esa-sceva/satcom-chunk-collection huggingface dataset:
# W-Band 4 th Order Waveguide Filter Based on Double Layer SU8 Microfabrication
## 4 Measurement Results and Discussion  
### Design of the Press Blocks and Construction of Measurement Platform  
The filter mentioned in this article is designed to be inserted into the center of a standard WR-10 flange to reduce insertion loss due to leaky waves. To eliminate the possibility of gaps between the SU-8 layers during the measurements, two kinds of pressure blocks are designed to help the measurement, as shown in Figure 10b. The difference between the two kinds is that pressure block I has a standard WR-10 straight waveguide with a length of 15 mm, and the fabricated filter needs to be measured through the waveguide transmission line of the pressure block. While the pressure block II can be directly connected to the waveguide port of the test equipment to the feeding rectangular waveguide. Pin holes and screw holes on the pressure block can ensure good alignment and tight contact between the SU-8 chip and the pressure block, as shown in Figure 10b. The measurement setup is shown in Figure 11, showing the SU-8 chips sandwiched between the two types of press blocks.  
Figure 10: Structure diagram of processing filter. (**a**) Filter composed of 3 layers of SU-8 and (**b**) a picture of the measuring platform with pressure block I on the left and with pressure block II on the right.  
Figure 9: Sensitivity analysis of the designed filter based on simulations, x represents a move in the X direction, and y represents a move in the Y direction.
# Digitalizing Industrialized Construction Projects: Status Quo and Future Development
## 5 Research Gaps and Future Directions
### Construction Phase  
Current research focuses on digitization, intelligence, and automation throughout the construction process. Cutting-edge technologies such as the IoT and AI are extensively harnessed for intelligent monitoring, predictive analytics, and the proactive optimization of construction progress, logistics, and safety protocols. Concurrently, industrial robots are adopted to propel the adoption of customization and automated assembly. Consequently, prospective avenues for future research can be delineated as follows:  
* Effective logistics planning necessitates the aggregation of voluminous data encompassing variables such as vehicle types, load capacities, and road tolls. Presently, an empirical understanding of the actual vehicle kilometers (vkm) attributed to the plethora of vehicles within the industry remains limited, with available information predominantly relying on speculative inference [75]. Future work can integrate a logistics information integration platform and optimization models to improve component delivery solutions.
* Compared with risk assessment, relatively scant attention has been devoted to automated real-time risk mitigation strategies. Existing risk management protocols predominantly rely on manual intervention [84]. Thus, prospective investigations should explore the synchronization of safety-risk prediction mechanisms with on-site construction activities. Additionally, devising methodologies to implement an automated risk response system warrants substantial efforts.
# Trajectory Tracking Control Method Based on Adaptive Higher Order Sliding Mode
## 3 Case Analysis
### Analysis of Simulation Results  
In the ADRC method, the plots of the simulation results reveal that the control convergence does not occur in the ADRC method, resulting in the nonconvergence of the state quantity, so that the simulation effect is relatively poor.  
In the LQR method, trajectory tracking curve and control curve are smooth. However, the problem is that the terminal position error is large, which is unacceptable. The method should balance the terminal state error and control instruction smoothness.  
In the AHSTC method, although the control quantity has some slight chatter, the terminal state error and the control quantity converge. The simulation results also show that the terminal state errors of the three methods are not very large, but the overall simulation performance based on the AHSTC method is good, and has good applicability, feasibility, and robustness.
# Key Issues in Using the Freezing Method in Power Tunnel Rehabilitation Projects
## 4 Analysis of Numerical Simulation Results
### X-Section Permafrost Curtain Development Law  
To gain a deeper understanding of how varying seepage velocities affect the vertical freezing three-dimensional freezing temperature field, X = 15.5 m profiles were selected for 30 and 60 day analyses. The temperature field cloud diagrams and isothermal line development changes were examined. Figure 12 illustrates the pattern of change in the temperature field cloud map and isotherm map for the X = 15.5 m profile under different seepage velocities.  
As illustrated in Figure 12, at 30 days of freezing, the permafrost curtain developed uniformly without any seepage velocity, with the isotherms measured upstream and downstream completing their intersection circle first due to the significant number of freezing pipes upstream and downstream. As the seepage velocity increased, driven by the groundwater flow upstream, the cold generated by the upstream freezing pipe was continuously transported downstream, leading to a substantial accumulation of cold within the internal freezing zone. When the head difference reached 3 m, the \(-1\)\({}^{\circ}\)C isotherm had completed its intersection circle, yet the development of the frozen curtain was uneven; this inhomogeneity increased with the seepage velocity. By 60 days of freezing, in the absence of seepage, the overall development of the permafrost curtain was stable and uniform. With increasing seepage velocity, the development of the frozen curtain on both the upstream and downstream sides became uneven. The thickness of the frozen curtain on the upstream side gradually decreased and approached the freezing pipe, while the downstream side exhibited a \"tailing\" phenomenon, with the freezing range expanding. When the head difference reached 4 m, the \(-1\)\({}^{\circ}\)C and \(-10\)\({}^{\circ}\)C isotherms on the downstream side did not intersect, failing to reach the freezing temperature. The unevenness of the frozen curtain development increased with the seepage velocity, which both expanded some frozen areas and diminished the overall freezing effect.
# Research on Radionuclide Identification Method Based on GASF and Deep Residual Network
## 5 Discussion  
This paper proposes a deep learning-based nuclide identification method that combines Residual Networks (ResNets) and Gramian Angular Summation Field (GASF) techniques. After dimensionality reduction of the gamma spectrum using the Piecewise Aggregate Approximation (PAA) algorithm, GASF is employed to transform the spectrum into two-dimensional images. This approach fully exploits the spatial features and dependencies within the data, exploring a new pathway for data processing in the field of nuclide identification. The experimental results show that the model achieves a recognition accuracy of 99.5% on the simulated data. When tested with actual measurement data containing unknown radionuclides, it still maintains an accuracy of 92.6% and is capable of  
Figure 9: Testing results with experimental data: (**a**) accuracy and loss curves; (**b**) confusion matrix of test results.  
successfully identifying the unknown radionuclides. This result validates the advantages and effectiveness of the proposed method in handling complex, noisy, real-world data.  
Additionally, the model proposed in this paper relies on large-scale labeled datasets for training, but, in practical applications, collecting and labeling high-quality, real-world gamma spectrum data may present challenges. Future research directions could explore how to use a small amount of labeled data for effective transfer learning or self-supervised learning, thereby improving the model's performance in data-scarce environments.  
All authors contributed to the manuscript and discussed the results. Conceptualization, S.Z. ([PERSON]); methodology, S.Z. ([PERSON]); validation, S.Z. ([PERSON]); [PERSON] provided criticism and revised the manuscript; funding acquisition, S.Z. ([PERSON]). 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 12165001.  
The study did not require ethical approval.  
The study did not involve humans.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Nuclear Data  
### Basic Nuclear Data Libraries  
For the analyses, several neutron transport libraries were used. including JEFF-3.2 [15], JEFF-3.3 [16] and TENDL-2017, 2019 [17; 18]. JEFF-3.2 library does not include covariance data for all nuclides and the latest version JEFF-3.3 provides the complete covariance data for all included nuclides. The TENDL neutron data library contains high-quality evaluations generated on the basis of the TALYS code [19] and includes, for each target nuclide, the covariance data as well a big set of the additional random data files (up to 300) that were produced for each \"standard\" evaluation included in the library.  
Materials included in the MCNP geometry models for the HCPB and WCLL DEMOs can be subdivided into three groups: structural materials that ensure the structural stability of the constructions, tritium breeding materials, and a neutron multiplier. In both breeder blanket concepts, the Eurofer steel serves as the main structural material. The tritium breeding material in both concepts is Li, which is included in the solid breeder ceramic composition of the HCPB (Li\({}_{4}\)SiO\({}_{4}\) plus 37 mol.% of Li\({}_{2}\)TiO\({}_{3}\)) and in a PbLi liquid eutectic of the WCLL DEMOs. In both concepts, Li with special \({}^{6}\)Li enrichment is used: in the former  
Figure 2: Comparison of the evaluated \({}^{6}\)Li(n,t)\(\alpha\) cross section with experimental data (**a**) and ratio \(\sigma_{\text{exp}}\) over \(\sigma_{\text{eval}}\) for the JEFF-3.2 and 3.3 libraries (**b**).  
case, the \({}^{6}\)Li enrichment is 60%--and 90% in the latter. The exact materials compositions used in the simulations are presented in [10; 20].
# Pulsed Thermography Dataset for Training Deep Learning Models
## 5 Discussion  
The advancement of applied AI heavily relies on the availability of quality datasets. Despite this, public datasets in the NDE field are scarce due to difficulties in data acquisition. To address this, we present a dataset for the training of deep learning models in the field of NDE. The dataset comprises of 19 measurements collected from 19 PVC specimens with artificially introduced defects.  
Unlike common NDE datasets that contain completely hidden defects, the defects in our dataset are visible from the bottom side, allowing for accurate and easy labeling. Our dataset is larger than many NDE datasets used for AI applications, for example, the dataset used in [18], which contains fewer than 10 specimens. A larger dataset can help reduce the risk of overfitting in deep learning models.  
Experiments are conducted using several deep encoder-decoder models for defect segmentation on the original dataset. The results indicate that the U-Net VGG11 model, without the use of pre-trained parameters, outperforms other models. The results also suggest that the use of a pre-trained model does not necessarily improve model performance, as observed in both U-Net and SegNet models. Incorporating VGG11 as the encoder backbone of the U-Net model improves its performance significantly, which is not the case for SegNet.  
The results in Table 2 demonstrate that PT data transformation methods, such as PCT and PPT, can effectively reduce the PT data size without sacrificing the performance of the models. Furthermore, the thermal profiles reveal that the most significant difference in thermal values between sound and defective areas occurs early in the measurement process. This issue is further explored by using only the leading frames as training data, where the results show that the model's performance barely decreased. Models that use only 200 frames even performed better than those that use the entire dataset, suggesting that a significant portion of the raw thermal images contains redundant information.  
The study has some limitations. Firstly, it does not consider the defects' depth information, which is essential for some applications. Secondly, the models are only tested on defects of a single shape, and it would be valuable to evaluate their ability to detect defects of various shapes. Lastly, the dataset only contains one type of material, PVC. However, the proposed methods have the potential to apply to other materials, such as CFRP and steel, provided that the defects are visible within PT data. Despite these limitations, the study provides a valuable contribution to the development of AI-based methods for defect detection in PT data. The dataset and the results can serve as a benchmark for future research in this area.
## 1 Introduction
transport network towards destination nodes. The switches in the network may be connected using both optical and radio links. A challenging issue in packet-based NGFI networks is quality of service (QoS) provisioning for the traffic flows that have strict latency requirements. These requirements are related to high requirements of 5G services and stringent latency constraints in baseband processing (DU/CU) in the radio access network. Buffering of packets at switch output ports makes the latencies unpredictable and complicates the provisioning of QoS in convergent packet-switched networks. Therefore, latency-aware placement of computational resources for the purpose of radio processing in selected PPs (namely, the placement of DU/CU entities), and proper handing of resulting traffic flows, which may have diverse latency and bandwidth requirements, is in general a difficult optimization task. We call this resource allocation problem a latency-aware DU and CU placement (LDCP) problem. In order to solve LDCP, dedicated optimization methods are required. These methods should be supported by reliable models estimating flows latencies in the packet-switched network to ensure that flows latencies do not exceed allowable limits. In this paper, we formulate and analyze the LDCP problem in the NGFI network by means of the mixed-integer linear programming (MILP) approach. To assure that the latencies of flows are within allowable limits, we introduce into the MILP formulation of LDCP a set of constraints that represent the worst-case estimates of flows latencies. To analyze the NGFI network performance in different network scenarios, we solve the MILP model using a general purpose mixed-integer programming solver (CPLEX) [11]. The related works and our main contributions are discussed below.
# Performance of Parallel K-Means Algorithms in Java
## 3 Parallel K-Means in Java  
In the basic K-Means algorithm shown in Figure 1, there is a built-in parallelism in both the \(Assign\) and the \(Update\) phases, which can be extracted to speed-up the computation. In other terms, both the calculation of the minimal distances of points \(x_{i}\) to current centroids, and then the definition of new centroids as the mean points of current clusters, can be carried in parallel.
## 3 Simulation Setup and System Parameters
All lasers were configured with a linewidth of 100 KHz, aligning with the typical ideal linewidth for CW lasers in coherent optical communication systems [22]. Additionally, setting this value to 100 KHz can effectively reduce the impact of phase noise on the system. All gain values of EDFA and output powers were optimized through multiple iterative simulations. By properly setting the gain value, the output signal power is maintained at the required level to drive subsequent logic operations.
## 2 Materials and Methods
### Satellite Precipitation Data  
This study evaluated two different satellite precipitation products SPPs, the TRMM 3B42 V7, and the GPM IMERG V05 compared to the gauge station at different time scales, from 1 September 2014 to 31 August 2017, in order to assess their reliability, and define their capabilities.  
The period was chosen according to the available precipitation and discharge data. The Taferiat station was installed in 2012, and the available flow data extends to 2017 only. This limited the temporal interval of the study. Moreover, the GPM satellite was launched in 2014, which explains the choice of the beginning year of the data series.
# Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
## 5 Experimental Evaluation  
The experiments proposed in this section evaluated the ability of AEs in addressing the desiderata for a novelty detection system introduced in Section 3. In particular, there are three research questions that we address:  
* (RQ1) Are AEs effective and robust in detecting novel operating conditions? (Section 5.1)
* (RQ2) Is it possible to classify the operating conditions of a machinery with an approach based on AEs? (Section 5.2)
* (RQ3) Can the AEs provide valuable explanations for their predictions? (Section 5.3)  
Figure 4: Raw signals from each operating condition (20 min per condition).  
Figure 3: The test rig (**left**) and its mechanical scheme (**right**).
# A Comparative Study of On-Body Radio-Frequency Links in the 420 MHz-2.4 GHz Range
## 2 Materials and Methods
### Methodology and Scope
An alternative model is given by:  
\[P_{\text{dB}}(d)=P_{0}+10\cdot\log_{10}\left(e^{-m_{0}d}\right)+N\left(0, \sigma_{p}^{2}\right), \tag{2}\]  
This model uses an exponential loss in power as function of distance. \(m_{0}\), is the loss per unit of distance. \(N\left(0,\sigma_{p}^{2}\right)\) is again a term quantifying the lognormal variance on the path loss and \(P_{0}\) is the baseline path loss. These path loss models are fitted to our measured data per technology and frequency. Path loss is in general a good proxy for other important link parameters such as communication range, packet delivery ratio, coverage, required transmitted power, etc. ([PERSON], 1996). Minimizing the influence of path loss is therefore an important aspect of optimizing wireless links. The studied path loss models describe the on-body channels' large-scale characteristics ([PERSON], 1996). The small-scale characteristics of the on-body channel are not studied in this manuscript.
## 5 Simulation
### Varying Nodes Density in Network
#### 5.3.1 Average End to End Delay  
As shown in Figure 18, WEAMR with hotline-assisted approach outperforms both AODV and AOMDV as the number of connections and packet generation are kept constant and the nodes density in the clusters is increased linearly. Average end-to-end delay stays much lower and almost unchanged for WEAMR with the increasing number of nodes as compared to other protocols. AOMDV and AODV suffer from performance degradation because as node density increases the number of hops also increases, resulting in more communication overhead at each intermediate node, this increase in density the communication takes longer as compared to a less dense network [29], whereas for WEAMR the number of hops remain almost constant due to hotline assisted routing.
# Treatment of Aqueous Bromate by Superparamagnetic BiOCl-Mediated Advanced Reduction Process
## 3 Materials and Methods
### Synthesis and Characterization of BiOCl-HAP\({}_{\mathrm{SM}}\)  
HAP\({}_{\mathrm{SM}}\) was synthesised as follows. First, 0.025 mol FeCl\({}_{2}\)\(\cdot\)4H\({}_{2}\)O and 0.05 mol FeCl\({}_{3}\)\(\cdot\)6H\({}_{2}\)O were dissolved in 50 mL ultrapure water, and 0.03 mol H\({}_{3}\)PO\({}_{4}\) was diluted with 50 mL ultrapure water. Then, the above-mentioned solution was mixed with continuous stirring at 200 rpm for 10 min. This mixture was stripped with nitrogen for 10 min to solution A. Afterwards, 0.5 mol Ca(OH)\({}_{2}\) was dissolved in 100 mL 70 \({}^{\circ}\)C Milli-Q water to produce solution B. Solution A was dosed into solution B with a peristaltic pump at a speed of 1.2 mL\(\cdot\)min\({}^{-1}\). The whole process was maintained in continuous stirring (400 rpm) and thermostatic (70 \({}^{\circ}\)C) under nitrogen protection. Ammonia solution was used to adjust the solution pH to 12-13. The final reaction slurry was transferred to a 1000 mL beaker, sealed with sealing film and settled for 72 h at 25 \({}^{\circ}\)C. The deposits were rinsed repeatedly with Milli-Q water, dehydrated at 70 \({}^{\circ}\)C under vacuum, and then ground. Pure HAP crystals were synthesised following the same steps of the synthesis of n-Fe-HA, except replacing the solution A by 100 mL H\({}_{3}\)PO\({}_{4}\) solution (0.3 M).  
The obtained HAP\({}_{\mathrm{SM}}\) (0.5 g) was added to 50 mL 0.01 M ethylene glycol solution of Bi(NO\({}_{3}\))\({}_{3}\). The mixture was ultrasonicated for 10 min to ensure uniform dispersion. Then, 50 mL 0.01 M ethylene glycol solution of KBr was dosed into the above solution with a peristaltic pump at a flow rate of 0.6 mL\(\cdot\)min\({}^{-1}\) with mechanically stirring at room temperature. The mixed solution was then autoclaved at 160 \({}^{\circ}\)C for 12 h. The formed precipitate was washed repeatedly with copious amounts of ultrapure water and anhydrous ethanol until the conductivity of waste liquid was maintained constant. The precipitate was then dried under vacuum for 12 h at 60 \({}^{\circ}\)C.  
Figure 12: (**a**) Mass balance of bromine element and (**b**) relationship of bromate decrease and bromide increase. Conditions: [Formate]\({}_{0}\) = 4 mM; BiOCl-HAP\({}_{\mathrm{SM}}\) dosage 0.5 g\(\cdot\)L\({}^{-1}\), [BrO\({}_{3}^{-}\)]\({}_{0}\) = 6.6 \(\upmu\)M, pH = 7.0 \(\pm\) 0.1, 25 \({}^{\circ}\)C.  
The X-ray powder diffraction pattern of HAP\({}_{\rm SM}\) powders was tested using a Rigaku Dmax-2000 diffractometer (Rigaku Co., Tokyo, Japan. The morphology and microstructure of the synthesized BiOCl-HAP\({}_{\rm SM}\) particles were observed by scanning electron microscopy (SEM; FEI Quanta FEG-650) and transmission electron microscopy (TEM; JEOL JEM-1230). X-ray photoelectron spectroscopy (XPS) analysis was conducted using an ESCALAB 250 Xi spectrometer. Magnetization measurements were performed using a Lakeshore 7304 vibrating sample magnetometer (Lake Shore Co. Ltd., Columbus, OH, USA) at 25 \({}^{\circ}\)C.
## 3 Methodology
### Image Compositing
We divided images into five groups (June through October) according to the month of acquisition. By converting the image collection to an array in the GEE platform, which is the process as used in Sentinel-2 described above, the median value of each monthly group was calculated for each pixel. (2) For the percentile composites, 10%, 25%, 50%, 75%, and 95% percentiles were calculated for SAR images as for optical images. In total, 148 features, including 126 features from optical images, 20 from SAR images, and two features regarding topography, were input into the classification task.