esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring of Surgically Treated Upper Arm Fracture by Implanted Antenna at 402 MHz
## 3 The Method
### Operating Frequency and Biological Effect
For a tissue density \(\rho_{i}\) (kg/m\({}^{3}\)), conductivity \(\sigma_{i}\) and dissipated power density \(P_{i}\) (W/m\({}^{3}\)), the average local SAR (W/kg) at position (i) is given by:  
\[\text{SAR}=\frac{P_{i}}{\rho_{i}}=\frac{\left|E_{i}\right|^{2}\sigma_{i}}{2\rho _{i}}\text{ W/kg} \tag{3}\]  
Figure 4 presents the variation in the SAR in the different tissues of the humerus at 400 MHz, 6 GHz, and 10 GHz. At the beginning of the tissue, the value of the SAR is high at the higher frequencies due to the electric field absorption and heat conversion. The following layers experience a low value of SAR at higher frequencies because most of the EM wave is already absorbed. As the penetration of the 400 MHz waves through the tissue is higher than the other frequencies, it shows a clear behavior of SAR along with the tissue. The curve shows a lower value of SAR in bone and fat tissue than in muscle and skin due to variations in the electrical properties and composition of the tissue. At high frequencies, it is required to decrease the strength of the initial electric field which has additional drawbacks on the signal penetration through the human tissue. The reason behind that is to save the increase in the SAR at the beginning of the model to safe levels. This reduction in penetration may block communication with the monitoring device outside the body. So, this distribution of SAR values gives an advantage of 400 MHz over the other studied frequencies. The different densities of human upper arm tissues are shown in Table 4. \begin{table}
\begin{tabular}{l l l l l} \hline
**Tissue** & **Bone** & **Muscle** & **Fat** & **Skin** \\ \hline \(\rho\) (kg/m\({}^{3}\)) & 1840 & 1060 & 920 & 1010 \\ \hline \end{tabular}
\end{table} 
Table 4: Density of humerus tissue [30]. Figure 3: Penetration of EM wave along with three layers human tissue model at different frequencies. The International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the IEEE C95.1-2019 standards set the SAR limit to 2 W/kg for the average of over 10 g of tissue [31].
# Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network
## 6 Experiments and Results
### Evaluation Indicators  
The evaluation indicators in the experiment are average delay, total throughput and packet drop rate.  
In SDSN, the average delay refers to a certain period of time when packets are sent from senders to receivers after it is transmitted through terrestrial network and satellite network. Assuming that the number of ISLs is N, the propagation delay of an inter-satellite link is \(T_{k}(k=1,2,3,\ldots,n)\) and the propagation delay of a satellite-ground link is \(T_{d}\). Transmission delay of each node is \(T_{tran,a}(a=1,2,3,\ldots,n,n+1,n+2)\), processing delay of each node is \(T_{pro,b}(b=1,2,3,\ldots,n,n+1,n+2)\) and the queuing delay of each node is \(T_{queue,c}(c=1,2,3,\ldots,n,n+1,n+2)\). Then average delay in SDSN is:  
\[T=\sum_{k=1}^{n}T_{k}+T_{d}+\sum_{a=1}^{n+2}T_{tran,a}+\sum_{b=1}^{n+2}T_{pro,b }+\sum_{c=1}^{n+2}T_{queue,c}\]  
Total throughput is the rate of traffic arriving at the destination, which can be calculated as total number of packets arriving at destination terminal divided by the simulation time. Assuming that total number of packets sent from sending terminal to destination terminal is P and the simulation time is \(T_{1}\), then total throughput is \(\frac{P}{T_{1}}\).  
Packet drop rate is calculated by packets sent and packets received. Assuming that total number of packets sent by sending node is \(P_{send}\) and total number of packets received by receiving node is \(P_{receive}\), packet drop rate can be calculated as \(1-\frac{P_{queue}}{P_{send}}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multicolumn{6}{c}{**Destination**} \\ \cline{2-7}  & **N. America** & **S. America** & **Europe** & **Africa** & **Asia** & **Oceania** \\ \hline N. America & 60 & 10 & 15 & 2 & 10 & 3 \\ S. America & 35 & 40 & 12 & 2 & 8 & 3 \\ Europe & 40 & 5 & 40 & 2 & 10 & 3 \\ Africa & 40 & 2 & 30 & 20 & 5 & 3 \\ Asia & 20 & 2 & 10 & 2 & 50 & 6 \\ Oceania & 40 & 2 & 10 & 2 & 12 & 34 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Distribution of Traffic Flows (%).
# An Experimental Tuning Approach of Fractional Order Controllers in the Frequency Domain
## 4 Controller Tuning
### Optimization Guidelines
The magnitude of the derivative part can be written as  
\[|1+T_{d}(j\omega)^{\mu}|=\sqrt{\left(1+T_{d}\omega^{\mu}\cos\frac{\mu\pi}{2} \right)^{2}+\left(T_{d}\omega^{\mu}sin\frac{\mu\pi}{2}\right)^{2}}. \tag{15}\]  
Opening the brackets and using the trigonometric property \(\sin^{2}\frac{\mu\pi}{2}+\cos^{2}\frac{\mu\pi}{2}=1\) gives  
\[|1+T_{d}(j\omega)^{\mu}|=\sqrt{1+2T_{d}\omega^{\mu}\cos\frac{\mu\pi}{2}+T_{d }^{2}\omega^{2\mu}}=15 dB. \tag{16}\]  
Replacing \(\omega=\omega_{r}=14 Hz=87.9 rad/s\), based on Figure 6, leads to  
\[|1+T_{d}(j\omega_{r})^{\mu}|=\sqrt{1+2T_{d}87.9^{\mu}\cos\frac{\mu\pi}{2}+T_{d }^{2}87.9^{2\mu}}=5.62. \tag{17}\]  
Knowing that \(\mu\in[0\;1]\) and evaluating the solution of (17) for \(\mu=0\) gives \(T_{d1}=-175.37\) and \(T_{d2}=0.1744\), whereas for \(\mu=1\), \(T_{d}=\pm 0.0629\). Note that \(T_{d}\) should always be greater than 0. Therefore, the starting point of \(T_{d}\) can be chosen any value around \([0.0629\;\;0.1744]\). The guidelines can be extended for the fractional-order PI or PID controller. Note that in the following subsections the PD controller transfer function is slightly different and the derivative gain \(k_{d}=T_{d}k_{p}\) interval is \(k_{d}\in[0.3535\ 0.9801]\).
# RETRACTED: Using Data Mining Methods for Predicting Sequential Maintenance Activities
## 3 The Proposed Model
### Generation of Frequent Spare Parts Groups  
In this step, we need to determine the frequently used spare parts for individual maintenance activities. Few spare parts are used in some maintenance operations and, in some cases, many spare parts are used for the same maintenance activity. Thus, by analyzing the historical consumption of spare parts demand, we can find the best group that is frequently used in the same type of maintenance activity. Example: We let Minsup be 40%. In Table 4 below,we observe that [S1, S2, S4] and [S1, S3, S4] have the highest spare parts coverage with support values of 42.13% and 41.71%, respectively. Thus, the best group of spare parts is the highest from among the both of them, i.e., [S1, S2, S4]. In this example, the data were collected from a mining company in southern India. The company has implemented integrated materials management since 2004 by integrating indentation, supplier sourcing, purchasing, and warehousing (inventory). Thus, all the spare parts consumption information is stored in a central database. The company has three large mining units that use a variety of special mining equipment, such as 28-wheel bucket wheel excavators, 12-wheel belt conveyors, 16-wheel spreaders, and 9-car trippers. The company manages nearly 150,000 items, of which 105,217 are spare parts that are mainly consumed by the mines and power generation equipment. \begin{table}
\begin{tabular}{c c} \hline
**Spare Part Groups** & **Support (\%)** \\ \hline S1 & 87.24 \\ S2 & 56.28 \\ S3 & 55.42 \\ S1, S2 & 50.42 \\ S1, S3 & 49.28 \\ S4 & 45.23 \\ S1, S2, S4 & 42.13 \\ S1, S3, S4 & 41.71 \\ S2, S4 & 40.57 \\ \hline \end{tabular}
\end{table} 
Table 4: The spare part groups with support. In this study, the equipment belt conveyor and the proposed model are considered to be applied to seven different belt conveyor related maintenance activities which consume approximately 2643 spare parts at various mining sites. We collected the spare parts from the consumption history. Regarding the maintenance activities of the 1800 conveyors in Mine-I (Table 5 below), the data show the seven maintenance activities and the few related spare parts used during the maintenance activities. We collected the maintenance data and spare part consumption data from April 2006 to February 2012. We used binary transaction data for the spare parts consumption. The number '1' indicated that the spare part is consumed and '0' indicated that the spare part was not consumed during the maintenance activity.
# Effects and Mechanisms of Microbial Remediation of Heavy Metals in Soil: A Critical Review
## 4 Microbial Remediation of Heavy Metal-Contaminated Soil
### Comparison of Microbial Removal Ability
#### 4.2.3 Kinetics of Adsorption  
Numerous sorption systems have been investigated during the past several years, and most have been reported as pseudo-first-order. However, pertaining to the microbial adsorption of heavy metal ions in contaminated soil, most fixed models are different, and whether a model is appropriate depends on several factors, including the type of metal ions, the type of microbe, and whether other metal ions exist. [PERSON] et al. compared four different models of metal ions biosorption by _Kappaphycus_ sp., and found that most fixed models were second-order, while the kinetic data did not follow the [PERSON] model [44]. [PERSON] et al. [48] also found similar results. However, [PERSON] et al. [64] showed that for _Nauclea diderichii_, most fixed models were first-order, and [PERSON] et al. [31] showed that for _Bacillus Subtilis_ second-order effects were better fitted. Recent comparison of sorption mechanisms is summarized in Table 4.
## 4 Conclusions  
The DGM(2,1) model is a type of grey model and is used for fluctuating and random sequences of data samples. Although the DGM(2,1) is capable of addressing some of the constrains of the traditional grey model, it still resulted in a worse performance. This study aimed to evaluate the forecasting performance of original DGM(2,1), and its modified models consisting of the MDGM(2,1), FDGM(2,1), and FMDGM(2,1) based on empirical analysis with data from three practical samples. The precision of the modified models was also compared with the original DGM(2,1). The results indicated that a combination of the Fourier series and the Markov chain in the modified models significantly improved the forecasting precision compared with the original DGM(2,1). The DGM(2,1) model had lower precision than the modified models, while the FDGM(2,1) had significantly better forecast precision than the MDGM(2,1). Furthermore, the results also showed that the FMDGM(2,1) had the best forecasting performance among all of the modified models and can be used for middle- and long-term forecasting.  
For this finding, the modified model FMDGM(2,1) not only has advantages with regard to the sample size requirement, but can also be flexibly applied to large fluctuations and random sequences with a high quality of estimation. In future studies, the accuracy of grey forecasting model is expected to improve by combining other approaches.  
In this paper, [PERSON] contributed to the collection of data and pointed out analysis ideas, while [PERSON] contributed to designing the theoretical verifications. [PERSON] analyzed data and prepared the manuscript. [PERSON] was involved in results discussions. All authors have both read and approved the manuscript.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coefficients** & **Sample 1** & **Sample 2** & **Sample 3** \\ \hline \(a\) & \(-0.2825\) & \(-0.1775\) & \(-0.221\) \\ \(b\) & \(-127.851\) & \(17.041\) & \(-9.525\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Coefficient \(a\) and \(b\) of DGM(2,1) in three practical samples.
# Penetration Depth of Initiated and Non-Initiated Diode Lasers in Bovine Gingiva
###### Abstract  
Background: This study aimed to evaluate the penetration depth of 445 and 970 nm diode lasers using both initiated and non-initiated fibers on bovine gingiva in an ex vivo model. Methods: In this in vitro experiment, 445 nm and 970 nm diode lasers were compared in pulsed (35 Hz, duty cycle: 50%) and continuous mode with both initiated and non-initiated tips. All experimental groups had laser output settings of 2 Watts with a 320 \(\upmu\)m thick glass fiber utilized for delivery. Two posterior teeth were randomly selected from four bovine mandibles for each group. The teeth were irradiated for 30 s buccal and 30 s lingual before the gingiva was harvested for histological evaluation. Samples were fixed in 10% formalin and stained via elastin Van Gieson. Sections were examined microscopically to evaluate collagen denaturation due to thermal effects, which were measured and compared using a mixed-effect model with Tukey adjustment. Results: The 445 nm wavelength groups displayed significantly higher penetration depths when compared to the 970 nm wavelength groups. The initiated tip groups tended to display a lower penetration depth than non-initiated groups; however, these values were similar (\(p>0.05\)) for the 445 nm pulsed lasers (initiated vs. non-initiated). Conclusions: We can conclude that the 445 nm laser wavelength displayed greater thermal denaturation in bovine gingiva ex vivo when compared to the 970 nm wavelength. Additionally, the pulsed settings displayed less thermal penetration depth when compared to the continuous emission modes of the same power output. However, additional studies are necessary to better compare initiated and non-initiated tips using the novel blue laser light.  
diode; diode laser; laser; laser irradiation; laser therapy; non-surgical therapy +
Footnote †: journal: Applied Sciences  
the tip, diode lasers can be used in a non-contact manner as a defocused beam to coagulate the targeted site, as well as in contact with the tissues for photobiomodulation [10].  
Given the flexibility of the usage of diode lasers, it is important to evaluate the laser-tissue interactions in periodontal tissues and compare initiated vs. non-initiated and pulsed vs. continuous laser beams for both visible and near-infrared wavelengths in a contact mode. This study aimed to evaluate the penetration depth and tissue denaturation of both 445 nm and 970 nm diode lasers.
# The Properties and Durability of Self-Leveling and Thixotropic Mortars with Recycled Sand
###### Abstract  
In recent decades, relevant environmental and economic reasons have driven an increasing interest in using a large amount of recycled aggregate in replacement of natural ones to produce mortar and concrete. The present study aims to investigate the effect of substituting 100% of natural sand with recycled aggregate on fresh properties, mechanical properties, and the durability of a thixotropic and a self-leveling mortar. Recycled aggregate was characterized using X-ray diffractometry and energy-dispersive X-ray spectroscopy. Its morphology was investigated using scanning electron microscopy and automated morphological imaging. Recycled aggregate mortars showed a moderate decline in initial workability, as well as higher shrinkage and porosity than the control ones. The compressive strength of self-leveling mortars produced with recycled aggregate was only 6% lower than mortars produced with natural sand. The gap increased to 40% in the case of thixotropic mortars. The self-leveling recycled aggregate mortar showed equivalent resistance to freeze-thaw cycles and better sulfate resistance than the control one. The thixotropic recycled aggregate mortar showed comparable sulphate resistance and only slightly lower resistance to freeze-thaw cycles than the control one. Their capacity to relief stresses, due to hydraulic pressures and the formation of expansive products, arises from their higher porosity. Thermal stability of the prepared mortars, after a curing period of 90 days, up to 700 \({}^{\circ}\)C, was also investigated. A significant decrease in ultrasonic pulse velocity is observed in the 200-400 \({}^{\circ}\)C interval for all the mortars, due to the dehydration-dehydroxylation of calcium silicate hydrate. The overall decline in the strength of both the recycled aggregate mortars was comparable to the control ones. The results reported in the present investigation suggest that the selection of high-quality recycled aggregate helps to obtain good-quality mortars when a large amount of natural sand is replaced.  
recycled aggregate mortar; mechanical properties; high temperature; sulphate resistance; freeze-thaw resistance +
Footnote †: journal: Applied Sciences
# Design and Implementation of Composed Position/Force Controllers for Object Manipulation
## 5 Grasping Stage
### Position-Force Hybrid PID Controller
#### 5.3.2 Force Control  
The force PID controller has a similar structure to the position PID controller.  
\[u_{f}(t)=u_{f}(t-1)+[K_{pf}+K_{if}+K_{df}]e_{f}(t)+[-K_{pf}+K_{if}-2K_{df}]e_{f}\] \[(t-1)+K_{df}e_{f}(t-2)\]  
where \(K_{pf}=0.5\), \(K_{if}=\frac{K_{pf}T_{sf}}{T_{if}}=0.005\), and \(K_{df}=\frac{K_{pf}T_{df}}{T_{sf}}=0.001\) are the proportional, integral, and derivative gains, respectively. \(u_{f}(t)\) is the control input, \(T_{if}\) is integral time, \(T_{df}\) is derivative time or rate time, and \(T_{sf}\) is the sampling period. \(e_{f}(t)=Ref_{force}-F(t)\) is the force error. The \(Ref_{force}\) is computed according to the object's weight and material; thus, damage on the object might be reduced.
## 4 Case Study
### Operation Results Without BESS  
In order to provide a basis for the subsequent calculation of the wind power curtailment and the auxiliary service cost mitigation by BESS, scheduling results of systems without BESS are given in Table 3. The dispatch output of each conventional unit and the wind curtailed energy during a scheduling period are described in Figure 6.  
* Case 1: The optimal scheduling results of systems without wind power integration is calculated.
* Case 2: With allowing wind power curtailment, the scheduling results with wind power integration is considered.  
While comparing case 1 with case 2 in Table 3, due to its anti-peaking characteristics and uncertainty of wind output, the unit coal cost of conventional units is significantly increased from 21.221 $/MW\(\cdot\)h to 21.534 $/MW\(\cdot\)h, and the total installed capacity of conventional units participating in auxiliary service is also increased by 55 MW. Figure 6 shows more intuitively that due to the limitation of the minimum output of conventional units, a large amount of wind power curtailment occurs at 2:00 to 6:00 during the difficult peaking period with high wind output and low load demand.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Units** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \(P_{max}\) (MW) & 455 & 455 & 130 & 130 & 162 & 80 & 85 & 55 & 55 & 55 \\
## 3 Certificate-Based Authentication  
In this section, we explain how a digital certificate can be used for user authentication in an industrial controller application. A conventional certificate-based user authentication scheme [18] can be described at a high level as follows.  
Suppose that a user \(u\) uses a device \(d\) (e.g., smartphone or laptop) with a hardware token holding the digital certificate \(cert_{u}\), and its corresponding sign key \(sk_{u}\) for an industrial controller \(c\). To protect \(sk_{u}\) in \(d\), \(d\) stores \(sk_{u}\) in an encrypted form (i.e., \(ENC(k,sk_{u})\)). An encryption key \(k\) is generated by using a key-derivation function \(KDF(\cdot)\) with the user's password \(pass_{u}\) (i.e., \(k=KDF(pass_{u})\)). Figure 1 shows the basic user authentication procedure.  
To perform the authentication, the user \(u\) should enter his/her password through the application installed on the device \(d\) (see Step 1 in Figure 1). Next, \(d\) sends a login request message containing the user's digital certificate \(cert_{u}\) (again containing the user identity \(id_{u}\) and its public (verification)  
Figure 1: User authentication using a digital certificate.  
key \(pk_{u}\)) to the controller \(c\) (see Step 2 in Figure 1). After receiving the login request message, \(c\) extracts \(cert_{u}\) from the message and verifies its validity by checking whether it was issued by a trusted certificate authority and has not expired or been revoked. If the received \(cert_{u}\) is valid, \(c\) replies to the request message by returning a random nonce \(n\) (see Steps 3 and 4 in Figure 1). After receiving \(n\), \(d\) computes the encryption key \(k\); if the entered password in Step 1 in Figure 1 is the same as \(pass_{u}\), \(k\) is successfully generated by computing \(KDF(\cdot)\) with the entered password, and \(k\) is then used to decrypt \(ENC(k,sk_{u})\). Therefore, \(sk_{u}\) is loaded into the memory in \(d\) during runtime (see Step 5 in Figure 1). Next, \(d\) digitally signs \(id_{u}\) and \(n\) with the user's sign key \(sk_{u}\) (i.e., \(SIGN(sk_{u},id_{u},n)\)) and replies to \(c\) (see Step 6 and 7 in Figure 1). After receiving \(SIGN(sk_{u},id_{u},n)\) from \(d\), \(c\) verifies its validity with the user's public key \(pk_{u}\). If the received message is valid, the user \(u\) can login to \(c\) for a transaction (see Step 8 in Figure 1).
## 4 Results and Discussion
### Camera's Network Latency  
A critical portion of the overall control feedback loop in our telerobotics experiments is the ability for sensor data and video feeds to be sent back from the robot environment to the operator client in order to observe the impact their control instructions have on the state of the robot. Thus, the camera latency measurements are an important consideration in these scenarios as well and are shown in Figure 14. As outlined in Section 3, the latency measured is the complete time for a frame to be captured, encoded, transmitted, decoded, and displayed on the client's screen. We conducted these measurements with the help of a high-speed, high-precision timer displayed on a screen. The transmission method utilized for these tests was lossless, as TCP packet transportation was utilized. This was performed to ensure the clarity of frames being captured, avoiding any artifacts that may appear with lossy transmission channels. Each test comprised 100 individual camera latency measurements over various network scenarios. Specifically, we studied the camera latency over three different scenarios:  
1. A Virtual Network within the same physical host, where the camera feed is captured and encoded in a Virtual Machine and transmitted to the VM's host computer for decoding and rendering over a Virtual Network Link;
2. A wired LAN environment between two different physical hosts;
3. A Wi-Fi network environment between a computer connected to the University's Wi-Fi and a computer connected to the University's Wired network environment, including the use of the VPN, similar to the previous latency tests conducted and presented in this paper.  
Given that the camera's internal processing introduces an average of 100 ms per captured frame, the latency presented for the Virtual Network communications and the wired network environment show only a minimal difference in latency. Once the VPN was introduced during the Wi-Fi camera latency tests, the latency increased to 200 ms per frame. This latency has the largest impact on the overall control feedback loop of the entire telerobotic system. To address this, multicast approaches may be implemented for transmissions, as most applications support UDP for fast, loss-tolerant systems. For the application presented in this paper, reliable network connections would be required, so  
Figure 13: Move Request latency comparison.  
protocols that have built-in acknowledgments, like TCP, or external tools for monitoring network conditions would be required.