esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Design of a Minimalist Step Climbing Robot
## 1 Introduction
Furthermore, researchers have also worked on cluster based stair climbing robots in which a single wheel is replaced by a cluster of three wheels. These wheel clusters are connected to the body of the robot at the centroid of a triangle formed by those wheels. Through a series of articles, [PERSON] et al. illustrated a step climbing robot which comprised two pairs of clustered wheels [27; 28; 29]. In order to improve the mobility of the robot, a modular approach was implemented [30; 31]. In order to improve stability of the climbing robot, the front pair of wheel cluster was replaced by a track-belt assembly [32; 33; 34]. As we may notice, many stair climbing robots are developed and can climb up or down the stairs; however, these systems have their own shortcomings. Robots with track-belts are efficient in climbing the stairs but are relatively slower in planes in comparison to wheel-based systems. In cluster-based and hybrid robots, the inclination angle varies drastically, limiting the robot to carry objects possessing a low center of gravity. Maintaining the inclination angle of the robot parallel to the ground is important while carrying a liquid or semi-liquid product. Hence, in this article, a novel robot is proposed, wherein two motors are attached to the front wheels of the robot and another one to the rear wheel along with an actuator, which is responsible for controlling the variation in the inclination angle of the robot. While climbing up the stairs, maintaining a proper inclination angle results in establishing a better grip between the wheels and the edge of the stairs. This reduces the risk of slippage. On the other hand, maintaining a proper inclination angle during descending reduces the probability of toppling, resulting in the improved stability of the robot. The fundamental question that we wanted to address in this study is whether the performance of the system (stair climbing robot) improves in terms of stability and dynamic equilibrium by adding a minimalistic linkage mechanism. A qualitative comparison of existing step climbing mechanisms is presented in the Table 1. Initial work towards the development of the proposed mechanism is available in the open literature [35]. In [36], the mathematical formulation and preliminary experiment results of the proposed mechanism were discussed. In the subsequent sections, details of the proposed mechanism, simulation and experimental results are explained. This paper is organized in the following manner: Section 2 explains the working principle; Section 3 defines the design parameters and modeling of the robot. In Section 4, the analysis of the effects of various parameters of a climbing robot is studied; in Section 5, development of a prototype and the results of experiments on the prototype are discussed. In Section 6, the importance of controlling the inclination angle of a robot while climbing a stair is explained. Section 7 concludes the article.
## 4 Experiments
### Ablation Experiment  
In this section, we evaluate our work through ablation experiments and comparisons. The experiments use CenterNet object detection model parameters on the COCO [43] dataset to initialize the algorithm model using a deep aggregation network (DLA-34) as the backbone network, which uses a tree structure to fuse semantic information and spatial features from the shallow and deep layers. The Adam optimizer is used to train 35 epochs on the MOT16 dataset, with the batch size set to 12, the initial learning rate set to \(e^{-4}\), and the attenuation at the 25 th epoch set to \(e^{-5}\). The response threshold for predicting the distance difference loss is set to 0.5; the Embedding matching threshold is 0.4; the IoU matching threshold is 0.5; the high detection score threshold is 0.6; the low detection score threshold is 0.2; and the trajectory initialization threshold is 0.7. In order to verify the effectiveness of each module, the corresponding ablation experiments were conducted on the MOT16 dataset, and common MOT official evaluation methods were used to verify the effectiveness of each part on the key parameters, and the specific results are shown in Table 1.  
By adding the Predicted Distance Difference Loss PDDL, MOTA improves by 2.2% and IDF1 improves by 2.8%, and the effective enhancement of IDF1 proves that the quality of trajectory tracking is optimized. By adding PDDL + EFGCN, MOTA is improved from 73.9 to 77.2, and IDF1 is improved from 78.2 to 80.3, which shows the effect of the two modules working together to effectively improve the detection accuracy, and also alleviate the dependence of Re-ID on the detection performance, which can effectively improve the tracking effect. Furthermore, adding ACM, MOTA increases from 77.2 to 81.1 and IDF1 increases from 80.3 to 82.4, which shows that the index is significantly improved when the three networks work together.
## 5 Conclusions  
This paper proposes an STGA-LSTM framework to predict the station-level short-term demand of a bike-sharing system by adopting multi-source data, including historical bike-sharing trip data, land-use data, weather data, and users' personal information.  
We used GCN to dig for spatial information and used LSTM to mine the temporal information from the data. The attention mechanism was applied to the spatio-temporal dimension simultaneously to deliver a more accurate prediction. We formulate the rental demand and return demand of shared bikes with a graph structure with our proposed STGA-LSTM model. The graph of the bike-sharing stations was constructed based on the demand connection among stations. In order to describe the relationship between stations and simplify the construction process, a learnable adjacency matrix was used in the model.  
Experimental results of the real datasets obtained from the Nanjing bike-sharing systems validated that the proposed model is effective. The proposed model is more accurate and efficient than the baseline models, and exogenous variables can only slightly promote the prediction accuracy. Furthermore, we discuss the impact of the temporal and spatial attention mechanism. Temporal attention scores illustrate that, compared to other time steps, the closest historical time step influences the prediction result the most. Spatial attention scores indicate that a higher density of bike-sharing stations causes a stronger interaction among stations.  
Further studies can be implemented from the following directions. First, the findings in this research are limited because the data used in this study only cover one-month docked bike-sharing data. Larger spatio-temporal datasets, such as metro passenger and dockless bike-sharing, can be used to conduct further investigations. Second, other types of external effects, such as metro passenger data and socioeconomic factors, should be considered to improve the prediction accuracy. In addition, the data for users' personal characteristics can be limited, as indicated by previous research [57, 58]. How to obtain the various and accurate personal data for research is worth investigating in future work. Finally, building a deeper network with our proposed model may promote accuracy, and maintaining a better balance between the cost of building a deeper network and the performance of the model should be considered during its implementation in the real world.  
The authors confirm their contributions to this paper as follows: X.M. and Y.J.: conceptualization, methodology, software; Y.Y., M.Z. and M.H.: investigation, writing--original draft preparation, writing--reviewing and editing. All authors checked the results and finalized the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 52172304 and 51908187, and The APC was funded by National Natural Science Foundation of China, grant number 52172304.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Simulation Results and Analysis
### Obstacle Area Deployment of UAV  
As the area of interest can have obstacles to evaluating our proposed work, we assume that the position of and obstacles are known in the geo-map, and we avoided deployment of UAVs in the area. In Figure 9, obstacles were placed in an area, and 200 nodes were distributed with obstacles in a random pattern. This shows the UAVs formed a network while avoiding the obstacles in the area, and also connected all UEs in area flocking model properties for the avoidance of collision between the UAVs and providing the quality of communication between the UAVs, which also helps with the avoidance of collision. We deployed the UAVs at a height that helps in avoiding obstacles. The result in all cases is not much affected. ICBM-UAVs perform a little higher than DFM, as ICBM-UAVs keep their table updated in a timely manner and always keep an active transmission route for packet transmission.
# AVO-Friendly Velocity Analysis Based on the High-Resolution PCA-Weighted Semblance
###### Abstract  
Velocity analysis using the semblance spectrum can provide an effective velocity model for advanced seismic imaging technology, in which the picking accuracy of velocity analysis is significantly affected by the resolution of the semblance spectrum. However, the peak broadening of the conventional semblance spectrum leads to picking uncertainty, and it cannot deal with the amplitude-variation-with-offset (AVO) phenomenon. The well-known AB semblance can process the AVO anomalies, but it has a lower resolution compared with conventional semblance. To improve the resolution of the AB semblance spectrum, we propose a new weighted AB semblance based on principal component analysis (PCA). The principal components or eigenvalues of seismic events are highly sensitive to the components with spatial coherence. Thus, we utilized the principal components of the normal moveout (NMO)-corrected seismic events with different scanning velocities to construct a weighting function. The new function not only has a high resolution for velocity scanning, but it is also a friendly method for the AVO phenomenon. Numerical experiments with the synthetic and field seismic data sets proved that the new method significantly improves resolution and can provide more accurate picked velocities compared with conventional methods.  
seismic velocity; AB semblance spectrum; AVO phenomenon; principal component analysis; spatial coherence +
Footnote †: journal: Medical Imaging  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# On a Framework for Federated Cluster Analysis
## 2 Materials and Methods
### Background: Related Work
#### 2.1.3 Davies-Bouldin Index  
The Davies-Bouldin index was introduced in [31] as a method to measure cluster validity. One of its advantages is that it only requires distances between a \"vector characteristic of a cluster\" (i.e., cluster center) and the vectors belonging to the cluster, as opposed to pairwise distances between all vectors in the dataset, as in other cluster validation methods. That makes it also particularly interesting for the federated clustering setting, where a pairwise distance matrix is hard to obtain, but distances to the cluster center can be calculated locally, shared, and averaged by the central server.  
Informally speaking, the validation measures how well \"cluster spread\" is balanced against \"dissimilarity between cluster centers\". A good clustering is achieved with low spread and high cluster center dissimilarity, but these goals are potentially conflicting. Formally, in [31], the nonfederated measure for a hard clustering is defined as:  
\[\bar{R} :=\frac{1}{\bar{K}}\sum_{i=1}^{K}R_{i} \tag{7}\] \[R_{i} :=\max_{i\
eq j}R_{ij}\] (8) \[R_{ij} :=\frac{S_{i}+S_{j}}{M_{ij}}\] (9) \[S_{i} :=(\frac{1}{\bar{T}_{i}}\sum_{j=1}^{T_{i}}||x_{j}-c_{i}||^{q})^{ \frac{1}{q}}\text{ ``cluster spread''}\] (10) \[M_{ij} :=(\sum_{k=1}^{D}|c_{i}[k]-cj[k]|^{p})^{\frac{1}{p}}\text{ ``center distances''}, \tag{11}\]  
where \(c_{i}\) is the characteristic vector (read: center) of cluster \(i\), and \(D\) is the dimension of the data. Note that \(R_{ij}\) is big when two cluster centers are close to each other (small \(M_{ij}\)), or the \"spread\" of the clusters is big (big \(S_{i}\)). Additionally, the cluster spread is usually smaller if we have many clusters. However, this often comes at the expense of closer centers. Roughly speaking, the index measures how well those two characteristics are balanced, and a smaller value indicates a better cluster result. In our experiments, we chose \(p=2\) and \(q=1\) for our computations.  
A soft version of the Davies-Bouldin index for fuzzy clustering was introduced in [32]. In soft clustering, every point can belong to every cluster, but in Equation (10), only points belonging to the same cluster are considered. Hence, the \"spread\" of a cluster must be defined differently. Authors of [32] propose the following adaptation:  
\[S_{i}^{f} :=U_{i}(\frac{1}{N}\sum_{j=1}^{N}||x_{j}-c_{i}||^{q})^{\frac{1}{q}}, \tag{12}\] \[U_{i} :=\frac{1}{N}\sum_{j=1}^{N}u_{ij}. \tag{13}\]  
Each \(x\in X\) can belong to each cluster \(i\). As a consequence, the spread of each cluster needs to be calculated by considering the whole dataset and is then multiplied by the average assignment for cluster \(i\), i.e., \(U_{i}\). The calculation of the index \(\bar{R}\) proceeds as outlined above, with \(S_{i}^{f}\) instead of \(S_{i}\).
## 4 Discussion
### Potential Applications of Crop Intensity Map  
The entire agricultural system has been subjected to a sizeable amount of change over the past few decades due to changes in socioeconomic conditions, human interference, and climate change. Because of the ever-increasing population, it is becoming extremely difficult for policymakers to meet the growing demand while simultaneously conserving the available land resources. Therefore, optimizing the use of land to its fullest potential is an extremely important component for nations like India in general and the current research  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Crop** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline Double & 0.88 & 0.86 & 0.76 & 0.81 \\ \hline Kharif & 0.95 & 0.97 & 0.91 & 0.94 \\ \hline Rabi & 0.88 & 0.70 & 0.89 & 0.78 \\ \hline Mean & 0.90 & 0.84 & 0.85 & 0.84 \\ \hline \end{tabular}
\end{table}
Table 3: Quality validation.  
area in particular. The availability of a map showing the previous cropping intensity is a fundamental requirement for the planning of an appropriate cropping system. This map can play a key role in maximizing agricultural land utilization in both the present and the future. The traditional census approach of measuring cropping intensity is associated with a restricted spatial extent and a higher cost [41]. Aside from that, the timely availability of the cropping intensity in relation to the spatial extent is a matter of great concern. In the current investigation, we combined geospatial technology and more conventional field surveys to generate the cropping intensity map in a shorter time while maintaining its reliability at a reduced cost.
## 3 Aggregated Fraction Model and Generalized Model: Comparison  
### Structural Model Compliance  
In order to obtain the model used by [PERSON] et al. (2018) and [PERSON] et al. (2019), it is sufficient to replace all variables \(c_{jrt}\) by expression \(W_{jr}f_{jt}\). Here \(f_{jt}\in[0,1]\) is an aggregated fraction continuous decision variable used to make decision about all resources involved in job implementation. There is  
Figure 2: Different model formulation time boxplots for instances of datasets _inst_j10_r5_ and _inst_j15_r5_.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Data Set** & \(|T|\) & \(d\) & \(|J|\) & \(|R|\) & \(|P|\) & \(L_{rt}\) & \(W_{jr}\) & \(p_{min,jr}\) & \(p_{max,jr}\) & \(e_{r}\) \\ \hline _inst\_j10_r5_ & 15 & 1 & 10 & 5 & 10 & \([0.0,70.0]\) & \([30.0,50.0]\) & \([1.0,5.0]\) & \([6.0,10.0]\) & \([1.0,4.0]\) \\ _inst\_j15_r5_ & 20 & 1 & 15 & 5 & 15 & \([0.0,70.0]\) & \([30.0,50.0]\) & \([1.0,5.0]\) & \([6.0,10.0]\) & \([1.0,4.0]\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Instance datasets parameters.  
another way to make our formulation equivalent to these models: We can strengthen the model with a constraint:  
\[\frac{c_{j_{\intercal}t}}{W_{j_{\intercal}}}=\frac{c_{j_{\intercal}2t}}{W_{j_{ \intercal}2}}\ \forall j\in J,\ \forall r_{1},r_{2}\in R,\ \forall t\in T. \tag{27}\]  
In this case, all values of \(f_{jt}\) have to be identical for every pair of resource.  
Further, we compare our generalized model with the aggregated fraction model. For this comparison, the aggregated fraction model is obtained by using \(f_{jt}\in[0,1]\) instead of \(c_{jrt}\) and with the transformation \(c_{jrt}=W_{jr}f_{jt}\) in all corresponding constraints.
# Chemical Migration from Wine Contact Materials
## 5 Wine Migration Studies on Chemical Contaminants
### Analytical Methods
#### 5.2.2 Liquid Chromatography
Limits of detection were below 5 \(\upmu\)g/kg in the food simulants and below 30 \(\upmu\)g/kg in champagne samples, while the migration levels ranged from 70 to 721 \(\upmu\)g/kg [75]. The same UPLC-IM-Q/TOF methodology was applied for the determination of migrating substances from EVA corks into aqueous simulants of alcohol 20% and acetic acid 3% [74]. After the migration test, no sample preparation was performed, and the extracts were analyzed through reversed phase liquid chromatography in positive and negative ESI polarity, acquiring data between 50 and 1000 \(m/z\) in high-definition mode. The compounds that were identified in this wide-scope untargeted UPLC-IM-Q/TOF study to migrate from EVA corks into aqueous simulants were, in total, forty-three. In more detail, the findings included seven additives, namely one cross-linking agent (Triethylene glycoldimethacrylate), four antioxidants (Butyl 4-hydroxybenzoate, Irgafos 168, Irganov 1076, Irganov 1010), and the degradation product of the latest, as well as two lubricants (N,N\({}^{\prime}\)-Ethylene bis oleamide or EBO and N,N\({}^{\prime}\)-1,2-ethanediylbisoctadecanamide). Furthermore, thirty compounds were identified as cyclic co-oligomers of the general formula (C\({}_{2}\)H\({}_{4}\))\({}_{n}\)(C\({}_{4}\)H\({}_{6}\)O\({}_{2}\))\({}_{m}\) with \"n\" ranging between 1 and 13 and \"m\" between 2 and 4. Furthermore, four compounds were identified as two amides bonded by ethylene. Reference standards were used for unequivocal identification of the additives, the degradation product of Irganov 1010, and the oxidation product of Irgafos 168 (Irgafos 168 OXO). The reported limits of detection ranged from 1 \(\upmu\)g/kg for Butyl 4-hydroxybenzoate up to 22 \(\upmu\)g/kg for Irganov 1010, and the quantifiable concentrations of the migrating substances ranged between \(<\)5.5 and 654 \(\upmu\)g/kg [74]. It is noted that the present study used two simulants, 3% \(v/v\) aq. acetic acid and 20% \(v/v\) aq. ethanol, and it was observed that all concentrations were systematically higher in the ethanolic simulant, as observed in the GC findings [74]. Multivariate analysis, in particular an orthogonal partial least-squares discriminant analysis (OPLS-DA), was performed on blank samples and extracts in order to identify the components/markers actually migrating in the simulant samples in both untargeted studies [74; 75].
## 5 Laser Driven Electron Beams for Radiotherapy  
Electron beams have been used to treat cancer for more than 50 years and a review of electron beam therapy physics was carried out in 2006 [81]. Most electron beams generated clinically are in the 5-20 MeV range and, due to the more limited penetration depth, such beams are normally used for treatment of superficial tumors.  
However electron beams above 150 MeV can readily be generated with laser irradiation of gas targets where much less laser intensity is required compared to the case for ion acceleration. They have a range in tissue of about 40 cm and hence can be used to treat deep seated tumors.  
Laser-driven electron beams typically feature a quasi-monoenergetic spectral profile and are better directed than laser-driven ions (less lateral spread [82]). Normally the laser is directed through a supersonic gas jet and the electron spectrum is measured by a magnetic spectrometer. A typical electron spectrum is shown in Figure 16.  
Although most effort exploring radiotherapy of laser-driven energetic particles have concentrated on proton acceleration, [PERSON] and his group have considered laser-driven electron beams for this application. They have published extensively in this area, researching: (a) radiotherapy with laser-plasma accelerators [84] both experimentally and using Monte Carlo simulation of deposited dose; and (b) measuring high energy electron induced damage in human carcinoma cells with electron bunches of ultrashort duration [85]. Treatment planning for laser accelerated very high energy electrons (now known as VHEE) has been carried out for prostate cancer and it has been found that the target coverage is better than that with a clinically approved 6 MV photon plan [86].  
Finally, Figure 17 illustrates a comparison made between a measured dose distribution from a laser-driven 120 MeV electron beam in a phantom target and that determined in Monte Carlo simulation [87].  
Figure 16: Quasi-monoenergetic 225 MeV electron beam with a width of 17 MeV generated in a 4 mm long gas jet at a density of \(7\times 10^{18}\) cm\({}^{-3}\) when irradiated with a 27 TW laser pulse [83]. Reproduced from [83]. Copyright 2009, AIP Publishing LLC.  
The authors conclude from this work that laser-driven electron beams are quasi monoenergetic with low divergence and potentially are of interest for electron beam therapy of deep seated tumors. The laser is more compact and delivers very high instantaneous doses within a time interval shorter than many chemical reactions. However the radiobiological effectiveness of these ultrahigh electron dose rates remains unknown.
## 3 System Overview
### Training the Model Using Federated Learning  
As a distributed learning framework, federated learning has the following advantages: First, each agent is trained on the local data set without centrally uploading a large amount of data to the server, which not only dramatically reduces the cost of communication, but also reduces the risk of privacy leakage. Second, the federated learning algorithm can deal with the problem of non-independent and identically distributed data and different amounts of proxy data. Third, although federated learning cannot surpass the centralized training model, it can still perform similarly. Based on the above reasons, we adopted the federated learning framework to train the DRL model, and the specific process is shown in Algorithm 1.  
```
1:Initialize global policy network parameters \(\theta\) and value network parameters \(\phi\)
2:Initialize all agents \(i\in N\)
3:for\(step=1,2,\ldots T\)do
4:for each agent \(i\in N\)do
5: Download the model parameters from the server so that \(\theta_{i}=\theta,\phi_{i}=\phi\)
6: Train the model with local data and update the model parameters \(\theta_{i}^{{}^{\prime}},\phi_{i}^{{}^{\prime}}\)
7: Randomly select \(m\) available agents to get the set of agents \(M_{step}\).
8:for agent \(i\in M_{step}\)do
9: Upload local model parameters \(\theta_{i}^{{}^{\prime}},\phi_{i}^{{}^{\prime}}\) to the server
10:endfor
11:endfor
12: Service-Terminal:
13: Receive all model parameters
14: Perform model parameter aggregation, update global model parameters \(\theta_{step}\), \(\phi_{step}\)
15:endfor
```  
**Algorithm 1** weight prediction model training algorithm based on federated learning