esa-sceva/satcom-chunk-collection huggingface dataset:
# AOA-Based Three-Dimensional Multi-Target Localization in Industrial WSNs for LOS Conditions
## 7 Conclusions  
In this paper, an ATML approach for locating the 3D positions of multiple MSNs simultaneously in a SIWSN is proposed. The MT-SIMO signal transmission scheme is designed by which two ANs capture the spread spectrum signals broadcast by MSNs using antenna arrays. The 2D AOAs of the LOS paths from every MSN to both ANs are estimated by using a simple iterative MLE based on the LPRVs. Based on the estimated AOAs and the skew line theory, the positions of multiple MSNs are determined. A testbed has been implemented for ATML. The simulation and field experiment results validate the effectiveness and feasibility of the proposed ATML in practice. It is shown that the ATML can estimate the positions of multiple MSNs simultaneously in a large range of SNR and with small antenna array dimensions. By exploiting the spread spectrum gain and the array gain, the ATML can achieve high accuracy and efficiency in multi-target localization. How to further improve the positioning accuracy, for example, by using the Kalman filtering and multi-domain signal fusion, will be investigated in our future works. To enhance the security of the electronic systems on MSNs for industrial applications is also an important issue to study [29; 30].  
Conceptualization, R.Z. and X.D.; Methodology, R.Z. and B.L.; Software, J.L. and B.L.; Validation, J.L. and B.L.; Formal Analysis, X.D.; Investigation, R.Z. and X.D.; Resources, R.Z.; Data Curation, J.L. and B.L.; Writing--Original Draft Preparation, R.Z. and J.L.; Writing--Review & Editing, X.D. and M.G.; Visualization, J.L. and B.L.; Supervision, X.D. and M.G.; Project Administration, R.Z.; Funding Acquisition, R.Z.  
This research was funded by National Natural Science Foundation of China grant numbers 61571370 and 61601365, National Science and Technology Major Project grant number 2016 ZX03001015, and Fundamental Research Funds for the Central Universities grant numbers 3102017 QQD091 and 3102017 GX08003.  
The authors declare no conflict of interest.  
Figure 12: Field experiment results of AOA estimation. (A: azimuth; E: elevation)
## 3 Experimental Results
### Qualitative Results  
We conducted qualitative analysis by comparing the output of our model with those of two other auto-encoder-based GAN models, BEGAN [12] and BEGAN-CS [18]. BEGAN-CS adds a latent constraint to BEGAN. The results are shown in Figure 5. The columns (a) to (c) represent 5, 10, and 15 epochs, respectively, while each row represents the qualitative results from the compared methods. The output images are produced by the generator and the input vectors are sampled randomly from a Gaussian distribution. It should be noted that the output of our model in (a) (5 epochs) has a lower resolution than the other models because of the progressive learning strategy it employs. The visual quality of the images improves as training progresses in all three models. However, the results from BEGAN contain some artifacts, such as checkerboard patterns, while BEGAN-CS produces blurred and unstructured facial images (Figure 5d). Once the size of the training images is increased, our model produces similar visual quality in Figure 5b and clearer images than the other models after 15 epochs (Figure 5c,d).
## 7 Passive Optical Network
### TWDM-PON Application
The XGS-PON is one of the viable solutions being considered. XGS-PON presents an enhanced business solution owing to the less costly elements being utilized.
# Towards Online Estimation of Human Joint Muscular Torque with a Lower Limb Exoskeleton Robot
###### Abstract  
Exoskeleton robots demonstrate promise in their application in assisting or enhancing human physical capacity. Joint muscular torques (JMT) reflect human effort, which can be applied on an exoskeleton robot to realize an active power-assist function. The estimation of human JMT with a wearable exoskeleton is challenging. This paper proposed a novel human lower limb JMT estimation method based on the inverse dynamics of the human body. The method has two main parts: the inverse dynamic approach (IDA) and the sensing system. We solve the inverse dynamics of each human leg separately to shorten the serial chain and reduce computational complexity, and divide the JMT into the mass-induced one and the foot-contact-force (FCF)-induced one to avoid switching the dynamic equation due to different contact states of the feet. An exoskeleton embedded sensing system is designed to obtain the user's motion data and FCF required by the IDA by mapping motion information from the exoskeleton to the human body. Compared with the popular electromyography (EMG) and wearable sensor based solutions, electrodes, sensors, and complex wiring on the human body are eliminated to improve wearing convenience. A comparison experiment shows that this method produces close output to a motion analysis system with different subjects in different motion.  
Keywords:human joint torque estimation; active power-assist; lower limb exoskeleton; inverse dynamics +
Footnote †: journal: Applied Sciences
## 2 Related Work
### Visual Object Tracking
#### 2.1.1 Regression-Based Trackers  
In recent years, some researchers have developed a deep regression network-based tracking approach [10; 36; 37] that uses bounding box regression to identify objects instead of classification models. This technique improves the chances of solving the tracking issue by training the model on trainable datasets using a loss function such as mean-squared or mean-absolute error. The datasets usually consist of the input pictures and the bounding boxes of the frame's object classes. However, if the object moves very quickly across the consecutive frames or if occlusion occurs, this approach may fail or produce poor results during the tracking process. Furthermore, to fully cover the consecutive frames' characteristics and their information, the aforementioned approaches require a more efficient searching algorithm for learning the dataset or environment by utilizing sliding-window or candidate-sampling strategies.
## 2 Wide Range Two-Dimensional High-Altitude Refractivity Estimation
The regional refractivity, \(M\), was obtained using the two-dimensional IDW interpolation at a fixed altitude of 5000 m and compared with the results of other conventional interpolation methods. From the results, the nearest-neighbor interpolation method obviously illustrated the discrete tendency, but other methods were similar to each other. The linear interpolation method had an apparent discontinuity inside the highlighted region, where the discontinuity line formed nearby the observatories of \(\mathbb{S}_{3}\) and \(\mathbb{S}_{5}\). Since the cubic method utilizes higher-order polynomials for the interpolation, it presented much smoother results than the linear interpolation in the discontinuity region. On the other hand, the IDW interpolation method represented no discontinuity over all regions; this  
Figure 3: Root-mean-square error (RMSE) of \(\
abla M\) between the observation-based and the interpolated refractivity data from conventional linear, cubic, nearest-neighbor, and IDW interpolation methods: (**a**) spring; (**b**) summer; (**c**) autumn; and (**d**) winter. method seems to better reflect the atmospheric phenomenon than others, with the lowest RMSE values of all methods. To specifically analyze the smoothness of the interpolation, we observed the standard deviation of the difference between the adjacent points for the interpolated result. Note that a smaller standard deviation indicates smoother interpolated results. Herein, the standard deviations for the smoothness of the IDW, linear, cubic, and nearest-neighbor interpolation methods were 0.1602, 0.7202, 0.8144, 1.3556, respectively. The proposed IDW interpolation consistently showed smoother and more continuous results than the other three methods. Figure 4: _Cont._
# A Novel Hybrid Ant Colony Optimization for a Multicast Routing Problem
## 3 Ant Colony Optimization and Cloud Model  
The ACO algorithm [24] is a popular-based search technique that simulates the foraging behavior of ants in nature as they find food. When the ants are foraging for food, they deposit a pheromone trail on the paths they have already passed. With the aid of these pheromones, these ants communicate and work together with others to discover the shortest route between their nests and food locations. As the ants move, the constant pheromone trail they deposit can attract other ants to follow them. The faster the pheromone trail increases, the more likely that other ants will choose that route. Over time, more ants can complete the shorter path on which pheromone trail accumulates faster. In contrast, pheromone trails on the longer routes have less pheromones deposited. In the end, the shortest route will be discovered. Ants can always find efficiently the shortest path from food sources to nests. Also, they can change the path to adapt to different environments. When new obstacles appear, they can seek the new shortest route.  
The cloud model [25] is a new transition model that can be considered for both qualitative and quantitative techniques according to both fuzzy and probability theory. It adopts a new method to illustrate the fuzziness and randomness of concepts. Because the model can effectively integrate fuzziness and randomness, we can use numerical characteristics for instance, expectation value \(Ex\), entropy \(En\), hyper entropy \(He\) to generate cloud drop with certainty degree by devising ACO algorithms. Suppose \(U\) is defined as the discourse universe, and \(C\) denotes a qualitative concept expressed by the characters (\(Ex\), \(En\), \(He\)) related with \(U\). \(\mu(x)\)\(\in\)[0, 1] denotes a stable tendency for any number \(x\)\(\in\)\(U\). If a number \(x\) represents a random instance of the concept \(C\) and accords with normal distribution \(x\)\(\sim\)\(N(Ex\),\(y_{i})\), where \(y_{i}\) also consistent with normal distribution \(y_{i}\)\(\sim\)\(N(En\),\(He)\), and the determinate degree of \(x\) meets Equation (7). So \(x\) is named as a cloud drop. If the \(\mu(x)\) distribution is normal in \(U\), it is named as the normal cloud.  
\[u(x)=e^{-\frac{(x-Ex)^{2}}{2y_{i}^{2}}} \tag{7}\]To illustrate the transformation, the model of normal cloud contains three numerical features, that is, expectation value \(Ex\), entropy \(En\), and super entropy \(He\). The \(Ex\) denotes the expectation of cloud drops which can be the most representative concept belonging to the qualitative concept. Considering for a center of cloud gravity, entropy \(En\) means the indeterminate measurement of the qualitative concept according to the arbitrary probability and fuzziness. Super entropy \(He\) denotes the dispersed degree of the entropy \(En\), representing the dispersion extent of cloud droplets. Figure 2 shows the diagram of the cloud model. It can be seen that the greater \(En\) represents the greater coverage of the cloud drops. Similarly, the greater the \(He\), the more dispersed the cloud drops are.
# Rifampicin-Loaded PLGA/Alginate-Grafted pNVCL-Based Nanoparticles for Wound Healing
## 3 Results and Discussions
### Matrix Characterization
#### 3.1.3 DSC Analysis  
Thermal properties of the polymeric nanoparticles with and without RF were tested to check the presence of the drug within the matrix and to determine the thermal stability and homogeneity of the prepared formulations. Figure 3 presents the DSC thermograms of pure RF and polymeric nanoparticles with and without RF.  
The DSC thermogram of the pure drug (Figure 3) showed an endothermic peak at 189 \({}^{\circ}\)C corresponding to the melting point of RF. The thermal decomposition process of RF occurred in two stages: the first thermal decomposition occurred around 200 \({}^{\circ}\)C and the second one at 257 \({}^{\circ}\)C. The thermal events observed on the DSC curve are consistent with the reported literature [23]. On the DSC of NP + RF, we detected endothermic peaks around 189 \({}^{\circ}\)C and 250 \({}^{\circ}\)C. [PERSON] et al. [24] also found out that RF had an exothermic crystallization peak at 213 \({}^{\circ}\)C and a degradation peak at 249 \({}^{\circ}\)C. The peak from 250 \({}^{\circ}\)C almost disappeared and overlapped with the degradation peak of the polymeric matrix, indicating the fact that Rf was encapsulated. When analyzing the thermogram of nanoparticles of AgA-g-pNVCL coated with PLGA (i.e., NPs), there were observed small peaks at 50 \({}^{\circ}\)C being assigned to the glass transition of PLGA and an endothermic peak around 217 \({}^{\circ}\)C being explained by the beginning of polymer degradation. Comparable results were found by [PERSON] et al. [25]. They found for sodium alginate a broad endothermic peak around 100 \({}^{\circ}\)C and a sharp exothermic peak at 250 \({}^{\circ}\)C, the latter one being connected with its thermal decomposition. The copolymer AgA-g-pNVCL showed three thermal events at 80 \({}^{\circ}\)C, at 218 \({}^{\circ}\)C, and 250 \({}^{\circ}\)C. It seems that PLGA coating induced a less thermal stability of the molecule as the peak from 217 \({}^{\circ}\)C indicated the beginning of the nanoparticles' degradation, especially the PLGA-attached moieties. A further degradation changed the macromolecular chains of AgA-g-pNVCL. The outcome from DSC analysis not only proved the complexity of the polymeric matrix but also confirmed the presence of RF and its thermal characteristics.  
Figure 2: Particle size distribution for the nanoparticles without rifampicin (**the upper graph**) and loaded with rifampicin (**downward**).
## 3 Results and Discussion  
### Analysis of Volatile Compounds in Tea with Different Types  
Volatile compounds in five tea types were shown in Supplementary Table S2. The results showed that 39 compounds (32.01 \(\upmu\)g kg\({}^{-1}\)) were found in white tea, including six terpenids, seven alcohols, four aldehydes, three ketones, eight acids, four esters, four heterocycles, and three hydrocarbons. Fifty-eight compounds (33.88 \(\upmu\)g kg\({}^{-1}\)) were found in green tea, including 12 terpenids, 10 alcohols, six aldehydes, eight ketones, five acids, five esters, eight heterocycles, and four hydrocarbons. Fifty-two compounds (33.23 \(\upmu\)g kg\({}^{-1}\)) were found in oolong tea, including 14 terpenids, six alcohols, nine aldehydes, five ketones, three acids, two esters, six heterocycles, and seven hydrocarbons. Fifty-four compounds (76.60 \(\upmu\)g kg\({}^{-1}\)) were found in red tea, including 11 terpenids, seven alcohols, seven aldehydes, four ketones, six acids, five esters, eight heterocycles, and six hydrocarbons.  
Forty compounds (25.48 ug kg\({}^{-1}\)) were found in dark tea, including nine terpenoids, nine alcohols, seven aldehydes, three ketones, three acids, two esters, two heterocycles, and five hydrocarbons.  
In terms of the number, it was green tea (58) \(>\) red tea (54) \(\approx\) oolong tea (52) \(>\) dark tea (40) \(\approx\) white tea (39). In terms of content (ug kg\({}^{-1}\)), it was red tea (76.60) \(>\) green tea (33.88) \(\approx\) oolong tea (33.23) \(\approx\) white tea (32.01) \(>\) dark tea (25.48). Many compounds, such as terpenoids (linalool and oxides, \(\beta\)-ionone, \(\beta\)-cyclocitral, dihydroactindiolide), alcohols (1-penten-3-ol, (_E_)-3-hexen-1-ol, benzyl alcohol, phenethyl alcohol), aldehydes and ketones (hexanal, (_E_)-2-hexenal, (_E_,_E_)-2,4-heptadien, benzaldehyde, 6-methyl-5-hepten-2-one), acids and esters (acetic acid, propionic acid, hexanoic acid, methyl salicate), and heterocycle compounds (2-pentylfuran and 2-acetyl pyrrole), were widely reported as tea aroma compounds [20]. It is apparent and impossible to clearly define the aroma intensity and odor characteristic of tea with different types depended on the number and the contents of volatile compounds in Table S2. Only a compound with OAV \(>\) 1 has its contribution to the aroma of a product [12]. The higher the OAV of a compound, the more the contribution to the overall aroma of a product. The overall aroma intensity of a product are related to the content and the threshold of the individual compounds [10]. The OAV method is widely used for defining the aroma intensity and odor characteristic in foods, wine and tobacco [13; 14; 15; 16]. Therefore, we calculated the OAVi of each compound to analyze the key contributors for tea aroma in the subsequent section.
# Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
## 3 Properties of Linear Photoacoustic
### Observation Angle  
Another way to generate a narrowband linear optoacoustic signal is by properly choosing the incident angle of the laser light and the observation angle of the receiver [39; 67]. A wavy water surface imposes further complexity to determine these angle values. Figure 8 shows the relationship between these angles for a wavy water surface. With the help of Equations (1)-(12) in [67], the power spectral density can be rewritten as:  
\[P(r,\omega)=A_{0}(\mu,p_{0},\omega,r,\tau,T)\times D(\theta,\theta,\varphi, \omega,\mu,a) \tag{15}\]  
where, \(A_{0}\) is the amplitude term whose value mainly depends on the laser parameters, \(\mu,p_{0},\omega,r,\tau,\text{ and }T\), and \(D\) is called directionality factor whose value depends on environmental parameters and observation angles, \(\theta_{r},\theta,\text{ and }\varphi\). Through some rigorous theoretical analysis, it is shown in [67] that when the sum of refracted angle, \(\theta_{r}\) and vertical observation angle, \(\theta\) is close to \(90^{\circ}\), narrowband acoustic signal generation is possible. Figure 9a,b illustrate these two scenarios where a laser beam is transmitted at an angle such that the sum of \(\theta\) and \(\theta_{r}\) is around \(90^{\circ}\). Figure 9a,b are based on a flat-water surface. Figure 9c shows the case for a wavy water surface. In [67], it was concluded that if the height of the water wave is small enough, a change in \(\theta\) and \(\theta_{r}\) does not impact their sum, i.e., if \(\theta_{r}\) decreases \(\theta\) increases in a similar amount so that their sum remains \(90^{\circ}\).  
Figure 7: Power spectral response of optoacoustic pressure wave for (**a**) \(S=0.5\), \(N=25\), \(\delta=6\) and (**b**) \(S=0.5\), \(N=25\), \(\delta=2.4\).  
From the above discussion we can conclude that, by choosing the optimum repletion rate and incident angle of the laser beam, a narrow band acoustic signal generation is possible for a linear optoacoustic.
## 4 Numerical Experiment
### Effectiveness Verification of MWT-MSSA Method
To highlight the detailed benefits of the proposed MWT-MSSA method, A-scan waveforms were extracted from the diffraction wing and vertex, as indicated by the white dashed lines in Figure 6b,e. The waveform comparison results are shown in Figure 7a-c. As revealed by the black dashed rectangle in Figure 7a,b, the energy around the wings of the diffraction separated by MWT-MSSA closely matches the synthetic A-scan, which indicates a minimal loss of energy. We use the energy-retention ratio to evaluate the performance of the method. Its formula is shown as follows  
\[R=E_{\mathrm{rec}}/E_{ori}\cdot 100\% \tag{11}\]  
Figure 5: The diffraction profile (**a**) and reflection profile (**b**) extracted from the second-level low-frequency approximation coefficient in Figure 4e by using the _k_-means clustering-based MSSA method. Figure 6: Comparison of diffraction and reflection profiles separated using MWT-MSSA and PWD. (**a**) Synthetic GPR data; (**b**) Diffraction profile and (**c**) reflection profile obtained using MWT-MSSA; (**d**) Dip field utilized for PWD separation; (**e**) Diffraction profile and (**f**) reflection profile obtained using PWD. where \(E_{\rm rec}\) represents the maximum amplitude of the separated diffraction peak and \(E_{ori}\) represents the maximum amplitude of the before separated diffraction peak in the original profile. Furthermore, as illustrated in Figure 7c, the diffraction separation using the MWT-MSSA method demonstrates a notable reduction in energy loss at the vertex, achieving an energy-retention ratio of approximately 35.6%. In contrast, the PWD method shows significant energy loss at the vertex, with an energy-retention ratio of only 10.7%. This comparison underscores the performance of the MWT-MSSA approach in conserving diffraction energy. These benefits confirm that the proposed method achieves more complete preservation of diffraction energy, leading to optimal separation performance. To specify the computational efficiency of the proposed method, the execution times are recorded and listed in Table 1. All the tests discussed in this paper were performed on a laptop (Windows 11 with 16 GB RAM and CPU frequency of 2.5 GHz) using MATLAB 2024b. As shown, PWD took approximately 38 min, while the execution of MWT-MSSA was completed in roughly 8 min, resulting in a 79% improvement in computational efficiency. Figure 7: Comparison of A-scan waveforms extracted from the wings (**a,b**) and vertex (**c**) of the diffraction located at the white dashed lines marked in Figure 6b,e.
## 4 The Proposed Method for Predicting the Reactive Power Adjustment
### Reactive Power Optimization Method Based on Big Data
#### 4.3.1 Data Preparation  
(a) Obtain the forecasting load data of the day ahead and establish the forecasting load random matrix \(P_{F}\), \(Q_{F}\) according to Equations(2) and (3). Then, establish the forecasting load augmented matrix \(A_{F}\).  
(b) Obtain the historical load data of the distribution network in recent years and establish the historical load augmented matrix \(A_{H}(t)\) of each day, where \(t=1,2,3,\cdot\cdot\cdot,L\) and \(L\) stands for the total number of days. Then, obtain the reactive power control devices dispatching scheme of each day, including the sequence of tap settings and the sequence of capacitor capacities in 24 h.  
(c) Divide the historical load augmented matrices into four groups according to seasons, as shown in Figure 3. Then, divide the historical load augmented matrices for each season into two subgroups, workdays and weekends; not that holidays are treated as weekends. Define \(\lambda\) as the seasonal grouping property and \(\mu\) as weekday grouping property. Define \(c(\lambda,\mu)\) as the subset of t after the grouping according to Figure 3. The groups of load are shown in Table 1.