esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Method  
### Model and Configurations  
The Advanced Research WRF (ARW) research model and WRF data assimilation (WRFDA), version 3.8.1 [20], were used for deterministic numerical weather forecasting. The fundamental configuration of WRF is listed in Table 1. There were two nested domains in the model, as shown in Figure 1. Domain 1 covered most of the Central Asia region, with a grid spacing of 9 km and 712 \(\times\) 532 grid points. Domain 2 was centered on Xinjiang, China, with a grid spacing of 3 km and 832 \(\times\) 652 grid points. There were 50 vertical computational layers with a pressure top of 10 hPa. Atmospheric and surface fields from the Global Forecast System of the National Centers for Environmental Prediction (GFS) model forecasts (the GFS forecasts) were introduced as the initial conditions. The physics schemes included WSM6 microphysics, ACM2 PBL, Kain-Fritsch deep convection, the RRTMG shortwave and longwave scheme, and the Unified Noah land-surface model. A three-dimensional variational (3 DVar) technique was used for data assimilation. Conventional observations in 6 h cycling assimilation provided the analysis fields, including SYNOP (surface SYNOPtic observations, shown as the red points in Figure 1), METARs (METeorological Aerodrome Reports, airport ground-based), AMDAR (Aircraft Meteorological DAta Relay), and RAOB (RAwinsonde OBservations). In addition to conventional data, radial velocities from radar observations were also assimilated in Domain 2. Ground-level (2m) temperature and relative humidity were used as forecast variables in the post-processing procedures.
## 3 Validation Cases
### Validation Sites  
#### 3.1.1 Walnut Gulch (WG)  
This area is located near the town of Tombstone in southeastern Arizona, USA. There are two sites in this area: Kendall and Lucky Hills. These sites are within the Walnut Gulch Experimental Watershed [24]. Figure 1 shows the location of the sites. The Kendall site has nine nodes, and the Lucky Hills site has seven nodes. The in situ sensors at each node are installed at depths of 5 cm, 15 cm, 30 cm, and 50 cm. In 2015, the sensors were installed, and the site was operational until Oct 2019. After a hiatus during the COVID-19 pandemic, the site was revived in August 2022.  
These sites are relatively dry all year round except in the monsoon season, which is usually in July, August, and the first half of September [25]. Thus, high vegetation water content (VWC) is expected during the monsoon season. Figure 2a,b show photos of the area during the dry season and the monsoon season, respectively, highlighting the presence of green vegetation during the latter.  
Figure 1: Satellite map of WG SoilSCAPE sites.  
To account for the presence of vegetation, an attenuation factor was applied to \(\sigma_{0}\). This is based on the vegetation model of [26] (Equation (11.4)). The normalized BRCS with vegetation is expressed as  
\[\sigma_{0,\text{VEG}}(\vec{\rho}\,)=\exp\left[-\kappa^{p}d\sec\left(\theta_{ \text{i}}\right)\right]\exp\left[-\kappa^{q}d\sec\left(\theta_{\text{s}} \right)\right]\sigma_{0}(\vec{\rho}\,) \tag{21}\]  
where \(\kappa^{p,q}\) is the p,q-polarized extinction coefficient (\(p,q\in\{v,h,l,r\}\)), \(d\) is the vegetation depth, \(\theta_{\text{i}}\) is the local incidence angle, and \(\theta_{\text{s}}\) is the local scattering angle. We refer to the model with this feature as IGOT-VEG. It is worth noting that when \(d\) or \(\kappa\) is zero, the IGOT-VEG reduces to the IGOT model.
# Time Segmentation-Based Hybrid Caching in 5G-ICN Bearer Network
###### Abstract  
The fifth-generation communication technology (5G) and information-centric networks (ICNs) are acquiring more and more attention. Cache plays a significant part in the 5G-ICN architecture that the industry has suggested. 5G mobile terminals switch between different base stations quickly, creating a significant amount of traffic and a significant amount of network latency. This brings great challenges to 5G-ICN mobile cache. It appears urgent to improve the cache placement strategy. This paper suggests a hybrid caching strategy called time segmentation-based hybrid caching (TSBC) strategy, based on the 5G-ICN bearer network infrastructure. A base station's access frequency can change throughout the course of the day due to the \"tidal phenomena\" of mobile networks. To distinguish the access frequency, we split each day into periods of high and low liquidity. To maintain the diversity of cache copies during periods of high liquidity, we replace the path's least-used cache copy. We determine the cache value of each node in the path and make caching decisions during periods of low liquidity to make sure users can access the content they are most interested in quickly. The simulation results demonstrate that the proposed strategy has a positive impact on both latency and the cache hit ratio.  
5G-ICN; mobility; hybrid caching; time segment +
Footnote †: journal:
# A 13.44-Bit Low-Power SAR ADC for Brain-Computer Interface Applications
## 2 Circuit Topology
### SAR Logic Array Design  
The proposed SAR control logic utilizes dynamic circuit techniques to enhance power efficiency, differing from conventional implementations such as ring counters with shift registers (static CMOS) or N flip-flops with combinational logic. As illustrated in Figure 14, this design incorporates event-driven operation that activates only during comparison phases, a dynamic latch array to store comparator decisions until the next reset, and non-overlapping clock control to prevent shoot-through currents.  
The timing diagram of the SAR dynamic logic unit operation is shown in Figure 15. During the sampling phase, when the D signal remains at a low logic level and the Valid signal transitions to a high logic state, the CLK signal asserts a high logic level, thereby pulling the voltage at node Q to ground potential. In this configuration, both the positive input (Pi) and negative input (Ni) of the comparator remain at low logic levels, causing the comparator's differential outputs Outn (negative output) and Outp (positive output) to settle in high-logic states. During the quantization phase, the Sample signal transitions to low logic, while the Valid signal remains highly asserted during the comparator's comparison phase. The CLK voltage is discharged to low logic through transistors M2 and M3. Upon completion of the comparator's comparison operation, one of the differential outputs (Outn or Outp) transitions to low logic. If Outn is driven to low logic, transistor M9 is activated, charging the Pi node to high logic, while the Ni node retains its low logic state. When the Pi node transitions to high logic, transistors M14 and M8 are activated,  
Figure 13: Monte Carlo simulation of comparator offset voltage.  
further charging the Pi node and discharging the Ni node, thereby latching the current comparison result. Subsequently, regardless of changes in the comparator's outputs, the latched result remains stable until a reset signal assertion occurs. During the reset phase of the comparator, the Valid signal is de-asserted (transitioning to low logic). The voltage at node Q is charged to high logic through transistors M4 and M5. This high-logic state at node Q propagates to the subsequent logic gate, triggering a state transition in the downstream circuitry, thereby ensuring that each comparison result is latched.  
The circuit augments the conventional architecture by integrating two bypass MOSFETs (M8 and M11) and two additional inverters. Upon the comparator's decision, this configuration enables faster result latching, relaxes the requirements for enabling signal transmission circuitry, and mitigates logic errors.  
Compared to static implementations, this architecture significantly reduces clock network power, decreases control signal fan-out, and minimizes parasitic capacitance at critical nodes. The absence of continuous DC paths and reduced switching activity collectively enable sub-\(\mu\)W power consumption while ensuring robust metastability handling.
# Birds Detection in Natural Scenes Based on Improved Faster RCNN
## 5 Conclusions  
Aiming at the problem of bird detection in natural scenes, an improved Faster RCNN model is proposed in this paper. The residual network ResNet-50 was used as the backbone network to extract the image features, and the multi-scale feature image fusion strategy was used to detect small scale birds, so as to improve the detection accuracy of difficult samples. On this basis, the soft non-maximum suppression method was used to solve the problem of bird object overlap, and multi-scale training strategy was introduced to further improve the detection accuracy and detection rate of the model. Experimental results show that the model is effective for bird detection in natural scenes, and the mAP of the model on the bird dataset is 89.0%. The performance of the model in this paper is better on mAP and F1_score, and the performance of the model is more stable. However, the model has a poor performance in detection speed and low FPS. Future experiments will focus on the detection speed of FPS, and we will conduct experiments in the future to solve the problem of FPS.  
Conceptualization, Z.S. and W.X.; methodology, Z.S.; software, W.X.; validation, W.X., Z.S. and X.W.; formal analysis, W.X.; investigation, Z.S.; resources, W.X.; data curation, X.W.; writing--original draft preparation, G.Z.; writing--review and editing, Z.S.; visualization, G.Z.; supervision, W.X.; project administration, X.W.; funding acquisition, W.X. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China (No. 51805312).  
Not applicable.  
The authors sincerely thank Shanghai University of Engineering Science, Beijing Jiaotong University, the State Key Laboratory of Automotive Safety and Energy and the School of Vehicles and Mobility, Tsinghua University, Beijing.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **mAP (IoU = 0.5)** & **FPS** & **Precision** & **Recall** & **F1\_Score** \\ \hline SSD300 & 71.23 & 45.64 & 70.86\% & 72.09\% & 71.47\% \\ YOLOv4 & 86.91 & 31.22 & 79.55\% & 81.13\% & 80.33\% \\ Faster RCNN & 84.60 & 12.52 & 73.15\% & 74.22\% & 73.68\% \\ Proposed model & 89.04 & 13.63 & 80.03\% & 79.68\% & 79.85\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of the proposed method and state-of-the-art methods.
# Developmental scRNASeq Trajectories in Gene- and Cell-State Space--The Flatworm Example
## 3 Results
### RNA Velocity Trajectories  
RNA-velocity analysis offers an independent approach of studying developmental dynamics. It calculates the change of mRNA abundance of each gene per unit of time in every single cell making use of the relation between spliced and unspliced mRNA and assumptions about the reaction kinetics of splicing [31]. RNA velocity is positive if the amount of mRNA increases. It refers to the situation when the amount of unspliced mRNA exceeds the amount of spliced mRNA compared with the steady state situation meaning that transcription and mRNA degradation are in equilibrium. Overall, one obtains a multidimensional RNA-velocity vector for each cell where each dimension refers to the rate change of transcription of one gene. The combination of velocities across genes then predicts the future expression state of the cell. In its original implication, the mRNA velocity vector points to direction of increasing mRNA abundance in sample space. For planarian single cell transcriptomics such plots show the expression state to which the cell is apparently moving in time [31].  
Here we apply this concept also to expression space, where RNA velocity refers to the rate change of mRNA expression in each metagene-pixel of the SOM-space. It results in a vector whose components are given by rate changes of all individual genes contained in the metagene-cluster. The resulting RNA-velocity vector points in the direction of local mRNA rate changes in SOM space. Overall mRNA velocity analysis thus generates a metagene-based vector-field pointing toward increasing transcript abundance. It consequently points in the direction of \"high\" expression spot areas containing either genes upregulated in differentiated tissues such as epidermal (spot \"J\"), neuronal (\"A\", \"Y\" and \"F\"), and muscular (\"L\") tissues or genes upregulated in stem cells (module \"N\"). Hence, overexpression spots represent attractors of RNA-velocity (compare with Figure 3C). In turn, areas of invariant expression lack measurable RNA rate changes. Hence, RNA velocity analysis provides information about local slopes of mRNA abundance in gene expression space. Interestingly, RNA-velocity \"vanishes\" in the kernel-area of maximum expression of the spots meaning, that expression indeed reached its asymptotic maximum value.  
In the next step, these vector field properties were transformed into \"vector\"-trajectories using the R program velocito [31]. It summarizes vectors along the main directions pointing toward the major attractor-modules separated by \"watersheds\" (Figure 6B). The trajectories obtained resemble the gene-state trajectories connecting source and sink modules. However, RNA-velocity trajectories are more diverse, particularly, showing branched paths which point toward the local attractors of overexpression. Taken together, both SOM-based dynamic and RNA velocity analysis provide similar trajectories indicating that differentiation points toward overexpression modules of differentiated tissue.
# A Novel Rolling Bearing Fault Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM
## 5 The Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM
### Diagnostic Case Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Number** & **IMF1** & **IMF2** & **IMF3** & **IMF4** & **IMF5** & **IMF6** & **IMF7** & **IMF8** & **IMF9** & **IMF10** \\ \hline
1 & 0.0278 & 0.4454 & 0.0871 & 0.1326 & 0.0537 & 0.0165 & \(-\)0.1641 & \(-\)0.1761 & \(-\)0.2020 & \(-\)0.2209 \\
2 & \(-\)0.0127 & 0.2431 & 0.3095 & 0.1650 & 0.0614 & \(-\)0.0283 & \(-\)0.1590 & \(-\)0.1753 & \(-\)0.1918 & \(-\)0.2118 \\
3 & 0.0611 & 0.3306 & 0.2607 & 0.1559 & 0.0237 & \(-\)0.0913 & \(-\)0.1882 & \(-\)0.1716 & \(-\)0.1823 & \(-\)0.1985 \\
4 & 0.0243 & 0.2993 & 0.3166 & 0.1179 & 0.0566 & \(-\)0.0794 & \(-\)0.1805 & \(-\)0.1891 & \(-\)0.1802 & \(-\)0.1855 \\
5 & 0.1275 & 0.4619 & 0.1291 & 0.0676 & \(-\)0.0368 & \(-\)0.0952 & \(-\)0.1517 & \(-\)0.1502 & \(-\)0.1724 & \(-\)0.1798 \\
6 & 0.0274 & 0.5054 & 0.1340 & 0.1041 & \(-\)0.0280 & \(-\)0.1411 & \(-\)0.1289 & \(-\)0.1295 & \(-\)0.1700 & \(-\)0.1733 \\
7 & \(-\)0.0157 & 0.4426 & 0.1010 & 0.0586 & 0.0445 & \(-\)0.0090 & \(-\)0.1345 & \(-\)0.1454 & \(-\)0.1733 & \(-\)0.1688 \\
8 & 0.0704 & 0.5200 & 0.1909 & 0.0291 & 0.0018 & \(-\)0.1494 & \(-\)0.1634 & \(-\)0.1509 & \(-\)0.1732 & \(-\)0.1753 \\
9 & 0.0525 & 0.4863 & 0.0815 & 0.0608 & \(-\)0.0111 & \(-\)0.0304 & \(-\)0.1660 & \(-\)0.1417 & \(-\)0.1570 & \(-\)0.1749 \\
10 & \(-\)0.0823 & 0.3017 & 0.2943 & 0.1209 & 0.0768 & 0.0123 & \(-\)0.1820 & \(-\)0.1602 & \(-\)0.1963 & \(-\)0.1851 \\ Effective & & & & & & & & & & \\ IMFs & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The value of the EWCS of each IMF for 10 groups of normal signals. Figure 14: A comparison of the DE-OSLEM and OSELM classification results. Figure 13: (**a**) original signal; (**b**) reconstructed signal. compared with the improved VMD and DE-OSELM diagnostic methods proposed in this paper, these are higher. This also further verifies the effectiveness and superiority of the proposed method.
## 5 Summary and Discussion  
In this work, we have extended the tight-binding model (3) of the previous studies [37; 38] and included the on-site Coulomb interactions (5). By this extension and our mean-field analysis, we have succeeded in opening a band gap at the Fermi level as shown in Figure 2e. Thus, we have established a lattice model which reproduces qualitative features of the electronic properties of 1T-TaS\({}_{2}\). We have further extended the model to treat the lattice distortion as a dynamical variable. Considering a specific PLD of the hexagram shape, we have modeled the lattice potential, reproduced the CCDW as a thermal equilibrium state, and formulated the coupled equations of motion for the electrons and the PLD.  
We have investigated the short-time charge dynamics induced by a pulse laser on the basis of our model. We have shown that the short-time dynamics are dominated by electrons and the hexagram CDW order is suppressed by the laser, while the lattice is effectively frozen. This CDW decrease is caused at the second order of the laser electric field, and the CDW order melts down as fast as \(20\,\mathrm{fs}\) for the strong laser amplitude \(E_{0}=23.1\,\mathrm{MV/cm}\) for the \(35\,\mathrm{fs}\) pulse. We note that the necessary amplitude \(E_{0}\) should be smaller for a longer pulse.  
The lattice distortion plays an important role on the long time scale \(\gtrsim\)100 fs. Its motion is dominated by the landscape of the free energy instantaneously converted from the equilibrium one (see Figure 6b). The CDW order may be washed out on the long time scale through the coupling to the lattice motion, even when the laser amplitude is not strong enough to melt the CDW on the short time scale.  
Figure 7: Time profiles of charge densities averaged over A, B, and C sites for laser amplitudes (**a**) \(E_{0}=4.61\) and (**b**) \(23.1\,\mathrm{MV/cm}\); (**c**) time profile of lattice distortion \(x(t)\) for several laser amplitudes \(E_{0}\).  
We comment on a few open questions beyond our present study. We have considered only one mode of the hexagram shape in lattice distortion, but there exist many other modes giving rise to the incommensurate CDW, the nearly-commensurate CDW, and so on. Including those relevant modes may make it possible to describe photoinduced transitions from the CCDW to the other CDW phases. In that case, our picture of the free-energy modulation is extended in multiple dimensions and several local minima may exist. We note that the phonon modes also work as a heat reservoir. Our present analysis does not include a coupling to any reservoir, or dissipation, and our results would be modified especially at long times. We leave these important issues as future works.
## 4 Multi-Objective Hybrid Resource Consolidation Algorithm
### Mathematical Modelling of Objectives Functions
#### 4.1.1 Service Level Agreement Model  
The SLA violations due to the _VM_ migration in Cloud data center environments are adapted from [49]. However, the authors only considered the migration of one resource. This research, on the other hand, considered not only the CPU but other resource components such as memory and storage. Furthermore, the SLA violation due to _VM_ migration is divided into service violation per active PM, which is the percentage of time active PM experiencing 100% utilization of its resource components that lead to SLA violation. Another potential situation is when the active PM performance is below the expectation due to the _VM_ migration, known as Performance Degradation (PD). SLA violation (\(SLAV\)) is modelled as in Equations (1)-(4),  
\[SLAV(PD)=\frac{1}{M}\sum_{j=1}^{M}\frac{\
abla_{VM_{i}}}{\Delta VM_{s}} \tag{1}\]  
where \(M\) is the number of _VMs_, \(\
abla_{VM_{i}}\) is the estimate of \(PD\) of \(VM_{i}\) due to migration, and \(\Delta VM_{s}\) is the total resource capacity of \(VM_{i}\) during its execution time.  
\[SLAV(PM_{active})=\frac{1}{N}\sum_{i=1}^{N}\frac{T_{PM_{j}}}{T_{PM_{i_{active}}}} \tag{2}\]  
where \(N\) is the number of PMs, \(T_{PM_{j}}\) is the total time \(PM_{j}\) reached 100% resource utilization, and \(T_{PM_{i_{active}}}\) is the total time of \(PM_{j}\) considered active for serving user request in form of \(VM\).  
Given the above estimation models for single _VM_ migration overhead factors, the overall \(SLAV\) migration overhead for a particular _VM_ consolidation plan for a cluster of PMs is modeled as the accumulated overhead of all the necessary _VM_ migrations within the cluster:  
\[SLAV=\left(\frac{1}{M}\sum_{j=1}^{M}\frac{\
abla_{VM_{i}}}{\Delta VM_{s}} \right)+\left(\frac{1}{N}\sum_{i=1}^{N}\frac{T_{PM_{j}}}{T_{PM_{i_{active}}}}\right) \tag{3}\]  
Here, the sum of all time needed for the resource utilization for all _VMs_ in data centers against active VMs is calculated for near average results.  
\[SLAV=\frac{Total\;VM_{i}\;Request-Total\;AllocatedVM_{i}}{Total_{PM_{i_{active}}}} \tag{4}\]
# Modeling and Performance Analysis of Satellite Network Moving Target Defense System with Petri Nets
## 7 Conclusions
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