esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network
## 4 Partitioned CenterPose Network
### Training and Inference
With the coordinates of body centers, the size of the bounding box \(\left(w^{k},h^{k}\right)\) and the offset of the part centers to the body center \(\overset{\rightarrow}{O}_{n}^{k}\) can be extracted from \(H_{bb}\left(\hat{x}_{0}^{k},\hat{y}_{0}^{k}\right)\) and \(H_{off}^{n,n+1}\left(\hat{x}_{0}^{k},\hat{y}_{0}^{k}\right)\), respectively. For the \(k\)th body center, we extract the coordinates of part center candidates \(\left(x_{n}^{k},y_{n}^{k}\right)\) from the joint heatmaps \(H_{bj}\), where the candidates are selected from inside the bounding box of the \(k\)th body center. Then, the offset \(\overset{\rightarrow}{O}_{ht}\) from the part center candidates to the body center are calculated by:  
\[\overset{\rightarrow}{O}_{ht}\ =\ \left(x_{n}^{k}-\hat{x}_{0}^{k},y_{n}^{k}-\hat{y}_{0}^ {k}\right) \tag{9}\]  
Next, each part center candidate is assigned by \(\text{argmin}\ i\in N_{c}\left(\overset{\rightarrow}{O}_{ht}-\overset{ \rightarrow}{O}_{n}^{k}\right)\) to identify the closest predicted offset vector \(\overset{\rightarrow}{O}_{n}^{k}\). Here, \(N_{c}\) is the total number of part centers. After grouping the part centers, we can extract the offset of the remaining joints to the part center \(\overset{\rightarrow}{O}_{n}^{k}\) from \(H_{off}^{m,m+1}\left(x_{n}^{k},y_{n}^{k}\right)\). If the part center is not visible, \(\left(x_{n}^{k},y_{n}^{k}\right)\) will be replaced by \(\left(\hat{x}_{0}^{k},\hat{y}_{0}^{k}\right)\ +\ \overset{\rightarrow}{O}_{n}^{k}\). Using the same strategy, we can group the remaining body joints to corresponding instances of a person. Finally, the complete human skeletons of multiple people are formed using the default connections between the predicted body joints. The network structure of the prediction heads is simple and lightweight, the body centers are obtained directly from keypoint estimation without the need for IoU-based non-maxima suppression or other greedy algorithms. In the inference post-processing, due to the constraints of the bounding box, the number of joint candidates can be reduced greatly to only in the candidates in small areas of the image, this not only improves accuracy it also reduces computing time. Therefore, in our method, post-processing does not take too long while the computational efficiency is similar to one-stage methods.
# The Suitability of Short Rotation Coppice Crops for Phytoremediation of Urban Soils
###### Abstract  
This experiment was aimed at verifying the usefulness of phytoremediation using Short Rotation Coppice (SRC) in an urban Zn-contaminated site. Besides elemental uptake and reclamation, the SRC method was applied to evaluate the additional benefits of a green infrastructure. Nine different plants with rapid growth and large biomass production were selected: three _Populus_ clones, three _Salix_ hybrids, and three _Robinia_ genotypes. Annual and biennial coppicing were evaluated. Poplar clones were more productive using annual coppicing, while _Salix_ and _Robinia_ produced higher biomass in blocks not coppiced. _Poplar_ had the highest phytoextraction rate during the second year, with 1077 g/ha. _Salix_ clones S1 and S3 extracted similar quantities using biennial coppicing. After two years, the bioavailable fraction of Zn decreased significantly using all species, from the 26% decrease of _Robinia_ to the 36% decrease of _Salix_. The short rotation coppice method proved to be useful in an urban context, for both landscape and limiting the access to the contaminated area. Improving the biomass yield through the phytomanagement options (fertilization, irrigation, coppicing, etc.) could make SRC phytoremediation an economic and effective solution to manage urban contaminated areas, coupling the added values of biomass production to the landscape benefits.  
Short Rotation Forestry; potentially toxic elements; metals; bioavailability; urban contamination +
Footnote †: journal: Applied Sciences
# Multiple Current Reversals Using Superimposed Driven Lattices
## 5 Experimental Realization  
Our scheme of multiple current reversal can be experimentally realized using cold atoms or colloids with optical lattices [14; 32; 38] and lattices designed using holographic trapping techniques [37; 54; 55; 56; 57]. The background lattice can be formed by 2D optical lattices where the periodic potential is generated by counterpropagating laser beams of perpendicular polarization. The spatially localized lattices of 2D Gaussian barriers can be obtained by reflecting a linearly polarized laser beam onto a spatial light modulator (SLM) displaying a computer generated hologram. The external driving force can be realized using a piezo-modulator [37].  
Translating our parameters to experimentally relevant quantities for an optical lattice setup with cold rubidium (Rb\({}^{87}\)) atoms and 780 nm lasers, we obtain the lattice height \(\bar{V}_{B}\sim 5E_{r}\), the width \(\frac{1}{\sqrt{\kappa}}\sim 350\) nm, the driving frequency \(\omega\sim\omega_{r}\) and the driving amplitude \(a\sim 0.003\ E_{r}/\text{nm}\), where \(E_{r}\) and \(\omega_{r}\) are the recoil energy and recoil frequency of the atom respectively. The timescales of the current reversals can be controlled by the spatial locations of the two lattices of Gaussian barriers. Further away the lattices are from the origin, i.e., near the initial location of the ensemble, the larger would be the reversal timescales. In contrast to Brownian ratchets, our mechanism does not depend on noise and operates in the purely Hamiltonian regime. The effect of weak noise typical for ratchet experiments with cold atoms and underdamped colloids [10; 58] represent minor fluctuations of the average velocity of the ensemble and this does not affect the functionality of the current reversal mechanism. Interaction and disorder have been shown to enhance accumulation of particles within the regular regions of the  
Figure 4: **(a,b)** Trajectories of two typical particles depicting the conversion between diffusive and ballistic dynamics in the setup III. The horizontal black lines at \(x=x_{1}\) and \(x=-x_{1}\) in the upper panels denote the left and right edges of the lattices \(V_{G1}\) and \(V_{G2}\) respectively. The particle in **(a)** is reflected twice by these two lattices due to the conversion between diffusive and ballistic dynamics. However, the particle in **(b)** undergoes such conversions multiple times over long timescales. The parameters are the same as in Figure 1.  
phase space [33; 59; 60], which would aid the conversion of chaotic to ballistic dynamics of particles. This would possibly decrease the reversal timescales.
# Transformer-Based Visual Object Tracking with Global Feature Enhancement
## 3 The Proposed Method
### The Training Loss  
The overall training loss of the proposed GFETrack can be divided into classification loss and regression prediction loss. The prediction head receives the feature vectors of \(H_{s}W_{s}\) and outputs binary classification and regression results. The feature vectors within the ground truth are viewed as positive samples, while the rest of the feature vectors are viewed as negative samples. All samples contribute to the classification loss, but only positive samples contribute to the regression loss. In order to mitigate the imbalance between positive and negative samples, the loss caused by negative samples has been reduced by a factor of 16. The standard binary cross-entropy loss is used for the classification, as shown in Equation (10):  
\[L_{cls}=-\sum_{i}[y_{i}log(p_{i})+(1-y_{i})log(1-p_{i})], \tag{10}\]  
where \(y_{i}\) is the label of the \(i\)th sample, \(y_{i}=1\) represents the foreground, while \(y_{i}=0\) indicates the background. \(p_{i}\) is the probability predicted by the learning model that belongs to the foreground. For regression, as described in [9], the \(L_{1}\) loss and the GIoU loss [30] are utilized in this paper. Thus, the regression loss can be expressed as:  
\[L_{reg}=\sum_{i}[\lambda_{g}L_{GIoU}(b_{i},\hat{b})+\lambda_{1}L_{1}(b_{i},\hat {b})], \tag{11}\]  
where \(b_{i}\) is the \(i\)th prediction bounding box, \(\hat{b}\) is normalized ground truth bounding box, \(L_{1}\) and \(L_{GIoU}\) represent the \(L_{1}\) loss and the generalized IoU loss functions, respectively, and \(\lambda_{1}L_{1}\) and \(\lambda_{g}L_{GIoU}\) are the hyperparameters determining the relative impact of the respective loss functions. We set it up in the experiment as \(\lambda_{g}=2\) and \(\lambda_{1}=5\). Finally, the overall loss is defined as:  
\[L=\lambda_{c}L_{cls}+L_{reg} \tag{12}\]  
where \(\lambda_{c}=8.3\) is the weight factor, and \(L_{reg}\) and \(L_{cls}\) are the losses of regression and classification, respectively.
# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 4 Discussion  
Compared with traditional scene classification algorithms, such as fixed threshold methods and stacked learning, this algorithm fully integrates temporal, spatial, and mathematical statistical features. It combines scene features with signal features, constructing a model that merges a dual-scale CNN network with a BiLSTM network optimized by the WOA algorithm. Experiments conducted on multi-source scene sensing in smartphones demonstrate that this algorithm exhibits superior performance and higher accuracy rates.  
The current algorithm can be applied to multi-source fusion localization in smartphones, providing enhanced a priori information and scene data for multi-sensor fusion localization, improving the accuracy of sensor fusion and switching. Additionally, the accuracy and recognition speed of this model in scene switching zones still hold significant potential for future research directions.  
In future research on algorithms for classifying complex indoor and outdoor scenes based on spatio-temporal features, we should continue to deepen the fusion and analysis of multi-source sensor data. Additionally, efforts should be made to enhance the robustness and real-time performance of the model in response to changes in complex environments. This research should focus on optimizing the structure of deep learning models, improving feature extraction capabilities, and exploring more efficient algorithm optimization techniques.  
At the same time, emphasis should be placed on the cross-scene applicability and interpretability of the model with the goal of developing a more accurate, reliable, and universal indoor-outdoor scene classification system. This system should be sensitive enough to classify scene feature data and rapidly aggregate the features that detect scene changes into a switching-awareness model, facilitating faster and more accurate indoor-outdoor fusion switching. Regarding the seamless integration of indoor and outdoor environments, it is essential to ensure that an accurate perception of both environments enhances the degree of seamlessness. We can improve the network model to achieve faster scene recognition and enable data prediction based on changes in data features, allowing for quicker and more accurate judgments regarding seamless scene switching.
# Probabilistic Analysis of Fatigue Behavior of Single Lap Riveted Joints
## 4 Statistical Analysis: Anderson-Darling Test
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Load Level** & **Average Value of Crack Length** & **Standard Deviation for Crack Length** \\
**[MPa]** & **[mm]** & **[mm]** \\ \hline
90 & 15.1 & 0.9085 \\
120 & 13.903 & 0.5915 \\
160 & 12.2564 & 0.448 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average and standard deviation values for crack length. The fatigue life dataset at 120 MPa fits both normal and log-normal distributions, as observable by probability plots in Figure 9.  
At 160 MPa, the fatigue life dataset (Figure 10) fits three supposed distributions: normal, log-normal and Weibull. About crack size datasets, all of them seem to better fit a distribution. Dataset at 90 MPa fatigue load (Figure 11) fits Weibull distribution, as verified by the Anderson-Darling test. Similar to the fatigue life dataset at 90 MPa, splitting the crack size data set into two sub-datasets, with separation at 14 mm due to higher dispersion (Figure 7) of previous data (the 14% of the whole dataset), both fit a normal distribution with A2 equal to 0.6714 and 0.7069, respectively. Both crack size datasets at 120 MPa (Figure 12) and 160 MPa (Figure 13) fatigue loads fit normal,  
+++ ==WARNING: Truncated because of repetitions==
Figure 10: Fatigue life at 160 MPa: probability plots for normal (**a**), log-normal (**b**), extreme value (**c**) and Weibull (**d**) distributions. Figure 9: Fatigue life at 120 MPa: probability plots for normal (**a**), log-normal (**b**), extreme value (**c**) and Weibull (**d**) distributions. Figure 11: Critical crack size at 90 MPa: probability plots for normal (**a**), log-normal (**b**), extreme value (**c**) and Weibull (**d**) distributions. Figure 12: Critical crack size at 120 MPa: probability plots for normal (**a**), log-normal (**b**), extreme value (**c**) and Weibull (**d**) distributions. Therefore, it can be concluded that fatigue life datasets at 120 MPa and 160 MPa stress levels can fit a normal distribution, while fatigue life dataset at 90 MPa cannot be fitted to any distribution, unless the dataset is split into two sub-datasets, both fitting normal distributions. For critical crack size, dataset at 90 MPa fits a Weibull distribution, while datasets at 120 MPa and 160 MPa both can fit a normal distribution.
# Fusing Appearance and Prior Cues for Road Detection
## 2 Proposed Algorithm
### Estimate of Appearance Model  
#### 2.2.1 Seed Superpixels Selection  
In order to deal with the highly dynamic nature of road surfaces, current algorithms usually estimate the road model online by defining a sufficient number of seed pixels, which is based on the assumption that the bottom region of the input image belongs to road area. This assumption comes from the fact that the bottom part of the input image usually corresponds to a distance of few meters from the ego-vehicle. Nevertheless, it suffers from a major challenge: in some scenarios, the bottom part of the image may correspond to lane markings or damaged road area which cannot be representative of the road surface and thus leads to a failed estimation of the road model.  
In this paper, we estimate the appearance model of road surface based on the generated superpixels. Specifically, we define 12 superpixels located at the fixed position of the input image as seeds. In view of the fact that these seeds may correspond to damaged road area and lane markings, we choose 6 of the 12 superpixels with the greatest similarity as the final seed superpixels. As shown in Equation (5), the Bhattacharyya coefficient is exploited to measure the similarity of any two superpixels, denoted as \(p\) and \(q\). The greater the value of \(\rho\), the more similar the two superpixels.  
\[\rho(p,q)=\sum_{i}\sqrt{p(i)q(i)}. \tag{5}\]  
Specifically, we first build the normalized histograms of the 12 superpixels using the grayscale channel of the input image. Then the similarity of any two superpixels is measured using Equation (5), where the value of \(i\) is 8, i.e., we build the normalized histogram with 8 bins. Thus we obtain a matrix of 12 \(\times\) 12. Next, we sum up the 12 values of each column in the matrix. Finally, we choose the 6 superpixels whose corresponding sum is bigger than the other ones as the final seed superpixels.  
The results of the seed superpixels selection are illustrated in Figure 3, where the blue superpixels show the seed superpixels defined initially and the green superpixels show the final seed superpixels selected by the Bhattacharyya coefficient.
## 3 Non-Dimensionalization and Parameter Discussions
### Discussions on Dimensionless Parameters
Because of the reciprocating motion, for each value the point heat source passes by, it will cause the accumulation of residual heat, so that the surplus temperature continuously increases over time. The \(\xi\)-\(\Theta\) curves have a peak value and symmetry about some point on the horizontal coordinate \(\xi\). As the point heat source periodically moves between \((0,0,0)\) and \((UT/2,0,0)\) on the \(x\) axis, the most affected point is located at \((UT/2)\), where the influence of the heat source continues for the longest time, and surplus temperature values are at the maximum. The point location at the converted horizontal coordinate is \(\xi=\beta/4\). What needs to be particularly indicated is that the heating conditions are not symmetrical about \(\xi=\beta/4\) at the early stage of the heat source motion, which is also the case for the temperature distribution. Only when the reciprocating time is enough, \(\xi=\beta/4\) is flanked by almost the same heating conditions, and the \(\Theta\) curve is considered symmetrical. Figure 3e further confirms the symmetry as peak points of the fluctuation amplitude \(a\) and \(b\), \(d\) and \(e\) are symmetrical about \(\xi=\beta/4\), respectively. The most violent oscillation appears in the adjacent regions outside the abscissa interval \([0,\,\beta/2]\) and then decreases quickly to almost zero. Due to shorter heating intervals, the fluctuation amplitude curves of surplus temperature reach a local trough of the wave at the symmetrical points, which indicates that, on the contrary, oscillation at the position of the heat source motion center is not violent. Fluctuation curves in the regions too far away from the symmetric center show more smooth features for the opposite reason. The regions of maximum surplus temperature and most violent fluctuation amplitude are abhorrent. Figure 3: Graphical exhibition of numerical results with various dimensionless parameter values: (**a**) Dimensionless surplus temperature profiles over time parameter for various values of \(\gamma\) with fixed \(\xi=0\) and \(\beta=2\); (**b**) Fluctuation amplitude profiles over heat conduction parameter for various values of \(\gamma\) with fixed \(\xi=0\); (**c**) Dimensionless surplus temperature profiles over time parameter for various values of \(\beta\) with fixed \(\xi=0\) and \(\gamma=2\); (**d**) Dimensionless surplus temperature profiles over location parameter for various values of \(\kappa\) with fixed \(\beta=2\) and \(\gamma=1\); (**e**) Fluctuation amplitude profiles over location parameter for various values of \(\beta\) with fixed \(\gamma=1\).
# In-Situ Nanoparticles: A New Strengthening Method for Metallic Structural Material
###### Abstract
With more quantities and homogeneous distribution of nanoparticles in the matrix, not only better strengthening effect but also lower damage to the elongation can be achieved. Powder metallurgy and internal oxidation are often used to fabricate dispersion-strengthened alloys [27; 43; 44; 45; 46]. However, these methods are difficult to be applied in mass production of bulk material due to complex fabrication process, high cost and high energy consumption [27]. In addition, mechanical alloying materials yield holes during subsequent high-temperature heat treatment, which significantly deteriorate the mechanical properties [47]. In contrast, fabrication of in-situ nanoparticles by conventional casting process is more feasible. As noted recently, second phase particles are expected to in-situ form in metal melt with nanoscale to realize dispersion strengthening. Simultaneously, these in-situ particles can act as nucleation sites during solidification process so as to realize the grain refinement. In-situ nanoparticles are more favorable than ex-situ nanoparticles, due to their uniform distribution and coherent or semi-coherent relationship with the matrix. In contrast, heterogeneous distribution and larger size scale (0.1-1 um, incoherent) of the ex-situ nanoparticles both always lead to unsatisfied mechanical response [27; 43; 44; 45; 46; 47; 48; 49; 50]. In addition, coherent or semi-coherent interface reduces the interfacial energy between particle and matrix, which results in increasing stability of particles. With respect to the fabrication process, the endogenous method is more low-cost, time saving and effective compared to external adding. In this review, we give a brief description of an innovative strengthening method of in-situ nanoparticles fabricated during conventional casting process by an overview of the fundamental and technical methods used for the fabrication and characterization of in-situ nanoparticles. Examples of in-situ nanoparticles applications in steel and Cu alloys are elaborately discussed in the context of fabrication techniques, their microstructure and mechanical properties. In-situ nanoparticles prepared in this way are uniformly dispersed and present a coherent/semi-coherent relationship with the matrix, indicating a remarkable strengthening effect akin to coherent interface strengthening. Effects of in-situ nanoparticles on grain refinement, inhibiting segregation and optimizing inclusions are also deeply discussed, which are conducive to plasticity. In a nutshell, this brand new in-situ nanoparticle strengthening method provides a guideline for fabricating strong and ductile bulk metals and it is expected to become a potential future direction in industrial mass production.
# Bounce: A High Performance Satellite-Based Blockchain System
## 7 Accumulator Implementation and Experiments  
The accumulator is implemented using the Microsoft FASTER database (version 2.6.5) [58]. To ensure fast and efficient updates, all wallet records are maintained in Random Access Memory. This potentially makes the FASTER hash structure vulnerable to power failure.  
There are several approaches to achieve fault tolerance:  
* Since any user can act as an accumulator by listening to commit messages broadcast by the Broadcast Ground Stations, a sufficiently large number of accumulators can provide high availability and redundancy, in case some accumulators suffer memory loss.
* Even if the structure is kept in memory, FASTER provides for periodic checkpointing to disk. Our timing experiments show that these checkpoints take 10 milliseconds per slot.
* As an alternative or complement to checkpoints, the hardware memory can be protected with battery-backed or non-volatile RAM. As of this writing [[https://www.nutronik24.com/product/infineon/s25](https://www.nutronik24.com/product/infineon/s25) fl064 labmfi013/14192262.html]([https://www.nutronik24.com/product/infineon/s25](https://www.nutronik24.com/product/infineon/s25) fl064 labmfi013/14192262.html) (accessed on 7 March 2025), non-volatile RAM costs roughly $1.00 per 8 megabyte unit. The price varies between vendors and is decreasing. Another option is battery-backed RAM, which is currently more expensive, but whose price is also decreasing.  
The Wallet and Transaction record structures are shown below.  
Wallet:  key: integer  balance: integer  highest_sequence_id: integer  
Transaction:  sender_wallet_id: integer  sequence_number: integer  recipient_wallet_id: integer  amount: integer  data: byte array  
Figure 9: The content of a commit record contains the slot id, reset id, hash of the previous commit record, the commit flag, and an array of Sending Station root groups. Each group contains the signature of the Sending Station and an array of multisigned Merkle roots in that Sending Station message.  
Figure 10 displays the processing time for different numbers of transactions with the implementation of the described algorithm. It can handle 8 million transactions in a 2-second slot.