esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 2 Methods
### Eligibility Criteria  
The review focused on professional truck drivers as the population of interest. Eligible studies evaluated monitoring technologies designed to monitor driving behavior, such as wearable devices, in-vehicle cameras, telematics systems, and artificial intelligence (AI)-based platforms. To qualify, studies had to report on safety-related outcomes, including reductions in accident rates, mitigation of driving risks, or behavioral improvements linked to the implementation of these technologies. Only original research articles published in peer-reviewed journals, written in English, and published between January 2009 and October 2024 were included to ensure the relevance and quality of the evidence.  
Studies were excluded if they targeted populations other than professional truck drivers, such as passenger vehicle or bus operators. Research addressing outcomes not directly tied to road safety, such as vehicle performance or driver comfort, was also excluded. Furthermore, review articles, editorials, opinion pieces, conference abstracts, and grey literature were rejected to maintain a focus on primary, scientifically validated evidence.
## 2 Results
### Multivariate Statistical Analysis for MCG with 5-10-15-Growth Years
#### 2.3.2 Chemo-Markers Discovery for Distinguishing MCG with 5-, 10-, 15-Growth Years  
To determine the variables responsible for the separation between \(\text{MCG}_{\text{5-years}}\) and \(\text{MCG}_{\text{10-years}}\), \(\text{MCG}_{\text{5-years}}\) and \(\text{MCG}_{\text{15-years}}\), \(\text{MCG}_{\text{10-years}}\) and \(\text{MCG}_{\text{15-years}}\), the OPLS-DA model was applied to the above dataset. As shown in Figure 3B-D, OPLS-DA score plots separated every two samples well into two blocks, with R\({}^{2}\)X(cum) values of 0.838, 0.9085, and 0.885, respectively, and Q2(cum) values of 0.704, 0.564, and 0.785, respectively. These models were subjected to 200 rounds of permutation tests to confirm their high predictability (Figure S1C,E,G). The ions intuitively responsible for the separation are marked in red in the S-plots (Figure 3E-G). As a result, with VIP \(>\) 1 and \(p\) \(<\) 0.05, six variables were determined as potential chemo-markers for discrimination between \(\text{MCG}_{\text{5-years}}\) and \(\text{MCG}_{\text{10-years}}\), seven chemo-markers were determined for the differentiation between \(\text{MCG}_{\text{5-years}}\) and \(\text{MCG}_{\text{15-years}}\), and 1 chemo-marker was determined for to distinguish between \(\text{MCG}_{\text{10-years}}\) and \(\text{MCG}_{\text{15-years}}\). Altogether, 12 potential growth-year-dependent markers, including (\(-\))-\(\alpha\)-neoclovene, \(\beta\)-panasinene, \(\gamma\)-gurjunene, \(\beta\)-neoclovene, \(\beta\)-chamigrene, heptanal, 2-oxo-pentanoic acid, benzaldehyde, octanal, (\(E\))-2-octal, (\(E\))-2-heptanal, and 3-octen-2-one, were determined and the information is summarized in Table 3. Of the 12 chemo-markers, except for benzaldehyde, the contents of the other 11 compounds increased with the growth years (Figure 3H), consistent with the market assessment that the higher the growth-years, the greater the value.  
Figure 3: Multivariate statistical analysis based on volatile components from MCG with 5-,10-, 15-growth years (**A**) PCA score plots; (**B–D**) OPLS-DA score plots; (**E–G**) S-plots; (**H**) The contents of the five growth-year-dependent markers to distinguish MCG with 5-,10-,15-growth years. (\(p\)-value * \(<\) 0.1; * \(<\) 0.01; * \(<\) 0.001).
## 4 Experimental Verification  
The fault data obtained by a single sensor are not comprehensive. Multiple different sensors can obtain fault information from different angles, which is more conducive to enhancing the characteristics of the fault and improving the effect of fault diagnosis.  
The time offset obtained by correlating the spatial positions of multiple sensors with the energy of multiple sensors is correlated to obtain the fusion data. In order to verify the effectiveness of the fusion method, SAE [38] deep learning method in the vibration test bench was used to compare and verify the original single data (OSD), average weighted fusion (AWF), neural network fusion (NNF) and STMF methods, and the superiority of STMF method is highlighted.
# A Taxonomic Survey of Physics-Informed Machine Learning
## 2 Taxonomy
### Driver  
The pipeline of physics-informed machine learning is given in Figure 3. The primary consideration when confronting the explainability of PIML modeling is the means by which the physics-based information is obtained for use in the machine learning process. This question crucially differentiates the qualifications for physics-based learning while further the broadening scope and promoting the introduction of physics-informed machine learning. The proposed taxonomy draws two distinctions: first, physics-information is enforced on the learning process through governing equations or physical structural patterns, and second, physics-information is encoded implicitly into the data through simulation or observation. The data may be structured in a physically relevant way, or physics-information is derived from traditional multiphysics models directly, as typically expressed by the governing equations. Figure 3 shows the adoption of the images of heat transfer problems, neural networks, and generic datasets as stand-ins for any physics-based model, learning machine, or physically structured dataset, respectively. In our generic example, \(u\) and \(v\) are physically relevant parameters in the feature set X and \(f(u,v,t)\) are labels. Consequently, \(\frac{du}{dt}\) and \(\frac{du}{dt}\) are governing physics equations underpinning the relationship between features and labels. Traditionally, expensive differentiation is employed to realize the predictive power of the physics-based model. However, Figure 3 displays the alternative PIML pipeline in the context of the two possible drivers of prior physics information.  
_Physics-model driven._ First--and most tangibly--physics information can be derived from preexisting physical models of a given problem. Commonly, governing equations are directly infused into the learning process. The learning machine might also realize these augmentations in the form of architectural augmentations which reflect physical symmetries. The features of the machine learning model are explicitly constrained by the problem's underlying physics.  
_Physics-data driven._ Second--and expressed broadly--physics information can be derived implicitly from numerically data-driven methods. It is not to say that all data-driven machine learning is \"physics informed\". Yet, if data have a physical structure informed by high fidelity, numerically derived training data or by empirical data quantifiable by a trustworthy physical model, such a model is--at least indirectly--constrained by the well-understood underlying physical priors. After all, it is the same relationships traditionally modeled by complex systems of differential equations that researchers are attempting to learn, in lieu of differentiating through expensive means.  
Figure 2: Diagram depicting the correlation of taxonomic categories also displayed in Table 1.
## 4 Experimental Results
### Calculation of NEP  
Substituting in Equation (4) the value B = 1 MHz (bandwidth of the LTF), the dark frequency of the LTF is f\({}_{\mathrm{D}}\) = 2 Hz, the average value of the output frequency noise with filter is \(\Delta\)fn = 0.9 Hz, and without filter is \(\Delta\)fn = 2.75 Hz, resulting in the NEPs:  
(a) With filter  
\[NEP=\frac{(0.9+2)}{134.77\times 10^{9}}=2.15\;\times\;10^{-11}\;W/\sqrt{\mathrm{Hz}}\]  
Figure 12: Difference in output frequency \(\Delta f_{mmw}\) as a function of MMW radiation power hitting the cross section of the GDD.  
(b) Without filter  
\[NEP=\frac{(2.75+2)}{115.32\times 10^{9}}=4.12\ \times\ 10^{-11}\ W/\sqrt{\rm Hz}\]
# Heterodyne Angle Deviation Interferometry in Vibration and Bubble Measurements
## 3 Experimental Results  
We performed two experiments to demonstrate the proposed method. The vibration and bubble measurements are described below.
## 3 Method
### Psychaocustic Parameter  
In order to determine affective dimension to automobile engine acceleration, psychoacoustic parameters and SPL were selected. In this study, five metrics of the psychoacoustic parameter: loudness, sharpness, roughness, fluctuation strength, and tonality and three metrics of SPL: SPL, SPL-A weighted, and SPL-C weighted were used. In addition, in this study, the parameters were designed considering the revolutions per minute (RPM) of the automobile in acceleration condition. All parameters are calculated and used in three different conditions: 2000 to 5000 RPM, 2000 to 3500 RPM, and 3500 to 5000 RPM. The descriptive statistics of each parameter are shown in Table 1 below. Skewness is a measure of the degree for symmetry. The value of skewness in the dataset with a symmetric distribution is zero. Kurtosis is a measure of the degree for the combined tails in data distribution. The value of the kurtosis in the normal distribution is three. According to [PERSON] et al. [42], if the values of skewness and kurtosis do not exceed 3.0 and 8.0 respectively, then the normality of the data distribution is assumed. The values of skewness and kurtosis the parameters used in this study were found to meet the relevant criteria.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameter** & **Min** & **Max** & **Mean** & **SD** & **Skewness** & **Kurtosis** \\ \hline Loudness2000-5000 & 6.30 & 58.40 & 18.92 & 10.48 & 2.25 & 5.63 \\ Sharpness200-5000 & 0.74 & 1.34 & 0.89 & 0.11 & 2.17 & 7.67 \\ Roughness2000-5000 & 0.52 & 3.67 & 1.70 & 0.54 & 1.47 & 4.38 \\ Fluctuation2000-5000 & 0.04 & 0.85 & 0.22 & 0.22 & 1.82 & 2.58 \\ Tonality2000-5000 & 0.03 & 0.27 & 0.12 & 0.06 & 1.04 & 1.00 \\ SPL2000-5000 & 79.42 & 99.64 & 90.25 & 4.91 & 0.20 & \(-\)0.34 \\ SPL-A2000-5000 & 50.54 & 85.72 & 66.31 & 7.42 & 0.96 & 1.08 \\ SPL-C2000-5000 & 72.93 & 98.25 & 83.74 & 5.34 & 0.94 & 0.91 \\ \hline Loudness2000-3500 & 9.95 & 66.90 & 26.97 & 12.66 & 1.71 & 2.54 \\ Sharpness2000-3500 & 0.77 & 1.81 & 1.08 & 0.17 & 2.21 & 7.38 \\ Roughness2000-3500 & 0.94 & 4.18 & 2.34 & 0.54 & 1.13 & 4.42 \\ Fluctuation2000-3500 & 0.04 & 0.39 & 0.13 & 0.09 & 1.50 & 1.14 \\ Tonality2000-3500 & 0.05 & 0.39 & 0.14 & 0.07 & 1.31 & 2.77 \\ SPL2000-3500 & 85.71 & 104.12 & 94.40 & 4.48 & 0.36 & \(-\)0.48 \\ SPL-A2000-3500 & 59.00 & 87.44 & 71.73 & 6.92 & 1.00 & 0.46 \\ SPL-C2000-3500 & 79.80 & 97.28 & 86.84 & 4.42 & 1.14 & 0.57 \\ \hline Loudness500-5000 & 8.11 & 62.70 & 22.87 & 11.44 & 1.98 & 4.01 \\ Sharpness2000-5000 & 0.83 & 1.57 & 0.99 & 0.14 & 2.35 & 7.91 \\ Roughness2000-5000 & 0.94 & 4.18 & 2.34 & 0.54 & 1.14 & 4.42 \\ Fluctuation3500-5000 & 0.01 & 0.23 & 0.04 & 0.05 & 2.52 & 6.77 \\ Tonality3500-5000 & 0.04 & 0.32 & 0.13 & 0.06 & 1.21 & 2.53 \\ SPL3500-5000 & 83.63 & 102.17 & 92.88 & 4.51 & 0.34 & \(-\)0.43 \\ SPL-A3500-5000 & 56.61 & 86.69 & 69.85 & 7.04 & 1.01 & 0.54 \\ SPL-C3500-5000 & 77.61 & 97.79 & 85.64 & 4.71 & 1.09 & 0.66 \\ \hline \end{tabular}
\end{table}
Table 1: Result of descriptive statistics for psychoacoustic parameters and acoustic parameters.  
Figure 2: Dummy head used in the study and an example of the installation.
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 4 Solution Using Matching Game-Based U-S. A and U-S. P Algorithms
### One-Sided Matching Based Solution Approach Sub-Problem (19)
#### 4.1.2 Base Stations Utility Function  
The base station's utility function must be efficient for the user-slice association. Where each BS wants to serve UEs with the highest rate to optimize the overall DL throughput based on the following utility function:  
\[\Psi_{b}(u)=R_{b\mu}, \tag{33}\]The major details of the U - S. A algorithm are depicted in Algorithm 1. After initialization, each UE erects its preference relations \(H_{u}\succ_{u}\) based on (31) and (32) (step 4). Similarly, the preference list for each BS \(H_{b}\) is constructed based on the preference utility (23). At each iteration \(It\), all unassigned UEs (eMBB and uRLLC UEs) send attachment requests to their preferred BSs (steps 5-9). Each BS will then determine whether to accept the requests or not based on its defined utility and quota \(\Theta_{b}\) (\(\Theta_{b}^{rem}\) and \(\Theta_{b}^{th}\) available radio resources and rate for each slice of a BS) that restricts the number of attached UEs to avert Quality of Experience (QoE) retrogression. If a BS has sufficient \(\Theta_{b}^{rem}\) and \(\Theta_{b}^{th}\) to accept, it accepts the request and updates \(\Theta_{b}^{rem}\), \(\Theta_{b}^{th}\) and \(\mu_{U-S.~{}A}^{(It)}(b)\) (steps 10-14). Otherwise, if the quota is not sufficient, but the utility (31)/(32) of the requesting UE \(u\) is more significant than a UE \(\hat{u}\) that was accepted in the previous round, the BS \(b\) determines all its current matches \(\hat{u}\) which have a worse ranking than according to steps \(H_{b}^{lt}\) (steps 15-17). Each least preferred \(\hat{u}\) is then sequentially removed, and \(\Theta_{b}^{rem}\), \(\Theta_{b}^{th}\), \(\mu_{U-S.~{}A}^{(It)}(b)\) and the minimum preference list (\(\xi_{mp}\)) is updated until it can be accepted or there is no more to reject (steps 18-29). After rejecting all UEs \(\hat{u}\) and the available PRBs at BS are still insufficient to admit a UE \(u\). Thus, it is refused and assigned to \(\xi_{mp}\) (steps 30-31), i.e., the refused UEs will send an attachment request in the next round to the next BS in their preference lists. Consequently, BS \(b\) removes \(\xi_{mp}\) its less preferred UEs from the respective lists \(H_{b}\). Similarly, these UEs remove a BS \(b\) from their preference lists (lines 32-33). After many iterations \(It\), there are no more bidding UEs; thus, the algorithm converges to a stable match.
## 3 Results and Discussion
### Prediction of Plant Yield
#### 3.2.2 Machine Learning
Thus, their performance is more sensitive to hyperparameter tuning and data noise than RF and CB. The CB model's effectiveness can be attributed to its rule-based structure, which excels at modeling sharp transitions and local patterns, whereas RF benefits from aggregating uncorrelated decision trees, improving stability. The optimal settings included 50 committees, one CB neighbor, and 100 trees, with mtry = 2 for RF. Previous research has employed various techniques and predictors in ML models for estimating PY, including cross-sectional and time-series predictions and predictors based on remote sensing and ground truth data (experimental plot-based designs) [65, 66]. These diverse approaches have led to inconsistent findings and varying performance levels in ML models for PY prediction. For example, RF and Support Vector Machine models showed similar efficacy, capturing between 50% and 70% of the spatial and temporal variance in yields for crops such as silage maize, winter barley, winter rapeseed, and winter wheat [67]. Furthermore, deep learning and RF models demonstrated strong performance at the field level for large-scale crop yield estimation, achieving mean R\({}^{2}\) values of 0.71 and 0.66 and RMSE values of 1127 kg/ha and 956 kg/ha, respectively [68]. The RF model in particular presented promising results in wheat yield prediction, with an RMSE of 434, normalized RMSE of 0.149 g per plot, and an R\({}^{2}\) of 0.74 [69]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **Metric** & **XGB** & **RF** & **CB** & **GB** \\ \hline \multirow{3}{*}{Data 1} & RMSE & 1229.65 \({}^{\text{a}}\) & 1297.74 \({}^{\text{a}}\) & 1142.77 \({}^{\text{a}}\) & 1509.56 \({}^{\text{a}}\) \\ \cline{2-6}  & R\({}^{2}\) & 0.86 & 0.84 & 0.88 & 0.79 \\ \hline \multirow{3}{*}{Data 2} & RMSE & 505.16 \({}^{\text{a}}\) & 493.22 \({}^{\text{a}}\) & 494.71 \({}^{\text{a}}\) & 651.19 \({}^{\text{b}}\) \\ \cline{2-6}  & R\({}^{2}\) & 0.75 & 0.77 & 0.76 & 0.57 \\ \hline \multirow{3}{*}{Data 3} & RMSE & 1460.60 \({}^{\text{a}}\) & 1577.07 \({}^{\text{a}}\) & 1422.36 \({}^{\text{a}}\) & 1929.11 \({}^{\text{b}}\) \\ \cline{2-6}  & R\({}^{2}\) & 0.79 & 0.76 & 0.80 & 0.65 \\ \hline \multirow{3}{*}{Data 4} & RMSE & 793.13 \({}^{\text{a}}\) & 809.99 \({}^{\text{a}}\) & 709.77 \({}^{\text{a}}\) & 1111.26 \({}^{\text{b}}\) \\ \cline{2-6}  & R\({}^{2}\) & 0.78 & 0.76 & 0.82 & 0.58 \\ \hline \hline \end{tabular}  
* Superscript letters (a, b) indicate statistically significant differences in RMSE within each dataset based on Tukey HSD post-hoc tests (\(p<0.05\)). Values sharing the same letter are not significantly different.  
\end{table} 
Table 5: The mean result of different ML algorithms across four datasets.
## 1 Introduction
Some of them have structural roles and responsibilities for the stability of critical biological molecules. Furthermore, some trace elements have necessary actions throughout biological processes, for example, iron (Fe), which can bind, transport, and release oxygen in the body. The established recommended daily amount (RDA) for Fe is 13.7-15.1 mg/day in children, 19.3-20.5 mg/day in men, and 17.0-18.9 mg/day in women, and for Zn, it is 8 mg/day for women and 11 mg/day for men. It is particularly vital for healthy skin and is essential for a healthy immune system and resistance to infection. Zn plays a crucial role in growth and cell division, where it is required for protein and DNA synthesis, in insulin activity, in the metabolism of the ovaries and testes, and liver function. Mn helps the body to form connective tissue, bones, blood-clotting factors, and sex hormones. It also plays a role in fat and carbohydrate metabolism, calcium absorption, and blood sugar regulation. Mn is also necessary for a healthy brain and nerve function. Co is an essential trace element for the human body, where it is a crucial constituent of cobalamin (the scientific name of vitamin B12). It also has a substantial role in the formation of amino acids and neurotransmitters [1]. It has been reported in the literature that the mineral element contents of plants are affected by the plant cultivar, soil conditions, weather conditions during the growing season, use of fertilisers, and the stage of plant maturity at harvest [12]. However, the variability of values of macroelements reported in the literature for cassava roots has revealed discrepancies that surpass those expected from the effects of the factors above but could be due to analytic inaccuracy, especially from the sampling method used. The correct estimation of the macro- and microelement content of cassava genotypes is vital from a nutritional point of view, especially for controlling and improving mineral intake in sub-Saharan Africa. There is a scarcity of information on the spatial distribution of both macro- and microelement contents of yellow-fleshed cassava storage roots. Based on this background, the present study aimed to evaluate the spatial distribution of macro and microelements in yellow-fleshed cassava root genotypes and establish the effect of the sampling method and harvesting time on their content. The knowledge generated from this study can be used by researchers, processors, dieticians, and policymakers in programming interventions.
## 2 State of Art
### Applications of Artificial Neural Networks  
Drawing parallels to biological learning, particularly in the human brain, ANNs seek to mimic human cognitive functions. This is accomplished through a network of interconnected units known as neurons. ANNs offer the advantage of addressing complex challenges efficiently by relying solely on examples, bypassing the necessity for a thorough understanding of the specific function or process involved ([PERSON] and [PERSON], 2018; [PERSON], 2018). The basic version of an ANN is the multilayer perceptron, featuring an input layer, one output layer, and possibly additional hidden layers, but does not include any internal recurrence. In contrast, recurrent neural networks are designed with cyclical connections and self-reinforcing elements. In the multilayer perceptron architecture, data flow forward through interconnected nodes, with each layer getting its input from the previous one. The sigmoid function is a popular activation function, modifying input features nonlinearly to produce the final outputs ([PERSON] and [PERSON], 2018; [PERSON], 2018).  
Backpropagation is the standard algorithm used to train multilayer perceptrons ([PERSON] and [PERSON], 2018). It involves two key phases: forward and backward propagation. In the forward phase, data flow through the hidden layers with random weights to produce the output. During the backward phase, the error, calculated as the difference between the actual and target outputs, is propagated backward through the network to adjust the weights. The backpropagation algorithm is controlled by two parameters: the momentum coefficient and the learning rate, with both parameters restricted to the range of 0 to 1. The momentum coefficient controls the direction of weight updates to maintain stability, while the learning rate controls the change applied to weights at each iteration ([PERSON] and [PERSON], 2018).  
Traditional ANNs, advanced methods that have evolved from them (e.g., deep learning, generative adversarial networks, or convolutional neural networks), and hybrid models integrating various computational intelligence techniques offer highly adaptable and flexible modeling capabilities applicable across many sectors. They find applications in financial markets for stock prediction ([PERSON] and [PERSON], 2018; [PERSON], 2018), in medicine for disease identification and diagnosis ([PERSON] and [PERSON], 2018; [PERSON], 2018), in industrial settings for enhancing production efficiency ([PERSON] and [PERSON], 2018), in environmental assessment and monitoring [32; 33; 34], fault diagnosis [35; 36], defect detection [37], optimization [38], and across various other domains.
## 3 Experiments and Discussion  
The experimental platform was constructed as shown in Figure 9. The camera was a Hikvision MV-CA013-21 UM, with a 1280 \(\times\) 1024 resolution. The focal length of the lens was 16 mm. Instead of utilizing a DLP projector, a low-cost LED projector was employed, which minimized the cost, reducing it by about 60%. The projector was the OPT structured light source OPT-SL03-R, with 16 \(\times\) 16 grid stripes. The camera and the projector were aligned with an angle. The system parameters were calibrated in advance. An electric sliding table (Zolix TSA200) and a grating ruler with 1 \(\upmu\)m accuracy were used to verify the precision of measurements. The algorithms and software were developed using Visual C# 2019 and opencv.  
Figure 8: Calibration procedures.  
It should be noted that the grid pattern-based structured-light vision method suffers from a monotonicity constraint, even though it has many advantages in terms of higher accuracy and lower cost, compared with time-of-flight (ToF) and stereo vision [24]. The method is not capable of measuring large depth changes due to the monotonicity constraint. There is an upper bound of depth change. When all the local depth changes in a scene were less than the upper bound, the monotonicity constraint was satisfied, and, thus, all the light stripes could be obtained in the image (Figure 10a). The monotonicity constraint was violated when measuring depth changes over the upper bound, resulting in loss of light stripes (Figure 10b).  
The upper bound of depth change, by employing the method is given by the following formula [24]:  
\[\Delta z=\frac{z_{0}^{2}\Delta d}{f_{p}B+z_{0}\Delta d} \tag{6}\]  
where \(f_{p}\) denotes the focal length of the projector, \(B\) denotes the length of the baseline, \(z_{0}\) denotes the reference depth, and \(\Delta d\) denotes the line interval in the projected grid. In our system, \(z_{0}=450\) mm, \(\Delta d=0.45\) mm, \(B=250\) mm, \(f_{p}=35\) mm. Thus, the \(\Delta z=10.179\) mm.