esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
## 3 Methods
### Coverage Calculation  
Our coverage metric is used to evaluate the extent to which the selected frames in all clips represent the entire video. It is calculated as the ratio of the total number of frames selected across all clips to the total number of frames in the video.  
The process begins with the clips generated in Algorithm 1, where the frames for each clip are selected and stored in the set \(\mathbb{S}_{clip}\). Each clip \(L_{i}\) in \(\mathbb{S}_{clip}\) contains a subset of frames selected from the video. The steps for calculating coverage are outlined in Algorithm 2.  
As shown in Algorithm 2, the total number of frames selected in all clips is given by Equation (7), where \(N_{clip}\) is the number of clips and \(F_{clip}\) is the number of frames in each clip. These clips may contain overlapping or non-overlapping frames.  
\[F_{sum}=N_{clip}\times F_{clip} \tag{7}\]To evaluate how well the generated clips represent the entire video, the coverage value \(Cov\) is calculated as the ratio of the total number of selected frames across all clips \(F_{sum}\) to the total number of frames in the video \(F_{total}\), as shown in Equation (8).  
\[Cov=\frac{F_{sum}}{F_{total}} \tag{8}\]  
A higher coverage value (\(Cov\approx 1\)) indicates that the selected clips effectively cover the content of the video, whereas a lower value suggests that some parts of the video may not be adequately represented. However, the optimal \(Cov\) value may vary depending on the application. In some cases, full coverage (\(Cov=1\)) may be required, while in others, a lower \(Cov\) may be acceptable if redundant or uninformative frames are excluded.
## 5 Experiment
### Ablation Studies  
To ascertain the efficacy of disparate components within the DRKG model, we devised ablation experiments with the aim of probing the influence exerted by the question decomposition module and the reasoning analysis module on model performance. We examined two experimental setups in particular. Experimental Setup 1 (ES1): Eliminate the fine-tuning process of the question decomposition module, and conduct direct multi-hop retrieval solely by leveraging the LLMs. Experimental Setup 2 (ES2): Omit the fine-tuning of the reasoning analysis module, and rely exclusively on question recognition for multi-hop reasoning.  
The experimental outcomes are presented in Table 9. In comparison with the fully fledged DRKG model, the removal of any module precipitates a significant deterioration in performance. Under Experimental Setup 1, notwithstanding the fact that the LLMs can accurately identify the topic words and formulate a reasoning plan, a marginal decline in performance is witnessed due to the absence of the fine-tuned question decomposition module. In Experimental Setup 2, the excision of the reasoning analysis module induces a substantial decrease in both hit@1 and F1 scores. This indicates that the precise identification  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **PathQuestion (2-hop)** & **PathQuestion (3-hop)** \\ \hline MemN2N & 89.9 & 77.0 \\ KVMemN2N & 93.7 & 87.9 \\ IRN & 96.0 & 87.7 \\ DRKG (Ours) & **96.6** & **89.3** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative results on PathQuestion dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**WebQSP**} & \multicolumn{2}{c}{**CWQ**} \\ \cline{2-5}  & **Hit@1** & **F1** & **Hit@1** & **F1** \\ \hline GraftNet & 66.4 & 60.4 & 36.8 & 32.7 \\ PullNet & 68.1 & - & 45.9 & - \\ EmbedKGQA & 66.6 & - & - & - \\ TransferNet & 71.4 & - & 48.6 & - \\ UNIKGQA & 77.2 & 72.2 & 51.2 & 49 \\ Hic-KGQA & 70.8 & - & 50.9 & - \\ ROG & 85.7 & 70.8 & 62.6 & 56.2 \\ DRKG (Ours) & **88.16** & **84.05** & **66.99** & **58.04** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative results on WebQSP and CWQ dataset.  
of topic words within a question is of paramount importance for multi-hop question answering tasks. Without appropriate semantic understanding, subsequent reasoning efforts prove fruitless. The ablation experiments corroborate the significance of both the question decomposition and reasoning analysis modules within the DRKG model.
## 3 Result Analysis of the Proposed MPA
### Simulated and Measured Results  
The simulated and measured S\({}_{11}\) plot is shown in Figure 3. The simulated and measured S\({}_{11}\) plot ensures that the proposed MPA will provide satisfactory performance across the whole X-band. Moreover, the S\({}_{11}\) graph shows that the antenna will be suitable for the entire X-band and a portion of the lower part of the Ku-band. The MPA provides two resonance frequencies of 9 GHz and 13 GHz with return loss magnitudes of S\({}_{11}-\)35 dB and \(-\)18 dB, respectively. The MPA offers a \(-\)10 dB impedance bandwidth of 5.8 GHz, ranging from 8.35 GHz to 14.15 GHz. However, the measured return loss graph is slightly different from the simulated return loss graph. It may have occurred because the antenna is excited using a waveguide port during simulation, but practically the antenna is excited using the SMA connector. The connector loss influences the response of the antenna. In addition, the near-field scattering objects, the losses due to the feed connector, and the  
Figure 1: (**a**) Three-dimensional view and (**b**) partial ground plane of the proposed X-band MPA.  
Figure 2: (**a**) Front view of the proposed MPA. (**b**) Fabricated Prototype.  
coaxial cable also affect the response of the antenna performance. Figure 4 shows that the VSWR is less than 1.5 at both resonance frequencies, ensuring good impedance matching characteristics.  
The surface current distribution of the designed MPA indicates that the antenna has the most current in the middle of its length and the least current near its edges, as illustrated in Figure 5. To validate the simulated and measured S\({}_{11}\), the equivalent RLC model of the proposed antenna is designed using Agilent ADS software and the resonance frequencies obtained from the RLC equivalent model of the proposed MPA using ADS Agilent software are almost equal to the simulated (using CST) and measured resonance frequencies. In addition, the S\({}_{11}\) using ADS is \(-34\) dB and \(-35\) dB at both resonance frequencies.  
The radiation pattern of the proposed antenna is depicted in Figure 6, which shows the main lobe direction, main lobe magnitude, side lobe level (SLL), and a 3-dB beam width. At the two distinct resonance frequencies, the 3-dB beam width is 148.2 degrees for 9 GHz and 71 degrees for 13 GHz. The SLL at a resonance frequency of 13 GHz is \(-6.3\) dB and at  
Figure 4: Simulated VSWR of the proposed MPA.  
Figure 3: Return loss (S\({}_{11}\)) vs. frequency plot of the proposed MPA.  
a frequency of 9 GHz it is \(-10\) dB. Gain is a measurement of the energy delivered to the main beam. The gain vs. frequency curve of the proposed antenna is presented in Figure 7. From the figure, the gain of the microstrip patch antenna varies from 2.2 dB to 6.25 dB in the entire simulated frequency range. The designed antenna gained 4.0614 dB at 9 GHz and 3.4589 dB at 13 GHz.  
Figure 5: Surface current distribution.  
Figure 6: Radiation pattern of the proposed MPA.  
Figure 7: Gain vs. frequency plot of the proposed MPA.
###### Abstract  
Hand\(-\)tracking systems are widely employed for telemanipulating grippers with high degrees of freedom (DOFs) such as an anthropomorphic robotic hand (ARH). However, tracking human thumb motion is challenging due to the complex motion of the carpometacarpal (CMC) joint. Existing hand\(-\)tracking systems can track the motion of simple joints with one DOF, but most fail to track the motion of the CMC joint, or to do so, there is a need for expensive and intricately set up hardware systems. This research introduces and realizes an affordable and personalizable tracking device to capture the CMC joint Flexion/Extension and Abduction/Adduction motions. Tracked human thumb motion is mapped to a robot thumb in a hybrid approach: the proposed algorithm maps the CMC joint motion to the first two joints of the robot thumb, while joint mapping is established between the metacarpophalangeal and interphalangeal joints to the last two joints. When the tracking device is paired with a flex glove outfitted with bend sensors, the developed system provides the means to telemanipulate an ARH with a four-DOF thumb and one-DOF underactuated digits. A three-stage framework is proposed to telemanipulate the fully actuated robot thumb. The tracking device and framework were evaluated through a device operation and personalization test, as well as a framework verification test. Two volunteers successfully personalized, calibrated, and tested the device using the proposed mapping algorithm. One volunteer further evaluated the framework by performing hand poses and grasps, demonstrating effective control of the robot thumb for precision and power grasps in coordination with the other digits. The successful results support expanding the system and further evaluating it as a research platform for studying human-robot interaction in grasping tasks or in manufacturing, assistive, or medical domains.  
CMC joint; anthropomorphic robot hand; thumb tracking; kinematic mapping; telemanipulation +
Footnote †: journal: Applied sciences
## 1 Introduction
In particular, they have designed ad-hoc strategies to process the both Sounder and SAR data. These strategies exploit the navigation information acquired during the flight and adopt model-based procedures, which are the microwave tomographic imaging for the Sounder and the back-projection procedure for the SAR. With the aim of obtaining a first assessment of the performances of this radar system, a helicopter-borne campaign has been conducted in 2018 over a desert area in Erfoud, Morocco. During the campaign, several tracks have been flown exploiting all the three different operational modes of the system. In this work, we present first results relevant to a small subset picked up from the huge dataset collected by the system during this campaign. The work is organized as follows. In Section 2 we provide a brief description of the system. The acquisition campaign is described in Section 3. The processing chain applied to the Sounder data is illustrated in Section 4 along with some first results. Similarly, the SAR data processing chain is described in Section 5 along with the corresponding first results. Section 6 is devoted to the concluding remarks.
## 1 Introduction
The combination of these two factors (costs and quality), has been the main motivation for the development of a complex field of research that aims to make better predictions of solar irradiance values at the ground level and, thus, be able to predict output values depending on the type of technology used. The observations with terrestrial instruments that use a sky camera and record complete images of the celestial dome are a powerful tool, since they represent a great opportunity to fill the prognostic gap at a low cost of computational processing [2; 15]. In this sense, the development of a robust and automatic Sun detection algorithm becomes an important step in the process of the forecasting of the solar resource. By knowing the Sun position with precision (solar centroid) and the cloud trajectory through the sky dome and towards the Sun (cloud vector), irradiance variance can be forecasted due to possible Sun occlusion. In this work, a low-cost solar nowcasting system based on artificial vision is proposed. With the purpose of capturing the image of the full sky reflection, the system is equipped with a dome-type convex mirror and a Raspberry Pi camera module pointing downwards. The camera acquires images every minute with a resolution of 1024 \(\times\) 780 pixels; while the mirror occupies 673 \(\times\) 641 pixels. To process the acquired images, a methodology addressing several challenges related to the computation of the position of the Sun has been proposed. This methodology is based on the implementation of several algorithms, which help to achieve a robust detection; since knowing the correct position of the Sun allows other processes to be carried out. For instance, knowing the position of a cloud with respect to the Sun enables us to identify if the Sun will be occluded, as well as the time it will take. In order to test this methodology, the system was installed in Aguascalientes city, Mexico (21\({}^{\circ}\)51\({}^{\prime}\) N 102\({}^{\circ}\)18\({}^{\prime}\) W).
## 2 Materials and Methods
### Optical and Color Changes Measurements  
Translucency (TP) and opalescence (OP) values were calculated for all samples, on both types of surfaces (polished and glazed), before (w) and after each step of aging (t1 and t2). Optical parameters were registered under a D65 illuminant, using a Vita Easyshade IV spectrophotometer (Vita Zahnfabrick, Bad Sackingen, Germany). It was calibrated before each measurement.  
To assess the measurements, two different backgrounds, black (b) and white (w), were selected, using the grey card WhiBal G7 (White Balance Pocket Card). L* represents the lightness-darkness of the material (L* = 0 for a perfect black and L* = 100 for a perfect white). a* represents the measure of greenness (negative value) or redness (positive value), and b* measures the blueness (negative value) or the yellowish (positive value) [50; 51; 52; 53].  
TP values were calculated using Equation (1).  
\[\text{TP}=[(\text{L}_{\text{b}}-\text{L}_{\text{w}})^{2}+(\text{a}_{\text{b}} -\text{a}_{\text{w}})^{2}+(\text{b}_{\text{b}}-\text{b}_{\text{w}})^{2}]^{1/2} \tag{1}\]  
OP values were calculated using Equation (2).  
\[\text{OP}=[(\text{a}_{\text{b}}-\text{a}_{\text{w}})^{2}+(\text{b}_{\text{b}} -\text{b}_{\text{w}})^{2}]^{1/2} \tag{2}\]  
The CR (contrast ratio) value was achieved by Equation (3).  
\[\text{CR}=\text{Y}_{\text{b}}/\text{Y}_{\text{w}}\text{Y}=[(\text{L}^{*}+16)/ 116]^{3}\times 100 \tag{3}\]  
w and b correspond to the white and black backgrounds, respectively. CR = 0 is equivalent to transparent, and CR = 1 to totally opaque [54].  
The color changes (\(\Delta\)E*), which represent the color difference between two stages, were calculated based on the CIE L*a*b*color system, for a black background, according to Equation (4).  
\[\Delta\text{E}^{*}=[(\Delta\text{L}^{*})^{2}+(\Delta\text{a}^{*})^{2}+(\Delta \text{b}^{*})^{2}]^{1/2} \tag{4}\]  
The National Bureau of Standards (NBS) system quantifies the levels of color change, relating to a clinical standard. \(\Delta\)E* values were thus converted into NBS units: NBS unit = \(\Delta\)E* \(\times\) 0.92 [55; 56; 57; 58; 59].
# Utilization of Machine Learning Algorithms
## 3 Results
### Gaps Identified via the Application of Machine Learning Interventions in HIV Testing  
While ML has demonstrated potential in the realm of HIV testing interventions, numerous challenges, limitations, and gaps warrant careful consideration. This study summarizes the gaps identified in Table 6. The efficacy of ML models heavily relies on the quality and representativeness of the training data. Biases within the training data, especially when they lack diversity or representativeness, can result in skewed models, potentially leading to inaccurate predictions and disparities in HIV testing outcomes (see Table S7).  
Moreover, a notable limitation identified from the reviewed studies is the restricted generalization of models. As such, models employed in a specific region or on a particular demographic may not be transferable to other regions or populations. Most studies examined in this review utilized retrospective designs, incorporating self-reported information vulnerable to missing data and recall bias. While some models experienced overfitting, a few of them were underfitted; in either case, the accuracy of the results is compromised. Additionally, several studies had from small sample sizes, lacking representativeness [14; 17; 41].  
Despite the various studies advocating for the integration of ML interventions in healthcare systems, obstacles such as compatibility issues, insufficient expertise, and the necessity for specialized training for healthcare professionals, particularly in low-resource settings, pose impediments to seamless integration. For example, studies have developed ML models based on mobile platforms to enhance HIVST and automate the interpretation of HIV results. However, these models may face usability challenges in low-resource settings due to insufficient expertise [17; 20; 48; 60].  
Additionally, some ML models, especially DL models, are often considered \"black boxes\" due to their complexity, which can be challenging to interpret. This makes it difficult to understand how they reach a particular prediction [70]. The intricacy of ML models and their unclear predictive capabilities raise ethical concerns surrounding data privacy. The vast majority of ML models predicting HIV rely on secondary data, such as hospital records. However, there is limited evidence of robust defense mechanisms in AI/ML systems to safeguard integrity, leading clinicians to question the reliability of integrating ML techniques into healthcare [70; 71].  
Furthermore, this review identified a significant discrepancy in the application of ML interventions in the context of HIV testing in SSA and developed nations. Of the 51 studies included in this analysis, 18 were conducted in SSA, of which 16 were from East and South Africa, with only two studies involving West and Central African countries. Thisnecessitates the promotion of ML integration into HIV testing interventions across other African regions for improved HIV testing (Tables 6 and S7).
## 3 Simulation Implementation
### Path Loss Simulation  
In the previous section, we simulated the optoelectronic characteristics at different stages. In this section, we establish the ultraviolet communication single-scattering channel based on the scattering model formula and simulate the variation curve of path loss with increasing distance. According to the single-scattering model, we estimated the path loss for communication distances ranging from 100 m to 1000 m. The specific parameters for this simulation are as follows: the elevation angle of the transmitter and receiver is set to \(10^{\circ}\), the emission angle is \(30^{\circ}\), the receiver's field of view angle is \(120^{\circ}\), atmospheric absorption loss ka is \(9\times 10^{-4}\), scattering coefficient ks is \(7.7\times 10^{-4}\), the receiving aperture Ar is \(5\times 10^{-4}\), the asymmetry factor g is \(0.72\), and f = \(0.5\). In Figure 7 of the simulation, it is evident that as the communication distance increases, the path loss of ultraviolet light transmission in the atmosphere also increases. This increase in path loss is measured in decibels.  
By establishing the aforementioned path loss simulation, we have laid the foundation for subsequent simulation analyses on the relationship between parameters such as BER and distance.
# Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
## 3 Fault Diagnosis Methods
### Data-driven Intelligent Diagnosis Algorithm
#### 3.3.3 Sparse Representation  
Sparse representation is a new theory which is the most representative methodology of linear representation methods. It can express the essential characteristics of the signals while compressingthem, so it can de-noise, anti-interference and reduce the data space. Its principle is to use the sparse linear combination of overcomplete dictionary atoms to represent the original signal [94].  
For an input signal \(x=\left[x_{1},x_{1},\cdots,x_{n}\right]^{T}\) of length \(n\), the expression of the sparse representation is:  
\[x=Da=\sum_{i=0}^{m}\alpha_{i}d_{i} \tag{14}\]  
where \(D=\left[d_{1},d_{2},\cdots,d_{m}\right]\) is the dictionary matrix, the column vector \(d_{i}\) is the dictionary atom and \(\|d_{i}\|=1\). \(\alpha=\left[\alpha_{1},\alpha_{2},\cdots,\alpha_{m}\right]^{T}\) is the sparse representation coefficient of the original signal. It can be seen from the above that the method of sparse representation mainly includes dictionary design [95; 96] and sparse coefficient solving. [PERSON] also developed a sparse representation classification (SRC) in the field of face recognition [97] by using training data as the dictionary to compare the residuals of different categories data and input data. It extends the application scope of sparse representation.  
In the field of fault diagnosis, sparse representation has been used in signal denoising, feature extraction and fault classification in recent years. [PERSON] et al. used the matching pursuit (MP) algorithm [98] to obtain the largest \(N\) sparse coefficients of PMSM with different faults and considered them as the features [99]. Based on this, [PERSON] et al. used the orthogonal matching pursuit (OMP) algorithm [100] and sent the feature vectors into SVM [101]. As for classification, SRC is not as accurate as SVM, although it is faster. Therefore, a joint model for bearing fault diagnosis which combined SRC with SVM was proposed by [PERSON] et al. [102].
## 5 Conclusions  
In this study, reinforced concrete (RC) samples were prepared considering the chloride concentration, cover depth, and water-to-cement (\(w/c\)) ratio. In addition, the open-circuit potential (OCP) of the embedded steel reinforcement was measured by conducting a cyclic drying-wetting test in a saltwater environment for two years, and its relationships were obtained considering durability design parameters and exposure period. The conclusions and research limitation drawn in this study are as follows:  
Under the conditions that considered saltwater, the OCP was evaluated to be lower than the critical potential (\(-450\) mV) regardless of the \(w/c\) ratio under the lowest cover depth condition (\(30\) mm) after six months. As corrosion progressed, the OCP varied due to the partial saturation and rust product.  
The averaged OCP measured for two years was derived, and each average value was analyzed with the cover depth. The corrosion potential decreased with increasing chloride exposure period and high chloride concentration. When the \(w/c\) ratio was \(0.4\), a significant drop in the OCP value began to be observed under the \(3.5\%\) and \(7.0\%\) conditions only after one year. Under the condition with a high \(w/c\) ratio (\(w/c\): \(0.6\)), values below \(-100\) mV were observed regardless of the cover depth after six months. The averaged OCP increased linearly in the negative direction with decreasing cover depth, and they showed a clearer tendency with higher chloride concentration. Regression equations were evaluated for the OCP behavior for each exposure environment considering the exposure period and cover depth.  
The measured OCP showed fluctuations with varying saturation and the corrosion pitting effect was not investigated. If the bearing capacity in the corroded RC beam with the mineral admixture such as fly ash and slag was evaluated through long-term accelerated conditions, the results from this work would show more reasonable ones that can be applied to a real RC member.  
Conceptualization, S.-J.K. and B.-Y.Y.; methodology, S.-J.K. and K.-M.L.; formal analysis, S.-J.K. and Y.-S.Y.; investigation, S.-J.K. and B.-Y.Y.; resources, S.-J.K. and K.-M.L.; data curation, S.-J.K. and K.-H.Y.; writing--original draft preparation, S.-J.K. and Y.-S.Y.; writing--review and editing, S.-J.K. and Y.-S.Y.; supervision, K.-H.Y. and K.-M.L.; funding acquisition, S.-J.K. and K.-M.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF-No. NRF-2020R1A2C2009462) and the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure, and Transport (Grant 22 SCIP-C158977-03).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.