esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The incidence of humerus greater tuberosity (GT) fractures is about 20% in patients with proximal humerus fractures. This study aimed to investigate the biomechanical performances of the humerus GT fracture stabilized by a locking plate with rotator cuff function for shoulder rehabilitation activities. A three-dimensional finite element model of the GT-fracture-treated humerus with a single traction force condition was analyzed for abduction, flexion, and horizontal flexion activities and validated by the biomechanical tests. The results showed that the stiffness calculated by the numerical models was closely related to that obtained by the mechanical tests with a correlation coefficient of 0.88. Under realistic rotator cuff muscle loading, the shoulder joint had a larger displacement at the fracture site (1.163 mm), as well as higher bone stress (60.6 MPa), higher plate stress (29.1 MPa), and higher mean screw stress (37.3 MPa) in horizontal flexion rehabilitation activity when compared to that abduction and flexion activities. The horizontal flexion may not be suggested in the early stage of shoulder joint rehabilitation activities. Numerical simulation techniques and experimental designs mimicked clinical treatment plans. These methodologies could be used to evaluate new implant designs and fixation strategies for the shoulder joint.  
greater tuberosity fractures; locking plate; shoulder rehabilitation activities; finite element analysis; mechanical tests +
Footnote †: journal:  
Article  
Numerical and Experimental Investigations of Humeral Greater Tuberosity Fractures with Plate Fixation under Different Shoulder Rehabilitation Activities
# Task-Driven Real-World Super-Resolution of Document Scans
## 4 Experimental Validation
### Experimental Results  
The mean reconstruction quality scores obtained using the investigated variants over the test datasets are reported in Table 3. It presents the summarized results, and the outcome of detailed statistical analysis is presented in Figures 2 and 3 and in Tables 4-11. Finally, examples of the qualitative results are showcased in Figures 4-9. From Table 3, it can be seen that all of the trained SR models receive worse image fidelity scores (PSNR, SSIM, and LPIPS) than bicubic interpolation, but the IoU scores are better on the models trained in a task-driven way from real-world images (for test sets composed of both simulated and real-world LR images). The violin plots showing distribution of the scores alongside median and quartile values are presented in Figures 2 and 3 for the simulated and real-world test sets, respectively. In Tables 4-11, we report the outcomes of statistical tests performed for PSNR, SSIM, LPIPS, and IoU metrics for all test sets--for each pair of methods, we indicate which one was significantly better considering the median score. It can be seen that the IoU scores are significantly better for the models trained in a task-driven way for both the simulated (Table 7) and real-world (Table 11) datasets. In Figures 4-8, we show examples of the reconstruction outcomes for images from the University Bulletin (Figure 4), Scientific Article (Figure 5), COVID Test Leaflet (Figure 6), Old Books (Figure 7), and MS COCO (Figure 8) datasets. It can be appreciated that the models trained in a task-driven way reconstruct more high-frequency features than the baseline \(\mathcal{M}_{\text{B}}\) configuration; however, this is achieved at a cost of some artifacts that are reflected in lower image fidelity scores reported earlier in Table 3. In Figure 9, we showcase the outcomes retrieved with the \(\mathcal{M}_{+++}^{R}\) and \(\mathcal{M}_{+++}^{S}\) models for scans of documents with various fonts and backgrounds. It can be seen that, in all cases, the SR models trained in a task-driven manner retrieve the text that is visually much clearer and sharper than in the outcomes of bicubic interpolation. Overall, the text appearance in super-resolved images is closer to that in the HR references. Figure 2: Violin plots illustrating the distribution of the IoU scores obtained with different models for the simulated images in the test set. The median and quartiles are indicated with the box plots. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Simulated Images**} & \multicolumn{6}{c}{**Real-World Images**} \\ \cline{2-10}  & **PSNR [dB] \(\uparrow\)** & **SSIM \(\uparrow\)** & **LPIPS \(\downarrow\)** & **IoU \(\uparrow\)** & **PSNR [dB] \(\uparrow\)** & **SSIM \(\uparrow\)** & **LPIPS \(\downarrow\)** & **IoU \(\uparrow\)** \\ \hline Int.
# Long-Range Correlations and Natural Time Series Analyses from Acoustic Emission Signals
## 3 Applications
### Three-Point Bending Test of a Glass Fiber-Reinforced Polymeric Plate
#### 3.1.2 Results
The results are shown in normalized time for easier identification of the possible entry point to a critical state, that is, the instant when criticality conditions, as described in Section 2.2 [64, 65], are fulfilled. There are three such points that are indicated in Figure 7d. The first one occurred right before a dense region of AE events, at normalized time 0.24, with an abrupt change in the AE energy rate (see Figure 7b). The second was probably related to the consecutive avalanches that were observed between normalized instants 0.42 and 0.52 (notice the increase of instantaneous hits in Figure 7b). Finally, there was a third and more extensive period (0.5288-0.5939) where parameters converge, which indicates the imminent structure collapse. Figure 7: **(a)** AE signals with \(b\)-value analysis; **(b)** Number of instantaneous signals, number of accumulated signals, applied load, and accumulated energy versus normalized time. The first was multiplied by 13 to maintain the same vertical scale for instantaneous and accumulated signals; **(c)** Application of Hurst and DFA methods to the AE data; **(d)** NT analysis of the AE time series with threshold values \((A_{\max}\).15)\({}_{Th}\) = 0.25 mV. The following auxiliary horizontal lines were added: \(\kappa_{1}\) = 0.070 (dashed red), \(\langle D\rangle\) limit = \(10^{-2}\) (dashed green), entropy limit \(Su\approx\) 0.0966 (dashed blue).
# Inter-Session Repeatability of Marker-Less Motion Capture of Treadmill Running Gait
## 3 Results
### Spatiotemporal Metrics  
Spatiotemporal metrics computed from events determined through ML kinematics were compared to these same metrics computed from the instrumented TM. Mean cadence (ML: 170.6 \(\pm\) 9.0 steps-min\({}^{-1}\); TM: 170.4 \(\pm\) 9.0 steps-min\({}^{-1}\); MD = \(-\)0.13; LoA: (\(-\)0.80, 0.54); and ICC = 1.0), mean stance time (ML: 0.234 \(\pm\) 0.019 s; TM: 0.233 \(\pm\) 0.021 s; MD = \(-\)0.001; LoA: (\(-\)0.014, 0.011); and ICC = 0.982), and mean step length (ML: 1.22 \(\pm\) 0.12 m; TM: 1.22 \(\pm\) 0.12 m; MD = 0.02 cm; LoA: (\(-\)0.80, 0.83); and ICC = 1.0) demonstrated excellent agreement between ML motion capture and instrumented TM when averaged across all trials (n = 63).
## 10 Future Research Directions
### Robust Communication Protocol Stack  
Many WSN OSs provide a communication API that allows applications to send and receive data. Underneath, these APIs use the communication protocol stack provided by the OS. There is no agreement on a unified protocol stack for sensor networks, hence almost each OS provides its own custom implementation of a communication protocol stack. As a consequence, no communication can occur between two motes using different OSs. In the recent past, Contiki has provided an implementation of a micro IPv6 stack for sensor networks. This enables sensors nodes to use IP addresses and communicate with other IP-enabled devices. Following the Contiki approach, TinyOS in its new version has provided support for IP that conforms to the 6 LowPan standard. Providing an implementation of the micro IP protocol stack allows two motes using different OSs to communicate. However, this approach may lack the capabilities to adequately handle multimedia streams.  
There is a need for research effort that designs and implement a suitable cross layer protocol for sensor networks. The protocol design needs to consider constraints on sensor nodes as well as new emerging application areas for sensor networks.
## 5 Experiment and Analysis  
In order to verify the positioning accuracy and robustness of the proposed method, we carry out experiments in the public TUM dataset [29], Euroc dataset [30] and dataset collected by ourselves. It is worth noting that there are few public available high dynamic datasets with pose groundtruth. Therefore, we synthesize three dynamic image sequences based on Euroc dataset. Euroc dataset is collected in a static scene, we consider dynamic objects as noise which is added into images in Euroc dataset artificially. After adding dynamic objects, the Euroc dataset is changed from a static scene to a dynamic scene, which can be used to evaluate the performance of Dynamic-DSO in high dynamic environments. Dynamic-DSO is based on the direct method. In order to show the effectiveness and superiority of the proposed system, we compare it with DSO, which is the state of art in direct methods.  
In Section 5.1, the TUM dynamic dataset is used to compare the candidate points extraction between Dynamic-DSO and DSO in high dynamic environments. In Section 5.2, We use TUM dynamic dataset, the modified Euroc dataset and the self-collected image sequence to evaluate the positioning performance of Dynamic-DSO in dynamic scenes. In Section 5.3, the comparison of semi-dense point cloud maps of Dynamic-DSO and DSO is conducted.  
All experiments are conducted on a workstation equipped with an Intel Xeon E5-2690v4 CPU (14 cores, 2.6 GHz), 128 GB memory and an NVIDIA Titan V GPU with 12 GB video memory. The operating system version is Ubuntu 16.04.5 LTS.
## 7 Nano-RK
### Scheduling  
Nano-RK provides priority scheduling at two levels: priority scheduling at the process level and priority scheduling at the network level. In this section, we only discuss the scheduling algorithms that are being used in Nano-RK for process scheduling. To support real-time applications, Nano-RK uses a fully preemptive priority driven scheduling algorithm, _i.e._, at any given instance the highest priority task is scheduled by the operating system. A rate monotonic scheduling algorithm is used for real-time periodic tasks and the priority of the task is set statically based upon the period of the job: the shorter the period of the job, the higher is its priority. Since rate monotonic scheduling algorithm statically assigns priorities to tasks, Nano-RK recommends to configure task parameters offline.  
Nano-RK also provides an implementation of rate harmonized scheduling for energy saving [32]. The main idea behind this scheduling algorithm is to eliminate CPU idle periods by grouping the execution of different tasks. Since initial arrival time, periodicity, and deadlines of tasks are known a priori, rate harmonized scheduling can be used to further save energy by eliminating idle cycles.  
Nano-RK also provides an implementation of the priority ceiling algorithm to bind the blocking time encountered by a higher priority process due to priority inversion, _i.e._, the shared resource needed by the higher priority process is being used by a lower priority process. To circumvent priority inversion, each mutex is associated with a priority ceiling. Whenever a mutex is acquired by a task, the priority of the task is set equal to the priority ceiling of the mutex. Once, the mutex is released, the priority of the task is set equal to its original priority.
## 4 System Model
### Existing Trajectory Design  
With the help of connected dominant set (CDS) nodes, the existing elliptical-shaped trajectory of the AUV route was developed. The CDS is a set of interconnected nodes that gives more than one path to the sink node of a network. The minimum spanning tree (MST) of CDS nodes was calculated after the selection of CDS nodes, and the Hamilton circuit (HT) was established after the construction of MST, as shown in Figure 1. The CDS nodes were designed in a random shape and did not allow us the flexibility to build a complete trajectory. However, HC turned it into the correct circular or oval trajectory. This creates an elliptical trajectory that is not constrained by network size. Previous work has not addressed elliptical size ratio fluctuations during network expansion or shrinkage.
## 4 Discussion
Quantitative results using HPLC also demonstrated that _ZmLC_ participated in the phenylpropanol biosynthesis process in _A. rugosa_ hairy roots, especially the out \begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**IC50 of DPPH**} & **IC50 of ABTS** \\ \hline Control & 0.7 \(\pm\) 0.03 & 0.63 \(\pm\) 0.03 \\ LC-10 & 0.53 \(\pm\) 0.00 & 0.48 \(\pm\) 0.01 \\ \hline \end{tabular} All results were carried out in triplicate, and the values are shown as mean \(\pm\) SD. The column without asterisks denotes no significant differences between the control and LC-10.  
\end{table} 
Table 3: IC50 values for DPPH and ABTS (mg/mL) from two _A. rugosa_ extracts. standing accumulation of three substances, namely trans-cinnamic acid, \(p\)-coumaric acid, and acacetin, which increased 3.46-, 4.35-, and 4.6-fold, respectively. The total polyphenol content increased 1.41-fold, and the flavonoid content increased 1.33-fold. These results are consistent with previous work showing light-induced phenolic biosynthesis and flavone accumulation in _A. rugosa_ hairy roots [15], flavonoid accumulation in _Alnus glutinosa_ roots [47], and phenylpropanoids, anthocyanins, and distinct proanthocyanidins in _Malus domestica_ Borkh. [27]. Treating _Scutellaria lateriflora_ with light and 15 mM of methyl-\(\beta\)-cyclodextrinrin increased phenolic compounds (baicalein, aglycones, and wogonin) [48], and this treatment increased rutin in _F. tataricum_ Hokkai T10 hairy roots [37]. Furthermore, caffeic acid derivatives were significantly increased in _Echinacea purpurea_ hairy roots [49]. The radical scavengers DPPH and ABTS are often used for the rapid assessment of the antioxidant activity of natural compounds [50]. Previous research has shown that all parts of the plant contain natural antioxidants [51], with flowers showing higher antioxidant capacity than stems and leaves in _A. rugosa_. However, flower harvesting depends on the weather and season; thus, it is difficult to proactively source raw materials for large-scale antioxidant production. Therefore, we evaluated the antioxidant activity of phenylpropanoids extracted from _A. rugosa_ hairy roots using ABTS and DPPH. The results showed that LC-10 had a more effective reduction ability than the control. At a concentration of 1000 mg/L, the LC-10 sample extract showed 86.2% scavenging activity against DPPH, while that in the control sample was 68.6%. ABTS removal was 88.03% in LC-10 and 75.63% in the control. An increase in the free radical content in cells will lead to ageing and diseases, including atherosclerosis, a weakened immune system, reduced intelligence, diabetes, and cancer [52]. Therefore, the antioxidant activity of _A. rugosa_ transgenic hairy roots in this report enrich the materials that can be used in the pharmaceutical industry to serve human health.
## 3 Experimental Results
The 3D reconstruction obtained without applying the matrix method to the energy resolved PiXirad data to separate the components is also shown for comparison. Figure 9 shows that the matrix approach is able to effectively separate bone from soft tissue using a lab based polychromatic X-ray source and just a single exposure per projection. However, the SNR in the retrieved images is degraded compared to the individual images corresponding to each of the four energy thresholds. The reduced SNR arises from the image subtraction implicit in solving Equation (7), i.e., noise will add in quadrature under following (weighted) image subtraction, with the net effect being a reduction in SNR [55]. This is a common artifact of matrix inversion and could be addressed using the 'brute force' approach of improving the statistics by collecting a larger number of images per projection (leading to longer data collection times). However, another common method to address this problem is matrix regularization which is able to reduce errors introduced by the inversion process by introducing modifications into the matrix operator that bias particular solutions with desirable properties. For example, Tikhonov regularization is commonly used to introduce high-pass operators to enforce smoothness if, for example, the underlying solution is known to be continuously varying [56]. In simulations of the experiment, we applied increasing amounts of experimental noise and where able to obtain an acceptable result up to a Poisson noise level of 6% (which is greater than expected for the experiment). Another limitation arises from energy-resolving X-ray detectors in general, which, to a greater or lesser degree depe  
Figure 8: Results for a single projection of the mouse hand sample at energy thresholds (**a**) \(E_{1}~{}=~{}16\) keV, (**b**) \(E_{2}~{}=~{}21.1\) keV, (**c**) \(E_{3}~{}=~{}23.9\) keV and (**d**) \(E_{4}~{}=~{}29.7\) keV. The corresponding result for the retrieved thickness after applying the matrix method is shown for (**e**) soft tissue and (**f**) bone. exhibit some charge-sharing effects. The quality of the final result for the retrieved sample components thus depends both on the SNR and the characterisation of the charge-sharing characteristics of the specific detector used. A number of factors contribute to the SNR of the data and the degree of charge-sharing artifacts. These include, the X-ray tube flux, the linear absorption coefficient for the sample, and the overall sample thickness. Here, we study a sample of just 5 mm \(\times\) 14 mm in dimensions, extending this method to e.g., patient limbs or even whole animals would require a source capable of delivering higher energy X-rays and most likely the implementation of additional matrix regularisation to maintain an acceptable result whilst minimizing the absorbed dose which would lead to lower SNR data.
###### Abstract  
Existing tunnels are often subject to longitudinal bending when undercrossing tunnelling. It is of significance to more accurately evaluate the longitudinal equivalent bending stiffness (LEBS) of the existing tunnel within the influential zone. A new analytical method is proposed for the LEBS of tunnel segmental lining joints with consideration of incorporating combined action of residual jacking force and bending moment. The solution can degenerate into a special case with no residual jacking force, which agrees well with other classical solutions and validates the model and solutions. Sensitivity analyses are carried out for the bending moment, tunnel geometry, tensile stiffness of bolts and concrete grade on the LEBS, and effective ratio of the LEBS considering residual jacking force. The LEBS and the effective ratio of LEBS increase nonlinearly as an S-curve with the residual jacking force and decrease with an increasing bending moment. The results show that the LEBS of the shield tunnel is variable stiffness, which exhibits a significant nonlinearity. The maximum increment of the LEBS reaches 80.3% as the ring width increases from 1 m to 2 m, and the LEBS of the shield tunnel increases by approximately 1.3 \(\times\) 10\({}^{7}\) kN-m\({}^{2}\) for every 4-bolt added. The influential order on the LEBS of shield tunnels is the tunnel diameter \(>\) lining thickness \(>\) bolts diameter \(>\) ring width \(>\) the number of bolts \(>\) elastic modulus of bolts. When the effective ratio of LEBS is more than 0.85, it does not change with the ring width, lining thickness, tensile stiffness of bolts, and concrete grade. The response characteristics of the tunnel parameters on the LEBS, considering the residual jacking force, could provide a theoretical basis for the design and deformation control of shield tunnels when undercrossing tunnelling.  
residual jacking force; analytical solution; shield tunnel; longitudinal equivalent bending stiffness (LEBS); effective ratio +
Footnote †: journal: Applied Sciences
## Appendix B Simulation Setup for LTE
### Physical Layer for LTE  
The downlink physical layer of LTE is based on orthogonal frequency-division multiple access (OFDMA).  
For our simulation, LTE is built based on a single transmitter and a single receiver. The LTE frame consists of 10 subframes that are individually generated. The frame is OFDM modulated. The LTE frame structure for the downlink is Frequency Division Duplexing (FDD). Each user is assigned to a different time/frequency Resource Block (RB). The simulation parameters of evolved NodeB (eNB) in LTE-subframe are presented in Table 10.
## 1 Introduction
In this paper, after introducing the measurement system and principle, to qualify the measurement accuracy, the systematic errors associated with the measurement procedure, including the miscalibration, misalignment and the roundness error of the gauge probes, as well as the slide motion error of the four-axis motion platform, are all evaluated and separated from the measurement results. A high aspect ratio aspheric workpiece, which has a tapered cone shape with a maximum inner radius of 40 mm and a maximum inner depth of 140 mm, is evaluated in experiments based on the proposed method. The experimental result is compared with that of a coordinate measuring machine (CMM), and the comparison result proves the feasibility of the proposed measurement system.
## 2 Results and Discussion
### HS-SPME Analysis of Area Nuts
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Regression**} & \multicolumn{2}{c}{**Standard**} & \multicolumn{2}{c}{**Degree of**} & \multicolumn{2}{c}{**Sum of**} & \multirow{2}{*}{**F-Value**} & \multirow{2}{*}{**\(p\)-Value**} \\  & **Coefficient** & **Error** & **Freedom** & **Squares** & & & \\ \hline Lack-of-fit & & & 3 & 9.375 & 1.071 & 0.478052 \\ Pure error & & & 3 & 8.750 & & \\ b0 & \(-\)55,2214 & 4.407486 & & & & \\ b1 & \(-\)2,1500 & 0.109332 & 1 & 3601.500 & 1234.800 & 0.000051 \\ b11 & \(-\)0,0096 & 0.000654 & 1 & 630.375 & 216.129 & 0.000683 \\ b2 & 0.8589 & 0.079995 & 1 & 560.667 & 192.229 & 0.000812 \\ b22 & \(-\)0.0062 & 0.000654 & 1 & 260.042 & 89.157 & 0.002518 \\ b12 & 0.0000 & 0.000534 & 1 & 0.000 & 0.000 & 1.00000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Coefficients of the central composite design for NoP fitted with the second-order polynomial model and analysis of variance (ANOVA) for the experimental results. Figure 1: Dependence of the number of peaks on the extraction conditions. To find which tested factors have effect results of extractions, analysis of variance (ANOVA) of the experimental data was performed (see Table 2). The significance of regression coefficients was tested for the \(F\)-value and the \(p\)-value. According to the determined values, both extraction parameters (time and temperature) can be considered significant in linear and quadratic terms. The interaction between the extraction time and extraction temperature is considered to be insignificant. Optimal HS-SPME conditions were evaluated, and the whole extraction process was as follows: Initially, the sample was incubated for 10 min at the extraction temperature, then the fiber was exposed to the sample headspace under the following conditions: extraction time of 70 min, extraction temperature of 112 \({}^{\circ}\)C.