esa-sceva/satcom-chunk-collection huggingface dataset:
# Computational Approaches for Grocery Home Delivery Services
## 3 Problem Description and Formal Model
### Vehicle Routing Problem with Time Windows  
The vehicle routing problem with time windows (VRPTW) is concerned with finding a delivery schedule having the minimal total travel time for a fleet of vehicles with given capacity constraints to deliver goods to customers within assigned time windows. The problem is known to be NP-hard (2018).  
A VRPTW instance is defined by a set of _customers_\(\mathcal{C},\ |\mathcal{C}|=p\), with a corresponding _order weight_ function \(c\colon\mathcal{C}\to\mathbb{R}^{>0}\), and a _service time_ function \(s\colon\mathcal{C}\to\mathbb{R}^{>0}\). In the considered AHD service, the individual items of an order \(a\in\mathcal{C}\) are consolidated into several boxes of fixed size. The number of required boxes defines the corresponding order weight \(c(a)\).  
Secondly, the VRPTW consists of a set of _time windows_\(\mathcal{W}=\{1,\ldots,q\}\), where each time window \(u\in\mathcal{W}\) is defined through the times of its begin and end \((B_{u},E_{u})\). We assume that the time windows are unique, i.e., there do not exist time windows \(u,v\in\mathcal{W},\ u\
eq v\) with \(B_{u}=B_{v}\) and \(E_{u}=E_{v}\). We consider overlapping and non-overlapping time windows. Two time windows \(u\) and \(v\) are _non-overlapping_ if and only if \(E_{u}\leq B_{v}\) or \(E_{v}\leq B_{u}\). Further, a function \(w\colon\mathcal{C}\to\mathcal{W}\) is given that assigns to each customer a time window, during which the delivery vehicle has to arrive at the customer. All vehicles depart from and return to a _depot_\(d\). The _travel time_ between all points \(a\in V=\mathcal{C}\cup\{d\}\) is given by a function \(t\colon V\times V\to\mathbb{R}^{\geq 0}\), where we set the travel time from a customer \(a\) to itself to 0, i.e., \(t(a,a)=0,\ a\in V\).  
Each vehicle has an assigned tour. A _tour_\(\mathcal{A}=(1,2,\ldots,n)\) contains \(n\) customers in the order they are visited by the vehicle and has an assigned capacity \(C_{\mathcal{A}}\). Furthermore, each tour \(\mathcal{A}\) has assigned _start_ and _end times_ that we denote as \(\mathit{start}_{\mathcal{A}}\) and \(\mathit{end}_{\mathcal{A}}\), respectively. Hence, the vehicle assigned to tour \(\mathcal{A}\) can leave from the depot \(d\) no earlier than \(\mathit{start}_{\mathcal{A}}\) and must return to the depot no later than \(\mathit{end}_{\mathcal{A}}\). A _schedule_\(\mathcal{S}=\{\mathcal{A},\mathcal{B},\ldots\}\) consists of \(|\mathcal{S}|=m\) tours.
# Evaluation of BDS-2 and BDS-3 Satellite Atomic Clock Products and Their Effects on Positioning
## 4 BDS-2/BDS-3 Clock Stabilities and Detum Discrepancy
### Comparison of Clock Datum in Different Products  
Based on the method in Section 2.1, three group differences between two sets of products from GFZ, WHU and broadcast ephemeris for the BDS-2/BDS-3 clock datum discrepancy were calculated and depicted in Figure 4. The insets enlarge respective plot of 3-day results for the time period MJD 58999-59002 to show more daily details.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**System** & **STD (ns)** \\ \hline GPS & 0.13 \\ BDS-2 & 0.16 \\ BDS-3 & 0.07 \\ Galileo & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average STD values for 1 h arc of broadcast clock offset.  
Figure 3: Long-term overlapping Allan deviation of each BDS satellite, all GPS Block IIF satellites and all Galileo FOC satellites. Average values for the period from 28 November 2019 to 31 December 2020 (MJD 58815-59214) using broadcast ephemeris are shown.  
The top plot of Figure 4 presents the difference between GFZ and WHU precise clock products. Most of results are between 1 and 6 ns with a mean of 3.30 ns, which means that the datum discrepancy between BDS-2 and BDS-3 in precise products does exist. The satellite clock datum discrepancy between BDS-2 and BDS-3 of different analysis centers may be due to the different processing strategy and stations. At the day boundaries, the difference exhibits a jump due to the clock offset discontinuities. However, on the whole, the difference time series are quite stable, with a standard deviation of 0.75 ns for a total of 400 days.  
The middle and bottom plots of Figure 4 present the difference between broadcast ephemeris and precise products derived from GFZ and WHU, respectively, having a mean of 3.04 ns and 6.37 ns, respectively. As mentioned above, the broadcast clock offsets with a 30 s sampling interval were obtained by second-order polynomial interpolation. Since the broadcast orbits referred to the center of mass for BDS-2, the PCO corrections are assimilated to the broadcast clock offsets [43]. Thus, the effect of satellite PCO in broadcast clock offsets must be corrected for BDS-2 when calculating the difference of clock datum discrepancy. Otherwise, it will introduce a bias of about 11 ns. Besides, the clock datum bias will jump every hour since BDS broadcast ephemeris is updated every hour and has a lager standard deviation, which is 1.61 ns for BRDC-GFZ and 1.55 ns for BRDC-WHU.
# Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm
## 3 Reinforcement Learning Environment Modeling and Design
### Action Space Design  
The action space of the agent reflects the trajectory control commands that can be applied at each time step. In the drilling process, the incremental adjustment of the deviation angle and the azimuth angle is the main means to realize trajectory steering and obstacle avoidance control. The action space is defined as a two-dimensional continuous space:  
\[a_{t}=[\Delta\theta_{t},\Delta\phi_{t}] \tag{27}\]  
The agent adjusts the deviation angle (\(\theta\)) and the azimuth angle (\(\varphi\)) within \([-0.15,0.15]\) radian (corresponding to \(\Delta\theta\leq\pm 0.5^{\circ}/\mathrm{m}\) and \(\Delta\varphi\leq\pm 1^{\circ}/\mathrm{m}\), as specified in API RP 67:2018). These bounds match the physical limitations of conventional rotary steerable systems (RSSs), preventing excessive steering that could cause tool wear or wellbore instability.
# Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval
## 3 Geo-DMP Mobile System
### The Routing Scheme Based on Map Segmentation and Movement History
**Case III**: Both \(N_{c}\) and \(N_{i}\) have visited the region(s) in \(R\) (lines 21-29)  
To decide which node has superiority over the other one when both of them declare that they have been to the region(s) in \(R\), we follow the principle that, intuitively, the node that has reached the target region on a deeper level can be expected to get closer to the content. According to the result of the intersection of \(V_{c}\) and \(V_{i}\) with \(R\), if \(V_{i}\) overlaps \(R\) to a greater depth than \(V_{c}\), \(N_{i}\) is qualified as the next best candidate. By this means, the bundle will gradually approach the destination. ```
1:\(N_{c}\gets N_{a}\)
2:for each node \(N_{i}\) that has an active connection with \(N_{a}\)do
3:if\(N_{i}\) is the destination of \(B\)then
4: deliver \(B\) to \(N_{i}\)
5:break
6:end if
7:\(V_{c}\leftarrow\) getVisitedRegions(\(N_{c}\))
8:\(V_{i}\gets\) getVisitedRegions(\(N_{i}\))
9:if\((V_{c}\cap R=\varnothing\) and \(V_{i}\cap R=\varnothing)\)then
10:\(d_{c}\gets computeShortestDistance(r_{3},V_{c})\)
11:\(d_{i}\leftarrow computeShortestDistance(r_{3},V_{i})\)
12:if\(d_{i}<d_{c}\)then
13:\(N_{c}\gets N_{i}\)
14:else
15: continue
16:endif
17:elseif\((V_{c}\cap R\
eq\varnothing\) and \(V_{i}\cap R=\varnothing)\)then
18:continue
19:elseif\((V_{c}\cap R=\varnothing\) and \(V_{i}\cap R\
eq\varnothing)\)then
20:\(N_{c}\gets N_{i}\)
21:elseif\((V_{c}\cap R\
eq\varnothing\) and \(V_{i}\cap R\
eq\varnothing)\)then
22:\(l_{c}\gets maximum\)\(l\) where \(r_{l}\in V_{c}\cap R\)
23:\(l_{i}\gets maximum\)\(l\) where \(r_{l}\in V_{i}\cap R\)
24:if\(l_{c}<l_{i}\)then
25:\(N_{c}\gets N_{i}\)
26:else
27: continue
28:endif
29:endif
30:endfor
31:if\(N_{c}\
eq N_{a}\)then
32: forward \(B\) to \(N_{c}\)
33:endif
```  
**Algorithm 1** The routing scheme based on map segmentation and movement history
## 4 Discussion  
As shown in Table 4, both the ANFIS and PCA-ANFIS models employed gbellmf membership functions, with the number of MFs set to three for each input, 3-3-3-3-3-3 topography for ANFIS, and 2-2-4-3 topography for PCA-ANFIS. The PCA-ANFIS model demonstrated higher efficiency due to PCA's dimensionality reduction, resulting in faster convergence times than the ANFIS model, which had redundant inputting variables, as evidenced by the time taken to run 50 epochs. In addition, Table 4 shows that the PCA-ANFIS model achieved higher performance accuracy than the ANFIS and ANN models.  
Because the carbonation depth data was collected from actual engineering, the prediction accuracy was likely affected by different criteria in data collection and calculation methods.  
The combined model PCA-ANFIS has strong learning and expression abilities, but the selection of fuzzy rule conclusion parameters and membership function parameters is a key factor hindering the improvement of ANFIS performance, which limits the application and forecast accuracy of the ANFIS model. Therefore, it requires optimization algorithms to enhance the ANFIS conclusion parameters and membership function parameters to obtain higher forecast accuracy for carbonation depth.
## 3 Results
### Micronucleus Assay
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Concentration** & **CBPI** & **RI** & **\% Cytotoxicity** & **SD** & \multicolumn{2}{c}{**\%**} & \multicolumn{1}{c}{\(p\)-Value**} \\  & & DKP 2 mg/mL & 1.00 & 0.00 & 100.00 & \(\pm\) & 0.0000 & - & - \\  & & DKP 0.67 mg/mL & 1.15 & 23.35 & 76.65 & \(\pm\) & 7.5445 & - & - \\  & & DKP 0.22 mg/mL & 1.79 & 125.71 & \(-\)25.71 & \(\pm\) & 1.1930 & 8.68 & 0.4506 \\  & & mitC 0.25 mg/mL & 1.23 & 35.88 & 64.12 & \(\pm\) & 0.5675 & 23.08 & 0.0070 \\  & & 1\% DMSO & 1.63 & 100.00 & - & - & - & 8.28 & - \\ \hline  & & DKP 2 mg/mL & 1.00 & 0.29 & 99.71 & \(\pm\) & 0.4086 & - & - \\  & & DKP 0.67 mg/mL & 1.08 & 11.12 & 88.88 & \(\pm\) & 3.6038 & - & - \\  & & DKP 0.22 mg/mL & 1.64 & 89.39 & 10.61 & \(\pm\) & 4.2261 & 8.93 & 1.035 \\  & & CP 0.25 uM & 1.16 & 22.05 & 77.95 & \(\pm\) & 2.4459 & 43.65 & \textless{}0.0001 \\  & & 1\% DMSO & 1.71 & 100.00 & - & - & - & 10.64 & - \\ \hline  & & DKP 2 mg/mL & 1.01 & 1.70 & 98.30 & \(\pm\) & 0.1532 & - & - \\  & & DKP 0.67 mg/mL & 1.01 & 0.83 & 99.17 & \(\pm\) & 0.3420 & - & - \\  & & DKP 0.22 mg/mL & 1.12 & 16.85 & 83.15 & \(\pm\) & 1.3788 & 33.33 & 0.0184 \\  & & Col 0.025 mg/mL & 2.27 & 181.55 & \(-\)81.55 & \(\pm\) & 21.9522 & 40 & 0.0086 \\  & & 1\% DMSO & 1.70 & 100.00 & - & - & - & 11.86 & - \\ \hline  & & DKP 100 nM & 1.65 & 104.03 & \(-\)4.03 & \(\pm\) & 3.5728 & 11.01 & 0.6404 \\  & & DKP 10 nM & 1.65 & 104.23 & \(-\)4.23 & \(\pm\) & 8.4285 & 9.67 & 0.9863 \\  & & DKP 1 nM & 1.64 & 102.88 & \(-\)2.88 & \(\pm\) & 2.5852 & 7.54 & 0.7308 \\  & & Col 0.025 mg/mL & 1.82 & 132.03 & \(-\)32.03 & \(\pm\) & 4.1326 & 51.40 & \textless{}0.0001 \\  & & 1\% DMSO & 1.62 & 100.00 & - & - & - & 9.16 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cytostatic effect of DKP indicated by RI (Replication Index) and CBPI (Cytokinesis-Block Proliferation Index). negative controls (\(p>0.05\), \(\alpha=0.05\)). Thus, genotoxicity was not observed. This means that, at physiological concentrations, the genotoxicity of DKP is unlikely.
# Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images
## 4 Weighted Coplanar Feature Clustering
### Content-Adaptive Weight  
The accuracy of the superpixels depends on the quality of the color and depth images when using Equation (3). When splitting the regions with better quality of color data than depth data, it makes sense to increase the proportions of \(d_{c}\) and \(d_{s}\) in the distance measure. Likewise, a dominant \(d_{\text{g}}\) in the distance measure will provide better results in the regions with better depth data. These two cases  
Figure 3: Geometric interpretation of PPL.  
usually occur in the same picture. Therefore, we introduced the content-adaptive weight to improve the performance. The content-adaptive weight (CAW) is defined as follows:  
\[\lambda_{b}=\left(1-\frac{d_{c}}{C_{m}}\right), \tag{7}\]  
which changes the distance measure to  
\[D=\sqrt{\left(\frac{d_{c}}{N_{c}}\right)^{2}+\left(\frac{d_{s}}{N_{s}}\right)^{ 2}+\lambda_{b}\left(\frac{d_{g}}{N_{g}}\right)^{2}}, \tag{8}\]  
which is named the dynamic distance measure. This makes it possible to dynamically adapt to the content of RGBD images according to the relationship between the color and depth data in different regions. In Equation (7), \(C_{m}\) is set to \(256^{2}\) = 65,536, that is, the maximum distance in the CIELIB color space, so the right part of Equation (7) represents the color similarity in the range between 0 and 1. In this case, the content-adaptive weight increases when there is considerable similarity in color so that the depth information dominates the clustering process. On the other hand, when the color of a pixel is significantly different from that of the cluster center, the small values of content-adaptive weight will make color information play a more important role than depth in the clustering.  
Furthermore, we compared the performance of the distance measure with and without the content-adaptive weight in practice, and the results confirmed that the content-adaptive weight improves the accuracy of superpixels.
# A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps
## 5 Conclusions  
This paper proposes a condition monitoring framework, and the models used were trained and tested on the experimental dataset. The test results in Section 4 prove that the aim of the proposed condition monitoring framework for ESWP is achieved, classical faults are found with 100% correct fault information, and the unlearned fault is found and sent to an expert for detailed information. Three conclusions can be drawn: (1) mean fusion is a good choice for multi-channel same-type signal processing. (2) The model preselection algorithm helps filter the most suitable UAD and FD models across existing models. (3) The information integration algorithm helps avoid lacks in specific fault information and misdiagnoses. The proposed condition monitoring framework's novelty is the parallel working of UAD and FD, the model preselection algorithm, and the information integration algorithm.  
The shortcoming of this method is that improving the framework requires expert assistance. Further work includes the fusion of different-format results from different models in depth and implementing an expert system. In addition, requesting actual pump data from SNPPs is another essential task to validate and improve the condition monitoring framework. We will continue to focus on the parallel implementation of anomaly detection and fault identification methods in the future. This method can concentrate on different models' advantages and avoid limitations, especially for monitoring critical and complex equipment conditions.  
**Author Contributions:** Conceptualization, Y.L. ([PERSON]) and Q.F.; data curation, Q.H.; formal analysis, R.Z.; funding acquisition, Q.F.; investigation, Q.H.; methodology, Y.L. ([PERSON]) and Q.F.; project administration, Q.F.; resources, Q.F.; software, Y.L. ([PERSON]), Y.L. ([PERSON]), and S.L.; supervision, Q.F.; validation, Y.L. (Yingqian Liu) and H.L.; visualization, Y.L. ([PERSON]); writing--original draft, Y.L. ([PERSON]); writing--review and editing, Q.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number U20A20292.  
**Data Availability Statement:** Restrictions apply to the datasets. The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to Professor [PERSON], [EMAIL_ADDRESS].  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm
## 4 Results
### Path Simulation Comparison
#### 4.2.6 Complex Non-Warehouse Environment  
In the warehouse environment, the LAPGWO algorithm exhibits excellent performance. To further verify the adaptability of the LAPGWO algorithm, this paper will continue to conduct tests in complex non-warehouse environments. The comparative algorithms for the test are the DSA-EGA algorithm, the LM-GWO algorithm, and the CWSO algorithm. The map environment is shown in Figure 12.  
The positions of the obstacles, the initial point, and the robot's target point are randomly assigned. The four algorithms' performance is evaluated by the path length, planning time, path smoothness, and average number of iterations, as shown in Figure 13a-d.  
As can be seen from Figure 13 and Table 7, the LAPGWO algorithm can still plan an optimal path in a complex non-warehouse environment. The planned path has few turning points, is relatively smooth, and has the shortest length. The algorithm's performance is superior to that of other comparative algorithms, further highlighting that the algorithm proposed in this paper has strong adaptability in different environments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Indicators** & **DSA-EGA** & **LM-GWO** & **LAPGWO** & **CWSO** \\ \hline Average Path Length (m) & 25.32 & 25.81 & 24.13 & 25.17 \\ \hline Single-Planning Time (s) & 3.417 & 3.524 & 3.191 & 3.208 \\ \hline Path Smoothness (Curvature) & 0.24 & 0.29 & 0.13 & 0.16 \\ \hline Average Number of Iterations & 62 & 94 & 31 & 61 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of the performance comparison.  
Figure 12: Complex scenarios in non-warehouse environments.
# ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments
## 2 Method
### C_OREPA Model  
Online Reparameterized Convolution (OREPA) [48] is a method designed to reduce the training cost and complexity of deep learning models through online convolutional reparameterization. It primarily involves two stages: firstly, optimizing the performance of online blocks using a specialized linear scaling layer; secondly, reducing training overhead by compressing complex training-time modules into a single convolution. This approach significantly reduces memory and computational costs during training, while also enhancing training speed. Figure 3 provides a detailed description of the composition of the C2f model.  
The C2f module is an improved convolutional module used in YOLOv8. It enhances the model's performance and efficiency by introducing additional skip connections and Split operations. The C2f module first splits the input tensor into two parts. One part passes directly through multiple bottlenecks, while the other undergoes shortcut connections after each operational layer. The final output is produced through a convolution operation. Figure 4 illustrates how the C_OREPA module works. The core idea of OREPA is to dynam  
Figure 3: C2f module structure diagram.  
Figure 2: ST-YOLOv8 model diagram.  
ically combine multiple convolution branches during training and reparameterize them into a single standard convolution layer during inference, achieving efficient computation.  
Here are the formulas:  
Dynamic Combination during Training, the weights of multiple convolution branches are dynamically combined using learnable vectors:  
\[\text{weight}_{\text{final}}=\Sigma\text{weight}_{i}\times\text{vector}_{i} \tag{1}\]  
During inference, all branch weights are merged into a single standard convolution weight:  
\[\text{weight}_{\text{fin}}=\text{weight}_{\text{ori}}+\text{weight}_{\text{ avg}}+\text{weight}_{1\times 1}+\cdots \tag{2}\]  
This process simplifies complex convolution operations into a single standard convolution.  
The proposed C_OREPA module enhances computational efficiency by replacing the original C2f block through decoupling spatial and channel operations via grouped convolutions. This design achieves a notable reduction in parameters while maintaining a high level of original feature expressiveness, as validated through ablation experiments. Furthermore, integrating OREPA's online re-parameterization within the C2f architecture enables replacement of complex convolutions with dynamically re-parameterized kernels. These kernels adaptively adjust weights during training to accommodate varying data distributions, thereby reducing computational complexity and improving end-to-end training/inference performance. This synergistic approach maintains model validity through kernel-level optimization while enhancing adaptability to diverse task requirements.
## 4 Conclusions  
In conclusion, we have successfully demonstrated a new application of ToF cameras as SPI bucket detectors in the presence of ambient light and scattering media. Our approach leverages the intensity information from ToF cameras, and applies four different algorithms to reconstruct images of target objects. Our findings show that this intensity information can be effectively used in ToF-based SPI systems. Specifically, CGI, BP, and CS algorithms have proven effective in reconstructing images of test objects. Additionally, untrained deep learning networks show significant advantages in ultra-low sampling conditions, achieving an image recovery success rate of 6.25%, well below the Nyquist limit. This proof-of-concept demonstration highlights the potential of ToF cameras in challenging environments such as haze, rain, snow, and underwater conditions. Our study not only expands the applications of ToF cameras, but also explores the potential for integrating them into other imaging systems.  
Conceptualization, T.-L.W.; methodology, T.-L.W. and LA.; validation, T.-L.W. and LA.A.; writing--original draft preparation, T.-L.W. and LA.; writing--review and editing, Z.-B.S.; data curation, T.-L.W. and LA.A.; supervision, Z.-B.S., N.H. and Y.-Q.W.; funding acquisition, Z.-B.S. and F.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Plots comparing the PSNR and SRs for the reconstruction of intensity images through scattering media using different algorithms.  
Figure 9: Experimental results of reconstruction using the intensity images through the scattering media at different SRs. (**a**) ToF image, (**b**) ToF image with added Gaussian noise. (**c–f**) the recovered images by CGI, BP, TVAL3, and DL. The SRs from left to right are 6.25%, 12.5%, 18.75%, 25%, 31.25%, and 37.5%.  
**Funding:** National key research and development program (2016 YFE0131500); Scientific Instrument Developing Project of the Chinese Academy of Sciences, Grant No. YJKYYQ20190008; National Key R&D Program of China under Grant 2023 YFF0719800.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Research data from this study will be made available upon request by contacting the authors.  
**Conflicts of Interest:** Author [PERSON] was employed by the company China Satellite Network Group Co., Ltd. The remaining author 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.