esa-sceva/satcom-chunk-collection huggingface dataset:
# Authentication and Billing for Dynamic Wireless EV Charging in an Internet of Electric Vehicles
## 4 System Model  
Different entities constitute the DWC infrastructure within the IoEV modeled in this work, namely: the charging service company (CSC), the charging pad owners (PO), the charging segments (CS), and the EVs. Each pad owner, \(PO_{i}\), owns \(\mathcal{S}_{i}\) charging segments. For simplicity, it is assumed that the charging segments are numbered \(1,2,3,\ldots,\mathcal{S}_{i}\) and that the EVs always enter the charging lane at segment 1 and are charged subsequently by the following segments. Each EV has to register and sign a contract with a specific CSC before a registered pad owner, \(PO_{i}\), provides dynamic charging to EVs. The EVs are, however, not billed directly by the PO. Instead, the PO submits the bills to the CSC, which pays for the EVs and then bills the subscribers accordingly. Each EV is assumed to be equipped with a tamper-proof device that carries out cryptographic computations, including message signatures, encryption and decryption. The proposed authentication and billing strategies in this work are developed to leverage on the advantages of the different approaches discussed in Sections 2 and 3 to provide fast, lightweight and privacy-preserving authentication with fair and efficient billing.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 5 Results and Comparison
### Performance Metrics and Evaluation
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**References \#** & **Platform** & **Frequency** & **Slices** & **LUTS** & **Time** & **T/Slices** \\  & & **(in MHz)** & & & **(in \(\mu\)s)** & \\ \hline \multicolumn{6}{l}{**Virtex 4 Results**} \\ \hline GBEC: \(d=59\)[16] & Virtex-4 & 127.261 & 17,158 & 2663 & 25.5 & 2.28 \\ BEC [24] & Virtex-4 & 48 & 21,816 & 35,003 & – & – \\ BEC halving [24] & Virtex-4 & 48 & 22,373 & 42,596 & – & \\ GBEC: \(d=59\ -3M\)[25] & Virtex-4 & 255.570 & 29,255 & – & 14.83 & 2.38 \\ GBEC: \(d=59\ -1M\)[25] & Virtex-4 & 257.535 & 12,403 & – & 32.81 & 2.45 \\ GBEC: \(d=59\)[29] & Virtex-4 & 195.508 & 3302 & 2723 & 32.1 & 9.43 \\ BEC: \(d=59\)[38] & Virtex-4 & 277.681 & 31,702 & – & 13.39 & 2.35 \\ \hline \multicolumn{6}{l}{**Virtex 5 Results**} \\ \hline GBEC: \(3M\ d1=d2=59\)[23] & Virtex-5 & – & 4581 & – & 51.46 & 4.24 \\ BEC: \(d_{1}=d_{2}=1\)[25] & Virtex-5 & 205.1 & 1397 & 4340 & 4560 & 0.1569 \\ GBEC: \(d=59\)[29] & Virtex-5 & 245.669 & 2714 & 2502 & 25.59 & 14.39 \\ GBEC: \(d=59\ -3M\)[39] & Virtex-5 & 337.603 & 9233 & – & 11.22 & 9.67 \\ GBEC: \(d=59\ -1M\)[39] & Virtex-5 & 333.603 & 4019 & – & 25.03 & 9.94 \\ \hline \multicolumn{6}{l}{**Virtex 6 Results**} \\ \hline GBEC: \(d=59\)[16] & Virtex-6 & 186.506 & 2664 & 22,256 & 17.39 & 21.5 \\ BEC: \(d1=d2=1\)[25] & Virtex-6 & 107 & 1245 & 3878 & 6720 & 0.119 \\ GBEC: \(d=59\)[29] & Virtex-6 & 290.92 & 1770 & 3597 & 21.61 & 26.14 \\ \hline \multicolumn{6}{l}{**Virtex 7 Results**} \\ \hline GBEC: \(d=26\)[16] & Virtex-7 & 179.81 & 2662 & 24,533 & 18.04 & 20.82 \\ GBEC: \(d=59\)[29] & Virtex-7 & 320.584 & 1771 & 4470 & 19.61 & 28.79 \\ \hline \multicolumn{6}{l}{**Our Work—Without Pipelining**} \\ \hline
**GBEC: \(d=26\)** & Virtex-4 & 92 & 3002 & 2223 & 39.78 & 8.37 \\
**GBEC: \(d=26\)** & Virtex-5 & 143.119 & 2314 & 2408 & 25.57 & 16.9 \\
**GBEC: \(d=26\)** & Virtex-6 & 188.223 & 1570 & 2897 & 19.445 & 32.76 \\
**GBEC: \(d=26\)** & Virtex-7 & 202.244 & 1572 & 3070 & 18.097 & 35.15 \\ \hline \multicolumn{6}{l}{**Our Work—Two stage Pipelining**} \\ \hline
**GBEC: \(d=26\)** & Virtex-4 & 199.2 & 3246 & 2846 & 25.07 & 12.2 \\
**GBEC: \(d=26\)** & Virtex-5 & 250 & 2624 & 2488 & 19.976 & 19.07 \\
**GBEC: \(d=26\)** & Virtex-6 & 294.92 & 1640 & 3326 & 16.93 & 36.01 \\
**GBEC: \(d=26\)** & Virtex-7 & 314.584 & 1641 & 3876 & 15.87 & 38.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: State-of-the-art methods comparison.
# A Dual-Connectivity Mobility Link Service for Producer Mobility in the Named Data Networking
## 5 A Producer Mobility Link Service in NDN Face  
An NDN face consists of a link service and a transport. The generic link service provides fragmentation, reassembly, failure detection, and reliability.  
As shown in Figure 2, the PMLS is added at the link service to conceal the link breakage caused by the movement of the producer. The PMLS supports two additional functionalities: one is the re-establishment of the link and the other is the re-synchronization of the on-going transmission. To re-establish the connection, the PMLS at NAR has a special transport that accepts the connection recovery request from the PMLS at the NMP. When the NDN face of the NMP detects relevant movement, the PMLS sends the Face Update message to the peer PMLS via the special transport at the previous NAR. Then, both PMLSs create new transports to connect to each other.  
After re-establishing the connection, the PMLSs must re-synchronize the on-going transmission. If the movement of the NMP occurs when it has received Interest and the corresponding Data packet transmission has yet to be completed, the partially transmitted Data packet is typically discarded. Then, the NMP must wait for the re-transmitted Interest, forcing the consumer to suffer from a long retrieval delay. Therefore, the remaining segments of the partially transmitted Data packet should be transmitted after the connection is fully re-established.  
Due to its functionalities mentioned above, the PLMS has the following advantages:  
* The PMLS restores links that have been damaged by the producer's movements so that consumers do not reissue the interests until the default re-transmission delay (3000 ms) expires. After the re-transmission delay expires, it retransmits the interest.
* The PMLS does not change the behavior of the existing NDN unlike the existing schemes that support the mobility of producers by changing the type of FIB or Interest packet.  
Figure 1: System Architecture Overview.  
* As the PMLS supports the mobility of producers by establishing new mobility link services. As it could be inserted and removed like a module in the NDN architecture, it does not require any other modification of the NDN architecture. Therefore, the PMLS which handles the mobility of the NDN has the advantage that it can be advanced regardless of the NDN architecture, and at the same time, the NDN architecture can also evolve regardless of the change of the PLMS.
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 2 Proposed WRTU-16T SRAM Architecture
### SEU Recovery Analysis
#### 2.5.2 SEU at Node S0  
When node S0 is affected by SEU, the logic state of node S0 changes from \"0\" to \"1\", which makes transistor P4 turn into temporary off and transistor N6 and N1 turn into temporary on. All nodes retain their logic values, but only the S1 node becomes high impedance. As a result, the S0 node is restored from logic '1' to logic '0' by activated P5 and N3. The SEU recovery operation is shown in Figure 4.
# _Forentation_
## 2 Materials and Methods
### Laboratory-Scale Fermentations in Sterile Apple Must
#### 2.3.2 Kinetic Parameters  
Kinetic parameters were calculated individually for each fermentation based on daily \({}^{\circ}\)Brix readings taken from the system. Two models were assessed:  
(i) The exponential decay function, previously applied by [PERSON] et al. [34]:  
\[\mathrm{Y=D+S*e-K*t},\]  
where Y represents the final \({}^{\circ}\)Brix value, t is the time (h), D is the value as t\(\rightarrow\)\(\infty\), S corresponds to the estimated change in value, and K is the kinetic constant (h \(-\) 1).  
(ii) The sigmoid or modified Gompertz decay function, used in prior work by [PERSON] and [PERSON] [35]:  
\[\mathrm{Y=A+C*e-e^{*}(K*(t-M))},\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Code**} & **Inoculation** & \multirow{2}{*}{**Inoculum (Cells/mL)**} \\  & **Strategy** & & **Inoculum (Cells/mL)** \\ \hline \multicolumn{4}{c}{_S. cerevisiae_ NIF8} & _P. kudriuzvevii_ NPCC1651 \\ \hline P & Pure culture & - & 2 \(\times\) 10\({}^{6}\) \\ S & Pure culture & 2 \(\times\) 10\({}^{6}\) & - \\ Sim 1:1 & Simultaneous 1:1 & 1 \(\times\) 10\({}^{6}\) & 1 \(\times\) 10\({}^{6}\) \\ Sim 1:10 & Simultaneous 1:10 & 1 \(\times\) 10\({}^{6}\) & 1 \(\times\) 10\({}^{7}\) \\ Seq P-S & Sequential & 1 \(\times\) 10\({}^{6}\) (48 h) * & 1 \(\times\) 10\({}^{6}\) \\ Seq S-P & Sequential & 1 \(\times\) 10\({}^{6}\) & 1 \(\times\) 10\({}^{6}\) (48 h) * \\ \hline \hline \end{tabular}  
* (48 h) indicates the inoculation time of the second yeast in sequential fermentations.  
\end{table}
Table 1: Code, inoculation strategies, and cell counts used in conducted apple must fermentations.  
where Y is the remaining \({}^{\circ}\)Brix in the must, t is time (h), A is the lower asymptote at t\(\rightarrow\)\(\infty\), K is the kinetic constant (h \(-\) 1), C represents the difference between the upper and lower asymptotes, and M is the time at which the inflection point occurs (50% of the total \({}^{\circ}\)Brix variation).  
Data analysis was performed using the non-linear module in Statistica 7.0 (StatSoft, Tulsa, OK, USA), with the Quasi-Newton method applied to minimize the sum of squared deviations between the experimental data and the model predictions. The adequacy of the fit was assessed by the coefficient of determination (r2), indicating the proportion of variance explained by the model. Both equations were evaluated, and the model with the highest r2 (r2 > 0.98) was selected. To compare the kinetic parameters, ANOVA and [PERSON]'s honest significant difference test (with \(\alpha\) = 0.05) were employed. The normality of the data was assessed using the Lilliefors test, while the homogeneity of variance in the residuals was evaluated with the Bartlett test.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 4 Research on Multi-Branch Line Fault Ranging Technology Based on Fault Branch Judgment Matrix
### Multi-Terminal Transmission Line Fault Location Method  
When a fault occurs in a multi-terminal transmission line that passes through multiple T-nodes, it can be analyzed using the T-type transmission line fault branch determination method proposed in the previous section. The schematic is shown in Figure 5.  
Multi-terminal transmission lines have multiple branch nodes. The corresponding element in the fault branch judgment matrix must be changed to the difference between the distance from the beginning to the fault point and the distance between the beginning of the fault branch and the nearest T-node from the end. Therefore, the fault branch judgment matrix is as follows:  
Figure 5: Multi-branch grid-connected line.  
\[\begin{bmatrix}M_{1}&N_{1}&N_{2}&N_{3}&\cdots&N_{k}&M_{2}\\ M_{1}&\times&d_{M_{1}FN_{1}}-L_{M_{1}T_{1}}&d_{M_{1}FN_{1}}-L_{M_{1}T_{2}}&d_{M_{1} FN_{3}}-L_{M_{1}T_{3}}&\cdots&d_{M_{1}FN_{1}}-L_{M_{1}T_{1}}&d_{M_{1}FM_{2}}-L_{M_{1}T_{1}}\\ N_{1}&d_{N_{1}FM_{1}}-L_{N_{1}T_{1}}&\times&d_{N_{1}FN_{1}}-L_{N_{1}T_{2}}&d_{N_{1} FN_{3}}-L_{N_{1}T_{3}}&\cdots&d_{N_{1}FN_{1}}-L_{N_{1}T_{2}}&d_{N_{1}FM_{2}}-L_{N_{1}T_{ 1}}\\ N_{2}&d_{N_{2}FM_{1}}-L_{N_{2}T_{1}}&d_{N_{1}FN_{1}}-L_{N_{1}T_{2}}&\times&d_{N_{ 2}FN_{3}}-L_{N_{2}T_{3}}&\cdots&d_{N_{1}FN_{1}}-L_{N_{1}T_{2}}&d_{N_{1}FM_{2}}-L _{N_{2}T_{1}}\\ N_{3}&d_{N_{3}FM_{1}}-L_{N_{3}T_{1}}&d_{N_{3}FN_{1}}-L_{N_{1}T_{1}}&d_{N_{3} FN_{2}}-L_{N_{1}T_{2}}&\times&\cdots&d_{N_{1}FN_{1}}-L_{N_{1}T_{2}}&d_{N_{1}FM_{2}}-L _{N_{1}T_{2}}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ N_{k}&d_{N_{1}FM_{1}}-L_{N_{k}T_{1}}&d_{N_{k}FN_{1}}-L_{N_{k}T_{1}}&d_{N_{1} FN_{1}}-L_{N_{1}T_{2}}&d_{N_{1}FN_{3}}-L_{N_{1}T_{3}}&\cdots&\times&d_{N _{1}FM_{2}}-L_{N_{1}T_{1}}\\ M_{2}&d_{M_{2}FM_{1}}-L_{M_{2}T_{1}}&d_{M_{1}FN_{1}}-L_{M_{2}T_{1}}&d_{M_{1} FN_{1}}-L_{M_{2}T_{2}}&d_{M_{2}FN_{3}}-L_{M_{2}T_{3}}&\cdots&d_{M_{2}FN_{3}}-L_{M_{2}T_{ 1}}&\times\end{bmatrix} \tag{28}\]  
There are three cases of multi-terminal transmission line faults: faults occurring on branch lines, T-node faults, and T-T inter-node faults. These are analyzed separately below.
## 6 Application of Algorithm to Various Scenarios for Fault Identification: Case Studies
### Effect of Type of Transformer Winding  
The nature of fault transients may be affected by the type of windings of the transformer installed between the fault location and the relay point which might affect the performance of the protection scheme [25]. Hence, the performance of the algorithm was tested using different types of windings for the transformer (XFM) installed between nodes 633 and 634 for all types of investigated faults. The fault index magnitude associated with all the phases during the incidence of an AG fault at node 671 in the presence of a transformer with windings such as star-star (\(Y-Y\)), where both windings are the start type with grounding (\(Yg-Yg\)), star-delta (\(Y-\triangle\)), delta-delta (\(\triangle-\triangle\)) and the primary winding is the start type with grounding and the secondary winding is delta (\(Yg-\triangle\)), provided in Table 6. It can be observed that the fault index has higher values than the threshold (\(2\times 10^{8}\)), corresponding to phase-A and low values corresponding to phases-B & C in the presence different types of transformer windings. It is also observed that the presence of delta winding reduces the FI. Further, the presence of ground connection also reduces the FI. Therefore, before installation of relay at a particular location, a proper weight factor should be selected to avoid any false tripping indication. Hence, it is established that the proposed algorithm is effective for the identification of different types of faults in the presence of different types of transformer windings.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Phase Name** & \multicolumn{5}{c}{**Fault Index Magnitude**} \\ \hline  & **0 \(\Omega\)** & **5 \(\Omega\)** & **10 \(\Omega\)** & **15 \(\Omega\)** & **20 \(\Omega\)** & **25 \(\Omega\)** \\ \hline Phase-A & \(5.8\times 10^{9}\) & \(1.4\times 10^{9}\) & \(9.2\times 10^{8}\) & \(8.1\times 10^{8}\) & \(7\times 10^{8}\) & \(5.9\times 10^{8}\) \\ \hline Phase-B & \(3.5\times 10^{6}\) & \(8\times 10^{5}\) & \(4.5\times 10^{5}\) & \(1.1\times 10^{5}\) & \(9.2\times 10^{4}\) & \(7.
## 4 Data Set Analysis for Simulation
Therefore, the device uniqueness \(U_{n}\) is defined as follows [29,41]. \[U_{n}=\frac{4}{K\cdot L\cdot N\cdot}\sum_{k=1}^{K}\sum_{l=1}^{L}\sum_{j=1,j\
eq n }^{N}(b_{n,k,l}\oplus b_{j,k,l})\] (7) When the sum of Hamming distance reaches the upper bound, \(U_{n}\) takes the highest value 1 and is considered as the ideal value. In our data set, we randomly applied 5500 different challenges for 100 different PUF instances, and investigated 5500 sets of 16-bit length responses. The uniqueness of the PUF is 0.9507 which is close to the ideal value of 1. The mean of the inter-HD is 47.93%. * **Steadiness**: Let \(S_{n,k,l}\) be the steadiness of the \(l\)-th bit of \(R_{n,k}\). Then, \(S_{n,k,l}\) is defined using min-entropy as follows [29,41]: \[S_{n,k,l}=1+\log_{2}max(p_{n,k,l},1-p_{n,k,l})\] (8) Then, the steadiness \(S_{n}\) is defined by taking the mean of \(S_{n,k,l}\). \[S_{n}=\frac{1}{K\cdot L\cdot}\sum_{k=1}^{K}\sum_{l=1}^{L}S_{n,k,l}\] (9) To evaluate steadiness, we obtained 256-bit length responses at temperatures of 0 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 85 \({}^{\circ}\)C and compared them to see if they were identical. A reference response was obtained at room temperature (25 \({}^{\circ}\)C). The results are shown in Figure 7d. The worst steadiness of the PUF is 0.9102 for a temperature of 85 \({}^{\circ}\)C.  
Figure 7: pm-PUF performance evaluation [29]: (**a**) randomness: occurrence probability of a 0 and 1 in the responses, (**b**) diffuseness: possibility of generating different responses with the same challenge, (**c**) uniqueness: possibility of generating different responses among different PUFs, and (**d**) steadiness under temperature variations (0 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, 50 \({}^{\circ}\)C and 85 \({}^{\circ}\)C).
## 2 System Model
### IRS Based Communication
The BS-IRS channel \(\mathbf{H}_{b}\) can be expressed as follows:  
\[\mathbf{H}_{b}=\sqrt{\frac{MN}{L_{1}}}\sum_{l_{1}=1}^{L_{1}}\beta_{l_{1}}\mathbf{ b}(\gamma_{l_{1}}^{H_{r}},\phi_{l_{1}}^{H_{r}})\mathbf{a}^{H}(\gamma_{l_{1}}^{H_{ r}},\phi_{l_{1}}^{H_{r}}), \tag{10}\]  
where \(L_{1}\) represents the number of paths between the BS and the \(i\)-th UE, \(\beta_{l}\) represents the complex gain of the paths, \(\mathbf{b}(\gamma_{l}^{H_{r}}\phi_{l}^{H_{r}})\) represents the steering vector related to the IRS, \(\mathbf{a}(\gamma_{l}^{H_{i}},\phi_{l}^{H_{r}})\) is the steering vector related to BS for the \(l\)-th path. Next, the channel between the IRS and UE can be defined as follows:  
\[\mathbf{H}_{ui}^{H}=\sqrt{\frac{N}{L_{2}}}\sum_{l_{2}=1}^{L_{2}}\beta_{l_{2}} \mathbf{a}^{H}(\gamma_{l_{2}}^{H_{r}},\phi_{l_{2}}^{H_{r}}), \tag{11}\]  
where \(L_{2}\) is the number of paths between the IRS and the \(i\)-th user, \(\beta_{l2}\) is the complex gain of paths, \(\gamma_{l2}^{H_{i}}\) and \(\phi_{l2}^{H_{i}}\) are the azimuth and elevation angle of departure of the signal, and \(\mathbf{a}(\gamma_{l_{2}}^{H_{i}},\phi_{l_{2}}^{H_{r}})\) is the steering vector. The cascade channel for BS to UE is as follows:  
\[ \begin{split}\mathbf{H}_{ca}=\sqrt{\frac{MN}{L_{1}L_{1}}}\sum_{ l_{1}=1}^{L_{1}}\sum_{l_{2}=1}^{L_{2}}\beta_{l_{1}}\beta_{l_{2}}diag( \mathbf{a}^{H}(\gamma_{l_{2}}^{H_{r}},\phi_{l_{2}}^{H_{r}}))\\ \mathbf{b}(\gamma_{l_{1}}^{H_{r}},\phi_{l_{1}}^{H_{r}})\mathbf{a}^ {H}(\gamma_{l_{1}}^{H_{r}},\phi_{l_{1}}^{H_{r}}).\end{split}  \tag{12}\]  
The channel matrix \(\mathbf{H}_{ui}^{H}\mathbf{H}_{b}\) can be expressed as follows:  
\[\mathbf{H}_{ui}^{H}\mathbf{H}_{b}=\mathbf{H}_{ui}^{H}diag(\xi)\mathbf{H}_{b}=\xi^{T}diag( \mathbf{H}_{ui}^{H})\mathbf{H}_{b}. \tag{13}\]  
Then, the following equation is obtained:  
\[\mathbf{H}_{ca}\xi^{T}=\mathbf{H}_{ui}^{H}\mathbf{H}_{b}. \tag{14}\]  
The total received signal \(\mathbf{y}_{it}\) can be written as follows:  
\[\mathbf{y}_{it}=(\mathbf{H}_{ca}\xi^{T}+\mathbf{H}_{d})\mathbf{x}+\mathbf{n}. \tag{15}\]
## 3 Methodology
### Binder Testing and Analysis
#### 3.1.1 Binder Master Curves and Glover-Rowe Parameter  
According to AASHTO T315, the binder specimens with 25 mm diameter were tested at the temperatures of 5, 15, 25, and 35 \({}^{\circ}\)C and loading frequencies within a strain range from 0.005 to 0.02. The complex modulus (G*) and phase angle (8) of binders were measured at testing frequencies and temperatures, and the binder master curves were generated at 25 \({}^{\circ}\)C reference temperature, using the time-temperature superposition principle, based on the Christensen-Anderson-Marasteanu Model (CAM) [24].  
Figure 2: Gradation curve of AC-13.  
[PERSON] et al. [25] developed the rheological parameter, G\({}^{\prime}\)/(\(\eta\)\({}^{\prime}\)/G\({}^{\prime}\)), as an indicator of cracking potential of binders. Later, [PERSON] [26; 27] simplified the Glover parameter, as Glover-Rowe (G-R) parameter, to evaluate the binder cracking properties. G-R parameter can be calculated from \(|\)G\({}^{\prime}\)\(|\)-(cos\(\delta\))\({}^{2}\)/sin\(\delta\), at temperature-frequency combination of 15 \({}^{\circ}\)C and 0.005 rad/s.
# Kinematic Analysis of Daily Activity of Touching Lateral Shoulder for Normal Subjects
## 3 Results
### Joint Angle for Different Groups  
The graphs of joint angle versus time for different age groups are shown in Figure 4. Table 3 shows the kinematic data of joint angles for subjects among the different age groups. The graphs of the joint angles of the shoulder and elbow showed smooth movement and similar profile patterns for both the lifting and returning phases while subjects performed the touching-the-contralateral-shoulder-task. Subjects in the 50s age group showed the greatest maximal extension and flexion in each phase for elbow joints and the greatest maximal abduction and adduction in each phase for shoulder joints. During the lifting phase, differences of joint angles for the shoulder and elbow among age groups were found to be 9.2\({}^{\circ}\) and 14.6\({}^{\circ}\), F\({}_{3,56}\) = 10.065, \(p\) = 0.000 for the shoulder joint and F\({}_{3,56}\) = 3.483, \(p\) = 0.022 for the elbow joint. During the resting phase, differences of joint angles for shoulder and elbow among age groups were found to be 13\({}^{\circ}\) and 9.2\({}^{\circ}\), F\({}_{3,56}\) = 10.061, \(p\) = 0.000 for the shoulder joint and F\({}_{3,56}\) = 10.061 for the shoulder joint and F\({}_{3,56}\) = 4.631, \(p\) = 0.006 for the elbow joint. During the returning phase, differences of joint angles for the shoulder and elbow among age groups were found to be 6.7\({}^{\circ}\) and 13.9\({}^{\circ}\), F\({}_{3,56}\) = 11.485, \(p\) = 0.000 for the shoulder joint and F\({}_{3,56}\) = 2.570, \(p\) = 0.064 for the elbow joint. Differences in angles among maximum and minimum values of shoulder and elbow joints during lifting were F\({}_{3,56}\) = 5.326, \(p\) = 0.003 and F\({}_{3,56}\) = 3.647, \(p\) = 0.018, respectively. ANOVA was carried out to compare the joint angles of the shoulder, elbow and wrist (dependent variable) in the different age groups (independent variable), which contained four age groups (20-29, 30-39, 40-49 and 50-59). An ANOVA test showed a significant difference among all age groups with \(p\) < 0.05 in all phases for the age group involving angles of the shoulder and elbow joints except during the returning phase for the elbow joint. A significant difference also occurred for the lifting and returning phases of the wrist joint. Post hoc tests showed that significant