particularly those with poor diabetes control, included in the study.
## 2 Related Works
### Quantitative Evaluation of Rectal Cancer using Deep Learning  
While radiomics features capture the essential characteristics of rectal tumor areas, we can extract new discriminative features using various CNN architectures. Using features of CE MRI extracted by 2D-CNN pre-trained on non-medical images, logistic regression classifiers can effectively predict the pCR of breast cancer (AUC = 0.85 [13] AUC = 0.77 [14]). With the features of multi-parametric MRI, T2, and CE, extracted by pre-trained 2D-CNN, an SVM classifier can accurately predict the pCR of breast cancer (AUC = 0.87) [15]. Given the multi-parametric MRI of DWI/ADC and CE, an MLP classifier has been shown to achieve higher accuracy and robustness by exploiting both 2D-CNN embedding and radiomics features of MRI [16].  
However, 2D-CNN cannot capture features of tumor volumes, because it analyzes each slice of MRI sequences separately. 3D-CNN can capture volumetric features by applying 3D filters across consecutive slices of an MRI sequence. Given 3D rectal CT/PET images of tumors, 3D-CNN has been used to extract volumetric features for pCR prediction [18]. This end-to-end deep learning method shows a 0.64 c-index score, which is higher than the Cox proportional hazards model (0.62) [26] and random survival forests (0.60) [27],based on a cohort of 84 patients. Given DWI/ADC MRI sequences obtained before nCRT, a logistic regression model on 3D-CNN embedding (AUC = 0.73) was shown to outperform a logistic regression model on its radiomics features (AUC = 0.64), based on a cohort of 43 rectal cancer patients [17].  
Our method differs from these works in three aspects. First, we focus on extracting the discriminative volumetric features of rectal cancer by applying 3D-CNN to multi-parametric MRI. Next, we exploit both our novel volumetric features and radiomics features of multi-parametric MRI to generate multi-parametric MRI embedding for pCR prediction of rectal cancer. Lastly, our experimental evaluation is based on a large collection of multi-parametric MRI scans of 912 rectal cancer patients. To the best of our knowledge, very few studies have used a cohort of more than 200 rectal cancer patients.