esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Reconstructed Grayscale Images  
Representative latewood and earlywood reconstructions from 1 mm cross-section specimens are shown in Figure 4. The two material classes in the images are cell wall and air filling the void spaces. The cell walls can be readily observed in the reconstructions and the expected anatomic features are present. Latewood has much thicker cell walls than earlywood, whereas lumina are much larger in earlywood. The specimens were nominally cut along the three primary wood axes and the radial and tangential orientations are indicated in Figure 4a,b. Ray cells (Figure 4a,b) and pits (Figure 4f) are also readily observed.  
Figure 4: Representative 8-bit reconstruction slices from 1 mm cross-section (**a**) latewood and (**b**) earlywood specimens with their corresponding histograms in (**c**) and (**d**), respectively. The close-up views in (**e**) and (**f**) are from the white boxes in (**a**) and (**b**), respectively. Intensity bars have units of grayscale values.  
The reconstructed images need to be segmented into cell wall and air components for quantitative analysis of cell wall swelling. For traditional histogram-based segmentation methods to be successful, a bimodal intensity histogram with distinct peaks corresponding to cell wall and air is needed. However, the intensity histograms (Figure 4c,d) do not display an obvious bimodal distribution. The histograms only have one obvious peak. Close-up views (Figure 4e,f) show that some regions of the cell wall have similar intensity values to the air. The line profile in Figure 3b also shows that the grayscale intensity values for cell walls and air-filled voids can overlap. Nevertheless, given the simplicity of the histogram-based thresholding, different traditional segmentation methods were tested in the Supplementary Materials. However, none of these traditional histogram threshold-based methods performed well. The resulting binary images lacked the expected anatomic features.
# Secure Delivery Scheme of Common Data Model for Decentralized Cloud Platforms
## 3 Results
### Our Encryption and Decryption Scheme for Delivering in SC-CDM  
#### 3.4.1 Integrity Auditing with the Digital Signature Based on Asymmetric Key Cryptography  
Figure 7 presents an example of how to encrypt and decrypt data in a file named a.out by using the encrypt program implemented the block encryption and decryption algorithm, as well as how to sign and verify it by asymmetric key algorithm. It can describe the state of data and the process of ensuring and preserving the validity and accuracy of data between two parties, the CDM provider and CDM consumer. One (CDM provider) encrypts data by using the public key in the file yrpubkey.pem, which is distributed from SC-CDM; the decryption key is derived from DID. To audit the integrity of CDM delivered from the outside to the SC-CDM, the other (CDM consumer) verifies the signature (in signfile.txt) of the CDM by yrprikey.pem, or its parts to be used later over encrypted data, and uploads the expected responses to the trust manager of SC-CDM.  
Figure 6: Starting the Inter Planetary File System (IPFS) as daemon.  
Our integrity auditing protocol is an asymmetric key based integrity auditing scheme. When applying our integrity auditing protocol, an asymmetric key derived from DID of a client can audit the integrity of CDM without generating additional metadata.
## 4 Experiments
### Experimental Results
#### 4.3.3 Comparison of ISIC 2018 Dataset  
The qualitative experimental results of MSLUnet, compared to other segmentation models on the ISIC 2018 dataset, are outlined in Table 4. The bolded section of the table highlights the optimal experimental results. According to Table 4, MSLUnet outperforms other models in four key metrics: mIoU, Dice, Precision, and Specificity. Moreover, the algorithm introduced in this paper shows improvements of 0.5% in Dice coefficient, 0.8% in mIoU, 0.4% in Precision, and 0.3% in Specificity over the state-of-the-art GCASCADE [45] network.  
For a visual enhancement of the comparative analysis, Figure 7 presents the segmentation results of each network on the ISIC 2018 dataset. The visualization indicates that for dermatological images without hair obstruction and with clear distinctions between the target and background, both the MSLUnet and other networks accurately segment the lesion regions. Most current advanced networks, however, struggle with incomplete segmentation when faced with dermatoscopic images that feature complex shapes, non-uniform colors, and intricate edges. In contrast, our MSLUnet network demonstrates superior segmentation capability, achieving more comprehensive segmentation. Nonetheless, it still exhibits some limitations in accurately segmenting edge regions with complex shapes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Architecture** & \begin{tabular}{c} **mIoU** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Dice** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Specificity** \\ **(\%)** \\ \end{tabular} \\ \hline UNet [8] & 85.1 & 86.8 & 91.3 & 93.5 & 78.7 \\ UNet++ [9] & 86.6 & 87.3 & 91.9 & 93.3 & 81.1 \\ Attention Unet [11] & 86.0 & 87.1 & 91.1 & 93.7 & 80.1 \\ ResUnet++ [25] & 85.7 & 86.6 & 90.6 & 93.8 & 79.5 \\ TransUnet [22] & 85.9 & 88.3 & 91.8 & 93.8 & 81.0 \\ ConvUNeXt [15] & 86.2 & 87.2 & 92.0 & 92.9 & 80.5 \\ CMUNeXt [43] & 86.9 & 88.5 & 91.3 & 93.4 & 79.9 \\ UNeXt [14] & 85.4 & 90.3 & **93.2** & 93.9 & 80.7 \\ ULite [29] & 86.0 & 87.0 & 90.8 & 94.0 & 80.0 \\ GCASCADE [45] & 86.5 & 90.9 & 93.1 & 94.7 & 80.9 \\ MSLUnet (our) & **87.3** & **91.4** & 92.9 & **95.1** & **81.2** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of experimental results between MSLUnet and other segmentation models on the ISIC 2018 dataset.  
Figure 6: The qualitative comparison results of U-Net, U-Net++, Attention U-Net, Res-UNet++, SwinU-Net, ConvUNeXt, CMUNeXt, GCASCADE, LM-Net, and MSLUnet on the Kvasir-SEG dataset. The blue boxes display the results of our designed MSLUnet network segmentation, while the red boxes indicate the segmentation regions where our model outperforms the other models.
# Flow-Induced Noise Characteristics Analysis of a Pipeline Structure in a Cabin Rigid Corner
## 5 Conclusions  
By using the acoustic phase theory, the double reflection method and the Graf addition theorem, the acoustic radiation function expression in the acoustic domain for vessel cabins with double rigid walls in quarter space was obtained. The acoustic radiation characteristic of the pipeline structure caused by flow excitation in the vessel cabins was performed. Through numerical calculation, the influence of the flow velocity, frequency and location in the vessel cabins was illustrated. The results of the research show that the distance from the rigid surface in the low-frequency band has undergone a significant enhancement effect. The presence of a rigid wall significantly increases the value of the acoustic radiation parameters of the cylindrical shell structure. As the distance between the centers of the pipeline structure to the rigid wall increases, the influence of the rigid wall decreases. In addition, with an increase in frequency, the influence of the rigid wall decreases. The results of present work can be used as a reference for in pipeline layout design for ships.  
[PERSON] put forward the ideas and theoretical model; [PERSON] and [PERSON] implemented the numerical simulations and data analysis; [PERSON] revised the manuscript; [PERSON] and [PERSON] organized the outline and the whole contents. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**2 (m/s)**} & \multicolumn{3}{c}{**5 (m/s)**} \\ \cline{2-7}  & **10–200 Hz** & **200–1000 Hz** & **10–1000 Hz** & **10–200 Hz** & **200–1000 Hz** & **10–1000 Hz** \\ \hline \(l=0,s=0\) & 54.92 & 79.82 & 79.84 & 62.18 & 87.82 & 87.83 \\ \hline \(l=0.25,s=0.25\) & 60.10 & 86.45 & 86.46 & 67.65 & 94.46 & 94.47 \\ \hline \(l=0.25,s=1.0\) & 65.51 & 86.14 & 86.17 & 72.82 & 94.14 & 94.17 \\ \hline \(l=1.0,s=1.0\) & 62.20 & 85.89 & 85.91 & 69.26 & 93.88 & 93.90 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of total level of acoustic power with different flow velocities (dB).
# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
## 6 Conclusions  
A radar gesture recognition method based on a finite-state machine combined with a bidirectional LSTM fusion neural network is proposed to address the issue of gesture recognition in human-computer interaction applications.  
Firstly, the characteristics and motion parameters of gestures are discussed. Based on the complexity of gesture motion trajectories, ten typical gestures are divided into six simple trajectory gestures and four complex trajectory gestures. Afterwards, for simple trajectory gestures, gesture motion trajectories are extracted by processing radar echo signals and six simple trajectory gestures are classified and recognized by solving trajectory parameters and utilizing a finite-state machine. For complex trajectory gestures, as they cannot be directly distinguished by a state machine, deep learning is used to recognize and classify complex trajectory gestures. A bidirectional LSTM fusion neural network is proposed to classify and recognize four complex trajectory gestures. Finally, multiple sets of gesture data are collected to validate the effectiveness of the proposed algorithm, including 480 sets of data for six simple trajectory gestures and 3000 sets of data for four complex trajectory gestures. After experimental verification, the average accuracy of simple trajectory gesture recognition based on finite-state machine reached 99.58%. For complex trajectory gesture recognition based on a bidirectional LSTM fusion neural network, the accuracy is 99.47%. In addition, the proposed neural network can achieve an accuracy of 98.77% for gesture data that are not included in the training dataset. The experimental results show that the proposed gesture recognition method has good recognition and generalization performance.  
Conceptualization, P.C.; methodology, Y.B. and Z.G.; validation, Y.B. and Z.G.; formal analysis, P.C.; writing--original draft preparation, Z.G. and Y.B.; writing--review and editing, Y.B. and P.C.; visualization, Y.B. and Z.G.; supervision, P.C.; project administration, Q.X.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Fund of Qianjiang Laboratory, Hangzhou Innovation Institute, Beihang University, grant number 2020-Y7-A-010.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We thank all the participants who helped with data collection during the experiment.  
[PERSON] was with the School of Electronic and Information Engineering, Beihang University, Beijing, China, and now is employed by Gaode-Ride Sharing Business, Alibaba (Beijing) Software Services Co., Ltd. The remaining authors 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.
## 4 Technologies for Nanocellulose Production
### Production of Nanocellulose from Cellulose
#### 4.2.4 Ball Milling  
Another method which is used for the preparation of NC is ball milling, also sometimes known as solid-state synthesis. It causes the defibrillation of cellulose fibers with the help of a planetary ball. In this process, cellulose dispersion is put in a hollow cylindrical container which is partially packed with balls. The cylindrical container rotates around its axis, which results in the generation of high shearing forces due to the centrifugal force from the rotating container. These forces cause collision among the balls, and friction between the balls and the container. This collision and friction lead to the fractionation of cellulose fibers,reduction in cellulose length and the degree of polymerization, and weakens hydrogen bonds to yield CNF [104; 105]. For the synthesis of nanocellulose using this method, milling in a wet condition is preferred to preserve the fibrous state and avoid defibrillation to an amorphous form. The characteristics of NC produced through ball milling majorly depends on the number and size of the balls, the milling speed, the state of milling (dry or wet state), the weight ratio between the balls and cellulose, and milling time [35; 106]. Some of the drawbacks of ball milling include the high usage of energy and power, as well as the production of a lot of heat during processing [30].
# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 5 Performance Analysis and Evaluation
### Discussion of Results  
Table 3 provides the achieved accuracy for LOF and GEO. Overall, LOF provided the best results in terms of outlier detection. The reason for this is related to the fact that LOF gives more importance to local outlier detection than other methods such as DBSCAN.  
Moreover, LOF is easier to parameterise because it varies by only one parameter (MinPts) and the variability of this factor can be tested more easily. LOF also shows better accuracy on dense datasets (GEO).  
DBSCAN is more difficult to parameterise due to the fact that one has to articulate two parameters, eps and MinPts, even if a refinement approach, such as kNN (as we have considered) is applied. DBSCAN exhibited better performance on the sparser dataset (PTM), but overall, lower performance in terms of outlier detection. In the same way, we can compare the performance of DBSCAN in the different datasets, highlighting the fact  
Figure 10: DBSCAN performance for the PTM dataset: precision, recall, and accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ML Approach** & **GEO** & **PTM** \\ \hline DBSCAN & 0.76 & 0.19 \\ PTM & 0.86 & 0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average accuracy of DBSCAN and LOF for the datasets GEO and PTM.  
Figure 9: LOF performance for the PTM dataset: precision, recall, and accuracy.  
that the PTM dataset is very small and, in addition, each trajectory is much smaller and covers a much smaller distance than that revealed by the GEO Dataset.  
However, as mentioned, LOF behaves very well in both datasets, showing an accuracy above 80%. While DBSCAN exhibits a more variable behaviour, as accuracy significantly lowers when DBSCAN is applied to a sparse dataset such as PTM.
# Review on the Recent Numerical Studies of Liquid Atomization
## 2 General Overview of Liquid Atomization
### Two Atomization Regions
#### 2.2.1 Primary Breakup  
Under different combinations of fluids properties and flow speeds, primary atomization would demonstrate clear visual distinction, forming the so-called different atomization regimes. The essential reasons resulting in these various atomization phenomena are the variation of the importance of different forces, such as the inertia force, surface tension force, and viscous force. For different layouts of nozzles, the regimes that can be exhibited with the increase of flow speed also change.  
For round jets that are not gas-assisted, dripping regime, Rayleigh regime, first wind-induced regime, second wind-induced regime, and atomization regime appear in sequence with the increase of the liquid speed, as shown by Figure 2a. For round jets with coaxial gas-assisted, three atomization regimes can be identified as Rayleigh-type breakup, membrane-type breakup, and fiber-type breakup with the increase of relative speed between phases. [PERSON] [30] reviewed the experimental investigations on confirming these regimes and exploring the underlying mechanisms. His review covered round jets, slot sheets, air-assisted round jets, and air-assisted slot sheets. The varying competing factors including turbulence energy, Rayleigh-Taylor (RT) instability, and Kelvin-Helmholtz (KH) instability contribute to the regime transitions.  
For crossflow atomization in Figure 2b, the jet is curved towards the gas flow direction by aerodynamic forces. At low gas speed, the liquid jet only breaks up at the end of the column, called column breakup. The column breakup resembles droplet breakup and can show bag mode, multimode, sheet-thinning mode, shear mode, and catastrophic mode. As the gas speed increases to some extent, surface breakup occurs  
Figure 2: Primary breakup regimes for round jet (**a**) and crossflow (**b**). (**a**) Reprinted from [PERSON] [30]. (**b**) Reprinted from [PERSON] and [PERSON] [31].  
Detailed reviews on the cross-flow atomization can be found in works of [PERSON] et al. [32] and [PERSON] and [PERSON] [33].
# A C-Based Framework for Low-Cost Real-Time Embedded Systems
## 7 Internet Access
### Design and Implementation
#### 7.2.2 Symbolic Name Translation  
Moving on to the second translation step, a variety of well-known techniques are appropriate, also because this function is carried out by the IMS, where plenty of processing power is available. For instance, a hash function can be applied to the character string that holds the symbolic name to derive the index of the corresponding entry in a hash table. An advantage of this approach is that it is quite straightforward and intuitive. However, it also has several downsides typical of hash tables:  
* The hash function may lead to a hash value that is quite large. Since this value indicates the position of a symbolic name in the hash table, the table itself may use a large amount of memory space, although only a small portion of its elements are filled with real data. On the other hand, a hash function with a smaller range incurs in a higher probability of collisions, thus reducing access efficiency.
* Even though the probability may be low, a hash conflict, which arises when multiple symbolic names are associated with the same hash value, cannot be ruled out. In this case, further checks and the overhead associated with them are needed to avoid operating on the wrong variable.  
Regardless of the implementation technique, the macro substitution technique illustrated previously is still useful for generating most of the required data structures and code at compile time. As an example, Figure 11 shows how to synthesize a comparison function that takes as argument a character string and resolves it into an enumerated value. The synthesis is performed completely at compile time and relies on compiler optimizations to enhance code performance and reduce footprint. It is suitable to handle a relatively small number of symbolic names efficiently, without resorting to memory-hungry data structures and complex algorithms at run time. If the input-dependent execution time of the function is a concern, it is possible to work around it by always performing the comparison of the input argument against all the symbolic names before returning the result to the caller.
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
## 3 Risk Analysis of Constellation Collision
### Phase 2: Operational Time at 550 km  
After reaching the mission orbit, the satellite will operate stably at 550 km altitude for five years. The time-dependent variation of the flux of \(\geq\)6 cm objects encountered by Starlink satellites is shown in Figure 3. Due to the constant height of the satellite, the debris flux fluctuates from \(4.50\times 10^{-6}/\text{m}^{2}/\text{a}\) to \(6.50\times 10^{-6}/\text{m}^{2}/\text{a}\), and the overall number has a downward trend.  
The average number of collisions and the relationship between the collision probability and the debris diameter of a single satellite and all satellites in the orbital layer during the working phase are shown in Table 3. During the 5-year operation, the collision probability of orbiting satellites with objects larger than 1 cm is 99.99%, and for objects larger than 6 cm is 63.36%.
# Effects of Vibration on Adjacent Pipelines under Blasting Excavation
## 5 Soil Vibration Response above the Buried Pipeline
### Soil Vibration Response above the Buried Pipe  
The surveying points were arranged to analyze the axial \(PPV\) distribution at 15 m intervals directly above the blast source. Figure 9 shows that as the tunnel and pipeline distance was reduced by the tunnel blasting excavation, the \(PPV\) increased. The maximum \(PPV\) appeared at the horizontal distance of 0 m from the explosion source when the axial distance was 0 m with a value of 2.6 cm/s. The minimum \(PPV\) appeared at the horizontal distance of 60 m from the explosion source when the axial distance was 40 m with a value of 0.7 cm/s. It can be observed in the same horizontal, with increased distance from the  
Figure 8: Pipeline cross-section dynamic response characteristics under this study’s construction conditions: (**a**) peak particle velocity; (**b**) peak effective stress.  
blast source, the vibration speed also displayed a decaying trend in a horizontal direction, and the _PPV_ above the blast source was greater than that of the two sides.  
To analyze the _PPV_ distribution of the ground soil directly above the blast source, several surveying points were selected in the surface soil on the tunnel centerline (Figure 10). The maximum _PPV_ appeared directly above the blast source with a value of 2.6 cm/s, and the minimum appeared at 40 m with a value of 1.42 cm/s. As their distance from the blast source increased, the excavation and unexcavated areas had different attenuation amplitudes. The attenuation amplitude of the unexcavated area was greater than that of the excavation area. This is because the latter area was excavated into a cavity as part of the vibration enlargement effect, forming a cavity effect.