esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Recovery of GPP after Tropical Cyclone  
The GPPbaseline, which is the multi-year average annual GPP of mangroves in Danzhou Bay without TC disturbance (SuperTY Sarika in 2016), was 1824.56 \(\pm\) 392.72 gC/m\({}^{2}\)/yr during the study period. Therefore, according to the mangrove recovery rules defined in this study, when the average of GPP of the mangrove ecosystem in Danzhou Bay can reach the standard value of 1732.80 (95% of GPPbaseline) for two consecutive years, it could then be considered as they have already recovered from disturbance. From 2015 to 2019, the GPP of the mangrove ecosystem in Danzhou Bay were 1848.31 \(\pm\) 397.22, 1511.22 \(\pm\) 371.02, 1698.01 \(\pm\) 400.37, 1844.30 \(\pm\) 397.40, and 1808.51 \(\pm\) 387.83 gC/m\({}^{2}\)/yr (spatial distribution can be seen in Figure S5). As shown in Figure 3a, the GPP generated by the mangrove ecosystem in Danzhou Bay in 2016 and 2017 was below the standard value and reached the standard for two consecutive years in 2018 and 2019. Therefore, it is judged that the recovery period was 2 years, and the mangrove ecosystem had recovered in 2018. Among them, from 2016 to 2017, 72.82% of the mangrove ecosystem GPP recovered to 95% of the baseline, and the rest of the mangrove ecosystem is still in the process of recovery; from 2017 to 2018, all mangroves recovered from the SuperTY Sarika impact (Figure 3b). Spatially, combined with Figure 4, the mangroves that recovered relatively quickly are mainly distributed on the inner side of the harbor (the side of the land facing away from the typhoon), while the part exposed to the open water recovered relatively slowly. The GPP of the mangroves farther from the typhoon's track recovered faster than those closer to it. Meanwhile, the mangroves in the same range of distance from the typhoon track, closer to the offshore, recovered faster.  
Figure 3: The timeseries of annual GPP of the mangrove ecosystem pre- and post- SuperTY Sarika (**a**) and the distribution of ratio of each year after the typhoon to the GPPbaseline (**b**).
# Analysis of Available Components and Performance Estimation of Optical Multi-Band Systems
## 3 Quantifying the Capacity and Transparent Reach of an OMB System  
### System under Study  
In the physical layer study of this section, we consider only Doped Fiber Amplifiers (DFAs) to compensate for the fiber losses as the DFA family shows similar operational and performance features with C-band EDFAs, and at the same time, it is a widely deployed type of amplifier. In particular, the DFAs provide a sufficiently flat gain over a substantial spectral range, allowing the effective exploitation of the low-loss attenuation spectrum of the fiber. Furthermore, they provide a relatively low-noise figure, e.g., 5.5 dB, leading to an improved physical layer performance compared with SOAs. In our case, the DFAs for the new bands are collocated in the same amplifier duct with the EDFAs, and in this way, the operator has the flexibility to add new DFAs on an \"as-needed\" basis, preserving the investment made on the existing networking infrastructure. Moreover, the S-band is split into S\({}_{1}\) and S\({}_{2}\) sub-bands, since a single Thulium-Doped Fiber Amplifier (TDFA) cannot ensure sufficient power per channel at its output, e.g., higher than \(-2\) dBm, due to the large amplification range (which is \(\approx\)55 nm).  
The system under investigation is a core network that consists of nodes with an inter-node distance of 150 km and employs commercially available state-of-the-art components (Figure 2). The link consists of three fiber spans of 50 km, where a fiber span is set as the distance between two consecutive amplification stages. Each amplification stage comprises four DFAs to compensate for the fiber losses and the loss induced by the band filters, which in our case is set to 2 dB. For all amplifiers, we consider the presence of Gain-Flattening Filters (GFFs), which can offer an effectively flat gain with \(<\)1 dB ripple across the band. This is mandatory in order to maintain similar power levels between the channels of each band and avoid an unpredicted impact of FWM and SRS, which may lead to a significant degradation of the OSNIR on specific channels. Furthermore, the power equalization is performed at the optical nodes, which are Wavelength-Selective-Switch (WSS)-based, maintaining the power at the egress of one node equal to the power at the egress of every other node. These technologies are available today as in [18]. The band details and the corresponding DFAs are shown in Table 5.
## 4 Experiment and Results
### Generalization Experiments of SIT-Conversion
#### 4.2.3 Performance with SOTA SNN Models  
In this Section, our ANN-to-SNN framework is compared to several state-of-the-art approaches from different technical routes, as shown in Table 7. Firstly, we presented the comparison results of the proposed SIT-conversion with other advanced ANN-to-SNN methods, including the MST [42] method based on the Transformer architecture, as well as the SNM [73] and QCFS [74] methods based on CNN architecture. Subsequently, we compared the results with some works that directly train SNN-Transformer models, including Spikformer [75] and Spikingformer [76].  
Compared to non-Transformer architecture-based ANN-to-SNN conversion methods, the accuracies of the SNNs generated by SIT-conversion on CIFAR10, CIFAR100, and ImageNet datasets are higher than those of SNM [73] and QCFS [74]. Additionally, our SIT-conversion requires only 12 time steps, significantly fewer than the time steps required by SNM and QCFS. However, due to their non-Transformer architecture, the proposed methods are not directly comparable to SNM and QCFS in terms of conversion loss and model parameter count.  
Compared to Transformer architecture-based ANN-to-SNN conversion methods, the conversion loss of SNNs generated by the SIT-conversion of EdgeNeXt, Next-ViT, and UniFormer on the three datasets is reduced by 1% to 1.89% compared to the MST [42] model. Moreover, the SIT-conversion achieves the conversion of the Transformer core and achieves nearly lossless conversion in only 12 time steps, which is significantly lower than the 256 time steps required by MST.  
SNNs generated by the SIT-conversion of EdgeNeXt, Next-ViT, and UniFormer models achieve higher accuracies on the CIFAR10, CIFAR100, and ImageNet-1K datasets compared to directly trained Spikformer [75] and Spikingformer [76]. Furthermore, the ANN-to-SNN conversion method could achieve performance comparable to leading ANNs without requiring prolonged training time.  
The results indicate that our proposed SIT-conversion method achieves nearly lossless ANN-to-SNN conversion in the evaluated network architectures and datasets using only 12 time steps, and truly realizes the self-attention mechanism in Transformer models through ANN-to-SNN conversion.
## 6 Conclusions  
This study obtained an analytical solution to unsteady flow with a suspended waterproof curtain and calculated the drawdown difference at various conditions. By investigating the effects of key parameters, including the groundwater drawdown inside the pit, the radius of the foundation pit, and the length of the waterproof curtain on the groundwater drawdown difference, the following conclusions are drawn:  
(1) The validity of the analytical method to predict the groundwater drawdown was verified by pumping tests and FEM simulations, and the analytical method was also applicable to the calculation of deviatering with suspended waterproof curtain. This relationship between the groundwater drawdown inside and outside the pit was established, and a good consistency was found between the analytical method and FEM simulations with an error of 11.76%.  
(2) \(\Delta\)/\(H_{0}\) increases with the increase of \(S_{h}/H_{0}\), and the increment of \(\Delta\)/\(H_{0}\) closes to 0.328. It is not suitable to adopt the scheme that the groundwater drawdown \(S_{h}\) exceeds the length of waterproof curtain \(L\).  
(3) \(\Delta\)/\(H_{0}\) decreases with the increase of \(R_{\text{un}}\)/\(H_{0}\). When \(Rw\)/\(H_{0}\) > 0.781, the decrease speed becomes slower, and the scheme of decreasing of \(Rw\)/\(H_{0}\) can be adopted to maintain the larger drawdown difference.  
(4) The drawdown difference increases with the increase of the length of the waterproof curtain. When \(L\)/\(H_{0}\) > 0.813, the difference tends to be stable, indicating the optimal length of the waterproof curtain to effectively maintain the drawdown difference.  
(5) When the dewatering time is within 48 h, the groundwater drawdown difference decreases rapidly. When the time exceeds 48 h or more, the groundwater drawdown difference stabilizes. The field measured data of foundation pit dewatering should be recorded in a timely manner to eliminate adverse effects; the above conclusions provide references for similar cases.  
Conceptualization, K.Y. and C.X.; methodology, K.Y. and C.X.; software, K.Y. and M.C.; validation, K.Y., M.C. and P.W.; formal analysis, K.Y.; investigation, K.Y. and M.C.; resources, C.X.; data curation, M.C. and P.W.; writing--original draft preparation, K.Y.; writing--review and editing, C.X., M.C. and P.W.; visualization, P.W.; supervision, C.X.; project administration, K.Y.; funding acquisition, C.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [the National Science Fund for Distinguished Young Scholars] grant number [No. 51725802], [the National Natural Science Foundation of China] grant number [No. 51878276], [Joint Fund of the National Natural Science Foundation of Zhejiang Province and Huadong Engineering Corporation Limited] grant number [No. LHZ19E080001] and [Study on Long-term Performance Evolution and Big Date Analysis for Roadbed of High-speed Railways] grant number [No. U1934208].  
Figure 13: Effect of \(t\) on the groundwater drawdown difference.
###### Abstract  
Cyber-physical systems (CPS) applied to safety-critical or mission-critical domains require high dependability including safety, security, and reliability. However, the safety of CPS can be significantly threatened by increased security vulnerabilities and the lack of flexibility in accepting various normal environments or conditions. To enhance safety and security in CPS, a common and cost-effective strategy is to employ the model-based detection technique; however, detecting faults in practice is challenging due to model and environment uncertainties. In this paper, we present a novel generation method of the adaptive threshold required for providing dependability for the model-based fault detection system. In particular, we focus on statistical and information theoretic analysis to consider the model and environment uncertainties, and non-linear programming to determine an adaptive threshold as an equilibrium point in terms of adaptability and sensitivity. To do this, we assess the normality of the data obtained from real sensors, define performance measures representing the system requirements, and formulate the optimal threshold problem. In addition, in order to efficiently exploit the adaptive thresholds, we design the storage so that it is added to the basic structure of the model-based detection system. By executing the performance evaluation with various fault scenarios by varying intensities, duration and types of faults injected, we prove that the proposed method is well designed to cope with uncertainties. In particular, against noise faults, the proposed method shows nearly 100% accuracy, recall, and precision at each of the operation, regardless of the intensity and duration of faults. Under the constant faults, it achieves the accuracy from 85.4% to 100%, the recall of 100% from the lowest 54.2%, and the precision of 100%. It also gives the accuracy of 100% from the lowest 83.2%, the recall of 100% from the lowest 43.8%, and the precision of 100% against random faults. These results indicate that the proposed method achieves a significantly better performance than existing dynamic threshold methods. Consequently, an extensive performance evaluation demonstrates that the proposed method is able to accurately and reliably detect the faults and achieve high levels of adaptability and sensitivity, compared with other dynamic thresholds.  
adaptive thresholds; model-based detection; equilibrium point; optimization +
Footnote †: journal: Applied Soft. 2018
## 2 Materials and Methods
### Data  
#### 2.2.1 Meteorological Data  
TerraClimate is a global dataset that offers monthly climate variables and water balance metrics specifically for terrestrial surfaces ([[https://www.climatologylab.org/terraclimate.html](https://www.climatologylab.org/terraclimate.html)]([https://www.climatologylab.org/terraclimate.html](https://www.climatologylab.org/terraclimate.html)), accessed on 5 March 2024), with a spatial resolution of 4 km. In the study, monthly precipitation (PRE) data and potential evapotranspiration (PET) data from 1981 to 2021 were selected to calculate the SPEI index. Due to its long temporal coverage and high spatial resolution, this dataset is well-suited for drought monitoring [33].  
ERA5-Land is a high-resolution reanalysis dataset developed by the European Centre for Medium-Range Weather Forecasts, providing gridded climate information (ECMWF) ([[https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)]([https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)), accessed on 5 March 2024), featuring both monthly and hourly temporal resolutions with a spatial resolution of 0.1\({}^{\circ}\). The soil moisture (SM) data derived from ERA5-Land are particularly applicable to arid and semi-arid regions [54] and have been extensively employed in the validation of agricultural drought indices [36]. In the study, monthly ERA5-Land soil moisture data from 2001 to 2021 were selected, and they were resampled to a 0.05\({}^{\circ}\) spatial resolution using the bilinear method to ensure consistency with the spatial resolution of other datasets. The soil moisture data were used to validate the accuracy of the VHI.  
The global land evaporation Amsterdam model (GLEAM) comprises a series of algorithms specifically designed to estimate land evaporation and root zone soil moisture using satellite data. GLEAM can provide potential evaporation, surface soil moisture, and RZSM. GLEAM RZSM products are available on a 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) regular grid at daily and monthly temporal resolution ([[https://www.gleam.eu/](https://www.gleam.eu/)]([https://www.gleam.eu/](https://www.gleam.eu/)), accessed on 5 March 2024). In the study, monthly GLEAM root zone soil moisture data from 2001 to 2021 were selected and resampled to a spatial resolution of 0.05\({}^{\circ}\) using the bilinear interpolation method to ensure consistency with the spatial resolution of other datasets. The soil moisture data were utilized to further validate the accuracy of VHI.  
Figure 1: Location map (**a**), elevation (**b**), vegetation cover types (**c**), and percentage of vegetation types (**d**) in the YRB.
## 5 Simulation Analysis
### Simulation Results and Analysis
Table 6 compares the performance of the five routing protocols under medium node density (300 nodes) and medium mobility speed (1.5 m/s). The results show that APC-EEDBR achieves a well-balanced performance in terms of network lifetime, energy efficiency, and transmission reliability. In contrast, DBR exhibits a higher packet delivery ratio but suffers from higher energy consumption and shorter network lifetime. Future work will consider incorporating opportunistic routing mechanisms to further enhance the adaptability and robustness of the protocol in dynamic environments.
# Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks
## 3 Model  
In this section, we describe the detailed configuration of spatial-temporal model and the flow of our method. According to the specifics of the actual application, we modify the 3D Resnet50 model in the following regards:  
* Recognizing the asymmetry of the spatial and temporal information, we introduce (2+1)D convolution to more effectively extract the spatiotemporal features;
* We embed non-local blocks to the network to take into consideration 'global' information;
* We employ multi-scale convolution to better capture the information of vehicles' varying sizes due to the varying distances between the vehicles and the camera.  
Since average vehicle speed estimation is a regression problem, we use **Mean Square Error** as the loss function:  
\[loss=\frac{1}{N}\sum_{i=1}^{N}\big{(}Y_{i}-\hat{Y}_{i}\big{)}^{2}. \tag{1}\]  
where \(N\) is the number of samples, while \(Y_{i}\) and \(\hat{Y}\) are the estimated value and ground-truth of sample \(i\), respectively. The loss is the mean of the squared Euclidean distances between all the real sample values and the predicted values.  
We discuss the model in greater detail in the following part of the paper.
# Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
## 4 Methodology and Implementation
### Attack Dataset  
The underlying choice of the dataset in NIDS research is not purely about modernity, but rather the comprehensiveness, applicability, and the depth of provided features. While our selected dataset KDDCUP'99 [39] might be considered historically grounded, its continued significance in recent research endeavors is evident [40; 41; 42]. As delineated in Sections 1 and 2, our study focuses on particular malicious activities and behaviors within network settings. Many of these behaviors echo the characteristics found in the KDD-99 dataset, indicating that foundational threat vectors remain deeply embedded in contemporary attack patterns. Furthermore, the mechanisms employed by computer worms are still pivotal in elucidating the propagation dynamics of infections in current computational models [43; 44; 45; 46].  
For experiment replicability and reliability purposes, it is essential to select a well-known but vast attack dataset publicly available to other researchers. The KDDCUP'99 [39] dataset developed by the MIT Lincoln Labs is widely used to validate most proposed NIDSs [47]. This dataset of approximately 2,000,000 network records has approximately 4,900,000 single connection threat vectors, with each threat vector having 41 normal or attack labeled features. Based on our selected threat description, we choose attacks that fall into the DoS category, and probing attacks as shown in Table 1. The malicious hosts selected in Section 4.2 are simulated as initial source nodes performing an attack. The attack dataset we use has a wide range of DoS and probing attack vectors, as detailed in Table 1, which allows our model a comprehensive array of attack scenarios, thereby enabling a rigorous validation of our detection capabilities against both volumetric (DoS) and reconnaissance (probing) activities.
# Equivalent Dynamic Analysis of a Cable-Driven Snake Arm Maintainer
## 5 Simulation and Experiments
### Equivalent Joint Dynamic Analysis  
The joint dynamics are obtained by equivalently transforming the SAM cable traction forces using the decoupling algorithm. To verify the accuracy of the decoupling algorithm, we also calculated the theoretical joint torques of the end joints under the same trajectory based on Adams dynamics software. The dynamics of the flexible cables are calculated by equating them to the linear springs with the addition of linear damping constraints. The relationship between the total equivalent joint torques and the theoretical joint torques calculated by Adams software are shown in Figure 13a,b. According to comparison of the results of the decoupling of the cable traction forces with the theoretical results of the joint torques, the forcibly equivalent joint torques obtained from the decoupling experiments have the same movement trend as the theoretical joint torques obtained from the Adams software. This proves that the decoupling algorithm for cable traction is effective.  
Figure 13c,d shows the error characteristics of the theoretical torques in relation to the equivalent joint torques. The maximum torque error is close to 0.38 Nm, but the mean error and standard deviation are small (less than 0.13 Nm). The large errors mainly reflect theoretically unbuilt model errors, such as the rigid-flexible coupling deformation of the cables and non-linear friction. In general, the SAM equivalent joint torques have the same motion trend as the [PERSON] theoretical torques, and the data in Figure 13 show that the difference between the theoretical joint torques and the experimental equivalent joint torques is significantly smaller than the experimentally equivalent joint torques. Therefore, equivalent dynamic control can improve the control effect. The experimental results can also guide the development of more accurate theoretical dynamics models.  
Figure 12: Test results of the single-joint group. (**a,b**) Vertical sinusoidal trajectory and cable force and (**c,d**) horizontal sinusoidal trajectory and cable force.
_appiled_  
_sciences_  
_Communication_  
3D Printing of Functional Assemblies with Integrated Polymer-Bonded Magnets Demonstrated with a Prototype of a Rotary Blood Pump  
[PERSON] 1,*, [PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 1, [PERSON] 1, [PERSON] 4 and [PERSON] 1  
Product Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland; [EMAIL_ADDRESS] (Y.H.); [EMAIL_ADDRESS] (S.B.); [PERSON]DDRESS] (M.S.D.); meboldt@ethz.ch.ch.M.J.  
Complex Materials, Department of Materials, ETH Zurich, 8093 Zurich, Switzerland; [EMAIL_ADDRESS]  
Arnold Magnetic Technologies AG, 5242 Birr-Lupfig, Switzerland; [EMAIL_ADDRESS]  
Laboratory of Ceramic Materials, Institute of Materials and Process Engineering,  
ZHAW School of Engineering, 8400 Winterthur, Switzerland; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]; Tel.: +41-44-63-20176  
Received: 15 July 2018; Accepted: 29 July 2018; Published: 1 August 2018  
**Featured Application: 3D printing with a polymer-bonded magnetic filament allows all-in-one print of a functioning prototype of a rotary blood pump.**  
**Abstract:** Conventional magnet manufacturing is a significant bottleneck in the development processes of products that use magnets, because every design adaption requires production steps with long lead times. Additive manufacturing of magnetic components delivers the opportunity to shift to agile and test-driven development in early prototyping stages, as well as new possibilities for complex designs. In an effort to simplify integration of magnetic components, the current work presents a method to directly print polymer-bonded hard magnets of arbitrary shape into thermoplastic parts by fused deposition modeling. This method was applied to an early prototype design of a rotary blood pump with magnetic bearing and magnetic drive coupling. Thermoplastics were compounded with 56 vol.% isotropic NdFeB powder to manufacture printable filament. With a powder loading of 56 vol.%, remanences of 350 mT and adequate mechanical flexibility for robust processability were achieved. This compound allowed us to print a prototype of a turbodynamic pump with integrated magnets in the impeller and housing in one piece on a low-cost, end-user 3D printer. Then, the magnetic components in the printed pump were fully magnetized in a pulsed Bitter coil. The pump impeller is driven by magnetic coupling to non-printed permanent magnets rotated by a brushless DC motor, resulting in a flow rate of 3 L/min at 1000 rpm. For the first time, an application of combined multi-material and magnet printing by fused deposition modeling was shown. The presented process significantly simplifies the prototyping of products that use magnets, such as rotary blood pumps, and opens the door for more complex and innovative designs. It will also help postpone the shift to conventional manufacturing methods to later phases of the development process.  
**Keywords:** 3D printing; rapid prototyping; additive manufacturing; fused deposition modelling; printable magnets; permanent magnets; rotary blood pump