esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Predictive Model Construction
It is useful for understanding how changes in the independent variables affect the period, but may not capture complex relationships [26]. * K-Nearest Neighbors (KNN): This model makes predictions based on the similarity of the test data to its nearest neighbors in the training data. KNN works well for capturing non-linear patterns but can be sensitive to noisy data [27]. * Support Vector Machines (SVM): SVM attempts to find the optimal hyperplane that separates the data into different classes or predicts continuous outcomes. It is effective for high-dimensional data but can be computationally intensive [28]. * Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds models sequentially, each new model correcting errors made by the previous ones. GBM is effective for handling complex, non-linear relationships but can require careful tuning [29]. * Extreme Gradient Boosting (XGBoost): XGBoost is a highly efficient implementation of gradient boosting. It improves performance by reducing overfitting and managing computational complexity, making it one of the most powerful models for this type of predictive analysis [30]. * Random Forests (RF): Random forests are an ensemble of decision trees where each tree is trained on a random subset of the data and features. This method reduces the variance of decision trees and provides robust predictions, but it can be less interpretable than simpler models [31].
## 3 Analysis and Clustering Methodology
### Data Pre-Processing  
A data pre-processing step is applied to the input acoustic database in order to obtain an STP for each sensed location, representing the acoustic energy distribution related to RTN in terms of both the considered time periods and the frequency bands. This step is integrated into the process due to the possible non-homogeneity of the original data, e.g., there might be different time periods of recordings provided for each sensed area.  
Figure 1: Block diagram of the clustering and analysis methodology of RTN acoustic environments from the data collected from \(N\) locations simultaneously.  
First, in order to perform a consistent analysis, a selection of 1-h periods where there are available data for all the sensed placements (both audio files and RTN labels) is performed. Secondly, only the 1-h periods with a minimum presence of RTN in the recorded passages are selected for further analyses. For that purpose, a _selection threshold_\(\gamma\) (see Figure 1) is defined as the minimum percentage of RTN frames that must be present within the \(T\) minutes of the recorded audio file per hour. This value is manually set as a trade-off between the minimum representative time to compute the hourly mean spectra of RTN and the maximum amount of discarded data (i.e., if the criterion is very restrictive, the subsequent analysis will lose too much data). The discarded 1-h periods represent data that are not sufficiently representative to compute the corresponding mean RTN energy curve. Finally, the missing STP values removed at these periods are filled using a 2D-based interpolation (i.e., in frequency and time) by considering the values of the STPs of their neighbour 1-h periods.
# Nature-Inspired Optimization Algorithms for the 3D Reconstruction of Porous Media
## 3 3D Reconstruction as an Optimization Problem
### Problem Formulation  
Based on the previous analysis, it was concluded that the application of a nature-inspired optimization algorithm will significantly enhance the 3D reconstruction of porous media. However, the application of any nature-inspired optimization algorithm is highly dependent on the successful formulation of the optimization problem related to the definition of the cost (objective) function being minimized. A generic objective function consisting of multiple correlation functions was proposed in this work for the set of \(N\) unknowns (the phases \(\{0,1\}\) of the 3D model's pixels) \(\mathbf{p}=\{p_{1},p_{2},p_{3},\ldots,p_{N}\}\), according to the following form:  
\[J(\mathbf{p})=\sum_{i=1}^{N_{f}}\frac{w_{i}\xi_{i}}{N_{f_{i}}}\big{(}\mathbf{f}_{i}( \mathbf{p})-\mathbf{f}_{i}^{Ref}(\mathbf{p})\big{)}^{2}, \tag{18}\]  
where \(N_{f}\) is the number of different functions (correlation and/or statistical) used to measure the morphological properties of the material; \(\mathbf{f}_{i}\) and \(\mathbf{f}_{i}^{Ref}\) are the \(i\)th function and its reference value (of the 2D slice image); \(N_{f}\) and \(\xi_{i}\) are the dimension and the normalization factor of the \(\mathbf{f}_{i}\) function, respectively; and \(w_{i}\) is its weight defining its importance satisfying the following condition:  
\[\sum_{i=1}^{N_{f}}w_{i}=1. \tag{19}\]  
Based on the objective function of Equation (18), the 3D reconstruction optimization problem is defined as follows:  
\[\underset{\mathbf{p}}{\text{min}}J(\mathbf{p}). \tag{20}\]  
The flowchart of the proposed reconstruction methodology, applied for each one of the algorithms of Section 2, is depicted in Figure 1. Based on this figure, the proposed 3D porous media reconstruction consists of the following phases: (1) initial random candidate solutions: in this phase, random material phases are generated and the proposed data representation scheme is applied to reduce the number of unknowns that need to be found by the evolutionary algorithm, (2) application of the specific operators of the applied evolutionary algorithm to produce solutions with higher fitnesses, (3) reconstruction of a 3D material model for each candidate solution, and (4) fitness calculation of each candidate solution using Equation (18). The execution of these four phases is repeated until a termination criterion is met. After the termination of the algorithm, the fittest solution corresponds to the reconstructed 3D porous media.
## 7 System Implemented and Real-World Evaluation
### Real-World Evaluation  
We monitored and collected the data of the system implemented, which is currently running in production, not only for _Mega Italia Media_ but also for other 6 companies which offer e-learning courses and provide the same chatbot to their users.  
From 1 November 2020 to 1 January 2022, more than 1400 messages were exchanged with users. Only 7% of them requested to talk with a human operator after interacting with our chatbot. In Section 4.2, we specified that the sub-group of the intentions selected covered 82.4% of the students' requests in the dataset extracted. With the new data coming from the performance evaluation, we have calculated that more than 93% of the requests belong to the 9 main intentions we selected for our experiments.  
In Figure 12, we show the distribution of the main intentions recognized by our system during the evaluation period. To evaluate the system performance, the possible cases (which are also shown in Table 4) are the following:  
1. **Success**: the NLU engine recognizes the underlying intention and the chatbot provides the correct answer to the student. In Table 4, we do not classify the Human Operator intent recognition as Success. In fact, this intention can only be triggered after the chatbot fails to recognize a user request.
2. **Disambiguation necessary**: the NLU engine does not provide a unique intention or the request is unclear, so the chatbot builds a disambiguation message as described previously. The student selects the correct intention among a subgroup of probable intentions. Therefore, the chatbot determines the correct answer and sends it to the student.
3. **Fail and redirect to human tutor**: the NLU engine recognizes the wrong intention and the chatbot consequently sends an incorrect answer, therefore users ask to talk to a human operator; in this case, the chatbot does not attempt to satisfy any request and the user is immediately redirected to a human tutor.  
Figure 12: Pie chart of the main intentions recognized by our chatbot during the performance evaluation (from 1 November 2020 to 1 January 2022).  
Nevertheless, we cannot directly compare these results with the ones obtained in the platforms comparison. In fact, while in the studies conducted in the previous sections the intention with the highest confidence is the one considered predicted, in the real-world implementation, the NLU engine provides an ambiguous prediction of the intention, the system implemented asks the student to manually indicate the intention from a list. However, given that a vast majority of the requests are satisfied and 79.0% of the intentions are recognized, we think our results are stable and promising even in the real world environment and with a larger test set.
# An Insight into the Chemistry of Cement--A Review
## 3 Hydration Step Involving OPC
### Chemical Reactions Occurring during the Hydration Process
#### 3.2.4 Reaction Involving C\({}_{4}\)Af  
The last component that participates in the hydration reaction is calcium aluminoiferrite (C\({}_{4}\)AF, or brownmillerite). This compound is the only variable phase present in cement with an unfixed composition. Brownmillerite constitutes a wide category of solid solution Ca\({}_{2}\)(Al\({}_{y}\)Fe\({}_{(2-y)}\))O\({}_{5}\), where the values of y are in the 0.00-1.33 range. Analogous to the previous component C\({}_{3}\)A, the hydration of C\({}_{4}\)AF is affected by the presence/absence of gypsum. The absence of gypsum favors the reaction of C\({}_{4}\)AF with water, forming metastable C(A,F)H hydrates (defined as hydroxy-Afm phase) onto the surface of the non-hydrated grains, and eventually the stable phase C\({}_{3}\)(A,F)H\({}_{6}\). Experimentally, it has been demonstrated that the Al\({}^{3+}\)/Fe\({}^{3+}\) ratio is usually lower in the products formed by the reaction with water with respect to the original C\({}_{4}\)AF [61]. The reduction in Al\({}^{3+}\)/Fe\({}^{3+}\) in the hydrated products can be clarified by means of the production of a secondary by-product, namely, an amorphous iron-rich gel or iron hydroxide [62].  
Conversely, in the presence of gypsum, C\({}_{4}\)AF produces hydration phases analogous to the case of C\({}_{3}\)A. However, the reaction rates of both components are quite different. In fact, in the presence of gypsum, C\({}_{4}\)AF reacts much slower than C\({}_{3}\)A, i.e., gypsum retards the hydration of C\({}_{4}\)AF more efficiently than in the case of C\({}_{3}\)A. However, the rate of hydration of C\({}_{4}\)AF is affected by the composition of the ferrite phase: the high amount of Fe corresponds to a low rate of hydration [63]. Therefore, gypsum favors the AF phase as the main product of reaction, whereas in a successive reaction step, the AFt phase evolves into AFm by reaction with further C\({}_{4}\)AF.
## 3 Results and Discussion
### Analysis of the Chemical and Biological Characteristics of the Grains
However, the presented results are specific to the context of evaluating the biochemical differences in white and brown rice cultivated under chemical and biological control methods, making them uniquely intrinsic to these conditions. Characteristics such as TPC or AA explicitly depend on factors like the type of cultivar, the degree of germination, the presence of spontaneous fermentation [4], moisture percentage, and temperature exposure before and during the assay [29; 30]. The disparity in the values of all variables between white and brown rice groups can be attributed to various causes. However, the most influential factor is the distinct structure of the grains. Unlike white rice, brown rice retains its embryo and bran (representing 8 to 10% of the total weight) [35]. The bran layer is an external structure of the grain, rich in cellulose and capable of storing different organic compounds, thus providing higher nutritional properties in certain aspects [23; 26; 36]. Therefore, the presence of bran increases certain characteristics, such as total phenolic compounds by up to 2.5 times compared to white rice samples, as well as antioxidant activity [26]. Studies by [PERSON] et al. in 2019, [PERSON] and [PERSON], and [PERSON] et al. in 2011 [23; 31; 37] attribute this difference to the same cause, demonstrating that \(\gamma\)-oryzanol, dietary fiber, and phytic acid are dependent or correlated, thus their levels also increase in brown rice samples.
# Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change +
## 2 Methodology
### Trajectory Re-Planning  
For each time instance during the lane change process, the algorithm will check the predicted movement of traffic vehicles until the end of planning horizon. The constraints of traffic gap are then updated to validate the current trajectory. If the planned trajectory exceeds the new gap constraint at any point, a trajectory re-planning needs to be conducted. During the re-planning control, the algorithm evaluates the original and target gap, then attempts to generate a new trajectory by the quadratic programming function. By utilizing the proposed longitudinal and lateral constraints, the time variant safety margin can limit the driving parameters in a safe range, and make sure the re-planning trajectory be collision-free. Since the safety margins \(s_{F}^{a}\) and \(s_{R}^{a}\) start from zero for each recalculation, constraints automatically become looser with each time step, so frequent re-planning due to minor constraint violation can be avoided.  
After the trajectory re-planning, the host vehicle have two kinds control results: continue the lane change, or abandon the maneuver and return to the original lane. Both of two control results will be conducted by the replanned trajectory. As an effort to avoid abrupt changes in acceleration, we limit the changing rate of both longitudinal and lateral acceleration during the replanning control. The constraint is designed as  
\[j_{min}-M_{j1}\leqslant\frac{a_{H}(k+1)-a_{H}(k)}{t_{s}}\leqslant j_{max}+M_{j2} \tag{23}\]  
where \(a_{H}\) refers to either \(ax_{H}\) or \(ay_{H}\), and the same applies to \(j_{min}\), \(j_{max}\), \(M_{j1}\) and \(M_{j2}\). The definitions of these parameters are similar to the Equation (12).
# A Systematic Approach to Studying Quark Energy Loss in Nuclei Using Positive Pions +
## 1 Introduction: Hadronization
### The Experimental Setup
#### 2.2 The CEBAF Large Acceptance Spectrometer (CLAS)  
The CLAS detector [6] was the primary detector used in Hall B during the 6 GeV CEBAF era. This detector works well for studying multi particle final states, as it can detect charged particles and neutral particles. The detector consists of six identical sectors, with a schematic shown in Figure 2. The beam enters from the left side of the figure, with the target positioned near the center of the detector. The six superconducting coils surrounding the beam axis generate a toroidal magnetic field that is largely uniform in the azimuthal direction. This design allows for precise momentum and angle measurements for charged particles using a set of three drift chambers (DCs). Scintillator paddles provide time-of-flight (TOF) measurements, while a gas Cherenkov detector and an electromagnetic calorimeter aid in particle identification, particularly for electrons and pions.  
Although it is primarily used for exclusive and semi-inclusive studies, as mentioned in [6], the CLAS detector can also handle inclusive electron scattering experiments. For the EG2c dataset, an open electron trigger was used, allowing for inclusive cross-section ratio measurements. During the EG2 experiment, both the Cherenkov detector and the electromagnetic calorimeter were included in the trigger, but their detection was limited to the forward direction, meaning that electrons could be detected at scattering angles of up to 55\({}^{\circ}\). The detector's ability to maintain a near-zero magnetic field near the target also enables experiments with polarized targets, enhancing the momentum resolution and providing clear separation between the charged particle trajectories. The separation of the detector into six sectors by the toroidal magnetic field coils further helps manage the particle detection across its nearly 4\(\pi\) solid angle coverage.  
Figure 1: Illustration of the induced energy loss dependence on the parton energy \(E\) (**left**) and on the medium size \(L\) (**right**).
## 2 Results and Discussion  
### Optimization of HS-SPME  
The effects of the main SPME variables such as extraction time (15 and 30 min), extraction temperature (60, and 80 \({}^{\circ}\)C), and fiber type (DVB/CWR/PDMS 80 \(\upmu\)m, and PDMS/DVB 65 \(\upmu\)m) were evaluated, in order to determine the optimum HS-SPME conditions for profiling volatiles in strawberry samples. Results showed that the optimum extraction temperature and time were 60 \({}^{\circ}\)C and 30 min, respectively. The DVB/CWR/PDMS fiber seemed to be optimal because it gave the best results in terms of number and peak areas of the detected volatiles in the strawberry matrix. The addition of NaCl solution (25%) increased the intensity of a few peaks, but decreased others; thus, it was better to work without the addition of NaCl salt. Therefore, 30 min extraction time and 60 \({}^{\circ}\)C extraction temperature, in addition to 20 min equilibrium time using DVB/CWR/PDMS fiber, were chosen as the optimum HS-SPME conditions. Taking into account the robustness of the method and repeatability of results, these optimized SPME parameters were selected for further investigations of analysis and comparison of the volatile fractions from dried and frozen strawberry fruits and their commercial jam with respect to fresh ones.
## 3 Current Techniques for OP Study
### Dual-Energy X-ray Absorptiometry
#### 3.1.2 DXA Limitations and Use of Calcaneal Quantitative Ultrasound (QUS)  
Despite several assets of this technique, namely the availability, minimal radiation exposure and short scan times, some limitations are associated with the use of DXA for the assessment of BMD [10]. In fact, the 2D nature of this imaging technique implies a lack of information about the depth of the scanned bone volume. Consequently, thinner bones will result in lower BMD due to the shorter X-ray path across the bone tissue [44].  
Moreover, since DXA does not distinguish cortical and trabecular bone contributions [45], it cannot provide information about the morphological alterations underlying the BMD variation, causing an incorrect OP diagnosis in the case of bone density alterations deriving from other diseases (e.g., osteomalacia) [46].  
In this frame, an alternative imaging technique such as calcaneal quantitative ultrasound (QUS) can be exploited for a more specific quantification of BMD in osteoporotic patients. This technique measures the propagation of ultrasound waves at different frequencies to obtain information about the properties of bone tissue. In detail, the measured velocity (termed as speed of sound, SOS) and attenuation (termed as broadband ultrasound attenuation, BUA) are then combined to define the stiffness index (SI) or quantitative ultrasound index (QUI). In addition, due to the high accessibility and the percentage of trabecular bone, calcaneus represents the most selected skeletal site for QUS analysis [47; 48].  
The main advantages of QUS lie in the lower costs and the radiation-free assessment of bone quality, compared to DXA analysis. However, the lack of consensus of diagnostic criteria, such as the use of devices, variables and cut-offs, significantly limits the ability of QUS to replace DXA as potential pre-screening tool for osteoporosis [48; 49].
## 2 Mathematical Model  
### Model Description and Basic Assumptions  
For the narrow and long excavation pit, its length is much larger than its width. The seepage field can be approximated as two-dimensional plane flow. Figure 1 shows the cross-section of the model for the strip excavation pit. The width of the strip pit is \(2a\), the thickness of the confined aquifer is \(M\) and the penetrating depth of the cut-off wall is \(d\). Additionally, the total pumping rate is set as \(Q\). The distance from the center of the excavation pit to the lateral boundary is \(R\). To establish the coordinate system for the problem, we take the center of the excavation pit bottom as the origin o, with the \(z\)-axis pointing downwards to represent the depth below the base.  
In order to simplify the seepage model and obtain the solution, the main assumptions are listed as follows:  
1. The recharge of the overlying unconfined aquifer is not considered;
2. The head of groundwater is constant at a distance \(R\) away from the strip excavation pit;
3. The cut-off wall is a no-flow boundary;
4. The entire excavation pit is regarded as a large well.
# High-Precision Ranging Method of 5G NR Co-Band PRS in Industrial Internet Scenarios
## 2 System Model
### Framework Structure  
In a 5G system, both communication data and positioning signals are transmitted using OFDM modulation. First, the generated 5G PRS signals and communication data will be mapped onto the time-frequency resource grid. Generally, in order to achieve improved localization performance, 5G PRS signals require a significant amount of time-frequency resources, which significantly restricts the bandwidth and data rate of communication signals. In this paper, an ultra-low-power positioning signal is superimposed on the communication signal which is 40 dB lower than the communication signal. This allows the PRS signal to be broadcasted on the same subcarrier and in the same frequency band as the communication data. It can avoid occupying communication resources and prevent much interference with communication signals. However, the reception and demodulation of the weak low positioning signal will also face challenges, which we will discuss later.  
The mapped and superimposed signals will be converted to the time domain using inverse Fast Fourier Transform (IFFT), and the orthogonality of the signals will be ensured by adding a cyclic prefix (CP) to each symbol. The signal is then filtered using a rising cosine roll-off function to prevent signal leakage. Finally, the baseband signal is modulated to RF and transmitted.  
On the receiver side, timing synchronization of the symbols and coarse estimation of the carrier frequency offset (CFO) need to be performed on the signal. Then, the impulse response of the channel in the time domain is estimated to determine the channel state and remove the effects of multipath on the signal. Finally, the code phase is finely estimated by a tracking loop to obtain precise ranging information. Figure 1 summarizes the block diagram of the transmission and reception of the signal in the 5G NR Co-Band communication and positioning fusion system.