esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Ablation Study
It was observed that, although \(X_{T_{d}}\) was unlabeled, using roughly aligned \(X_{T_{d}}\) to predict \(X_{T_{n}}\) was quite important and also played a key role in DLA-Net. It reduced the segmentation results by 36.8% without using \(X_{T_{d}}\), indicating that the training in the daytime domain is quite critical in the network. The LIE-SubNet and the corresponding loss function \(L_{cn}\) also contribute to the whole network. Meanwhile, the utilization of the decoupling body and edge loss \(L_{de}\) in this paper yielded superior results when compared to applying the cross-entropy loss directly to computing the segmentation loss. The performance disparity between the two methods is notable, and not using the \(L_{de}\) loss outperformed using the cross-entropy loss by 38%. In addition, the adoption of probabilistic reweighting in experiments enhanced the segmentation performance, affirming its effectiveness as an auxiliary tool. This chapter presents comparison and ablation experiments on DLA-Net and its components across multiple datasets. The results demonstrate that DLA-Net efficiently segments images in nighttime scenes without using labeled images or synthetic datasets. LIE-SubNet effectively brightens low-light images, and the decoupling body and edge segmentation effectively predict feature maps with a uniform body and clear edge. However, DLA-Net struggles with domain adaptation when faced with large domain gaps caused by differences in styles and inherent variations between datasets, such as in urban street scenes. Future work will focus on conducting in-depth research to better understand these differences and adapt to a wider range of scenes and datasets. Figure 6: Visualization results of ablation experiments with different loss functions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **mIoU** \\ \hline ProCA [51] & 25.47 \\ DiGA [52] & 25.21 \\ \hline AdaptSegNet-CityScapes\(\rightarrow\) DZ-night [6] & 19.13 \\ \hline w/o \(X_{T_{d}}\) & 22.58 \\ w/o LIE-SubNet \& \(L_{cn}\) & 32.45 \\ w/o \(L_{cn}\) & 33.86 \\ \hline w/o \(L_{de}\) & 20.19 \\ w cross-entropy loss in \(L_{de}\) & 32.96 \\ \hline w/o probability reweighting & 31.68 \\ \hline w/o pre-trained segmentation model & 29.78 \\ \hline DLA-Net & 35.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ablation study of several DLA-Net (DeepLab-v3+) modules proposed in this paper on Dark Zurich-val.
# A Deep Learning-Based Course Recommender System for Sustainable Development in Education
## 4 Experiments
### Baselines and Settings  
This study evaluated the proposed model's performance compared with that of the most popular algorithms in recommendation studies. We used state-of-the-art baseline methods such as ItemKNN, MF, and Neural Collaborative Filtering (NCF), and these methods were divided into memory-based, model-based, and deep learning-based approaches, respectively.  
ItemKNN is a standard item-based CF method based on neighborhood models in recommender systems. It has been applied to Amazon [63], MovieLens [64], and other online recommendation applications to optimize their performance. We followed the settings in the existing literature to adapt ItemKNN to an implicit dataset and tested the method with several recommendation sizes [65].  
MF is an efficient model-based CF that obtains accurate recommendations, and it is easy to understand and implement. It also focuses on dimensionality reduction, where a reduced set of hidden factors contains user preferences [62].  
NCF is a deep neural network-based recommender system that can capture complex nonlinear interactions between users and items. Additionally, it utilizes embedding vectors representing users and items as the inputs for capturing user-item interactions. Furthermore, it only uses implicit feedback and a pointwise loss function [32].  
We also determined the impact of changing the number of latent factors (within [8; 16; 32; 64]) on model performance. The number of hidden layers was also equally set to {32, 16, 8} for comparison with existing studies. We set the optimal learning rate in the model training process to {0.0001, 0.0005, 0.001, 0.005} and conducted various experiments to change the parameters and optimize the proposed model. Based on the common strategy found in previous studies', the proposed model for used an Adam optimizer. This study set the batch size to {32, 64, 128, 256, 512} and then selected the optimal size. All experiments were conducted in a computer environment with 64 GB of memory and a 2080 Ti GPU.
## 4 Discussion and Future Work  
In this study, we utilized an SE-CNN to invert the travel time data of crosshole ground-penetrating radar and analyzed the error and efficiency of the MCMC inversion method based on network forward models. Based on the work presented in this article, three topics need to be discussed:  
1. The advantage of an SE-CNN network is that as the training size increases, the number of model errors gradually decreases. A high-precision forward model can be obtained by increasing the training size. The disadvantage is that as the training size increases, the computational cost also increases. Compared with traditional ray methods, the computational efficiencies of both methods are similar, but the model errors of SE-CNN method are smaller when the training size exceeds 20,000.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Forward Model** & **\(\mathbf{\mathcal{S}}_{\text{td}}\)** & **\(\mathbf{\mathcal{S}}_{\text{1000}}\)** & **\(\mathbf{\mathcal{S}}_{\text{5000}}\)** & **\(\mathbf{\mathcal{S}}_{\text{10000}}\)** & **\(\mathbf{\mathcal{S}}_{\text{20000}}\)** & **Ray** \\ \hline Calculation Time(s) & 1015.8 & 2.03 & 1.97 & 1.94 & 1.92 & 1.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computation time analysis of different forward models.  
Figure 13: (**a–e**) The residual of the inversion results obtained using trained neural network and ray method.  
At present, the methods of using neural networks for MCMC inversion mainly focus on processing simulated data. The field data quality may not be ideal due to factors such as terrain, geometry systems, and noise. The targeted optimization of network structures is needed. To achieve the application conditions of field data, a large number of field cases need to be analyzed and tested, and this endeavor is a challenge to be addressed in our future research work.
3. The attention modules used in this article are SE attention modules. Currently, with the development of neural networks, various attention modules have sequentially emerged. We can try to introduce more types of attention modules into the network model and test their effectiveness. In response to the MCMC inversion problem, the introduction of attention modules not only improves the accuracy of the network but also ensures that the neural network maintains high computational efficiency. Therefore, the design of attention modules and network structures is very important and will be one of the focuses of future research.
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 6 Monte Carlo Simulation-Based Data Generation
### Case Study: Data Generation for German LV Networks  
The Monte Carlo algorithm introduced in Section 6.1 is applied to generate realistic synthetic data of voltage notch waveforms within the context of German industrial LV networks. The fixed parameters are initialized as indicated in Table 8. Furthermore, synthetic data are generated for scenarios 1-4 from Table 6, with variants related to the inclusion of a detuned or non-detuned capacitor bank and different levels of background unbalance and distortion. \(LL_{on}\), \(CB_{on}\), and \(Det_{on}\) are set accordingly. A total of \(N=10\) exemplary simulations are run for each scenario and variant.  
The flow diagram in Figure 21 shows that some parameters of the simulation are randomly generated, including \(S_{sc}\), \(S_{r}\), \(\alpha\), \(X_{com}\), \(L_{D}\), \(U_{rm}\), selection of the design curve, \(R_{sn}/R_{base}\) ratio, \(S_{ll}\), \(PF_{ll}\), and \(QF\). Table 9 indicates the range for each random parameter, the type of probability distribution, and the probability corresponding to the respective range in case various ranges are adopted for the case study. Data on probabilities for \(S_{sc}\) obtained from extensive surveys at European LV networks is extracted from [16]. Ranges, types of probability distribution, and range probabilities of the rest of the parameters are computed and chosen according to the procedures and typical values described in Sections 3 and 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Equation** & **Parameter** & **Equation** & **Parameter** & **Equation** \\ \hline \(L_{s}\) & (10) & \(U_{rm}\) & 0.8\(\cdot\)\(U_{rm}\) & \(R_{sn}\) & (16) \\ \(L_{com}\) & (9) & \(U_{r}\) & \(U_{rm}\)/\(f(R_{sn}/R_{base})\); see Figure 5 & \(C_{sn}\) & (17) \\ \(I_{D}\) & (5)–(6) & \(di/dt\) & (15) & \(P_{ll}\) & (19) \\ \(U_{D}\) & (11) & \(I_{rm}\) & \(f(di/dt)\); see Figure 6 & \(Q_{ll}\) & (19) \\ \(R_{D}\) & \(U_{D}/I_{D}\) & \(R_{base}\) & (13) & \(Q_{cb}\) & (20) \\ \(P_{D}\) & \(U_{D}\)-\(I_{D}\) & \(C_{base}\) & (14) & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Computed parameters and corresponding equations.
## 1 Introduction
Two different high-frequency analysis techniques were used to detect erosive cavitation in the previous studies: analyzing the RMS (root mean square) value at high-frequency bands (over 10 kHz) and applying amplitude demodulation techniques, such as the Hilbert transform [25], to obtain the low-frequency oscillation of the cavitation cloud. Figure 1 shows an example of a prototype Francis turbine analyzed with the aforementioned technique. In this case, the power spectrum and amplitude demodulation of a high-frequency band (10-15 kHz) are shown for an accelerometer located in the turbine bearing. Three different operating conditions were analyzed: 70%, 85%, and 100% of the guide vane opening or the maximum power. It is observed than the amplitudes of the spectrum are higher for the 100% of the maximum power and lower for the 70% operating condition. Moreover, the main frequencies related with the rotating speed, RSI, and their harmonics are present when demodulating the signal for the case of 100% of the maximum power, whereas they are not seen in the 70% operating condition. After several hours of operation at 100% of maximum power, severe erosion damage was found in the blades, which was associated with unstable inlet leading edge cavitation. Therefore, it seems that the power spectrum amplitude increases and that different peaks appear in the demodulation of the high-frequency bands when erosive cavitation is present in comparison with the non-cavitation case. However, there are still some problems to be solved with this method:  
* The transmission path from the runner to the measuring position can only be known in air (the runner could be impacted during an overhaul [17]) but not in water and under operation. * No correlation between existing cavitation and the measured signals can be obtained: the cavitation cannot be visualized in the prototype [25]. * The excitation characteristics of the cavitation are an unknown. * It is not clear which high-frequency bands have to be used for every sensor for the analysis. In this paper, all of these uncertainties are studied using an experimental test rig. The test rig consists of a rotating disk submerged and confined in water. The disk has been instrumented with a PZT (piezoelectric patch) [26; 27; 28; 29; 30] and different sensors (accelerometers, AE, and a microphone) have been located in the rotating and stationary parts of the test rig. In that way, the transmission of different excitation patterns simulating cavitation to the different parts of the test rig can be studied in detail. Therefore, since the excitation is completely known, the best transmission path from the excitation to the measuring point can be obtained, as well as the best sensor for its detection. The results obtained in this study permit improving the current methods to detect and monitor [20; 31; 32] erosive cavitation in hydraulic turbines.
## 2 Materials and Methods
### Final System Implementation  
In this section, we delve into the specifics of the sensor node's final implementation, which is built upon a microcontroller platform. To keep things simple, we focus on thoroughly explaining the various power-saving strategies integrated into the programming tools associated with the TI-CC2650 Launchpad MCU. It is important to note that further investigations are conducted on all the MCU platforms mentioned earlier. For the final sensor node implementation using the TI-CC2650 Launchpad MCU, we have two viable approaches. One option is to utilize its internal timer, which operates and tracks time even in the ultra-low-power sleep mode. Alternatively, we can opt for an external TPL5110 timer connected to the MCU system.
### The Present Study
#### 1.2.1 EEG Features: Event-Related Band Power and Amplitude  
Typically, a clear separation is made between the analysis of the EEG in the frequency domain (Spectral analysis; measurements: power, coherence, phase, etc.) and the analysis of the EEG in the time domain, whose most common form is the Event-Related Potentials (ERPs), obtained by averaging activity associated with events of the same nature (reported measurements: amplitude, latency, topography).  
However, most recently, hybrid approaches have also been devised in which time and frequency domain EEG features are examined as covariates (for example see [43; 44; 45; 46]), particularly, to investigate spectral changes associated with stimulus or task time-course, as opposed to resting brain activity. In line with these approaches, in the present study we focused on two EEG event-locked or task-related features: Event-related band frequency amplitude and power. As described by [PERSON] [47], the event-related **amplitude** is the 'magnitude' of an oscillation, reflecting the distance between the maximal positive- and negative-going points (phase) of an oscillatory cycle. The related feature of **power** is the under-the-curve integral of the event-related amplitude response (to a stimulus and/or task) within the entire frequency band during a test period.
# Qutrit Control for Bucket Brigade RAM Using Transmon Systems
## 4 Discussion  
The results suggest that under the appropriate logical mappings, the qutrit control operations required to operate addressing switches in QRAM based on the BBA architecture can be realized by using the same entangling gates as the one used for quantum computation based on qubits. Although this was achieved at the extra cost of some additional single-qutrit gates, we demonstrated that single-qutrit transitions can be easily defined and calibrated for the case of qutrits that are realized using the energy levels of transmon systems. A methodology for using existing tools to optimize the pulses needed was also presented; this pipeline can be parametrized both in terms of the transmon parameters (e.g., resonance frequencies and anharmonicities) and in terms of the definition of the native gates used, which can be set as target operators that the parametrized signals must approximate. The results suggest that using transmon systems to address quantum registers under the Bucket Brigade scheme may be a promising technique for implementing a QRAM based on BBA in transmons. However, there are various considerations and further steps that need to be taken before the model can be adopted experimentally.
# On the Control and Validation of the PARA-SILSROB Surgical Parallel Robot
## 3 Results
### Medical Protocol Used for Experimental Tests
#### 3.2.1 Testing the PARA-SILSROB Robot Using the Simulation and Training Kit Used for a Laparoscopy  
During the test stage, three training modules were used and they were placed inside the simulation and training kit used for laparoscopy. Based on these training modules, the following operations were performed:  
* Handling the plastic kit elements within the intraoperative field;
* Manipulating and resection of tissue;
* Simulating tissue suturing procedures.  
To perform the previously described operations using the PARA-SILSROB robot, it was essential to first move the robot to its homing position. Subsequently, using the master console (as detailed in Section 2.2), the robot was positioned above the designated insertion point. This procedure relied on real-time data transmitted to the master console by the laparoscope attached to the mobile platform. Following the positioning, the laparoscope was inserted into the intraoperative field, and its RCM point was recorded. The attachment and insertion of the active instruments into the orientation and insertion modules were performed manually (Figure 11).  
During the manipulation stage, which involved plastic springs within the intraoperative field, two commercial active instruments, each with a diameter of 8 mm, were used,  
Figure 11: Active instruments developed by the Intuitive Surgical used for performing the insertion into the laparoscopic training kit.  
alongside a laparoscope with a diameter of 10 mm. All instruments were inserted through the SILS port in the training simulator.  
The results from this stage are presented in Figure 12, which shows the arrangement of the plastic springs on the plastic training module. This module was also equipped with a set of plastic rods.  
During the manipulation stage, which involved putting a cable through the rings, two commercial active instruments with an 8 mm diameter, which were equipped with articulated tips, were used, alongside a 10 mm laparoscopic camera. The outcome of this stage is illustrated in Figure 13.  
During the simulation of the tissue-cutting stage, a silicone module was used. This stage involved the use of an 8 mm diameter articulated tip active instrument for tissue grasping, an 8 mm diameter curved scissor-type active instrument, and a 10 mm diameter laparoscopic camera. The outcome of this stage is illustrated in Figure 14.  
Figure 12: Manipulation stage of the plastic springs.  
Figure 13: Cable manipulation using the training module with rings.  
The final stage of this testing phase of the PARA-SILSROB robot involves simulating tissue suturing. For this operation, two commercial active instruments (EndoWrist large needle driver and EndoWrist flexible grasper, developed by Intuitive Surgical, Sunnyvale, CA, USA, 2014), both with an 8 mm diameter, along with a 10 mm diameter laparoscope, were used. The outcome of this stage is illustrated in Figure 15.
# Adaptive Emergency Call Service for Disaster Management
## 3 System Model
### Emergency Call Procedure  
The emergency call procedure can be triggered either by human intervention or automatically (e.g., after a car accident detection). Once the call is triggered, the emergency application starts incorporating the data related to the victim, i.e., voice, text, video message, the UE id emergency call id, indicators about the emergency degree (level), RSSI indicator, and device position. If the emergency caller is within the coverage of a network gNB, then the call is directly sent to the gNB using the classical communication mode. However, if the caller is out of coverage, the call is locally diffused using the out of coverage D2D procedure.  
Once a relay receives an emergency call and decides to relay it, the relay adds its identifier, position, and RSSI value to the relayed message. Then the relayed message is sent using either the traditional mode if the relay is covered by a gNB or by D2D communication if the device is out of coverage. The emergency source position and relay's positions help the public safety center precisely localize the source of the emergency call. Further, once an emergency call is received by a given gNB, a notification is diffused to all the UEs under coverage, indicating the successful transmission of that emergency call with its specific ID. That way, redundant uplink transmissions of the same emergency call are reduced.
## 3 Materials and Methods
### Overview of Loss Functions Used
#### 3.8.3 Soft-F1 Loss  
An additional loss function used during training is the Soft-F1 Loss, a function introduced by [PERSON] et al. in 2013 [54]. As described in the article, this loss function was specifically designed for imbalanced datasets, where traditional loss functions often do not yield good results. This makes it particularly suitable for tasks like anomaly detection, where the F1-Score is a more relevant metric than Accuracy. However, since the F1-Score is non-differentiable, it cannot be used directly for training models via backpropagation. The Soft-F1 Loss addresses this issue by making the F1-Score continuous and differentiable, treating predictions as probabilities, so that it can be used within the training process directly as a loss function. The idea behind the Soft-F1 Loss is to replace True Positives (TPs), false positives (FPs), and False Negatives (FNs) with their probabilistic versions, defined as follows:  
\[TP=\sum_{i=1}^{m}o^{(i)}\cdot t^{(i)},\quad FP=\sum_{i=1}^{m}o^{(i)}\cdot(1-t^ {(i)}),\quad FN=\sum_{i=1}^{m}(1-o^{(i)})\cdot t^{(i)} \tag{4}\]  
where the following are true:  
* \(o^{(i)}\) is the model's output, i.e., the predicted probability.
* \(t^{(i)}\) is the actual target value, either 0 or 1.  
The calculation of the Soft-F1 follows the standard F1-Score formula, but with these probabilistic versions:  
\[\text{Soft-F1}=\frac{2\cdot TP}{2\cdot TP+FP+FN} \tag{5}\]  
This function allows for directly optimizing the F1-Score during the training process, thus maximizing the model's ability to balance Precision and Recall. An additional extension of the Soft-F1 Loss is the \(Soft-F\beta Loss\), a parameterized version of the F1-Score that allows for giving more weight to either Precision or Recall. This is particularly useful in cases where it is preferable to optimize one of the two metrics over the other. In anomaly detection tasks, for example, it might be preferable to optimize Recall more to ensure that most anomalies are correctly identified, even at the cost of a slight increase in false positives. The formula that defines the \(Soft-F\beta Loss\), as reported by [PERSON] et al. [54], is as follows:  
\[\text{Soft-F}_{\beta}=\frac{(1+\beta^{2})\cdot\text{TP}}{(1+\beta^{2})\cdot \text{TP}+\beta^{2}\cdot\text{FN}+\text{FP}+\epsilon} \tag{6}\]  
where \(\beta\) is the parameter that regulates the model's behavior based on the specific needs of the task, meaning it favors either Precision or Recall. These differentiable versions of the F1-Score and F\(\beta\)-Score are therefore very useful in contexts like anomaly detection, where the primary objective is the identification of anomalies. They allow the optimization of the model to focus on forecasting rare events like anomalies. The integration of these functions directly into the backpropagation process could lead to an improvement in the model's performance in these specific contexts compared to the use of traditional loss functions.