esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring Forest Phenology in a Changing World
## 2 Monitoring Phenology
### Trade-Offs among Phenological Monitoring Techniques  
Visual assessment from the ground, sometimes using binoculars, has historically been the most common method for monitoring phenology [79], remains one of the most commonly deployed methods, and is heavily relied upon for the validation of sensor based monitoring [116]. Several long-term research studies have been set up using this method, where a group or individual collects data at a single research site or within a single habitat over an extended period of time [117]. Many of the long term visual assessment studies focus on observing one aspect of phenology such as leaf emergence, flowering or fruiting [112; 118], rather than recording all phenological events from a representative sample of plant species [3]. Most studies have also focused on temperate regions [119], with phenology having been monitored for more than 10 years in only 26 sites across tropical America, Africa, and Asia [112]. Visual assessment is time and labour intensive, and of only limited effectiveness in environments where vegetation is dense, terrain is steep and plants exceedingly tall such as that of many tropical forests (Table 1). Arguably, however, the biggest limitation of ground surveys is the subjective bias of observers which can reduce the strength of quantitative conclusions that might be made from visually assessed phenology [116]. Nonetheless, ground surveys remain especially important as a means for validating the data collected from other monitoring methods, and schemes and guidelines do exist to help reduce observer bias [120]. Other methods provide alternatives for visual assessment that monitor phenology both directly and indirectly. The following sections summarise the trade-offs between these methods in relation to the decision factors normally associated with choosing a method (Table 1).
# Using Aspect-Level Sentiments for Calling App Recommendation with Hybrid Deep-Learning Models
###### Abstract
Our study collected an apps reviews dataset of five well-known calling apps available on the Google Play store--namely, Whatapp, Skype, Wechat, Telegram, and IMO and makes the following contributions. * A novel deep-learning-based ensemble model is presented that combines a convolutional neural network (CNN) and gated recurrent unit (GRU). A large dataset was collected containing reviews regarding different famous calling apps. The performance of the proposed model is analyzed with respect to individual long short-term memory (LSTM), CNN, GRU, and recurrent neural network (RNN). * Aspects are extracted from the collected dataset using the Latent Dirichlet Allocation (LDA) algorithm, while the dataset is annotated using the valence-aware dictionary for sentiment reasoning (VADER). LDA-extracted aspects are used to recommend a particular app regarding the users' requirements of particular features. * Sentiment analysis is performed using several machine-learning models, including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT). In addition, three feature engineering approaches are also studied: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Estimated Revenue** & **No. of Users** & **No. of Download** \\ \hline
2014 & $1 million & 0.5 billion & 0.8 billion \\ \hline
2016 & $0.2 billion & 1 billion & 2 billion \\ \hline
2018 & $1.3 billion & 1.5 billion & 3.4 billion \\ \hline
2020 & $5.5 billion & 2 billion & 4.9 billion \\ \hline \hline \end{tabular}
\end{table} 
Table 1: WhatsApp revenue stats in recent years [3]. term frequency-inverse document frequency (TF-IDF), bag of words (BoW), and hashing vector. The rest of the paper is organized as follows: Section 2 describes the related work that has been previously performed in the field of sentiment analysis of mobile apps. Section 3 illustrates the methodology, machine-learning models, and the proposed approach. Section 4 elaborates the results obtained from experiments. Finally, Section 5 concludes the study with future implications.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 4 Results and Discussion
### Results after Varying the Number of Vehicles
#### 4.3.3 Simulation Results of Nakagami Propagation Model regarding Number of Vehicles  
The mRSU in the Nakagami propagation model operates well in terms of throughput but performs poorly in terms of average latency owing to multi-path fading. However, due to the MIMO feature of the Nakagami propagation model, the packet delivery ratio of the mobile RSU grows as the number of vehicles rises, while the PDR of the static RSU drops as the number of nodes increases.  
Figure 11: Computational modeling outcomes pertaining to the deployment of mRSUs and sRSUs in the context of propagation models, in relation to the variation in the packet size. (**a**) Average delay analysis of mRSU and sRSU with regard to packet size; (**b**) hop count analysis of mRSU and sRSU with regard to packet size; (**c**) PDR analysis of mRSU and sRSU with regard to packet size; (**d**) energy consumption analysis of mRSU and sRSU with regard to packet size; (**e**) throughput analysis of mRSU and sRSU with regard to packet size.
Non-invasiveness and high resolution have promoted OCT towards a wide range of applications, such as biomedical diagnosis (in ophthalmology [9; 10; 11], dermatology [16], dentistry [17; 18; 19; 20], and endoscopy [21]) and non-biomedical testing (of glass, plastic, and semiconductors [22; 23], as well as of profiles of metallic surfaces [24]). The advantages, as well as the limitations/complementarities of OCT in relationship to other investigation methods have been reported in respect to imaging teeth [25; 26; 27] or different types of dental constructs [28; 29; 30; 31; 32; 33]. Relevant to non- destructive testing, targeted here, the impact of the OCT light source, of its central wavelength, as well as of specular reflection, have already been considered [23]. In this study, we demonstrate that MS/SS-OCT can be used to identify, more frequently than at standardized periods, possible undesired variations of the oven temperature. The application of the MS/SS-OCT is simple, fast and non-invasive, based on the imaging of ceramic constructs only, which are simply produced each day. The aim is to achieve this operation independently to other calibrating procedure recommended by the manufacturer. With such a method, the technician may immediately infer that a temperature calibration is mandatory.
# Strategies for Reducing Sodium Intake in Bakery Products, a Review
## 4 Methods for Sodium Content Reduction in Bakery Products
### The Use of Dry Sourdough as a Substitute for Sodium Chloride in Bakery Products  
Another possibility of partial substitution of sodium chloride in bakery products is the use of sourdough. Sourdough is defined as a fermented semi-finished product obtained from flour and water in the presence of its own, natural microbiota and then dried in conditions to keep lactic bacteria in a viable state [112]. The industrial production of dry sourdough has over 40 years old and was initially used for obtaining products with a high acidity. Further, its main uses were to obtain bakery products with specific taste and aroma [113; 114]. The advantage of using sourdough in baking is that it eliminates the leaven phase by shortening the time for bread making process. It allows obtaining the bread dough in a single phase leading to bakery products of a very high quality from the technological and sensory point of view, similar to those obtained through dough making in a double or a triple phase. Nowadays, a wide range of sourdoughs are available on the market, which differs according to the flour used, as well as the specific flavor that exist on each sourdough. Sourdough in the form of an ingredient for bakery products corresponds to the new trend of clean labels, natural products, including a reduced use of additives [115]. The advantages of using dry sourdough in low-salt products to create healthy foods are evident. [PERSON] et al. [24] suggested that the dry sourdough may compensate the effect of salt reduction on bread flavors and may lead to the good sensory characteristics of the finished products, such as crumb texture. Moreover, they also reported that the addition of sourdough in bread making, fermented with _Lactobacillus amylovorus_, for obtaining a low-salt bread, extended the shelf life compared to a control sample. Bread containing lactic acid bacteria (LAB) from fermented wheat germ had a saltier taste compared to a control bread. The salty taste was thought to be a combined effect of acidification and proteolysis. Due to the addition of a sourdough from rye malt fermented with glutamate that accumulated bacteria of the _Lactobacillus reuteri_ species, it may be possible to reduce the salt content of bread from 1.5 to 1% (compared to flour), maintaining the taste and other characteristics of a standing quality. Sourdough improves the perception of salty taste and brings an additional intake of aromatic compounds. This is a useful functional ingredient for low-salt bread. Moreover, the use of dry sourdough is not limited to bread. One possibility is to incorporate sourdough into pastries or croissants to improve their flavor, texture and therefore palatability [12].
# Clipping Noise Compensation with Neural Networks in OFDM Systems
## 2 Clipping Noise Compensation
Then, the equalized signal \(z[n]\), which is simply the IDFT of \(Z_{k}\), will be used as the signal within the unclipped part, while \(x_{r}[n]\) will be used as the reconstructed signal within the clipped part. The overall schematic is shown in Figure 1. Note that the clipping threshold can be obtained beforehand via communication protocols.
## 3 Finite Element Simulation
### Result Analysis
The transfer matrix also kept constant with even higher moving speeds. The acceleration at Point 6 in these new scenarios was estimated by applying the original scenario's transfer matrix to the simulated accelerations of these scenarios. Figures 8 and 9 present the comparative analysis between the estimated and simulated results. The consistency observed in both the time and frequency domains substantiates that the obtained inter-section transfer matrix is equivalent to the actual transfer matrix, thereby validating the proposed method. Figure 6: Two surrogate displacement mode shapes from the FastICA mixing matrix. (The first surrogate mode shape is indicated with circles, while the second is denoted by diamonds, across the seven monitored sites and at both end supports). Figure 7: Comparison between the estimated acceleration through the proposed method and the simulation result at Point 6 (higher modes are marked with ellipses and similarly marked below). In addition, discrepancies are evident in Figures 8 and 9, manifesting in both the time and frequency domains. As depicted in Figure 8, these inconsistencies are particularly pronounced in the vibrations of higher modes. It is assumed that in this study, we have focused exclusively on the first two vibration modes, and accordingly, the number of signal sources has been set to two. Consequently, the proposed algorithm automatically isolates two independent signal sources along with their corresponding mixing matrix. As established in Section 2.2, the first two modes predominantly influence the overall bridge response; thus, the two signal sources and the mixing matrix should be primarily associated with these initial two modes. However, for vibrations of higher modes, which are distinct from the first two modes, the current algorithm's configuration, which only accounts for two signal sources, is insufficient for accurate estimation. To achieve precise estimation of higher modes, which necessitates the consideration of more independent signal sources, the algorithm's parameter settings, specifically the number of signal sources, should be reevaluated. Furthermore, acceleration measurements should be collected from a greater number of cross-sections, rather than the current limitation of just two, which corresponds to the available signal resources.
## 5 The General Model of an ADR Based on Photodiodes (PDs)  
To mathematically model an ADR, it is necessary to precisely define how the signals received by the PDs are combined and how they relate to their orientations (normal vectors). In the following, a detailed model for ADRs is developed that incorporates the normal vectors of the PDs, the angles of incidence of the signals, and other relevant parameters of the VLC system.  
The ADR consists of several photodiodes oriented in different directions. Each photodiode \(k\) has an effective area \(A_{\text{PD}_{k}}\) and a specific orientation described by its normal vector \(\mathbf{n}_{k}\). The ADR is used to capture signals from multiple angles and improve reception in an environment with reflections or NLoS conditions. The general mathematical and theoretical parameters of the ADR are as follows:  
* Number of photodiodes: \(N_{\text{PD}}\) (in the ADR).
* Effective area of each PD: \(A_{\text{PD}_{k}}\).
* Position of ADR receiver: \(R=(x_{r},y_{r},z_{r})\).
* Photodiode orientation \(k\): \(\mathbf{n}_{k}=(n_{x,k},n_{y,k},n_{z,k})\), which is a unit normal vector that defines the direction in which the photodiode is oriented.
* Angle of incidence on PD \(k\): \(\psi_{k}\), which depends on the orientation of the PD \(k\) and the direction of the incoming signal.
## 2 Materials and Methods
### Finite Element Method  
Parametric FEM simulations were performed using ANSYS Mechanical APDL R1. The following assumptions for the numerical models were made: struts are idealized as beam elements; cross-sections are assumed to be ideally round and were modelled as solid cross-sections with variable diameters; material properties are constant and isotropic; a consistent distance between struts and rigid connections between struts are assumed. Table 1 gives the geometric parameters of the numerical models:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name of Parameter** & **Geometric Parameter** \\ \hline \(d\_s\) & Diameter of struts \\ \(d\_n\) & Diameter of struts in node areas \\ \(a\_xy\) & Space of pores in \(xy\) plane \\ \(a\_z\) & Pore height (\(z\) direction) \\ \(a\_p\) & With of node area \\ \(number\_x\) & Number of pores in \(x\) direction \\ \(number\_y\) & Number of pores in \(y\) direction \\ \(number\_z\) & Number of pores in \(z\) direction \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric parameters of numerical models.  
Figure 1: Geometrical properties of PCL scaffolds. \(d_{n}\): Diameter of PCL struts in node areas; \(d_{s}\): Diameter of PCL struts in areas distant from nodes; \(a_{xy}\): Clear width between two struts in \(xy\) plane; \(a_{z}\): Clear width between two struts in \(z\) direction.  
FEM models of PCL scaffolds were implemented based on three-dimensional 2 nodes Beam188 Timoshenko elements, which can model normal and shear forces and bending moments. In contrast to Bernoulli elements, no shear rigidity of the cross-sections is assumed. Thus, stocky geometries can also be analyzed [26]. The size of elements were varied via the variable \"_element_size_\". Parameterized geometries of numerical models were defined via key points and generated via lines. The basic geometric development is illustrated in Supplementary Figure S1. Elastic-plastic and viscoelastic material models based on a Prony-Row were implemented [27; 28]. Material properties were implemented via the variables \"_emodulus_pcl_\",\"_f_y_\", \"_prony_value_\" and \"_prony_time_\". Loads were applied as path-controlled node loads. The basic structure of a numerical model and the comparison of the scaffold and model node areas are demonstrated in Figure 2.  
For validation of the numerical model, the following force ratios between the results from the tensile testing of scaffolds and numerically determined values were defined (\(\varepsilon\) = strain, n = number of tensile tests). Equation (1) is defined as the ratio between the determined force during the tensile testing and the numerical calculated force at a certain strain. Equation (2) describes the average of Equation (1) for 1%, 2% and 3% strains. Ratio (3) is the ratio of the average of all tensile testing and the numerical determined force.  
\[\mu_{i}=\frac{F_{Numerical,\varepsilon=i}}{F_{\text{TensileTesting, \varepsilon=i}}} \tag{1}\]  
\[\mu_{sum}=\frac{\mu_{0.01}+\mu_{0.02}+\mu_{0.03}}{3} \tag{2}\]  
\[\overline{\mu}=\frac{F_{Numerical}}{\left(\sum_{i=1}^{n}F_{\text{TensileTesting, \varepsilon}}\right)/n} \tag{3}\]
# Development of Attached Cavitation at Very Low Reynolds Numbers from Partial to Super-Cavitation
## 2 Experimental Study Overview  
### Experimental Test-Bench and Protocol  
The experiments are conducted in a test-bench of the _LIFSE_ facilities especially designed to study cavitation in silicone oils (see Figure 1). A Pollard MPLN 142 volumetric pump (1) impels the silicone oil in the loop. A tank of 100 liters (2) is equipped with a temperature sensor ThermoEst PT100 (3). The tank is connected to compressed air supply or to a vacuum pump (4). The oil then flows in a 40 mm inner diameter pipe and through an ultrasonic flowmeter KHRONE Optisonic 3400 (5) placed 1.5 m upstream of the test section (7). The inlet and outlet pressures are monitored with two JUMO dTrans p30 pressure sensors (6).  
The test section consists of a Venturi geometry with convergent/divergent angles of respectively \(18^{\circ}\) and \(8^{\circ}\)[10]. It presents an inlet section \(S_{in}=20\times 10\) mm\({}^{2}\) and a square section at the throat \(S_{throat}=10\times 10\) mm\({}^{2}\). Both side and top visualizations are possible. Two round-to-rectangle contraction nozzles with an area ratio of 6.3 are placed on each sides of the test section to connect it to the pipe system.  
All experiments are realized following a protocol to eliminate a maximum of dissolved air. The pressure in the setup is first decreased to an absolute pressure of \(\simeq\)70 mbar, then the oil is kept circulating for this pressure at slow velocity during two hours, while removing periodically the air that accumulates in the vertical tube situated above the outlet of the test section and visible on the top part of the sketch on the left in Figure 1. The measurements are then operated at roughly constant Reynolds number, increasing progressively the pressure in the test-bench.
## 2 Literature Review
### Machine Learning  
Due to the complex nature of CBM, which involves analyzing vast datasets prone to uncertainties and non-stationary processes, integrating ML methodologies like Neural Networks (NNs), genetic algorithms (GAs), Fuzzy Logic (FL), clustering and a mix of others has become crucial for improving data analysis and predictive capabilities [1].  
In Ref. [14], an ML-based tool was developed to predict seismic responses in Reinforced Concrete Moment-Resisting Frames (RC MRFs). Using Artificial Neural Networks (ANNs), the study predicted the Maximum Interstory Drift Ratio and spectral acceleration, which are essential for assessing the seismic performance and safety of these structures, achieving accuracies of up to 92.9%, enhancing the reliability and precision of seismic performance assessments for RC MRFs. In Ref. [15], a predictive maintenance planning model is introduced. It starts by utilizing the Java-based Sea Lion Optimization (J-SLnO) algorithm to refine feature selection, minimize data redundancies, and optimize the weights of the Recurrent Neural Network (RNN). Subsequently, the optimized RNN is employed to predict component failures and to further enhance prediction accuracy, and a Support Vector Machine (SVM) is integrated to identify the prediction network across different ranges. The authors of Ref. [16] present a problem involving the optimization of flexible job shop scheduling and flexible PM using a Digital Twin, considering fixed and human resources. They apply a double-layer Q-learning algorithm as an optimization method, which has been adapted for dynamic environments with proven low computational overhead. In Ref. [17], a combined model of a multi-population memetic algorithm and Q-learning is employed for the optimization of a hybrid flow shop scheduling problem with flexible predictive maintenance. The study concludes that the proposed methodology yields satisfactory results compared to traditional evolutionary techniques.  
As seen in these examples, ML is widely used in maintenance. In Ref. [4], a comprehensive review examining the employment of ML techniques in CBM is conducted. The review reveals that Neural Networks are utilized in 50% of CBM studies, followed by Fuzzy Logic in 13%, and clustering methods and evolutionary algorithms in 8%. Additionally, the study analyzes the types of datasets used in these studies, with results showing that approximately 47% utilize simulated or synthetic data, 29% use public datasets, 18% use real operational data, and only 6% rely on industry case studies.  
It is clear that there is a lack of exploration of clustering techniques and evolutionary algorithms in CBM. Moreover, the utilization of these techniques on real operational or industrial datasets is lacking. However, these gaps are primarily due to the substantial challenges in integrating covariates from authentic datasets into the models [18]. Therefore, this study aimed to address these obstacles by implementing optimization and clustering ML techniques, while also leveraging real operational data in the proposed PHM-ML model.
## 1 Introduction
In addition, the scale transformation and occlusion of the objects will also have a great impact on data association. More importantly, many multi-object tracking algorithms do not attach importance to the real-time performance. They tend to pursue accuracy and ignore speed. However, in the era of pursuing high efficiency in all aspects, the real-time performance of the algorithms is of vital importance. In this paper, we propose a hierarchical matching based online and real-time multiple object tracking approach with deep appearance features (DAFs) to solve the aforementioned problems (low FPS, false detection, unreliable features). Through our method, we can significantly reduce IDS, FP, and the number of track fragmentation (Frag) while maintaining real-time analysis and accurate results. In summary, our main contributions are as follows:  
To handle problems like false detection, missed detection, and occlusion, we designed four different trajectory states and transition conditions in each state. Each object can use different strategies to tackle problems under different situations. * In order to obtain discriminative appearance features, we exploited an extraction CNN which divided the input image into several different regions. Moreover, we trained the network on a large-scale pedestrian re-identification dataset, and evaluated it on the test set. * As for the data association, we proposed the calculation equations of trajectory confidence and the hierarchical matching strategy. By adopting the equations, we divided the trajectory set into different confidence sets, representing different reliability levels. Furthermore, we designed a hierarchical matching strategy, associating the trajectory set and detection set after measuring the similarity distance between each of them from the aspect of appearance and motion. * We carried out some ablation studies on our algorithm as well as the experiments on the MOT15 benchmark dataset [13; 14] and MOT17 benchmark dataset [15]. The experimental results demonstrate the effectiveness of our proposed approach. The rest of this paper is organized as follows. In Section 2, we introduce a detailed description of the proposed hierarchical-matching-based online and real-time multi-object tracking with deep appearance features. Experimental evaluation of our tracking approach is carried out in Section 3. Section 4 presents our conclusions.