esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Defocus Robust Source and Mask Optimization Using Sensitive Penalty
## 2 DRSMO Modeling
### DRSMO Optimization Algorithm  
In our method, the DRSMO process can be regarded as a machine learning process, training variable \(\beta_{i}\) as a stochastic disturbance in the cost function to solve this multi-objective optimization problem. Table 1 illustrates the optimization flow of the SGD and MBGD algorithm, respectively. In our previous works [25; 26], SGD was adopted to calculate the gradient in a single training sample \(\beta_{i,k}\) in each iteration with fast speed. However, due to the wider sample range of defocus disturbances, the SGD algorithm could not guarantee each iteration was conducted in the global optimal direction.  
In order to give consideration to both optimization speed and accuracy, mini-batch gradient descent (MBGD) was proposed to traverse a part of the random defocus samples \(\beta_{i,k},\beta_{i+1,k},\cdots,\beta_{i+l_{batch}-1,k}\) in each iteration, and \(l_{batch}\) is the batch number in each iteration [27]. Different from the SGD method, MBGD updates part of the training set, thus leading to a relatively correct search direction, so it is easier to converge to the global optimal solution.  
Both the SGD and MBGD algorithm need to calculate the analytic gradient expression of cost function, which can be formulated as  
\[\
abla_{J}G_{i} =\
abla_{J}F_{i}+\omega\
abla_{J}Y_{i}, \tag{8}\] \[\
abla_{M}G_{i} =\
abla_{M}F_{i}+\omega\
abla_{M}Y_{i}. \tag{9}\]  
where \(\
abla_{J}\) and \(\
abla_{M}\) are the gradient to the source parameter \(\Omega_{J}\) and mask pattern \(\Omega_{M}\), respectively. We directed a large amount of study toward the derivation of the analytic gradient formula about sensitive penalty \(\
abla_{J}Y_{i}\), \(\
abla_{M}Y_{i}\), and more details can be found in Appendix B, Equations (A11) and (A13). Similarly, the expansion of \(\
abla_{J}F_{i}\), \(\
abla_{M}F_{i}\) can be found in Appendix C, Equations (A19) and (A20).
# Distributed Global Function Model Finding for Wireless Sensor Network Data
## 5 Experimental Section
### Data Resources  
In this paper, four representative datasets (including two real-life datasets and two UCI (University of California Irvine) standard datasets) are considered to evaluate the performance of the proposed algorithm. In two real-life datasets, all data are collected by various photo sensors and meteorological sensors. The first dataset is estimation of leaf biochemistry and leaf water status with remote sensing data obtained from websites [47]. In the first dataset, we use _spec_aux.txt_ in LOPEX (Leaf optical properties experiment) 93 to find model between spectrum and the relative auxiliary measurements. The second dataset is provided by the EUNITE (the European Network of Excellence on Intelligent Technologies for Smart Adaptive Systems) network during the daily peak load competition [48]. For the dataset, the organizer of the competition provided the following data to the competitors: half  
Figure 4: Distributed data mining on grid computing platform using web services with gene expression programming (GEP) and function finding algorithm using gene expression programming (FF-GEP) and global model generation algorithm based on unconstrained nonlinear least squares (GMG-UNLS).  
hourly electricity load demand from January 1997 to December 1998, average daily temperature from 1995 to 1998, and holiday's information from 1997 to 1999. We focus on mining model between daily peak load and average daily temperature and between daily peak load and holiday. Two UCI standard datasets are also available on the UCI machine learning archive [49]. In Gas Sensor Array Drift Dataset (GSADD), this contains 13,910 measurements from 16 chemical sensors utilized in simulations for drift compensation. In Dodgers Loop Sensor (DLS), loop sensor data were collected for the Glendale on ramp for the 101 North freeway in Los Angeles. All datasets in this paper are shown in Table 1.  
To facilitate the calculation of the algorithm proposed, we linearly normalize all inputs and output to be within the range [0,1] to avoid the masking effect.
# Reducing Defense Vulnerabilities in Federated Learning:
## 4 Attack Scenarios and Results
### Performance Analysis
#### 4.3.1 Data Poisoning Scenario and Results (IID)  
Table 3 presents the performance comparison for label-flipping attacks on CIFAR-10, where dog (label 5) is flipped to cat (label 3). Even with 40% malicious participants, our OC-SVM variant maintains 72.24% accuracy, significantly outperforming baseline methods.  
Even when the attacker rate is increased to 40%, as seen in Table 3, the NC-FLD method we propose effectively maintains its model success when compared to other studies. However, in our proposed method, k-NN classification cannot detect label-switching attacks as the attacker rate increases. This is because the false positive rate increases during classification, and model success decreases as it treats malicious users as benign.  
Table 4 shows the results for label-flipping attacks on F-MNIST (shirt/t-shirt swap) and MNIST (digits 7/1 swap). Our approach demonstrates superior robustness particularly at higher attack rates.  
Table 4 shows the state-of-the-art method performance comparison with the proposed NC-FLD method. Especially at a higher rate of malicious participants, the OC-SVM and Agg-C classification algorithms can distinguish benign and malicious users from each other more effectively.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & \multicolumn{3}{c}{**F-MNIST**} & \multicolumn{3}{c}{**MNIST**} \\ \hline  & **\%0** & **\%20** & **\%40** & **\%0** & **\%20** & **\%40** \\ \hline Krum [14] & 64.49 & 63.62 & 55.78 & 85.34 & 82.83 & 81.74 \\ Median [16] & 79.11 & 76.34 & 69.34 & 98.01 & 96.08 & 88.15 \\ TMeans [16] & **80.84** & 76.12 & 68.33 & 97.68 & 98.11 & **92.72** \\ Ours OC-SVM & 79.93 & **78.02** & **73.84** & **98.65** & 98.18 & 92.64 \\ Ours Agg-C & 79.05 & 77.62 & 72.12 & 98.51 & **98.21** & 89.47 \\ Ours k-NN & 78.61 & 76.65 & 69.54 & 97.84 & 97.51 & 85.64 \\ \hline \end{tabular}
\end{table}
Table 4: Label-flipping attack results for F-MNIST and MNIST.  
\begin{table}
\begin{tabular}{l c c} \hline  & **\%0** & **\%20** & **\%40** \\ \hline Krum [14] & 66.83 & 65.18 & 64.34 \\ Median [16] & 76.35 & 76.25 & 68.23 \\ TMeans [16] & 73.91 & 72.76 & 67.37 \\ Ours OC-SVM & **77.21** & **76.40** & **72.24** \\ Ours Agg-C & 77.03 & 75.26 & 71.26 \\ Ours k-NN & 76.92 & 74.34 & 68.46 \\ \hline \end{tabular}
\end{table}
Table 3: Label-flipping attack results for CIFAR-10.
## 2 Materials and Methods
### RMF Joining and Laser Joining Processes  
For the RMF Joining process, the Ni/Al reaction was ignited via a 13 V DC power source. A total of 5 MPa pressure was applied to ensure close contact between the CFRP, RMFs, and aluminum. The schematic image of the joining process is shown in Figure 1a. The RMF joining parameters are listed in Table 1. The laser joining process is shown in Figure 1b. The temperature achieved by the PC during RMF joining was estimated computationally using ANSYS. The simulation parameters are shown in Table 2[19]. The thermal conductivity and heat capacity were assumed to be constant. A slice of cross-section was applied on analysis of the temperature during the RMF joining. For the laser joining process, the laser used for joining was a 300 W continuous wave diode laser, the size of the laser beam was 5 mm \(\times\) 2 mm, and the wavelength of the laser was 806 nm. The laser joining parameters are listed in Table 3. The laser irradiation was on the top aluminum surface, and the irradiation time was 5 s. The lap tensile test was used to determine the tensile strength of the joints, as shown in Figure 1c.  
Figure 1: Schematic image of (**a**) the self-propagating joining using Ni/Al multilayer film; (**b**) the laser joining; (**c**) the lap tensile testing.
## 2 Materials and Methods  
### Materials  
Certified high-purity reference standards (HPLC grade) of mono- and disaccharides, including arabinose, fructose, galactose, glycerol, glucose, inositol, xylose, lactose, maltose, mannitol, mannose, rhamnose, ribose, sucrose, sorbitol, sorbose, and trehalose, were purchased from Sigma-Aldrich Chemie Ltd. (Steinheim, Germany) and used as external standards to create calibration curves. Acetonitrile LiChoslov(r) (CH\({}_{3}\)CN) was obtained from Supelco(r) (Bellefonte, PA, USA). Ultrapure water was produced using the PureLab Flex Elga water purification system via reverse osmosis (Veolia Water Technologies, Paris, France). Gallic acid (97.5%), Folin Ciocalteu phenol reagent, 2,2-diphenyl-1-picrylhydrazyl (DPPH) (99%), and (\(\pm\))-6-hydroxy-2,5,7,8-tetrameyhylchromane-2-carboxylic acid (Trolox) (97%) were purchased from Sigma-Alrich (Buchs, Switzerland). All other chemicals used were of analytical grade.
# Model Experiment Study on Vertical Bearing Characteristics of Large-Diameter Ring Piles
###### Abstract
A new p-y curve was proposed with better predictions for large-diameter piles. [PERSON] (2010) [8] showed that the dynamic load test for large-diameter open concrete piles is limited and frequently underestimates pile-bearing capacity. Therefore, static load tests are recommended, whose results should be combined with those of dynamic load tests to promote the design and construction of large-diameter piles. [PERSON] (2020) [9] and [PERSON] et al. (2013) [10] clarified the load-transfer mechanism of large-diameter rock-socked piles and determined the performance characteristics of ultimate end resistance and ultimate side resistance. The pile-end resistance increases linearly with the settlement of the pile end, and the rock ultimate side resistance coefficient of the rock-socked section has no significant correlation with the pile diameter of the rock-socked section. However, the ultimate resistance coefficient increases with an increase in pile diameter. However, when the pile diameter is less than 1000 mm, the correlation is not obvious. On the pile diameter problem, [PERSON] (2016) [11] also proposed a different method to evaluate the pile diameter effect for the finite element simulation software. The commonly used LPILE program ignores the volume of the pile, and the results are ideal when the deflection is less than 60 mm. However, after more than 60 mm, the load displacement curve will diverge, and the analysis results will not accurately represent the interaction between the pile and the soil. Therefore, it is necessary to calibrate the data according to the static load experiment to deal with the unreliability of the p-y curve. The existing research has revealed the settlement mechanisms and mechanical properties of pile foundations in detail from both theoretical and practical perspectives, making significant contributions to the field of pile foundation. However, the mechanical propertiesof LDRPs and whether they have similar physical and mechanical properties to LDSPs must still be determined. The hollow part is the key structure of LDRPs, and the effect of the size of the hollow on the overall mechanical properties of the pile must be determined. The appropriate size that not only ensures the ultimate bearing capacity of the pile but also reduces the use of concrete and provides economic benefits is of great interest. In view of the above problems, we adopted the method of combining a scale vertical static load model with numerical simulations and introduced a new parameter, the thickness-to-diameter ratio, \(K\), to quantitatively distinguish between five LDRPs with the same pile diameter but different hollow ranges. The overall mechanical properties of LDRPs are revealed from the perspectives of pile settlement, pile stress, and pile strain. The range of the thickness-to-diameter ratio, \(K\), given in the conclusion, provides a new reference for the design and applications of LDRPs in the future.
## 6 Simulation Results
### Performance Analysis  
We compare the proposed MADDQN algorithm with two existing algorithms:  
1. GPSR [22] (greedy perimeter stateless routing) is often used in the transmission of ad hoc networks, which collects the geographic location information of neighboring nodes and finds the next-hop node with the nearest geographic location to the destination through a greedy algorithm.
2. The random selection scheme randomly selects the next-hop node within the communication range without any optimization strategy.
# Fault Detection in Active Magnetic Bearings Using Digital Twin Technology
## 9 Discussion  
The study emphasizes the significance of fault detection in Active Magnetic Bearing (AMB) systems, given how important they are in various industries like aerospace, power generation, and manufacturing. The paper proposes using a digital twin of the AMB system to monitor and analyze its performance in real-time. The digital twin acts as a standard to detect failures by simulating the system's behavior. The research proposes to use a neural network as the primary tool for digital twin fault identification. The neural network is trained using real-time sensor data (including normal condition, imbalance and misalignment) from the digital twin. Real-time sensor data from the digital twin model is supplied into the neural network once it has been trained. This allows the network to analyze, realize, and classify various types of AMB system failures.  
The experimental results were presented to validate the methodology. Because of the implementation of digital twin technology, the model proved to have a high fault detection accuracy compared with the literature studies, as shown in Table 4.
# Convolutional Adaptive Network for Link Prediction in Knowledge Bases
## 5 Results and Discussion
### Parameter Sensitivity  
_(1) Label Smoothing_: When measuring loss values, overfitting can be avoided by utilizing a method called label smoothing. Accordingly, several comparison experiments on the WN18 and FB15k datasets were extended to reveal the importance of these parameters in the results of CANet. Figure 4 depicts the outcomes. When we set the label smoothing value to 0.1, CANet outperformed both datasets for the Hits@10 results. As a result, label smoothing could increase the efficiency of our method to some degree. Increasing the label smoothing value resulted in underfitting. Thus, it is recommended that the impact of label smoothing should be analyzed on a per-dataset basis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**YAGO3-10**} \\ \cline{2-5}  & \multirow{2}{*}{**MRR**} & \multicolumn{2}{c}{**Hits**} \\ \cline{3-5}  & & **@10** & **@3** & **@1** \\ \hline DistMult & 0.340 & 0.540 & 0.380 & 0.240 \\ ComplEx & 0.360 & 0.550 & 0.400 & 0.260 \\ ConvE & 0.440 & 0.620 & 0.490 & 0.350 \\ \hline CANet & **0.504** & **0.662** & **0.562** & **0.423** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The MRR and Hits@k of the link prediction results on the YAGO3-10 dataset.  
Figure 3: The Hits@10 performance with various kernel sizes on four datasets.  
(2) Dropout Rate:To improve the generalization potential of our approaches, we used the dropout strategy. On four datasets, we performed multiple experiments with dropout rates ranging from 0.0 to 0.5 to validate the influence of various dropout rates. The findings of our research are depicted in Figure 5. We focused on how different input matrices and a hidden layer dropout rate affected CANet's Hits@10 results. The findings show that using the dropout strategy provided better results than those obtained without using it. As a result, the dropout approach has the potential to significantly increase the method's efficiency. For the FB15k and WN18 datasets, the input matrix dropout rate of 0.2 and hidden layer dropout rate of 0.3 were the highest. However, for the WN18 RR and FB15k-237 datasets, the input matrix dropout rate of 0.4 and hidden layer dropout rate of 0.4 were the best. This result could be due to the fact that the first two datasets contained reversible relations, which made prediction easier.
# Energy Efficiency Perspectives of PMR Networks +
## 5 Technical and Economic Considerations on Energy Efficiency of Next Generation PMR Systems
### Business Models for Mobile Broadband PMR
#### 5.1.3. Deploy Dedicated Network Services through a Public-Private Partnership Project  
The Public-Private Partnership Project (PPP) business model features a dedicated and standalone LTE network, which is deployed, operated and maintained by an MNO and/or another independent operator. This type of network is typically owned by a telecom operator, which provides the service to the public safety agencies while usually assuming the financial, technical and operational risk of the service offer. One of the key benefits of the PPP model is that the public safety agency is the only entity using the network. CAPEX and OPEX can be reduced through synergies in the reuse of antenna sites, backhaul and technical skills contributed by the private partner. Public safety communications requirements are assured and customized, with full control over such critical specifications as latency, coverage and resilience. The main challenge is that this model requires having access to a dedicated broadband public safety spectrum and negotiating with a partner to invest the upfront CAPEX to build the network. However, many synergies can exist to minimize this upfront investment.
## 5 Discussion
The differences in PERSIANN-CDR could be linked with GPCP precipitation product with a pixel scale resolution of 2.5 degrees [47; 53], which is very coarse as compared to the resolution of PERSIANN-CDR. However, PERSIANN-CDR provides long term precipitation information which results have shown promising results in station rich regions [2; 61]. The point source precipitation may not be able to capture exact complex behavior of precipitation variability with simple error metrics. The regional precipitation variation is subjected to latitudinal increase and after certain degrees of elevation it started decreasing while longitudinal increase/decrease is subjected to summer and winter seasons. The two precipitation patterns produce more precipitation in the southeast and southwest during monsoon and winter seasons respectively [14; 36]. The differences reported in statistical metrics could possibly be associated with rain shadow regions, seasonal wind patterns and location of in-situ stations in the lee sides of the mountains and valleys. Thus, when making any comparison at regional scale about the performance of GPPs, it might be prudent to keep in mind the regional distribution of precipitation pattern, wind and topography of the region.
## 1 Introduction  
Even though technology drives ships to be highly automated to support human factors, on-scene or remote ship operators are inevitably responsible for safe ship handling [1; 2; 3; 4; 5]. Accordingly, ship-handling training methods should also follow the trend of maritime technology development [6; 7; 8]. As ship control primarily relies on rudder steering rather than engine adjustment, except for highly urgent emergencies, the simulated training should also measure how skillfully the navigator steers the rudder in a given situation [9]. Trainees are considered well-trained when they achieve appropriate ship-handling skills like professional navigators [10].  
Many researchers have pointed out how vital ship handling is, considering that maritime accidents happen due to inappropriate maneuvering [11; 12; 13], energy losses from excessive rudder controls [14], and even maintaining the quality of goods [15]. However, evaluation methods for simulated ship-handling training have been merely based on scenarios, such as the restriction of exceeding the maximum value [16; 17; 18], watching the trainee's physical condition [19], or robust methods under succeeding or failing rules [20]. Here, this research considered a more specific evaluation model, not only accepting that a trainee has passed the simulation test if they keep within the limit line but also by being closer to the experts' performance.  
Collecting professional navigators' data in each situation is challenging for many reasons. Data usage is restricted by the company's policies or privacy issues, the actual ship test boarded with professional navigators is complex [21], and a simulation test with experienced navigators also has risks due to the limited chances of simulation [22]. Accordingly, this research used the university training ship's data and applied probabilistic estimation methods to populate the uncollected range of data to find specific patterns diverted along the different altering angles.  
Therefore, this research aims to develop a probabilistic steering angle estimation model using the actual navigator's steering data from the target ship to obtain a sufficient professional navigators' database. The specified process is enumerated, including data collection, preprocessing, probability calculation, and comparing the trainee's results with the experts' steering patterns.
# High-Speed Alumina Stereolithography
## 1 Introduction
The parts were reported to have superior mechanical performance than traditionally molded ZTA, however, a long sintering schedule was described. Lithography based ceramic manufacturing has also been used to fabricate complex alumina parts such as gear wheels, turbine blades, and cellular structures [13]. A high fabrication speed of 6000 mm/s was reported for stereolithography of alumina ceramic using a 355 nm UV laser at 1.2 W in [14], however, a higher fabrication speed is not commonly reported in other publications. Ceramic paste composition is an important consideration for stereolithography. The solid content of the paste has a significant impact on processability and part properties [15]. A solid loading of greater than 50% is recommended to prevent the cracking of parts during binder removal and reduce shrinkage during sintering [16]. A high solid loading is generally recommended for improved part density and mechanical performance, however, the effect of composition on paste rheology is an important consideration [17]. A high solid content may increase the paste viscosity to an extent that homogenous layer spreading cannot be achieved, or may change the paste rheology from thixotropic to dilatant [18]. A low solid content may reduce the paste viscosity whereby paste layers can no longer self-support and flow freely, reducing processability. The balance between solid content and viscosity is an important factor for ceramic stereolithography. In this research, ceramic paste composition was computationally modelled to predict the maximum solid loading of alumina particles in an acrylic resin matrix, to reduce the time and cost of experimental paste optimization. Stereolithography processing conditions such as laser power, scan speed, and spot size, influence the extent of photopolymerization and therefore the curing depth and curing width [19]. A curing depth of 1.5 \(\times\) the layer thickness is preferred to ensure sufficient inter-layer lamination. To achieve a dimensionally accurate part, the measurement of the curing width is required to define a suitable laser offset to account for over-curing. These parameters are affected by the scattering of laser radiation by the solid particles in the resin matrix, therefore material properties such as particle size distribution and refractive index are important [20; 21]. Modelling of the paste material and optical analysis was used in this research to predict the curing behavior of the ceramic slurries, and to estimate suitable laser processing parameters to achieve sufficient curing depth.