esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology: Development of the Evaluation Framework
### Building the Cost-Benefit Function
#### 3.3.2 Normalisation and Scaling  
There exist many normalisation techniques and according to [30], \"both robustness (insensitivity against the existence of extreme values) and efficiency (estimated value close to the expected optimum when the real data distribution is unknown) must be balanced\". Through the application of normalization, the values used to feed the evaluation matrix will turn into non-dimensional, taking all of them values in the interval [0,1], and where in all of them 0 represents the worst value for the indicator (the less sustainable) and 1 represents the best value (the most sustainable).  
Normalisation is aimed at transforming current values for each criterion that is measured under different units, into a common new criterion that follows the same unit. According to [31], three different methods can be applied, being the most suitable to address a synthetic transformation of the value of the indicators following a fixed scale unit change. The normalisation and scaling method in this methodology intends to reduce the amount of subjectivity in the process by making use of the results derived from the calculation of the baseline scenario. Thus, instead of fixing the minimum and maximum values to implement a min-max normalisation, relative values compared to the baseline situation are used.  
Then, since most DPIs measure the savings obtained in each category (for example \"savings in energy consumption\" measured from 0% to 100%) this can be easily related to the best possible obtainable value, which is a 100% of savings = \"1\", while the worst value (\"0\") would be a 0% of savings.  
However, not all DPIs can be calculated as savings against the baseline situation (as, for example, the investment), using in these cases the values given by the stakeholders as boundary condition to transform the criteria into a homogeneous framework that can be weighted and aggregated.
## Abbreviations  
The following abbreviations are used in this manuscript:  
ADAMAdaptive Moment Estimator  
CANController Area Network  
CNCentralized Network Configuration  
CUCentralized User Configuration  
DQNDeep Q-Networks  
DRLDeep Reinforcement Learning  
I2 IInfrastructure-to-Infrastructure  
ILPInteger Linear Programming  
IoVInternet of Vehicles  
MARLMulti-Agent Reinforcement Learning  
MDPMarkov Decision Process  
MLMachine Learning  
PERPrioritized Experience Replay  
QoSQuality of Service  
RLReinforcement Learning  
RSCE Roadside Cabinet Electronics  
RSURoadside Unit  
SCMSSecure Credential Management System  
SDNSoftware-Defined Network  
TASTime-Aware Shaper  
TFCTransportation Field Cabinet  
TMCTraffic Management Center  
TMSTraffic Management System  
TSNTime-Sensitive Networking  
V2 IVehicle-to-Infrastructure  
V2 VVehicle-to-Vehicle
## 5 Automatic Monitoring and Control Method for Middle Rock Pillars
### Automatic Monitoring Process
#### 5.4.1 Monitoring Point Tracking  
Through the association between the spherical camera and microwave radar, the monitoring points arranged on the section can be located, and the displacement generated during construction can be tracked by it and can be visualized in the images collected by the spherical camera. The laser rangefinder determines the initial position of the measuring point by sending a laser, and the radar transmits the sensed change in the position coordinates to the laser rangefinder. The laser rangefinder can track the laser rangefinder from the spherical camera and microwave radar to the tiny rotation angle \(\alpha\).
## 4 Conclusions  
In this work, MIP was prepared by precipitation polymerization using SLX as the template molecule, HEMA as the functional monomer, and EGDMA and TRIM as crosslinkers. MIP optimized using molecular dynamics simulation according to a templatemonomercrosslinker ratio of 1:6:20 showed stronger interaction from the RDF point of view. No hydrogen bond interaction of TRIM with SLX from the MD result shows that the crosslinker will not affect the binding between template and functional monomer and will result in better molecular recognition with the latter. TRIM-based MIP greatly decreased non-specific adsorption compared to EGDMA-based MIP. The present polymer made with precipitation polymerization had better selectivity over structural analogues and better capacity than in our previous work made with bulk. Moreover, MIP was successfully applied in a solid phase extraction in serum analysis and effectively extracted SLX from the complex serum matrix, confirming the elimination of interference and achieving high SLX recovery.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/molecules27113619/s1](https://www.mdpi.com/article/10.3390/molecules27113619/s1)]([https://www.mdpi.com/article/10.3390/molecules27113619/s1](https://www.mdpi.com/article/10.3390/molecules27113619/s1)). Table S1: The Compositions of synthesized MIPs and NIPs using precipitation polymerization.  
S.S.--original draft preparation; M.M.--supervision; Y.R.-- conceptualization and methodology; A.N.H.--funding acquisition, review and editing of the draft. All authors have read and agreed to the published version of the manuscript.  
APC was funded by the Directorate of Research and Community Services Universitas Padjadjaran.  
Not applicable.  
Directorate of Higher Education Ministry of Education and Culture Indonesia for Penelitian Disertasi Doktor Research Scheme 2021-2022.  
The authors declare no conflict of interest.  
Figure 11: FTIR spectrum of (**a**) MIP1 and NIP1, (**b**) MIP2 and NIP2, (**c**) MIP3 and NIP3, and (**d**) MIP4 and NIP4. Red lines: MIP before extraction, black lines: MIP before extraction, blue lines: NIP.
## 3 Results  
### Calculations  
Estimation of the data range of the configured link was performed using the mathematical model of the OWC system. The main task was to determine the changes in the signal-to-noise power ratio in function of the distance considering parameters of its components for different atmospheric conditions defined by the extinction coefficient and the  
Figure 4: Scheme diagram of the OWC lab setup.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Spectral Characteristics** \\ \hline Active area & 1 \(\times\) 1 mm\({}^{2}\) & \\ Detector resistance & 60 k\(\Omega\) & \\ Current noise density & 2.4 pA/\(\sqrt{\text{Hz}}\) & \\ Responsivity & 2.25 A/W & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detector parameters and spectral response.  
scintillation level (\"weak\" \(C_{n}^{2}=10^{-15}\) m\({}^{-2/3}\), \"moderate\" \(C_{n}^{2}=10^{-14}\) m\({}^{-2/3}\) and \"strong\" \(C_{n}^{2}=10^{-13}\) m\({}^{-2/3}\)). Calculations of SNR allow relating the obtained results to different modulation standards. Additionally, some analyses of using higher power ICLs to increase the operating range were also carried out. In Figure 5, calculated SNR changes for different weather condition scenarios and laser power were presented.  
A strong influence of scintillation at short link distances (Figure 5a) was observed. Increasing the scintillation level causes a dynamic decrease in the SNR value, especially at a distance of less than 500 m. For the configured OWC system, the \"last mile\" data transmission is achieved with the SNR = 10 if the structure parameter does not exceed \(10^{-14}\) m\({}^{-2/3}\), assuming good other weather conditions. This level corresponds to the turbulence that can occur during the operation of terrestrial OWC systems, for example, placed near the ground. Such systems are installed considering, e.g., distance and type of surface, wind, opening area, sun shading. For different weather conditions causing light scattering, a significant range limitation occurs below the visibility of 1 km (Figure 5b). It corresponds to the formation of haze. If the weather conditions are excellent, the range is mainly determined by geometric loss and radiation absorption in the air. Increasing the power of the laser pulses does not directly increase the registered SNR value at short distances, where scintillation is the main limitation. However, higher power is required at longer distances to compensate for the radiation losses caused by the extinction coefficient and beam divergence (Figure 5c). The described OWC configuration provides to obtain SNR = 10 at the distance of about 2 km for weather conditions defined by a structure parameter of \(10^{-15}\) m\({}^{-2/3}\) and visibility of 1 km. It should also be noticed that the four times increase in the laser power extends this distance only by 0.5 km for these conditions.  
Figure 5: OWC data range for different propagation conditions determined by (**a**) scintillation, (**b**) visibility, and (**c**) pulse laser power.
# Scalable Resource Provisioning Framework for Fog Computing Using LLM-Guided Q-Learning Approach
## 6 Results and Discussion
### Processing Delay  
Figure 2 shows the graph of the number of fog nodes versus processing delay. It is observed from the graph that the processing delay of LLM_QL is consistently less (50-200 ms) as it takes the optimal Q value at the step of learning through LLM guidance. Similarly, the processing delay of F_QL remains moderate (450-800 ms) as the learning process slows down and requires many episodes of training to arrive at the optimal solution. In contrast, the processing delay of BAY_L and CC remains very high with the increase in the number of fog of nodes (800-900 ms) as it involves a large number of parameters and posterior distributions are influenced by the prior distribution.
## 3 Results
### Evaluation Metrics  
Several metrics were utilised to evaluate the performance of the proposed models. The mean bias error (Equation (4)) represents whether a model overestimates or underestimates on average. The root mean square error (Equation (5)) measures the average magnitude of prediction errors in a model. The normalized values of these were used (Equations (6) and (7)) to compare different models. Lastly, the coefficient of determination (Equation (8)) between the estimated values and the observations was calculated to determine how well a linear regression can explain the relationship between these two variables. The more positive this coefficient, the more linearly correlated these variables are. If the coefficient of determination is negative, the mean of the data would be a better fit.  
\[\text{MBE}=\frac{1}{n}\sum_{i=1}^{n}(\hat{y}_{i}-y_{i}) \tag{4}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{5}\]  
\[\text{nMBE}=\frac{\text{MBE}}{g}\times 100\ (\%) \tag{6}\]  
\[\text{nRMSE}=\frac{\text{RMSE}}{g}\times 100\ (\%) \tag{7}\]  
\[R^{2}=1-\frac{\sum_{i}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i}(y_{i}-\hat{y})^{2}} \tag{8}\]  
where \(\hat{y}_{i}\) is the estimated value and \(y_{i}\) the predicted value for the i instance from a dataset of n instances.
# Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions
## 6 Empirical Results and Analyses
### Calculation Results and Analyses  
The relevant parameters are set as referenced in \"Table 1: Model Parameters\", utilizing MATLAB programming and conducting simulations. Using the model and algorithm presented in this paper, a comparative simulation experiment was conducted against the traditional genetic algorithm based on the aforementioned example. The comparison results are illustrated in Figure 5.  
Figure 5 shows that the total cost, carbon emission costs, and transportation costs achieved by the GA-LNS algorithm developed in this study are the lowest. This suggests that the proposed algorithm effectively decreases carbon emissions, which subsequently leads to a reduction in the total distribution cost. For this logistics and distribution center, the reduction in total cost improves the company's profitability and enables it to provide a more competitive offer in a price-sensitive market; the decrease in carbon emission costs facilitates compliance with environmental regulations, enhances the company's green image, supports long-term development objectives, and attracts environmentally conscious customers and investors; the reduction in transportation costs reflects optimized routes and loads, improved logistics efficiency, and decreased waste of energy and resources.  
This case simulation provides robust evidence supporting the development of energy-saving and carbon reduction strategies in China. The effective application of the GA-LNS algorithm enables companies to achieve economic benefits while simultaneously contributing positively to environmental protection. These results provide practical references for policymakers when establishing and implementing energy-saving and carbon reduction targets, underscoring the importance of promoting such optimization algorithms within the logistics industry. In the future, continued exploration and application of these findings will not only facilitate the green transformation of the logistics sector but also contribute to achieving national environmental protection goals.
## 1 Introduction
Figure 2: Dependency of causality constraint on the direction of noise source in in-ear headphones. Blue: reference microphone, Green: error microphone, Red: speaker. for the feedforward ANC, by including the estimated secondary path model \(S^{\prime}(z)\) (Figure 3) in the algorithm; this inclusion, also, reduces the convergence time. The digital-filter transfer function \(W(z)\) is adjusted to minimize the noise inside the ear canal. To mitigate the causality constraint in the feedforward ANC, which uses the FxLMS algorithm, the sampling rate is increased to 96 kHz, but for 90\({}^{\circ}\) incidence, the causality constraint is difficult to meet in in-ear headphones; this complication degrades the noise attenuation bandwidth to 850 Hz at 90\({}^{\circ}\) incidence, compared to 1500 Hz at 0\({}^{\circ}\) incidence. This degradation is considered to be due to the limitation of the FIR filter, with its finite number of 24 taps, to predict future sounds [15]. In this work, a convolutional neural network (CNN) is used, instead of the FxLMS algorithm in the feedforward ANC, to solve the causality constraint. A dilated convolution enables observation of the previous 327 ms of data (=(15,355 samples in the input receptive field)/(sample rate 46,875 S/s)), whereas the FxLMS algorithm [15] observes only the previous 250 \(\upmu\)s of data (24/96 k); this increase in the observation of previous data increases the noise attenuation bandwidths to 2000 Hz,for both 0\({}^{\circ}\) and 90\({}^{\circ}\) incidence, and reduces the total power to 14.8 dB at 0\({}^{\circ}\) and 14.3 dB at 90\({}^{\circ}\). The rest of this paper is organized as follows. Section 2 explains the proposed architecture for in-ear headphones. Section 3 describes its hardware implementation. Section 4 demonstrates the measurement results, while Section 5 discusses them. Section 6 concludes the paper.
## 2 Materials and Methods
### Photon Density Wave Spectroscopy  
A PDW spectrometer built by the University of Potsdam was used for the measurement of the absorption coefficient \(\mu_{\mathrm{a}}\) and the reduced scattering coefficient \(\mu_{\mathrm{s}}\)'. Identical devices are commercially available at PDW Analytics GmbH (Potsdam, Germany). The general set-up of the PDW spectrometer was described by [PERSON] et al., as follows: \"A schematic set-up of the spectrometer is shown in Figure 1. Light from a laser diode with wavelength \(\lambda\) [m] (typ. 400-1000 nm) is sinusoidally intensity modulated by a vector network analyzer (typ. f = \(\omega\)/(2\(\pi\)) = 10-1300 MHz). The light is then coupled into the material via an optical fiber, acting as a point-like light source. A second optical fiber, positioned at a distance r to the emission fiber (typ. r = 5-30 mm), collects light of the PDW and guides it onto an avalanche photodiode (APD) as detector.\" [13].  
To integrate the multifiber PDW spectroscopy _in-line_ probe into the system, a DN25 safety Ingold socket (elpotech GmbH & Co. KG, Germany) was welded onto the lid of the bioreactor. The probe was mounted before autoclaving and sterilized with the bioreactor inside the autoclave. The optical fibers of the probe were connected to the PDW spectrometer after autoclaving. \(\mu_{\mathrm{s}}\)' and \(\mu_{\mathrm{a}}\) were analyzed at 638 nm with a temporal resolution of 0.8 min\({}^{-1}\). A 10-point moving average was used to reduce the signal noise.
## 3 System Model
### Ground Segment  
The ground segment consists of two sub-segments:  
* User sub-segment: the sensor devices and terminals of rescuers and victims in the remote disaster area;
* Core network sub-segment: the service provider and national rescue institute in the normal area.  
The remote sensor devices and terminals of rescuers and victims are multi-radio portable devices [33], which support the LTE/WiMAX communicating with the HAPs and the DVB-RCS communicating with the satellite. The core network sub-segment is responsible for routing and exchanging information with the national rescue institute.  
As a consequence, an IHS network is then available for emergency scenarios. The devices and terminals can select the relay HAPs or the GEO satellite, which is in view, to achieve an urgent information transfer. At the same time, the service provider and national rescue institute in the normal area can transmit rescue information, including voice, video and data, via the GEO satellite to the devices and terminals. In the paper, we will mainly investigate the former information transfer due to the fact that devices and terminals use more energy when transmitting a signal and less constant energy when receiving a signal.
# _Applied Sciences_
## 4 Experiments and Evaluation Metrics
### Experiments  
The main objective of the experimentation proposed in this research is to demonstrate that a model is able to learn how to automatically detect contradiction types and contradictions with high accuracy from the ES-Contradiction dataset.  
The BETO model has been configured as indicated in Section 4.1, and the following experiments were performed:  
1. Predicting all classes: This experiment uses ES-Contradiction dataset and the model is trained with the training set, resulting in the prediction of the test set. The classes to predict are Compatible, Contradiction, and Unrelated.
2. K-fold cross-validation: This experiment makes a cross-validation with our training set without unrelated examples. Cross-validation is a statistical technique that involves partitioning the data into subsets, training the data on a subset, and using the other subset to evaluate the model's performance. Cross-validation enables all available data to be used for training and testing [29]. In this experiment, k-fold cross-validation with k = 5 is used.
3. Detecting contradiction vs. compatible information: This experiment focuses on detecting only the contradictory and compatible examples from our dataset (Table 1 without the unrelated examples). The classes to predict are Compatible and Contradiction.  
Detecting specific type of contradictions: This experiment uses only the contradictory examples of the dataset described in Table 2 to detect the types of Contradiction between pairs. The training and test set are used for training and testing.
5. Comparison between XNLI and our dataset: This experiment trains by using machine translation into Spanish of the XNLI dataset ([[https://github.com/facebookresearch/XNLI](https://github.com/facebookresearch/XNLI)]([https://github.com/facebookresearch/XNLI](https://github.com/facebookresearch/XNLI)), accessed on 29 March 2021), and uses the Spanish test set of the XNLI corpus and our test set. The XNLI dataset has 3 classes: (Entailment, Contradiction, and Neutral). Therefore, it was necessary to match them with our dataset. The Neutral class of the XNLI dataset and the Unrelated class of our dataset were eliminated, whereas the Entailment class was associated with our Compatible class and the Contradiction class with our Contradiction class.
## 3 Results
### Effectiveness of the Light Intervention
#### 3.1.4 Light Intensity (lx)  
Table 4 shows the results of the randomization tests for illuminance (inlx, log10 transformed; see Supplementary Material Table S4 for the descriptives of lux values.). The table shows that in phases A\({}_{1}\) and B\({}_{1}\), for most participants, the light intervention did not result in illuminance values. Only participants 8 and 12 showed (marginally) significant effects. The sum of the individual \(p\)-values was 3.612 and hence is not significant, \(p\) = 0.152, indicating that no overall effect of the light intervention emerged in the replicated single-case design. The overall mean difference was 0.039, and [PERSON]'s \(d\) was 0.14, indicating a small effect size. For the comparison of phases B\({}_{1}\) and A\({}_{2}\), we expected the illuminance values to decrease. Most participants indeed showed a decrease in mean illuminance value. The randomization tests showed that there was a significant decrease for participants 1, 2, 4, and 7. The sum of all individual \(p\)-values, 3.159, was significant, \(p\) = 0.02. The overall mean difference was \(-\)0.111 (log10 of illuminance), and [PERSON]'s \(d\) was 0.355, indicating a small-to-medium effect size. The comparison of the second baseline A\({}_{2}\) and the reintroduction of the lamp in phase B\({}_{2}\) showed higher illuminance values for most of the participants, except for participants 2 and 5. The randomization tests showed that these differences were significant for participants 1, 3, 4, 6, and 10. The sum of all individual \(p\)-values, 2.046, was significant, \(p\) < 0.001, indicating that there was an overall effect of the lighting system intervention in the replicated single-case design model. The overall mean difference was 0.142, and [PERSON]'s \(d\) was 0.367, indicating a small-to-medium effect size. The difference in the light intervention between the first control phase A\({}_{1}\) and the intervention phase B\({}_{1}\) was not significant, but the light intensity was significantly higher in phases B\({}_{1}\) and B\({}_{2}\) than in phase A\({}_{2}\). As full spectral data, unfortunately, could not be acquired with these wearable sensors, similar analyses could not be performed for EDI\({}_{\text{mel}}\). Correlated Color Temperature (CCT)  
The mean differences between the CCT values in phases A\({}_{1}\) and B\({}_{1}\) showed significant differences for participants 9, 11, and 12 in Table 5. The sum of all individual \(p\)-values was 4.41, which was not significant, \(p\) = 0.458. The overall mean difference was 39, and [PERSON]'s \(d\) was 0.065, indicating a very small effect size.