esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
One of the most devastating aspects of a spinal cord injury (SCI) at a cervical level is the impairment of arm and hand functions, which has a great impact on one's level of independence and determines, for the most part, the extent of handicap [1]. Therefore, therapy of the upper extremities is very important in the rehabilitation of persons with tetraplegia. However, quantitative evaluation of rehabilitation therapy still constitutes an open field. Recording muscle activity by means of surface electromyography (EMG) may provide an insight into the development of new motor programmes, and the formation of new muscle coalitions during and after treatment of the aforementioned patients [2]. Furthermore, motivation is an important factor in rehabilitation and it is frequently used as a determinant of the outcome of the rehabilitation. Haptic interfaces and virtual reality (VR) technology can positively influence patient motivation and thus improve exercise adherence [3]. Hence, the combined use of VR and EMG recording, as performed by [PERSON] et al. [4], improves the therapists' assessment of rehabilitation therapy.  
[PERSON] et al. [5] analysed EMG biofeedback effectiveness as a rehabilitation tool in the treatment of the hemiplegic hand with positive results. [PERSON] and [PERSON] [6] assessed the reliability of surface EMG for volitional motor tasks, providing evidence of the potential use of EMG for the evaluation of the central nervous system motor control. By following this insight, [PERSON] et al. [7] proposed a system that provides continuous assistance in extension torque, which is proportional to the amplitude of the subject's EMG signal from the triceps. Hence, performance was analysed by measurements derived from the EMG and torque.  
Aimed at merging these strategies and creating a measurement protocol to assess rehabilitation improvement, we designed the UVa-NTS platform [8]. This platform is a human-machine interface that makes real-time EMG signal processing, in order to command a set of interactive rehabilitation applications in a computer. This paper shows that the EMG upper limb training applied to individuals with C7 SCI has benefits as well as its suitability in the rehabilitation program. Results have been measured in terms of time to achieve a predefined task, as well as in terms of performance indexes related to gaming abilities. These performance indexes represent a new strategy to measure the evolution in the rehabilitation process by means of gaming scores, which are straightforward and only dependent on the patient and the human-machine interface.
## 4 Effect of Design Parameters on Seismic Performance of SCMSW Specimens
### Effect of Vertical Reinforcement Ratio on Hysteretic Response of SCMSW Specimens  
Figure 14 illustrates the effect of the amount of flexural reinforcement on the wall behavior which is evaluated based on the results of two SCMSW specimens which had an identical aspect ratio and same magnitude of axial loading. The maximum observed lateral load increased from 115 kN for SW4 to 149 kN for SW5, resulting in a strength increase of 30% when the vertical reinforcement ratio increased from 0.46% to 0.718%.
# In-Home Evaluation of the Neo Care Artificial Intelligence Sound-Based Fall Detection System
## 2 Materials and Methods
### Analysis  
Descriptive statistics (n and %) were used to describe the frequency of the event detection and response categories. The data were then used to calculate the sensitivity and specificity of fall detection and response. The sensitivity refers to the device's ability to correctly identify participants who experienced a fall and was calculated as: [true positives/(true positives + false negatives)]. The specificity refers to the device's ability to correctly identify participants not experiencing a fall, was calculated as: [true negatives/(true negatives + false positives)]. In particular, in the context of fall detection, sensitivity refers to the ability of the Neo device to correctly identify actual falls (i.e., how well the device detects falls when they do occur), while specificity refers to the ability of the Neo device to correctly identify non-fall events (i.e., how well the device avoids false alarms by not triggering when a fall has not occurred). In the context of fall response data, sensitivity refers to the ability of the Neo device to correctly alert a caregiver when a fall actually occurs (i.e., how well the device responds when intervention is needed), while specificity refers to the ability of the Neo device to correctly refrain from initiating an unnecessary response (such as alerting a caregiver) when a fall has not occurred (i.e., how well the device avoids false alerts in situations where no intervention is needed). Sensitivityand specificity with exact Clopper-Pearson confidence intervals [2] were calculated in STATA (version 17, StataCorp LLC, College Station, TX, USA).
## 5 Discussion  
As discussed in the review, although the improved YOLOv5s algorithm (YOLOv5s-ESC) exhibits a slight decrease in accuracy and detection speed, its recall rate and mean average precision (mAP) increased by 3.6% and 2.2%, respectively. The standard monitoring frame rate in the industry typically ranges from 25 to 30 fps. The enhanced detection speed of the improved model ensures that it meets the real-time detection requirements for water conservancy construction sites.  
While achieving promising performance in sluice renovation environments, this study exhibits three critical limitations requiring scholarly attention. First, the dataset's geographical concentration on Jiangsu Province infrastructure projects introduces latent bias--ethnic homogeneity in worker demographics and regional construction practices may degrade model generalizability for multinational deployments. Second, the current framework's scene adaptability remains constrained by its training paradigm; specialized safety protocols in high-risk vertical construction (e.g., skyscraper steelwork) and confined-space operations (e.g., tunnel boring) demand customized feature representations not encapsulated in our horizontal-structure-focused training data. Third, temporal scalability challenges  
Figure 7: Monitoring in different construction scenarios.  
emerge from static model assumptions--evolving PPE designs (e.g., photoluminescenceentvest materials) and emerging safety regulations necessitate continuous model updating mechanisms absent in our architecture. Future research should integrate federated learning frameworks to aggregate multinational safety datasets, while preserving data sovereignty, coupled with domain-adversarial neural networks to bridge cross-environment feature disparities. Developing synthetic data engines incorporating procedural generation of diverse worker physiognomies, dynamic weather patterns, and novel hazard scenarios could mitigate current sampling biases.
## 2 Proposed Methodology
### Prediction Module Based on the Gated BiLSTM Unit  
Although BiLSTM improves feature extraction by combining forward and backward LSTM layers, it assigns equal weight to all input features and struggles to filter out redundant or noisy information. Additionally, as sequence length and data complexity increase, issues like gradient vanishing can slow training convergence. To address these challenges, we propose the gated BiLSTM unit, which utilizes parallel linear and BiLSTM layers for feature extraction, global information capture, and sequence dependency modeling. The outputs were fused through element-wise multiplication to adaptively suppress redundant features and emphasize key ones. The design of the gated BiLSTM unit is shown in Figure 1.
## Nomenclature  
\begin{tabular}{l l} \(a\) & Axial induction factor \\ \(a^{\prime}\) & Tangential induction factor \\ \(\overset{\rightarrow}{a}_{p}\) & Linear acceleration of the structure (m\(\cdot\)s\({}^{-2}\)) \\ \(AM_{RP}\) & Added-mass due to radiation at the reference point \\ B & Number of the blades \\ \(c\) & Leaf chord length (m) \\ \(C\)_Hydrostastic_ & Hydrostatic-restoring matrix \\ \(C_{tt}\) & Coefficients for the normal force \\ \(C_{t}\) & Coefficients for the thrust force \\ EA & Cross section axial stiffness (kN) \\ \(\overset{\rightarrow}{F}_{AM}\) & Total added mass forces from all contributions (kN) \\ \(\overset{\rightarrow}{F}_{HS}\) & Hydrostatic forces at the reference node (kN) \\ \(\overset{\rightarrow}{F}_{Line}\) & Mooring tension (kN) \\ \(\overset{\rightarrow}{F}_{RD}\) & Radiation memory-effect force at the reference point (kN) \\ \(\overset{\rightarrow}{F}_{W}\) & Incident-wave excitation force at reference point (kN) \\ \(\overset{\rightarrow}{F}_{WAP}\) & Total loads at the reference point (kN) \\ \(\overset{\rightarrow}{F}_{Wind}\) & Wind loads applied to the blades and tower (kN) \\ \(F_{H},F_{V}\) & Applied horizontal and vertical force at the fairlead, respectively (kN) \\ \(g\) & Acceleration due to gravity (m\(\cdot\)s\({}^{-2}\)) \\ \(K\) & Hydrostatic restoring force matrix \\ \(K_{1}\) & Radiation kernel from potential flow theory \\ \(L\) & Upstretched line length (m) \\ \(S_{\xi}^{2-sided}(\omega)\) & Wave spectrum (P-M spectrum in this paper) \\ \(t\) & Time (s) \\ \(V_{0}\) & Volume of the immersed part of the platform (m\({}^{3}\)) \\ \(V_{1}\) & Wind speed (m\(\cdot\)s\({}^{-1}\)) \\ \end{tabular}
## 4 Discussion
### Comparison of This Research with Previous Results  
THz generation using InAs QD arrays distributed in GaAs has been reported previously [14; 15; 16; 17]. The maximum THz power output was \(\sim\)0.9 \(\mu\)W for pulsed InAs-QD emitters when pumped with 50 mW 800 nm laser power, and \(\sim\)0.15 nW for InAs-QD photomixers when pumped with 30 mW 1200 nm laser power. While the pulsed emission has a much better conversion efficiency (\(\sim\)1.8 \(\times\) 10\({}^{-3}\)%) than the photomixing (\(\sim\)5 \(\times\) 10\({}^{-7}\)%), no ringing was observed in its time-domain profile. The working mechanism was attributed to reduction of the photocarrier recombination time by an Auger-related process [14], not the superradiance proposed in this research.
## 4 Experiment Results and Data Analysis
### Eye-Tracking-Data Analysis
[PERSON] [38] gave useful rules of thumb about what to regard as a \"large\", \"medium\", or \"small\" effect. On the basis of the above analysis of the \(p\)-values and [PERSON]'s d-values, the relationships between final similarity choices and TFDs in ROIs were not significant. (2) FPV in each ROI refers to the degree of fixation concentration when the participants were viewing the stimuli. In our research, this indicator was used to reflect whether a participant's eye-movement was local attention or global scan. Since Texture S had the same global features as those of Texture A and the same local features as those of Texture B, if participants captured the global features of the visual stimuli, the fixation points were dispersed; if the participants paid more attention to the local features of the visual stimuli, the fixation points were concentrated. Therefore, we hypothesized that, during the process of visual searching, the fixation points in Texture A were more dispersed than those in Texture B. To verify this hypothesis, we calculated the FPV in each ROI. The coordinates of all fixation points in each ROI could be accessed from Tobii Studio. We first computed the FPVs in Textures A and B through the following steps. I. Calculate the central point of all fixations in each ROI. \[(a,b)=\frac{1}{n}\sum_{i=1}^{n}{(x_{i},y_{i})}, \tag{4}\]  
where \((x_{i},y_{i})\) is one of the fixations in each ROI and \((a,b)\) is the central point among all fixations in each ROI. II. Calculate the Euclidean distance between each fixation and the center point. \[d_{i}=\sqrt{(x_{i}-a)^{2}+(y_{i}-b)^{2}}, \tag{5}\]  
where \(d_{i}\) is the Euclidean distance between each fixation and the center point in each ROI. III. Calculate the variance from all distances. \[Var=\frac{1}{n}\sum_{i=1}^{n}{d_{i}}, \tag{6}\]where \(Var\) is the variance from all distances. It represents the concentration of all fixations in each ROI. The average and SD of FPVs in the ROIs of Textures A and B for four scenes are shown in Table 2. We conducted a paired t-test to estimate differences in global and local features on the basis of FPVs for Textures A and B. The independent variable used in the t-test was the FPVs, which were calculated from 30 participants' eye-movements. The \(p\)-values and [PERSON]'s d-values indicated that significant differences existed between global and local features on the basis of the FPVs for Textures A and B at the significance level of 0.05. Results validated the hypothesis that fixation points were more dispersed in Texture A than in Texture B. Overall, the fixation points in Texture A were scattered because Textures A and S had the same global features; fixation points in Texture B were concentrated because Textures B and S had the same local features. An example of the fixation-point distribution is shown in Figure 5. From this figure, it is obvious that the fixation points in Texture A were more dispersed than the fixation points in Texture B.
## 3 Materials and Methods
### Volatile Organic Compounds (VOCs) Analysis
#### 3.3.2 Gas Chromatography-Mass Spectrometry Analysis (GC-MS)  
The chromatographic separation of the volatile compounds from juices and peels was performed using a gas chromatographer, model GC 7890A, equipped with a capillary column HP-Innowax and coupled to a mass spectrometer 5975 C (system from Agilent Technologies, Santa Clara, CA, USA). The oven temperature was initially held at 40 \({}^{\circ}\)C for 1 min and then ramped to 220 \({}^{\circ}\)C at 2.5 \({}^{\circ}\)C/min and kept for 10 min, for a total GC run time of 83 min. Helium was used as the carrier gas at a flow rate of 1 mL/min. The injection port operated in the splitless mode at 250 \({}^{\circ}\)C. The temperatures of the transfer line, the quadrupole, and the ionization source were set at 270, 150, and 230 \({}^{\circ}\)C, respectively. The electron impact (EI) mass spectra were acquired at 70 eV and the mass range was 30-300 m/z. The electron multiplier was established to the auto-tune mode and the ionization current was 10 \(\mu\)A. VOCs were identified through a comparison of their mass spectra with those listed in the NIST05/Wiley07 libraries database. Furthermore, the Linear Retention Indices (LRIs) were calculated using a series of n-alkanes (C8-C22) and compared with the available retention data reported in the literature for the polar column (www.pheobase.com, www.flavornet.org, www.ChemSpider.com, [[https://pubchem.ncbi.nlm.nih.gov](https://pubchem.ncbi.nlm.nih.gov)]([https://pubchem.ncbi.nlm.nih.gov](https://pubchem.ncbi.nlm.nih.gov)), webbook.nist.gov; accessed on 1 July 2022). Identifications were also confirmed by comparison of the retention times of the chromatographic peaks with those, when available, of commercial standards analyzed under the same conditions. Compounds that were not identified using an authentic chemical standard were considered putatively identified. For individual volatiles, the peak area was calculated from the total ion chromatogram (TIC) and semi-quantified by relative comparison with the peak area of the IS (Relative Peak Area, RPA%), according to [PERSON] et al. (2020) [14].
## 4 The Evaluation Results and Discussions
### The Effect of SE Methods in Speech Recognition for Noisy Speech
In our opinion, directly compensating speech features used for a noisy recognition system is probably more helpful in reducing WERs than enhancing speech signals by SE methods. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Signal-To-Noise Ratio (SNR)**} \\ \hline  & & **–6 dB** & **–3 dB** & **0 dB** & **3 dB** & **6 dB** & **12 dB** \\ \hline  & baseline & 1.031 & 1.036 & 1.044 & 1.062 & 1.096 & 1.271 \\ \cline{2-7} PESQ & MMSE & 1.040 & 1.054 & 1.080 & 1.130 & 1.237 & 1.647 \\ \cline{2-7}  & IRM & 1.032 & 1.036 & 1.044 & 1.063 & 1.098 & 1.275 \\ \hline  & baseline & 0.577 & 0.648 & 0.722 & 0.790 & 0.850 & 0.935 \\ \cline{2-7} STOI & MMSE & 0.584 & 0.643 * & 0.697 * & 0.734 * & 0.766 * & 0.840 * \\ \cline{2-7}  & IRM & 0.578 & 0.649 & 0.723 & 0.792 & 0.852 & 0.937 \\ \hline  & baseline & 66.1 & 62.1 & 57.0 & 49.8 & 44.8 & 34.4 \\ \cline{2-7}  & MMSE (TR, TS) & 70.0 * & 66.7 * & 61.9 * & 56.5 * & 50.5 * & 40.5 * \\ \cline{2-7} WER (\%) & IRM (TR, TS) & 67.3 * & 63.1 * & 58.3 * & 52.5 * & 47.3 * & 37.5 * \\ \cline{2-7}  & MMSE (TS) & 70.3 * & 66.8 * & 60.4 * & 55.0 * & 49.7 * & 40.1 * \\ \cline{2-7}  & IRM (TS) & 65.8 & 61.9 & 56.5 & 50.4 & 44.4 & 34.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The various evaluation results of MMSE, IRM, and the baseline for the test set in noise environment “**white**”.The results worse than the baseline are marked with a superscript “*”. In particular, we also give the same marking for the results worse than the baseline in the other tables.
## 5 Simulation Results and Discussion
### Comparison of KK and Coherent Systems without Scintillation
If the linewidth is increased to 1 MHz, the system based on coherent receiver will incur a serious penalty (even with digital phase noise compensation) whereas the performance of the system based on KK transceiver will not be impacted. We note that the 2.9 dB SNR penalty is the price to pay for using inexpensive KK transceiver instead of coherent transceiver, although this penalty is lower at lower SNRs. In this comparison (and in Figures 9 and 10 as well), the waste of transmitter power due to pilot beam in the KK scheme and the mode mixing loss in the coherent detection scheme are not considered. Figure 8: Comparison between coherent and KK transceivers performance versus electrical SNR (For KK transceiver: \(K=10\)). Figure 7: Example of received constellation diagram for QAM-16 in FSO KK system (downlink, \(K=10\)).
# Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition
## 5 Experimental Results
### Experiment on CAS-PEAL Dataset  
#### 5.3.1 Experimental Results without Subset  
The CAS-PEAL dataset is a wide-ranging Chinese face database. The progress of face recognition is facilitated by providing face images from different sources with differences in gestures, expressions, lights, and accessories. The CAS-PEAL databset includes 99,594 faces images of a total of 1040 people, 595 men and 445 women. In this experiment, 10,311 face images from 1040 people are selected. Figure 8 shows a facial image of a subject in the CAS-PEAL database.  
Several classes are randomly selected from the lighting, expression, and subset to constitute a training set, and the alternative training samples are obtained in the same way. One image of per subject is used to compile a gallery set. The test set comprises the remaining face images. For the sake of fairness, all methods use the same LBP features.  
Since KRDD combines three algorithms: SRC, KDA, and PCA, it is not a simple stack of the three algorithms. To prove that KRDD is fundamentally different from SRC and KDA, KRDD is compared with PCA, SRC, ESRC, KDA, KDA + SRC, KDA + ESRC and KED. All methods were tested using LBP features. Again, PCA is applied to reduce the feature dimensions to 600, 700, and 800 to compare these advanced methods. In this experiment, KRDD learned from the first 10, 15 and 20 components of the real disturbance variable and the simulated disturbance variable in kernel space.  
It can be seen in Table 5 that the recognition rate of the simple association of KDA and SRC is indeed nearly 20% higher than the rate of KDA and SRC alone. However, KRDD is not a simple addition of KDA and SRC. The recognition rate of KRDD is approximately 5.8% higher than that of KDA plus SRC and KDA plus ESRC. KRDD also achieves higher recognition accuracy than taht of KED. Among all the methods, KRDD achieved the best results. Figure 9 shows the classification time for several algorithms. Although ESRC has a higher recognition accuracy than SRC, it takes a much longer time because it expands the quantity of atoms in the dictionary. \(D\) in KRDD is based on low dimensions and only adds a small number of atoms, which is faster than SRC. It is remarkable that KRDD has a higher recognition rate and takes less time than KED for the same number of selected occlusion components. Additionally, a positive correlation could be observed between selected  
\begin{table}
\begin{tabular}{c c} \hline
**Method** & **Mean Accuracy** \\ \hline VMRS [41] & 92.05 \\ Sub-SML [42] & 90.75 \\ high-dim LBP [43] & 93.18 \\ KRDD & 92.46 \\ ConvNet-RBM [38] & 91.75 \\ GaussianFace [39] & 98.52 \\ DeepID3 [40] & **99.53** \\ \hline \end{tabular}
\end{table}
Table 4: Mean recognition accuracy for Advanced Methods on LFWA Dataset.  
Figure 8: Example Images of one Subject on the CAS-PEAL Database.  
occlusion components and the recognition rate of KRDD. Nevertheless, selecting more occlusion components results in longer time computation times.
# Coefficient of Thermal Expansion of RCA Concrete Made by Equivalent Mortar Volume
## 5 Conclusions  
The present study was conducted to experimentally verify that the COTE of RCA concrete is dependent on the volume of the original virgin aggregate in the total RCA concrete mix. Three types of RCA (one crushed from railroad concrete sleepers; one crushed from precast (PC) culverts; and one obtained from a commercial recycling plant) were mixed by two concrete mixing methods (conventional mix method and EMV method) and by varying the replacement ratio relative to that of natural coarse aggregate. Three mix series were used in the experiment. The conclusions acquired from the experimental results are described below. It should be noted that because of limited test variables and test numbers in this study, further study is needed for the findings of this study to be generalized to other RCA concrete types (i.e., using RCA from other sources, or having different parameters and exposure conditions).  
1. At the same RCA replacement ratio (volumetric ratio of RCA in the total coarse aggregate) of 68%, the COTE of RCA concrete prepared by EMV mix design was over 6-7% lower than that of RCA concrete prepared by conventional mix method, and it was similar to or 1-2% lower than that of the reference natural coarse aggregate concrete.
2. For RCA concrete prepared by conventional mix method, which does not take into account the residual mortar content (RMC) attached to RCA, the volumetric ratio of the original virgin aggregate was relatively small and the volumetric ratio of the mortar (or cement paste) was relatively high, and thus the COTE may have increased.
3. The COTE of RCA concrete is represented in terms of the volumetric ratio of coarse aggregate of the individual mixes by the best fit quadratic curves. However, further study is needed to verify if the COTE value of concrete is linear or nonlinear to the volume ratio of total coarse aggregate.
4. Finally, the predicted COTE obtained by applying the composite series model was slightly higher than the measured COTE, but the determination coefficient was higher than usual, indicating that experimental results obtained in this study are reliable.  
[PERSON] conceived, designed the experiments, and wrote the paper; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data. Both authors have read and agreed to the published version of the manuscript.  
This study was conducted under research Project (21 POQW-B152690-03) funded by the Ministry of Land, Infrastructure and Transport (MOLIT) and the Korea Agency for Infrastructure Technology Advancement (KAIA). The authors would like to thank the members of research team, MOLIT and KAIA for their guidance and supports throughout the project.  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest.