esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Effect of LiDAR Pulse Density on Foliage Profile Estimation  
The forest foliage profiles were estimated from the airborne small-footprint full-waveform LiDAR data at different pulse densities from 0.25 pulse/m\({}^{2}\) to eight pulses/m\({}^{2}\) to explore the influence of pulse density on the forest foliage profile, and the results are shown in Figure 15. Figure 15a shows the estimated foliage profiles at all pulse densities, and Figure 15b-g show the histograms of the estimated foliage profiles at the pulse densities of 0.25 pulse/m\({}^{2}\), 0.5 pulse/m\({}^{2}\), one pulse/m\({}^{2}\), 2 pulses/m\({}^{2}\), four pulses/m\({}^{2}\), and eight pulses/m\({}^{2}\). Figure 15 shows that when the pulse density increased from 0.25 pulse/m\({}^{2}\) to eight pulses/m\({}^{2}\), the retrieved foliage profiles were all very close to each other, and there was no obvious change rule. Therefore, when the pulse density increased from 0.25 pulse/m\({}^{2}\) to eight pulses/m\({}^{2}\), the effect of pulse density on the foliage profile at plot level was weak.  
Figure 14: The _RMSEs_ of the foliage profiles (**a**) and the _Diffs_ of the total _LA_Is (**b**) at flying altitudes from 1000 m to 4000 m at 1000 m intervals (the scanning angle and pulse density are 20\({}^{\circ}\) and one pulse/m\({}^{2}\), respectively).  
Figure 15: The estimated foliage profiles at pulse densities from 0.25 pulse/m\({}^{2}\) to eight pulses/m\({}^{2}\) (the scanning angle and flying altitude are 20\({}^{\circ}\) and 2000 m, respectively). (**a**) the estimated foliage profiles at all pulse densities; (**b–e**) the histograms of the estimated foliage profiles at pulse densities of 0.25 pulse/m\({}^{2}\), 0.5 pulse/m\({}^{2}\), one pulse/m\({}^{2}\), two pulses/m\({}^{2}\), four pulses/m\({}^{2}\), and eight pulses/m\({}^{2}\).  
The _RMSEs_ of the foliage profiles and the _Diff_s of the total _LAI_s at all pulse densities were calculated to quantitatively analyse the foliage profile retrieval results, and the results are shown in Figure 16. Figure 16a shows that when the pulse density was between 0.25 pulse/m\({}^{2}\) and eight pulses/m\({}^{2}\), the _RMS_Es were all similar, and they were all very small and between 0.16 and 0.18. Figure 16b shows that the _Diff_ changed slightly when the pulse densities were between 0.25 pulse/m\({}^{2}\) and eight pulses/m\({}^{2}\), and they were all small and within the range of 0.01 to 0.09. Therefore, when the pulse density increased from 0.25 pulse/m\({}^{2}\) to eight pulses/m\({}^{2}\), the estimation accuracies of the foliage profiles were similar and remained high.
# Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation
## 5 Experiment
### Dataset  
Four real world datasets were selected for experimental comparative analysis. The public benchmark dataset Yelp2 contains users, businesses, and rating information across 11 metropolitan areas in four countries. Two subsets of public benchmark dataset Yelp2 ([[https://www.yelp.com/dataset](https://www.yelp.com/dataset)]([https://www.yelp.com/dataset](https://www.yelp.com/dataset)) (accessed on 15 January 2022)), North Carolina(NC) and Wisconsin (WI), were selected as the experiment datasets. The NC dataset includes 225,580 rating records, 22,737 users and 12,502 items, and 111,394 trust social relations. The WI dataset includes 80,643 rating records, 8386 users and 4593 items, and 34,099 trust social relations. The Epinions dataset includes user trust relationships, ratings and item categories, which is crawled from the popular social network website Epinions (www.epinions.com (accessed on 15 January 2022)). This dataset contains 764,352 rating records, 18,088 users and 261,649 items, 355,813 trust relationships. The LastFM ([[https://grouplens.org/datasets/hetrec-2011/](https://grouplens.org/datasets/hetrec-2011/)]([https://grouplens.org/datasets/hetrec-2011/](https://grouplens.org/datasets/hetrec-2011/)) (accessed on 15 January 2022)) dataset contains 1874 users and 2828 items, 71,411 rating records and 25,174 relations of trust. Some of their important properties are summarized in Table 1. A brief introduction of them follows.
# Tamed Warping Network for High-Resolution Semantic Video Segmentation
## 5 Experiments  
### Experimental Setup  
We report our major results on the Cityscapes dataset [1], which contains 5k images finely annotated with 19 classes. The models are trained on the 2975 training images and evaluated on the 500 validation images. We also obtain results on the 1525 test images, reported by the test server. Each image is the 20 th frame of a \(1024\times 2048\) video clip. We also conduct experiments on the CamVid dataset [52], which can be found in Section 5.4.  
The training is divided into two steps, i.e., the training of per-frame CNN and the training of NKFC. To train the per-frame model, we use the 2975 fine-annotated training images (i.e., the 20 th frames in the video clips). We use MobileNet [17] pre-trained on ImageNet [53] as the encoder of the per-frame CNN and three cascaded lateral connections [47] as the decoder. We adopt the Adam optimizer [54] to train for \(90K\) iterations with the initial learning rate of \(1\times 10^{-2}\) and a batch size of 8. We update the pre-trained parameters with a 100 times smaller learning rate. Weight decay \(\lambda_{0}\) is set to \(1\times 10^{-7}\). Training data augmentations include mean extraction, random scaling between 0.5 and 2.0, random horizontal flipping, and random cropping to \([800,800]\). We implement the model using TensorFlow 1.12 [55] and train it on a GTX 1080 Ti GPU card.  
Figure 5: Choices of different layers for feature warping. The chosen layer is indicated with red color. The dotted arrows and boxes denote skipped operations.  
After training KFC, the parameters in it are fixed, and we start to train NKFC. In each training step of NKFC, we first send a batch of the 19 th frames into KFC to extract their context features. Then, we perform warping and correction for the corresponding 20 th frames and calculate the loss according to Equation (8). Note that the 20 th frames should also be sent to KFC to calculate the context features. Random cropping is not adopted since the warping operation may exceed the cropped boundary. We keep the size of \([1024,2048]\) with a batch size of 4.  
At inference time, we conduct all the experiments on video clips at a resolution of \(1024\times 2048\). During evaluation, the key frame is uniformly sampled from the 9 th to the 20 th frame in the video clip, and the prediction of the 20 th frame is used for evaluation. No testing augmentation is adopted. The accuracy is measured by mean intersection-over-union (mIoU), and the speed is measured by frames per second (FPS). Our models run on a server with an Intel Core i9-7920X CPU and a single NVIDIA GeForce GTX 1080 Ti GPU card.
## 4 Results and Discussion
### Selecting the Power of the Electrolyzer  
In Section 2.2. we suggested that when selecting the electrolyzer power, several main technological criteria should be assessed: (1) the possibility of using the by-product of electrolysis--oxygen; (2) the possibility of using the main product of electrolysis--hydrogen; (3) the possibility of using the by-product of electrolysis--heat and (4) the availability of renewable electricity for green hydrogen production.  
Based on the data presented in Section 3, the need for pure oxygen in Kaunas WWTP for aeration is 5.824 million m\({}^{3}\). If the primary use of hydrogen is biogas methanation, the need for hydrogen depends on the amount of biogenic CO\({}_{2}\) generated. Currently, the need for hydrogen is 5.682 million m\({}^{3}\), and considering sorted food waste as an additional source of raw materials--7.246 million m\({}^{3}\). The total heat demand in Kaunas WWTP is 16,129 MWh.  
Theoretically, the largest amount of renewable electricity could be produced at Kaunas WWTP if all biogas were used in cogeneration for electricity production. In the current situation, this would be 9991 MWh. After assessing the collection of sorted food waste, solar electricity and hydroelectric potential, renewable electricity production could reach 15,976 MWh.  
When selecting the electrolyzer power, it is assumed that the electrolyzer will operate with electrolyzer capacity factor equal to 80% and with electrolyzer efficiency equal to 60% [13].  
The options for selecting the electrolyzer power are presented in Table 4. The figures are given for two cases: V1--current situation (without additional waste, without solar power plant expansion and hydroelectricity); V2--after assessing the collection of sorted food waste, the potential of solar power and hydroelectricity.
# Multi-Objective Optimisation of Welding Parameters for AZ91D/AA6082 Rotary Friction Welded Joints
## 1 Introduction
The authors [24] intensively explored the effect of process parameters on the weld pool geometry using the Taguchi method. A statistical response surface method for optimising welding parameters was developed [25; 26]. Multi-regression methods and neural network models to predict optimal weld parameters were also analysed in [27; 28; 29]. The neural networks have been used to identify the welding parameters in real-time [30]. ANNs successfully predicted the desired weld beam geometry in gas metal arc welding. The optimal process parameters like feed rate, welding voltage and weld speed have been found to predict the beam height and depth of penetration of the weld beam [31]. Simulated annealing (SA) was also applied as an intelligent method for optimising welding parameters and weld pool in the TIG welding process [32]. The neural networks and genetic algorithm as the hybrid intelligent method were also studied to search optimal process parameters [33; 34; 35]. The evolutionary algorithms (EAs), particle swarm (PSO) and SA algorithms have been used to determine welding parameters to maximise tensile strength and minimise metal loss in friction welding steel [36]. A multi-objective optimisation for maximising the tensile strength and minimising the flash diameter and the HAZ width for the ductile iron with low-carbon steel joints was carried out in the study [37]. A genetic algorithm modeling temperature distribution during heating and cooling AZ31B magnesium alloys with 7075 aluminium alloy friction welded joints was investigated [38]. Rotary friction welding is a suitable welding method for joining light (Mg and Al) alloys [39; 40]. In addition, FRW between dissimilar materials has recently received much attention [41]. However, more research is needed to optimise dissimilar FRW between Mg and Al alloys with relatively high strength using the conventional friction welding method [42; 43; 44; 45]. In addition, it is difficult to fully understand the effects of basic processing parameters such as friction force, upsetting force and friction time on the dissimilar FRW between magnesium and aluminium alloys. In this study, the previous joining of AZ91D with 6 xxx series alloy bars by the direct-drive friction welding method was conducted. Then, the influences of the welding parameters on tensile properties and metal loss of the joints were experimentally and numerically investigated.
## 3 Results
### Characteristics of Studies
[35], had a gap of, respectively, several weeks and 3-4 months between the two tests. A prospective cross-sectional or cohort design was the most commonly adopted design, but ten studies were retrospective [13; 15; 27; 28; 33; 35; 38; 40; 46; 47]. Among the 28 studies included, the mean age of children was 7.47 years. Sample size varied from 32 to 4191 participants. The sample population consisted mainly of children referred to sleep centers for suspected OSA or sleep-disordered breathing; two studies were limited to pediatric patients affected by obesity [23; 34], one by Down syndrome [31], and one by laryngomalacia [46]. Fourteen studies excluded patients with serious comorbidities, such as craniofacial abnormalities, neuromuscular disease, syndromic diseases, chronic pulmonary diseases, or congenital heart disease [15; 24; 25; 27; 28; 33; 36; 38; 40; 42; 43; 47]. The remaining studies failed to report the type of comorbidities. Nine different parameters to define abnormal oximetry recordings were found; the most commonly used were a 3% oxygen desaturation index (ODI3) and a McGill score [12] > 1. Twelve out of 28 studies adopted the 2007 rules of the American Academy of Sleep Medicine for the Scoring of Sleep and Associated Events as the scoring criteria.
# A New Pabs Model for Quantitatively Diagnosing Phosphorus Nutritional Status in Corn Plants
## 2 Materials and Methods
### Data  
In this study, multispectral data for calculating vegetation indices were sourced from Google Earth Engine ([[https://eartheengine.google.com](https://eartheengine.google.com)]([https://eartheengine.google.com](https://eartheengine.google.com)) (accessed on 1 July 2024)). Sentinel-2, offering high-spatial-resolution optical imagery at 10 m resolution in both visible and near-infrared bands, is well-suited for large-scale environmental monitoring, agricultural assessments, and vegetation analysis [21].  
For maize classification in Changchun, a 10 m resolution crop type map was utilized, derived from Sentinel-2 imagery and processed using a random forest algorithm on the Google Earth Engine platform [22].  
Climate data, including daily maximum (Tmax) and minimum (Tmin) temperatures, precipitation (Rain), solar radiation (SRAD), relative humidity (RH), wind speed (WS), and potential evaporation (PET), were retrieved from the China Meteorological Administration platform ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)) (accessed on 1 May 2024)). These parameters were integral to APSIM model simulations, facilitating an in-depth analysis of crop responses to climate variability [23].
## 4 Simulation and Experimental Analysis
### Method Comparison
It can be seen that the prediction results of the load profile can track the measurement data accuracy in different sample time intervals, and the sampling time will influence the prediction results. The results in Figures 10 and 11 show that the proposed multi-layer stacked bidirectional LSTM method is more accurate than the other methods mentioned such as the BP neural network, ELM, and traditional LSTM neural network.
## 6 Experimental Results and Discussion
### Room Temperature Experiments
This confirms the high sensitivity of EMATs to lift-off; more particularly, the lift-off limitations of EMATs differ depending on the application the EMAT is designed for. EMATs for thickness measurements can still operate efficiently when the lift-off exceeds 1 mm; however, EMATs for guided waves are more sensitive to lift-off. A parameter that influences the performance of EMAT regarding lift-off is the impedance of the coil. The impedance changes with lift  
An experimental evaluation of these EMATs regarding their power requirements was also conducted. The power level decreased gradually from 100% to 20% with a step of 5%; we stopped there as no useful information could be retrieved from the signal received when the power level was smaller than 20%. Figure 10b shows how the amplitude of the signal transmitted increases with power supply increase; it can be observed that the amplitude increases almost linearly with the power increase. Similar to lift-off, the impedance of the coil affects the power requirements of EMATs. If the pulser/receiver unit drives the EMAT transmitter through an output resistor, the magnitude of the impedance of the coil should be equal to the output resistor of the pulser unit, so that the voltage drop in the coil will be minimized. Therefore, impedance matching is always required, which means that either a unit with zero output impedance should be chosen or an impedance matching network should be added between the pulser and the EMAT transmitter. Impedance matching should be used for the coil to be driven with the maximum power possible so that strong signals will be obtained. Nevertheless, the power supply level of EMATs remains high, leading to the conclusion that EMATs are considered to be more efficient as receivers rather than transmitters.
# Context-Aware Beam Selection for IRS-Assisted mmWave V2I Communications
## 2 Related Works
### Our Contributions
In addition, vehicles can share their perception of the traffic light status through Cooperative Awareness Messages (CAMs) or Basic Safety Messages(BSMs), which include location, speed, heading, and, in some cases, braking events that suggest deceleration due to traffic light stops. These standardized messages allow the base station to build a synchronized and real-time environmental context, which includes the dynamic state of the traffic signal affecting each vehicle's route. In summary, our contributions encompass not only context-aware beam and IRS selection for prolonged connection times but also a practical testing tool to optimize IRS deployment strategies. Moreover, by harnessing waiting times at traffic lights as predictive context, we enable the development of proactive strategies to enhance mobility management and resource allocation in mmWave V2X communication. These innovations collectively pave the way for more robust, reliable, and efficient vehicular communication systems, crucial for the advancement of smart and safe transportation networks. The remainder of this paper is organized as follows: Section 2 describes the considered scenario and formulation of the beam selection problem. In Section 3, the C-MAB based beam selection algorithm is elaborated. The experimental results are explained in Section 4 to show the effectiveness of our proposed algorithm. Finally, we draw important conclusions in Section 5.
## 5 Limitations of the Study  
* Focus on in vitro studies: This review focused exclusively on in vitro studies to evaluate the fit accuracy of RPDs. While these studies provide controlled conditions for comparison, they do not fully capture the complexities of the oral environment, such as patient variability, oral fluids, and functional stresses. As a result, the clinical applicability of the findings may be limited;
* Exclusion of clinical data: Due to the scope of this study, clinical data were excluded. This limits our ability to assess the long-term clinical performance and patient outcomes associated with digital and conventional fabrication methods. Future studies incorporating clinical trials or observational data would provide more comprehensive insights;
* Lack of direct comparison between specific digital techniques: Although our meta-analysis broadly compared digital methods (e.g., CAD/CAM and 3D printing) to conventional techniques, we did not perform detailed comparisons between different digital fabrication methods. The small number of studies focusing on specific digital techniques and the variability in measurement methodologies made it difficult to conduct meaningful subgroup analyses in regard to these techniques;
* Heterogeneity in study design and methods: Despite efforts to reduce heterogeneity, variability in the study design, sample size, and measurement techniques persisted across the included studies. This residual heterogeneity may have introduced bias, although we addressed it by conducting sensitivity analyses and excluding outlier studies to improve the robustness of the results;
* Limited statistical power for subgroup analysis: Subgroup analysis according to specific digital techniques (e.g., SLM, DMLS) did not yield statistically significant results, due to the limited number of studies available for each subgroup. Future research with larger, more standardized datasets is required to evaluate the comparative performance of these techniques.
# Optimal Harmonic Mitigation in Distribution Systems with Inverter Based Distributed Generation
## 2 Problem Formulation
### Objective Functions
#### 2.1.1 Minimizing the Total Harmonic Distortion (f\({}_{1}\))  
THD of the system should be kept as low as possible according to the following equation [27].  
\[f_{1}=\min\sum_{i=1}^{N}\left(\frac{1}{V_{1i}}\sqrt{\sum_{h=2}^{H}V_{hi}^{2}}\right) \tag{1}\]  
where \(V_{1i}\) is the fundamental bus voltage, \(V_{hi}\) is the harmonic order (\(h\)) voltage at bus \(i\), and \(N\) is the total number of buses.
# SARDIM: High-Speed Near-Memory Processing Architecture for Synthetic Aperture Radar Imaging
## 2 Background  
In large-scale data processing applications, addressing the issue of bottlenecks is crucial. This problem arises due to the high data transfer rates between memory and processors, and it can be solved through NMP. With the recent increase in large-scale AI applications, many studies have focused on using NMP to resolve the significant issues related to execution time delays and high energy consumption caused by bottlenecks. For example, refs. [27; 28; 30] highlight the bottlenecks in the deep learning recommendation model (DLRM). Specifically, they demonstrate performance improvements by performing the embedding lookup operations and vector addition operations near the memory, which require substantial data transfer. Refs. [29; 31] address bottlenecks in graph neural networks (GNNs), identifying sparse aggregation, combination computation, and matrix multiplication operations as the causes, and propose NMP as a solution.  
RDA, a SAR imaging algorithm, also requires processing large volumes of data, which typically involves substantial data transfer from memory to computational units. Recognizing this similarity, we see the potential of applying NMP to SAR imaging to achieve significant improvements in speedup and energy efficiency. Unlike previous studies focused on AI applications, our research uniquely implements critical operations in RDA such as FFT, IFFT, and matched filtering using an NMP architecture. This approach represents a novel application of NMP technology in the domain of SAR imaging, differentiating our work from existing studies and demonstrating the versatility and potential of NMP. The following sections will provide a detailed explanation of the RDA and NMP, including their principles, challenges, and the innovations presented in this study.