esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Random Forest Based on Particle Swarm Optimization
### Optimized Random Forest Based on PSO  
In this paper, PSO was applied to RF for parameter optimization in order to achieve a more accuracy classification result. The purpose of the algorithm is to explore the relationship between algorithm performance and the number of decision tree _nTree_ in order to find the difference between the best parameter combination and traditional value selection.  
The objective function is as follows:  
\[f(n\text{{Tree}}^{*},\{\,Attribute_{i}|i=1,2,\ldots M\})=\text{argmin}(\text{ avg}OOBerror). \tag{16}\]  
where _OOBerror_ means out-of-bag error.  
The specific steps of PSO optimization for the RF parameter _nTree_ are as follows:  
1. Initialize the parameters of PSO, including: group size, learning factor, maximum number of iterations, initial location and speed of particles.
2. The RF of each particle vector is used to predict the learning sample respectively. The prediction error of the current position of each particle is obtained, which is used as the fitness value of each particle. The current fitness value of each particle is compared with the optimal fitness value of that particle. If the current fitness value is smaller, the present position of the particle will be treated as the optimal position of the particle.
3. Compare the optimal position fitness value of each particle with the fitness value of the optimal position of the group. When the fitness value appears the minimum valve, the optimal position of the particle is taken as the optimal position of the group.
4. Check whether the end condition of the search is satisfied. If it satisfies, end the optimization and get the best _nTree_. Otherwise, go back to the step (2) to again.
# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 2 Proactive Service: Gains, Costs, and Needs
### Memory Cost to Be Paid for Caching  
A cost to be paid arises from the memories at edge nodes and end devices, which are inexpensive but not free [4]. The memory cost is determined by not only how many bits are cached, but also how long they are cached [21, 22].  
The average memory size of user devices continues to grow. However, no matter how large a memory is, overflow occurs if the cached contents are never evicted. As a result, a content item should be evicted from the edge node if it becomes unpopular or outdated, or from a user's device if it has been already read by this user. After its eviction, the occupied memory space will be released, which enables memory sharing in the time domain. The eviction time is a challenging issue. Evicting too early may cause possible future requests to be missed, evicting too late wastes the storage resource.  
On the other hand, if a file is cached much earlier than its request time, the storage resource is also wasted. If it is cached too late, it may miss user requests, thereby losing the caching gain. In addition, it is inefficient to update memory when the channel remains poor or the spectrum is busy. Hence there is a need to jointly optimize pushing and memory updating, which generalizes the concept of cross-layer design as both wireless links and memories are involved. Such communication-storage coordination becomes very challenging with preference skewness, radio environment dynamics, and coded caching/prefetching.  
Figure 2: A hierarchical RAN with partial mmWave coverage, in which caching helps to reduce the required density and deployment costs of mmWave BSs [20].  
To attain high memory efficiency, when and how long a content item is cached should be carefully optimized. A memory-efficient eviction policy is presented in [21] to minimize the average memory consumption at user devices. A memory-efficient eviction policy for edge caching is presented in [21] to increase the storage efficiency at BSs. In addition, when the cache memory is full, less popular files can be replaced newly pushed ones [23; 24]. Moreover, when a user is experiencing a poor channel condition or the spectrum is crowded, content placement, and memory update are ineffective in terms of transmission costs. Therefore, the physical-layer status should also be considered in the eviction policy. The physical-layer constraints and dynamic environments make the decision policy on the placement time more challenging. Joint optimization of the memory control and content placement emerges as a novel issue of cross-layer scheduling.  
In summary, with asynchronous user requests, time-domain memory sharing can be adopted to enable caching with finite memory. To attain high memory efficiency, wireless links and memories should be jointly scheduled from a cross-layer perspective, based on the prediction of what and when a user will request.
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 2 Materials and Methods
### Accuracy Assessment  
Two independent assessment measures were conducted to assess the accuracy of the U-Net and QLUMP classifications of banana plantations in the Tully River catchment.  
The first assessment was based on a stratified random sample of 9805 points using the ALUM Classification tertiary classes as the strata, following the method described in [53]. As the scale of the QLUMP data was different to the high-resolution imagery used to map land use classes, every point was visually inspected to ensure they were correctly classified as a banana plantation or other land use. If there were inconsistencies between the classification of a point and the imagery, the point was reclassified to the correct land use class. For example, if a banana plantation point fell on an area of fallow or a narrow road between the banana plantations, these points were reclassified as land-in-transition or road. The points were used to calculate the user's, producer's, and total accuracies for the U-Net and QLUMP classifications of banana plantations. In total, 701 out of 9805 points were located on banana plantations (7.15%).  
The second measure of accuracy for both classifications was conducted using a similarity coefficient, the Jaccard Index defined in Equation (1). The benefit of using the Jaccard index for measuring the accuracy of the banana plantation classification is that it accounts for all the validation data excluded from the training of the U-Net model for the Tully River catchment. It is a similarity index and compares how well the validation and classification banana plantation locations match.  
\[J(C,G)=\frac{C\cap G}{C\cup G} \tag{1}\]  
where \(C\) is the classification (U-Net or QLUMP) and \(G\) is the validation data.
## 2 Materials and Methods
### Statistical Analysis  
All obtained data are described as arithmetic means \(\pm\) standard error (SE). The results were assessed by SPSS 20 (SPSS, Chicago, IL, USA). One-way ANOVA followed by [PERSON]'s post hoc test were done to evaluate and compare the significance between testing groups. Values of \(p<0.05\) were referred to as significant.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Primer Sequence** & \\ \hline BDNF & Forward: 5\({}^{\prime}\)-AGTGATGACCATCCTTTCCTTAC3\({}^{\prime}\) & Gene ID: 24225 \\  & Reverse: 5\({}^{\prime}\)-CCTCAAATGTGTCATCCAAGGA-3\({}^{\prime}\) & NC\_005102.4 \\ \hline \multirow{3}{*}{P2Y12R} & Forward primer: & Gene ID: 6803 \\  & 5-CCAGTGCATCCCTAAAATTTCC-3 & Accession number on \\  & Reverse primer: 5-CCAGCGTTCCCATATACCAG-3 & chromosome: NC\_005101.4 \\ \hline \multirow{3}{*}{P2X7R} & Forward primer: & Gene ID: 29665 \\  & GAACCTCGAGTGAGCCACAACCAGAACACT & Accession number of chromosome: \\  & Reverse primer: & NC\_005111.4 \\ \cline{1-1}  & GACAAGATCTATGGCCCAAGGAGCTCGGT-3 & NC\_005111.4 \\ \hline \multirow{3}{*}{Beta actin} & Forward: & Gene ID: 11461 \\ \cline{1-1}  & 5\({}^{\prime}\)-GCACCCAGCACAATAGATCAAG-3\({}^{\prime}\) & Accession number of chromosome: \\ \cline{1-1}  & Reverse: 5\({}^{\prime}\)-TCATACTCCTTGCTGATCC-3 & NC\_000071.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primers used for real-time quantitative PCR.
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
###### Abstract  
Building point cloud completion is the process of reconstructing missing parts of a building's point cloud, which have been affected by external factors during data collection, to restore the original geometric shape of the building. However, the uncertainty in filling point positions in the areas where building features are missing makes it challenging to recover the original distribution of the building's point cloud shape. To address this issue, we propose a point cloud generation diffusion probability model based on building outline constraints. This method constructs building-outline-constrained regions using information related to the walls on the building's surface and adjacent roofs. These constraints are encoded by an encoder and fused into latent codes representing the incomplete building point cloud shape. This ensures that the completed point cloud adheres closely to the real geometric shape of the building by constraining the generated points within the missing areas. The quantitative and qualitative results of the experiment clearly show that our method performs better than other methods in building point cloud completion.  
building point cloud completion; building contour constraint; DDPM
# A Novel Decomposed Optical Architecture for Satellite Terrestrial Network Edge Computing
## 5 Scalability and Discussion
### Discussion  
Based on the above experimental and numerical assessments, it is shown that the NOS-based optical network can provide stable and fast switching interconnection among hardware cubes under various network scales. Compared with the microseconds access latency of current electrical network-based solutions in [16; 17], the implemented decomposed optical computing architecture performs lower target data access latency of 148.5 ns under a 4096-cube network and an MCAR of 0.9. Meanwhile, the proposed NOS-based optical computing architecture can achieve a higher aggregated bandwidth (up to hundreds of gigabits) utilizing commercially available transceivers and Dense Wavelength Division Multiplexing (DWDM) technologies.  
Compared with milliseconds switching time in an Optical Circuit Switch (OCS)-based network in [21] and microseconds switching time in a micro-ring resonator-based network in [23], the implemented NOS-based decomposed computing prototype can achieve nanoseconds switching time (43.4 ns), while keeping a stable interconnect link with none packet loss. These assessment results demonstrate the superiority of the proposed optical decomposed computing network based on NOS with respect to existing works.
# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 3 Applications of Supervised Learning in Additive Manufacturing
### Quality Detection
The authors constructed a test set comprising 30 distinct parts to evaluate the framework, yielding 1200 images. Three pre-trained CNN architectures, MobileNetV2, ResNet-50, and VGG16, were employed, with VGG16 achieving the highest average part classification accuracy [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018). Furthermore, the authors enhanced the industrial applicability of their workflow by integrating physics simulation into the rendering process. In an industrial case study with 215 distinct parts,the part classification accuracies reached 99.04% for the top three predictions and 90.37% for the top one. Recently, [PERSON] et al. integrated a photodiode sensor with ML algorithms to establish a robust framework for surface roughness classification [129]. Data were collected by printing 42 parts with 21 distinct parameter combinations through LPBF. Several ML algorithms were employed, including RF, gradient boosting, SVM, ANN, and LSTM. They found that RF resulted in superior performance, achieving a weighted F1 score of 0.71. In another study, [PERSON] et al. employed a CNN-based segmentation process. They developed a porosity investigation strategy to examine porosity defects in AM products using micro-computed tomography (micro-CT) [130; 131]. The training data included 120 manually annotated CT slices, and the analysis revealed correlations between pore size, pore sphericity, pore evolution, and the discovery of pore-free zones. Furthermore, [PERSON] et al. proposed a novel feature-learning engineering framework for AM quality monitoring [132]. Their approach integrated engineering knowledge with a convolutional autoencoder and incorporated 3D neighborhood models to characterize spatiotemporal variations in melt pools during AM processes.
## 1 Introduction  
Reliable, accurate, and efficient modeling of electromagnetic radiation transport in turbid media has important applications in studies of Earth's climate by remote sensing. For example, such modeling is needed to develop forward-inverse methods used to quantify types and concentrations of aerosol and cloud particles in the atmosphere, as well as dissolved organic and particulate biogeochemical matter in lakes, rivers, coastal water, and open-ocean water, and to simulate the performance of remote sensing detectors deployed on aircraft, balloons, and satellites. Accurate radiative transfer (RT) modeling is also required to compute irradiances and scalar irradiances that are used to compute warming/cooling and photolysis rates in the atmosphere, solar energy deposition in the cryosphere including frozen fresh water (lakes and rivers), sea ice, and glaciers, as well as primary production rates in the water. Finally, RT modeling is needed to compute the Stokes vector describing the polarization state of the radiation field, which is desired in many remote sensing applications.  
Accurate, efficient, and easy-to-use radiative transfer (RT) simulation tools are important because they (i) can be used to generate irradiances as well as total and polarized radiances (including degree of polarization) at any location and direction; (ii) will provide accurate results for given input parameters and specified inherent optical properties (IOPs); (iii) will lead to significant progress in research areas such as remote sensing algorithm development, climate research, and other atmospheric and hydrologic applications.  
Available tools for atmospheric applications include: (i) SBDART [1], Streamer [2], and LibRadtran (www.libradtran.org), which all apply to the atmosphere only; there is no coupling to an underlying surface consisting of e.g., solid (snow/ice) or liquid water; (ii) Hydrolight, which applies to water only, provides water-leaving radiance, but not top-of-the-atmosphere (TOA) radiance; there is no coupling to the atmosphere (assumed to be a boundary condition). To remedy this situation a new Accurate Radiative Transfer (AccuRT) tool was developed to facilitate well-tested and robust RT simulations in coupled systems consisting of two slabs with different refractive indices. Please note that we here use the word \"water\" generically to describe the solid phase (i.e., snow and ice) as well as the liquid phase. The AccuRT tool accounts for reflection and transmission at the interface between the two slabs, and allows each slab to be divided into a sufficient number of layers to resolve the variation in the IOPs with depth.
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 5 Our Privacy-Preserving Swarm Learning
### System Initialization
#### 5.2.1. Local Model Training  
In the \(t\)th iteration, each \(\textbf{MC}_{k}\) obtains the encrypted global model \(\llbracket\textbf{w}_{t-1}\rrbracket_{pk_{FHE}}\) from the latest block \(B_{t-1}\) on the blockchain. Then, each \(\textbf{MC}_{k}\) calls \(\textbf{Collab\_Decryption}(\cdot)\) to obtain the local model \(\textbf{w}_{t-1}\). Finally, using \(D_{k}\) and \(\textbf{w}_{t-1}^{k}\), each MC computes the local gradient \(\textbf{g}_{t}^{k}\), as shown in Equation (4):  
\[\textbf{g}_{t}^{k}=\triangledown L(\textbf{w}_{t-1}^{k},D_{k}) \tag{4}\]  
where \(L(\textbf{w}_{t-1}^{k},D_{k})\) represents the experiel loss function and \(\triangledown\) represents the derivation operation.
# Valorization of Date Fruit (_Phoenix dactylifera_ L.) Processing Waste and By-Products: A Review
## 4. Valorization Approaches of Date Processing Waste
### Extraction of High-Value Compounds  
Date processing waste is a significant source of high-value compounds such as antioxidants, flavonoids, phenolic acids, and dietary fibers [17]. Different methods can be used to extract high-value compounds, especially phenolic compounds, from date processing waste, including solvent extraction, supercritical fluid extraction, and microwave-assisted extraction. Solvent extraction is one of the most commonly used methods, which involves the use of solvents such as methanol, ethanol, and acetone to extract target compounds from the waste [105]. For instance, [PERSON] et al. [76] conducted a study investigating the extraction of phenolic compounds from date pits using reflux solvent extraction. In their results, the extraction process was efficient, and the phenolic extracts had high antioxidant activity. Similarly, a study by [PERSON] et al. [10] explored the use of supercritical fluid extraction to extract phenolic compounds such as phenolic acids and flavonoids from date palm pits. This method utilizes supercritical carbon dioxide to extract the compounds. The study found that the extraction process had higher efficiency than conventional extraction and Soxhlet extraction, and the extracts had potential applications in the food and pharmaceutical industries. This method has the advantage of being environmentally friendly and producing high-quality extracts [10]. Extraction with microwave assistance is a relatively newer method that involves using microwave radiation to extract the compounds from the waste. This method is known to be efficient, fast, and environmentally friendly and was previously applied by [PERSON] et al. [106] to recover phenolic compounds from date palm seeds, and the extracts were obtained within 5 min as a fast extraction.
## 1 Introduction
A dataset was created and the performance of the convolutional neural network (CNN) models YOLOv3-Tiny [9], MobileNet-SSD V2 [10] and ResNet-50 [11], which were considered the most promising, was evaluated. It was concluded that YOLOv3-Tiny is the most promising model, given the accuracy recorded and the computing power required. Following our previous study, this paper aims to propose, implement, test and validate a low-cost prototype demonstrator, based on the Internet of Things (IoT), computer vision and the concept of opportunistic networks, to meet this challenge. The prototype should work in the scenario illustrated in Figure 3, where it is assumed that cycling and hiking trails are used by a variety of pedestrian, cyclist and motorcyclist users, which we aim to identify and count. It is assumed that a set of IoT devices, which will be responsible for detecting and counting the different types of users, will be strategically positioned along the trails, in places where there may not be an Internet connection (e.g., using a mobile network). These devices have non-volatile memory that can store the information on the user count for extended periods until an opportunity arises to propagate it [12]. This concept of opportunistic communication is defined by Delay Tolerant Networks (DTNs) [13; 14] and can be applied to scenarios such as interplanetary networks, military and tactical systems, disaster recovery networks, vehicular communications and wildlife tracking/monitoring. Figure 3: An illustration of the scenario. To make it possible to collect the data stored on these devices, it is assumed that some of the users of these trails will use a mobile application on their smartphone which, in addition to information about the routes, could for example provide indications about the wild flowers and plants that can be found along them, as described in [15; 16]. When a user with the mobile application approaches one of these IoT devices, a contact opportunity arises, and user count information can be sent to the smartphone. Later, when the user has Internet access on their smartphone, the data with the counts of the different types of users will be sent to a remote server. Once again, this process takes place automatically and transparently for the user. This information will then be aggregated, processed and made available to the managers of these infrastructures, for example via a web application. The rest of this paper is organized as follows. Section 2 presents the prototype developed as part of this study. It describes and justifies the architecture adopted, the hardware and software components, and the implementation process. Section 3 describes the tests conducted to validate the prototype. Finally, Section 4 presents the conclusions of this study, highlighting the results obtained and discussing possible directions for future work.
## 3 Results and Discussion  
### Structural and Microstructural Results  
The XRD patterns for BCTZ (\(x\) = 0.10, 0.15, 0.18) sintered samples are shown in Figure 1. All compositions exhibited a perovskite-type structure. For \(x\) = 0.18, a secondary phase of CaTiO\({}_{3}\) was detected. The XRD patterns indicate that Ca\({}^{2+}\) and Zr\({}^{4+}\) ions were incorporated completely into the BaTiO\({}_{3}\) lattice (for \(x\) = 0.10 and 0.15 compositions) to form a solid solution.  
A fine-scanning XRD pattern in the 20 range of 44\({}^{\circ}\)-46\({}^{\circ}\) (inset Figure 1) was performed to determine the dependence of the phase structure. A rhombohedral structure with a single (200) peak at 45\({}^{\circ}\) was revealed. A further increase in the \(x\) content was followed by a pronounced splitting of the (002) peak from the (200) peak, demonstrating a phase transition from rhombohedral to tetragonal phases [14]. The CaTiO\({}_{3}\) secondary phase (PDF#042-0423) was detected for \(x\) = 0.18. It was speculated that the rhombohedral and tetragonal phases may coexist in the composition range of 0.10 \(\leq x\leq\) 0.20, and consequently, there exists a temperature-independent morphotropic phase boundary (MPB) [15]. The presence of a MPB enhances the electrical performance of BCTZ electroceramics.  
Figure 2a shows the SEM images for sintered pellets at 1400 \({}^{\circ}\)C for 2 h with \(x\) = 0.10, 0.15 and 0.18. All ceramics revealed a dense microstructure (\(\approx\)98%). Increasing the Ca\({}^{2+}\) content at the Ba\({}^{2+}\) sites decreased the average grain size of the ceramic (22.6, 17.9 and 13.3 \(\upmu\)m); moreover, as \(x\) = 0.18, the presence of the secondary CaTiO\({}_{3}\) phase was promoted due to the proximity of the solubility limit, as revealed by the XRD analysis. Figure 2b shows the EDS spectra for the expected cations of BCTZ and CaTiO\({}_{3}\) grains (\(x\) = 0.18 sample).  
Figure 1: XRD patterns of BCTZ (\(x\) = 0.10, 0.15, 0.18) sintered ceramics (1400 \({}^{\circ}\)C, 2 h in air). The inset shows a magnification in 2\(\theta\) between 44\({}^{\circ}\) and 46\({}^{\circ}\).  
Figure 2: _Cont._
# Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
## 2 System Model  
In this section, we present a MIMO-OFDM system that comprises a transmitter sending signals to a receiver as illustrated in Figure 1. The transmitter and receiver antenna arrays have \(N_{T}\) and \(N_{R}\) antennas, respectively, therefore creating an \(N_{T}\times N_{R}\) MIMO channel model that is modeled by the 5G channel profile.