esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Classification Results and Evaluation  
#### 3.2.1 Performance Analysis of Transfer Learning Method  
As a means of performance comparison to the transfer learning approach with and without GAN-based data augmentation, the results of the _AlexNet_ model that was trained using the real images were initially compiled. The dataset was geometrically augmented by random rotations, translations, and reflections. Later, the model was trained again from the initial condition with both the real and generated images to analyze the potency of image data augmentation through GAN. Henceforth, 200 new images were generated for each class to support the training set. Briefly, the combined dataset utilized for the final model had 2000 images in total (400 images per class), out of which 75 real images from each class were reserved for testing purposes. Table 2 clearly summarizes the image distribution for the training and testing.  
After training with the original (real) dataset, the CNN model produced a classification \(F1\)-score of 0.970. After adding artificial images, the \(F1\)-score of the CNN (denoted as GAN-TL) improved remarkably and reached a value of 0.986. The statistical classification results on the test set have been recorded in Table 3. In addition, the accuracy improved to 98.40% from the previously attained 97.07% (without GAN augmentation) in the test data. A remarkable increment in the performance metrics was observed for Shepherd's Purse and Fat Hen, while the results remained unchanged for the maize and Cranesbill classes (Table 3). Overall, image augmentation with the help of a conditional GAN resulted in an improved classification result through the transfer learning method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Charlock** & **Fat Hen** &
\begin{tabular}{c} **Shepherd’s** \\ **Purse** \\ \end{tabular} & **Cranesbill** & **Maze** \\ \hline Real images & 200 & 200 & 200 & 200 & 200 \\ \hline Artificial images & 200 & 200 & 200 & 200 & 200 \\ \hline Total & 400 & 400 & 400 & 400 & 400 \\ \hline Training images & 325 & 325 & 325 & 325 & 325 \\ \hline Test images & 75 & 75 & 75 & 75 & 75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of data distribution for each class.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Class Name**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**\(F1\)-Score**} \\ \cline{2-7}  & **TL** & **GAN-TL** & **TL** & **GAN-TL** & **TL** & **GAN-TL** \\ \hline Charlock & 0.9493 & 0.9615 & 1.0000 & 1.0000 & 0.9739 & 0.9804 \\ \hline Fat Hen & 0.9136 & 0.9868 & 0.9867 & 1.0000 & 0.9487 & 0.9934 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of the classification results based on the transfer learning method. TL and GAN-TL refer to the models trained with the real image data, and combined real and artificial datasets, respectively.
## 3 Detection of Damage Occurrence
### Time Domain Methods
#### 3.1.3 Data-Based Statistical Methods  
Structural damage affects the dynamic properties of a structure resulting in a change in the statistical characteristics of the measured responses. Over the past two decades, the data-driven time series methods utilizing statistical signal processing techniques have formed an important and rapidly evolving class of vibration based methods [16; 36]. These methods rely on the well-established statistical concepts instead of human expertise to extract features that change with the onset of damage [37], which eliminates possible individual biases and requires very few assumptions regarding the physical structure. This method is represented by the Auto-Regressive (AR) family method such as the AR or Auto-Regressive Moving Average (ARMA) models [37; 38; 39; 40], AR model with exogenous inputs (ARX) [41; 42], Auto-Regressive Moving Average Vector (ARMAV) model [43], vector autoregressive (ARV) models [44], and the time frequency autoregressive moving average (TFARMA) model [45]. The ARMA or other similar models are used to model the time-domain vibration signals obtained from the structure, and then the model coefficients estimated using statistical methods are used to identify the system dynamic parameters and to extract features that indicate the damage occurrence. The AR approach is solely based on signal analysis of the measured vibration data, making this approach very attractive.
## 2 Overview of the Power Distribution Control
### Operation Modes of HESS  
The HESS control structure uses a two-level hierarchical approach. The lower level is composed of the primary controllers for the bidirectional power flow of each ESD.  
The upper level is the secondary control and defines the operation modes when there is no power balance between the SST, the ChPs, and the HESS. The secondary control also establishes the references for the different primary controllers of the lower level in each operating mode.  
Figure 3 summarizes the operation modes of the HESS and their respective transition conditions. This state machine is a reflection of the operating modes defined in the centralized control in [14], which were classified as grid-tied and islanded operating modes. For the centralized control, the transition among UFCS operating modes is carried out through power balancing in the 1500 V dc bus, establishing the same Equation (2) in terms of power. For grid-tied operation, there are four operating modes, namely, direct, complementary, limited loading, and standby. If there are no EVs to charge, the UFCS is in the standby mode and intends to keep the ESDs at optimal levels by charging them constantly. The direct loading mode appears when the power supplied by the SST is sufficient to  
Figure 2: Circuit diagram of HESS and 1500 V dc bus.  
feed the demand of all ChPs. The remaining power from the SST is used to charge the HESS when necessary. If the power of the ChPs exceeds the maximum power of the SST, additional power will be injected from the HESS to supply the power balance. This will set the complementary loading mode in the UFCS if the combined power from the SST and HESS maintains the power balance; otherwise, it will establish the limited loading mode, in which the demanded power in ChPs is limited to the maximum power of the SST and HESS.  
In absence of power from the SST, the UFCS will work in islanded operation, in which the HESS will be the main supplier to the ChPs. This operation of the UFCS has three modes depending on the SOC of the battery pack: the autonomous, burnout, and disconnection modes. The first one is the default mode in islanded operation and supplies the ChPs up to the maximum power of the HESS. If the SOC of the battery pack falls under 40% during the autonomous mode, the UFCS will enter into the burnout mode. In this condition, the UFCS will supply only the EVs that are already charging and will stop providing service to the future EVs. After fulfilling the charging of the EVs, no service is provided until the SST is active again.  
Thus, the charging mode in Figure 3 corresponds to the direct loading and standby modes. The power injection mode corresponds to complementary and limited loading, and the voltage regulation mode in turn corresponds to the autonomous one. Finally, the burnout mode of the HESS represents the burnout and disconnection modes.  
Figure 3: State machine with operating modes of HESS.
# A Fast, Decentralized, Self-Aligned Carrier Method for Multicellular Converters +
## 1 Introduction
increases the system flexibility in terms of allowing to expand the range of voltage and the power requirements by adding or removing the number of cells in parallel and/or serial connections [22; 23; 26; 27; 28; 29]. In addition, the system reliability is high by implementing the decentralized approach, which is based on the modularity of the cell local controller. Nevertheless, the time to configure the system depends greatly on the processing speed of the local controller [25; 30; 31], the controller sampling time, the number of system local controllers, and the initialization condition of the system [10]. For this controller type, it should be noted that the time spent to adjust the phases of system carrier waveforms in a case of reconfiguring the converter is one of the main issues. A slow phase adjustment leads to a long time to reach steady-state and thus degrades the dynamic performance of the whole system. This paper deals with the third research direction mentioned above, in which a fast, self-aligned method for a decentralized controller of a multilevel or multiphase converter is proposed. With this method, each cell of the converter synchronizes its carrier frequency and simultaneously updates its own carrier phase-shift (PS) or level-shift (LS) that is based on the information exchanged with its close neighboring serial (multilevel converter) or parallel cells (multiphase converter). The exchanged information is the carrier angle/level, the index number, and the total number of active cells involved in the converter. The objective of the proposed self-aligned carrier generation method is to obtain an accurate and fast arrangement of the PS and the LS carriers in a multilevel or multiphase converter with any number of cells, especially when a dynamic configuration and reconfiguration of the system with a large number of cells is required.
# Dilated Filters for Edge-Detection Algorithms
The classical edge detection methods Sobel [14], Prewitt [15] and Scharr [16], together with more complex boundary detection algorithms such as Canny [13], Marr-Hildreth [17] or Shen-Castan [18] are considered for testing with our proposed dilation technique. With this extended research work we wish to prove the research hypothesis that dilatation of kernels can bring forward better edge maps. The main contributions of this paper are summarized as follows:  
1. Extend our previous analysis on first-order derivative orthogonal gradients based algorithms and Canny algorithm, that was performed in [PERSON] et al. [11]. 2. Analyze and evaluate the effect of dilated filters upon first-order derivative compass gradient based algorithms, Frei-Chen algorithm [19,20] and Edge Drawing algorithm [21]. 3. Analyze and evaluate the effect of dilated filters upon second-order derivative based algorithms such as: Laplacian [22], Laplacian of Gaussian [23], Marr-Hildreth or Shen-Castan. 4. Analyze and evaluate if the effect of dilated is maintained when using different kernels for the mentioned algorithms. 5. Analyze and evaluate the effect of noise level of an input image upon the dilated filters. 6. Prove the research hypothesis that dilated filters in general bring forward better results than classical or extended versions of them. In order to complete our evaluation of the dilation benefits over an edge-detection filter, we will not limit our analysis to the first-order derivative gradient-based edge-detection filters but consider the second-order filter too. Details regarding the new approached algorithms and steps we used in order to obtain the edge map format are presented in Section 3. Section 4 highlights the new results of our hypothesis regarding the dilated filters. For completing the comparison we used two different datasets and evaluation measures, one based on natural images and one based on synthetic images, that are presented in Section 3.11. Figure 2: Example of edge map results using Canny algorithm configured with different parameters. Columns are the low and high threshold combinations used: (20, 60); (50, 90); (70, 110); (90, 130); (110, 150); (130, 170); (150, 190). Rows represent the filter used: 3 \(\times\) 3 filter edge; 5 \(\times\) 5 dilated filter; 7 \(\times\) 7 dilated filter.
## 4 Simulation Results
### Non-Circular Orbit Optimization  
To further explore the mobility freedom of the UAV sensor, next, we discuss the results of the optimization of the non-circular orbit optimization. Eight patrol points are selected,  
Figure 8: Optimized sensor placement (circular orbit).  
Figure 10: CDF of localization error (circular orbit).  
Figure 9: Localization error distribution (circular orbit).  
and the coordinates including the altitude of those points are optimized. Figures 11 and 12 show the optimized patrol points of receiver sensor route and the corresponding estimation errors, respectively. Compared to the circular path on which the UAV's patrol points are restricted to a certain circle of optimized radius, the patrol points of the free-path case have more freedom to visit the whole area to collect more reliable RF fingerprints, knowing that the RSSI fingerprints in this paper might be contaminated by noise, especially when the distance between the UAV sensor and the emitter is long, the path is obstructed by buildings, etc. Since the patrol points of the circular path are restricted on a specific circle, even after the optimization process, some patrol points need to fly over the rooftop of a big building in the center area of the map. Since this building has a higher probability of obstructing most of the propagation path between these patrol points and emitters (i.e., NLoS environments), the localization estimation accuracy of the circular path is expected to be poorer than that of the free path. Indeed, it can be seen from the result of the free-path optimization that one patrol point is selected in the northern area where it is easy to secure an LoS, and some other patrol points are concentrated in the southern area with a denser distribution of buildings that made the environment more multipath vulnerable. As a result, it is easier to secure an LoS condition compared to both scenarios of fixed sensors and circular orbits evaluated in Section 4.1. Owing to this benefit, the position estimation error is not degraded even in areas where buildings are densely populated.  
Next, the cumulative distribution function (CDF) of the position estimation error is shown in Figure 13 and Table 5. Compared with the CDF value of 90%, which is the objective function set in this study, the error is 55.02 m for the fixed sensor and 28.59 m  
Figure 11: Patrol points of UAV sensor (non-circular orbit).  
Figure 12: Localization error map (non-circular orbit).  
for the circular orbit UAV sensor, while it is 12.91 m for the UAV sensor with optimized patrol points. Thus, it can be seen that the position estimation accuracy is further improved compared to the case of optimization with a circular orbit.
# PV Sizing for EV Workplace Charging Stations--An Empirical Study in France
## 5 Sizing Procedure Examples
### Estimating the Number of EVs Given an Existing PV Installation and a Target Mean Power Price  
Let us now suppose that the owner of an existing PV installation of 100 kWp in the same region wants to determine the number of EVs that it could charge with the Mean Power strategy and with a targeted Mean Power price of \(Pr_{CPO}=265\)\(\upcelsius\)/MWh. The procedure for deriving the number of EVs that it can support is as follows, according to Figure 3:  
* From PVGIS: Annual PV Production \(E_{PV}=100[\mathrm{kWp}]\times 1.4\left[\frac{\mathrm{MWh}}{\mathrm{KWP}} \right]=140\) MWh;
* From Equation (7), \(SPR=\frac{Pr_{CPO}-Pr_{ESEV-CPO}}{Pr_{CPO-LP}-Pr_{ESEV-CPO}}=\frac{265-424}{197 -424}\approx 0.7\);
* The Production-to-Consumption ratio for different strategies is determined using Figure 12: \(PTC_{MP}=2.5\);
* Annual EV Charging Consumption (using the MP strategy) is then \(E_{EV}=\frac{E_{PV}}{PTC}=\frac{140[\mathrm{MWh}]}{2.5}=56\) MWh;
* From Equation (8), the optimal number of EVs is then: \(\frac{E_{EV}}{(12\times 100)}=\frac{56\) MWh}{12\times 100\(\mathrm{kWh}}\approx 46\).
# Advancing Model Explainability: Visual Concept Knowledge Distillation for Concept Bottleneck Models
## 3 Methodology  
### Preliminaries  
#### 3.1.1 Concept Bottleneck Models  
Concept bottleneck models (CBMs) are structured as \(f(g(x))\), where for an input \(x\in\mathbb{R}^{d}\), the model predicts intermediate concepts \(c\in\mathbb{R}^{k}\), and based on these predicted concepts it predicts the final target \(y\in\mathbb{R}\). Specifically, \(g:\mathbb{R}^{d}\rightarrow\mathbb{R}^{k}\) is a function that maps the input to a concept space, and \(f:\mathbb{R}^{k}\rightarrow\mathbb{R}\) is a function that maps the concepts to the final prediction.  
During the training process of CBMs, the dataset \(\{x^{(i)},y^{(i)},c^{(i)}\}_{i=1}^{n}\) is utilized, comprising input data \(x\), corresponding concepts \(c\), and final targets \(y\). The objective of the model is to minimize two loss functions. The first loss function, \(L_{C_{i}}:\mathbb{R}\times\mathbb{R}\rightarrow\mathbb{R}+\), quantifies the disparity between the predicted and actual values for the \(j\)-th concept. In contrast, the second loss function, \(L_{Y}:\mathbb{R}\times\mathbb{R}\rightarrow\mathbb{R}+\), assesses the discrepancy between the final prediction and the ground truth. Three main types of CBM are introduced in [4]:1. **Independent bottleneck**: Concept and final predictions are conducted independently in this model. During training, the ground truth concepts \(c\) are utilized to train \(f(c)\), but not to train \(\hat{g}(x)\). During testing, the output of \(f(\hat{g}(x))\) is employed.
2. **Sequential bottleneck**: This model predicts concepts from the input data and then predicts the final target based on these predicted concepts. The output of \(\hat{g}(x)\) is directly utilized as the input to \(f\).
3. **Joint bottleneck**: Similar to the sequential bottleneck model, this model predicts concepts \(c\) from the input data \(x\) and then predicts the final target \(y\) based on these predicted concepts. However, it additionally considers the loss function for concept prediction. In other words, it simultaneously minimizes the concept prediction loss \(\mathcal{L}_{C}\) and the final target prediction loss \(\mathcal{L}_{Y}\) during training. To balance the contributions of these two loss functions, a weight \(\lambda>0\) is introduced, defining the total loss function as \(\mathcal{L}_{joint}=\mathcal{L}_{Y}+\lambda\cdot\mathcal{L}_{C}\). Consequently, the joint bottleneck model performs concept and final target predictions simultaneously, considering the relationship between the two prediction results during training.
## 3 Results
### Pix2 Pix Performance
#### 3.1.2 Examples of Images Generated  
Figure 5 shows an example of an image generated by Pix2 Pix from a real mask, illustrating the model's ability to generate realistic and detailed images. In addition, Figure 6 shows images generated from modified masks, where the size of the polyps has been altered.  
Figure 4: Workflow for evaluating classifier limits using generated examples. The series of masks with controlled polyp size variations are fed into the trained Pix2 Pix model. The resulting synthetic images, which vary in polyp size but preserve the background context, are then input into the trained classifier (EfficientNet or ViT). The classifier’s output confidence for each image is recorded to analyse how its predictions change in response to the controlled variations.
# Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation
## 2 Background
### Batching and Memory Usage  
After all data samples are prepared according to the domain-specific data preparation steps, the system will start the batch formation process. Batching plays a fundamental role in the training of transformer-based models. It refers to the process of grouping training data into smaller subsets called batches, which are then sequentially fed to the model during training iterations. This technique offers several advantages, including computational efficiency during training by leveraging the parallel processing capabilities of modern GPUs, improving model convergence through gradient averaging, and mitigating noise inherent in individual samples. However, the batching process requires careful consideration of memory usage. The preparation and loading of each batch from the CPU to the GPU's dedicated memory creates a temporary increase in peak memory consumption. This increase, coupled with the model's memory requirements, can create a bottleneck, especially when working with large transformer-based models or substantial batch sizes.  
Figure 2 outlines the default batching process. Step 1, sequence preparation, involves various data preparation steps. The outputs of step 1 are sequences of tensors. Step 2 is the batch assembly process. For time series data analysis, the batch assembly entails grouping windowed sequences of ordered data. By default, batch assembly is performed on the CPU after the data preparation (sliding-window operation) is applied to the ordered data based on pre-configured batch size. As a result, there are unnecessary data copies  
Figure 1: Time-based sliding window.  
accumulated in memory as shown on the right side of Figure 1. The amount of memory usage increases drastically with large window sizes and especially with high dimensional data (multivariate). During each training iteration, each batch of tensors goes through the following steps: data transfer from CPU to GPU (labeled 3 in Figure 2), batch processing during the forward pass (labeled 4 in Figure 2), and unloading during the backward pass. After the model processes each batch, the processed batch will be released from the GPU memory, and the system will start transferring the next batch of tensors. This data transformation is facilitated by a data-loading pipeline described in detail in the next subsection.