esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review  
### VRP with Asymmetric Costs  
The rich VRP [7] was developed to tackle multiple real-life VRP considerations, including uncertainty, the use of heterogeneous fleets, and realistic time or distance factors. One important branch of the rich VRP is the VRP with asymmetric costs. Extensive studies regarding the effect of asymmetric costs on the traveling salesman problem (TSP) [8] and VRP [4] argue that asymmetry has a large effect over the solution methods and quality, and that asymmetry should be considered when proposing and improving routing algorithms. Lead and founder of OptaPlanner [3], [PERSON], reported that solving a VRP with air distances and then applying asymmetric road distances was suboptimal by almost five percent. Despite the inevitability and significance of asymmetry, insufficient attention has been paid to ACVRPs in the existing literature [9; 10].  
A number of exact algorithms for ACVRPs were addressed in [11; 12]. Subsequently, researchers have focused on numerous ACVRP variants, including backhaul [13], simultaneous pickup and delivery [14], heterogeneous fleets [10], exclusive lanes [15], stochastic demands [16], and time windows [17]. However, few researchers paid attention to the classical ACVRP itself. This was not because the scientific community had conquered the problem. Rather, this was due to the fact that the ACVRP lacks benchmark instances or published best-known solutions.  
A well-known set of benchmark instances was provided in [PERSON]'s work [18], which included eight instances with the number of nodes, or the dimension, ranging from 34 to 74. By modifying the original vehicle capacity of 1000 to different levels, instances grew in terms of population [19], but not in terms of dimension. The small-sized ACVRP instances were not sufficient to test state-of-the-art algorithms. A breakthrough was achieved by a study that evaluated the effects of asymmetry in VRPs [4]. The research provided 1350 freely available asymmetric road distance instances with dimensions ranging from 50 to 500 [20]. The real road distances were obtained using the Google Maps API [21]. Each instance was provided with two different levels of vehicle capacity, which made for a total of 2700 instances. Of course, 2700 symmetric instances were also prepared to match the asymmetric instances. Although they solved all instances using well-known heuristics, the best solutions found were not reported. This was because the focus of the research was on analyzing the effects of asymmetry, not on proposing an efficient routing algorithm. Other than the research carried out by [PERSON] [10], in which 20 instances with dimensions of 50 and 100 were deployed, the new instances remain untapped. OptaPlanner has also created benchmark instances for ACVRPs that are available online [22]. The OpenStreetMap API [23] was used to acquire real road distances and times. For each category of asymmetric road distances, asymmetric road times, and symmetric air distances, five instances sized from 50 to 2750 were provided, none of which had any record of the optimal or best-known solutions.
## 4 Experiments
### Results  
For the IEMOCAP and CMU-MOSEI benchmarks, this study compared the proposed method with baselines from [PERSON] [1] and state-of-the-art models in the same domain. The baselines include three unimodal approaches corresponding to the three modalities and two multimodal late-fusion methods using long short-term memory (LSTM) and a transformer, denoted as the LF-LSTM and LF-Trans methods, respectively. The text-centered EmoEmbs [10] model mapped global textual embeddings to acoustic and visual sequences to generate modality-specific emotion embeddings. [PERSON] et al. [33] implemented intermodal and interbimodal interaction modules based on multihead attention for the fusion network BIMHA. Tables 3 and 4 present the results of different approaches on the IEMOCAP and CMU-MOSEI datasets, respectively. The proposed XMBT trained with the DRA objective loss scored best in almost all evaluation metrics. Compared to the runner-up model by [PERSON] [12] for the IEMOCAP dataset, the proposed method obtained an increase of 3.7% in the F1 score and 0.7% in accuracy. For the CMU-MOSEI dataset, the models significantly improved by 6.5% in weighted accuracy and 2.0% in the F1 score compared to the second-best model. The proposed method notably enhanced the prediction performance for the fear and surprise samples, with F1 scores exceeding 30%, a threshold not surpassed by previous studies. The weighted average scores were boosted by more than \(11\%\) for these two emotion labels. Several methods were explored in the analysis of the weighting strategies for multimodal losses, including equal weighting (or average weighting), DWA, relative regularization adjustment (RRA), and traditional training using a single loss function (\(\mathcal{L}_{\text{fuse}}\)) derived from the late-fused logit. Figure 4 illustrates the evaluation scores of these approaches on the IEMOCAP and CMU-MOSEI datasets. The naive approach of using mean average weighting resulted in suboptimal performance because no regularization occurred between the loss elements. Although separately applying DWA or RRA could enhance performance in some circumstances, the improvement was insignificant. However, combining these strategies in the DRA approach yielded a consistent advancement in performance on the IEMOCAP and CMU-MOSEI datasets. This work plotted the value of the evaluation metrics by epoch during the training process to clarify the effect of UW techniques on the learning process. This analysis offers
## 3 Methodology
### Preliminary Study Mode  
The objective of the preliminary study is to understand the procedures involved in the EEG signal classification process using a machine learning algorithm. Time and frequency features are extracted and used for classification. The features are extracted in MATLAB and exported to Jupyter Lab for KNN classification.  
Time features, excluding the zero-crossing and peak-to-peak features, are extracted from the filtered EEG signals. Frequency features such as average powers of beta- and alpha-frequency bands are extracted using [PERSON]'s method rather than Fast Fourier Transform (FFT) since it produces smoother results due to its averaging method (Figure 7). [PERSON]'s method is also more robust against non-stationary signals such as EEG.  
All the extracted features are exported from MATLAB into a python-compatible data type so that classification can be performed using the K Nearest Neighbor (KNN) classifier. The first 10 extracted features for event 1 can be seen in Table 3.  
Before the classification is performed, the feature dataset is visualized with the help of scatter plots. Figure 8 shows one such visualization. The features of the events can be observed as closely associated with one another, meaning it will be troublesome for the KNN model to perform the classification, even between two events. The result of this is discussed further below.  
Since the data of events are very similar, the classification result is expected to be quite low unless the distribution of the data can be separated. KNN classification works by finding the nearest k neighbors from the data point of interest and taking the mean of those points to obtain the prediction for the data point. The classification of the algorithm performs best at k = 3.  
Figure 7: Comparison between [PERSON]’s and FFT on the same signal.
## 2 Evolution of AI in GPR Interpretation
### From Rule-Based Systems to Classic Machine Learning  
The earliest attempts to automate GPR interpretation employed rule-based systems and handcrafted algorithms designed to detect specific patterns such as hyperbolas (characteristic signatures of buried objects). These methods relied heavily on domain expertise and threshold-based logic but lacked robustness and adaptability across different environments.  
Classic ML methods emerged in the early 2000s as researchers began exploring data-driven techniques to overcome these limitations. Algorithms such as SVMs, random forests, k-nearest neighbors (KNN), and Naive Bayes classifiers were used to classify GPR reflections based on manually extracted features, such as amplitude, time-of-flight, and frequency-domain attributes.  
Feature engineering played a pivotal role in this phase. Techniques such as wavelet transforms, histograms of oriented gradients, texture analysis, and principal component analysis helped reduce dimensionality and improve classification accuracy. For exam ple, wavelet-based feature extraction combined with SVMs was found to be effective for classifying pipe and cable signatures in noisy environments [18; 19; 20].  
However, classic ML approaches have inherent limitations. They are sensitive to feature selection, require extensive tuning, and often struggle to generalize across different soil conditions or GPR hardware configurations. These drawbacks set the stage for the rise of deep learning. A comparison of rule-based systems with classic ML is summarized in Table 1.
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 3 Materials and Methods
### Multinomial Naive Bayes  
Multinomial Naive Bayes (MNB) is a variant of Naive Bayes and is useful for classifying the discrete features of crop data. Here, the text data are pre-processed and converted into a vector format. In MNB, at the beginning, the soil and season feature vectors are identified, such as the frequency of a particular word in a document. Then, it computes the probability of a feature in each class and then finds the highest probability that a document belongs to that class.  
Let us consider the features \(w_{1}\), \(w_{2}\), \(w_{3}\),...\(w_{n}\).  
\(D\) is the document and \(C\) is the class; then, Equation (22) shows that \(P(C|D)\) the posterior probability of \(C\) for data \(D\), \(P(C)\) is the prior probability, and \(P(w_{i}|C)\) is the product of likelihoods.  
\[P(C|D)\propto P(C)\prod_{i=1}^{n}P(w_{i}|C) \tag{22}\]
## 2 Theoretical Analysis and Design of the MRD-Based Roller Cage Shoe
### Design of MRD  
#### 2.3.1 Structural Design of MRD  
Considering the actual lifting system conditions on the vibration-damping requirements for the buffer cage shoe, the inner diameter of the cylinder of the MRD can be determined using the following formula [19]:  
\[D_{1}=\sqrt{\frac{4F_{0}}{\pi[p](1-\lambda^{2})}} \tag{19}\]  
where \(D_{1}\) is the inner diameter of the cylinder; \(F_{0}\) is the maximum damping force that the damper needs to withstand; \([p]\) is the maximum pressure allowed by the damping, generally taking 2\(\sim\)4 MPa; and \(\lambda\) is the ratio of the diameter of the piston rod to the inner diameter of the cylinder barrel, generally values of 0.4\(\sim\)0.5. The piston rod diameter can be calculated using the formula \(d=D\times\lambda\). Considering the requirements of strength and stiffness of the piston rod at the same time, the strength of the piston rod is calibrated with the following equation:  
\[\sigma_{\text{max}}=\frac{F_{\text{max}}}{\pi d_{0}^{2}/4}<[\sigma] \tag{20}\]  
where \([\sigma]\) is the allowable stress of the material, \(\sigma=\frac{\sigma_{s}}{n}\), \(\sigma_{s}\) is the yield strength of the material (20 steel yield strength of 245 MPa), and \(n\) is the safety factor (the design requires \(n\geq 1.2\)); in this paper, \(n\) takes the value of 2.  
From Equation (3), it can be seen that the Coulomb damping force of the damper is inversely proportional to the damping gap (\(h\)), and the viscous damping force is inversely proportional to the cubic equation of the damping gap, so the damping gap is a crucial parameter that affects the magnitude of the damping force, and decreasing the value of the damping gap will largely improve the damping force of the damper. And at the same time, when the number of turns of the excitation coils is constant, the small gap will make the magnetic resistance at the gap small and increase the magnetic permeability. However, under the condition that \(h\) decreases by the same value, the viscous damping force decreases faster. Therefore, when the damping force is increased by the decreasing \(h\), the power adjustment range is reduced. When determining the damping gap (\(h\)), the damping force and adjustable range should be considered comprehensively, and usually, \(h\) is selected between 0.5 and 2 mm.  
The equivalent length of the damping channel signifies the piston's effective operational length. Any alteration in its value directly impacts both the Coulomb and viscous damping forces. Augmenting the effective working length of the piston amplifies the MRD effect, consequently upscaling the damper's output damping force. Nonetheless, an excessively extended working length may result in unwieldy dimensions and stroke for the damper. To ensure uniform magnetic field distribution in the MRD and minimize magnetic leakage, it is crucial to maintain a slenderness ratio of \(2h/L\leq 0.2\).
# Determination and Risk Assessment of Flavor Components in Flavored Milk
## 3 Results and Discussion
### Quantitative Analysis of Flavor Concerned Components in Flavored Milk  
Fifteen components of concern were quantitatively analyzed in flavored milk samples and shown in Table 4. Benzenemethanol, 2,3,5-trimethylpyrazine, furfural, and benzaldehyde were all detected in 100% of flavored milk samples. Benzenemethanol is a colorless liquid with a mild pleasant aromatic odor naturally produced by fruits and teas. Pyrazines are nitrogen-containing heterocyclic compounds that contribute significantly to the flavor of various grilled, roasted, and similarly cooked foods, including baked potatoes, nuts, and meats [46]. Differences in furfural and benzaldehyde levels in flavored milk were generated by the Maillard reaction and the protein denaturation reaction. Benzaldehyde is an aromatic aldehyde bearing a single formyl group and an almond odor, and can be extracted naturally and is widely utilized in the production of aniline dyes, perfumes, flavorings, and medicines. In addition, the detection rates of 5-methylfurfural (96.4%), maltol (96.4%) and 1-hexanol (92.9%) were higher than 90%. Maltol is one of the byproducts of sugar degradation. There were differences in the content of flavor components of concern in various flavored milk brands, which were influenced by additive amount and manufacturing techniques. Maltol concentrations ranged from 0.83 ug kg\({}^{-1}\) to 1682.11 ug kg\({}^{-1}\). The maximum content of benzenemethanol (14,995.44 ug kg\({}^{-1}\)) was determined, which was significantly higher than 2,3,5-trimethylpyrazine (2387.18 ug kg\({}^{-1}\)), furfural (3840.42 ug kg\({}^{-1}\)), and linalool (4958.30 ug kg\({}^{-1}\)).
## 2 Materials and Methods
### Outcome Measures
#### 2.3.2 Secondary  
Neck disability index (NDI). The questionnaire consists of 10 items, each scored from 0 to 5. The total score is interpreted as a percentage, where 0 points or 0% indicates no activity limitation, and 50 points or 100% indicates complete activity limitation. The NDI is a reliable and valid tool for assessing patients with neck pain, with intraclass correlation coefficients ranging from 0.50 to 0.98. Patients scoring between 0-4 points (0-8%) are considered to have no disability, 5-14 points (10-28%) mild disability, 15-24 points (30-48%) moderate disability, 25-34 points (50-64%) severe disability, and 35-50 points (70-100%) complete disability [32]. The minimum detectable change (MDC) and the minimal clinically important difference (MCID) on the NDI are 8.4 and 3.5 points, respectively [33].  
Cervical spine range of motion (ROM). ROM was measured using a bubble inclinometer. For neck flexion and extension, the inclinometer was placed on the top of the patient's head along the sagittal plane while the patient was seated. Lateral flexion was assessed from the same starting position. The inclinometer was placed on the frontal plane atop the patient's head. For neck rotations to the left and right, measurements were taken from the supine body position; the inclinometer was placed on the forehead. The reading of the inclinometer was set to 0 at the starting position. ROM readings were taken at the endpoint of movement. In this study, the average of three measurements [34; 35] was considered for each movement and used for analysis.  
Hand grip strength. Grip strength was measured using a Saehan SH5001 hand dynamometer (SAEHAN Corporation, Eschborn, Germany). Measurements for both the dominant and non-dominant hands were taken with the individual seated, while the shoulder was in neutral rotation and adduction, and the elbow was bent at 90 90-degree angle. The patient's wrist and forearm were in a neutral position. The maximum grip strength was recorded in kilograms (kg). Three measurements were taken. There was 60 s break between trials. For analysis, the average result was taken [36].
## 5 Empirical Results and Discussion
### Regression Results
In other words, if a province has a high PM\({}_{2.5}\) concentration, its neighboring provinces will also experience elevated PM\({}_{2.5}\) levels. In conclusion, PM\({}_{2.5}\) pollution has spatial effects and is not confined to a single area. Both the rho and lambda variables can be interpreted similarly, as they represent spillover effects, but they differ in terms of their estimation technique, as rho is estimated using the Lag model, while lambda is estimated using the Error model. Table 8 depicts the results of the Spatial Durbin Panel Random Effects Regression model. The results are mostly consistent with those of the Non-spatial Panel Regression Model and the Spatial Lag and Error Panel Regression Models. The estimated results show a U-shaped relationship between PM\({}_{2.5}\) concentration and economic growth (_LnGPPpc_ and _LnGPPpc\({}^{2}\)_), which contrasts with the Environmental Kuznets Curve (EKC) theory, while other variables are statistically insignificant. The weighted variables indicate that the PM\({}_{2.5}\) concentration (\(W_{1}PM_{2.5}\)), GPP per capita (\(W_{3}LnGPPpc\)), GPP per capita square (\(W_{4}LnGPPpc^{2}\)), and energy consumption per capita (\(W_{6}LnEnergy\_cons\_pc\)) are statistically significant and exhibit spillover effects on nearby provinces. This means that if the PM\({}_{2.5}\) concentration (\(W_{1}PM_{2.5}\)) in one province increases, the PM\({}_{2.5}\) concentration in nearby provinces will also increase. If the GPP per capita (\(W_{3}LnGPPpc\)) of one province increases, the PM\({}_{2.5}\) concentration in nearby provinces will decrease. Moreover, the GPP per capita square (\(W_{4}LnGPPpc^{2}\)) shows that an increase in one province's economic growth above the threshold would increase the PM\({}_{2.5}\) concentration in nearby provinces. These findings suggest that not only does the increase in economic growth in the provinces themselves contribute to environmental degradation, but so does the rise in economic growth in nearby provinces, and if the energy consumption per capita (\(W_{6}LnEnergy\_cons\_pc\)) of one province increases, the PM\({}_{2.5}\) concentration in nearby provinces will increase. Finally, as seen in Table 9, the affluence elasticities of impact coefficients (EE\({}_{\rm IA}\)) were estimated. The results indicate that the elasticity value of the EE\({}_{\rm IA}\) for income levels ranging from USD 470 to USD 46,950 is negative, meaning that as the GPP per capita increases within this range, the PM\({}_{2.5}\) concentration continues to decline at a diminishing rate (the average GPP per capita (Purchasing Power Parity, USD) of Thailand was USD 16,450 in 2022). However, once income reaches the inflection point of USD 56,715, the PM\({}_{2.5}\) concentration begins to increase as the GPP per capita continues to grow.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
###### Abstract  
With the rapid development of future wireless networks, device-to-device (D2D) technology is widely used as the communication system in the Internet of Things (IoT) fifth generation (5G) network. The IoT 5G network based on D2D communication technology provides pervasive intelligent applications. However, to realize this reliable technology, several issues need to be critically addressed. Firstly, the device's energy is constrained during its vital operations due to limited battery power; thereby, the connectivity will suffer from link failures when the device's energy is exhausted. Similarly, the device's mobility alters the network topology in an arbitrary manner, which affects the stability of established routes. Meanwhile, traffic congestion occurs in the network due to the backlog packet in the queue of devices. This paper presents a Mobility, Battery, and Queue length Multipath-Aware (MBMQA) routing scheme for the IoT 5G network based on D2D communication to cope with these key challenges. The back-pressure algorithm strategy is employed to divert packet flow and illuminate the device selection's estimated value. Furthermore, a Multiple-Attributes Route Selection (MARS) metric is applied for the optimal route selection with load balancing in the D2D-based IoT 5G network. Overall, the obtained simulation results demonstrate that the proposed MBMQA routing scheme significantly improves the network performance and quality of service (QoS) as compared with the other existing routing schemes.  
D2D; IoT; 5G; back-pressure; MARS; MBMQA +
Footnote †: journal: Applied Soft Computing
# An Energy-Efficient and Robust Multipath Routing Protocol for Cognitive Radio Ad Hoc Networks
## 5 Performance Evaluation
### Simulation Environment  
We consider a network area of 1000 \(\times\) 1000 m\({}^{2}\) where a set of PUs and SUs are randomly deployed. In addition, we consider 11 channels, one of which is dedicated as a control channel and the rest are assigned to PUs as licensed channels. The PU activity is based on an ON/OFF model in which the ON and OFF times of PUs are exponentially distributed. During the ON state, the PU occupies the channel assigned for the calculated time and, conversely, frees the same channel during the OFF state. The transmission ranges of PUs are set to 150 m and are assumed to be completely static. On the other hand, SUs have the transmission range of 250 m and are expected to move in a random direction with a random speed within a range of 0-5 m/s and a pause time of 30 s. All routing control packets are transmitted through a dedicated CCC, which is shared by all SUs. The data transmission is accomplished through the data channel selected from the channel selection policy. The weight factors of residual energy and stability are set as \(\alpha\) = 0.5 and \(\beta\) = 0.5. The rest of the simulation parameters are listed in Table 2.
## 3 Experiments and Discussions
### Error Analysis  
The measurement error of the 3 DOFLM system was significantly reduced after correcting the laser diode wavelength. However, the measurement accuracy of the 3 DOFLM system is still a little lower than commercial laser interferometers. The reason could be that the measurement accuracy of the wavelength corrector is influenced by the linearity error of the Lissagous circle, the incident beam angle and its drift, thermal deformation of the grating pitch, etc., which were not considered in this study. As a report for the first step of the research, this paper focuses on the design and construction of the 3 DOFLM system. The improvement of wavelength measurement accuracy will be carried out in the next step of the research for the proposal of a practical application of the nanopositioning stage.