esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving the Sensory Quality of Black Tea by Blending Varieties During Processing
## 2 Materials and Methods
### Sample Preparation  
Clonal tea leaves were harvested from three varieties, including _Jinguanyin_, _Jinxuan_, and _Fudingdabai_. They were obtained from the Shenzhou Tea Comprehensive Experimental Base of the Tea Research Institute, Chinese Academy of Agricultural Sciences (Shengzhou City, Zhejiang Province). Fresh tea leaves were picked according to the standard that specifies one bud and one leaf. The manufacturing process for black tea was adapted from our previously used methods with a slight modification [14]. In brief, the harvested tea leaves were withered in bamboo sieves until the moisture reached around 62%, and they were then rolled using a machine (6 CR-45, Shangyang Machinery Ltd., Hangzhou, China) at 38 rpm for 1.5 h. After rolling, tea leaves underwent fermentation in a machine (Y7-6 CHF-7, Jiayou Machinery Intelligent Technology Co., Ltd., Quanzhou, China) at 30 \({}^{\circ}\)C and a relative humidity of 90% for 3.5 h. A two-phase drying method was performed in this present work; the first drying was conducted at 120 \({}^{\circ}\)C until the moisture level reached 25%, followed by the second drying at 90 \({}^{\circ}\)C until the moisture content decreased to around 5% (Y7-6 CHZ-9B, Jiayou Machinery Intelligent Technology Co., Ltd., Quanzhou, China).  
The method of blending fresh leaves has been used by many tea manufacturers, but scientific research based on chemical components is still lacking. Therefore, the treatments of this work were mainly based on practical experiences. Each variety of tea leaves was manufactured individually, resulting in three distinct black tea samples, which were labeled as JGY (_Jinguanyin_ cooked), JX (_Jinxuan_ cooked), and Fud (_Fudingdabai_ cooked), respectively. For blending processing, according to our preliminary research, the ratio of fresh tea leaves used was 85% _Fudingdabai_ to 15% _Jinguanyin_ or _Jinxuan_. The blend of _Fudingdabai_ with _Jinguanyin_ cooked black tea was designated as FJG, while the blend with _Jinxuan_ was labeled as FJX. Additionally, finished tea blends were created using the same ratio, combining 85% Fud with either 15% JGY or 15% JX, resulting in two blending samples named AFJG and AFJX, respectively. Therefore, a total of seven black tea samples were prepared based on the difference in varieties and blending methods (fresh leaf blending and finished tea blending). The sample names and prepared methods are summarized in Table 1. Among them, a single-variety treatment was used to compare the flavor differences between Fud, JGY, and JX. Blending fresh leaves treatment was used to confirm the contribution of adding oolong variety to flavor. As for the blending finished tea treatment, it was used to analyze the differences between blending fresh leaves and blending finished tea. All treatments are single-factor comparisons.
## 4 Results
### Participant Characteristics
#### 4.1.2 Discriminative Validity  
Table 3 demonstrates the results of unilateral testing: (1) AROM, (2) grip, and (3) ULSPT divided into three groups--control, RCRSP painful side, and RCRSP non-painful side.  
The ULSPT demonstrated significant differences between the control group and both the painful and non-painful shoulders in the RCRSP group (\(p\) = 0.02). However, there was no significant difference in throwing distance between the painful and non-painful sides in the RCRSP group.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{3}{c}{**Control (n = 34)**} & \multicolumn{2}{c}{**RCRSP (n = 30)**} \\
**Outcome Measure** & **Mean** & **SD** & **Mean** & **SD** \\ \hline QuickDASH (11–55) & 0.38 & 1.50 & 26.20 & 10.54 \\ FABQ PA (0–24) & 0.29 & 1.20 & 12.40 & 6.20 \\ FABQ work (0–42) & 0 & 0 & 8.80 & 9.03 \\ FABQ total (0–66) & 0.29 & 1.20 & 21.2 & 11.10 \\ VAS start (/10 cm) & 0 & 0 & 3.57 & 2.50 \\ VAS pre ULSPT (/10 cm) & 0 & 0 & 3.77 & 2.60 \\ VAS post ULSPT (/10 cm) & 0 & 0 & 4.60 & 2.43 \\ VAS pre-post ULSPT difference & 0 & 0 & \(-\)0.35 & 2.43 \\ \hline \end{tabular} Controps reported no pain before, during, and after the procedure, and reported minimal level of disability and FABQ. RCRSP—rotator cuff-related shoulder pain, SD—standard deviation, QuickDASH—disability arm shoulder hand, FABQ—fear-avoidance beliefs questionnaire, PA—physical activity, VAS—visual analog scale.  
\end{table}
Table 2: Self-reported results for disability (QuickDASH), fear avoidance (FABQ), pain intensity (VAS), and VAS change throughout the procedure.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{3}{c}{**Rotator Cuff-Related Shoulder Pain**} \\  & \multicolumn{3}{c}{**Control**} & \multicolumn{3}{c}{**(n = 30 Participants, 60 Shoulders in Total)**} \\
**Outcome Measure** & **(n = 34, 68 Shoulders)** & \multicolumn{3}{c}{**RCRSP Non-Painful Side**} \\  & & & **(n = 30)** & **(n = 30)** \\ \cline{2-5}  & **Mean** & **SD** & **Mean** & **SD** \\ \hline ULSPT (m) & 2.51 & 0.93 & **2.04** & 0.80 & **2.03 * & 0.81 \\ SMBT (m) & 3.39 & 1.24 & **-** & - & 2.85 & 0.98 \\ AROM (\({}^{\circ}\)) & 175.10 & 8.20 & 167.90 & 29.70 & **166.20 ***,\#** & 10.00 \\ Grip (kg) & 34.80 & 11.70 & 32.40 & 10.70 & 33.50 & 11.70 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result of physical outcome measures by three groups: control, RCRSP painful side, and RCRSP non-painful side.  
The SMBT, the only bilateral physical measure, did not show a significant group difference, although RCRSP participants threw to shorter distances.  
AROM showed significant differences between the control and painful sides of the RCRSP group (\(p\) = 0.0001), with higher AROM demonstrated by controls. The RCRSP painful side had lower ROM compared with the non-painful side (\(p\) = 0.02).
## 3 System Models
### Latency-Aware Client Clustering  
We denote \(\tau\) as the deadline of a global iteration. The FL server would accept all the models uploaded from the clients before the deadline \(\tau\) and reject the rest in each global iteration. Hence, we cluster the clients into several tiers, and \(x_{ij}\) is used to indicate whether client \(i\) is in Tier \(j\) (i.e., \(x_{ij}=1\)) or not (i.e., \(x_{ij}=0\)). Basically, if client \(i\) can finish its local model training and uploading before deadline \(\tau\), i.e., \(t_{i}\leq\tau\), then client \(i\) is in Tier 1, i.e., \(x_{i1}=1\). Similarly, if client \(i\) can finish its local model training and uploading between \(\tau\) and \(2\times\tau\), i.e., \(\tau<t_{i}\leq 2\times\tau\), then client \(i\) is in Tier 2, i.e., \(x_{i2}=1\). The following equation provides a general mathematical expression to cluster client \(i\) into a specific tier.  
\[x_{ij}=\left\{\begin{array}{ll}1,&\text{if }\tau\times(j-1)<t_{i}\leq\tau \times j,\\ 0,&\text{otherwise},\end{array}\right. \tag{7}\]  
where \(j\) is the index of tiers.
# Capability of Advanced Ultrasonic Inspection Technologies for Hydraulic Turbine Runners
## 3 CIVA Modelling and Analysis
### Simulation of Phased Array Ultrasonic Testing (PAUT)  
A linear array of 64 elements assembled to a flat refracting wedge was selected as the probe to simulate phased array ultrasonic testing. In all cases, this probe was used to sectorial scan the sample at angles ranging from 40\({}^{\circ}\) to 70\({}^{\circ}\)(with & without focusing) as shown in Figure 11. If there was focusing, the focal depth was either 22 or 50 mm. The electronic focusing enables optimizing the beam shape at the expected defect location and further optimizes the detection probability. Also, focusing at multiple depths improves the ability to size critical flaws for volumetric inspections.  
The T-joint sample model was the same as the one used for conventional UT simulation; same material options, same flaws nature, size, and distribution. According to HydroQuebec's procedure, the numerical system's sensitivity was calibrated, namely three side-drilled-holes with a diameter of 1.5 mm at depths of 15, 30, and 45 mm, respectively. The study used the computed time corrected gain (TCG) in this calibration process.  
The following conclusions were obtained from the simulation results: Sample alloys appear to have little to no impact on the detection rate of the system; A 16-element configuration has a better detection rate than a 32-element one (Figure 12). The result agrees with the experimental measurements and tends to prove our hypothesis, namely, the impact of the beam exit point and the width of the refracted beam on the sensitivity of these techniques; by phased-array sectorial scanning, we are sweeping the volume with a variety of angles. Even if the targets are not exactly aligned with the center ray as indicated by CIVA, there will be components of the wavefront that might hit the targets with a preferred reflection coefficient and directivity. Of course, the S-scan's fixed standoff  
Figure 10: Simulation results from Conventional ultrasonic testing probe.  
is a limiting factor that could cause flaws at the extremes of the sectorial sweep to have a weaker response. This will be a factor in the way we set up the 16 element and 32 element apertures; for the same refracted angle, the 32 element aperture has an exit point farther back from the weld. So we are approaching the targets from slightly different angles (and distances).  
PAUT simulated detection rate is significantly better than the conventional UT one. As mentioned previously, this could be because the fixed-angle beam can miss a flaw more easily than with ultrasounds impinging the flaw from different directions, as is the case with sectorial scans. Also, with a fixed angle and using a raster scan, detection will be a variable that can be a function of the wedge size (limiting the probe's access to the weld) and the beam divergence.
# Evaluation of NOx Reduction Effect and Impact on Asphalt
## 3 Experimental Results and Analysis
### Asphalt Aging Evaluation by UV  
#### 3.3.1 FT-IR Spectroscopy Analysis  
TiO\({}_{2}\) is known to accelerate the oxidation and aging of asphalt because it uses UV as an energy source. Therefore, a UV aging test was conducted to determine the effect of the surface treatment agent produced in this study on asphalt aging. The UV aging experiment was repeated 10 times for 5 h each, and the total time of exposure to UV was 50 h so that the asphalt was sufficiently aged by UV. The UV aging test was conducted on three specimens: general asphalt, asphalt mixed with TiO\({}_{2}\), and asphalt mixed with TiO\({}_{2}\) and the rejuvenator. The TiO\({}_{2}\) used for the specimen preparation was type-1. After UV aging, the asphalt binder was extracted and analyzed using FT-IR to confirm the changes in the molecular structure and functional groups of the asphalt. The analysis results are shown in Figures 11 and 12.  
Figure 11 show the results of the FT-IR experiments of asphalt and TiO\({}_{2}\) mixed asphalt after UV aging. The peak at 2926 cm\({}^{-1}\) corresponds to the sp\({}^{3}\) C-H stretching vibration of saturated hydrocarbons, the peak at 1456 cm\({}^{-1}\) corresponds to the -CH\({}_{2}\) bending vibration, and the peak at 1376 cm\({}^{-1}\) corresponds to the -CH\({}_{3}\) bending vibration. The peak at 1738 cm\({}^{-1}\) corresponds to the C=O stretching vibration of the ester carboxyl functional group (COO) and is used as an index to predict the degree of oxidation of asphalt [44].  
A comparison of the intensity of the peak at 1738 cm\({}^{-1}\) by aligning the peaks at 2926 cm\({}^{-1}\) in both spectra with the same intensity revealed that the intensity of the peak at 1738 cm\({}^{-1}\) of the asphalt containing TiO\({}_{2}\) compared to the asphalt without TiO\({}_{2}\) increased by approximately 30%. This means that the asphalt mixed with TiO\({}_{2}\) is highly affected by photooxidation, and the asphalt in the area in direct contact with TiO\({}_{2}\) is partially and  
Figure 11: FT-IR spectrum of asphalt and asphalt with TiO\({}_{2}\).  
Figure 12: FT-IR spectrum of Asphalt mixed with surface treatment agent.  
rapidly oxidized. Therefore, TiO\({}_{2}\) in asphalt is expected to accelerate the aging of asphalt owing to oxidation by UV.  
Figure 12 show the FT-IR test results of asphalt samples mixed with TiO\({}_{2}\) and the rejuvenator. The peak at 3000 cm\({}^{-1}\) corresponds to the -OH stretching vibration, which is an absorption peak that appears mainly in asphalt-containing polymers [45]. Since the peak at 1738 cm\({}^{-1}\) is much larger than that of general asphalt, it seems that the rejuvenator contains an SBC polymer additive and a component with an ester functional group.  
The peak change before and after the UV aging test showed that the intensity of the C=O stretching vibration at 1738 cm\({}^{-1}\) increased by 10% based on the peaks at 2926 and 1456 cm\({}^{-1}\). Therefore, it was found that oxidation was less advanced when the rejuvenator was mixed than when only TiO\({}_{2}\) was mixed with asphalt.
# Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction
## 4 Algorithm Design
### PPO-Based SFC Migration Algorithm
#### 4.2.2 Proximal Policy Optimization Algorithm
Use the Adam optimizer to update the parameters based on the computed gradient information:  
\[\
abla_{\theta_{\epsilon}}L(\theta)\theta\leftarrow\theta-\alpha\cdot\frac{m_ {t}}{\sqrt{v_{t}}+\epsilon} \tag{42}\]  
where \(\alpha\) is the learning rate; \(m_{t}\) and \(v_{t}\) are the first- and second-moment estimates of the gradient, respectively; and \(\epsilon\) is a small constant to prevent division by zero. The policy parameters \(\theta_{a}\) aim to maximize policy rewards by minimizing the negative terms in the loss function:  
\[\theta_{a}\leftarrow\theta_{a}-\alpha\cdot\
abla_{\theta_{a}}\Big{(}-L^{\text{ CLIP}}(\theta)-c_{2}S_{\pi_{\theta}}\Big{)} \tag{43}\]  
The value function parameters \(\theta_{c}\) aim to minimize the value function loss \(L^{\text{VF}}(\theta)\):  
\[\theta_{c}\leftarrow\theta_{c}-\alpha\cdot\
abla_{\theta_{c}}L^{\text{VF}}(\theta) \tag{44}\]  
Through these steps, PPO effectively balances exploration and exploitation during policy optimization, stabilizes the training process, and enhances sample efficiency.
# Strengthening Design of RC Columns with Direct Fastening Steel Jackets
## 2 Review of Previous Work  
### Mechanical Behavior of Shear Connectors joined by Direct Fastening  
The connections used in this novel steel jacket resist shear loading incurred as a result of concrete expansion or lateral load. The load transfer process of these connections is similar to that of single lap joints subjected to tensile force at the ends. However, the behaviors of this type of connection have rarely been studied and relevant design specifications are thus not available. Therefore, the experimentation on the single lap joints shown in Figure 4 was carried out to study the mechanical behavior of direct fastening connections [22].  
Two kinds of failure modes--bearing and shear fracture--were examined in the tests. Similar to the bearing failure seen in bolted or screwed connections, this failure occurs with enlarged nail holes and bulging of the material around the fastener holes due to large plastic deformation, leading to desirable ductile behavior (see Figure 5).  
Figure 3: Strengthening procedure.  
Figure 4: Illustration of connection test joined by direct fastening.  
Figure 5: Mechanical behavior of connections: (**a**) bearing failure; (**b**) shear load versus displacement curve.  
Although equations for predicting the maximum bearing resistance of bolted and screwed connections are available in current specifications EN-1993-1-8 [26], ANSI/ AISC 360-10 [27], and AS 4100 [28], these equations cannot be applied to estimate the maximum bearing resistance (yield strength) of connections joined by direct fastening without modification. Therefore, a unified design equation is developed to evaluate the maximum bearing resistance of the connections:  
\[F_{b}=\psi_{fp}\psi_{fp}\alpha_{br}d_{n}t_{p}f_{pu} \tag{1}\]  
where \(\alpha_{br}\) is the bearing resistance factor, which is taken as 1.6; \(\psi_{fp}\) is the factor that considers the effect of protuberance; \(\psi_{fi}\) is the factor that takes the effect of knurling into account; \(d_{n}\) is the nominal fastener diameter; \(t_{p}\) is the thickness of the connected steel plate; and \(f_{up}\) is the ultimate strength of the connected steel plate.  
The two factors caused by the effects of protuberance and knurling are given as:  
\[\psi_{fp}=\begin{cases}1.35&\text{without pre -- drilled holes on connected steel plate}\\ 1.0&\text{with pre -- drilled holes on connected steel plate}\end{cases} \tag{2}\]  
\[\psi_{fk}=\begin{cases}1.17&\text{fastener with knurling}\\ 1.0&\text{fastener without knurling}\end{cases} \tag{3}\]
# Closed-Loop Robotic Arm Manipulation Based on Mixed Reality
## 2 State of the Art
Apart from the safety aspect, one of the key challenges of industrial HRC is the interaction and coordination between human and robot resources, as presented in [28]. More similar to this research work, a context-aware MR approach was used in car door assembly and tested against two standard methods, i.e., printed and screen display instructions [29]. In addition, the authors of [30] focused on enabling human operators to communicate with mobile dual arm robots, namely, Mobile Robot Platforms (MRPs), via an AR-based software suite. The novelties of the systems proposed lie in the end-to-end (E2E) integration of the human side interface AR-based framework, with mobile robot controllers exploiting the Digital Twin capabilities of the production entities [31]. Moving on, a recent study presenting the problems of Human-Robot Interaction (HRI) [21] suggests that AR interfaces can enhance the process of interaction by manipulating robots. Moreover, MR has been used in order to embed the user in a virtual environment deeper than AR. Furthermore, a similar study in [32] proposed an intuitive robot programming based on MR. A methodology to plan the geometric path, including orientation, has been developed. Shared autonomy systems enhance the ability of people to carry out everyday life tasks using robotic manipulators. The authors of [33] describe a robotic cell that manipulates, assembles, and packages geometrically complex products using cognitive control and actuation systems. Individual mechatronic components, such as a 6 DoF (Degrees of Freedom) gripper and a flexible assembly mechanism, were designed by decomposing the actual assembly and handling tasks into functional components. Additionally, a problem for users that cannot change their point of view has been addressed in [33] with the introduction of the InvisibleRobot, which is a diminished reality-based approach that overlays the background information onto the robot in the FoV of the user, through an Optical See-Through Head-Mounted Display. The authors of [34] developed an AR system allowing for safer online programming of industrial robots. Lastly, [35] presented the results of a project to develop an AR-based HRC system to improve safety when working with robots, the solutions consisting of volumes of safe working zones and audio and visual instructions to indicate danger. Furthermore, the levels of collaboration between an operator and a robot are classified in [36] as (1) Coexistence, (2) Cooperation, and (3) Collaboration, these being the three pillars of coexistence. As a result, as defined in ISO/TS 15,066 [37], different levels of collaboration necessitate different safety actions and measures. Therefore, following the literature investigation, there is only a limited number of similar studies proposing a method for near real-time wireless robot manipulation with MR capabilities. Additional user-experience-enhancing features, such as safety zones, robot reachability zones, and so on, are also supported.
## 7 Computational Results
### Inverse Problem without Noise  
First, we solve the problem with the integral measurements, taken directly from (53). In Table 1 the results of mesh refinement analysis are presented in the case of \(\
u=0.1\), \(\
u_{m}=0.2\), \(c=1\), \(X=\pi/2\), and \(T=1\). Theoretically, the linearized finite difference scheme for the direct problem is of second order with respect to time and to space; see [28].  
But, due to the first order of the approximations of \(U_{x}(X,t)\) and \(U_{x}(0,t)\), the proposed algorithm leads to first order both for \(U,b\) and also for \(f(t)\). The numerical results confirm the theoretical rate of convergence, i.e., \(O(\tau+h)\) for the flow velocity \(u(x,t)\) and for the magnetic field \(B(x,t)\).  
Shown in Figure 1a is the the numerical solution \(U_{num}\) for the corresponding problem (17), (18) and shown in Figure 1b is the numerical values of the flow velocity \(u(x,t)\) of the original system (1)-(4), (7) for \(N=40\) and \(M=32\). In Figure 2a the error \(U_{xx}-U_{num}\) and in Figure 2b) the error \(b_{xx}-b_{num}\) are depicted for the same values of the mesh parameters. It is clear to see the good approximation of the numerical solution to the exact one.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**u**} & \multicolumn{3}{c}{**B**} & \multicolumn{3}{c}{**f**} \\ \hline \(M\) & \(N\) & \(err_{u}\) & **Ratio** & **Order** & \(err_{B}\) & **Ratio** & **Order** & \(err_{f}\) & **Ratio** & **Order** \\ \hline
5 & 5 & 9.2310e-03 & - & - & 6.5473e-04 & - & - & 1.1485e-02 & & \\ \hline
10 & 10 & 5.0943e-03 & 1.81 & 0.86 & 1.6591e-04 & 3.95 & 1.98 & 6.1159e-03 & 1.88 & 0.91 \\ \hline
20 & 20 & 2.6823e-03 & 1.90 & 0.93 & 4.5833e-05 & 3.60 & 1.85 & 3.1974e-03 & 1.91 & 0.94 \\ \hline
40 & 40 & 1.3803e-03 & 1.94 & 0.96 & 1.6712e-05 & 2.72 & 1.45 & 1.6416e-03 & 1.95 & 0.96 \\ \hline
80 & 80 & 7.0073e-04 & 1.97 & 0.98 & 7.1775e-06 & 2.32 & 1.21 & 8.3274e-04 & 1.97 & 0.98 \\ \hline
160 & 160 & 3.5312e-04 & 1.98 & 0.99 & 3.3274e-06 & 2.14 & 1.11 & 4.1952e-04 & 1.99 & 0.99 \\ \hline \end{tabular}
\end{table}
Table 1: The error in maximum norm of the flow velocity \(u(x,t)\), the magnetic field \(B(x,t)\), and the source function \(f(t)\), obtained for \(\
u=0.1\), \(v_{m}=0.2\), \(c=1\), \(X=\pi/2\), and \(T=1\).  
Figure 2: Mesh parameters \(M=32\), \(N=40\): (**a**) The error in maximum norm for the flow velocity \(U(x,t)\); (**b**) The error in maximum norm for the magnetic field \(b(x,t)\).