esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Electromyography-Based Action Recognition and Manipulator Control
## 2 Method and Mathematical Background
### Evaluation of Classification  
The confusion matrix is an error matrix that is commonly applied in pattern recognition. It depicts the relationship between the true attributes of the sample data and the recognition result, and is a common method to evaluate the performance of the classifier. The confusion matrix is a square matrix with the size (\(n\_classes\times n\_classes\)), where \(n\_classes\) represents the number of classes. Each row of the matrix represents the instance in the true class, and each column represents the instance in the prediction class. Through the confusion matrix, it is easy to see whether the classifier confuse the multiple classes or not [45; 46].  
Suppose that for the classification task of \(L\)-classes, the recognition data set \(D\) includes \(T_{0}\) samples, each type of mode contains \(T_{w}\) data (\(w=1,\,2,\dots,\,L\)). A certain recognition algorithm is used to construct the classifier R. \(rw_{st}\)\(s,t=1,\,2,\dots,\,L\) represents the number of the data of the \(s\)th class, judged as the \(t\)th class by the classifier R to the total number of the \(s\)th class samples, and then the \(L\times L\) dimensional confusion matrix is gained [47]:  
\[\left(\begin{array}{ccccc}rw_{11}&rw_{12}&\cdots&rw_{1s}&\cdots&rw_{1L}\\ rw_{21}&rw_{22}&\cdots&rw_{2s}&\cdots&rw_{2L}\\ \vdots&\vdots&&\vdots&&\vdots\\ rw_{s1}&rw_{s2}&\cdots&rw_{ss}&\cdots&rw_{sL}\\ \vdots&\vdots&&\vdots&&\vdots\\ rw_{L1}&rw_{L2}&\cdots&rw_{Ls}&\cdots&rw_{LL}\\ \end{array}\right) \tag{14}\]  
The row subscripts of the elements in the confusion matrix correspond to the true attributes of the class, and the column subscripts correspond to the recognition attributes generated by the classifier. Diagonal elements represent the number of each class that can be correctly identified by the classifier R, while the non-diagonal elements represent the number of incorrect judgments. In the ideal situation, the predicted category of each sample is correct, then the confusion matrix becomes a diagonal matrix. In this study, due to the fact that we chose 5 representative actions--fist clenching, hand opening, wrist flexion, wrist extension, and calling me, we finally got a confusion matrix with 5 rows and 5 columns.
## 3 Results
### Regional Scale Simulation  
#### 3.2.1 Simulation Results  
In 2020, the total NPP in the study area was 4.25 \(\times\) 10\({}^{11}\) g C a\({}^{-1}\), with an average of 564.71 g C m\({}^{-2}\) a\({}^{-1}\). The annual NPP and average NPP of different stand types were different (Table 3). The stand type with the largest average NPP was silver birch, which was 647.84 g C m\({}^{-2}\) a\({}^{-1}\), followed by coniferous and broad-leaved mixed forest, camphor pine, spruce, evergreen and deciduous coniferous forest, northern Chinese larch, sparse forest and shrub forest. The total NPP was related to the area of the stand type. Because northern Chinese larch was widely planted, the stand type with the largest total NPP was northern Chinese larch, which was 1.73 \(\times\) 10\({}^{11}\) g C a\({}^{-1}\), followed by silver birch, camphor pine, coniferous and broad-leaved mixed forest, spruce, sparse forest and shrub forest, evergreen and deciduous coniferous forest. It can be seen that for the primary productivity of each stand type in the study area, broad-leaved forest > coniferous and broad-leaved mixed forest > coniferous forest, evergreen forest > deciduous forest.  
As can be seen from Figure 9, the NPP in the study area in 2020 was mainly concentrated in 300-700 g C m\({}^{-2}\) a\({}^{-1}\), and in a few area it was below 300 g C m\({}^{-2}\) a\({}^{-1}\), and in some areas it reached more than 700 g C m\({}^{-2}\) a\({}^{-1}\). The quality of forest land in the eastern part of the study area was high, and its annual average NPP was generally higher than 700 g C m\({}^{-2}\) a\({}^{-1}\).  
Figure 8: Annual average daily variation of air temperature and simulated canopy temperature of the flux tower.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Stand Type**} & **Annual NPP** & **Total NPP** \\  & **(g C m\({}^{-2}\) a\({}^{-1}\))** & **(g C a\({}^{-1}\))** \\ \hline Northern Chinese Larch & 518.05 & 1.73 \(\times\) 10\({}^{11}\) \\ Camphor Pine & 576.95 & 7.81 \(\times\) 10\({}^{10}\) \\ Spruce & 565.48 & 1.95 \(\times\) 10\({}^{10}\) \\ Silver Birch & 647.84 & 1.22 \(\times\) 10\({}^{11}\) \\ Coniferous and Broad-leaved Mixed Forest & 661.35 & 2.52 \(\times\) 10\({}^{10}\) \\ Evergreen and deciduous Coniferous Forest & 558.05 & 2.46 \(\times\) 10\({}^{9}\) \\ Sparse Forest and Shrub Forest & 292.60 & 5.11 \(\times\) 10\({}^{9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: NPP statistics of different stand types.
###### Abstract
We evaluate our framework using several LLMs, including Mistral-7B-Instruct-v0.3, Llama-3-70B-Instruct, GPT-3.5-turbo, and GPT-4o across twelve diverse low-resource language pairs in both directions: Thai, Gujarati, Marathi, Pashto, Burmese, and Sinhala paired with English. Our evaluation reveals an interesting asymmetry: the retrieval-based approach significantly outperforms random example selection in many-to-one (X\(\rightarrow\)English) scenarios but shows comparable performance in one-to-many (English\(\rightarrow\)X) settings. This directional difference provides valuable insights into the dynamics of cross-lingual transfer and the impact of example selection across different language directions. A key innovation of our work lies in examining how the effectiveness of retrieval-based in-context learning varies across translation directions. Through comprehensive analysis, we investigate the relationship between example similarity and summarization quality, revealing distinct patterns across different translation directions (X\(\rightarrow\)English versus English\(\rightarrow\)X). Furthermore, we analyze the relationship between example similarity and summarization quality, providing insights into how retrieval-based methods enhance cross-lingual transfer. Our findings demonstrate that intelligently selecting contextually relevant examples can substantially improve XLS performance by providing LLMs with more informative demonstrations, particularly when summarizing from low-resource languages into English. Table 1 summarizes the key differences between our approach and existing cross-lingual summarization methods, highlighting the unique advantages of MuRXLS in terms of example selection strategy and cross-lingual effectiveness. The main contributions of our work are as follows:  
* We demonstrate significant performance improvements across twelve language pairs, with particularly strong gains in X\(\rightarrow\)English summarization. * We identify a notable directional asymmetry where retrieval-based selection benefits high-resource target languages substantially more than low-resource targets. * We establish a strong correlation between example similarity and summarization quality, providing statistical evidence for the efficacy of our approach. * We verify the effectiveness of method across multiple LLMs of varying sizes and capabilities.
## 4 Conclusions  
In this work, we used PTAA/NiO bilayers as the HTL in PSCs with p-i-n structures and showed significant improvement in device PCE compared to devices with an HTL of NiO. When the PTAA/NiO bilayer was used as the HTL, the PCE increased from 17.29% to 19.05%. Because PTAA has a lower work function compared to NiO, the energy band alignment of PTAA/NiO bilayer-based PSCs is better than that of NiO monolayer-based PSCs when PTAA is present between the perovskite and NiO. Moreover, the analysis of the charge transport behavior showed that the device using the PTAA/NiO bilayer HTL had high performance due to its improved carrier transport, carrier extraction and reduced recombination. It can, therefore, be suggested that the PTAA/NiO bilayer as an HTL controlled by interface engineering is a promising pathway to further improving the stability of PSCs.  
Y.-S.L. and S.-N.K. equally contributed to this work. Y.-S.L.: Writing--original draft, Methodology and Data Curation. S.-N.K.: Writing-review and editing, Formal Analysis, and Visualization. S.-I.N.: Conceptualization. D.K.: Conceptualization, Supervision. S.-W.K.: Supervision, Writing--review and editing, Funding Acquisition. All authors have read and agreed to the published version of the manuscript.  
This work was supported by SKKU Excellence in Research Award Research Fund (S-2021-2596-000), Sungkyunkwan University, 2022.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 8: Stability of normalized PCE of NiO and PTAA/NiO bilayer devices (**a**) under air atmosphere at room temperature (unencapsulated); (**b**) under 85% RH/85 \({}^{\circ}\)C temperature environment (encapsulated).
## 3 Feature Extraction and Application to Floor Classification and Landmark Detection
### Semisupervised Discriminant Analysis
#### 3.2.3 FDA  
Let \(\{(y_{i},w_{i})\}_{i=1}^{l}\) be a set of \(l\) labeled training data points, where \(w_{i}\in\{1,2,\ldots,C\}\) is the label of the RSSI vector \(y_{i}\). For example, the label can be either floor level or a user-defined landmark. In FDA, the between-class covariance matrix \(S_{b}\) and the within-class covariance matrix \(S_{w}\) are defined as  
\[S^{b} = \sum_{c=1}^{C}l_{c}(\mu_{c}-\mu)(\mu_{c}-\mu)^{\mathrm{T}}, \tag{24}\] \[S^{w} = \sum_{c=1}^{C}\sum_{i:w_{i}=c}(y_{i}-\mu_{c})(y_{i}-\mu_{c})^{ \mathrm{T}}, \tag{25}\]  
where \(l_{c}\) is the number of the labeled data points in class \(c\in\{1,2,\ldots,C\}\) with \(l=\sum_{c=1}^{C}l_{c}\), \(\sum\limits_{i:w_{i}=c}\) indicates summation over \(i\) such that \(w_{i}=c\), and \(\mu_{c}\) is the mean vector of the data points in class \(c\), given by  
\[\mu_{c} = \frac{1}{l_{c}}\sum_{i:w_{i}=c}y_{i}. \tag{26}\]  
The transformation matrix for FDA is given by  
\[T^{\mathrm{data}}=\mathrm{argmax}_{T}\left[\mathrm{tr}\left(T^{ \mathrm{T}}S^{b}T(T^{\mathrm{T}}S^{w}T)^{-1}\right)\right].\]  
Equations (24) and (25) can be expressed by the following weight matrices.  
\[W_{ij}^{b} = \left\{\begin{array}{cl}\frac{1}{l}-\frac{1}{l_{c}}&\text{if }w_{i}=w_{j}\\ \frac{1}{l}&\text{if }w_{i}\
eq w_{j}\end{array}\right. \tag{27}\] \[W_{ij}^{w} = \left\{\begin{array}{cl}\frac{1}{l_{c}}&\text{if }w_{i}=w_{j}\\ 0&\text{if }w_{i}\
eq w_{j}\end{array}\right. \tag{28}\]
# Kinesthetic Learning Based on Fast Marching Square Method for Manipulation
## 3 Learning Manipulation Trajectories via Fast Marching Square
### Addition of Obstacles in the Workspace  
Real environments are rarely completely free and are usually dynamic. This is why obstacle inclusion is necessary. Obstacles can belong to the environment from the beginning, which allows the robot to be taught to take them into account, or they can be added later once kinesthetic learning has already taken place. Due to the advantages of FML and FM\({}^{2}\), both cases can be solved in a simple way.  
If the obstacles in the environment are initially known, the solution is trivial because the kinesthetic teaching will take these obstacles into account, thus generating paths that allow the obstacles to be circumvented. An example of this case in 2 dimensions is shown in Figure 14.  
In the case of initially having a free environment (\(W_{o}\)), the learning process will be carried out without taking into account any obstacle, generating a certain experience \(E\), which will have a velocity map \(F\) associated with it. As soon as any obstacle appears dynamically, it will be necessary to create a new velocity map \(F_{o,sat}\), which will be saturated. For this work, we considered a dynamic object as one that, after having learned a series of trajectories, appears and interferes with previously learned data. In other types of algorithms, where only the information that has been learned is used and there is no exploration, this type of situation requires either a new learning procedure of the modified environment, or systems for detecting and processing this type of situation. In the presented algorithm, the auto-learning process through the use of exploration determines a new path that is able to avoid the dynamic object. After verifying its feasibility, the new plan is added as a learned path, thus extending the knowledge of the environment as well as accelerating the process in case the object remains in that position. As such, to obtain the final velocity map, it will be necessary to apply Equation (7) to all the points in which \(F_{o,sat}\) is not saturated.  
\[F:=min\big{(}F_{o,sat},F\big{)}\ \ \ \ \ \forall i\in F_{o,sat}|F_{o,sat}(i)<sat \tag{7}\]  
That process has to be repeated any time a new obstacle appears in the environment. The method is the same for 2D and 3D. An example in two dimensions of the dynamic appearance of an object in a free environment can be seen in Figure 15 with \(sat=0.1\) and \(aoi=25\ px\).  
An example of the process in three dimensions is represented in Section 4.2.  
Figure 14: Environment with the initial obstacle. Demonstrations (red dotted lines) initially take into account the object, so the velocity matrix is created accordingly.
## 5 Experimental Results and Analysis
### Indicators for Model Evaluation
#### 5.1.3 Evaluation of Random Forest Model  
Table 22 demonstrates that the average Accuracy was 87.19%, the average Precision was 86.12%, the average Recall was 79.07%, and the average F1 score was 73.06%. The average Loss value was 3.725, the maximum Loss value was 4.928, the minimum Loss value was 2.738, and the standard deviation of Loss value std was 0.335.  
The values of the loss function for the random forest model, with the loss values reaching an average of 3.7 per round, represent a significant decrease relative to the SVM model.
# Focus on Disaster Risk Reduction by ResNet-CDMV Model After Natural Disasters
## 4 Experimental Results
### Secondary Disaster Event Monitoring and Early Warning  
The possible secondary disaster factors in the disaster site can be identified by the ResNet-CDMV algorithm, and the information can be relayed to the emergency rescue person in the shortest possible time. In this study, we conducted real-time on-site identification of disaster events through the visualization platform. The construction of the visual platform for disaster event detection and early warning is shown in Figure 5.  
Figure 5A is our inspection robot system, which is equipped with the ResNet-CDMV algorithm to inspect the area that needs to be monitoring secondary disaster, and (B) shows the layout of the information displayed on the warning platform. On the left side of the platform are the types of secondary disaster factors detected, including six objects, namely, fire, flammable objects, explosive objects, toxic substances, trapped persons, and dangerous buildings. In the middle of the platform, the visual scene of the disaster collected by the image detection equipment and the probability values of the corresponding six monitoring objects are displayed. The right side of the platform is a sign of the danger of a secondary disaster event. In Figure 5C, the colored and gray symbols correspond to fire, flammable objects, explosive objects, toxic substances, trapped persons, and dangerous buildings. When the monitored secondary disaster factor's AP is greater than 0.5, it is considered that a corresponding disaster event will likely occur, and the corresponding event identifier will be displayed in a colored style. When the monitored secondary disaster factor's AP is less than 50%, it is considered that the corresponding disaster event will not occur, the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Detection Method**} & \multicolumn{1}{c}{**Faster-RCNN**} & \multicolumn{1}{c}{**SSD**} & \multicolumn{1}{c}{**CornerNet**} & \multicolumn{1}{c}{**CenterNet**} & \multicolumn{1}{c}{**YOLOv7**} & \multicolumn{1}{c}{**ResNet-50**} & \multicolumn{1}{c}{\
\begin{tabular}{c} **ResNet-50**} \\ **CDMV** \\ \end{tabular} \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Secondary \\ disaster \\ factor \\ (\%) \\ \end{tabular} } & Fire & 79 & 78 & 74 & 78 & 82 & 91 & **95** \\  & Flammable & 84 & 82 & 81 & 84 & 86 & 81 & **88** \\  & Explosive & 68 & 62 & 63 & 66 & 68 & 71 & **79** \\  & Toxic & 78 & 75 & 77 & 77 & 80 & 81 & **89** \\  & Trapped persons & 73 & 73 & 70 & 71 & 72 & 75 & **85** \\  & Dangerous buildings & 84 & 81 & 79 & 80 & 85 & 84 & **86** \\  & mAP (\%) & _77.67_ & 75.17 & 74 & 76 & 78.83 & 80.5 & **87** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative experiments of different models.  
Figure 5: Real-time secondary disaster management platform.  
corresponding event is displayed in gray style, and the emergency rescue person carries out targeted emergency rescue work according to one or more disaster events indicated by the colored identifier.
## 2 Experimental Setup  
The experimental setup of the proposed mode-locked fiber laser based on CNTs generating the CVB is shown in Figure 1. The fiber laser has a ring cavity that consists of a wavelength division multiplexer (WDM), a 12 m erbium-doped fiber (EDF) with a dispersion parameter D of \(-16\) ps/nm/km, a three-port circulator, a TM-FBG, and an output coupler (OC). The three-port circulator, which is inserted into the ring cavity, not only incorporates the TM-FBG into the cavity by using the offset splicing technology, but also ensures that the light travels unidirectionally in the cavity. The fiber Bragg grating (FBG) is written on the TMF to discriminate the LP01 and LP11 modes. The total length of the SMF and TMF is 5.4 m and 0.4 m. The polarization controllers (PC1 and PC2) which are installed at the front and rear positions of the offset splicing spot are used to control the polarization of the input fundamental mode to adjust the coupling efficiency from the fundamental mode to higher-order modes and refine the polarization of the output higher-order mode, respectively. The PC3 is used to adjust the polarization state of light in the cavity. The fiber laser is pumped by the 980 nm laser diode with a maximum pump power of 700 mw through a 980/1550 nm WDM. The output1 is the 10% OC port. The mode-locking mechanism is the saturable absorption of the CNTs which are sandwiched between two fiber connectors. The insertion loss and modulation depth of the CNT saturable absorber are around 1.8 dB and 17%, respectively. The saturable intensity of the CNT-SA is 15.5 MW/cm\({}^{2}\). An optical spectrum analyzer (Yokogawa AQ-6370D), a commercial frequency-resolved optical gating (Frog SCAN ultra), a radio-frequency analyzer (FSV30), and a digital storage oscilloscope (LeCroy SDA 6000A) with an electro-photonic detector are used to monitor the laser output1 simultaneously. The CVB is recorded by a charge coupled device (CCD camera (CinCam IR) from the output2 of the TM-FBG.  
Figure 2 shows the offset splicing technique which is used to convert the fundamental mode in SMF to the first group of high-order modes in TMF, as in Figure 2a, which shows a sketch map of the offset splicing (OSS) of the SMF and TMF. This is to say that when the SMF and TMF are spliced with an offset value, the radially and azimuthally polarized beams can be excited in the TMF. The micrograph of the OSS after splicing the SMF and the TMF is shown in Figure 2b. The reflection spectra of the  
two-mode FBG under different OSS values have been experimentally measured, as shown in Figure 3. When there is no lateral misalignment, the left peak of the TM-FBG cannot be seen, which means that the high-order mode was not excited. With the amount of OSS increasing, the second-order mode is excited, corresponding to the leftmost reflection peak in Figure 3. The best lateral misalignment to effectively excite is about 5 \(\upmu\)m through the experimental measurement and the theoretical calculation.
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 3 Verification with Cantilever Beam Vibration
### Results of Displacement Time History  
The displacement time history of the beam can be extracted by tracking the movement of the detected edge points in all the video frames. Figure 9 illustrates the displacement time histories of the tracked edge shown in Figure 7 using the Canny operator and the above three subpixel edge-detection methods. As can be observed in Figure 9, the displacement by the Canny operator contains many horizontal segments, implying that the displacement difference less than one pixel cannot be detected, which results in low accuracy of the displacement identification, especially for the cases with small vibration amplitudes. Figure 9-d show the displacement time histories from the edges detected by the three subpixel algorithms, i.e., the algorithm in the literature [31], the PAE-based algorithm, and the proposed algorithm. By comparison, it is seen that the result of the proposed algorithm shows a good periodic pattern and is much better than the other two algorithms.
# Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review
In addition, numerous methodologies have been developed to identify and detect partial discharge (PD), including electrical detection [29; 30; 31; 32; 33], electromagnetic detection [26; 34; 35; 36; 37; 38], optical detection [39; 40; 41], acoustic detection [42; 43; 44; 45; 46], gas presence detection [6; 47; 48; 49], and integrated approaches [42; 50; 51]. In order to pinpoint the spots and PD activities, these methods have been utilized to determine the PD signals. In online PD monitoring, it has been found that there are challenges to detecting the PD signals and their exact proposition due to the complicated geometrical of power transformers, the shielded structure of power transformers, and interruption of noise (i.e., noise interference). For instance, electrostatic and electromagnetic interference, radio frequency interference, and crosstalk brought on by nearby cables are all examples of external disturbances which may lead to noise interference in PD detection. Additionally, the transformer's core and windings produce the majority of the internal noise. Therefore, an applicable method to detect PD is needed to prevent any breakdown happening in the power transformer or gas-insulated switchgear. Partial discharge detection methods encompass a range of techniques, in which each offering unique insights into the early identification and localization of PDs within electrical systems. The method that is currently being used to detect PD include the UHF method. The UHF method utilizes UHF sensors or antennas placed near the transformer or GIS to capture and analyze the electromagnetic emissions generated by PD events. This method offers high sensitivity, accurate PD localization, and the ability to detect PDs in their early stages. It is commonly used for both online and offline monitoring [52; 53]. Other than that, Transient Earth Voltage (TEV) method also can be used to detect PD. The TEV method detects the radiated electromagnetic waves resulting from PD events in a power transformer or GIS. It involves placing sensors on the transformer's surface or GIS surface to measure high-frequency transient signals. The TEV method is non-intrusive, highly sensitive to PD events, and suitable for online monitoring systems [54; 55]. Apartfrom that, acoustic detection also is one of the methods that are currently being used to detect PD. Acoustic PD detection relies on the measurement of sound waves produced by PD events. Acoustic sensors or ultrasonic detectors are used to capture and analyze acoustic emissions. This method is effective for detecting PDs in power transformers or gas-filled compartments of GIS and provides information about the discharge location based on time-of-flight analysis [56; 57].