esa-sceva/satcom-chunk-collection huggingface dataset:
# A Filter Pruning Method of CNN Models Based on Feature Maps Clustering
## 3 Framework of Retinanet
### Fpn  
Before the appearance of FPN (feature pyramid network), most target detection algorithms only used top-level features to predict. However, this kind of method has its drawbacks because although the semantic information of image samples acquired at the high lever is richer than that at the low level, the target location is rougher than that at the low level. In addition, although an algorithm using a multi-scale fusion method has been proposed, it usually uses the fused features to predict. FPN fuses feature maps from different layers through top-down connections, bottom-up connections, and horizontal connections so that it performs better in small target detection.
# User-Information-Aware D2D Multicast File Distribution Mechanism
## 8 Conclusions  
D2D communication, as a promising technology to solve the shortage of wireless spectrum resources, can provide more high-quality multimedia services for users. By D2D multicast file distribution technology, the redundant transmissions of multimedia files can be reduced, then dramatically improving the efficiency of spectrum and energy. So, substantial research effort has been undertaken to improve the performance of D2D multicast communication. However, the distribution of user's service requests in the time domain is not fully considered in the existing design of D2D multicast communication system. There are a large number of users who have the same service demands with different service request times, which cause repeat transmission for same content, and the efficiency of spectrum and energy can be further improved. In this paper, the willingness of potential service requesters to accept the video push service is predicted firstly, and then the video is pushed to potential service requesters when distributing video for service requesters. The user-information-aware D2D multicast file distribution mechanism is proposed and the efficiency of spectrum and energy are improved dramatically by increasing the number of multicast receivers. To guarantee the QoE of potential service requesters, the users' interests and status information of user's device are fully taken into account in the calculation of the willingness of potential requesters. With the consideration of the social relationships and the channel quality information about users, the D2D multicast utility metric is proposed, and then a cluster head selection scheme is developed to maximize the utility value. The simulation results show that the file distribution mechanism proposed in this paper can greatly improve the system's throughput, thus improving the efficiency of spectrum and energy, and the transmission rate also has an excellent performance.  
The work presented in this paper is a collaborative development by all of the authors. [PERSON] and [PERSON] proposed the idea and involved in the theoretical performance analysis; [PERSON] and [PERSON] designed and optimized the algorithm; [PERSON] and [PERSON] wrote the paper; [PERSON] participated in discussion and made many valuable suggestions.  
This work was supported in part by the National Natural Science Foundation of China under Grant No. 61871062, and in part by the Scientific Research Foundation of CQUPT under Grant No. A2018-07.  
The authors declare no conflict of interest.
# Weight Effects on Vertical Transverse Vibration of a Beam with a Nonlinear Energy Sink
###### Abstract  
Reductions in the vibration of a continuum system via a nonlinear energy sink have been widely investigated. It is usually assumed that weight effects can be ignored if the vibration is measured from the static equilibrium configuration. The present investigation reveals the dynamic effects of weight on the vertical transverse vibrations of a Euler-Bernoulli beam coupled with a nonlinear energy sink. The governing equations considering and neglecting weights were derived. The equations were discretized with some numerical support. The discretized equations were analytically solved via the harmonic balance method. The harmonic balance solutions were compared with the numerical solution via the Runge-Kutta method. Finite element simulations were performed via ANSYS software (version number: 2.2.1). Free and forced vibrations, predicted by equations considering or neglecting the weights, were compared with the finite element solutions. For the forced vibrations, the amplitude-frequency responses determined by the harmonic balance method agree well with those calculated by the Runge-Kutta method. The free and forced vibration responses predicted by the equations considering the weights are closer to those computed by the finite element method than the responses predicted by the equation neglecting the weights. The assumption that weights can be balanced by static deflections leads to errors in the analysis of the vertical transverse vibrations of a Euler-Bernoulli beam with a nonlinear energy sink.  
vertical transverse vibration; beam; nonlinear energy sink; harmonic balance method; finite element method 2025  
151380  
1
## 3 Numerical Simulation of Tunnel Face Reinforcement Scheme
### Analysis of Simulation Results for Tunnel Face Reinforcement Schemes
#### 3.2.3 Analysis of Maximum Ground Deformation  
The previous section qualitatively analyzed the cross-sectional and longitudinal deformation patterns in the reinforced zone of the tunnel. To quantitatively assess the maximum ground deformation at various tunnel positions, the maximum settlement and heave values during excavation for both Scheme 1 and Scheme 2 were extracted. The results are illustrated in Figures 5 and 6.  
As illustrated in Figure 5, with the advance of tunnel excavation, the maximum ground settlement under both schemes initially increases, then decreases, increases again, and eventually stabilizes. The maximum settlement occurs at the interface between the reinforced and unreinforced zones, reaching \(-\)59.53 mm in Scheme 1, which exceeds the deformation warning threshold. Scheme 2 exhibits a maximum settlement of \(-\)26.94 mm. Specifically, settlement in Scheme 1 ranges from \(-\)59.53 mm to \(-\)31.35 mm, far beyond the warning threshold, with a settlement range of 28.18 mm. In Scheme 2, settlement ranges from \(-\)26.94 mm to \(-\)6.80 mm, remaining within the warning threshold, with a range of 20.14 mm. The settlements under Scheme 2 are consistently smaller, and the distribution is narrower, indicating lower deformation variability due to excavation.  
As illustrated in Figure 6, with the advancement of tunnel excavation, the maximum ground heave under both schemes exhibits a pattern of increase, decrease, and stabilization. Scheme 1 reaches the peak heave of 42.83 mm at segment ring 7, exceeding the warning threshold, while Scheme 2 reaches a maximum of 29.83 mm at segment ring 30, close to the threshold. Specifically, Scheme 1 shows heave values from 18.73 mm to 42.83 mm, with a range of 24.10 mm, partially exceeding the warning threshold. Scheme 2 shows heave values from 9.52 mm to 29.83 mm, all within the threshold, with a range of 20.31 mm.  
Figure 5: Maximum settlement values of strata under different conditions of reinforcement schemes.  
Figure 6: Maximum uplift values of strata under different conditions of reinforcement schemes.  
The heavy observed under Scheme 2 is consistently lower and the fluctuations caused by excavation are less pronounced.  
At the 8-10 m range, the shield machine had already exited the reinforced area and was no longer cutting through the jet grouting piles. Meanwhile, the unreinforced stratum is comparatively weaker than the reinforced zone. As the thrust force remained constant during excavation, both the maximum settlement and maximum uplift exhibited abrupt changes. As the shield machine gradually moved away from the reinforced zone and the soil properties around the machine became more uniform, the maximum settlement and uplift values gradually decreased and stabilized. These results are consistent with the findings of [PERSON] [22] and [PERSON] [23].
## 2 Material and Methods
### Top-of-Canopy Spectral Measurements of Reflectance and Sun-Induced Fluorescence  
TOC spectral radiances were collected on 11 July 2015 at midday from 11:00 to 14:20 solar time under clear sky conditions at nine plots (V1-V9) located along the main boardwalk in S-N direction and representing different vegetation--i.e., from _Carex_ and _Typha_ (high biomass of vascular plants) to _Sphagnum_ (low biomass of vascular plants) dominated groups (Figure 2, Table 2). The two spectrometers (HR4000, OceanOptics, Largo, FL, USA) covering different wavelength ranges were used to obtain TOC reflectance and SIF. One spectrometer operated in the visible and near-infrared (350-1050 nm) spectral range with a FWHM of 1 nm, suitable for computation of incident irradiance, visible to near-infrared reflectance, and VIs. The other spectrometer covered the 650-840 nm spectral range with a spectral resolution of 0.2 nm (FWHM) and was designed particularly for the estimation of SIF in the oxygen absorption bands O\({}_{2}\)-A and O\({}_{2}\)-B positioned at 760 nm (F760) and 687 nm (F687). Both spectrometers were housed in a Peltier thermally regulated box (model NT-16, Magapor, Zaragoza, Spain), keeping the internal temperature at 25 \({}^{\circ}\)C in order to reduce dark current drift (i.e., changes of the instrument noise level caused by temperature variations) [PERSON] et al. (2018). The spectrometers were spectrally calibrated with known standards (CAL-2000 mercury argon lamp, OceanOptics, Largo, FL, USA), while the radiometric calibration was inferred from cross-calibration measurements performed with a reference calibrated FieldSpec spectrometer (Analytical Spectral Device, Boulder, CO, USA). Bare fiber optics with a field of view (FOV) of 25\({}^{\circ}\) were used to measure a white reference calibrated panel (Labsphere Inc., North Sutton, NH, USA) and the vegetated targets. To facilitate these measurements, the fibers were mounted on the tripod. The manual rotation of the mast allowed sequential measurements over the white reference panel and the vegetated targets [for details, see 23]. The targets were measured from nadir at a distance of 100 cm, corresponding to a sampling area of about 40 cm in diameter. \begin{table}
# Closing Editorial for Computer Vision and Pattern Recognition Based on Deep Learning
## 6 Compression  
[PERSON] et al. [27] proposed RBENN, a Quality Enhancement Network dedicated to improving reference blocks in inter prediction for hybrid video coding frameworks. It operates on luma reference blocks pre-motion compensation, enhancing them for better coding efficiency.  
[PERSON] et al. [28] proposed an optimization strategy for video codecs in cloud gaming by incorporating deep learning networks. Specifically, it introduces CMGNet, a camera motion-guided network, for enhancing reference frames in cloud gaming videos, thereby reducing bitrate consumption. CMGNet utilizes camera motion information to improve frame alignment and fusion, enhancing the quality of reference frames for better compression efficiency.  
[PERSON] et al. [29] introduce an end-to-end learning-based approach for compressing point cloud attributes (PCACs) to reduce transmission and storage costs. It utilizes a sparse convolution-based variational autoencoder (VAE) structure and incorporates an attention mechanism, specifically a non-local attention module, to capture local and global correlations in both spatial and channel dimensions. Additionally, a modulation network enables variable rate compression within a single network, eliminating the need to store multiple networks for different bitrates.
## 2 Methods: Experiment and Numerical Simulation
### Theory of Numerical Simulation Method
#### 2.2.3 Fluid Equations of Motion
Therefore, the comparative studies on turbulence models are ignored in this paper, and only the optimal turbulence model is selected for calculation, i.e., SST \(k\)-\(\omega\), which is also to establish a constant reference benchmark. The SST \(k\)-\(\omega\) model has been shown to predict the onset of flow separation under adverse pressure gradients [28; 29] without computation intensively. Moreover, the SST \(k\)-\(\omega\) model solves the boundary layer with the aid of wall functions [28], which model the wall velocity profile when the near-wall y+ values are larger than 12, thereby avoiding fully resolving the viscous sublayer with a very large number of mesh elements. In the numerical solution process, the continuum equation, momentum equation and turbulence equation are discretized by second-order upwind scheme, and the pressure-velocity coupling is solved iteratively using Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm. The hydrodynamic forces and moments applied on the hull at different times are evaluated by integrating pressure and shear data along the surface. Combined with the 6 DOF equation of the submarine, the time-varying curves of the emergency rising motion are solved. In the meantime, the colorful cloud map of flow field information such as velocity, pressure and vorticity can be captured.
# A Trustworthy Framework for Skin Cancer Detection Using a CNN with a Modified Attention Mechanism
## 4 Results and Discussion
### Model Performance Metrics  
This subsection focuses on the performance evaluation metrics to evaluate the effectiveness and robustness of the proposed model. To evaluate this capability, we used an extensive range of performance indicators such as accuracy, precision, recall, and F1-measure.  
Accuracy assessment is a critical parameter for evaluating the overall efficacy of the skin cancer detection models. This metric quantifies the ratio of accurately classified skin cancer instances to the total number of instances included in the dataset. Mathematically, the accuracy is determined by the following calculation:  
\[Accuracy=\frac{Correct\ predictions}{Total\ number\ of\ predictions} \tag{6}\]  
A high accuracy score indicates that the model detected skin cancer and noncancer instances well, but other measures were required for imbalanced datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Class** & **Before Preprocessing** & **After Preprocessing** \\ \hline
1 & Akiec & 327 & 1099 \\
2 & Bcc & 541 & 1099 \\
3 & Bkl & 1099 & 1099 \\
4 & Df & 155 & 1099 \\
5 & Nv & 6705 & 6705 \\
6 & Mel & 1113 & 1113 \\
7 & Vasc & 142 & 1099 \\ \hline \end{tabular}
\end{table}
Table 1: Data with and without preprocessing.  
Figure 4: Sample images of each class from the HAM10000 dataset.  
Precision is a metric that quantifies the proportion of correctly identified positive cases relative to the overall number of positive predictions generated by the skin cancer detection model. In the context of our study, precision plays a crucial role in evaluating the capacity of the model to accurately detect instances of skin cancer while minimizing the occurrence of false-positive classifications for noncancer cases. Precision was computed using the following formula:  
\[Precision=\frac{True\;Positives}{True\;Positives+False\;Positives} \tag{7}\]  
Recall, sometimes referred to as the sensitivity or the true positive rate, measures the capacity of the model to accurately identify and include all skin cancer occurrences in a dataset. This measures the ability of a model to correctly identify all positive instances out of the total number of positive instances. The concept of recall is commonly referred to as:  
\[Recall\;(Sensitivity)=\frac{True\;Positives}{True\;Positives+False\;Negatives} \tag{8}\]  
A higher recall indicates that the model has a lower rate of missing positive instances, meaning that it is better at capturing all instances of the positive class, which is crucial in medical diagnosis to avoid missing potential cases of disease.  
The F1-score combines precision and recall, offering a consolidated measure for evaluating the overall performance of a model. The harmonic means of the precision and recall were computed using the following formula:  
\[F1-Score=2\times\frac{Precision\;\times\;Recall}{Precision+Recall} \tag{9}\]  
When the positive and negative classes are imbalanced, the F1-score helps balance the false positives and negatives in the datasets.
## 5 Analysis of the Transmission Delay  
The target of this section is to introduce an analytical model for the end-to-end transmission delay caused by the beacon-less timer setting in a route setup phase, i.e., to assess the expected value of the \(T_{i}^{c}\) components of the overall content delivery time \(T_{\rm e2e}\) in Equation (1).
# Blockchain and IoT Convergence--A Systematic Survey on Technologies, Protocols and Security
## 7 Blockchain-IoT Projects
### Iota  
IOTA [34; 35] is the project with highest market cap [81] between all the IoT-Blockchain projects analyzed in this paper. The validation process is not based on consensus, no classical mining scheme, no fee required for the transactions. The data structure used to handle the transaction is a direct acyclic graph-based ledger called 'The Tangle' [35], in which when a node submits a transaction the system uses Markov Chain Monte Carlo (MCMC) algorithm to select two unconfirmed transactions to check if these do not produce conflicting results (Figures 3 and 4).  
IOTA uses Proof of Work as an anti-Sybil [82] measure. In this way the consensus and validation are done by the whole network of active participants by giving everyone an equal say in the network regarding the transaction making process. This is a radical shift of mind because differently from canonical Blockchain schemas, the Tangle is probabilistic. Full consensus for a transaction is only reached once (almost) all network participants have repeatedly certified that the transaction is more valid than another transaction. There is no global consistency in the tangle architecture. There is eventual consistency. This is related to the CAP theorem [83]. If a transaction is referenced directly or indirectly by every new transaction then it can be considered \"confirmed\" with high likelihood. Advantages over traditional Blockchain technology:  
* Highly Scalable: Instead of storing transactions in blocks with a limited size, each transaction lives on its own and must approve two other transactions. With this method, the number of transactions that can be handled in a certain amount of time increases with the number of transactions.
* No fees: Each node validates its own and two other transactions. No block-mining required.
* Availability: Very high availability tanks to the Tangle architecture with no transaction to be inserted and mined in blocks with the danger of never being verified.
* Partition-Tolerant: Very High partition-tolerant system except for the presence of Coordinators.
* Quantum Computing resistant: A quantum-resistant algorithm called the Winternitz One-Time Signature Scheme is used to providing much better inherent security against the future quantum computer threat. Main drawbacks:
* Coordinators: Is meant for assuring security in the early stage of the network as it grows. It will be eventually removed when the network becomes sufficiently large. Presently is a limit for decentralization and for partition-tolerance of the system.
* Milestones: Will be eliminated in the future but are important to avoid attack to the structure. Without them, the size of the ledger would grow to sizes too big to be handled by most nodes, primarily by IoT devices.
* Low consistency: There is no global state which everybody agrees to. Only after milestones, but will be eliminated in the future.  
Figure 3: Classical Blockchain structure vs. Tangle.
# Human Appropriation of Net Primary Production: From a Planet to a Pixel
## 4 Results
### HANPP at County Mesoscale: The U.S. Case  
The mesoscale, here represented by U.S. counties, is a critical scale through which to examine HANPP because it captures most ecological interdependencies between HANPP-consuming areas--focused on metropolitan areas--and HANPP-producing areas--the agricultural and forestry belts upon which they depend for food, feed, biofuel and biofiber. International trade in biomass products is increasingly important but, because they usually have a low value per unit weight and are variably perishable, most trade occurs over more moderate distances along transport infrastructures that teleconnect these ecological interdependencies [26].  
The variation in HANPP (harvest) among US counties in 2012 varies enormously, from 0.0000363 to 1752 kilotones, a seven order of magnitude range (Figure 3a). Countywide HANPP density in gCm\({}^{-2}\)yr\({}^{-1}\) varies from 0.0003027 to 783 gCm\({}^{-2}\)yr\({}^{-1}\), a six order of  
Figure 2: National-scale comparisons of (**a**) NPP, (**b**) HANPP (harvest), (**c**) NPP (ecological), (**d**) HANPP (harvest/NPP), and (**e**) NPP (ecological)/NPP.  
magnitude range (Figure 3b). We have therefore mapped county-level HANPP (harvest), totaling crops, grazing and forestry, on a logarithmic scale. Methods used to derive this figure are described in [PERSON] et al. (2021).  
Foremost among centers of HANPP (harvest) are intensively cropped regions including, in order of importance, the Midwestern corn and soybean belt, the spring and winter wheat belts of the Great Plains, the Lower Mississippi Valley, the Central Valley of California, the Palouse and Snake River Plain of the northwest, and pockets of crop production on dispersed fertile soils across the eastern U.S. Second in importance are intensively timbered areas spread across much of the southeast, the Pacific northwest and Maine. Grazing harvests NPP at lower levels throughout much of the U.S. (2021). These are the ecosystems upon which Americans and major U.S. export markets rely for products derived from biomass. Estimating embedded HANPP (consumption) and deriving the specific teleconnections among HANPP-exporting and HANPP-importing cities, counties, states and regions awaits further research.
## 4 Experimental Results
### Objective Evaluation  
For the subjective evaluation presented above, the difference is caused by human factors, resulting in errors in the evaluation of the processing results. Therefore, an objective evaluation is essential. This study used the information entropy, mean square error (\(MSE\)), peak signal-to-noise ratio (\(PSRN\)), structural similarity (\(SSIM\)) and processing time as the evaluation criteria, which can effectively prevent the interference of human factors and simultaneously evaluate the processing results of the image in a more comprehensive manner. In addition, the experiments for the proposed method are conducted with the MATLAB R2021a environment on the Windows 10 platform with CPU Intel(R) Core(TM) i5-6300 HQ and 12 GB running memory (Table 1).