esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Results of Joint Angle Prediction
#### 3.3.2 Prediction Results of Different Input Signals  
In order to verify the superiority of the MMG signal, the rotation angle data collected by the IMU were also used to predict joint angles. The rotation angle data of the IMU located at the forearm were used to predict the joint angle of elbow joint, and the rotation angle data of the IMU located at the upper arm was used to predict the joint angle of shoulder joint. Table 3 shows the results of training. The findings revealed that the prediction accuracy of MMG is higher.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & & \multicolumn{4}{c}{(**a**)} \\ \hline
**Input Signals** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** & _p-Value_ \\ \hline MMG & 0.0019 \(\pm\) 0.0001 & 0.0366 \(\pm\) 0.0012 & 0.0098 \(\pm\) 0.0006 & 0.970 \(\pm\) 0.002 & / \\ IMU rotation angle & 0.0052 \(\pm\) 0.0003 & 0.0701 \(\pm\) 0.0019 & 0.0531 \(\pm\) 0.0036 & 0.941 \(\pm\) 0.005 & 0.0065 \\ \hline \hline  & & \multicolumn{4}{c}{(**b**)} \\ \hline
**Input signals** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** & _p-Value_ \\ \hline MMG & 0.0020 \(\pm\) 0.0001 & 0.0387 \(\pm\) 0.0009 & 0.0108 \(\pm\) 0.0007 & 0.963 \(\pm\) 0.001 & / \\ IMU rotation angle & 0.0054 \(\pm\) 0.0003 & 0.0768 \(\pm\) 0.0026 & 0.0695 \(\pm\) 0.0037 & 0.936 \(\pm\) 0.003 & 0.0053 \\ \hline \hline \end{tabular}
\end{table}
Table 3: (**a**) Prediction results of different input signals during arm side raises. (**b**) Prediction results of different input signals during elbow curls.  
Figure 16: (**a**) Real-time shoulder joint angle prediction results during arm side raises; (**b**) Real-time shoulder joint angle prediction results during arm front raises; (**c**) Real-time elbow joint angle prediction results during elbow curls.  
Experiments on real-time prediction of joint angles were also conducted. During arm side raises, the RMSE of shoulder joint angle prediction result was 0.0631 and 0.0842, respectively; during arm front raises, the RMSE of shoulder joint angle prediction result was 0.0593 and 0.0776, respectively; during elbow curls, the RMSE of elbow joint angle prediction result was 0.0579 and 0.0791, respectively. In all three exercises, the prediction errors based on MMG are much smaller than those based on the IMU.
# Rockburst Hazard Evaluation Using an Extended COPRAS Method with Interval-Valued Fuzzy Information
## 5 Discussion
### Comparison Analysis
Then, the global benefit values of the samples were calculated as \(\widehat{G}_{i}=\{[0.9145,\) 1.0855\(]\), [0.7042, 0.8231\(]\), [0.5873, 0.6829], [0.4477, 0.5219], [0.5052, 0.5915], [0.3678, 0.4307], [0.3128, 0.3527], [0.4819, 0.5643], [0.2762, 0.3186], [0.4972, 0.5944], [0.3068, 0.3776]\(]\), and the individual regret values of the samples were calculated as \(\widehat{P}_{i}=\{[0.1755,\) 0.2179\(]\), [0.1582, 0.2001\(]\), [0.1468, 0.1870\(]\), [0.1369, 0.1744], [0.1175, 0.1443], [0.0863, 0.0882], [0.1024, 0.1068], [0.1360, 0.1733], [0.0908, 0.0927], [0.1533, 0.1953], [0.1074, 0.1368]\(]\). Afterwards, by using the weight for the strategy of maximum group utility \(\beta=0.5\), the compromise sorting scores were calculated as \(\widehat{Z}_{i}=\{[1.0000,\) 1.0000\(]\), [0.7387, 0.7601\(]\), [0.5829, 0.6185\(]\), [0.4178, 0.4648\(]\), [0.3540, 0.3944], [0.0718, 0.0731], [0.0939, 0.1192], [0.4398, 0.4883], [0.0176, 0.0251], [0.5486, 0.5926], [0.1422, 0.2260]\(]\). Finally, the corresponding ranking result was \(C_{5}>C_{2}>C_{3}>C_{7}>C_{1}>C_{W}^{*}>C_{4}>C_{6}>C_{1}^{*}>C_{1}^{*}\). It can be seen that the ranking results and corresponding hazard levels under the extended COPRAS and TOPSIS methods were similar. The ranking results of \(C_{5}>C_{7}>C_{3}>C_{2}>C_{4}>C_{6}>C_{1}\) were identical, and difference only occurred in \(C_{2}\). As for the extended VIKOR method, deviations arose from \(C_{1}\), \(C_{2}\) and \(C_{7}\). However, the hazard levels of \(C_{3}\), \(C_{4}\), \(C_{5}\), \(C_{6}\) and \(C_{7}\) were still the same as those under the extended COPRAS and TOPSIS methods. Therefore, the effectiveness of the extended COPRAS method was demonstrated. In summary, the highlights of the proposed method are concluded to be as follows:  
(1) The indicator values are expressed by IVFNs instead of crisp numbers, and thus can better capture the intrinsic uncertainty of indicator values. (2) The extended DEMATEL and entropy methods are combined to obtain comprehensive weights, which can guarantee the stability of the evaluation results. (3) The COPRAS method is extended with IVFNs, which can cope well with the evaluation problems of rockburst hazards under uncertainty. (4) The extended COPRAS method was applied in the Jiaojia gold mine, and credible ranking results and hazard levels were obtained, which demonstrates the effectiveness and reliability of the proposed methodology. \begin{table}
\begin{tabular}{l c
## 5 Clinically Approved Nanostructured Drug Delivery Systems  
Nanostructured drug delivery systems have gained significant attention in immunotherapy, with several formulations receiving FDA approval. LNP-based and liposomal drugs are particularly prominent due to their biocompatibility, drug-loading capacity, and ability to reduce toxicity. One of the most representative examples is Doxil(r), the first FDA-approved nanodrug (1995), which uses PEGylated liposomes to enhance the delivery of doxorubicin in cancer treatment. Other approved formulations using liposomes as nanocarriers, such as Myocet(r), DaunoXome(r), and Onivyode(r), have been developed to treat various cancers, offering prolonged circulation time and reduced side effects. LNPs also played a critical role in mRNA vaccine development during the COVID-19 pandemic, with Comirnaty(tm) and Spikevar(r) demonstrating the versatility of nanotechnology in modern medicine [151; 152; 153; 154]. Polymer-based nanostructured drug delivery systems have also been explored in immunotherapy, although fewer formulations have received regulatory authorities' approval compared to lipid-based counterparts. One key example is Abraxane(r), an albumin-bound nanoparticle formulation approved in 2005 for delivering paclitaxel to treat various cancers. Additionally, polymer micelle-based nanosystems like Genexol(r), Nanoxel(r), and Apeale(r) have been developed for delivering paclitaxel or docetaxel to treat advanced cancers, offering improved solubility, reduced toxicity, and enhanced drug delivery to tumor sites. These polymeric systems, along with lipid-based nanoparticles, emphasize the tremendous potential of using nanostructured drug delivery systems in dealing with malignant diseases, offering new perspectives for improving immunotherapeutic treatment options and raising hopes for translating under-research formulations to the clinical setting [1551; 152; 154; 33]. To provide a comprehensive overview of the current landscape of clinically approved nanostructured drug delivery systems, we have compiled a summary of key examples, including lipid- and polymer-based formulations. In this respect, Table 2 highlights critical attributes such as drug formulation, therapeutic benefits, target indications, and approval status, offering insights into the diverse nanomedicines that have successfully transitioned from research to clinical use. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
# Central and Periodic Multi-Scale Discrete Radon Transforms
## 2 Conventional 2D Discrete Radon Transform
### Radon Adjoint Transform  
To achieve an inverse Radon transform, it is necessary to define, previously, the adjoint transform.  
As can be seen in the next terms: the forward transform computes from an input image, the summation of pixel values through a set of lines, which is complete in slopes and displacements.  
The adjoint transform is such that, for every point in the Radon domain, corresponding to a certain slope and displacement, it redistributes uniformly that summation value back to the image domain, assigning the same quantity to every pixel traversed by the line. That, obviously, is not the inverse transform, but (and that is the key of the contribution from [PERSON] [16]) it is a sufficiently good starting point for inducing an iterative refinement process.  
The adjoint discrete Radon transform is defined by the inverse mapping of Equation (5):  
\[\widetilde{f}^{\,m}(\boldsymbol{\sigma}|\,0,\mathbf{v}|\,d)+ =\widetilde{f}^{\,m+1}(\,s_{n-m-1},\boldsymbol{\sigma}\,|\, \mathbf{v}|\,d)\] \[\widetilde{f}^{\,m}(\boldsymbol{\sigma}|\,1,\mathbf{v}|\,d+s_{n- m-1}+\lambda(\boldsymbol{\sigma}))+ =\widetilde{f}^{\,m+1}(\,s_{n-m-1},\boldsymbol{\sigma}\,|\, \mathbf{v}|\,d) \tag{6}\]  
Figure 7 shows the adjoint operator working independently on the four quadrants of a forward DRT, as well as the sum of all of them.  
Figure 6: Schemes of behavior of the 4 quadrants of a full DRT. Each quadrant covers \(45^{\circ}\) angles of projection. Together they cover all projection angles. (**a**) Quadrant covering \(0^{\circ}:45^{\circ}\) angles; (**b**) \(135^{\circ}:180^{\circ}\equiv 0^{\circ}:-45^{\circ}\); (**c**) \(90^{\circ}:45^{\circ}\); (**d**) \(-90^{\circ}:-45^{\circ}\equiv 90^{\circ}:135^{\circ}\).
###### Abstract  
Intelligent gauge sorting with high precision is of vital importance for improving coal quality. To tackle the challenges associated with coal gangue target detection, including algorithm performance imbalance and hardware deployment difficulties, in this paper, an intelligent gangue separation system that adopts the elevated YOLO-v5 algorithm and dual-energy X-rays is proposed. Firstly, images of dual-energy X-ray transmission coal gangue mixture under the actual operation of a coal mine were collected, and datasets for training and validation were self-constructed. Then, in the YOLOv5 backbone network, the EfficientNetv2 was used to replace the original cross stage partial darknet (CSPDarknet) to achieve the lightweight of the backbone network; in the neck, a light path aggregation network (LPAN) was designed based on PAN, and a convolutional block attention module (CBAM) was integrated into the BottleneckCSP of the feature fusion block to raise the feature acquisition capability of the network and maximize the learning effect. Subsequently, to accelerate the rate of convergence, an efficient intersection over union (EIOU) was used instead of the complete intersection over union (CIOU) loss function. Finally, to address the problem of low resolution of small targets leading to missed detection, an L2 detection head was introduced to the head section to improve the multi-scale target detection performance of the algorithm. The experimental results indicate that in comparison with YOLOv5-S, the same version of the algorithm proposed in this paper increases by 19.2% and 32.4% on mAP@.5 and mAP@.5%, respectively. The number of parameters decline by 51.5%, and the calculation complexity declines by 14.7%. The algorithm suggested in this article offers new ideas for the design of identification algorithms for coal gangue sorting systems, which is expected to save energy and reduce consumption, reduce labor, improve efficiency, and be more friendly to the embedded platform.  
hardware device; gangue recognition; object detection; YOLOv5; lightweight network; deep learning 2020 1 1 2021 1
## 4 Antenna Modeling Using RLC Components  
The transmitter and receiver in the communication system pose as an electrical system; therefore, analyzing the antenna as an equivalent circuit is useful for circuit simulations and time domain analysis. Such an electrical model can be highly beneficial in investigating the antenna's integration with the transceivers. The circuit theory analyzes the antenna as a passive single-port network that imparts impedance to the source. The projected antenna equivalent circuit is modeled in AWR design environment 15, as depicted in Figure 14. The impedance model approach is used to build an analogous circuit model. [28]. A 50 \(\Omega\) RF source is used to excite the input port. Different contiguous resonances generated by parallel resonators connected in series accomplish the wideband impedance matching. The UWB antenna exhibits two sharp resonances at 4.5 GHz and 8.5 GHz, and this is represented using two parallel RLCs connected in a series arrangement, as depicted in Figure 14a. The series RLC configuration resonates when the inductance and capacitance are equal, helping to tune applications. The series RLC provides a sharp curve at the resonating frequencies. The circuit arrangement used for the UWB antenna is modified to achieve an equivalent circuit to a dual-band notch UWB antenna. The RLC configuration used for achieving two notches is based on the impedance curve, and the associated circuit theory diagram is represented in Figure 14b.  
Figure 14: The antenna equivalent circuit using lumped elements. (**a**) UWB: resistors (\(\Omega\)): R1 = 1, R2 = 21, R3 = 3 K, and R4 = 50; inductors (nH): L1 = 12.3, L2 = 1.4, L3 = 0.254, and L4 = 0.79; and capacitors (pF): C1 = 0.79, C2 = 2, C3 = 0.25, C4 = 0.2, and C5 = 0.09. (**b**) UWB band notch: resistors (\(\Omega\)): R1 = 3.8 K, R2 = 5 K, R3 = 3 K, and R4 = 50; inductors (nH): L1 = 1.31, L2 = 1, L3 = 0.112, L4 = 0.032, and L4 = 0.5; and capacitors (pF): C1 = 1, C2 = 6.3, C3 = 0.63, and C4 = 0.78.
# Chronic Obstructive Pulmonary Disease Warning in the Approximate Ward Environment
## 2 Fundamentals of Breathing
### WCI System with Breathing  
The time invariance property of multipath components also holds for breathing. Assuming only one multipath component got affected by breathing, from [18] we can write that,  
\[\zeta_{1}(t)=\zeta_{1}\bigg{(}1+\frac{\Delta L_{1}}{L_{1}}\sin\theta\sin\frac{ 2\pi b}{60}t+\phi\bigg{)}^{-\psi} \tag{7}\]  
In Equation (7), \(\zeta_{1}\) gives us the gain of the multipath component (MPC) number 1 and \(L_{1}\) gives the length for that MPC. The breathing causes the MPC 1 to be displaced or change its position and is represented by \(\Delta L_{1}\). The path loss exponent is represented by \(\psi\) and the angle between the EM wave and the subject is represented by \(\theta\). The breathing rate is also very important and measured in breath per minute and represented by b and the breathing initial phase is represented by \(\phi\).  
Breathing shows significant effects on the MPC 1 and varies its path length \(L_{1}(t)\), which is represented as,  
\[L_{1}(t)=L_{1}+\Delta L_{1}\sin\theta\sin\bigg{(}\frac{2\pi b}{60}t+\phi\bigg{)} \tag{8}\]  
The channel information which we showed in Equation (5) will be represented as,  
\[H_{a}(t)=\zeta_{1}e^{-j2\pi\frac{L_{1}(t)}{\lambda_{a}}}+\sum_{l=2}^{M}\zeta _{l}e^{-j2\pi\frac{L_{l}}{\lambda_{a}}}+e_{a}(t) \tag{9}\]  
Further explanation of Equation (9) is shown as,  
\[H_{a}(t)=\zeta_{1}e^{-j2\pi\frac{L_{1}(t)}{\lambda_{a}}}e^{-j2\pi\frac{ \Delta L_{1}\sin\theta\sin\big{(}\frac{2\pi b}{60}t+\phi\big{)}}{\lambda_{a}} }+\sum_{l=2}^{M}\zeta_{l}e^{-j2\pi\frac{L_{l}}{\lambda_{a}}}+e_{a}(t) \tag{10}\]  
Jacobi-anger expansion plays an important role in Equation (10) and by decomposing the first term of Equation (10) into finite summation [19], we get,  
\[e^{-j2\pi\frac{\Delta L_{1}\sin\theta\sin\big{(}\frac{2\pi b}{60}t+\phi\big{)} }{\lambda_{a}}}=\sum_{n=-\infty}^{\infty}(-1)^{n}J_{n}(v_{a})e^{jn\frac{2\pi b }{60}t}e^{jn\phi} \tag{11}\]The value for \(v_{a}=2\pi\sin\theta\Delta L_{1}/\lambda_{a}\) and the nth order Bessel function is shown as \(J_{n}\) (\(z\)) with argument \(z\).  
The practice shows that if the value of \(\mid n\mid\geq 2\) then the function \(J_{n}\) (\(v_{a}\)) decays in faster manner by giving the ordinary \(v_{a}\) values. With this information our Equation (11) shows the approximation with the values of n as \(\pm 1\) and the DC component where \(n=0\). So, the new \(H_{a}\) (\(t\)) can be represented as,  
\[H_{a}(t)\approx\underbrace{\zeta_{1}e^{-j2\pi\frac{t_{1}}{\lambda_{a}}}\sum_{ n=-1}^{+1}\left(-1\right)^{n}J_{n}(v_{a})e^{jn\frac{2\pi b}{\theta\Omega}t}e^{jn \phi}}_{U_{a}}+\underbrace{\sum_{l=2}^{M}\zeta_{l}e^{-j2\pi\frac{t_{l}}{ \lambda_{a}}}+e_{a}(t)}_{TI_{a}} \tag{12}\]  
In the above equation, the \(U_{a}\) represents the signal for the monitoring of breathing activity on the subcarrier a. On the other hand, time invariant part is represented by \(TI_{a}\), which exists as the response of a static environment and is considered as the interference.
## 4 Results and Analysis
### Relationship Between Strong Electromagnetic Pulses and Lightning Discharges
It is important to highlight that among the events, 10 TISPs did not find corresponding matches in the WWLLN observations within both \(\pm\)150 s and \(\pm\)300 s. These events \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Event Types**} & \multirow{2}{*}{**Event Count**} & \multicolumn{8}{c}{**WWLLN**} & \multicolumn{8}{c}{**GHMLLS**} \\ \cline{3-11}  & & & \(\pm\)**3 ms** & & \(\pm\)**30 s** & & \(\pm\)**150 s** & \(\pm\)**300 s** & & **+3 ms** \\ \cline{3-11}  & & **MR 1 (\%)** & **MMC 2** & **MR (\%)** & **MMC** & **MR (\%)** & **MMC** & **MR (\%)** & **MMC** \\ \hline ALL & 1061 & 9.61\% & 1.02 & 90.96\% & 35.19 & 98.49\% & 162.97 & 99.06\% & 329.42 & 2.07\% & 1.00 \\ TISP & 226 & 7.08\% & 1.00 & 88.50\% & 29.88 & 95.58\% & 125.18 & 95.58\% & 269.25 & 0.00\% & 0.00 \\ TIPP & 835 & 10.30\% & 1.02 & 91.62\% & 41.11 & 99.28\% & 173.20 & 100\% & 370.78 & 2.63\% & 1.00 \\ \hline \hline \end{tabular}  
* MR denotes the proportion of WEMPD-detected events that coincide with those identified by the ground-based lightning location network under specific spatial and temporal parameters relative to the overall count of the WEMPD-detected events. 2 MMC is the ratio of the cumulative number of events from the ground-based lightning location network that meet the required temporal and spatial criteria to the number of the WEMPD-detected events that match the ground-based network’s detections. An MMC value of 1 indicates a one-to-one correspondence between the WEMPD-detected events and those detected by the ground-based lightning location network. The spatial window for matches, as presented in the table, is confined to an 800 km radius from the subsatellite point.  
\end{table} 
Table 1: Correlation of the WEMPD-detected events with the WWLLN and GHMLLS data across various time windows. exhibited radio-wave birefringence, characterized by distinct linear polarization, suggesting that they were likely not associated with lightning discharges. The nature of the radiation sources for these occurrences merits further investigation, which will not be addressed in the current paper. Furthermore, the mean match counts (MMCs) between the TIPP events and the WWLLN observations within +3 ms, \(\pm\)30 s, \(\pm\) 150 s, and \(\pm\)300 s are 2.00%, 37.58%, 38.36%, and 37.71% higher than that of the TISP events, respectively. The presence of a TIPP event suggests an increased frequency of lightning discharges within the thunderstorm clouds.
## 2 Related Work
### Background
#### 2.3.2 Collection Tree Protocol  
Collection Tree Protocol (CTP) is a best-effort, multi-hop routing protocol designed for low power and low data rate WSN. For each node in the network, CTP proactively maintains a spanning tree rooted in the node itself that minimizes the cost of the path from any node to the root. The _cost_ of a link is  
Figure 2: BoX-MAC-2 Tx/Rx scheme.  
evaluated by using the ETX (Expected Transmissions) metric [32,33], which is the estimation of the number of transmissions required to successfully transmit a packet along the link.  
Every node maintains a routing table which records the neighbors' identifiers and their cost to the root. To build this table, all the nodes in the network exchange beacon messages containing their cost to reach the root.  
The purpose of CTP is to achieve robustness and reliability, and the results show that it successfully achieves high delivery packet ratios ranging between 90.5% and 99.9% [34]. However, to obtain this performance, CTP burdens the network with control packets (ACK, beacons, _etc._). The cost of dealing with the dynamics of real networks (e.g., loop detection, packet loss, temporary lost of connectivity) generally results in higher energy consumption.
## 6 Evaluation Experiment
### Baseline
#### 6.2.1 Cityscapes  
Figure 15 depicts the confusion matrix for the Cityscapes test subset images. After normalization in each class, each matrix element is displayed in heatmap color distributions. The diagonal color pattern from the upper left to the bottom right represents high temperature. However, the color patterns for IDs 7, 8, 9, 14, 18, and 19, which correspond to pole, traffic light, traffic sign, rider, train, and motorcycle, show high temperatures in the other classes of the non-diagonal positions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Source** & **Baseline** & **Exp. A** & **Exp. B** & **Exp. C** & **Exp. D** \\ \hline Source images & ✓ & & & ✓ \\ CUT [71] & ✓ & & ✓ & ✓ \\ pix2 pixHD [65] & & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data combination for each experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **CUT [71]** & **pix2 pixHD [65]** & **DeepLabV3+ [103]** \\ \hline Backbone & ResNet-50 [34] & VGG-19 [69] & Xception-65 [116] \\ Training iterations [epoch] & 200 & 300 \\ Training coefficient & 0.0002 & 0.0010 \\ Mini-batch size & 4 & 2 \\ Sampling size [pixel] & 256 \(\times\) 256 & 480 \(\times\) 360 \\ Number of sampling & 2500 & 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters and setting values.  
Table 4 presents IoU scores of the semantic segmentation result in each class of Cityscapes. The corresponding class IDs are apparent in Table 1. For the training subsets, the IoU scores of 10 classes, which respectively correspond to unlabeled, road, sidewalk, building, vegetation, terrain, sky, car, bus, and train, are greater than the mean score. For the test subsets, the IoU scores of eight classes, which respectively correspond to unlabeled, ground, road, sidewalk, building, vegetation, sky, and car, are greater than the mean score.  
Figures 16 and 17 respectively denote representative successful and failed test subset images. The mIoU threshold to classify these cases was set as 0.266. Figure 17 depicts false segmentation samples in the _car_, _bus_, and _truck_ classes. We infer that these similar classes have difficulty learning differences among object features.