esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Spatial multispectral imaging technology can selectively image in specific spectral bands, and the filter wheel is a core component for multispectral selection. At present, there are relatively few types of spectral bands for the filter wheel under limited space/weight constraints. Addressing the challenges presented by this issue, this paper introduces an innovative design approach for the development of a double-layer or even multi-layer filter wheel that is operated by a solitary motor in conjunction with a differential gear mechanism, enabling a vast array of spectral segment combinations within a highly compact layout. A detailed design is implemented for the double-layer filter wheel, including comprehensive modal and dynamic analyses. The results of the modal analysis attested to the structural stability of the component, and the outcomes of the dynamic analysis validated the component's timely and reliable switching capabilities. A prototype was meticulously crafted and subjected to rigorous testing. The switching functionality was validated during these tests, concurrently affirming the accuracy of the finite element analysis results. Additionally, spectral and application testing confirmed the number of spectral segments and the practical utility of the components. The research presented in this article introduces an innovative design concept for multispectral imaging filter-wheel mechanisms, providing a valuable reference and profound insights for the design and arrangement of a double-layer or even multi-layer filter wheel.  
spatial multispectral imaging; double-layer filter wheel driven by single motor; compact layout; finite element analysis; performance test +
Footnote †: journal: Applied sciences  
0000-0001-5000-0001-5000][PERSON]  
0000-0001-5000-5000][PERSON]  
0000-0001-5000-5000][PERSON]  
0000-0001-5000-5000-5000][PERSON]
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 5 Simulation
### CBR Performance
#### 5.1.3. Average Hop Count  
Average hop count is studied by varying the vehicular moving speeds. As shown in Figure 11, when the moving speed is increased, the hop count becomes lower because the rapidly changed topology makes packets have more chances to be carried. The average hop count of the proposed CBR is higher than the other two schemes. It is because the delivery paths selected by CBR have higher traffic density, and thus increases the forwarded chances. It is worth to compare results in Figure 11 with results discussed above. Although the high moving speeds increase the delivery speeds of carried packets, the network becomes disconnected more frequently. Packets prefer to be forwarded rather than carried since the speed of vehicle moving is not competitive to the speed of packet forwarding. Therefore, the higher hop count of the CBR results a lower end-to-end delay and a higher success rate.  
Figure 11. _Cont._  
Figure 10. Packet delivery ratio vs. vehicular density.
## 4 Evaluation
### Key Results
#### 4.2.1 Model Performance  
The framework achieved a testing accuracy of 95%, demonstrating robust predictive capabilities in IIoT scenarios. The federated approach allowed the model to leverage diverse local data distributions across client nodes, contributing to improved generalization. Training logs showed a consistent reduction in loss, culminating in a final training loss of 0.0150 and an accuracy of 0.9972, as shown in Figure 5. These metrics validate the framework's suitability for predictive maintenance and other real-world applications.  
In this experiment, FL enabled the aggregation of locally trained models, ensuring that the global model benefited from diverse and distributed data while preserving privacy. The use of weighted federated averaging further optimized contributions based on node-specific data quality and volume, improving overall performance.
# Melodic Similarity and Applications Using Biologically-Inspired Techniques
## 5 Analysis of Melodic Stability
### Setup  
We are interested in applying the best alignment configuration (as established on Section 4) to the TuneFam-26 and Csv-60 melodic datasets. We can later perform an analysis on the aligned cliques (tune families or cover song melodies) by using an appropriate measure of stability applied on each column of the MSA. The results from Section 4 have indicated that the best MSA algorithm for melodic sequences is Mafft, while its globalpair and localpair strategies are indistinguishable in terms of alignment quality. Gap settings have little or no effect per strategy, Mafft options and gap penalties had minimal effect on alignment quality, so we explored several parameterizations: Mafft-globalpair with \((-4,-2)\) gap penalties, Mafft-globalpair-allowshift with \((-4,-2)\) gap penalties and Mafft-localpair-allowshift with \((-2,-1)\) gap penalties.  
A quantitative measure of stability, suitable for music sequences, does not exist as a result of the lack of supporting literature and research. Nevertheless, [PERSON] and [PERSON] [67] use a probabilistic interpretation of the WSOP measure that aims to answer the following question: given that we observe a single, randomly chosen melodic element, what is the probability for this element to appear unchanged when we observe a new, unseen a variation of it. In practice, given a set of \(k\) alignedsequences of length \(m\) such as \(S_{i}:s_{i,1},,s_{i,2},...,s_{i,m}\), the stability of the non-gap symbol \(e\) in position \(j\) is defined as:  
\[stab(e,j)=\frac{\sum_{i=1}^{k}|s_{i,j}=e|-1}{k-1} \tag{10}\]  
while the stability of the \(j\)-th MSA column is simply \(PS_{j}=\sum stab(e,j)\) over all unique \(e\).  
It is worth examining the related bioinformatics literature regarding the equivalent concept of conservation scores. [PERSON] (2007) mentions that \"there is no rigorous mathematical test for judging a conservation measure\". A scoring method can be only judged with respect to biochemical intuition, and therefore a number of conservation scores have been proposed through the years ([PERSON], 2007). The same authors list a number of intuitive prerequisites that a conservation score should fulfil, including sequence weighting (to avoid bias due to near-duplicate sequences) or the consideration of prior amino acid frequencies. However, applying the same prerequisites to music sequences is not supported by any musical literature. Consequently, our analysis adopts two widely used and interpretable conservation scores from bioinformatics: the WSOP score (already discussed thoroughly) and Information Content (IC). Based on [PERSON]'s entropy, the IC score of the \(j\)-th column is defined as such:  
\[IC_{j}=\sum_{i=1}^{N_{a}}P_{e,j}log(\frac{P_{e,j}}{Q_{e}}) \tag{11}\]  
where \(N_{a}\) is alphabet size, \(P_{e,j}\) is the frequency of a particular symbol \(e\) in the \(j\)-th column, while \(Q_{e}\) is the expected frequency of symbol \(e\) in the dataset (prior). It should be noted that symbols in a column with zero frequency are not taken into account.
## Materials
### Data used for Geoscience Laser Altimeter System (GLAS) Leaf Area Index (LAI) Inversion
#### 2.2.2 GlobeLand30 Landcover Data  
The GlobeLand30 landcover images from paths 112-123 rows 23-30 were used to mask out the non forest-covered GLAS data in Heilongjiang Province so that we could focus on the forest. This product was produced by using multisource high-resolution remote sensing data, which include Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) multispectral images and Chinese Environmental Disaster Alleviation Satellite (HJ-1) multispectral images [41]. Ten land cover types were classified in GlobeLand30, as shown in Figure 1, and the accuracy of GlobeLand30 was approximately 83% at the global scale [42].
## 5 Conclusions  
The surface crack of a gear tooth is considered as an early fault that causes the spalled defect. However, there has been few discussions on the dynamic modelling of surface cracking. Thus, an analytical calculation model of the mesh stiffness of gear with tooth surface crack is developed. Then, based on the presented model, the effects of the surface crack propagation on the meshing stiffness and dynamic characteristics of gear system are investigated and discussed. Several conclusions are obtained:  
(1) The mesh stiffness of gear system will decrease gradually with the surface crack propagation, and it decreases significantly when the crack propagation evolves into a spalling fault.  
(2) Compared with the time domain responses of DTE and acceleration, the sideband frequencies in the frequency domain responses are more sensitive to the surface crack propagation.  
(3) The side frequencies of DTE increase faster and change more obviously than those of the acceleration during the surface crack propagation, which is more conducive to the surface crack fault diagnosis.  
It is worth mentioning that the calculation accuracy of the proposed model has not been verified in this paper. Therefore, the authors hope to use the finite element method, experimental method, or other effective methods to verify its computational accuracy in future research work, and finally apply it to the field of early spalling fault diagnosis.  
Figure 16: BAR of different fault cases: (**a**) DTE, (**b**) acceleration.  
Author ContributionsWriting--original draft, L.-t.Y.; conceptualization, Y.-m.S. and L.-m.W.; methodology, L.-k.Z.; validation, J.X. and L.-m.W.; software, W.-w.J. and L.-t.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"the National Natural Science Foundation of China, grant number 51905053, 52035002\", \"the China Postdoctoral Science Foundation, grant number 2019M6533 35\" and \"the Chongqing Postdoctoral Science Foundation, grant number cstc2019 jcyj-bshX0119\".  
Data Availability StatementData is contained within the article (Grant No. 51905053, 52035002), the China Postdoctoral Science Foundation (Grant No. 2019M653335) and the Chongqing Postdoctoral Science Foundation (Grant No. cstc2019 cyj-bshX0119).  
The authors state that there are no conflict of interest in this paper.
## Network Setting and Computational Model
### Computation-Offloading Model
#### 3.4.2 mMEC Offloading  
Computational tasks can be executed on the mMEC following initial transmission to the SBS through the wireless access link (from MD to SBS) and then migrated to the mMEC through a fiber-optic link (from SBS to MBS). We denote the transmission data rate of the fiber-optic link between each SBS and the MBS as \(\beta\). The transmission time when the computational task \(I_{m}\) migrates from the SBS \(n\) to the mMEC via the fiber-optic link is calculated as follows:  
\[T_{mm}^{tm}=\frac{D_{m}}{\beta}. \tag{8}\]  
Let \(f_{0}\) denote the computing resources allocated to each offloading MD by the mMEC, which is assumed to be constant and identical to all offloading MDs [10; 16]. The computation time for the computational task on the mMEC is given by  
\[T_{mn}^{em}=\frac{C_{m}}{f_{0}}. \tag{9}\]  
The completion time \(T_{mn}^{mbs}\) when the computational task \(m\) is executed on the mMEC can be computed by the sum of (1) the transmission time between MD \(m\) and SBS \(n\) according to Equation (4)via wireless access, (2) the transmission time \(T_{mn}^{tm}\) from SBS \(n\) to the mMEC through the fiber-optic link, and (3) the computation time \(T_{mn}^{cm}\) at the mMEC:  
\[T_{mn}^{mbs}=T_{mn}^{t}+T_{mn}^{tm}+T_{mn}^{cm}=\frac{D_{m}}{R_{mn}}+\frac{D_{m }}{\beta}+\frac{C_{m}}{f_{0}}. \tag{10}\]  
Similarly, the energy consumption when task \(I_{m}\) is offloaded to the mMEC (denoted by \(E_{mn}^{mbs}\)) is equal to the transmission energy consumed \(E_{mn}^{t}\) according to Equation (7):  
\[E_{mn}^{mbs}=E_{mn}^{t}=p_{m}\frac{D_{m}}{R_{mn}}. \tag{11}\]
# An Open Data Approach for Estimating Vegetation Gross Primary Production at Fine Spatial Resolution
## 4 Discussion
### Challenges and Future Work  
Even though the fine-spatial-resolution GPP can reflect the spatial heterogeneity of the GPP, its modeling accuracy is affected by the acquisition of remote sensing data. Unlike the coarse-spatial-resolution remote sensing data, which have more than two observations at any given location of the land surface each day, the Landsat series satellite produces sparse observations. On average, for each Landsat satellite had 23 observations at the same location each year, but due to cloud cover, 50% of the cloud-free data were missing [14; 15; 26]. Our results showed that the total amount of reconstructed NDVI based on the long-term gap-filling method is around 50% for most of the vegetation types (Figure 8A), which leads to high uncertainties in the \(\mathrm{GPP_{L}}\) (Figure 8B). Similarly, a recent study showed that around 50% of Landsat data are cloud-covered, leading to uncertainties in crop yield estimations [27; 28].  
This indicates that the modeling accuracy of fine-spatial-resolution GPP is highly affected by the data gaps in the time series of fine-spatial-resolution remote sensing data. There are some solutions for this: first, different sources of fine-scale data can be harmonized, such as in the harmonization of Landsat and Sentinel-2 data [29], but this approach is limited by the shorter availability of Sentinel-2 data; second, researchers use data fusion methods with coarse-spatial-resolution remote sensing data [30; 31], as in the case of MODIS-Landsat data generation, but this approach requires the recalibration of the reflectance at each channel [32; 33]; third, researchers use a data assimilation method to generate Landsat time-series data with climate data [34], but this approach needs huge amounts of computing resources, so it is not available for large-scale evaluation on the cloud platform. Therefore, future work on mapping the GPP on a continental scale should apply fine-scale spatio-temporally reconstructed cloud-free remote sensing data.
## 3 Results and Discussion
### CuBTC@PET Characterization Analysis
#### 3.2.1 Superhydrophobic CuBTC@PET Water Stability Analysis  
Figure 10b shows the CuBTC@PET water contact angle of 77.2\({}^{\circ}\). When the water droplets stayed on the composite fibers for one minute, it was observed that the water  
Figure 8: FTIR patterns of different fiber membranes.  
Figure 9: (a) XPS spectra of different fiber membranes, (b) C 1s spectra of SCP.  
was gradually absorbed by the CuBTC@PET composite fibers. This phenomenon may be attributed to the porous structure of CuBTC, which promotes the absorption of water molecules. Figure 10a shows that the hydrophobicity of SCP is significantly improved, and the water contact angle reaches \(146.4^{\circ}\). The water droplets remained intact off the fiber surface after one minute, and no water absorption by CuBTC was observed.  
Figure 11 shows the XRD spectrum before and after hydrophobic modification at different immersion times. Through Figure 11a, it can be seen that the initial CuBTC@PET fiber membrane has diffraction peaks at \(2\theta=6.61^{\circ}\), \(9.42^{\circ}\), \(11.50^{\circ}\), \(13.30^{\circ}\), \(14.55^{\circ}\), \(14.90^{\circ}\), \(16.35^{\circ}\), \(17.36^{\circ}\), \(18.90^{\circ}\), \(20.10^{\circ}\), \(24.01^{\circ}\), \(25.83^{\circ}\), \(29.20^{\circ}\), \(35.13^{\circ}\), and \(39.09^{\circ}\), which correspond to the characteristic peaks of CuBTC [34], and the characteristic peaks appeared at \(2\theta=7.82^{\circ}\) and \(9.04^{\circ}\) with the increase in immersion time, which may be the low-angle diffraction characteristics of the large-size pore structure of Cu-based MOFs [45]; the characteristic peaks at \(2\theta=7.82^{\circ}\) and \(9.04^{\circ}\) also tend to disappear when the immersion time was further increased up to \(6\) h. The characteristic peaks at \(2\theta=7.82^{\circ}\) and \(9.04^{\circ}\) also tend to disappear. It can be proven that the crystal structure of MOFs is completely decomposed after \(6\) h of immersion. Figure 11b shows the XRD patterns of the hydrophobically modified composite fiber membranes soaked for different times, and it can be seen that the composite fibers after hydrophobic modification by POTS still retained the characteristic peaks of the crystal structure of CuBTC after soaking for \(24\) h. After \(96\) h of immersion, the characteristic peaks at \(2\theta=7.82^{\circ}\) and \(9.04^{\circ}\) showed a trend of broadening, which may be due to the hydrolysis of CuBTC crystals, resulting in the non-uniformity of the crystal microstructure and grain size.
## 3 Results
### Contribution of LOS Variation to GPP Increment  
Based on the framework proposed in Section 2.3.3, we conducted a quantitative analysis of the relative contribution of LOS changes to boreal GPP increment during the two distinct periods. Employing the SMIPP model, we simulated pixel-level annual GPP by integrating LOS and \(GPP_{max}\). The developed SMIPP model demonstrated a strong capability to explain over 80% (R\({}^{2}\) > 0.8) of the inter-annual variability in annual GPP across most boreal vegetation regions from 1982 to 2018.  
To isolate the contribution of LOS variations to GPP increases, we conducted a control experimental analysis using the pixel-level SMIPP model. Our findings indicate that, influenced by contrasting changes in boreal vegetation phenology and annual GPP during the two periods, the mean RIE from LOS extension has increased by 22% in the boreal terrestrial ecosystem, rising from 3.20 in P1 to 3.93 in P2 (Figure 8g). The changes in RIE displayed spatial heterogeneity across different vegetation types and geographical locations. While the mean RIE from forestlands LOS extension decreased from 5.08 in P1 to 3.45 in P2, those of shrublands and grasslands experienced substantial enhancements, increasing from 0.51 and 2.36 in P1 to 5.92 and 3.39 in P2, respectively (Figure 8d-f). Spatial patterns of RIE during both periods also reveal contrasting contributions of LOS changes to GPP increments at lower and higher latitudes. In the earlier period, positive RIE values were predominantly concentrated at lower latitudes, which are primarily covered  
Figure 7: Long-term trends in pixel-level and regional mean annual GPP of boreal vegetation during the two periods. Panels (**a**,**b**) present the spatial distribution of pixel-level GPP trends for the two study periods. Panel (**c**) shows the percentage of pixels exhibiting positive trends for different vegetation types. Panels (**d–f**) display the frequency of pixel-level linear trends in forestlands, shrublands, and grasslands during the two periods. Panels (**g–i**) present the time series and linear trends of regional mean GPP during P1 and P2.  
by boreal forests. Conversely, in the recent period, positive RIES have shifted northward to higher latitudes, primarily dominated by shrublands. This northward expansion suggests that during P2, the contribution of LOS extension to GPP increments has become more pronounced, potentially due to enhanced warming in these high-latitude regions.
## 1 Introduction
In search and rescue scenarios--where environments are highly complex and individuals may be obscured or deeply integrated with challenging backgrounds--PSPNet is particularly well suited for personnel segmentation. To address the need for efficient computation in image processing [15], researchers have developed various lightweight neural network architectures, including SqueezeNet [16], ShuffleNet [17], MobileNetV1 [18], and MobileNetV2 [19]. SqueezeNet reduces the number of parameters by replacing some 3 \(\times\) 3 convolutions with 1 \(\times\) 1 convolutions, utilizing the Fire module, which combines Squeeze layers (1 \(\times\) 1 convolutions) with Expand layers (1 \(\times\) 1 and 3 \(\times\) 3 convolutions) to enhance feature complexity. ShuffleNet reduces computational cost by introducing pointwise group convolutions and employs a channel shuffle mechanism to alleviate information isolation. MobileNetV1 leverages depthwise separable convolutions, which break down convolutions into depthwise and pointwise steps, substantially lowering both computational load and parameter count. MobileNetV2 improves upon V1 by incorporating an inverted residual structure and a linear bottleneck design, greatly enhancing accuracy while maintaining a compact model size. These lightweight architectures significantly reduce computational resource requirements while maintaining high performance, making them ideal for mobile search and rescue applications and real-time processing. The task of segmenting occluded individuals in search and rescue images often suffers from poor recognition accuracy, especially in complex backgrounds with image blurring and limited computational resources. To overcome these challenges, we propose the MHC-PSPNet algorithm, an enhanced version of PSPNet, which achieves the high-precision segmentation of individuals in search and rescue scenarios. The subsequent sections will introduce the MHC-PSPNet algorithm in detail and validate its segmentation accuracy for personnel images in complex rescue environments through experimental results.
## 3 Sensor Network Architectures for Monitoring Underwater Pipelines
### Integrated Wired/RF Wireless Sensor Networks  
To enhance the communication of the architecture described in Section 3.4, radio frequency channels can be used instead of acoustic communication channels, as shown in Figure 7. In this architecture, a buoy equipped with a radio transceiver is released when there is a cut or fault in the wire connecting a pair of nodes as shown in Figure 7. The buoys are attached to the nodes and they only float on the surface if there is a need for them. The transceivers on the floating buoys are activated to provide communication and connectivity among them thus replacing the broken link underwater.  
Figure 6: Integrated wired/acoustic wireless sensor network.  
This architecture is very similar to the one discussed in the previous section; however, it can provide better communication as radio frequency channels are used instead of the acoustic channels that have lower network properties. This architecture will provide better physical security protection for the network than the RF wireless sensor network architecture described in Section 3.3 as the buoys will not appear unless there is a problem with the wired connections. In addition, only sparse pairs of buoys will be deployed at a time thus they will not reveal the full location or spread of the pipelines.
## 6 Performance Evaluation
### Simulation Results
#### 6.4.5 Permanent Node Failure Rate Test (Fifth Simulated Scenario)
In conclusion, ASAART protocol proves to be energy efficient and offer a high resiliency against faults and errors in the presence of permanent node failure rate and in highly congested and scalable network compared with SSR and SHR protocols. Figure 11: Permanent node failure rate test (fourth simulated scenario) with source node = 20: (**a**) Average packet delivery rate (success rate); (**b**) End-to-end delay; (**c**) Energy consumption; (**d**) Transmitted MAC layer packets. Figure 12: _Cont._