esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Ecosystem Performance Modeling
#### 2.2.3 Ecosystem Performance Anomalies (EPA)  
GSN (actual performance) was regressed on EEP (expected mature forest performance) to assess model prediction accuracy and to derive the 90% confidence limits for the regression. Data points that fell below the regression line were underperforming relative to an undisturbed boreal forest but onlysignificant (\(p<0.10\)) when they were below the lower 90% confidence interval. The inverse would be for overperforming observations being above the regression line and above the upper 90% confidence interval. Between the upper and lower confidence interval (\(\pm 69\) in rescaled NDVI units) was considered mostly the range of normal performance variability and pixels that fell outside the 90% confidence intervals were Ecosystem Performance Anomalies (EPA) [41]. Picking a level of significance can be user specific so EPA map color ramps show graduations but all significant anomalies were red tones or green tones and labeled as two levels of significant overperformance or underperformance. EPA are essentially the residuals of the GSN regressed on the EEP model predictions, which were significant outliers at \(p<0.10\). Maps of EPA were produced for each year. Setting a confidence limit threshold standardizes the sensitivity of the EPA to the EEP model error and provides quantification of both anomaly magnitude and a statistical significance of anomalies. Since the EEP model largely accounts for both site conditions (though the site potential variable) and weather, EPA values are standardized, detrended, or normalized for weather and site conditions resulting in an interannual and spatially consistency that facilitates interannual comparisons and analysis.
## 3 Methodology
### Analysis of Experimental Data  
The parking occupancy prediction, based on deep learning methods, aims to predict the future parking occupancy rate according to the historical parking data and contextual weather information. Because the time series is characterized by randomness, trend, periodicity, and time variability, it is necessary to analyze the data of the time characteristics, i.e., the parking occupancy rate per minute, to obtain the possible trend fluctuation and periodicity of the parking occupancy rate, and to analyze the influence of weather features and adjacent parking lots on the parking occupancy rate.  
This paper randomly selected the parking change of the grouped parking lot in a week. The period of each data collection for an interval of 60 min in PL6 is shown in Figure 5. From Figure 5, we can see the changing trend of parking and the regularity of periodicity in the parking lot; the amount of parking on weekdays and non-weekdays conforms to the basic laws of people's activities: more parking during the day, less parking at night. Compared with the weekend, the number of parking occupancy in working days is relatively large. The possible reason maybe that the parking lot is close to the commercial center. People usually work from Monday to Friday, while they might go shopping on Saturday and Sunday; a small peak appears at 20:00 every night, indicating that people come out for dinner or get together during this period. Figure 5 also shows that the peak of the working day usually occurs at 8:00 a.m. and 6:00 p.m. The peak at 6 p.m. indicates that people are busy getting off work in the evening rush hour, driving from the company's  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Describe** \\ \hline Parking lots & Unique identification of parking space sensor \\ Arrival time & Date and time when the sensor detected that the vehicle arrives \\ Departure time & Date and time when the sensor detected that the vehicle departs \\ During time & Time difference between arrival time and departure time events, in seconds \\ Street name & Street signs for each group of parking spaces \\ \hline \hline \end{tabular}
\end{table}
Table 2: Formats of parking data sets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Describe** \\ \hline Temperature & \({}^{\circ}\)C \\ Wind & km/h \\ Pressure & mmHg \\ Relative humidity & Pa/Pa \\ Precipitation in the past hour & mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Formats of weather data sets.  
parking lot to the roadside for dinner, shopping, or other activities. From 1:00 a.m. to 6:00 a.m., there are a few cars parked on the roadside.
# Research on Three-Dimensional Scanning Path Planning of Casing Parts Based on Industrial Robot
## 1 Introduction
For complex large-scale structural parts, it is time-consuming to plan the scanning path by manual teaching programming, and the spherical scanning head is easy to collide with the measured part. The accuracy of the scanning path also directly affects the scanning efficiency and the reconstruction rate of the measured parts. Therefore, the planning of the scanning path is particularly important for popularizing automated 3D scanning systems in the measurement of large structural parts [19]. In this paper, the constraints such as scanning area range, proximity distance and scene width angle of the tracking 3D scanner are analyzed. Combined with the geometric structure characteristics of the casing parts, the mathematical model of the annular scanning path was established by using the mathematical discretization modeling method. The change rules of scanning stroke, robot attitude transformation times and scanning time under circular scanning path were analyzed. The Robot Studio software platform was used to simulate the automatic scanning trajectory, which verifies that the scanning trajectory meets the constraints. The influence of the scanning path on the scanning reconstruction rate was analyzed by scanning experimental results.
# Super-Resolution Reconstruction of an Array Lidar Range Profile
## 3 Experimental Results
### Real Detecting Data Experiment and Result Analysis  
The GM-APD lidar system diagram (a) and the range profile acquisition schematic (b) constructed for the experiment are shown in Figure 5. The system is mainly divided into the laser launching system and echo receiving system; the main process of its detection and imaging principle is to set up a good delay and gate width in the host computer in advance and control the laser to launch laser pulses after a delay time \(t_{s}\). After the GM-APD detector began to work, while the timer began to work, the echo pulse signal triggers the GM-APD; the GM-APD and timer stop working and this time is recorded as \(t_{d}\).  
Finally, the distance values corresponding to the triggering of each array element are obtained using the ToF Formula (20):  
\[D_{i}=\frac{t_{s}+t_{d}}{2}\cdot c \tag{23}\]  
where \(D_{i}\) is the distance value recorded by the \(i_{th}\) probe array element and \(c\) is the speed of light.
# Study on Optical Properties and Internal Quantum Efficiency Measurement of GaN-based Green LEDs
## 2 Materials and Methods  
The sample investigated in this work is a commercial green InGaN/GaN MQW epitaxial wafer grown by metal organic chemical vapor deposition (MOCVD) on a c-plane sapphire substrate manufactured by Xiamen Changelight Co., Ltd. A schematic illustration of the experiment setup is shown in Figure 1a. The sample was mounted on the cold head of optical helium closed-cycle cryostat where the temperature can be tuned from 10 K to 300 K. A steady-state 405-nm laser diode whose output power can range from 0 mW to 250 mW was chosen as the excitation source and the laser was focused by a lens with a focal length of 30 cm. A sharp knife was mounted on a motorized linear translation stage placed at the focal point of the lens. The position of the knife was changed continuously to cut the laser with its edge and the change of the power was monitored with a power meter, which is shown in the inset of Figure 1b. Considering the transverse distribution of the laser is gaussian, the derivative of the results obtained by the power meter could be calculated and fitted that with gaussian function to acquire the spot radii. The spot radii on the sample were about 0.05585 mm, 0.05602 mm, and 0.05554 mm at excitation power of 250 mW, 150 mW, and 50 mW, respectively determined by this knife-edge method [18] which did not change much when changing the output power. The photoluminescence (PL) signals were collected and reflected to the entrance of a HORIBA Jobin Yvon Triax 550 monochromator with lens #2, mirror and lens #3 shown in Figure 1a. The incident PL signals were dispersed with a grating for spectral resolution and the intensities of different wavelengths were detected by a GaAs photomultiplier tube (PMT) which was connected to a computer to collect data.  
Figure 1: (**a**) A schematic of the experimental apparatus in which the power meter shown with dashed line is used to improve the accuracy of the experiment mentioned in the section of discussion; (**b**) The gaussian fit result of derivative of measured data at excitation power of 250 mW as described in the inset to determine the spot radius on the sample using the knife-edge method.
# Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
## 4 Methods
### Quantitative Structure Models  
In addition to the classical forest inventory parameters described above, i.e., DBH, tree height, CPA, we quantified the above-ground wood volumes for each extracted tree individual using QSMs. QSMs are a state-of-the-art approach [10; 35] to quantify the 3D structure of a tree including its branch topology. In contrast to common allometric equations [59], which mainly use height and DBH, QSMs can deliver a much more accurate estimate of wood volume. QSMs are a description of the tree as a hierarchical collection of geometric primitives (here: cylinders) that are fitted into the point cloud from which topological and geometric tree characteristics can be derived. To generate the QSMs, we used the _TREEQSM_ (2.30) software developed by [PERSON] et al. [10] running in _Matlab(tm)_ (MathWorks, Natick, MA, USA) version R2016a. The method first segments the point cloud of a tree into stem and individual branches and simultaneously determines its topological branching structure. In a second step the method creates a surface and volume model of the segments by fitting cylinders. The segmentation and modeling process is sensitive to specific method parameters [10; 11] that, for instance, define the size and number of segments (patches of points) or the minimum/maximum branch diameters that are allowed in the modeling. Therefore, we conducted a parameter optimization test using a subset of three trees (small, medium and large) by comparing the modeled QSMs with the original point cloud. This led to two different sets of parameters. For the TLS point clouds, we used the following parameter values: first minimum patch size: 8 cm; second minimum patch size: 3 cm. For the MLS data we used: first minimum patch size: 9 cm; second minimum patch size: 6 cm. For both datasets we used: second maximum patch size: 6 cm; relative cylinder length: 4; relative radius for outlier removal: 5 [35]. In addition to the QSM we computed the CPA for each tree using a concave hull (alpha-shape with \(\alpha\)-value = 0.3) from the projected XY-points [60].  
Figure 6: MLS leaf-off point cloud of plot 1 with voxel tree templates extracted with the TLS leaf-off point cloud of a _Carpinus betulus_ tree (**green**) and a _Fagus sylvatica_ tree (**red**). Small box: top view. The dotted black line with arrows indicates the MLS trajectory.
## 3 Results  
### Acoustic Signal Analysis  
The recorded acoustic signals exhibited noticeable spectral variations depending on the robot's structural state. FFT analysis revealed that the removal of structural components, such as the head, arms, hands, and legs, resulted in shifts in spectral characteristics. These spectral differences indicate that structural modifications affect the robot's acoustic response in measurable ways. Figure 6 presents the power spectral density corresponding to each structural condition. The power spectra were computed from FFT magnitude values extracted from each recording and averaged for each class. A Hamming window was applied prior to FFT computation, and the resulting magnitude spectra were saved in CSV format. The power values were plotted using Matplotlib (3.9.2) with smoothing via cubic spline interpolation to enhance visual clarity.  
While most structural conditions resulted in broadly similar spectral shapes, the spectrum for the \"leg removed\" condition appeared markedly different from the others. This deviation is especially evident in the mid-frequency band and is thought to be associated not just with the absence of the limb itself but also with the resulting shift in the robot's overall physical posture. Unlike other component removals, detaching a leg causes the robot to tilt slightly, altering the way acoustic waves reflect and resonate within its body. Although this was not intentionally part of the experimental manipulation, such posture-induced variation likely contributed significantly to the distinctive spectral response observed in that condition.  
Figure 6: Power spectra corresponding to different structural conditions of the robot.  
To ensure the reliability of results under varying joint configurations, all other experimental conditions were kept strictly consistent. The variations in arm position were included in the training data to allow the model to learn their influence and minimize the impact on structural condition classification.  
To improve feature clarity and reduce the influence of irrelevant noise, frequency components outside the 200 Hz to 3200 Hz range were filtered out. This filtering process enhanced the robustness of the feature vectors and improved classification consistency across all models. Spectral energy patterns were sufficiently distinct across different structural states to enable reliable feature extraction, forming the basis for machine learning-based classification. While the classification accuracy achieved by each model varied, the overall performance trends suggest that the acoustic features were sufficiently informative to capture structural differences under controlled settings. It is worth noting that the test conditions, though carefully constructed, still represent a simplified version of real-world variability. This emphasizes the importance of gradually extending such experiments to include more realistic sources of variation--such as surface material changes, external vibrations, or minor assembly inconsistencies--to better evaluate model generalizability.
# Numerical Study of Cone Penetration Tests in Lunar Regolith for Strength Index
###### Abstract  
The cohesive properties of lunar regolith, combined with a low-gravity environment, result in it having a distinct mechanical behavior from sandy soil on Earth. Consequently, empirical formulas derived from cone penetration tests (CPTs) for calculating the shear strength parameters of Earth's sand cannot be directly applied to lunar regolith. This study utilized the three-dimensional discrete element method (DEM) to numerically simulate triaxial shear tests and cone penetration tests in a lunar environment. The particle contact model for lunar regolith in the discrete element method (DEM) simulation incorporated the hysteresis effect of van der Waals forces, thereby simulating the cohesive properties of lunar regolith in a lunar environment. We proposed a relationship for calculating the shear strength index of lunar regolith based on normalized cone tip resistance using the results from triaxial and CPT simulations and referencing empirical formulas derived from ground-based CPT data. The results of this study provide a valuable reference for future lunar CPTs.  
discrete element method; lunar environment; cone penetration test; numerical analysis; penetration mechanism +
Footnote †: journal: Applied sciences
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 3 Dynamic RID Model with Sliding Time Window
### Objective Function  
Three objective functions and subjected constraints are set for the dynamic RID model. The minimisation of unmet demand for relief items at all demand regions and the minimisation of total vehicles' travel time for the distribution and minimisation of the total cost are the objectives defined for this model. The delay factor (service time) is also considered in the cases where any vehicle distributes relief resources to more than one demand region. For this model, a duration of 30 min is set as the delay time for the relief items distribution processing time at each intermediate demand region in the transportation route. A number of variables, as listed in Table 1, are defined to formulate the RID problem as follows:  
1. Minimisation of unmet demand for relief items (f1): \[\text{Min f1 (RS)}=\text{Nc}-\sum_{i=1}^{\text{Nv}}\sum_{j=1}^{\text{yi}}f \text{sd}(\text{r}_{\text{ij}}),\ 1\ \leq\text{yi}\ \leq\ \text{kmax}\]
2. Minimisation of total time spent (f2): \[\text{Min f2 (RS)}=\sum_{i=1}^{\text{Nv}}\sum_{j=1}^{\text{yi}}\text{T}_{ \text{ij}}+30*\text{nV}_{\text{t}}+\text{T}_{\text{offset}},1\ \leq\text{yi}\ \leq\ \text{kmax}\]  
Figure 1: Sliding Time Window Approach.  
where n is the total number of vehicle tours with multiple demand regions. Subject to:  
\[\text{f(x)}=\left\{\begin{array}{rl}\frac{\sum_{\alpha=1}^{1}\text{D}\left(f \text{dx,dy}(\text{r}_{\text{ij}-1}).\text{dx,dy}(\text{r}_{\text{ij}})\right)}{ \Phi_{\text{i}}},&\text{if r}_{\text{ij}-1}\text{ and r}_{\text{ij}}\
otin\Phi\\ 0,&\text{Others}\end{array}\right.\]
3. Minimisation of total vehicles' transportation cost (f3):  
\[\text{Min f3}\left(\text{RS}\right)=\sum_{i=1}^{\text{Nv}}\Psi_{\text{i}}\]  
In this dynamic RID model, the following assumptions are postulated:  
1. The geographical location of disaster regions and supply points are known.
2. The total affected population of the disaster regions are known.
3. Relief items demand is proportional to the population suffering in the corresponding disaster regions.
4. Heterogeneous relief items are bounded in a single bundle and can be loaded into any vehicle.
5. Connecting links between disaster regions and supply points along with corresponding distances are known.
## 3 Results
### Head Dataset Results
Mean Absolute Error, PSNR and Pearson coefficient within the soft-tissue on the head data set obtained using U-net with different convolutions and reconstructions. \begin{table}
\begin{tabular}{c c c c} \hline  & **MAE** & **PSNR** & **Pearson** \\ \hline
**2D** & \(27.94\pm 3.98\) & \(33.75\pm 1.124\) & 0.329 \(\pm\) 0.045 \\ \hline
**3D-32** & \(30.36\pm 4.07\) & \(33.10\pm 1.482\) & 0.296 \(\pm\) 0.074 \\ \hline
**3D-16 av** & \(27.96\pm 3.79\) & \(\mathbf{33.04\pm 1.407}\) & **0.334 \(\pm\) 0.078** \\ \hline
**3D-16** & \(27.79\pm 4.08\) & \(32.66\pm 1.708\) & 0.332 \(\pm\) 0.090 \\ \hline \end{tabular}
\end{table} 
Table 11: Head dataset Soft-tissue with Atrous-Net. Mean Absolute Error, PSNR and Pearson coefficient within the soft-tissue on the head data set obtained using Atrous net with different convolutions and reconstructions. \begin{table}
\begin{tabular}{c c c c} \hline  & **MAE** & **PSNR** & **Pearson** \\ \hline
**2D** & \(173.62\pm 29.88\) & \(22.57\pm 1.56\) & 0.650 \(\pm\) 0.079 \\ \hline
**3D-32** & \(176.96\pm 43.70\) & \(22.13\pm 1.77\) & 0.630 \(\pm\) 0.087 \\ \hline
**3D-16 av** & \(173.45\pm 42.72\) & \(\mathbf{22.61\pm 1.70}\) & 0.654 \(\pm\) 0.080 \\ \hline
**3D-16** & \(\mathbf{172.99\pm 45.22}\) & \(22.88\pm 1.821\) & \(\mathbf{0.662\pm 0.083}\) \\ \hline \end{tabular}
\end{table} 
Table 8: Head dataset Fat with Astrous-Net. Mean Absolute Error, PSNR and Pearson coefficient within the fat on the head data set obtained using Astrous network with different convolutions and reconstructions. \begin{table}
\begin{tabular}{c c c c} \hline  & **MAE** & **PSNR** & **Pearson** \\ \hline
**2D** & \(173.62\pm 29.88\) & \(22.57\pm 1.56\) & 0.650 \(\pm\) 0.079 \\ \hline
**3D-32** & \(176.96\pm 43.70\) & \(22.13\pm 1.77\) & 0.630 \(\pm\) 0.087 \\ \hline
**3D-16 av** & \(173.45\pm 42.72\) & \(\mathbf{22.61\pm 1.70}\) & 0.654 \(\pm\) 0.080 \\ \hline
**3D-16** & \(\mathbf{172.99\pm 45.22}\) & \(22.88\pm 1.821\) & \(\mathbf{0.662\pm 0.083}\) \\ \hline \end{tabular}
\end{table} 
Table 9: Head dataset Fat with Residual-Net. Mean Absolute Error, PSNR and Pearson coefficient within the fat on the head data set obtained using Residual net with different convolutions and reconstructions.
# Bacteria-Mediated Modulatory Strategies for Colorectal Cancer Treatment
## 3 Microbiota in CRC
### Influence of Microbiota on Drug Metabolism
With the wide spectrum of impacts of bacteria-induced chemical metabolism, such as pharmacological activation [144], inactivation [145] or toxicity [141], pinpointing the bacteria or their characteristics causing a specific metabolic effect is currently one of the most challenging aspects of treatments. For example by influencing the TNF response or ROS production [146], metabolic processes of glucuronidation conjugating pharmaceuticals to glucuronic acid (GlcA) in the liver, inactivates and detoxifies medicines. These glucuronides are then taken to the gut and are eliminated from the body [147]. However, once in the colon, these compounds can be reactivated by gut bacterialglucuronidases (GUS) enzymes by removing the GlcA, resulting in local acute toxicity [148]. Furthermore, as customized medicine is becoming increasingly important, research is currently being conducted into the extent to which individual drug metabolism can be harnessed. [PERSON] et al. created a technique to find metabolites formed by microbiome-derived metabolism (MDM) enzymes in a series of 23 orally applied medicines in human healthy donors in order to describe metabolic interactions between microbiota and therapeutical agents [149]. This study included different methodologies, including microbial community cultures, small-molecule structural assay, quantitative metabolomics, metagenomics, mouse colonization and bioinformatic analysis, making it a very extensive and technically heavy approach. The authors demonstrated the efficacy of this technique in identifying MDM enzymes in a high throughput manner utilizing medicines from several groups with varying mechanisms of action [149]. [PERSON] et al. used a related attempt to assess the in vitro ability of 76 naturally occurring bacteria in the human gut to metabolize 271 orally administered pharmacokinetics from various groups based on their mode of action. Surprisingly, at least one of the microbes studied was shown to metabolize up to two-thirds of the medications tested [150]. Furthermore, a single microbe had the ability to digest up to 95 distinct medicines and they were able to discover distinct drug-metabolizing gene products that are accounting for the conversion of medicines into metabolites using metabolomics, mass spectrometry and DNA sequence analysis [150]. Finally, in silico techniques have been created to enable the characterization of pharmaceuticals and their metabolites by certain bacterium species [140] as well as the prediction of toxicity events using data on bacteria composition, drug activity and food preferences [151]. When it comes to medication metabolism in the human body, more evidence has pointing out the importance of gut microbiota, as bacteria and their metabolites can affect pharmacokineticsand pharmacodynamics, which is a significant finding in context to therapy. In the next chapter, we will focus on how the microbiome affects traditional CRC therapy.