esa-sceva/satcom-chunk-collection huggingface dataset:
# VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron
###### Abstract  
Gait is a pattern of a person's walking. The body movements of a person while walking makes the gait unique. Regardless of the uniqueness, the gait recognition process suffers under various factors, namely the viewing angle, carrying condition, and clothing. In this paper, a pre-trained VGG-16 model is incorporated with a multilayer perceptron to enhance the performance under various covariates. At first, the gait energy image is obtained by averaging the silhouettes over a gait cycle. Transfer learning and fine-tuning techniques are then applied on the pre-trained VGG-16 model to learn the gait features of the attained gait energy image. Subsequently, a multilayer perceptron is utilized to determine the relationship among the gait features and the corresponding subject. Lastly, the classification layer identifies the corresponding subject. Experiments are conducted to evaluate the performance of the proposed method on the CASIA-B dataset, the OU-ISIR dataset D, and the OU-ISIR large population dataset. The comparison with the state-of-the-art methods shows that the proposed method outperforms the methods on all the datasets.  
gait; gait recognition; deep learning; pre-trained model; multilayer perceptron +
Footnote †: journal: Medical  
being utilized for gait recognition are ResNet [1], DenseNet [2], AlexNet [3], and VGG [4]. Integration of the pre-trained models using transfer learning and fine-tuning techniques improves the accuracy on a large scale. Nevertheless, not many researchers have applied the pre-trained models in their work.  
Consequently, the incorporation of a pre-trained model and a multilayer perceptron is proposed in this paper to achieve better accuracy under various factors. The method first averages the gait silhouettes over a gait cycle to attain the gait energy image (GEI). A pre-trained VGG-16 model is then employed to extract the gait features of the obtained GEIs. The multilayer perceptron is used to identify the association between the features and the subjects. Finally, the subjects are classified based on the features in the classification layer.  
The main contributions of this work are specified below:  
* The extraction of deep features by exploiting the pre-trained VGG-16 model using transfer learning and fine-tuning techniques.
* The correlation of the obtained gait features and the associated subject is determined by utilizing the multilayer perceptron.
* The generalization capability of the proposed method was assessed on CASIA-B, OU-ISIR D, and OU-LP datasets.
# A Hybrid State Estimator Based on SCADA and PMU Measurements for Medium Voltage Distribution System
## 4 Case Studies
### Comparison of State Estimation Results under the Normal Condition  
Table 1 shows a comparison of convergence, iteration, execution time, and the mean absolute error (MAE) of voltage amplitude between the different methods at the SCADA data acquisition time. Also, the MAE of the bus voltage amplitude is defined using Equation (16).  
\[r_{\text{MAE}}=\frac{\sum\left|\hat{\mathbf{x}}-\mathbf{x}_{pf}\right|/ \mathbf{x}_{pf}}{n} \tag{16}\]  
where \(\hat{\mathbf{x}}\) is the state estimation results and \(\mathbf{x}_{pf}\) is the power flow results.  
Under the normal measurement condition, the four estimation methods could effectively converge in less than 0.5 s, which would not collide with the set time in Figure 5, and the MAE of each method were all less than \(10^{-4}\), which shows the high estimation accuracy. The improved IGGIII has certain advantages in iterations compared with the exponential equivalence robust estimation. Furthermore, it shows that the reasonable measurement weights could speed up the iteration process. However, the execution time of the improved IGGIII was longer than that of WLS and initial IGGIII. This was because the recalculated weights and robust thresholds in each iteration increased the execution time.  
Table 2, Figures 6 and 7 show the dynamic state estimation results at bus 6 (without PMU installation) and 30 (with PMU installation). The proposed IIGGIII-LSE could track the changes of the states, whose effect was similar to the method WLS-UKF. While at the bus with the PMU installation, the proposed IIGGIII-LSE could track the changes more accurately compared to the bus without PMU installation, which indicates the effectiveness of using PMUs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Estimation Method** & **Convergence** & **Iterations** & **Execution Time(s)** & **MAE of Bus Voltage Amplitude** \\ \hline WLS & Yes & 22 & 0.144 & 3.17033 \(\times\)\(10^{-5}\) \\ EXP & Yes & 37 & 0.496 & 6.57133 \(\times\)\(10^{-5}\) \\ IIGGIII & Yes & 32 & 0.357 & 1.72627 \(\times\)\(10^{-5}\) \\ IGGIII & Yes & 26 & 0.182 & 9.44840 \(\times\)\(10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of five estimation methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Time/s**} & \multicolumn{3}{c}{**MAE of Bus Voltage Amplitude**} & \multicolumn{3}{c}{**The Relative Error of the Estimated Voltage Phase**} \\ \cline{2-7}  & \multicolumn{3}{c}{**Bus 6**} & \multicolumn{3}{c}{**Bus 30**} & \multicolumn{3}{c}{**Bus 6**} & \multicolumn{3}{c}{**Bus 30**} \\ \cline{2-7}  & **WLS-UKF** & **IIGGIII-LSE** & **WLS-UKF** & **IIGGIII-LSE** & **WLS-UKF** & **IIGGIII-LSE** \\ \hline
20 & 0.00106 & 0.00134 & 0.00018 & 0.00042 & 0.00106 & 0.00070 & 0.00044 & 0.00002 \\
20.5 & 0.00142 & 0.00124 & 0.00010 & 0.00056 & 0.00119 & 0.00091 & 0.00055 & 0.00019 \\
21 & 0.00133 & 0.00083 & 0.000
## 2 Materials and Methods
### RNA Sequencing Procedure and Data Analysis  
#### 2.2.1 Total Ribonucleic Acid (RNA) Extraction and Library Construction for RNA Sequencing  
Total ribonucleic acid (RNA) was extracted from the 27 samples (1 genotype \(\times\) 3 P treatments \(\times\) 3 maturity stages \(\times\) 3 biological replicates) using the Monarch(r) Total RNA Miniprep Kit (NEB #T2010), and the concentration was evaluated by Qubit 4.0 Fluorometer instrument and the RNA BR Assay Kit reagent (Invitrogen, Carlsbad, CA, USA). The Nucleic acid integrity and the ratio of 25 S/18 S ribosomal molecules (rRNA) were determined by the Fragment analyzer system, Agilent (Reagent code DNF-471). The accepted-to-proceed samples were within acceptable qualification limits according to the literature (plants > 6.5 RIN). Library preparation was performed with NEBNext(r) Ultra(tm) II RNA Library Prep Kit for Illumina (NEB#7770 and 7775 mRNA selection) according to the manufacturer's instructions, with an approximate fragment size of 420 base pairs (bp) as input in the final library. The 5400 Fragment Analyzer (Agilent, DNF-91, Santa Clara, CA, USA) was used to estimate the size of the libraries through capillary electrophoresis, and it was established that the fragment size of the two pooled sample groups was on average 419 bp and 448 bp, respectively. Finally, the libraries were quantified with qPCR using the KAPA Library Quantification Kit for Illumina(r) sequencing platforms (#KK4824, Roche, Basel, Switzerland) on a Rotor-Gene Q thermocycler (Qiagen, Hilden, Germany). The constructed Libraries were eventually sequenced on the Illumina(r) NextSeq(r) platform using the NextSeqR 500/550 v2.5 High Output Kit (300 cycles) (#20024908, Illumina, San Diego, CA, USA). The sequencing procedure for the NextSeq(tm) 500 Sequencing System (Illumina, San Diego, CA, USA) includes the high Output Flow Cell with a capacity of up to 400 M reads, a Read Length of 2 \(\times\) 150 base pairs, and a final Output of 150-200 Gb.
###### Abstract  
The aim of this study was to analyze the association between the amount of daily movement measured with a triaxial accelerometer (MIAMORI-Gait) and motor symptoms in patients with Parkinson's disease (PD). The subjects were 50 consecutive patients with untreated PD free of dementia. The amount of overall movement over 24 h was measured with the portable MIMAMORI-Gait device and its association with the modified Hoehn and Yahr stage and UPDRS part II and III scores was analyzed. In patients with PD, the amount of overall movement measured with MIMAMORI-Gait was significantly associated with the UPDRS part II score (\(\beta=-0.506\), \(p<0.001\)) and part III score (\(\beta=-0.347\), \(p=0.010\)), but not with the modified Hoehn and Yahr stage. The amount of overall movement measured with MIMAMORI-Gait can potentially be used for evaluation of motor symptoms and ADL in PD patients.  
Keywords:triaxial accelerometer; MIMAMORI-Gait; amount of daily movement; Parkinson's disease +
Footnote †: journal: Applied Sciences
## 4 Performance Evaluation
### QoS Parameters  
#### 4.2.1 Frame Loss  
Simulation results for the different tested scenarios show that GeoQoE-Vanet has lower frame loss values compared with GPSR and GPSR-2P as illustrated in Figure 12. This is due to the fact that GeoQoE-Vanet selects the next hop vehicle which can enhance the perceived quality of transmitted video. Figure 12 shows that the frame loss percentage inversely proportional to the number of vehicles. As the number of vehicles increases, the probability of finding a best candidate vehicle be the next forwarder becomes high which decreases the frame loss.
# Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
## 5 Sustainable/Green Computing in Fog/Edge
### Security Sustainability  
IoT sustainability emphasizes the security of data and devices. Securing data involves handling confidentiality and integrity aspects, whereas device security concerns defense against stealth attacks. Energy-harvesting chips are susceptible to malicious attacks, including DoS attacks that disrupt sensors. Both the criteria of energy efficiency and security characterize the IoT sustainability while, at the same time, challenging IoT progress [163] as IoT devices are power constrained, which demands a refined, lightweight energy and security framework.  
According to a study, 70% of connected devices are at risk of cyber-attacks [164]. Furthermore, vulnerable smart devices are estimated to cause 25% of all industrial attacks [158]. As IoT devices are resource-constrained, they are highly prone to attack than desktops or laptops. As the battery size decreases, it can hold less energy, which in turn reduces the availability of resources that provide security. Hence, lightweight security mechanisms suitable for power constraint devices are essential, as traditional security solutions designed for resource-rich devices consume more energy, owing to more computations. Research shows that the advanced encryption standard, as well as the elliptic curve cryptography, offer a lightweight cryptographic solution with an evaluation based on resource limitations, chip space, latency, and throughput [165]. For the IoT systems to be sustainable, the balancing of aspects such as energy efficiency, power consumption, performance, and security is required [158].
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 4 Metal Telluride Thin Films
### Zinc Telluride Films  
Zinc telluride (ZnTe) belonged to II-VI compounds. Nanostructured zinc telluride was produced by using sodium telluride and zinc sulfate (ZnSO\({}_{4}\)) solutions on microscope glass slides [127]. The surface morphology and structure analysis revealed that the obtained films were free from cracks and were polycrystalline in nature (two peaks, 2\(\theta\) = 27\({}^{\circ}\) and 30.05\({}^{\circ}\)), respectively. Other results, such as the elemental ratio of 53:47 (Zn:Te) and the band gap, increased (2.75 to 3.15 eV) with the increasing film thickness (75 to 270 nm). [PERSON] and co-workers [128] proposed that this film could be used as an optical window due to transmittance mostly occurring in the far infrared region.
# A 1064 nm Dispersive Raman Spectral Imaging System for Food Safety and Quality Evaluation
## 2 Raman Spectral Imaging System
### Spectral Calibration and Resolution  
The X-axis in the Raman spectral measurement, termed as Raman shift (wavenumber, cm\({}^{-1}\)), is the change in the energy state from the excitation source. Typically, chemicals with known wavenumber shifts are used to assign a wavenumber to the corresponding pixels along the spectral dimension. The process is called spectral calibration. In this study, spectral calibration was done using polystyrene and naphthalene. American Society for Testing and Materials (ASTM) International enlists six chemicals (polystyrene, naphthalene, 1,4-bis(2-methylstyryl) benzene, sulfur, toluene-acetonitrile (concentration 50/50 by volume), 4-acetamidophenol, benzonitrile, and cyclohexane) as Raman shift standards for spectrometer calibration [46]. Polystyrene and naphthalene were packaged separately in two Petri-dishes and scanned covering the surface area of 10 mm \(\times\) 10 mm with a step size of 1 mm for X- and Y-axes, collecting a total of 100 spectra from each Petri-dish. Figure 2a shows an average Raman spectrum of naphthalene (polystyrene spectrum not shown). Seven spectral peaks were identified for naphthalene between 513 to 1576 cm\({}^{-1}\). Similarly, five peaks were identified for polystyrene in the wavenumber range of 620 to 1602 cm\({}^{-1}\). Spectral calibration was performed using the twelve peaks covering the wavenumber range of 513 to 1602 cm\({}^{-1}\). Quadratic fitting was used to develop spectral calibration model. The quadratic model was used to determine the wavenumbers of corresponding pixels. The Raman system covered the wavenumber range of 142 to 1820 cm\({}^{-1}\) (Figure 2b) after spectral calibration. The spectral resolution of the Raman spectrometer is 12 cm\({}^{-1}\) at full width half maximum (FWHM).  
Figure 2: Raman spectrum of naphthalene for spectral calibration (a); quadratic fitting of naphthalene and polystyrene Raman peaks for spectral calibration (b).
## 3 Results and Discussion  
### XRD Analysis  
The XRD spectrum, as can be seen in Figure 1, proves the successful formation of AuNPs using sonochemical methods. All diffraction peaks fit perfectly into the face-centered cubic phase (fcc). The results of the XRD are in good agreement with the report of [PERSON] et al. (2016). The characteristic diffraction peaks of (111), (200), (220), (311) and (222) are identified at 20 values of 38.1\({}^{\circ}\), 44.4\({}^{\circ}\), 64.6\({}^{\circ}\), 77.8\({}^{\circ}\), and 81.6\({}^{\circ}\), respectively, which are consistent with those in the reference (ref. code 01-089-3697). The XRD peaks are distinct because of their relatively high intensity. This finding can be attributed to the ultrasound's ability to control the process of crystallization through acoustic cavitation ([PERSON] et al., 2017). The sonochemical approach, therefore, provides better crystalline AuNPs as the XRD spectral characteristics (intensities and peak shifts) denote, comparing with other methods ([PERSON] et al., 2017; [PERSON] et al., 2017).
# HIEN: A Hybrid Interaction Enhanced Network for Horse Iris Super-Resolution
## 3 Experiment and Results
### Comparison with State-of-the-Arts Models
#### 3.2.1 Quantitative Results  
As shown in Table 3, the comparative results demonstrate that our proposed model achieves competitive performance across multiple metrics. In terms of PSNR, our model achieves a score of 30.5988. Although HAN reports a slightly higher PSNR of 30.7414, our model remains highly competitive and outperforms most other methods, including the recent RGT (30.5905). For the SSIM metric, our method achieves a score of 0.8552, which is slightly lower than CDFormer (0.8631) and HAN (0.8572). However, our method exhibits superior performance in the downstream iris-recognition task.  
Where our model particularly excels is in the recognition-based metrics. Our method achieves Precision (0.8148), Recall (0.7438), and F1-Score (0.7777), significantly outperforming all other methods in these categories. These results validate our model's effectiveness in recovering fine details within iris images while preserving overall structure. The recognition-related metrics show a marked improvement over even the closest competitors, SwinIR (F1-Score: 0.7533) and CDFormer (F1-Score: 0.7506).  
While some methods like HAN and CDFormer demonstrate excellent performance in specific traditional image quality metrics (HAN with the highest PSNR and CDFormer with the highest SSIM), our proposed HIEN model offers a better overall balance between perceptual quality and recognition performance. This is particularly important for practical applications where downstream recognition tasks depend on the quality of recovered fine iris details. Our model's performance in Precision, Recall, and F1-Score demonstrates its enhanced capability in preserving the discriminative features crucial for iris-recognition systems.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Year** & **PSNR** & **SSIM** & **Precision** & **Recall** & **F1-Score** \\ \hline Bicubic & - & 26.5949 & 0.7631 & 0.5713 & 0.4646 & 0.5125 \\ EDSR & 2017 & 30.3418 & 0.8502 & 0.7388 & 0.6938 & 0.7156 \\ RDN & 2018 & 30.5361 & 0.8533 & 0.7394 & 0.6979 & 0.7181 \\ SRFBN & 2019 & 30.5082 & 0.8541 & 0.7381 & 0.6917 & 0.7141 \\ HAN & 2020 & **30.7414** & 0.8572 & 0.7389 & 0.6958 & 0.7167 \\ SwinIR & 2021 & 30.5412 & 0.8542 & 0.8044 & 0.7083 & 0.7533 \\ ELAN & 2022 & 30.3353 & 0.8507 & 0.7337 & 0.6979 & 0.7154 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative comparison between our proposed method and several state-of-the-art super-resolution methods. The best and second best results are bold and underlined, respectively.
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 2 Related Work
### Link Estimation Methods  
In the past decades, abundant literature has been proposed for link quality estimation (LQE) to improve routing efficiency. The existing approaches are well surveyed in [5]. Hardware based LQE uses RSS (received signal strength) [6; 7; 8], LQI (link quality indicator) [9; 10; 11] and SNR (signal noise ratio) [12; 13; 14] as indicators while software based LQE exploits metrics such as PRR (packet reception ratio) [15; 16; 17], ETX (expected transmission count) [18; 19; 20] and 4-bit [2]. Typical work [2; 3; 18; 21] takes either network layer, link layer or physical layer information as input to get the estimation of link qualities. These methods can well fit in a stable network without environmental change or manual intervention. However, once network condition changes, existing methods such as 4-bit [2] have to replay the estimation process and therefore incur a significant overhead due to information exchange, especially in dynamic networks. Different from the existing work, LESS essentially decreases the information requirements by sparse sampling. As a supplementary technique, LESS can be easily integrated into other methods to enhance their performance.
# Developmental Screening System for Patient Vibration Signals with Knee Disorder
###### Abstract  
In this paper, we introduce a simple sound signal diagnostic method to evaluate anterior cruciate ligament (ACL) injury before and after reconstructive surgery. Sixty-five recruited participants were divided into control (n = 27) and experimental (n = 38) groups. Dynamic time warping of sound signals was applied to evaluate the healthy and injured limbs before surgery with those after surgery via analysis of variance and Z-test analysis. In the control group, the average differences among three sensing points ranged from 7.7 \(\pm\) 3.4 to 18.9 \(\pm\) 10.6, over the frequency range of 250 Hz to 4 kHz. In the experimental group, the average differences were between 6.2 \(\pm\) 2.8 and 57.4 \(\pm\) 21.3. The greatest significant wavelet coefficient difference was observed in the range of 125-250 Hz. Our preliminary results demonstrate that the proposed approach produces significant signal variations at the ACL test point (TP\({}_{\text{ACL}}\)) with regard to identifying ACL injury, with swing cycles conducted within the 4-kHz band of 1-7 wavelet coefficients. Thus, wavelet analysis of knee sound can be used to evaluate the recovery status after single ACL reconstruction surgery. After a 1-year follow-up of the 38 patients with ACL injury, the frequency band difference among sensing points was reduced from 274% to approximately 600%.  
a pacs: 9
## 3 Simulation Results
### Simulated-Signal vs. Airborne Lidar Measured-Signal
As Figure 7c,d shows, the LUT method has relatively smaller residual than that by ICDF method in almost all data. In general, the results indicate that the LUT method is effective for solving complicated or even unsolvable inverse cumulative distribution function of various scattering phase functions, and it significantly improves modeling accuracy. Figure 6: Simulated-signal vs. airborne lidar measured-signal: (**a**) Airborne lidar photo, (**b**) chlorophyll and \(c(z)\) profiles, (**c**) simulated signal vs. lidar measured-signal, and (**d**) error curve of comparison.
## 5 Discussion
### Development Working Time Coefficient  
The current section aims at disclosing the nature of the development working time coefficient (DWTC).  
For intermediate estimation [9], the relationship between PWT and DWT reflects the structure of developer working time:  
\[\overline{W}_{D}=\sum_{m\in\overline{T}_{D}}\sum_{i\in\overline{K}}\overline{ W}_{i}^{m}=\sum_{m\in\overline{T}_{D}}\sum_{i\in\overline{K}}\left[\sum_{s\in \overline{Q}}\left(\overline{D}_{i}^{m(s)}+\overline{A}_{i}^{m(s)}\right)+ \overline{G}_{i}^{m}+\left(\overline{O}_{i}^{m}+\overline{I}_{i}^{m}\right) \right], \tag{25}\]  
where \(\overline{W}_{D}\) is the development project working time; \(\overline{T}_{D}\) is the set of development project roles; \(\overline{K}\) is the set of project phases; \(\overline{Q}\) is the set of development specializations; \(\overline{W}_{i}^{m}\), \(\overline{D}_{i}^{m}\), \(\overline{A}_{i}^{m}\), \(\overline{G}_{i}^{m}\), \(\overline{O}_{i}^{m}\), \(\overline{I}_{i}^{m}\) are, respectively, the project working time (PWT), the development working time (DWT), the supplementary development activities, the general project activities, the leave time, and the idle time for development role \(m\in\overline{T}_{D}\) in project phase \(i\in\overline{K}\) (further information about these variables is provided in [9]).  
The DWTC from (8) is introduced in order to decrease complexity by reducing the number of variables in the system of working-time balance equations. Therefore, for the preliminary estimation, development project working time, \(W_{D}\), is expressed as follows:  
\[W_{D}=\sum_{m\in T_{D}}W^{m}=\xi\sum_{m\in T_{D}}D^{m}, \tag{26}\]where \(\xi\) is the development working time coefficient (DWTC); \(D^{m}\) is the development working time (DWT) of role \(m\in T_{D}\). Following the concept of the ELC, a preliminary estimate is supposed to be \"wider\" than the corresponding intermediate estimate (given that the NDE is the same for both estimates); on the other hand, \(W_{D}\) does not have to be too big to prevent making a wrong impression regarding the project duration and budget:  
\[q\cdot\overline{W}_{D}\geq W_{D}\geq\overline{W}_{D}, \tag{27}\]  
where \(q>1\) is the coefficient defining the high boundary of PWT, \(W_{D}\), received as the outcome of preliminary estimation; \(\overline{W}_{D}\) is the corresponding PWT resulting from the intermediate estimation.  
Summarizing the above, DWTC depends on what is defined by (25): (a) the structure of software developer working time; (b) the project release plan (splitting into project phases); (c) the development team composition (roles, development specializations, involvement in different project phases). And the coefficient's boundaries are limited according to (27). To define DWTC, it is suggested that one rely on the data from historical projects implemented in a particular company as well as on experts' judgment. For practice usage, it is recommended that a decision tree is created that allows one to obtain a value of DWTC based on the specifics of a particular project.