esa-sceva/satcom-chunk-collection huggingface dataset:
# Multipoint Thermal Sensing System for Power Semiconductor Devices Utilizing Fiber Bragg Gratings
## 5 Results and Discussion
### Prediction Using AI  
The thermal imaging analysis of IGBTs plays a crucial role in predicting potential failures before they occur, leveraging the power of advanced artificial intelligence (AI) tools. These tools utilize neural networks (NNs) to process and interpret the thermal images captured from IGBTs under various operating conditions. By examining the heat distribution patterns, the NN is trained to recognize subtle anomalies that may indicate early signs of wear or damage.  
In Figure 11, thermal images captured at two different time intervals are shown, providing a comparative view that helps in tracking the thermal behavior of the IGBT over time. Figure 11a shows a lower average temperature compared to Figure 11b. However, these differences are not easily discernible to the naked eye, making it challenging to identify areas that require attention. This comparison is crucial for understanding the development of potential issues. Figure 12 enhances this analysis by employing a neural network that identifies specific regions within the IGBT with abnormal heat signatures, highlighted as brighter areas in the image.  
The NN architecture used in this work is similar to [23], with descending layers followed by ascending convolutional layers. The network contains skip connections in order to avoid the vanishing gradient problem. The dataset includes thermal images of IGBT modules captured under varying operating conditions. Labels indicating healthy and anomalous regions were provided by the domain experts. The network was trained on labeled datasets where areas of abnormal heating were annotated based on expert knowledge and physical inspections. A portion of the data was reserved for validation  
Figure 10: FBG temperature response to varying load power with minimized airgap effect.  
to ensure generalization. Data augmentation techniques, such as rotation, flipping, and scaling, were applied to increase the diversity of the training samples. The network was trained using a supervised learning approach optimized for cross-entropy loss to classify regions as normal or anomalous. The output of the neural network was postprocessed (annotated) to generate the thermal map, as shown in Figure 12. The brighter areas (circled) in the image represent those regions where the neural network detected unusual thermal patterns. This visualization aids in identifying hotspots that may require further inspection. This approach not only enhances the accuracy of the damage prediction but also aids in proactive maintenance, ensuring the longevity and reliability of the IGBT components in various applications.
# Optimized Gateway Placement for Interference Cancellation in Transmit-Only LPWA Networks
###### Abstract  
We study the placement of gateways in a low-power wide-area sensor network, when the gateways perform interference cancellation and when the model of the residual error of interference cancellation is proportional to the power of the packet being canceled. For the case of two sensor nodes sending packets that collide, by which we mean overlap in time, we deduce a symmetric two-crescent region wherein a gateway can decode both collided packets. For a large network of many sensors and multiple gateways, we propose two greedy algorithms to optimize the locations of the gateways. Simulation results show that the gateway placements by our algorithms achieve lower average contention, which means higher packet delivery ratio in the same conditions, than when gateways are naively placed, for several area distributions of sensors.  
wireless sensor network; interference cancellation; capture; gateways location 1  
[PERSON] _et al._ Article
## 4 Feasibility of Agro-Waste Utilization in Reinforced Concrete (RC) Elements
### Effects of Agro-Wastes on RC Elements
#### 4.3.1 Mechanical Properties and Structural Performance  
In the reviewed studies, concrete beams prepared with AW used in RC beam production were compared with traditional concrete beams. An assessment was conducted to determine whether they met the requirements specified in standards such as BS8110 [25], ASTM C330 [37], and ACI-318 [72]. The geometric properties of the beams used in these studies, as well as the effects of AW, are summarized in Table 4.  
The effects of agro-waste on the mechanical strength of load-bearing systems necessitate the examination of mechanical properties such as compressive strength, modulus of elasticity, and tensile strength. Determining these effects is crucial for evaluating the mechanical performance and structural durability of materials. Studies have demonstrated that the fibers contained in AW positively influence the strength and resilience of beam materials. Experimental data reveal that AW improves the structure and durability of beams while enhancing resistance to corrosion. These findings support the broader adoption of AW in the construction sector, particularly for sustainable applications [89; 90]. However, further studies are required to comprehensively evaluate the effects of AWs on corrosion resistance under aggressive environmental conditions, such as chloride penetration and carbonation. Limited data indicate that AW materials may influence the alkalinity of the concrete matrix, potentially impacting the protective environment for embedded steel reinforcement. Additionally, environmental effects must also be considered, and waste management strategies should be developed. Preliminary tests conducted on concrete containing various proportions of AW have assessed mechanical properties, which are summarized in Table 5. Table 5 presents the results of mechanical tests performed to evaluate the effects of AW on RC elements. The table includes information on the types of AW used in concrete mixtures, contribution ratios, tested mechanical properties (e.g., compressive strength, modulus of elasticity, flexural strength), test conditions, and performance comparisons.
## 3 Preliminaries
### Recurrent Neural Network (RNN)  
A type of neural network commonly used for handling sequential data is a recurrent neural network (RNN). One of its defining characteristics is its ability to remember past inputs and states, allowing it to make predictions for current outputs [40]. The two primary types of RNN are the long short-term memory (LSTM) and Gated Recurrent Unit (GRU). The GRU utilizes hidden states, also known as hidden memory, and internal connections to effectively store long sequences of input patterns by forming a directed graph [41]. It employs two gates, known as the update and reset, to regulate the information flow and determine which input is crucial for retention. However, the issue of vanishing gradient poses a challenge for a GRU, as it impedes its ability to effectively learn long-term connections between data points within a sequence. To overcome this issue, a long short-term memory (LSTM) network utilizes specialized gating mechanisms to control information flow and avoid the vanishing gradient problem [42]. The LSTM network incorporates three gates, forget, input, and output, to regulate the information flow. The forget gate removes irrelevant historical information, while the input gate determines which new information is relevant and should be retained. The outputs of these gates are then combined to update the present cell state, with the output gate playing a role in determining which specific components of the cell state should be carried on to the succeeding LSTM cell.  
Figure 2: Flowchart of K-means clustering.  
Typically, the primary element of an RNN is its transition function that operates at each time step \(\tau\). In this case, both the previous hidden state \(S_{\tau-1}\) and the present time information \(C_{\tau}\) are considered to compute the present hidden state according to the following equation:  
\[S_{\tau}=F(C_{\tau},S_{\tau-1}), \tag{1}\]  
where \(F\) refers to a non-linear transformation or activation function, such as Softmax. Because of its recurrent structure, the hidden state \(S_{\tau-1}\) in Equation (1) can be seen as a remembrance of the preceding inputs. Therefore, RNNs have the capability to store and recall previous inputs in their internal state. This property makes RNNs highly applicable for tasks, e.g., text generation [42], that involve predicting the subsequent word in a sentence by considering the preceding words. Furthermore, RNNs are often applied in speech recognition and synthesis [43].
# Feature-Level Camera Style Transfer for Person Re-Identification
## 3 Feature-Level Camera Style Transfer
### Problem Modeling  
The person re-ID problem is related to determining whether the two input images belong to the same person. Traditional methods often consist of feature extraction and metric learning. Camera style variance can harm the representational ability of traditional identity features. This paper proposes extracting an additional camera style feature to provide camera-related information. Furthermore, a generative model is proposed to make camera style transfer to alleviate camera style variance for the re-ID problem.  
**Identity Feature and Camera Style Feature.** Generally speaking, a deep learning-based person re-ID approach consists of two main parts: representative feature extraction and distance metric learning. In a typical approach, a feature extractor \(E_{I}\) takes a pair of person images \((x_{i},x_{j})\) with labels \((y_{i},y_{j})\) as input and outputs the extracted identity features \((f_{i}^{l},f_{j}^{l})\). Then, the distance of the features is measured to determine the similarity of images \(x_{i}\) and \(x_{j}\). If the two images have the same label, i.e., \(y_{i}=y_{j}\), the approach should determine that \(x_{i}\) and \(x_{j}\) are of the same person. However, if \(x_{i}\) and \(x_{j}\) were captured by different cameras and thus have different camera labels \(c_{i}\
eq c_{j}\). The camera style information contained in the extracted features makes it hard to make the right decision. An extra feature extractor \(E_{C}\) is used to capture features related to camera style \((f_{i}^{C},f_{j}^{C})\). Then, the extracted identity feature \((f_{i}^{l},f_{j}^{l})\) and camera style feature \((f_{i}^{C},f_{j}^{C})\) are used in a feature-level generative model to complete the camera style transfer of a given feature. The necessity of the extra independent feature extractor \(E_{C}\) is discussed in Section 5.6.1.  
**The Generative Model for Camera Style Transfer.** The generative model aims to make camera style transfer at the feature level. Thus, it takes a pair of features \((f_{i}^{l}|y_{i},f_{j}^{C}|c_{j})\) as input where \(f_{i}^{l}|y_{i}\) denotes the source identity feature with identity label \(y_{i}\), which is extracted from source image \(x_{i}\) by extractor \(E_{I}\). Similarly, \(f_{j}^{C}|c_{j}\) denotes the target camera style feature with camera label \(c_{j}\), which is obtained from target image \(x_{j}\) by an extra camera style feature extractor \(E_{C}\). Then, the generator \(G\) generates feature \(f^{l}\) conditioned on identity label \(y_{i}\) from \(f_{i}^{l}\) and camera label \(c_{j}\) from \(f_{j}^{C}\). The whole generation process of the generative model can be expressed as:  
\[\begin{split} f_{i}^{l}|y_{i}&=E_{I}(x_{i})\\ f_{j}^{C}|c_{j}&=E_{C}(x_{j})\\ f^{l}|y_{i},c_{j}&=G(f_{i}^{l},f_{j}^{C})\end{split} \tag{1}\]  
The generated feature \(f^{l}|y_{i},c_{j}\) has the same identity label \(y_{i}\) as the source feature \(f_{i}^{l}|y_{i}\) and the same camera label \(c_{j}\) as the target feature \(f_{j}^{C}|c_{j}\).
## 4 Experiments
### Experiment Setup  
The reviews presented in the dataset were cleaned of non-English words and pre-processed. Tokenization, N-grams generation, text vector building, and designing of the CNN layers was conducted using python programming language in the virtual laboratory (Google Colaboratory). The obtained vector was split into two subsets, 80% of the dataset was used for training the CNN model, and the other 20% was used for evaluating the classification performance. Since the dataset contained multiple sentences (reviews), pooled output was used in the BERT embedding. The rectified linear unit (RELU) was used as the activation function, with 100 neurons for the hidden fully connected layer. The output dense layer was set up with two (2) neurons since the texts were to be classified into two classes. Softmax was used as the activation function, which was in line with the text classification problem at hand. In the study, we used 50-dimensional Glove word embeddings trained on Google News, 250-dimensional Word2 Vec embeddings trained on Wikipedia, and 128-dimensional BERT embeddings trained on English Wikipedia corpus. In the experiments, we used tensor flow tools to prepare the data and build our proposed model. Among the training set, a small potion (100) of the reviews was used for validation. In Section 4.3, we present the model parameters of the designed CNN model.  
Figure 3: The CNN model.
# A Hybrid Linear Quadratic Regulator Controller for Unmanned Free-Swimming Submersible
## 6 Experimental Results
### Comparison and Discussion  
This section compares the heading and pitch parametric values achieved by the proposed controller with [4; 23]. Table 8 shows a quick comparison in terms of rising time, settling time, and steady-state error for controlling pitching angle and heading. It can be seen that the proposed controller (HLQR) performs well and settle the disturbances more quickly and efficiently. Results of [4; 23] show that the proposed controller has a comparatively sharp rise time. Moreover, it effectively removes the steady-state error. It is important to note that the work in [23] also removes the steady-state error completely, however, the work in [4] removes it to some extent. In addition to it, the work in [4] controls disturbances without requiring the mathematical model dynamics and kinematics of vehicles. It helps to handle the hydrodynamic disturbances with a minimum overshoot. The other two require re-modeling of dynamics of the vehicle.  
Similarly, ref. [24] has presented the development of a novel self-adaptive fuzzy PID controller for an AUV based on a non-linear MIMO topology. Authors have employed a combination of adaptive techniques and dual PID controllers. As a result, the uncertainty problem in PID parameters and AUV modeling uncertainty may be solved more effectively. However, exposure to hydrodynamic disturbances in depth leads to uncertainty in performance, whereas our controller provides exceptional dynamic, spectacular steady-state qualities, and remarkable stability at all depth levels.
# Edge Couplers in Silicon Photonic Integrated Circuits: A Review
## 4 Structural Transformations of Edge Couplers in Vertical Direction
### Edge Couplers Based on Cascaded Multi-Stage Tapers
Alignment tolerance with a fiber is also measured in this design, with 2.5 \(\upmu\)m and 2 \(\upmu\)m for 1-dB excess in horizontal and vertical directions, separately. The quantity of stages in the cascaded multi-stage edge coupler is unconstrained, but it should be proper, since too many layers will increase the fabrication complexity with excessive steps of deposition and lithography, while too few layers may lead to insufficient mode conversion into the critical silicon layer. Generally, three to five layers are most commonly used for cascaded multi-stage  
Figure 11: Two regimes of multi-stage tapers with (**a**) a forward taper and (**b**) an inverse taper. Figure 12: Simulated optical field distribution in a multi-stage edge coupler under the regime depicted in Figure 9b. (**Left**) top view and (**Right**) side view of modal distribution. Reproduced with permission from [82], Copyright IEEE, 2016.  
edge couplers. The complexity of the fabrication process is determined by how many stages are utilized, since lithography, etch, and polishing are required to prepare the taper structure in each layer. In addition, the sidewall roughness of the taper in each layer can also lead to excess loss to coupling efficiency [83; 84; 85; 86; 87]. Interlayer taper tip alignment should be considered, and thick cladding needs to grow upon the traditional SOI structure to reduce the mode size mismatch with the fiber core. To summarize, the fabrication of a multi-stage edge coupler is compatible with the typical CMOS process, and the main processes include optical lithography, etching, deposition, and polishing. However, the fabrication can be very complicated and time-consuming due to the existence of multiple stages, which constrains the practicality and feasibility of wide application. In research scenarios where a single mode fiber is used, this kind of configuration is quite suitable because of its large spot size, low modal mismatch, and high alignment tolerance. However, its path toward commercialization is blocked.
## 3 Results and Discussions  
Spatially explicit linear regression results are shown in Figure 2. Crophlands resulted in a regression coefficient close to 1, while forested areas resulted in higher intercept values and a consequently lower regression coefficient. A high coefficient of determination values demonstrates the robustness of the linear regression model.  
The LAI biophysical processor calibrated on S2 spectral bands at 10 m spatial resolution overestimates the LAI values, compared with the biophysical processor calibrated on the S2 full spectrum and with the biophysical processor calibrated on the L8 (Figure 3). The LAI estimates from the biophysical processor calibrated on L8 have a strong linear relationship with the LAI estimates from the model calibrated on the S2 full spectrum. This evidence demonstrates how red-edge and short-wave infrared spectral reflectances provide key information related to vegetation status, which complements plant photosynthetic activity revealed from visible and near-infrared wavelengths.  
The LAI biophysical processor calibrated on S2 spectral bands at the 10 m spatial resolution overestimates the LAI values for the different plant functional types (Figure 4). This overestimation was found to be higher for grasslands and lower for needleleaved forests. Further improvements require in situ measurements to complement the linear relationship analysis with the evaluation of accuracy metrics.  
Figure 2: Spatially explicit linear regression results: (**a**) linear regression coefficient; (**b**) linear regression intercept; (**c**) coefficient of determination; (**d**) spatial detail of linear regression coefficient over plain croplands; and (**e**) spatial detail of linear regression coefficient over hilly croplands.  
Figure 4: Density scatterplot showing the linear relationship for LAI estimated using the two Sentinel-2 MSI models calibrated on different band sets for main land cover classes: (**a**) croplands; (**b**) grasslands; (**c**) broadleaved forests; and (**d**) needleleaved forests.  
Figure 3: Density scatterplot showing the linear relationship for LAI estimated from different spectral bands sets. (**a**) Sentinel-2 MSI 10 m spectral bands set against Sentinel-2 MSI full spectral bands set; (**b**) Landsat01 local spectral bands set against Sentinel-2 MSI full spectral bands set; (**c**) Landsat01 local spectral bands set against Sentinel-2 MSI 10 m spectral bands set.  
The plot in Figure 5 shows how LAI estimates from multiple satellite sensors, with an unbiased linear relationship, can constitute denser time series from virtual satellite constellation, contributing to improve temporal analysis, such as vegetation phenology estimation, and consequently enhancing agroecosystems monitoring capacity. The weak spatial coherence of Sentinel-2 acquisitions over time requires an image co-registration procedure to be applied in order to reduce shaky time series [12].
# A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments
## 3 Methodology
### MUSIC Spectral Function Calculation and Peak Search  
The noise subspace of the received signal is obtained through Equation (14). In order to reduce the submersion of the non-direct path signal to the direct path signal, we have improved the calculation method of the spectral function in the traditional MUSIC algorithm. The improved calculation method is as follows:  
\[P_{norm}=\big{|}\textbf{a}(\tau_{i})\textbf{U}_{\textbf{c}}\big{|} \tag{36}\]  
\[P_{MUSIC}=10\big{(}\frac{\max(P_{norm})-P_{norm}}{\max(P_{norm})-\min(P_{norm} )}\big{)} \tag{37}\]  
where \(P_{MUSIC}\) is the spectral function.  
Then, find the delay corresponding to the peak value of PMUSIC as the estimated value of propagation delay:  
\[\hat{\tau}=\text{argmax}(P_{MUSIC}) \tag{38}\]  
After the above process, the estimated propagation delay can be obtained.
## 3 Simulation of Thermal Power Characteristics
### Simulation Results and Analysis
#### 3.2.3 Power Loss Analysis at Extreme High Speed  
As shown in Figure 9, internal gear pumps exhibit two distinct low-efficiency zones. In the low-speed high-pressure zone, insufficient hydrodynamic film load capacity in friction pairs and weakened velocity sealing effects lead to a reduction in volumetric efficiency. Conversely, the high-speed low-pressure zone suffers from deteriorated mechanical efficiency due to sharp growth in frictional power consumption of moving components and degradation of lubrication performance. The study demonstrates that at the rated speed of 2000 r/min, pressure elevation from 7 MPa to 21 MPa dominates total loss modulation with a 1.37 kW increase, while speed variation between 500 r/min and 2000 r/min induces a weaker influence of only 0.899 kW increment. Notably, under extreme high-speed conditions at 3500 r/min, rotational speed sensitivity surpasses pressure effects: accelerating from 2000 r/min to 3500 r/min at constant 21 MPa results in a 1.99 kW loss surge, significantly exceeding the 1.529 kW increase caused by pressure elevation from 7 MPa to 21 MPa. It can be obtained that the power loss under the extremely high speed condition is more sensitive to the speed change.  
Figure 8: Volumetric loss distribution cloud diagram of internal gear pump.  
Figure 7: Mechanical loss distribution cloud map of internal gear pump.