esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Framework
### Entities for the Framework Design  
1. **Central Cloud**: The central cloud connects to Regionally Organized Clouds via the core network. The facility houses all the media files of a content provider. It also serves data upon regional requests. When new content is published, the central cloud updates all cache catalogues on the regional clouds.
2. **Regionally Organized Cloud (ROC)**: A ROC connects to MEC access nodes via wireless or fiber backhaul. ROCs function as central clouds, but with fewer computing and storage capacities. A ROC caches content the region needs. It also computes  
Figure 1: Edge caching scenario.  
the popularity of content based on consumption data reports from the Edge Cache Manager. It correspondingly transcodes and serves the remaining parts of media content in the regional cache once an edge cluster manager requests for them. At the regional level of a cache network, users have similar preferences compared to those at a global level. The advantage realized is that a ROC can efficiently serve its region's needs, whiles following the region's data protection and rights requirements. Content providers can upload directly onto their ROCs to better serve their regional users.
3. **Edge Cluster Manager**: The edge cluster manager resides on a cluster's head MEC access node server at the edge of the network. Its primary function is to perform cache management locally. Users are served directly from the cache at a MEC access node if the requested file is in the cache. If the desired file is not in the cache, the edge cluster manager sends a cache search request to neighbor MEC edge nodes. It also directs user requests to ROCs directly for users to be served when files are not found in any of the clustered MANs.
4. **MEC Access Node (MAN)**: The MAN is the physical access point of the system. The user equipment (UE) connects to the entire network via a wireless or wired medium. Aside from functioning as connection media between user devices and the core network, MEC access nodes are equipped with both compute and storage capabilities. These are in the form of edge servers--transcoding servers and streaming servers. A transcoding server converts mostly higher resolutions and bitrates of video files to acceptable bitrates commensurate with users' network quality to realize jitter-free and shorter buffering times. A streaming server, however, fetches requested videos from the cache and serves them to users. If the video is not available at the MEC cache, the edge cluster manager redirects the streaming server to serve video streams from either neighbor MEC access nodes or ROCs. The MAN is also responsible for the provision of the wireless resource allocation strategy.
5. **User equipment**: Different users have different devices which are capable of streaming content over a wireless or wired network. Most users stream wirelessly via mobile devices such as smartphones, tablets, laptops. A small fraction of users still prefers wired connection streaming on devices such as desktop computers, laptops, smart TVs, and other smart home devices with screens.
## 3 Results and Discussion
### Microbiological Analysis
Error bars represent standard deviation (SD) of mean values of treatments (n = 3) tested using the Duncan multi-range test at 95% confident interval (\(p\leq\) 0.05) and different lower-case letters indicate significant difference in means (\(p\) < 0.05). Similar lower-case letters are not significantly different. Continuous dashed line indicates baseline measurement. The descriptions of treatments: AEW = acidic electrolyzed water (200 mg L\({}^{-1}\)); AIEW = alkaline electrolyzed water (200 mg L\({}^{-1}\)). Furthermore, the decline in microbial counts on the fresh-cut apples in this study was associated with the available chlorine concentration [12]. At the end of 9 days storage, decontamination efficacy revealed that AEW \(>\) AIEW \(>\) control at the end of storage for TAMB. Similarly for yeast and mould count, AEW was more effective compared to AIEW treatments. The final AMB and Y&M were below the allowable limits, set by the Foodstuffs, Cosmetics, and Disinfectants (FCDA) Act 54 of 1979 in South Africa and the International Commission for Microbiological Specifications for Foods. As a result of treatment, the highest microbial load in the treated samples was significantly below the maximum limits permitted for fresh fruit; thus, the apples could be deemed safe at the end of their storage.
## 3 Methods
### Substructure Extraction  
Through our research, we found that the convolution operation collects information from different substructures, and each substructure updates the feature vector \(\mathbf{h}_{i}^{(l+1)}\) (as shown in Equation (5)) centered around \(i\). Meanwhile, its preactivation vector \(\mathbf{z}^{(l+1)}\) contains substructure information composed of nodes \(s\in\mathcal{N}_{i}\cup i\). As we move from the previous layer to the next, the range of the local neighborhood expands. In addition, some nodes that are not part of the substructures are also considered in the attention Equation (2), enabling the learning of correlations between different substructures.  
In the GAT layer, all substructure information (represented by a single-node feature vector \(\mathbf{i}^{(l)}\)) is aggregated (as shown in Equation (4)), and each substructure is represented by a learnable parameter \(\beta_{i}\in\mathbf{R}\), which can be understood as its importance. We use \(\mathbf{z}_{i}^{(l)}\) instead of \(\mathbf{h}_{i}^{(l)}\) here because we only need the current layer's output rather than the output modified by the nonlinear layer \(\mathbf{h}_{i}^{(l)}\). Therefore, all drug substructure information \(G_{x}\in G\) identified in the \(l\)-th layer can be represented by the following equation:  
\[\mathbf{g}_{x}^{(l)}=\sum_{i}^{n}\beta_{i}\mathbf{z}_{i}^{(l)}. \tag{8}\]  
SAGPooling [47] is deployed to discern the significance \(\beta_{i}\) of every node within the graph. When presented with a graph characterized by \(\mathbf{X}\) and \(\mathbf{A}\), SAGPooling computes the importance vector \(\beta\), with its elements being the coefficients \(\beta_{i}\):  
\[\beta=\text{softmax}(\text{SAGPooling}(\mathbf{A},\mathbf{X})) \tag{9}\]  
SAGPooling takes into account both the contextual and the topographical data of nodes to gauge their significance within the overarching graph.
# Long-Term Behavior of Concrete Containing Wood Biomass Fly Ash
## 2 Materials and Methods  
### Properties of Wood Fly Ash  
Wood fly ash (WFA) was collected from two power plants in Croatia. Both plants are cogeneration biomass power plants that produce heat and electrical energy and use the same technology of incineration by grate combustor. Power plant 1, which produces WFA1, is located in the northern part of Croatia and has a production capacity of 2.75 MW of electrical energy and 15 MW of heat energy. The types of wood used as fuel in Plant 1 are beech, oak, fir, and spruce, which are used in the form of wood chips from roundwood and thinning residues including twigs and tops, branches, bark, needles/leaves, and wood industry waste (including bark). Plant 2, which produces WFA2, has an electric capacity of 1 MW and a heat capacity of 4.1 MW, and is located in the mountainous part of Lika\(-\)Senj County. Plant 2 uses beech, oak, and hornbeam as fuel in the form of wood chips made from roundwood and thinning residues including twigs, tops, and branches. Fly ash particles from the combustor are collected by bag filters [30]. The data on the incineration temperature of wood biomass came from the power plant technologists and range from 700 to 950 \({}^{\circ}\)C in Plant 1 and up to 800 \({}^{\circ}\)C in Plant 2.  
The chemical properties of the WFAs used in this work are presented in Table 1. Elemental composition was determined by X-ray fluorescence according to the standard ISO/TS 16996:2015, loss on ignition (LOI) according to ASTM D7348-13, and pH value according to EN 12176:2005 [31; 32; 33]. WFA1 has a CaO-Al\({}_{2}\)O\({}_{3}\)-SiO\({}_{2}\) ratio in the range of coal fly ash [2; 34] and a pozzolanic oxide content higher than the minimum value for class C pozzolans according to ASTM C618-19 [35]. WFA2 had a high CaO content and a relatively low amount of pozzolanic oxides. The density was determined according to ASTM C188-17 [36]. WFA1 contained a significant amount of unburned wood particles, which made accurate density determination difficult because these particles tended to float on the petroleum. WFA2 did not contain any unburned wood particles.  
Particle-size distribution (PSD) was analyzed by a laser diffraction method using a dry measurement (Shimadzu SALD 3101 instrument, Kyoto, Japan). The median particle size (\(d_{50}\)) of WFA1 ranged from 107 to 144 \(\upmu\)m and of WFA2 from 57 to 72 \(\upmu\)m. Cement had finer particles than WFA with \(d_{50}\) ranging from 9.0 to 9.6 \(\upmu\)m.
# DCSPose: A Dual-Channel Siamese Framework for Unseen Textures Object Pose Estimation
## 3 Materials and Methods  
### Framework  
We devised a dual-channel Siamese framework to estimate object pose parameters in real-time detection scenarios. Given the RGB and depth images of a target region during real-time detection, this framework can locate the most similar template from the template set constructed through automated template creation and use the template's corresponding pose as the estimation result.  
The inference process of the entire framework can be divided into two parts: offline and online, as illustrated in Figure 2. The offline part encompasses template rendering and feature extraction, with the goal of constructing a template library and a feature repository for the objects to be detected. The template set was generated offline using a CAD model, which is elaborated in Section 3.2. The online part involves feature extraction from data collected in real time, as well as performing similarity retrieval against the template feature set to ascertain the best-matching pose.  
The feature extraction process is facilitated through a dual-channel feature extractor capable of extracting and integrating both RGB textures and depth features, elucidated in Section 3.3. The feature extractor was trained using contrastive learning, employing a Siamese structure, without separating the offline and online phases. Hence, the entire framework simultaneously employs two feature extractors, dedicated to processing synthetic templates and real-world scene data, respectively. Each feature extractor comprises two channels responsible for extracting RGB and depth features, resulting in a total of four data pathways. Details regarding model training are elaborated in Section 3.4.  
In this framework, the term \"dual-channel\" refers to the two processing pathways for RGB and depth features in the feature extractor, distinct from the two sub-networks used in the Siamese network structure for handling real and simulated scenes.  
Figure 2: The dual-channel Siamese framework. This framework achieves object pose estimation of objects by matching online scenes and offline templates.
## 2 Materials and Methods
### Flood Return-Period Maps
#### 2.3.2 Near Real-Time Operations
This tends to select for true waterline segments on straighter sections of exterior boundaries of water objects. To suppress false alarms further, waterline heights in regions that are sufficiently far (11 m) from high (>0.5) DSM gradients are selected, provided that they arealso within \(\pm 1.5\) m of the mean water height. This avoids false alarms near high DSM slopes, which may give rise to shadow/layover areas [42]. At this stage also, waterlines from permanent water bodies are excluded using the land use map. In order to find the mean waterline height in the rural area in each sub-domain, a histogram is constructed of the waterline heights, and the positions of the histogram maxima are found. Generally, the mean waterline height in the sub-domain is set to correspond to the height of the largest maximum. However, if any substantial maxima greater than half that of the largest maximum are present at a higher waterline height, the highest of these is chosen instead. This latter rule copes with the situation where a substantial number of erroneous low waterline heights in the interior of water objects have not been eliminated, leading to a largest maximum at an incorrect low height. An example histogram is shown in Figure 6 of [40].\" A standard deviation for the mean waterline height is estimated using the histogram frequencies lying above it. A further check is carried out in the case where the domain is divided into two subdomains, one in the upper and one in the lower part of the reach. Occasionally it may happen that, in one of the subdomains, the maximum value \(h_{1}\) chosen from the histogram is less than the overall mean calculated not from the histogram but directly from all the waterline heights in the subdomain \(w_{1}\). In this case, \(h_{1}\) is corrected using the value in the other subdomain (\(h_{0}\)) and the difference between the mean waterline values in the upper and lower domains (\(w_{0}-w_{1}\)), i.e., \(h_{1}=h_{0}-(w_{0}-w_{1})\). The waterline heights in the subdomains are then interpolated over the whole domain using bilinear interpolation. A small positive guard height may be added to cope with bias introduced by the waterline heights not including the height of any flooded vegetation at the flood edge. The rural SAR segmentation algorithm does not take into account the fact that there may be emergent vegetation at the flood edge that will not be classed as flooded due to the high backscatter it produces [40]. Because of the difficulty of estimating this (usually short) vegetation height, the guard height is treated as a free parameter that must be optimized by calibration.
## 2 Materials and Methods
### Statistical Analyses  
All statistical analyses were computed using the statistical program SPSS for Windows (version 22.0, SPSS, Inc., Chicago, IL, USA). Shapiro-Wilk testing was performed for all variables and revealed that the data obtained were normally distributed. Pearson correlation coefficients were used to find the relationships between the reactive agility performance and reaction, sprint speed, and muscle strength parameters. A correlation coefficient (r) less than 0.30 was considered small, 0.31 to 0.49 moderate, and 0.50 and above as a large effect [16]. Additionally, coefficients of determination (R\({}^{2}\)) were used to estimate the contributions of measured variables to reactive agility. The level for statistical significance was set at \(p<0.05\). Descriptive statistics (mean \(\pm\) SD) were calculated for all results. The statistical power analysis and effect sizes were calculated using G*power 3.1 for Windows. An a priori sample size estimate for an alpha level of 0.05, effect size of 0.5, and power (1-8 probability error) of 0.80 was calculated. A total sample size of 26 participants appeared to be necessary to detect the relationship between reactive agility tests and the reaction, speed, and strength parameters; however, the post hoc sample size estimate for an alpha level of 0.05 and effect size of 0.71 (calculated from R\({}^{2}\) = 0.50) dropped the power to 0.57. The sample size in this study was below the limit because the inclusion criteria required participants to be active in handball and train regularly, which made participation difficult due to COVID-19 restrictions at the time.
# FhMYB108 Regulates the Expression of Linalool Synthase Gene in _Freesia hybrida_ and _Arabidopsis_
## 3 Results
### The Activation of Sesquiterpene and Monoterpene Synthase Genes by FhMYB108 in Arabidopsis thaliana  
Due to the unavailability of a reliable genetic transformation system in _F. hybrida_, we conducted a transient transformation of FhMYB108 in _A. thaliana_ protoplasts to validate its regulatory role in terpenoid metabolism. FhMYB108 demonstrates a close evolutionary relationship with MYB21, thereby suggesting its potential regulatory role in modulating the expression of _A. thaliana_ sesquiterpene synthase genes _AtTPS11_ and _AtTPS21_. Additionally,linal biosynthesis is catalyzed by _AtTPS14_ in _A. thaliana_. Therefore, we cloned the promoter sequences of _AtTPS11_, _AtTPS14_, and _AtTPS21_ and subsequently assessed the activation potential of FhMYB108 on these promoters (with HA-pUC19-CAT serving as a negative control) in accordance with the previous experiments on transcription factor activation assays. The results demonstrated that the co-transfection of FhMYB108 with the promoters of _AtTPS14_ and _AtTPS21_ resulted in a significant increase in the GUS values, thereby indicating the specific binding ability of FhMYB108 to the promoter regions of AtTPS14 and AtTPS21, consequently activating their transcription (Figure 4A).  
Figure 3: Transcriptional activity analysis of FhMYB108. (**A**) The localization of FhMYB108 in cells. (**B**) The measurement of _GUS_ reporter gene expression. (**C**) The binding assay of FhMYB108 to _TPS1_ promoter. (**D**) The activation validation of FhMYB108 to the truncated promoter of _TPS1_. In (**B**,**C**,**D**), data are shown as the mean \(\pm\) SD of three biological replicates. Differences were as-sessed using a two-tailed _t_-test (***, \(p<0.001\)) in (**B**,**C**).  
When FhMYB108 was transiently expressed solely in prototoplasts, we observed a pronounced upregulation of only _AtTPS14_, responsible for linalool biosynthesis, compared to the control group. However, there were no notable changes detected in the expression of _AtTPS21_ (Figure 4B).
# Radiomics Analysis in Ovarian Cancer: A Narrative Review
## 2 How Is a Radiomic Analysis Performed?
### Feature Extraction  
The feature extraction involves the use of dedicated software or programming packages such as 3 DSlicer or Pyradiomics [20]. In order to customize the feature extraction and allow the use of more filters on original images, this step may include pre-processing equipment, such as discretization, resampling, and normalization.  
These features can be categorized as follows:  
* Statistical features;
* Shape-based features;
* Textural features.  
Generally, the number of features extracted could be from 50 to 5000. This number decreases due to the selection of reproducible and informative characteristics and to prevent over-fitting, which is building an analytical process correlating closely to a specific dataset, with the scarce capability of generalizing on independent data [21].
# SDR-Based 28 GHz mmWave Channel Modeling of Railway Marshaling Yard
## 4 Results  
As mentioned in Section 3.2, data samples are collected on the selected Tracks \(T_{1\&2}\) and \(T_{3}\) using the self-developed LabVIEW data processing software mentioned in Section 3.1.2. The channel sounding data are processed and the corresponding channel parameters, including received power and time delay, as well as the phase value of each multipath component, are extracted; at every position, 30 samples of each parameter are obtained, and the 95% statistical value are calculated for each parameter. The 95% statistical value of channel parameters at the position of 12.4 m (horizontal distance from the bridge) on \(T_{1\&2}\) are shown in Table 5 and Figure 15; here, we show that the index of the MPC increases as the power decreases and the delay increases  
Based on all the channel parameters derived from the collected sounding data, further analyses are made from the following two aspects.
## 5 Conclusions
The remote lab, like the virtual one, is a technological tool composed of software but, in addition and unlike the virtual one, it is also composed of hardware. The remote lab allows teachers and students to carry out, through the Internet, their practice as if they were in a conventional lab (on-site). Users use and control the resources available through the use of sensors and instrumentation that allow them to interact with real equipment instead of using simulations and without requiring physical presence in the lab. Users can therefore become familiar with interfaces that correspond to real equipment that they operate remotely. An outstanding advantage of remote labs is reducing space and time limitations, as they do not require the physical presence of the user in the lab. In this way, the labs are available beyond school hours or scheduled classroom practice, which allows students to increase the number of hours of practice, an essential requirement for learning in engineering careers. This encourages autonomy in learning, since it does not require concurrence with the teacher. Another aspect to highlight is the possibility of repeating the same practice or the implementation of variations in the initial practice according to the needs and interests of the students, contributing to personalized learning and respecting the individual needs of each user. That said, remote labs should not be seen as a substitute for conventional labs but as a complement. Consequently, it is necessary to make rational use of remote laboratories in conjunction with conventional laboratories to provide students with the most complete set of skills in the new digital era. Conceptualization, F.S. and J.M.A. ; Methodology, F.S. ; software, M.M. ; validation, M.M. and F.S. ; formal analysis, M.M. ; resources, F.S. and J.M.A. ; writing--original draft preparation, M.M. ; writing--review and editing, F.S. ; project administration, F.S. ; funding acquisition, J.M.A. All authors have read and agreed to the published version of the manuscript. This research was funded by Erasmus+ Programme, grant number Ref. 2020-1-IT02-KA226-HE-095424 RE-OPEN project; ERASMUS+ Programme 2020--KA2; and the APC was funded by Ref. 2020-1-IT02-KA226-HE-095424 RE-OPEN project, founded by ERASMUS+ Programme 2020--KA2. Ethical review and approval were waived for this study due to practices with remote laboratories have been carried out inside the facilities of universities participant in the project, within the classes lessons and with internal personnel. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to internal agreement between project partnership.
# Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest
## 2 Random Forest and Its Improvements  
### Random Forest Algorithm  
Random forest [19] is a machine learning algorithm that was proposed by [PERSON] in 2001. The algorithm has a very high accuracy rate. It is not easy to overfit and has good antinoise ability. In addition, it is not sensitive to abnormal points, is easy to implement, and performs quickly in training. Because of its simple structure and superior performance, it has been widely used in various fields for classification and regression tasks. Random forest is achieved through bagging and random selection. Random forest uses a random sampling method to generate multiple subtraining sets and corresponding test sets from the original data. Repeated resampling means that duplicate data exist in each training subset, which avoids the problem of falling into local extremes. Then, different decision tree models are trained by bagging data to obtain the final decision. Unlike decision trees, the random forest algorithm randomly selects features. The process of random forest, from sampling to final voting, is shown in Figure 2[26].  
Figure 1: The framework of a fault diagnosis system.  
The construction process of random forest is as follows:  
1. Randomly select \(n\) subtraining sets and corresponding test sets from the original dataset.
2. When each node of the decision tree needs to be split, \(m\) attributes are randomly selected. Select one attribute from the \(m\) attributes according to the method of information gain as the split attribute.
3. Repeat step 2 until it can no longer be split.
4. Repeat steps 1-3 to build a large number of decision trees to form a random forest.