esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study investigates an innovative hybrid treatment for compost-derived wastewater, combining aluminum-based electrocoagulation (EC), zeolite addition, and magnet assistance. Key experimental variables--presence/absence of magnet, stirring speed (250 and 350 rpm), and contact time (10-30 min)--were systematically varied to analyze process efficiency, electrode dissolution and mass loss, solid-liquid separation dynamics, and quantify energy input and Faraday efficiency (FE). Magnet-assisted processes achieved higher COD reduction at longer treatment times of 30 min and lower mixing speeds of 250 rpm, with up to 89.87%. The highest turbidity reduction of 98.59% is achieved after 20 min at 350 rpm. The magnetic field does not significantly affect the dissolution of Al electrodes, but over time, it helps reduce localized electrode damage, thereby supporting both process efficiency and electrode longevity. Magnetic fields improved sludge settling in shorter treatments by promoting faster aggregation. However, the energy input was generally higher with magnetic assistance. FE in the range of 50.89-65.82% indicates that the actual electrode loss is lower than theoretical. For the experiments conducted according to the L8 Taguchi experimental design, given the significance and contribution of factors to the process, the optimal combination is the absence of a magnet, 350 rpm, and 20 min.  
hybrid treatment; magnet assistance; electrocoagulation; aluminum electrode; zeolite; process optimization Article
## 5 Findings and Discussion
### Synthesizability of the Two Perspectives  
Synthesizability has been widely used in synthetic fiber and synthetic information systems [50]. It intends to combine separate parts to form a new system, and this new system will function better than its counterparts. In this study, designers and constructors play different roles in the development of steel structures. The synthesis of these two perspectives helps to develop a new sustainability indicator system that can be used to resolve conflicts and inconsistencies between design and construction. Traditionally, constructors have to compromise with designers' initiatives if the used sustainability assessment indicators are design-oriented. Similarly, designers are forced to compromise with constructors' needs if the indicator system is based on construction activities.  
This new indicator system reflects the importance of synthesizing the divergent views of designers and constructors to attain sustainable steel structures. In practice, both design and construction are complex processes that contain many interlocked know-hows. Conflicts will occur given the poor coordination between designers and constructors in the design and construction process. As suggested by the newly developed indicator system, designers can better forecast potential construction-related issues before the construction phase and address constructors' demands in project schematics. Meanwhile, constructors can detect the potential issues caused by design errors and react to these issues effectively.
## 3 Experiments and Results
### Performance Analysis
#### 3.2.4 Test Scenario-4  
This casework is segmented into two parts--training results and testing results. Tables 1 and 2 show the training accuracies measured for each classifier for different lengths of shapelets for \(p=1\), \(i_{max}=15\) and \(p=3\), \(i_{max}=15\), respectively. From Table 1, it is evident that ensemble and \(k\)-NN identify all the devices correctly for \(p=1\) and each length. From Table 2, it can be stated that ensemble outperforms other methods by identifying all the devices correctly for \(p=3\).  
Figure 5: Accuracy vs length of shapelets for ensemble and binary DT for \(p=1\) and \(i_{max}=15\) & 60.  
Figure 6: Accuracy vs \(p\) values for \(i_{max}=15\) & 60 with different values of \(m\) (ensemble).
# Task Offloading Scheme Based on Proximal Policy Optimization Algorithm
## 4 Model System  
Edge computing can reduce transmission delay, enhance privacy security, and reduce cloud load by sinking computing resources. However, limited by hardware resources and power consumption, edge devices make it difficult to independently complete computationally intensive tasks, and a single task offloading strategy cannot fully optimize computing efficiency, energy consumption, and resource utilization. Therefore, how to allocate computing tasks reasonably between the cloud and the edge and achieve a balance between computing efficiency, energy consumption control, and latency optimization has become a research hotspot.  
This article proposes a task offloading scheme that utilizes the Proximal Policy Optimization (PPO) algorithm. The basic framework of this method is shown in Figure 2. In the traditional cloud edge collaboration architecture, the collaboration mechanism between edges is introduced to improve the utilization of computing resources by optimizing task offloading paths and resource allocation. Simultaneously, service caching technology is utilized to effectively reduce data transmission latency and network load.
###### Abstract  
Polytetrafluoroethylene (PTFE) has been widely used as a high-frequency dielectric substrate due to its excellent dielectric properties and thermal stability. However, with its low intrinsic thermal conductivity, PTFE falls short in meeting the escalating heat dissipation demands of high-power density, high-frequency communication systems. Although the thermal conductivity of PTFE composites can be effectively improved by the high thermal conductivity fillers, it is always accompanied by a decline in dielectric properties. Molecular chain ordering is regarded as an effective strategy to improve the intrinsic thermal conductivity of polymers while maintaining dielectric properties. Unfortunately, the conventional preparation methods for ordered molecular chains, such as electrostatic spinning and uniaxial stretching, are not applicable to the preparation of PTFE substrates. In this work, fluorinated graphite (FGi) is employed to induce the in-plane orientation of PTFE molecular chains. As a result, the PTFE composite with 0.5 wt% FGi loading exhibits an in-plane thermal conductivity of 1.21 W\(\cdot\)m\({}^{-1}\)\(\cdot\)K\({}^{-1}\), six times higher than the in-plane thermal conductivity of pure PTFE. In addition, this composite exhibits a superior dielectric constant of 2.06 and dielectric loss of 0.0021 at 40 GHz. This work introduces a facile method to achieve improved thermal conductivity of PTFE while maintaining its excellent dielectric properties.  
high-frequency dielectric substrates; molecular chain orientation; thermal conductivity; high-frequency dielectric properties +
Footnote †: journal: materials
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 3 Advances in Numerical Modeling Techniques for LPBF Defect Detection
### Melt Pool Defect Modeling
#### 3.1.2 Thermo-Mechanical Simulations of LPBF Defects Using FVM
A regression model was employed to correlate the calculated molten pool dimension and calculated porosity evolution. An in-house semi-analytical thermal model was reported to predict the printed component's thermal history and features [82]. The investigated powder material was IN718. Various scanning strategies, such as chess hatching, contouring hatching, stripes hatching, and meander hatching, were performed for a triangle-shaped printed part to analyze how the scanning strategies affected sharp corners. It was determined that vector lengths decrease, and multiple nearby scans can cause significant energy input increases, leading to large melt pools and a lower accuracy when printing pointed features. A numerical study presented a spatially fully adaptive smoother particle hydrodynamics (SPH) scheme that simulated the melt pool behavior in LPBF for SS304 and IN718 [83]. The recoil pressure from the laser created a slight depression zone around the laser spot, resulting in a considerably higher velocity field destabilizing the molten liquid pool. The liquid is pushed into the pores of the metal powder, and the Marangoni effect is caused by the wettability effect, which bulges the surface outward on both sides, resulting in increased surface roughness. An analytical function of SS316L parts predicted the final roughness in LPBF [84]. Figure 6 compares the experimental results for the surface roughness and the analytically predicted surface roughness. A similar study proposed an analytical model to estimate the residual stress of LPBF-processed geometries by calculating the resultant temperature gradients and the thermal stresses from a point body load approach [85]. Table 3 lists the thermo-mechanical analytical models of the melt pool behavior and defect generation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Powder** & **Defect(s)** & **Laser Energy Rate (W), Velocity (mm/s), and Diameter (mm)** & **Code(s)** \\ \hline IN718 & Systematic discontinuity, irregular melt pool & & \\
[82,83,85] & surface, large residual stresses, resultant & 42–250, 100–8000, 38–100 & Semi-analytical, analytical \\
[82,83,85] & shrinkage stresses, porosity, gas trapping, splattering, balling & & \\ \hline SS 304 [83] & Porosity, gas trapping, splattering, balling & 42–200, 76–120, 38 & Analytical \\ \hline Ti6 Al4V [80,81] & Large residual stresses, large porosities & 50–195, 500–1200, 54 & Diablo, Analytical \\
[80,81] & Surface roughness, gas bubbles, ablated particles & 100–200, 400–2000, 54 & Analytical (Fortran) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Compiled list of thermo-mechanical analytical models of the melt pool behavior and defect generation.
## 4 Conclusions  
Downscaled ensemble climate variables using Climate-smart Decision Support System (CSDSS) together with field experimental data and recommended model parameters were used as inputs to AquaCrop model, and rice yield and water productivity (WP) indicators were simulated for 2010-2039, 2040-2069, and 2070-2099 future periods relative to the 1976-2005 base period in Malaysia. The projected increasing trend of the maximum and minimum temperatures in two crop seasons (off-season: January-June and main season: July-December), increasing rainfall during the main season, and decreasing rainfall during the off-season will likely exert multi-layered impacts on rice production and WP. The simulated increasing trend of WP with increasing CO\({}_{2}\) concentration, temperature, and rainfall under RCP4.5, RCP6.0, and RCP8.5 emission scenarios indicates an improved synergy for rice growth under the projected water-heat-carbon status. However, there remains some degree of uncertainty in the magnitude of projected WP, since the AquaCrop model, to some extent, systematically overestimated rice production. The reason of overestimation might be the highly daily variable and short-period rainfall patterns, and not accounting for the impacts of climate change on agronomic practices (e.g., irrigation, drainage, weeding, pest control, and fertilizer application). Despite this weakness, the proposed model would fairly predict rice yields under the likely evolved future climate change and different water regimes. The information on WP would help to devise a strategic water management plan during periods of both shortage and excess of water as currently occurring during the two rice-growing seasons in the Northwest Selangor Irrigation Scheme. The model can be adopted in planning crop and water management to increase WP in any region, especially in areas with water shortage. The AquaCrop model can be used as a decision support tool to boost water productivity. However, development of a user interface to access the information interactively will enhance decision-making, especially in complex environments, for adaptation of the AquaCrop model.  
Conceptualization, M.R.K.; methodology, A.A.H. & M.R.K.; software, A.A.H.; validation, A.A.H. and M.R.K.; formal analysis, A.A.H. & M.R.K.; investigation, A.A.H.; resources, M.A.M.Z. & B.M.R.; data curation, A.A.H.; writing--original draft preparation, A.A.H. & M.R.K.; writing--review and editing, M.A.M. & M.R.K.; visualization, A.A.H.; supervision, M.R.K. & M.A.M.Z.; project administration, M.A.M.Z. & B.M.R.; funding acquisition, M.R.K., M.A.M.Z. & B.M.R. All authors have read and agreed to the published version of the manuscript.  
Geran Putra Berimpak, Universiti Putra Malaysia & No: 9678600.  
Not applicable.  
We acknowledge Universiti Putra Malaysia for financial support from Putra Grant No: 9678600. The authors thank [PERSON] for his scholarly scientific advice on the sink effect on rice production.  
The authors declare no conflict of interest in publishing this research.
# A Deep Learning-Based Crop Disease Diagnosis Method Using Multimodal Mixup Augmentation
## 2 Related Works  
### Deep Learning Approach to Crop Disease Diagnosis Using Multimodal Data  
Multimodal data refer to the input of data from various sensors, including visual, auditory, and temperature sensors, encompassing diverse types of information with distinct characteristics. In deep learning, the utilization of multimodal data for learning purposes is commonly known as multimodal deep learning [16].  
The analysis of multimodal data using deep learning has been studied in various fields, including healthcare [17; 18], automotive [19], and facial recognition [20] domains. [PERSON] et al. introduced a deep learning-based method for the driver's stress level classification using ECG (electrocardiogram) signals, vehicle dynamics data, and environmental data [19]. It was found that when using multimodal data, the accuracy increased by up to 14.9% compared to when using unimodal data (ECG signals). [PERSON]. [PERSON] et al. analyzed data from speech, handwriting, and gait using a multimodal deep learning model to diagnose [PERSON]'s disease [18]. When multimodal data were used, the accuracy improved by 5.3% compared to using unimodal data (speech).  
However, most of the deep learning studies for crop disease diagnosis have relied heavily on unimodality (e.g., mostly images). They have leveraged structural improvements of a CNN (convolutional neural network), utilization of multiple resolutions, and ensembles the maximization performance from a given single datum [21; 22; 23].  
In this study, we would like to verify whether performance improves when environmental data as well as images are used to diagnose diseases of crops. We would like to emphasize that in this study, we do not propose a novel model structure for processing multimodal data. To date, most studies on diagnosing crop diseases using deep learning have focused on data using a single modality. However, in this study, we aim to demonstrate that a deep learning model using multimodal data outperforms a model using unimodal data. To the best of our knowledge, this is the first attempt to perform crop disease diagnosis using multimodal data and confirm performance improvement compared to unimodal data.
# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 3 Deep Learning Surface Defect Detection Methods for Industrial Products
### Semi-Supervised
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Year** & **Method** & **Target** & **Performance** \\ \hline
[80] & 2021 & YOLOv4 & Fabric & Can quickly and accurately locate defects, and can also be used in other defect \\  & & & & detection industries \\ \hline
[81] & 2022 & UNet & Fabric & Detection accuracy rate: 99\% \\ \hline
[82] & 2022 & SVM & Non-woven fabric & It is highly accurate and performs well in real time \\ \hline
[83] & 2021 & Faster R-CNN & Aluminum & In comparison with the original algorithm, this algorithm achieved 78.8\% mean average accuracy (mAP), which is 2.2\% higher \\ \hline
[84] & 2018 & CNN & Copper clad lamination surface & Accuracy rate: 98.2\% \\ \hline
[85] & 2019 & Faster-RCNN and feature fusion & (GAPR) texture defect & Performs well under various conditions and has good adaptability \\ \hline
[86] & 2022 & Autoencoder and morphological operation & Textile & Superior performance to other prevailing models \\ \hline
[87] & 2019 & Faster R-CNN & Weel hub & It is simpler, faster, and more accurate than both R-CNN and YOLOv3 methods for wheel hub defects \\ \hline
[88] & 2022 & YOLOv3 & Polarizer & There is a slight increase in its mAP over YOLOv3, and it has a detect speed increase of 44\% to 121 frames per second \\ \hline
[89] & 2021 & Faster R-CNN & Belt Layer of Radial Tire & False negatives and false positives decrease by 7.79\%, 3.4\%, and 5\%, respectively, compared with the vanilla Faster R-CNN \\ \hline
[90] & 2017 & CNN & Pavement crack analysis & Accurately detects pavement cracks and evaluates their types \\ \hline
[91] & 2022 & YOLO v5 & Solar Cell & Solar cell EL images were used to train the model, which achieved 89.64\% mAP \\ \hline
[92] & 2017 & CNN & Mangosteen & Recognition accuracy: 97\% \\ \hline
[93] & 2017 & CrackNet & Crack detection on 3D asphalt surfaces & With 200 3D images, CrackNet achieved high precision \\ \hline
[94] & 2021 & R-CNN & Textile fabric & Defect detection accuracy improved by 4.09\% to 95.43\% \\ \hline
[95] & 2020 & CNN & AigleRN and DAGM2007 & Can achieve high detection accuracy and efficiency \\ \hline
[96] & 2019 & Faster R-CNN & Aluminum profile & With regard to the multiscale defect-detection network, it achieved a 75.8\% mAP over Faster R-CNN \\ \hline
[97] & 2022 & MobileNetV3 & Sanitary ceramics & With the Faster R-CNN method, detection speed is improved by 22.9\%, precision is improved by 35.0\%, and memory consumption is reduced by 8.4\% compared to the SSD, YOLO V3, and one-stage SSD methods \\ \hline
[98] & 2017 & CNN & Welding & Recognition accuracy rate: 95.83\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
# From 5G to 6G--Challenges, Technologies, and Applications
## 3 Sixth-Generation Network's Aspects
### Sixth-Generation Network's Footprint  
#### 3.1.1 Social Impact  
Currently, there are multiple subjects receiving little attention when it comes to communication networks. Some of these topics include: users' personal data accessing rights, operators' subscription plans, and social awareness about sharing data between users on the community and individual level. These points hold important social value as they can heavily influence the public's opinion about sensitive topics. One famous example of such an issue was the case of _Cambridge Analytica_, the infamous British political consulting firm, which was able to access users' data through the Facebook Open API and link them to other available data, such as other social media platforms and online purchases, to collect over 5000 data points on 230 million U.S. citizens [30]. The data were claimed to have been used to impact the U.S.'s presidential race. Another example is with respect to the conventional spectrum allocation scheme, where regulators auction off the license to use certain frequency bands to the highest payer. This has negative consequences such as hiking up the data plan and telecommunication service prices for the end users, as well as the device costs. Therefore, it is important to propose novel spectrum regulation policies and reconsider the available data accessing options. Furthermore, as reported in the Digital 2020 July Global Statshot Report [31], 4.57 billion people are connected to the Internet; this is only a little over half the planet's population. This raises another challenge for 6G networks, i.e., bringing the world together and focusing on installing network infrastructure in third-world countries that have the least Internet access. This became clearer than ever before in 2020, as COVID-19 forced the entire world to almost operate entirely digitally. More importantly, the expansion of the Internet should consider the living inequalities among people worldwide by offering substantially less-expensive Internet access options to realize the aspiration of considering connectivity as a basic human right. This is achievable by offering near-free data plans and popularizing device leasing options. Due to the global language and cultural barriers, the emerging Internet services should consider the differences between people and work to integrate them under one umbrella. In other words, the offered network services should be tweaked to suit the demographics  
Figure 4: A hierarchical approach for the discussion of 6G’s aspects.  
of the geographical area in which they are being provided. One famous example of this can be found with Google Maps, where disputed territories are displayed as belonging to different countries based on the geographical location of the map viewer.
## 3 Virtual Admittance Feedforward Compensation and Phase Correction to Reduce Current Distortion
### Overall Control Diagram  
The overall block diagram of the proposed control strategy is shown in Figure 9. By realizing the proposed VAFC, the phase difference between input current and voltage caused by limited bandwidth of the current loop is reduced. The influence of the input capacitor is eliminated with the proposed reference current phase correction. Additionally, a phase lock loop is adopted in the controller, to reduce feedforward interference caused by input voltage sampling noise. A notch filter is adopted to filter line frequency ripple in output voltage sampling.  
Figure 9: Proposed control strategy for CCM totem-pole PFC converter.  
Traditional average current control methods directly use the sampled input voltage to generate reference current, which is sensitive to the delay and disturbance of the sampling circuit. To solve this problem, PLL is adopted to process the sampled input voltage. It generates an ideal sine wave that is in phase with the input voltage. The structure of PLL based on the second-order generalized integral is shown in Figure 10. The principle of the second-order generalized integrator is shown in Figure 11. The second-order generalized integrator generates the signal \(v^{\prime}\)\({}_{in}\) and the orthogonal signal \(qv^{\prime}\)\({}_{in}\), and the d-axis component \(v_{d}\) and q-axis component \(v_{q}\) are obtained with Park transformation. Then, the q-axis component \(v_{q}\) is adjusted to \(v_{q}\)\({}^{*}\) with the PI controller, to realize phase-locking. The output of PLL is an ideal sine wave that is in phase with the input voltage, which is used to generate a current loop reference and solve the problems caused by the delay and disturbance of the sampling circuit. Due to the sampling and calculation delay of the PLL system, the input phase of the Park transformation (\(\hat{\theta}\)) lags behind the real system, which induces an error in the Park transformation. However, given that the sampling and calculation frequency (20 kHz) is much higher than the frequency of the input signal (50 Hz), the relative error is small. Specifically, the sampling and calculation delay is 1/20 kHz = 50 \(\upmu\)s, and the relative error is calculated as 50 \(\upmu\)s/20 ms-100% = 0.25%.  
A Saber/Simulink co-simulation model was built to verify the above theory. Figure 12a shows the simulation waveform without feedforward compensation when the input power is 600 W, while Figure 12b shows the simulation waveform with the proposed VAFC. In Figure 12a, the inductor current shows a phase difference where an input voltage and current distortion exists near the input voltage zero-crossing point. With the proposed VAFC, the phase difference and current distortion is almost eliminated and the input current tracking performance is significantly improved. The harmonic analysis results of the input current show that T  
Figure 11: Second-order generalized integrator.  
Figure 10: PLL structure based on second-order generalized integrator.