esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Artificial Neural Network (ANN) Approaches  
Artificial neural networks have been extensively used to model blast-induced flyrock. Early studies demonstrated that ANN models can significantly outperform conventional statistical methods. For example, [PERSON] et al. [22] applied back-propagation neural networks (BPNNs) to predict flyrock distance in Indian limestone quarries and found the ANN's predictions to be far more accurate than multivariate regression analysis (MVRA) on the same data. In a follow-up study, [PERSON] et al. [22] simultaneously predicted flyrock distance and rock fragmentation using a BPNN. The network yielded substantially lower root-mean-square error (RMSE) and mean absolute error (MAE) than MVRA, confirming the ANN's superior accuracy. [PERSON] et al. [23] trained a three-layer ANN on 192 blast records from Sangan iron mine (Iran) and likewise observed reliable predictions, identifying key influential inputs, such as powder factor, stemming length, and charge-per-delay. Notably, by using the ANN's insights to adjust blast design (e.g., reducing the charge per delay), the mine was able to reduce observed flyrock distances from 165 m to 25 m, illustrating a direct practical benefit of ANN modeling in the field. Overall, these studies established that well-trained ANNs can capture the highly nonlinear relationships governing flyrock and serve as a better predictive tool than regression models [22; 23], with typical reported coefficients of determination (R\({}^{2}\)) above 0.90 in training and validation phases.  
Researchers have also explored optimizations and variants of ANN to further improve accuracy. [PERSON] et al. [24] introduced a neuro-genetic model, using a genetic algorithm (GA) to optimize an ANN's architecture and parameters. This hybrid ANN-GA model (sometimes called a genetic algorithm optimized neural network) achieved higher predictive efficiency than a standard neural net on the same dataset. Similarly, other works integrated evolutionary algorithms with ANN training (see the Hybrid Models Section below). In general, ANNs have proven highly effective for flyrock prediction, consistently outperforming empirical equations and providing actionable insights (e.g., sensitivity analysis from ANN weights often highlights the most critical factors controlling flyrock throw).  
However, one limitation noted is that an ANN's performance and derived insights are only as good as the training data--if the data come from a single site or do not span a wide range of conditions, the network may not generalize well to other mines (a point revisited later). To mitigate this, some studies trained ANNs on multi-mine datasets: for instance, [PERSON] et al. (2015) pooled data from four different limestone mines (125 blast events) to train an ANN and still achieved a high prediction accuracy (the ANN's outputs correlated much better with observed flyrock distances than any regression fit). This indicates that with sufficient and diverse data, ANN models can be robust tools for flyrock forecasting in practice.
# Numerical-Experimental Analysis to Optimize the Geometry of a Prosthetic Abtment
## 4 Numerical Simulation
### Numerical Analysis and Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dental Cement Type** & **Young Modulus [GPa]** & **Poisson Ratio** & **Tensile Strength** & **Compressive Strength** \\  & & & **[MPa]** & **[MPa]** \\ \hline Zinc Phosphate & 15–22 & 0.35 & 5–10 & 48–133 \\ \hline Zinc polycarbonate & 15–18 & 0.35 & 10–12 & 55–100 \\ \hline Glass ionomer & 7–22 & 0.35 & 19–21 & 93–226 \\ \hline Composite resin & 8–14 & 0.28 & 40–44 & 140–265 \\ \hline Self-adhesive resin & 11–18 & 0.30 & 49–50 & 184–224 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mechanical properties and strength of dental cements. Among the considered configurations, the one with cusps and chamfer connection was chosen: having a higher compressive stress value than other configurations, it ensures the minimum tensile stress value. This constitutes an important safety margin for the tensile strength of the cement, which is notoriously the weak element of any dental prosthesis. However, the stress values obtained with the cusp + chamfer configuration, excluding the obsolete zinc phosphate-based cement, are well below the strength limits of cements summarized in Table 6.  
Figure 17: Comparison of cementless models to tensile stress areas. Figure 18: Comparison of cementless models to compressive stress area. In the history of dental cements, a step forward in terms of mechanical resistance was made with the replacement of zinc component with glass ionomer, which has a double average compressive strength. The development of resins and their application in the dental sector have finally allowed us to obtain dental cements with high compressive strength, together with a good tensile strength, on average 4-5 times that of zinc cements. However, each resistance value depends strongly on the different cement manufacturers, and this explains the high fluctuation found in the values of the same type of cement [42; 43; 44; 45; 46]. As found by the result simulations and confirmed by the values in Table 6, the best performances in terms of tensile strength and dislodgement resistance were achieved by using self-adhesive resin as luting cement. The tensile and compressive stresses obtained with cusps and chamfer connection using luting cement are shown, respectively, in Figures 19 and 20. By comparing Figures 15 and 16 (with no luting cement) and Figures 19 and 20 (with luting cement), the damping action carried out by the concrete layer is evident. In addition to decreasing the stress state values on the crown-abutment coupling, the luting cement completely cushions the dislodgement action of the chewing load.
## 3 The Semantic Social Network of Things Middleware (S2 NeTM)
### Trustworthiness Management (TM)
The semantic annotation module is an essential part of the S2 NeTM, as it enables the creation of a more intelligent and interconnected system by providing a common understanding of the data exchanged between devices and users. Ontology alignment is a mechanism included in SEs, which enables the integration and merging of different ontologies. This is necessary when data from different sources need to be combined and interpreted. Ontology alignment is achieved by mapping the concepts and relationships in different ontologies, and identifying their similarities and differences. The following general steps of the alignment process are used in S2 NetM for determining the relationship between two or more ontologies (Figure 2):1. The two ontologies are given as input. 2. Ontology alignment techniques, such as lexical, word matching and semantic similarity methods, are used in conjunction with methods such as weighted averaging to compute the similarity of classes, object properties, and data properties in the input ontologies. 3. Having obtained the similarity value between the entities, those entities with a value greater than a predefined threshold (e.g., threshold = 0.5) are selected. If the similarity value of the matching is equal to or greater than the selected threshold, then the entities of the input ontologies are considered related; otherwise, the entities are considered unrelated. 4. Finally, the ontology alignment process is completed by refining and adjusting the mappings between the entities of the two ontologies. Reasoning is another critical mechanism provided by the SEs component, which allows an SIoT system to derive new knowledge from the existing data. Reasoning involves applying logical rules and inference mechanisms to the data to infer new relationships and concepts. For example, if an SIoT system knows that two users are friends and that they both use a particular IoT device, it can infer that they are likely to use the device together. To support these functions, the SEs component requires a semantic repository to store and manage the ontology and data in the S2 NeTM. In this case, a triple store knowledge graph, in particular the Neo4j, is used to store semantic data. Neo4j is a graph database that allows for efficient and flexible querying of the data, making it an ideal choice for a semantic repository. The SEs also provide support for querying and retrieving data from the S2 NeTM using SPARQL or CYPHER, which are semantic query languages for RDF data. These queries can be used to extract data based on specific criteria, such as device type, location, user preferences or social relationships. Figure 2: Ontology alignment process in S2 NetM.
## 1 Introduction
In this paper, we extended this baseline design to build a single photodiode receiver equipped with a LCD shutter array controlled by an automated signal selection protocol to differentiate reception from multiple LED transmissions (conceptual diagram in Figure 1). This protocol helps relax the assumption (considered in our prior work) of the apriori knowledge of which LCD shutter should be opened (or closed), and enables on-the-fly determination of the intended shutter state. The proposed receiver architecture, to be referred to as _pixelated shutter_ receiver for the rest of the paper, sets the foundation for future VLC system architectures to achieve MIMO communication using only a single photodiode receiver. Achieving MIMO first requires multiple access reception capability demonstration, and to be best of our knowledge, our work in this paper presents the first design and evaluation of a novel multiple access VLC receiver. In summary, the key contributions of this paper are:  
1. Design of an automated shutter control protocol for selective reception of multiple LEDs using a pixelated shutter receiver. 2. Implementation of a prototype _pixelated shutter_ receiver multiple access system with 2 \(\times\) 2 LCD panels, single photodiode and a software-defined radio. 3. Experimental trace based evaluation of the _pixelated shutter_ receiver which employs the bespoke proposed automated shutter protocol, for 2 independent LED transmitter scenario using (a) bit-error, (b) packet error and (c) latency metrics. 4. Experimental evaluation of the automated shutter control protocol under controlled mobile environment.
# A Real-Time Novelty Recognition Framework Based on Machine Learning for Fault Detection
## 3 Proposed Predictive Maintenance Architecture
### Maintenance Control Unit
The agent works in a specific operative range composed of low and high thresholds (lowest and highest bounds). The lowest bound represents when the agent starts its prediction, while the highest bound represents the maximum error allowed. This last value is used for the RTR prediction;
* Performs an \"Average\" computation of the parametrized \(k\) errors selected back in time; when the lowest bound is overcome, the system triggers the related software agent that takes the last \(k\) errors back in time, dividing this errors-set into two different parts, an \"oldest\" and a \"newest\" part; at the end, an error and time average are computed for both the first and the second part selected, obtaining two points useful for the following trend identification phase;
* Performs the trend degradation analysis by training a Linear Regressor (LR) on the two points computed in the phase before and making a prediction on the time of reaching a certain error value;
* Apply a condition-action-based rule on the predicted time to degradation value to decide whether the model changes. The notification is performed if the predicted time is closer to a parametrized notification prevention tolerance time. Otherwise, the agent program ends. This approach avoids notifying model changes, which error threshold value will most likely be reached in a long time;
* Notify model change for a given predictor and error type. The notification is performed thanks to the interface actuation function that permits emitting a notification with a related message. The notification message contains the detected predictor that changes with the time prediction of reaching the error threshold value. The MCU application architecture is composed of different MCDAs that operate independently over time. Each MCDA executes its AP periodically. To summarize, MCU is implemented as a leader-follow, time-triggered agent system. Both system and application architecture are entirely developed using multi-threading and Object Oriented Programming approaches. Additionally, inheritance and polymorphism are used to develop the hereditary hierarchy of agents. Thanks to its code reusability  
Figure 10: The Agent Manager main process that schedules and executes all ready agents. optimization and extendability, this MAS allows the development of new applications easily through new agents. The framework's code will be available at the following link: [[https://github.com/PIC4](https://github.com/PIC4) SeR/PdM_NoveltyDetectionFramework.git]([https://github.com/PIC4](https://github.com/PIC4) SeR/PdM_NoveltyDetectionFramework.git) (accessed on 1 November 2022).
## 1 Introduction
### Challenges for The GNSS Receiver  
Many difficulties will be encountered by the GNSS signal through its transmission in the atmosphere. It should be noted that the atmospheric noise not only reduces the signals from the GNSS satellites [32; 33; 34], but also the SAR signals whose performances may be largely reduced when they pass through the ionospheric and tropospheric layers and influenced by the turbulent atmosphere [29; 35; 36; 37]. The ionospheric modelling is considerably a tricky issue towards the orbital satellite signal processing, and the signal power would be dramatically attenuated by the unintentional turbulence. The multipath interference forms another large challenge faced by the GNSS signal, since it randomly occurs. It is a type of biased error source that is hard to be modelled as well [38; 39; 40; 41]. If the incoming signal is influenced due to the previously mentioned interference, the received signal strength may be highly attenuated and such weak signal is extremely hard to be handled through the baseband signal processing of the GNSS receiver. In addition, when the GNSS receiver are faced with the dynamic situation, the less accurate measurements can be produced as well referring to the characteristics of the loop filter in terms of the receiver tracking procedure [42]. It is worth noticing that such application towards increasing the performance of the dynamic GNSS signal processing is also embraced with great significance for some aerial system applications, e.g., mapping with unmanned aerial system [43] and airborne radiometric surveys [44]. Some advanced techniques have been recently presented to deal with the these challenging signals [32; 45; 46]. The vector tracking (VT) technique is capable of remarkably improving the performance on the GNSS signal processing in weak and dynamic environments with the assistance of the position and velocity solutions.
## 2 Motivation and Related Works
The listen-only period in the network increases the convergence time. [PERSON] et al. [10] described the necessary elements for the node mobility support in RPL, and an RPL protocol is proposed that is optimized for upward routing from a mobile node to a sink node in a fixed network. The algorithm in the paper dynamically adjusts the DIS message interval to enable faster detection of network topology changes due to node movement. The algorithms of [6; 10] are to adjust the trickle timer, which could support node mobility, but there are a few disadvantages. In [6], the algorithm untimely set the time interval for control messages in order to solve the short-listen problem. When inconsistent information is received, a control message is transmitted due to the redundancy constant. This method can cause frequent transmission and reception of control messages. In addition, the methods in [6; 10] adjust the trickle timer by arithmetically operating a constant value without considering the speed and the moving distance of the mobile node. Therefore, if two algorithms are applied to the LLN with a mobile node having a variable speed, packet loss and energy consumption may be increased when the mobile node selects a parent node and transmits information. In this paper, we propose an algorithm that dynamically allocates the time interval of selecting the parent node by considering the speed and the moving distance of a mobile node. We also present a scenario for selecting parent nodes considering the situation of moving based on the dynamic time interval.
# Influence of Benzyladenine on Metabolic Changes in Different Rose Tissues
###### Abstract  
Two modern rose varieties, Floribunda and Hybrid Tea, were used to analyze and identify metabolic changes after foliar application with benzyladenine (BA). Volatile organic compounds (VOCs) as metabolites were detected. Two pairs of doses of BA, at 11.16 and 17.87 mg/cm\({}^{2}\), and 7.17 and 12.26 mg/cm\({}^{2}\) were applied to the foliage of Hybrid Tea and Floribunda, respectively. Sampling time was optimized and treatment duration was 4 weeks. After treatment, the volatiles from the treated and untreated control roses were extracted using headspace solid-phase microextraction (HS-SPME) technology by three-phase fiber 50/30 \(\upmu\)m divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) and analyzed by gas chromatography (GC) coupled with a flame ionization detector (FID), and with mass spectrometry (GC-MS).The results showed that BA and its dose rate led to metabolic changes of treated roses in comparison with untreated controls. The number of VOCs extracted and detected from leaves, stem, rhizosphere and whole plants from the two rose varieties at doses rate of 17.87 and 12.26 mg/cm\({}^{2}\) were 43, 65, 40 and 68 compounds for each plant material, respectively, for both rose varieties. Whilst the VOCs extracted and detected from both rose varieties for leaves, stem, rhizosphere and whole plants were 38, 61, 34 and 66 compounds for each plant material, respectively. The results demonstrate that some volatiles, such as 4-Heptyn-2-ol, Phenyl methyl ether and 3-Methyl-apopinene, increased with increasing doses of BA; these compounds are aroma chemicals with a very powerful smell. This study shows that BA treatments can have a significant effect on metabolite changes in different rose tissues. This method could be applied to other floriculture plants.  
rose; benzyladenine; headspace solid-phase microextraction (HS-SPME); VOCs +
Footnote †: journal: plants
## 3 Optimization
### Objective Functions and Boundaries  
This study considers the shaking force (**ShF**(**X**)) and the shaking moment (\(ShM(X)\)) as the objective functions for the optimization of the balance of the mechanism. To ensure dimensionless quantities for **ShF**(**X**) and \(ShM(X)\), normalization factors \(\beta_{ShF}(X)\) and \(\beta_{ShM}(X)\) are introduced, defined as follows:  
\[\beta_{ShF}(X)=\frac{rms(^{o}\textbf{ShF}(\textbf{X}))}{rms(\textbf{ShF}( \textbf{X}))}=\sqrt{\frac{\sum_{k=1}^{N}(^{o}ShF_{ik}(X)^{2}+^{o}ShF_{jk}(X)^{2 })}{\sum_{k=1}^{N}(ShF_{ik}(X)^{2}+ShF_{jk}(X)^{2})}} \tag{12}\]  
\[\beta_{ShM}(X)=\frac{rms(^{o}ShM(X))}{rms(ShM(X))}=\sqrt{\frac{\sum_{k=1}^{N} {}^{o}ShM_{k}(X)^{2}}{\sum_{k=1}^{N}ShM_{k}(X)^{2}}} \tag{13}\]  
\(\beta_{ShF}(X)\) (Equation (12)) is defined as the ratio of the root mean square (RMS) value of the **ShF** reaction for the optimized mechanism (\(rms(^{o}\textbf{ShF}(\textbf{X}))\)) to the RMS value of the **ShF** for the original, unbalanced mechanism (\(rms(\textbf{ShF}(\textbf{X}))\)), both evaluated over time period \(T\). The values of **q**, \(\dot{\textbf{q}}\), and \(\ddot{\textbf{q}}\) for each time step (\(k\)) can be obtained through direct kinematics or by modeling the original mechanism using CAD software such as Solidworks 2023.  
Analogously, \(\beta_{ShM}(X)\) (Equation (13)) is determined by a similar ratio, where \({}^{o}ShM(X)\) represents the \(ShM\) of the optimized mechanism and \(ShM(X)\) is a constant representing the \(ShM\) of the unbalanced mechanism.  
A solution to the problem (\(X\); Equation (14)) is a vector comprising all the design variables associated with the five counterweights.  
\[X=[x_{1},y_{1},t_{1},x_{2},y_{2},t_{2},x_{3},y_{3},t_{3},x_{4},y_{4},t_{4},x_{5 },y_{5},t_{5}] \tag{14}\]  
The constraints on the design variables, corresponding to the position of the mass center (\(x_{i},y_{i}\)) and the thickness (\(t_{i}\)) of each counterweight (\(1\leqslant i\leqslant 5\)), are imposed based on the physical limitations of the model and are defined in Equation (15).  
\[\begin{split}-0.16\leqslant x_{i}\leqslant 0.16\\ -0.16\leqslant y_{i}\leqslant 0.16\\ 0.005\leqslant t_{i}\leqslant 0.04\end{split} \tag{15}\]
## 2 Related Work
[27] developed an approach that differs from the grouping technique used in PointNet++. They defined two neighborhoods in both world space and feature space by using K-means clustering and K-nearest neighbors (KNN). Additionally, they introduced pairwise distance loss and centroid loss to better regularize feature learning. To simulate interactions between different points, PointWeb investigates the relationships between all point pairs within a local region by densely constructing locally fully connected networks [28]. They proposed an adaptive feature adjustment module for information exchange and feature refinement, which aids in learning discriminative feature representations. PointWeb can effectively extract the local features of the point cloud, but it processes the point cloud in a grid-based way. Therefore, PointWeb does not work well when dealing with irregular point cloud shapes. [PERSON] et al. [29] presented a permutation-invariant convolution method based on the statistical quantities of concentric spherical shells. This method queries a set of multiscale concentric spheres, summarizes the statistical \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Methods** & **Dataset Used** & **Characteristic** & **Limited** \\ \hline \multirow{4}{*}{Voxel-based} & SPLATNet [22] & RueMonge2014, ShapeNet & These methods convert the point cloud data into a voxel grid. They use voxel & These methods may result in information loss. They have high time and space \\  & FCPN [23] & ScanNets, ShapeNet & & \\  & SSCNs [24] & ShapeNet, NYU Depth (v2) & 3D objects. They can retain neighborhood information. & difficult to select the correct voxel resolution. \\ \hline \multirow{4}{*}{Point-based} & PointNet [25] & ModelNet40, ShapeNet, Stanford 3D & These methods directly process 3D point cloud & \\  & PointSIEF [26] & S3 DIS, ScanNet & data and preserve & Most of these methods adopt expensive neighborhood \\  & PointWeb [28] & S3 DIS, ScanNet, ModelNet40 & details in the point cloud. & search mechanisms. They require large scale, high \\  & ShellNet [29] & ScanNet, S3 DIS, Semantic3D & They can effectively deal with the sparsity of the point cloud. They have low & quality annotated data. They are computationally complex. \\  & RandLA-Net [5] & Semantic3D, S3 DIS & & memory consumption. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._data using max-pooling operations in different shells, and obtains the final convolution output using MLP and 1D convolution. However, ShellNet's processing efficiency for large-scale point cloud data is low. The number and radius of shells need to be manually set, which may affect their generalization ability. The above methods exhibit lower accuracy in semantic segmentation of large-scale 3D point cloud data in urban environments. [PERSON] et al. [5] proposed an efficient and lightweight RandLA-Net that can be used for colored point cloud segmentation. This network utilizes random point sampling to achieve high efficiency in terms of memory and computation, and further proposes a local feature aggregation module to capture and preserve geometric features.