esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Fabric Defect Detection Model for High-Resolution Images
## 2 Proposed Model Based on Cascade R-CNN
### Cascade R-CNN  
Faster Region-based Convolutional Neural Network (Faster R-CNN) is a commonly used two-stage detection network [17]; however, this network has limitations. During model training, the Region Proposal Network (RPN) generates many candidate boxes, which are identified as positive samples based on the Intersection over Union (IoU) between the candidate and true boxes. However, it is difficult to select an appropriate threshold for IoU; when the threshold is significantly small, some candidate boxes that are not the detection targets may get misidentified as positive samples, whereas when the threshold is large, many small target candidate boxes may be omitted, resulting in positive and negative samples imbalance owing to a small number of positive samples. Therefore, it is necessary to design a more robust candidate box screening method.  
The overall architecture of Cascade R-CNN is shown in Figure 1. INPUT indicates the input image, Conv indicates the backbone, NECK indicates the neck network, Pool indicates region-wise feature extraction, H(1,2,3) indicates the region of interest heads, B(1,2,3) indicates bounding box regression, C(1,2,3) indicates classification, and B0 indicates proposals in RPN. Cascade R-CNN can solve the problem of threshold selection in a region of interest (ROI) by applying a multistage Region-CNN structure. The network model sets multiple detection heads, as shown in Figure 1, and the output boxes of each stage can be used as input candidate boxes for the next stage. In this study, different thresholds were set for each detection heading simultaneously, and they were gradually increased so that the proposals from the previous stage could adjust to the next step with a higher threshold, thus enhancing the detection accuracy. The thresholds here are hyperparameters that need to be adjusted for different types of data sets.
# Automatic Translation between Mixtec to Spanish Languages Using Neural Networks
## 2 Theoretical Foundation  
Before describing the proposed methodology, it is essential to address the fundamentals of recurrent neural networks, Transformer networks, the BLEU evaluation metric, and Translation Edit Rate (TER).
## 3 Description of Control Schemes
### Integrating SMO with MPC  
This section presents the mathematical formulation of the SMO that is used in feedback mode to estimate the states of the system, as illustrated in Figure 1. There are two main stages of SMO, namely the sliding phase and the reachability phase. The stator voltages and currents are being utilized as inputs, and the SMO estimates the unknown Back EMF. The voltages are extracted from the switching states of the pulses as:  
\[\left[\begin{array}{c}v_{a}\\ v_{b}\\ v_{c}\end{array}\right]=\frac{v_{dc}}{3}\left[\begin{array}{ccc}2S_{a}&-S_{b}& -S_{c}\\ -S_{a}&2S_{b}&-S_{c}\\ -S_{a}&-S_{b}&2S_{c}\end{array}\right] \tag{14}\]  
A sliding surface is designed by the error difference equation between the estimated currents and measured currents to reach the sliding phase and the resulting error reduces to zero. A reaching phase control law is designed as follows [11]:  
\[u=\left[\begin{array}{cc}K_{sw}\text{sgn}(\vec{t}_{d})&K_{sw}\text{sgn}( \vec{t}_{q})\end{array}\right]^{T} \tag{15}\]  
Figure 2: Finite Control Set-Model Predictive Control (FCS-MPC) internal modeling.  
The signum function is used to reduce error when the sliding phase condition is met. The observer dynamics for currents are expressed as:  
\[\frac{d\tilde{i}_{dq}}{dt}=-\frac{R_{s}}{L_{s}}\tilde{i}_{dq}+\frac{1}{L_{s}}v_{dq }-\frac{K_{sw}}{L_{s}}\text{sgn}(\tilde{i}_{dq}) \tag{16}\]  
where \(\tilde{i}_{dq}=\tilde{i}_{d}-i_{dq}\). A positive definite Lyapunov candidate function is chosen as \(V=\frac{1}{2}S^{2}\) and \(K_{sw}=\text{max}(|v_{d}|,|v_{q}|)\) is derived as the reachability condition for the SMO. Both conditions are called Lyapunov asymptotic stability conditions. Currents obtained through Equation (16) are used as feedback reference currents for the MPC to compare with the measured currents.
## 4 Results and Discussion
### Limitations  
An efficient parallelization of SpMV requires optimal and balanced distribution of input matrix to processors with minimum inter-processor communication by exploiting the properties of sparse matrices and underlying machine architecture. As a result, different storage formats were proposed to efficiently utilized computation resources and to minimize overall communication and execution cost. Most of the storage schemes are customized to target the optimization of the sparse matrices with specific patterns. The overall work set of SpMV algorithm is reduced with blocking as these methods need to maintain single index per block. Selection of appropriate block size is not straight forward. Fixed size blocks require padding and variable size blocking requires additional data structure.  
Manual process of trial-and-error block size is error prone and time consuming. Different sparse matrices have different structures which is an unknown entity before runtime, thus we used machine learning based optimization of SpMV kernel using BCSR. The cost associated with format conversion from CSR to BCSR increases with irregular patterns of non-zero elements which ultimately requires excessive padding. The variable blocking requires processing of additional data structures. The proposed methodology works with fixed size blocking and becomes more complex with variable block size and increasing number of cores and threads. We have experimented with, around 700 small to medium scale sparse matrices. The overall performance of the proposed methodology can be improved by increasing the data set size (sparse matrices of any dimension) and including more sparse features with low computation complexity. For the ease of implementation, we have considered only square sparse matrices and features are extracted from real world sparse matrices that require some computations to be performed to prepare dataset for training machine learning algorithms. Some of these features requires a full scan of sparse matrix and have computation complexity of \(\Theta\) (M) and their standard deviation with complexity \(\Theta\) (2M). There is a need to incorporate advance heuristics and performance models to accurately predict complex scenarios with variable block sizes that also adapt to modern multi-core architectures.
# Innovative Approaches to Semi-Transparent Perovskite Solar Cells
## 6 Applications of ST-PSCs
### Silicon/CIGS-Perovskite Tandem Solar Cells
They used the additives MACI and MAH\({}_{2}\)PO\({}_{2}\) to enhance the perovskite grain size and to passivate grain boundaries; as a result, the top ST-PSC generated a current that matched well with the bottom Si cell. [PERSON] et al. demonstrated a certified efficiency of 25.7% and good device stability with the use of solution processes to make perovskite top cells on textured Si bottom cells [146]. [PERSON] et al. reported an efficient and stable 2T perovskite-Si tandem solar cell with a certified efficiency of 26.2% and an operational efficiency of 26.7%, for which they used anion-engineered additives to increase the stability and performance of perovskite top cells [147]. As a new fabrication approach for realizing R2R, [PERSON] et al. demonstrated a TCO-free ST-PSC via an entirely vacuum-free and low-temperature solution process [110]. They utilized extremely conductive n-PEDOT:PSS as both the bottom and top transparent electrodes and stacked TCO-free ST-PSCs on the c-Si bottom cell to build a 4T P-TSC, achieving an overall PCE of 19.2%. This approach is not only ideal for creating flexible devices but is also promising for large-scale TSCs in the future. P-TSCs based on bottom PSCs with a narrow band-gap (\(\sim\)1.2 eV) also have been investigated. [PERSON] et al. reported a 22.1% efficiency using mechanically stacked 4T P-TSCs using narrow-band-gap perovskite and CIGS top cells [132]. [PERSON] et al. reported a monolithic two-terminal perovskite-CIGS tandem solar cell (2T P-PSC) with a verifiedconversion efficiency of 22.43%, which was fabricated with PTAA as the HTL layer, PCBM as the ETL layer and ZnO nanoparticles as a protective layer [148]. Furthermore, a tandem cell composed of only perovskite was studied, in which a narrow band-gap (\(\sim\)1.2 eV) PSC was used as a bottom cell and a wide-band-gap (up to 1.8 eV) PSC was employed as a top perovskite cell. [PERSON] et al. reported the first perovskite-perovskite tandem solar cell (2T and 4T P-PSC) with a PCE of 17.0% using a FA\({}_{0.83}\)Cs\({}_{0.17}\)Pb(I\({}_{0.5}\)Br\({}_{0.5}\))\({}_{3}\) top cell (1.8 eV) and a FA\({}_{0.75}\)Cs\({}_{0.25}\)Pb\({}_{0.5}\)Sn\({}_{0.5}\)I\({}_{3}\) bottom cell (1.2 eV) [149]. [PERSON] et al. employed Cs\({}_{0.05}\)(MA\({}_{0.17}\)FA\({}_{0.83}\))Pb\({}_{1.1}\)(I\({}_{0.83}\)Br\({}_{0.17}\))\({}_{3}\) (1.63 eV) as a perovskite layer for the top 2T P-TSC and attained a TSC with an efficiency of 25.5% in the configuration of a back-side texture mixed with a front-side light management foil [150].
## 3 Path Model Design
### R-Curve Path Model
#### 3.1.2 Stage 2: Mobility Motion Description  
As shown in Figure 3b, the mobile node will begin at point M1 in the sub-field F1 and will travel in a straight line vertically to the upper direction, pausing for one minute at point M2. After that, it will move in a straight line vertically to the upper direction stopping for one minute at M3 in sub-field F3. Then, it will move in straight line horizontally to the right direction stopping for one minute at M4. Then, it moves in straight line vertically downwards and reaches M0, which is the center point of the four sub-fields. Finally, it moves in a diagonal line with an angle 45\({}^{\circ}\) toward M5, stopping for one minute at M5 in sub-field F2.  
There are two scenarios for the role of the mobile anchor node; in the first scenario as shown in Figure 5, the mobile anchor node acts as an access point with a specific IP address and creates a network, and the mobile node pauses at each position for one minute and sends messages to the unknown nodes with the pausing point number and position X, Y. Hence, every unknown sensor acts as a server in each sub-field that has at least information from three different mobile pausing points in its communication range. Then, it will select the three saved messages which have the largest RSS values and acquire its position using the accuracy-priority trilateration technique (APT), which evaluates the positions of non-localized nodes by taking the three nearest messages from the all-accepted messages [32]. APT has high localization accuracy in comparison with the trilateration technique [32]. The data are available on requirement from the webpage of the specified IP.  
In the second scenario, as shown in Figure 6 the mobile anchor node acts as a server, which saves the positions of all pausing points and their numbers. It pauses at each position for one minute and sends \"Hello\" messages to all the unknown sensor nodes, then each unknown sensor plays two roles, the access point which starts the network, and the client which replies at each \"Hello\" message from the server with a message containing its number. The mobile node saves each message and the equivalent RSSI. Then, for each unknown node, it selects the largest RSSI values and the pausing point position where it acquires them and calculates the position using APT. Then, it automatically sends all the data to its webpage with the nodes number and their positions.  
Using the two scenarios, the accuracy of the position of the unknown sensor node depends on selecting an effective path model for the existing obstacle.  
The overall distance taken by the mobile node at le = 1 and resolution L is denoted by (3):  
\[4\;*\;\text{L}+\sqrt{2}\;\text{L} \tag{3}\]  
At le = 2 as in Figure 4a the overall distance is denoted by (4):  
\[\left(4\;*\;\text{L}+\sqrt{2}\;\text{L}\right)\;*\;  
Figure 5: First scenario.
## 2 Description of Experiments
### Similarity and Differences of The Navair Test Rig and The Laboratory Testing  
The purpose of the UIC laboratory experiments is to simulate fatigue crack growth other than simulating fretting noise. The burst type AE signals due to the crack growth of the UIC fatigue test is embedded into the streamed AE signals that were obtained from the Navair gearbox test to understand the detectability of crack signal from the streamed AE signals. The purpose is to identify the operational noise signal from the Navair gearbox test and the crack AE signal from the UIC fatigue test for developing an efficient signal processing method to extract the crack signal from the streamed waveforms. Therefore, it is important to identify if two test conditions are comparable.  
The comparison of the Navair gearbox test and the UIC fatigue test is summarized in Table 1. The AE sensor types and spline geometry of both experiments are similar. The motion of spline gear in the helicopter is rotation, and the fatigue crack initiates and grows after certain numbers of rotational loading. The direction of crack growth is typically perpendicular to the spline based on the Navair statistical data.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
Figure 3: The comparison of cracks directions in the Navair gearbox test and the UIC fatigue test, (**a**) gear spline; (**b**) planar spline; (**c**) microscopic image of crack growth at the planar spline; and (**d**) the acoustic microscope image of the crack measured from the back surface of the planar spline.  
Figure 2: The UIC fatigue test, (**a**) the loading machine with the sample, (**b**) the planar spline sample and dimensions, and (**c**) the positions of AE sensors and strain gauge.  
The crack directions from the NavaIR test rig and the UIC fatigue test are compared in Figure 3. The crack generated in the actual spline is in the circumferential direction of splines similar to the crack growth pattern of the planar geometry. For the driving loads of crack, there are two types of loaded splines in rotorcraft that experience refetting that would lead to either wear or crack initiation. The first loading condition is more common in drive shafts, and there is little moment that is applied to drive shafts that can produce a crack that grows normal to the axis of the shaft. This condition generally produces wear that erodes the spline until all of the splines are too thin to transfer the torque at which point overload occurs. The second loading condition occurs when the splines are in tension from the preload, and the gyroscopic loads are very high and cause a reverse bending situation. This condition causes the crack initiated by spline refetting to grow perpendicular to the shaft axis.
# Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging
## 3 Experimental Results and Discussions
### Resampling Methods  
To address the challenge of imbalanced data in the Titanic dataset, six resampling methods were applied and evaluated. The purpose of this process was to balance the number of survivors and non-survivors, which is critical for improving the model's ability to generalize and accurately predict survival outcomes. The six methods used were SMOTE (Synthetic Minority Oversampling Technique), which generates synthetic samples from the minority class; SMOTETomek, a combination of SMOTE and Tomek links to remove noisy data points; ADASYN, which generates synthetic data focused on the minority class instances that are harder to classify; SMOTE+ADASYN, a hybrid of SMOTE and ADASYN techniques; TomekLinks, an undersampling method that removes overlapping majority class samples; and _ENN_ + SMOTE, which combines Edited Nearest Neighbors (_ENN_) to clean noisy majority samples with SMOTE for minority oversampling.  
Among these methods, as shown in Figure 8, _ENN_ + SMOTE demonstrated the best performance, with a cross-validation accuracy of 0.89210, outperforming the other methods. This method's strength lies in its ability to not only generate synthetic minority samples but also to clean the majority class by removing borderline or misclassified samples through _ENN_, which enhances model generalization and reduces the risk of overfitting. SMOTETomek and TomekLinks also performed well, with accuracies of 0.84102 and 0.83650, respectively, while SMOTE, SMOTE + ADASYN, and ADASYN yielded lower performance.  
Figure 7: Overfitting comparison: Non-PCA vs. PCA accuracy.  
By selecting _ENN_ + SMOTE, the model benefits from a cleaner, more balanced dataset that better represents both classes, leading to improved predictive performance and more reliable survival predictions on unseen data.
## 2 Analysis of the State of the Art
### OpenDDR
#### 2.3.6 Device Discussion  
Apart from all the cited techniques for considering devices and their capabilities, we need to remark how technology evolves in a way that makes difficult to predict how we will work with smart devices in the near future. A few years ago context was understood as the result of the mixture of every physical sensor deployed in the environment. Temperature, light, noise,... all these context variables have been historically collected through several sensors located strategically. Now these sensors (and more) are also embedded in these smart devices. They have become wearable, and using their capabilities of the devices they area embedded in they have improved their performance. Touchable screens, network capabilities, parallel computing resources and high storage space embedded in small devices have changed the way context and smart devices interact. This situation makes us contemplate how this interaction channel would be in the near future. Smart watches, glasses and other wearable devices are, undoubtedly, changing the way smart environments will sense, behave and react.
###### Abstract
A long-term accessibility study showed that a cool roof reduced summer peak temperatures by up to 4.7 \({}^{\circ}\)C [24]. An urban heatwave response project estimated temperature reduction rates using a microclimate analysis modeling approach [25]. A thermal infrared image acquired by a drone was analyzed to determine the effects of heat island reduction techniques during a heatwave [26]. The solar reflectance index (SRI) is also used to evaluate cool roofs [27]. Reflective concrete pavement has been reported to lower temperatures by 0.2-0.4 \({}^{\circ}\)C [28], resulting in a 60% higher albedo, an indicator of the amount of light reflected by an object, for the coated area and a 40% lower surface temperature for the painted surface, compared with an uncoated surface [29]. In Rome, cool pavements installed to mitigate high urban temperatures resulted in a significant temperature decrease that was closely associated with solar reflection [30]. Cool pavements have been compared with existing asphalt in alleys to determine their effects on temperature [31]. Multiple studies have also examined the economics of the urban heat island effect and the impact of cooling pavements on climate change [32]. In some studies, the heat island effect was simulated using weather research and forecasting urban canopy models [33], or actual measurements are analyzed using high-precision GPS data [34]. Furthermore, previous studies have demonstrated that packaging materials significantly affect surface temperatures [15]. Although various heatwave mitigation technologies are available, their proper applications have not been thoroughly investigated. To understand how the installation of heatwave mitigation measures affects adjacent buildings or pedestrians in a complex city, the interactions of those measures with elements of the city must be examined. In particular, empirical studies are needed to understand how heat stress directly affects building users and pedestrians during a heatwave [35]. In this study, we quantified the effects of cool roofs and cool pavements on surface temperatures; we also conducted a survey of the users of these systems to determine their perceptions of the temperature effects of these heatwave mitigation measures. A heatwave mitigation facility was installed in the Jangyumuoye district of Gimhae, Republic of Korea, from August to October 2019. In terms of the recording period, Republic of Korea is characterized by frequent rainy days during the rainy season in July and August, which is why measurements were conducted between late August and October. To examine temperature differences after heatwave mitigation measure installation, we measured temperatures at the mitigation facility and at control sites. A flowchart of the procedures followed in this study is provided in Figure 1. Our quantitative analysis of the results will be useful for establishing future heatwave policies using the examined technologies.
# Experimental Quantum Message Authentication with Single Qubit Unitary Operation
## 2 Quantum Message Authentication Protocol  
Quantum message authentication, which is similar to conventional message authentication, should provide message integrity and origin authentication. What differentiates quantum message authentication from conventional message authentication [41; 42] is that the former uses quantum states \(\ket{0}\) and \(\ket{1}\) as a message represented by a sequence of \"0\" and \"1\" bits. In addition, using arbitrary quantum states as a message enables more information to be delivered at once [43; 44]. Moreover, there is a significant difference that is described below. In modern cryptography, asymmetric key cryptography easily provides message integrity, message origin authentication, and nonrepudiation. Unfortunately, a quantum asymmetric key cryptosystem based on the quantum trapdoor one-way function do not exist, making the design of quantum authentication and quantum signature protocols difficult. To overcome this difficulty, we propose a new quantum message authentication protocol based on [PERSON]'s three-pass protocol [1; 35]. [PERSON]'s three pass protocol has the advantage that two parties, e.g., [PERSON] and [PERSON], can share information without exposing their own private keys. In the implementation, the central idea is that the commutative property [19] of exponential operation in [PERSON]'s three-pass protocol is implemented using single-qubit rotation operators consisting of linear combinations of wave plates. To our knowledge, this is the first time a quantum message authentication protocol has been proposed using the quantum three-pass protocol, though other applications of the quantum three-pass protocol, such as direct communication [32], quantum key distribution [30], and quantum signature [19], have been proposed theoretically. Figure 1 schematically shows the quantum message authentication protocol that we implemented. Our quantum message authentication protocol consists of preparation, quantum message authentication, and verification phase.