esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A novel PIC structure that can be employed as a low-cost optical transmitter system in the next generation short-reach networks is presented. The device consists of two lasers integrated in a master-slave configuration, where the master laser injects light into the cavity of the slave laser to improve its spectral characteristics. A set of static and dynamic characterizations are conducted on the PIC to analyze several important parameters of consideration for next-generation short-reach optical networks, including optical linewidth, RIN, and frequency response. Furthermore, the enhancements that OIL can provide to these parameters are presented. The reduction in linewidth of the PIC from 8 to 2 MHz due to OIL is demonstrated. Moreover, the RIN measurements show averaged RIN values as low as \(-154.3\) dB/Hz, which allows the use of the device with multi-level modulation formats. Furthermore, it shows the reduction of RIN by applying OIL from the master laser. On the dynamic characterization part, the PIC shows a 4.5 GHz (45%) improvement in the modulation bandwidth under injection. Transmission performance of the injected case shows a 2 dB improvement in the minimum optical power required to achieve a bit error rate of \(3.8\times 10^{-3}\) (hard decision forward error correction limit) as a result of reduction in chirp by OIL.  
These improved spectral characteristics, achieved due to the OIL, are accompanied by the attractive features of photonic integration, qualifying the multi-section PIC to be used in next-generation optical short-reach networks. In the future, we will aim to improve the PIC design to further reduce the linewidth of the master laser as well as to increase the control of the power injected from the master into the slave laser. This will result in further enhancements of the device spectral characteristics and performance.  
Conceptualization, M.N.H., A.K.-A., P.L., and P.M.A.; Methodology, M.N.H., A.K.-A., and P.M.A.; validation, M.N.H., A.K.-A., M.D.G.P., G.J., and P.D.L.; formal analysis, M.N.H., G.J., and A.K.-A.; investigation, M.N.H., A.K.-A., P.D.L., G.J., P.L., and M.D.G.P.; resources, P.M.A., A.K.-A., and M.D.G.P.; data curation, M.N.H.; writing--original draft preparation, M.N.H. and P.D.L.; writing--review and editing, M.N.H., P.L., A.K.-A., M.D.G.P., P.D.L., and P.M.A.; visualization, M.N.H., A.K.-A., P.L., and P.M.A.; supervision, P.M.A., P.L. and A.K.-A.; project administration, P.M.A., P.L., and A.K.-A.; funding acquisition, P.M.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science Foundation of Ireland's (SFI) career development award (15/CDA/3640), SFI 17/IFB/5385, and Regional Development Fund (13/RC/2077).  
We would like to acknowledge the support given by the technical officers ([PERSON], [PERSON]) in the school of Electronic Engineering, Dublin City University.  
The authors declare no conflict of interest.
# Enhanced Fully Generalized Spatial Modulation for the Internet of Underwater Things
## 5 Simulation Results
### Power Saving Evaluation  
As shown in Section 4, to obtain the power efficiency of the proposed EFGSM, examine the claim that the IoUT sensor nodes are required to transfer data with a speed of 10 bit/s/Hz. In this case, without SMSs, suppose the IoUT node use M (1024) QAM. When using SMSs, there will be a decrease in M relationship to the number of bits which are sent by SMSs. Accordingly, power will be saved. The power saving ratios of SM, GSM, FGSM, and the proposed EFGSM are calculated in (34), (35), (36) and (37), respectively. The power saving ratio of different SMSs in respect to the number of transmitted antennas \(N_{T}\) is shown in Figure 5, and for \(N_{T}=4\), the proposed EFGSM saves up to 40% of the used power, while SM, GSM, and FGSM save up to 20%, 20%, and 30%, respectively.  
Figure 4: The ABER of the proposed EFGSM, FGSM, GSM and SM for 9 bpcu transmissions.  
Figure 3: The ABER of the proposed EFGSM in comparison with the ABER of FGSM, GSM, and SM for 8 bpcu transmissions.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 5 Discussion
### Spatiotemporal Pattern Discovery Using ML  
Pattern recognition and generalization is a key challenge in ML application. Usually, one model working for a single power plant should be re-trained and calibrated before being applied to another power plant, because the patterns and atmosphere context are different. Observing the results in Section 4, the emission of power plants was highly  
Figure 31: XGBoost models’ feature importance using a single power plant (Alabama) dataset.  
correlated to the day of the year in ML model training, which is reasonable as the summer and winter are the highest peak of electricity demands and require the plants to operate for longer hours. ML captured that correlation and memorized it in its weight/decision trees. Considering the spatial variances, we also trained the models on all the power plants and compared them to the model trained only on one plant. The performance of the models trained on all power plants was relatively lower than the models trained on single plants. This means that the ML was struggling to fit on all the plants and made compromises to maintain a relatively higher accuracy on every plant. This is reasonable as at some power plants the NO\({}_{2}\) was very high in TROPOMI, but EPA observations was lower than other plants, or vice versa. Therefore, at present, we recommend training and testing the approach on a single plant and retraining the models when transferring to a new plant. Meanwhile, it is possible to directly reuse trained models if the atmosphere context and the power plant capacity are similar, for example, two power plants located in the rural mid-west region with 3000 MWh (megawatt hour) net generation.
## 4 Numerical Examples
### Inversion Analysis of a High Earth-Rockfill Dam Using the Step-by-Step Method
#### 4.2.2 Step-by-Step Inversion Analysis  
The step-by-step inversion analysis method is applied to get the material parameters of the bottom of the core wall (marked by red lines in Figure 6) during construction periods. Calculated material parameters can be found in Table 7. It can be seen from Table 7 that the trend of change of the material parameter \(K\) is quite obvious; this trend will be further studied in the following section. The compression values of the bottom of the core wall by different methods are all close to the original values; detailed values are listed in Figure 8.  
In order to compare the accuracy of different inversion methods, the relative errors between the monitoring data and calculated data are calculated by Equation (9):  
\[err=\frac{\sum_{i=1}^{m}\left|\frac{q_{i}-p_{i}}{q_{i}}\right|}{m}\times 100 \%\,, \tag{9}\]  
where \(err\) is the relative error, \(m\) is the number of monitoring data, and \(q_{i}\) and \(p_{i}\) are the monitoring data and calculated data in the \(i\)th construction step. The relative errors of step-by-step inversion and conventional inversion are 4.6% and 14.4%, respectively. Thus,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sequence** & \(K\) & \(n\) & \(R\)/ & **Sequence** & \(K\) & \(n\) & \(R\)/ \\ \hline
32 & 2000 & 0.25 & 0.65 & 73 & 1000 & 0.5 & 0.65 \\
33 & 1400 & 0.25 & 0.75 & 74 & 1800 & 0.25 & 0.7 \\
34 & 400 & 0.45 & 0.7 & 75 & 1000 & 0.3 & 0.8 \\
35 & 1600 & 0.45 & 0.6 & 76 & 1800 & 0.45 & 0.75 \\
36 & 1000 & 0.35 & 0.7 & 77 & 2000 & 0.4 & 0.55 \\
37 & 1200 & 0.45 & 0.85 & 78 & 800 & 0.35 & 0.55 \\
38 & 1800 & 0.5 & 0.8 & 79 & 1200 & 0.25 & 0.8 \\
39 & 400 & 0.2 & 0.55 & 80 & 1400 & 0.35 & 0.55 \\
40 & 400 & 0.35 & 0.6 & 81 & 1800 & 0.4 & 0.65 \\
41 & 600 & 0.35 & 0.75 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Construction Steps** & \(K\) & \(n\) & \(R\)/ \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 7: Calculated material parameters.  
results closer to monitoring data can be obtained by the step-by-step inversion analysis from the perspective of relative error.
## 4 Results
### Similarity between Human and CNN Attention Maps  
#### 4.2.1 Dice Score  
For the Dice score, the 3 (human-CNN comparison: spontaneous gaze vs. Grad-CAM, gaze-pointing vs. Grad-CAM, drawing vs. Grad-CAM) \(\times\) 3 (image type: objects, indoor scenes, landscapes) ANOVA revealed a main effect of human-CNN comparison, \(F\)(1,4,80.9) = 6.438, \(p\) = 0.006, \(\eta\)p\({}^{2}\) = 0.101, a main effect of image type, \(F\)(2,57) = 23.558, \(p\) < 0.001, \(\eta\)p\({}^{2}\) = 0.453 and an interaction between the two factors, \(F\)(4,114) = 10.484, \(p\) < 0.001, \(\eta\)p\({}^{2}\) = 0.269 (see Figure 5A, red bars). The main effect of human-CNN comparison indicated that the human attention maps that overlapped most with Grad-CAM were those elicited by drawing. That is, Grad-CAM had a higher overlap with drawing than with spontaneous gaze (0.30 vs. 0.24, respectively), \(p\) = 0.019, while comparing the overlap between Grad-CAM and drawing to that between Grad-CAM and gaze-pointing (0.26) just missed significance, \(p\) = 0.050. Conversely, the two types of eye movement maps did not differ in their overlap with Grad-CAM, \(p\) = 0.683. The main effect of the image type indicated that the overlap between human attention maps and Grad-CAM was highest for objects (0.43), followed by indoor scenes (0.26) and landscapes (0.11), all \(p\)s \(<\) 0.005. Finally, the interaction revealed that the differences between human-CNN comparisons strongly depended on image type. For objects, the overlap with Grad-CAM was most dependent on the human task. Here, Grad-CAM overlapped more with drawing (0.53) than with spontaneous gaze and gaze-pointing (0.40 and 0.37, respectively), both \(p\)s \(<\) 0.002. Conversely, the two eye movement tasks showed similar overlap with Grad-CAM, \(p\) = 0.179. For indoor scenes, spontaneous gaze overlapped least with Grad-CAM (0.20) and this overlap was lower than for gaze-pointing and drawing (0.28 and 0.31, respectively), both \(p\)s \(<\) 0.011. Finally, for landscapes, all three human tasks showed very little overlap with Grad-CAM (with 0.13, 0.13, and 0.06 for spontaneous gaze, gaze-pointing, and drawing, respectively) and no differences between them were found, all \(p\)s \(>\) 0.188.
## 3 Proposed Work
### Message Flow Diagram during Inter-Domain Handover in PMIPv6 for VANET  
When a vehicle changes its attachment point from one MAG to another MAG in a different PMIPv6 domain, an inter-domain handover is the result. Figure 5 depicts the sequence diagram of messages that are traded among various network entities such as the LMA/MAG and TTP during an inter-domain handover. Upon receiving the attachment request from the vehicle in step 1, the new MAG forwards the authentication request  
Figure 3: Sequence diagram when a vehicle connects to the PMIPv6 domain.  
to the TTP present in the local domain in step 2. The TTP multicasts the authentication request to the neighboring TTPs in step 3, and if the vehicle was already authenticated, then its authentication information would have been communicated earlier, during the initial connection or during some intra-domain handover to the PMIPv6 domain. The TTP having the authentication information then unicasts the authentication information back to the local TTP in step 4. Upon successful authentication, the TTP will send the authentication response consisting of the vehicle and MAG profile to the MAG and LMA in step 5. In addition to this, the TTP also multicasts this profile information to the neighboring TTPs so that, later, during the other inter-domain handover, this information can be used. In step 6, when a new MAG seeks an attachment, the vehicle's router will send a solicitation message; the new MAG then transmits a PBU message to the new LMA in step 7 and the previous LMA receives a PBU message from the new LMA in step 8. In step 9, the new LMA receives the PBA message from the previous LMA. Consequently, this creates a bidirectional tunnel connecting the previous and new LMAs during step 10. Step 11 of the process updates the BCE by coupling the same HNP to the new MAG, and in step 12, the PBA message is sent to the new MAG by the new LMA. In step 13, a new, bidirectional tunnel will be constructed between the new LMA and the new MAG. In step 14, the new MAG sends the vehicle a router advertisement message with the same HNP. The vehicle will not be aware of any of the above because the HNP is preserved throughout the transfer.  
Figure 4: Sequence diagram for intra-domain handover for a vehicle in the PMIPv6 domain.
## 5 Design of Longitudinal and Lateral PD Controllers  
Using the calculated direction and distance between the target and the robot as direct control inputs can lead to excessive overshoot, negatively impacting driving performance [31]. To address this, a proportional-derivative (PD) controller is designed to reduce overshoot and ensure robust transient response, as shown in Figure 10 [32]. The controlsystem follows a feedforward structure, where a 2D LiDAR sensor provides input signals for the PD controller, modeled by \(K(s)\). The system dynamics are captured by \(G(s)\), and the overall transfer function is expressed as \(T(s)\). For more detailed mathematical formulations and derivations, please refer to Equations (4) and (5).  
\[K(s)=K_{p}+K_{d}s \tag{4}\]  
\[T(s)=\frac{G(s)\big{(}K_{p}+K_{d}s\big{)}}{1+G(s)\big{(}K_{p}+K_{d}s\big{)}} \tag{5}\]  
Figure 11 shows that simulation results with \(K_{p}\) set to 0.5 and \(K_{d}\) set to 0.3 showed that the PD controller effectively reduced oscillations and allowed for rapid convergence to zero, outperforming basic proportional control.
## 3 Experimental Study
### Time-Frequency Conversion  
The signal collected by the MFL sensor is a one-dimensional MFL signal. To better suit convolutional neural networks and residual network models, it is necessary to convert the one-dimensional MFL signal into a two-dimensional time-frequency image using time-frequency analysis technology. This article utilizes continuous wavelet transform (CWT) to extract time-domain and frequency-domain features of MFL signals. These features are  
Figure 6: (**a**) Steel wire rope inspection test bench: 1—loading device; 2—detector; 3—movable tray; 4—cord lock; 5—fixing pin. (**b**) Signal acquisition system: 1—LabVIEW program; 2—NI 4496 card; 3—DC power supply.  
Figure 7: The one-dimensional raw signal collected by the MFL sensor.  
then used as inputs for quantitative identification in neural networks. The continuous wavelet transform of signal \(f(t)\) can be expressed as follows:  
\[CWT_{f}(u,s)=\langle f(t),\psi_{u,s}(t)\rangle=\int\,\,^{+\infty}_{-\infty}f(t) \frac{1}{\sqrt{s}}\psi^{*}\bigg{(}\frac{t-u}{s}\bigg{)}dt \tag{3}\]  
where wavelet time-frequency atoms \(\psi_{u,s}(t)=\frac{1}{\sqrt{s}}\psi\big{(}\frac{t-u}{s}\big{)}\) are obtained by scaling (\(s\): scale factor) and translating (\(u\): shift factor) the mother wavelet. After the wavelet transform, the one-dimensional signals \(f(t)\) are decomposed into a series of wavelet coefficients related to scale and translation factors, which are then projected into a two-dimensional time-frequency distribution image.  
Taking the inner and outer damage samples of a 20 mm diameter steel wire rope as an example, the time-frequency images obtained through continuous wavelet transform (CWT) are shown in Figure 8. The horizontal axis in the picture represents the time domain, and the vertical axis represents the frequency domain.
# Study on the Effect of Anchor Cable Prestress Loss on Foundation Stability
###### Abstract  
Anchor cable prestressing is one of the key factors in maintaining the stability of the supporting structure and controlling ground deformation. In order to further understand the influence of anchor cable prestress loss on the stability of the foundation pit, an underground station pit of Qingdao Metro Line 6 is taken as the engineering background, and numerical simulation research is carried out by using FLAC3D, which calculates the surface settlement, the deformation of enclosing piles, the support axial force, and the axial force of anchor cables in the process of excavation of the foundation pit and compares it with the on-site monitoring data, to get the law of the impact of prestressing force on the stability of the foundation pit in order to provide a reference for the design of the soil-rock Combined pit support system design and engineering construction to provide reference.  
p _applied_  
**Keywords:** prestressing anchors; prestress loss; foundation pit; numerical modeling
## 3 Research Methodology
### Environment Setup  
The Keras program classifies the models, which are then executed on an AMD Ryzen 7 with 32 GB of RAM. The proposed system employs Python for all computational tasks. The models are trained using 80% of the data from dataset 1, while 20% of the data from both datasets are used for testing purposes. Accuracy, precision, recall, specificity, the F1 measure, and a confusion matrix are employed to evaluate our method. The model's performance is denoted by true negative (\(TN\)), true positive (\(TP\)), false negative (\(FN\)), and false positive (\(FP\)) values in the confusion matrix. In this investigation, the subsequent efficacy metrics were implemented:  
Accuracy:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
Precision:  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
Sensitivity:  
\[Sensitivity=\frac{TP}{FN+TP} \tag{7}\]  
Specificity:  
\[Specificity=\frac{TN}{TN+FP} \tag{8}\]  
F1 measure:  
\[F_{1}=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{9}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Learning Models** & **Hyperparameters** \\ \hline LR & C = 100; Max iteration = 300; Intercept scaling = 10 \\ \hline SVM & C = 100; Kernel = rbf; Cache size = 200 \\ \hline DT & Criterion = entropy; Max depth = 30; Min sample leaf = 6; Min sample split = 2 \\ \hline KNN & Leaf size = 1; Neighbors = 5; Penalty = 1 \\ \hline RF & Criterion = entropy; Max depth = 500; Bootstrap = True \\ \hline HGB & warm\_start = True; max\_iter = 150; max\_leaf\_nodes = 42 \\ \hline XGB & Objective = multi:softmax; Colsample by tree = 0.75; Max depth = 6; Min child weight = 5; N\_estimators = 100; Booster = dart; Sampling method = gradient-based \\ \hline Ensemble Model & Estimators = (RF, XGB, HGB); voting = hard \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimal hyperparameters of the models employed in this study.  
Precision quantifies the quantity of genuine affirmative samples, whereas accuracy assesses the classifier's capacity to forecast classes. Specificity evaluates a model's ability to predict negative outcomes, while sensitivity evaluates its ability to detect positive data. The harmonic mean of accuracy and sensitivity is used to calculate the F1 score after false positives and negatives are taken into consideration.
# Relief Aircraft Dispatch Strategies Based on Different Levels of Information Sharing Systems
## 6 Numerical Experiments
### Result of Numerical Experiment
#### 6.2.3 Effectiveness of Different Levels of Information for Dealing with Different Relief Scenarios  
1. The Gini coefficient and D/C affect the effectiveness of the transport methods. Table 4 and Figure 9 show that a larger Gini coefficient in the same D/C or larger D/C in the same Gini coefficient could increase the flight time, finishing time, and waiting time.
2. When D/C is larger than 1 (such as 1.2 or 1.4), the methods in Level two and three mostly have the same performance. In this case, when the Gini coefficient is smaller, the demand in each shelter is often larger than the aircraft capacity; thus, the injured in one shelter can be transported by a single aircraft. When the Gini coefficient is larger, most demand is focused on one or two shelters, and thus, other shelters do not consume as much flight resources. Hence, when D/C is larger than 1, regardless of the Gini coefficient, the methods in Level two and Level three have the same performance.
3. When D/C is smaller than 1, the difference between the two methods can be seen at Level two. One destination (two-I) is better than multiple destinations when the Gini coefficient is smaller (such as 0.1 or 0.3), whereas multiple destinations are better when the Gini coefficient is larger (such as 0.5 or 0.7). This is because when D/C is smaller than 1 and the Gini coefficient is small, there are sufficient flight resources and the injured in each shelter may be less than the capacity of the aircraft. Under these conditions, the one-destination method is fast and efficient. When D/C is smaller than 1 and the Gini coefficient is large, more flight resources are available, but the distribution of the demand is imbalanced. In some shelters, the injured might exceed the capacity of the aircraft. In this condition, multiple destinations could be better.
## 1 Introduction
Notice that if the power profile is measured to be used as an input for the design process, it will be typically obtained using digital data loggers. Thus, the raw data will inherently be provided in a stepwise format. Obviously, the shorter the discretization step the better the approximation to the actual power profile. By using the mathematical expressions provided in this paper, the temperature at the end of each time step can be straightforwardly obtained from the initial values of the same interval. Besides this generalization, there are a good number of industrial pieces of equipment, such as uninterruptible power supplies (UPSs), in which the SCs are typically commanded to provide grid support by feeding an essentially constant power to loads such as emergency lighting, electronic measurement devices, monitoring systems, etc. In these widespread applications, the estimation of the SCs temperature can be conducted directly for the full discharge process by using the analytical expressions presented in this paper. The proposed formulas can also be used to simplify the development of new control techniques for SCs arrays. The present study is, therefore, aimed at providing an alternative mathematical formulation that eliminates the simplifications used by the authors in [56]. This way, it is possible to obtain the strictly accurate value of the temperature vs. time evolution of an SC cell, as well as any other electrical variable, when it is used in all constant-power industrial applications. The equations presented in this paper, which are based on the traditional electrical and thermal models, as well as on the features of the so-called Incomplete Gamma  
Figure 1: Actual and discretized power profiles. Function [57; 58; 59; 60; 61], allow a fast and explicit calculation of the temperature of the SC cell to be made, avoiding the use of more complex simulations or experimental tests at the initial design stage. This fact opens the door to an effortless analysis of different scenarios during the design process: different ambient temperatures, different initial temperatures of the device, different power profiles (either constant or not), and so on. The rest of the paper is organized as follows: Section 2.1 shows a summary of the electrical analysis of an SC cell subjected to constant-power charge and discharge; in Section 2.2, the Incomplete Gamma Function is presented; finally, in Section 2.3, the entire temperature analysis of an SC cell is developed. The results are presented in Section 3, which that include several case studies that prove that the proposed formulation is valid for all possible conditions as well as a summary that briefly shows the practical application of the new equations. Section 4 shows the conclusions of the study.