esa-sceva/satcom-chunk-collection huggingface dataset:
# Feasibility Pump Algorithm for Sparse Representation under Gaussian Noise
## 4 Conclusions  
In this paper we have presented a version of the Feasibility Pump algorithm adapted for the sparse representation problem with \(l_{2}\) norm of the error. Our tests show that the Feasibility Pump gives a better solution when compared with FISTA and OMP, especially at higher sparsity levels. The addition of the weight term for the error proves to be a very important factor for the performance of the algorithm, as it forces the representation error to be smaller. The regularization and the big-\(M\) trick limit the magnitude of the values of the coefficients and thus allow the use of this algorithm for ill-conditioned problems. Future lines of research will focus on improving the randomization  
Figure 8: Mean errors for all algorithms, dictionary size \(80\times 400\), condition number 1000.  
Figure 7: Mean errors for all algorithms, dictionary size \(80\times 200\), condition number 100,000.  
step, using different regularization terms, treating other norms of the error, implementing recent modifications of FP, like those from [5; 9], and adapting the algorithm for other sparse problems reformulations and dictionary learning.  
Conceptualization, B.D. and F.I.M.; Methodology, B.D. and F.I.M.; Software, F.I.M.; Validation, F.I.M. and B.D.; Formal analysis, B.D. and F.I.M.; Investigation, F.I.M.; Data curation, F.I.M.; Writing--original draft preparation, F.I.M.; Writing--review and editing, B.D.; Visualization, F.I.M.; Supervision, B.D.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# FTGM: Fast Transformer-Based Global Matching for Particle Image Velocimetry
## 2 Related Work
[29] constructed the WSSflow framework based on RAFT components, which offers significant advantages in providing high spatiotemporal resolution of wall shear stress (WSS) dynamics, particularly in near-wall regions. Beyond these, other notable deep learning-based algorithms for flow estimation in PIV exist. [PERSON] et al. [30] introduced UnLiteFlowNet-PIV, while [PERSON] et al. [31] proposed an unsupervised deep learning algorithm named URAFT-PIV and introduced a proxy loss function. [PERSON] et al. [32] developed Deep-TRPIV, a novel multi-frame architecture designed for the prediction of optical flow in sequences of particle images, inspired by the RAFT framework. [PERSON] et al. [33] introduced ARaft-FlowNet, a deep recurrent network based on the RAFT architecture with an attention mechanism, improving trace particle motion recognition. [PERSON] et al. introduced latent dynamical models [34] to extract underlying physical mechanisms. They plan to further enhance their work by integrating the URAFT-PIV network with causal risk frameworks [35]. This integration will enable the direct learning of causal flow relations from particle images. According to their speculation, (U)RAFT-PIV is also capable of deriving important flow quantities [36], such as the instantaneous wall-shear stress distribution over a relatively large spatial extent. Despite achieving significant performance improvements in various tasks by using learning-based flow regression methods, these studies still rely on cost volumes and iterative refinements as primary guiding principles. These principles involve applying convolutions to different local cost volumes at various iterative stages to progressively expand the global search space. However, the extensive sequential refinements lead to a linear increase in processing time, limiting their potential for application in practical systems. Consequently, the question arises whether it is possible to achieve high precision and efficiency without relying on a large number of iterations.
## 2 Materials and Methods
### Proposed Method  
The proposed framework for the segmentation task includes a hybrid encoder and decoder, as illustrated in the figure. The encoder comprises convolutional and dense layers to enhance the extraction of characteristics from the input images. After extracting details, they are passed through a squeeze-and-excite (SE) block, which only sends the required details to the decoder for creating the preliminary segmentation mask. The SE block promotes feature representation by capturing inter-channel dependencies. As a result, the model can prioritise crucial features while suppressing less informative ones. Integrating softmax and ReLU activation functions signifies the utilisation of non-linear mapping, while incorporating the recurrent residual convolutional layer (RrCL) helps capture deeper layer characteristics by enhancing the RrCL block by incorporating two RCL units. The RCL expansion strategy involves expanding the RCL to two-time steps (T = 2) to extract valuable features further.  
Additionally, the features of the encoder are improved using Attention Gating Modules (AGMs) that focus on specific areas of interest without losing the spatial resolution of the feature maps. The resulting feature maps are then up-sampled and sent to the decoder, along with a zero-shot linear classifier (ZsLC) that generates the final classified segmentation output with improved accuracy. The flow diagram in Figure 4 illustrates the process of the proposed methodology.  
Figure 4: Process flow diagram of the proposed SellaMorph-Net model.
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 3 Results
### Establish of Forest Stand Age Inversion Model in Loess Plateau  
#### 3.3.1 Spectral Feature Extraction Using PCA  
There is a significant correlation between the spectral data, as shown in Figure 6. In the application of quantitative remote sensing, the representative worth band or vegetation index without duplicate information is generally selected to achieve linear or nonlinear dimensionality reduction of spectral information. In this study, the principal component analysis method (PCA) was used to reduce the dimensionality of spectral information. Figure 6 shows the covariance matrix (M) of the principal components. The grid value is the covariance between each vector element, which is a natural generalization process from a scalar random variable to a high-dimensional random vector.  
Figure 5: Relationships between vegetation indices and forest stand age (the black line is the linear relationship between vegetation index and forest age, and the red line is the nonlinear relationship between vegetation index and forest age). (**(a)** is a scatter plot of the relationship between \(SR\) and forest stand age, (**b**) is a scatter plot of the relationship between \(NIR_{\mathrm{P}}\) and forest stand age, (**c**) is a scatter plot of the relationship between \(NDVI\) and forest stand age, (**d**) is a scatter plot of the relationship between \(GVI\) and forest stand age).  
Figure 6: Principal component covariance matrix (M) of canopy spectral information.  
Figure 7 shows the cumulative contribution rate of the principal components of canopy spectral information. When the first three principal components are selected, namely PC1 (\(SR\)), PC2 (\(NDVI\)) and PC3 (\(B_{5}\)), 98% of canopy spectral information can be interpreted. In particular, the structure vegetation index \(SR\) can explain 61% of the spectral information, so for forests, the structural parameters of forests are the main difference from other features. PC2 (\(NDVI\)), which quantifies forest photosynthesis and forest greenness by the difference between near-infrared (strong leaf reflection) and red light (leaf absorption), provides 30% of the spectral information. PC3 (\(B_{5}\)) is able to interpret 9% of the spectral information. Therefore, the three principal components were used in the estimation of forest stand age in the Loess Plateau.
## 5 Simulation
### Simulation Results
Basically, if \(\tau\rightarrow+\infty\), LESSON acts as FedAvg, and if \(\tau\to 0\), LESSON acts as asynchronous FL. Figure 8 shows the test accuracy of LESSON by having different values of \(\tau\) and \(\beta\) for CIFAR-10 over global iterations, where we can find that \(\tau=60\) seconds incurs the highest test accuracy for both \(\beta=0.1\) and \(\beta=1\). This is because a larger \(\tau\) (1) reduces the number of tiers in the system, thus alleviating the performance degradation caused by stale issues, and (2) increases the average number of clients in uploading their local models in a global iteration, which can mitigate the impact caused by non-IID. Also, we can see that the blue curve, i.e., \(\tau=10\) s, is more sensitive to the change in \(\beta\) than the other two curves. This is because as \(\tau\) reduces, LESSON acts more like asynchronous FL, which has the convergence issue under non-IID. Figure 9 shows the test accuracy of LESSON by having different values of \(\tau\) and \(\beta\) for CIFAR-10 over time. From Figure 9a, we can find that \(\tau=60\) exhibits the slowest convergence rate with respect to the time because a larger \(\tau\) implies a longer latency of a global latency, i.e., \(\tau=60\) runs the fewest global iterations than \(\tau=10\) and \(\tau=20\) within a time period. Therefore, we conclude that changing \(\tau\) can adjust the trade-off between the model accuracy and model convergence rate with respect to time. A large \(\tau\) can increase the model accuracy but reduce the model convergence rate, and vice versa. Meanwhile, as the data distribution is closer to IID (i.e., as \(\beta\) increases), the difference in the convergence rate among the three algorithms increases, as shown in Figure 9b. Figure 8: Test accuracy over the number of global iterations for CIFAR-10, where (**a**) \(\beta=0.1\) and (**b**) \(\beta=1.0\). Figure 7: Test accuracy over the time for CIFAR-10, where (**a**) \(\beta=0.1\), (**b**) \(\beta=1.0\), and (**c**) \(\beta=10.0\). Figure 9: Test accuracy over the time for CIFAR-10, where (**a**) \(\beta=0.1\) and (**b**) \(\beta=1.0\). The two foundational approaches select clients based on their availability. FedAvg emphasizes the importance of client presence but suffers from delays caused by slow participants, known as stragglers. On the other hand, FedCS prioritizes models that have been recently updated. LESSON integrates the strengths of both approaches: it places stragglers in higher tiers to minimize system disruption and effectively incorporates less up-to-date models to enhance the global model's generalization capability. When dealing with clients who drop out intermittently, FedAvg can experience significant delays, and FedCS does not account for the contributions of clients with unstable connections in the first place. Although LESSON cannot offer guarantees, it provides an opportunity for such disconnected clients to make a partial contribution to the global model.
###### Abstract  
The Loess Plateau, with a fragile ecological environment, is one of the most serious water- and soil-eroded regions in the world, which has been improved by large-scale projects involving returning farmland to forest and grassland. This work is mainly aimed at exploring a more reasonable and efficient ecological forest restoration mode and revealing synergistic restoration mechanisms. This study sampled typical Loess Plateau areas and designed the restoration modes for pure forests of _Armeniaac sshirica_ L. (AR), _Amygdala davidaiana_ (Carriere[PERSON]. (AM), _Medicago sativa_ L. (MS), and mixed forests of apricot-peach-alfalfa (AR&AM&MS), using abandoned land (AL) as a control treatment. The effects of these modes on the physical and chemical properties and enzyme activities of various soils were investigated in detail. Moreover, the soil microbial diversity and community structure, functional gene diversity, and differences in the restoration modes were deeply analyzed by meta-genomic sequencing technology, and the inherent driving correlation and mechanisms among these indicators were discussed. The results showed that the soil water content and porosity of the AR, AM, and AR&AM&MS treatments increased significantly, while the bulk density decreased significantly, compared with AL. Moreover, the total carbon, total nitrogen, nitrate nitrogen, total phosphorus, available phosphorus, total potassium, and available potassium contents of the AR&AM&MS restoration mode increased significantly. Compared to CK, there was no significant change in the catalase content of pure forest and mixed forest; however, the contents of urease, phosphatase, sucrose, B-glycanase, and N-acetylglucosamine in the restoration mode of the mixed forest all increased significantly. The species diversity index of the restoration modes is similar, and the dominant bacteria in soil microorganisms include _Proteboucateria_, _Acidobacteria_, _Actinobacteria_, _Bacteroidetes_, and _Gemmatimonadetes_. The mixed forest restoration mode had the highest microbial abundance. The functional gene diversity of the different restoration modes was also similar, including kegg genes, eggNOG genes, and carbohydrate enzymes. The functional genes of the mixed forest restoration mode were the most abundant, and their restoration mechanism was related to the coupling effect of soil-forest grass. After evaluation, the restoration mode of mixed forest was superior to that of pure forest or pure grass. This is attributed to the fact that the mode can improve soil structure, retain soil moisture, enhance soil enzyme activity, optimize soil microbial community structure, and improve microbial diversity and functional gene activity. This provides key data for the restoration of fragile ecological areas, and the promotion of sustainable management of forests and grass in hilly areas of the Loess Plateau.  
**Keywords: restoration mode; hilly loess plateau; macrogenome sequencing; soil enzyme activities; microbial diversity; functional gene**
## 4 MG Communication Technologies
### Wireless Technologies
#### 4.2.5 Lpwan  
This technology was introduced to provide equilibrium between energy consumption and data rate in WAN. Cellular networks have been widely used to transmit high data rates in WAN but with high energy consumption. LPWAN could defeat this weak point of a cellular network by introducing SIGFOX and LoRa. These technologies have been applied to a star topology, which will result in the simplicity of the system and less power consumption. LoRa technology is divided into two subsets, including LoRa modulation and LoRaWAN. LoRa is a physical layer protocol using ISM bandwidth modulation. Despite Frequency Shifting Key (FSK) used by legacy wireless protocols, LoRa applies Chirp Spread Spectrum (CSS) brought by a higher data rate for LoRa. While LoRa modulation is a physical layer protocol, LoRaWAN contains the MAC layer as well as the physical layer and improves data rate concerning power consumption [51; 52; 53; 54].
## 4 Numerical Experiments
### Comparison between Experiments and Simulations  
In this section, we conduct a comparative study with the actual experimental data to validate our numerical simulation. There is an actual experimental study on whether geometric features affect tissue growth by using four different cross-sectional shapes (triangle, square, hexagon, and circle) in [18]. Figure 10 illustrates the domain of experiment which consists of initial perimeter \(P\) and projected tissue area \(A\). Because we do consider the effects of chemicals, we omit the description of substrate. The experiment is conducted by the assumption that any position of the tissue surface normally moves to the surface by distance \(ds\) during the time step \(dt\). A simple 2D mathematical formulation is expressed as follows in [18]: \(ds/dt=-\lambda x\), where \(\lambda\) is a constant of proportionality and \(\kappa\) is local curvature. We conduct a numerical simulation under the same conditions to match ours with the experiment, i.e., \(\lambda=0.97\) and \(P=3.14\) are taken for the numerical test. Note that we conduct the simulation only for \(P=3.14\) for simplicity of exposition. As shown in Figure 11, the average thickness of cell, \(A/P\), is good agreement with the experimental result.  
Figure 8: Temporal evolutions of the volume and those of the surface area of cell layer over TPMS. (**a**) is the volume of the cell layer over each TPMS and (**b**) is the surface area of the cell layer over each TPMS. Note that the horizontal spacing between each marker is \(100\Delta t\).  
Figure 9: Temporal evolution of the averaged local curvature of cell layer over TPMS. Note that the horizontal spacing between each marker is \(170\Delta t\).
## 3 Case Study
### Ergonomic Assessment
#### 3.3.2 Critical Issues  
Once the results about ergonomics indexes have been obtained, according to the methodological framework in Figure 1, if one or more indexes do not fall within the low risk area, it is appropriate to investigate which sub-phase or specific characteristic of the working cycle mostly contributes to the value of the index.  
The analysis of the results related to the case study described shows that the values of exerted forces and the lifting index (NIOSH) exceed the threshold values for the low-risk area; hence, it is necessary an intervention for reducing the injury risk due to biomechanical overload.  
Regarding the exerted forces, the value of the counter-reaction force, due to the tightening of the bolt, exceeds the maximum exercisable value. To decrease this force value, it is necessary to use a different type of screwdriver, with a higher lever arm. An angled screwdriver, with a distance between the spindle and the actuation button higher than a gun screwdriver, will significantly reduce the value of the counter-reaction force absorbed by the worker arm.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Average (\(I_{\mu}\))** & **Standard Deviation (\(I_{o}\))** & **Medium Risk Threshold Value (\(I_{t}\))** \\ \hline OCRA index & 10.0 & 0.9 & 11.0 \\ \hline \end{tabular}
\end{table}
Table 4: OCRA score: statistical values.  
Figure 9: OCRA scores for each working cycle and their statistical values.  
About the lifting index, according to NIOSH equation, it was deduced that the main contribution to is given by the vertical distance to be covered in handling and the maximum horizontal distance between the handled component and the body, which is excessive especially when the assembly is placed in the cart. In order to reduce the index, it is necessary to think about a reconfiguration of the workstation, lowering the shelf where the components are placed and raising the support surface of the assembly inside the cart.  
The next section describes a possible modification of the workstation layout and the type of screwdriver. A simulation will show how this contributes to reduce the value of the risk indexes.
## 3 Analysis of Results
### First-Order Shovel Blade Excavation Test Study
#### 3.2.3 Optimal Parameter Verification  
From the previous analysis, it is clear that the soil disturbance on the upper and lower surfaces of the shovel body near the shovel blade is extremely serious. When the digging depth is set to 100 mm, some of the Fritilariae ussuriensis maxim bulbs with a shallow growth depth will be damaged, so the digging depth of the shovel body should be greater than 120 mm. Compared to the inclination angle of the shovel surface, the impact of operating speed on the excavation resistance of the shovel body is less significant. Therefore, the speed of operation can be increased to improve the harvesting efficiency while ensuring the quality of operation. The Optimization module of the Design Expert software is used to analyze and process the data, following the principle of minimizing the excavation resistance of the shovel body under the premise of high operational efficiency and low damage rate. Combining the boundary conditions of each factor, a nonlinear planning parameter model is constructed, as shown in Equation (5).  
\[\left\{\begin{array}{l}\begin{array}{l}\min R_{1}(A,B,C)\\ \max A\\ C\geq 120\\ \end{array}\\ \begin{array}{l}s.t.1\end{array}\right\{\begin{array}{l}0.3\leq A\leq 0. 5\\ 20^{\circ}\leq B\leq 30^{\circ}\\ 100\leq C\leq 120\\ \end{array}\end{array}\end{array} \tag{5}\]  
where \(R_{1}\)--digging resistance of shovel body, N; \(A\)--operating speed, m/s; \(B\)--shovel surface inclination, \({}^{\circ}\); \(C\)--digging depth, mm.  
The optimal solution can be found in the Post Analysis module of the software: the operating speed \(A\) is 0.5 m/s, the shovel face inclination angle \(B\) is 25\({}^{\circ}\), the digging depth \(C\) is 120 mm. Under the optimal parameters, the digging resistance \(R_{1}\) of the shovel body is 344.84 N. The experiment was conducted in five rounds, and the results were taken as the average, as shown in Table 14.  
As seen from Table 14, the measured values of the test bench do not differ much from the predicted values of the optimal parameter, and the average relative error is less than 5%, indicating that the parameter is highly reliable. The research method can be used to optimize the design of future Fritilariae ussuriensis maxim excavation shovels.
# Complete Blood Cell Detection and Counting Based on Deep Neural Networks
## 4 Experiments and Results
### Experiment Results  
Tables 4 and 5 show the experiment results in the validation data of all of the models under a confidence score of 0.9 and 0.8, respectively. We note that the recall of blood cell detection also refers to the ratio of the predicted to ground truth boxes. For accurate blood cell counting, we prefer models with higher recalls. Models 1, 2, and 3, trained with preprocessed images in both grayscale and RGB color spaces, are much more powerful than the others. For the WBC recognition task, Model 1 is advantageous. For RBC recognition tasks, Model 3, trained with enlarged (1.5\(\times\)) images, achieves a prominent recall and F1 score. Model 5, using only RGB images, achieves the highest precision; however, it scores worst in recall. For the platelet recognition task, Model 2, trained with enlarged (1.25\(\times\)) images, achieves the best F1 scores.  
Figure 6 shows the total training loss of all of the models. The loss might be relevant to the size of the input image. Model 2 is trained with enlarged images and achieves higher measurement indices for RBC recognition tasks. Since RBCs are the majority of the BCCD dataset, the total training loss is quickly reduced at the beginning of the epoch time.  
We notice that some blood cells on the input images are unclear and heavily overlap with each other. The background-like color further causes the successful detection to be difficult. To investigate the performance of the five models in depth, we selected a representative image of each kind of blood cell and discussed the experiment results.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Type** & \multicolumn{3}{c}{**Red Blood Cell**} & \multicolumn{3}{c}{**White Blood Cell**} & \multicolumn{3}{c}{**Platelets**} \\ \hline
**Index*** & \(P\) & \(R\) & \(F1\) & \(P\) & \(R\) & \(F1\) & \(P\) & \(R\) & \(F1\) \\ \hline Model 1 & 0.737 & 0.744 & 0.741 & **0.761** & **0.95** & **0.845** & 0.4  
The models generally can recognize sparse RBCs well and return excellent scores. Nevertheless, their performance depends on their recognition capabilities for overlapping blood cell detection. For example, although Models 3 and 5 have impressive precision for RBC detection, as seen in Figure 8, the differences in recalls between Models 3 and 5 are 38.1% (42.4-14.3%) and 47.6% (61.9-14.3%) under a confidence score of 0.9 and 0.8, respectively. The recalls of RBC detection in Figures 7 and 8 of Model 5 are 78.5% and 14.3%, respectively. As a result, the recall of RBC detection on the validation set of Model 5 is significantly lower than those of the other models. On the other hand, Model 3 still has recalls of 52.4% and 61.9% for RBC detection in Figure 8 under confidence scores of 0.9 and 0.8, respectively.