esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Calibration
### Calibration Method of Calorific Absorption Correction Coefficient of Heat Flux Sensor  
In the measurement of heat of absorption, the correction coefficient related to the heat flux sensor needs to be calibrated. The 304L stainless steel was also used as the calibration material, and its volume specific heat value varying with temperature (\(C_{V-304}\) (\(T\))) was measured by a HotDisk analyzer. The relationship between the volume specific heat and temperature is expressed in Equation (23).  
\[C_{V-304L}(T)=3.8254+0.0014\times T \tag{23}\]  
In each temperature step, the correction coefficient is considered to be linear. When the temperature is changed from \(T_{a}\) to \(T_{b}\), the change of areal enthalpy \(h_{A}\) (\(T_{a}\), \(T_{b}\)) can be expressed as \(h_{A}\) (\(T_{mem}\)). Based on Equations (8)-(11), the correction coefficient \(S_{HFM}\) (\(T_{mem}\)) can be calculated by Equation (24) at each mean calibration temperature, which represents the heat stored by the heat flux sensors within the temperature step from \(T_{a}\) to \(T_{b}\).  
\[S_{HFM}(T_{mem})=\frac{h_{A}-C_{V-304L}(T_{mem})\times\delta_{304L}\times \Delta T}{\Delta T} \tag{24}\]  
During the calibration process, the initial temperature of both heating plates was set to 25 \({}^{\circ}\)C, and the temperature step was 10 \({}^{\circ}\)C. The calibration average temperature range was from 30 to 150 \({}^{\circ}\)C. Equation (24) was used for every temperature step to calculate the correction coefficient at its mean temperature. After the calibration process was finished, the calibration results were fitted to obtain the function of the correction coefficient varying with the average temperature. The fitting function is shown in Equation (25), and the fitting curve is shown in Figure 7.  
\[S_{HFM}(T_{mem})=1.2971+0.0190\times T_{mem}+5.9365\times 10^{-5}\times T_{mem} \tag{25}\]
# An Overview of Key Technologies in Physical Layer Security
## 5 Conclusions  
We have provided a comprehensive review of PLS in wireless networks based on optimization techniques. We have also shown that PLS is an auspicious technology for strengthening the confidentiality and secrecy of information transmission in both existing and emerging wireless networks, which can be used to augment conventional cryptographic methods. To emphasize the benefits of PLS, we first compared conventional encryption using cryptography and PLS. This work mainly focused on the provision of a comprehensive review of both the design and the optimization of PLS schemes. We also discussed some of the main challenges facing PLS and outlined some of the promising solutions to these problems and how they can benefit future wireless communication networks. In short, we have shown that PLS is a very promising technology in ensuring safe and secure wireless communication.  
**Author Contributions:** Conceptualization, A.S. and B.B.; methodology, A.S.; software, A.S. and G.A.M.; validation, A.S. and G.A.M.; writing--original draft preparation, A.S.; writing--review and editing, A.S., G.A.M., T.M.L.J. and L.M.; supervision, B.B. and J.M.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Botswana International University of Science and Technology grant number R00067 and S00081. The APC was funded by R00067 and S00081.  
**Acknowledgments:** We would like to acknowledge the Botswana International University of Science and Technology (BIUST) for research funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
### Main Findings  
One of the actual results of this review is that monitoring and assessing vertical jump height is a general procedure used in wearable research. As jump height is a marker of athletic performance in volleyball [25; 26; 27], its accurate measurement in training and competition and the ability to track changes over time seems critical. Furthermore, as the vertical jump is a decisive factor in players' performance [28], a top volleyball player must demonstrate enough strength to jump as high as possible, block, react quickly, attack the ball, and combine these actions with a high technical level [3]. Thus, vertical impulsion has become increasingly important, supporting specific motor skills and actions [29; 30]. Specifically, the shot, the block, the pass in suspension, and the service in suspension are performed through vertical impulsion [31]. Therefore, as it becomes evident, evaluating the lower limbs' muscle power is crucial in volleyball players, contributing to maximizing their performance [32].  
The moderate to high correlations found in this review on the evaluated devices may be due to the homogeneity of testing a collegiate sample of volleyball players, while a more varied sample would likely result in a higher correlation [33]. The greater degree of underestimation may be due to the type of surface, the type of jump, or the specifications of the device itself [19].  
In the following paragraphs, a brief mention of the different wearable devices used in volleyball research will be given.
## 2 Methodology for Determination of Beam Pointing Dynamics
### Derivation of Expression for Received Power  
The size of the common volume and its height range, in a long-distance tropo link, are considerably large in spite of the narrow beamwidth (1\({}^{\circ}\) to 2\({}^{\circ}\)). Propagation conditions depend on the scattering properties of the troposphere, which change with height even within the same common volume, owing to its large size. In order to factor in the effect of changing scattering conditions within a given common volume, transmit and receive  
Figure 1: Link geometry for the determination of beam pointing dynamics.  
antenna beams are considered to comprise M sub-beams each. Each set of corresponding transmit and receive sub-beams encloses a portion of the common volume called the sub volume. The value of M is chosen in such a way that the height range of each sub volume is small enough to assume uniform scattering conditions within the sub volume.  
Figure 2 shows the splitting of the lowest transmit and receive antenna beams into M sub-beams. Sub volumes, formed by the intersection of the corresponding transmit and receive sub-beams, are always contiguous and vertically stacked, shown as shaded regions in Figure 2. The received power is separately calculated for each of the M sub-beam sets, thus taking into consideration the scattering conditions at different heights. The total received power for a given set of corresponding transmit and receive antenna beam elevation angles is obtained by adding the received powers for its M sub-beam sets. The received power for each of the N sets is calculated using this method.
# An Optimization Design of Bi-log Hybrid Antenna with Taguchi's Method for EMI Measurements
## 3 Antenna Optimization using Taguchi's Method
### Optimization of Antenna Design
In addition, a low-frequency antenna unit is made up of a bowtie antenna set that evolves from the biconical structure. This type of antenna is one of the few antennas that can use mathematical derivation to analyze its working principle. The impedance (real part and imaginary part) of the antenna can be derived to design a bowtie structure. The trade-off from the flare angle is relatively uniform in terms of the real part, imaginary part, and VSWR and conforms to the broadband characteristics. The best performance is achieved with the bowtie element, which has a 52\({}^{\circ}\) flare angle and a triangle height of 60 cm. All of these elements are mounted on either side of a parallel metal boom. The well-designed LPDA and bowtie could be then integrated. The performance of the integrated antenna is shown in Figure 4. Within the considered frequency band, the antenna demonstrates well the impedance matching, a linear AF, and a consistently flat gain curve. Figure 5 shows the simulated radiation patterns with good directivity characteristics. Accordingly, the  
Figure 3: The numerical model of analyzed Bi-log hybrid antenna with NEC2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type of Element**} & **Number of** & \multirow{2}{*}{**Length (mm)**} & **Width/Diameter** & **Scaling Factor** \\  & **Elements** & & **(mm)** & **(r)** \\ \hline \multirow{8}{*}{Planar Plate Dipoles} & 1 & 4.3 & 1.0 & 0.80 \\  & 2 & 5.4 & 1.0 & 0.81 \\  & 3 & 6.7 & 1.5 & 0.80 \\  & 4 & 8.4 & 1.5 & 0.79 \\  & 5 & 10.6 & 2.0 & 0.80 \\  & 6 & 13.2 & 3.0 & 0.80 \\  & 7 & 16.5 & 3.0 & 0.83 \\  & 8 & 20.0 & 3.5 & 0.80 \\  & 9 & 25.1 & 3.5 & 0.80 \\  & 10 & 31.2 & 4.0 & 0.85 \\  & 11 & 36.5 & 5.0 & — \\ \hline \multirow{8}{*}{Cylindrical Dipoles} & 12 & 48.3 & 4.0 & 0.84 \\  & 13 & 57.5 & 4.0 & 0.79 \\  & 14 & 73.1 & 5.0 & 0.79 \\  & 15 & 92.3 & 5.0 & 0.76 \\  & 16 & 121.6 & 5.0 & 0.80 \\  & 17 & 151.5 & 7.0 & 0.80 \\  & 18 & 189.9 & 7.0 & 0.79 \\  & 19 & 239.8 & 7.0 & 0.80 \\  & 20 & 299.8 & 9.0 & 0.84 \\  & 21 & 357.0 & 9.0 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimized dimensions of the proposed Bi-log hybrid antenna.
## 3 Major Problem Statement
* This work only identifies the hole and is unable to heal the hole which plays a vital role in coverage enhancement. In addition, the efficacy of the coverage hole detection can be measured only after the recovery process. Thus, this work is inefficient to improve the coverage of the network. Authors in [31] aim to improve energy efficiency in the network through optimal cluster formation and sleep scheduling. Firstly, the network is segregated into multiple clusters by base station (BS). For optimal cluster head (CH) selection, a probability value which is composed of energy and traffic rate metrics is formulated. In each cluster, the nodes which are located nearby are paired. The nodes which have no neighbor nodes are isolated and the nodes directly communicate with the cluster head (CH). The time division multiple access (TDMA) scheduling technique is revised to support data gathering. The sleep scheduling is performed among the paired nodes. The problems encountered in this research work are:  
* Here the sleep scheduling is rotated among only paired nodes, i.e., isolated nodes have no opportunity to sleep. This scheduling increases energy consumption of the isolated nodes which further results in the early death of nodes. * Among the paired nodes, the sleep decision is made in a distributed manner which increases control packet overhead and energy consumption in the network. * CH is selected with the limited metrics (energy level and traffic rate) and the same CHs are used to route the packets. This way of routing increases the packet loss. Authors in [32] propose a routing algorithm to improve the energy efficiency of the sensor network. For that, a dynamic multi-hop routing protocol is presented in this work. Initially, the network is split into multiple clusters by using a k-means algorithm. In each cluster, optimal CH is selected by using an artificial bee colony (ABC) algorithm. The ABC algorithm considers multiple objectives such as energy level, traffic rate, etc., for optimal CH selection. Then, inter-cluster routing is enabled by using a multi-hop routing approach. The routing approach constructs the objective to minimize hop count and selects a route with a minimum hop count. The following are the issues faced in this research work:  
* In the k-means algorithm, initialization of k is relatively difficult and also initialization of centroid nodes also affects the overall clustering process. * The ABC algorithm has a slow convergence rate and high computational complexity. It selects non-optimal CHs even with large time consumption. Table 1 illustrates the research gaps encountered in previous works from which the proposed work is presented as a solution to these problems by enhancing the coverage and energy efficiency to achieve better QoSensing.
# Implementation of a Blood Cold Chain System Using Blockchain Technology
## 3 Design of Blood Cold Chain System
### New Blood Cold Chain System Based on Blockchain Technology
#### 3.2.3 General Blood Supply Situation  
The distributed ledger architecture of the blockchain blood cold chain system proposed in this paper is shown in Figure 4. It is based on the block structure of Hyperledger Fabric, and shows the transactions that are created each time blood flows through its base.  
There are transactions in Block 1000, where 15 blood banks across the country produce blood for transfusions, and in Block 1001 and Block 1002, there are transactions from a blood bank to an inspection center via transportation vehicles. Transactions that send blood back to the blood bank can be confirmed by Block 1003 and Block 1004. Block 1005 and Block 1006 represent the transfer of blood from a blood bank to a hospital in the event of a blood order. Finally, Block 1007 shows the use of blood  
Figure 4: Architecture of distributed ledger.  
Figure 3: Conventional centralized blood cold chain system (**left**); proposed blood cold chain system (**right**).  
stored in a hospital. Each block's KVS is responsible for preserving transaction processing results to ensure an up-to-date status. The KVS has the same contents in all verification codes, and the hash value is recorded in the blockchain.  
As blood is a temperature-sensitive resource requiring the use of a dedicated refrigerator to maintain a constant temperature, the new system checks the temperature and records the transaction to check the temperature. This is done to ensure that blood having a temperature management problem is disposed of. In Hyperledger Fabric, chaincode is a program for executing transactions, which is the basis of smart contract execution. Therefore, a transaction in Hyperledger Fabric can be interpreted as the execution of chaincode. When checking temperature information at a specific point in time, it can be recorded as a transaction through chaincode, which is recorded in the blood asset information.  
One of the important reasons for implementing the proposed blood cold chain system in blockchain is information visibility. Many medical institutions wherein blood transfusions are made generate data that records when the final blood is used or disposed of, which can improve blood inventorying as well as blood-demand predictability. The participant consensus algorithm of Hyperledger Fabric sequentially records transactions over time and manages information transparently so as to prevent forgery or alteration.
# Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL
## 5 Guided Image Filtering
### OpenCL Code
#### 5.2.2. Kernels  
In the OpenCL implementation, the guided image filter algorithm is split into two kernels, as shown in Figure 7. The first kernel calculates steps 1, 2 and 3 in Listing 1, while the second kernel calculates steps 4 and 5. Kernel 1 starts by reading the filtering image \(I\) and the guidance image \(G\) from global memory. It then slides over \(I\) and \(G\) and calculates \(mean_{I}\), \(mean_{G}\), \(corr_{IG}\) and \(corr_{G}\). Based on these four arrays, it then calculates the values in Step 2 and Step 3. The result of these first three steps, arrays \(a\) and \(b\), are sent directly to the second OpenCL kernel, together with the guidance image, which is required in Step 5 of the algorithm. Kernel 2 slides \(f_{mean,r}\) over \(a\) and \(b\), which results in arrays \(mean_{a}\) and \(mean_{b}\). Based on \(mean_{a}\) and \(mean_{b}\), the filtering output \(O\) is calculated and written back to global memory.  
The main motivation to split the calculations into two kernels is the ease of programming. Kernel 1 contains the sliding window implementation of the four \(f_{mean,r}\) functions in Step 1 of the algorithm. In kernel 2, on the other hand, the sliding window implementation of the two \(f_{mean,r}\) functions in Step 4 is implemented. Separating these two algorithmic steps decreases the implementation complexity. Because it is possible in OpenCL to directly send data between kernels, no extra bandwidth to global memory is required for this configuration.  
Figure 6: Stream of image data.  
Figure 7: Kernel 1 computes the first three steps in the guided image filter algorithm, while kernel 2 computes the last two steps.
## 5 Experiments
### Results and Discussion
#### 5.2.1 Observed Signals Extraction  
Raw radar data are first aligned along the range points, and then we proceed to the observed signals extraction step. In order to more intuitively display the effect of range migration compensation, here we selected the radar data with a detection time of 5 min, and the result is shown in Figure 6. Figure 6a is raw radar data, and the data collection system is about 2 m from the subject, from which we can see that during the detection process, there is a problem that the target is located in different range bins. Figure 6b is the radar data processed by the range migration compensation, in which case the target is in the same range bin. The result shows that on the basis of retaining the complete signal of interest, this method can effectively eliminate the movement of the UAV platform across the range bin and ensure the accuracy of subsequent observed signal extraction.  
The observed signals extracted from the radar data after range alignment by the range sampler are shown in Figure 7, and they are mixed signals of chest movement and radar movement from multiple range channels. After pre-processing, the observed signals are taken as the input of ICA to blindly estimate respiration of the human target.  
Figure 6: Range profiles of a static subject. **(a)** Radar data without range migration compensation. **(b)** Radar data with range migration compensation.
## Materials and Methods
### Sampling
#### 2.5.2 Pelagic Endpoints  
Pelagic primary production was measured five times during the experiment (t1, t3, t6, t10, t13), though only data from t1, t6, and t13 is used in this paper. Two 10 mL water samples were taken (in the morning) from each microcosm and placed in two 20 mL scintillation vials. One hundred \(\upmu\)L \({}^{14}\)C was added as buffered NaH\({}^{14}\)CO\({}_{3}\) (specific activity 185 kBq mL\({}^{-1}\)) and the samples were incubated at ambient temperature for a few hours, one of the samples in the light and the other in the dark (covered in black plastic). Three drops of 10% HCl was then added to stop primary production, excess \({}^{14}\)C bubbled off, and 10 mL Lumagel Safe scintillation cocktail (Perkin Elmer, Waltham, MA, USA) was added. \({}^{14}\)C activity in the samples was measured in a 1214 Rackbeta scintillation counter (LKB Wallac, Turku, Finland) and decays per minute (dpm) converted to mg\({}^{14}\)C L\({}^{-1}\) h\({}^{-1}\). Net primary production was calculated as the difference between \({}^{14}\)C uptake in the light minus that in the dark.  
All other pelagic endpoints were measured at t1, t6, and t13. In order to measure pelagic chlorophyll a, phyto- and zooplankton community composition, different fractions of the overlying water were obtained. Firstly, the overlying water was siphoned off into a bucket from which a well-mixed water sample of known volume was filtered through a 90 \(\upmu\)m sieve. The fraction retained on the sieve (zooplankton) and the fraction that had passed through the sieve (largely phytoplankton) were fixed separately in Lugol solution and saved in the dark until quantification and identification to the lowest possible taxonomic level. A further 1-2 L was filtered through a 90 \(\upmu\)m sieve to remove the zooplankton and the filtrate was further filtered on 0.7 \(\upmu\)m cellulose-nitrate filters and stored at \(-20\)\({}^{\circ}\)C until the analysis of chlorophyll a, a measure of phytoplankton production/biomass. At that time, frozen filters were thawed and extracted in 5 mL acetone; the extract was analyzed with a U-2000 spectrophotometer (Hitachi, Tokyo, Japan) according to [21].
# Control of Flow around an Oscillating Plate for Lift Enhancement by Plasma Actuators
## 4 Results and Discussion
### Effects of Driving Conditions
#### 4.3.2 Effects of Time Offset  
In this section, the effects of the time offset, \(t_{0}/T\), on the lift for the control only on the top surface with \(R=50\%\) is discussed. Figure 19 depicts the lift coefficient for \(t_{0}/T=0.0\)-0.8 for the baseline case. The time-averaged lift coefficient is increased under the conditions of \(t_{0}/T=0.0\), 0.2, 0.8 compared to baseline case, while lift is decreased under the conditions of \(t_{0}/T=0.4\), 0.5, 0.6. Especially, the conditions of \(t_{0}/T=0.0\) and 0.5 generated the highest and lowest coefficients, respectively. The  
Figure 18: Iso-surfaces of second invariant (\(q/(U_{0}/b)^{2}=0.1\)) of velocity gradient tensor and contours of pressure coefficient (\(p\)-\(p_{0}\))/(\(0.5\rho_{0}{U_{0}}^{2}\)) around the oscillating plate for the control only on the top surface (\(t_{0}/T=0.0\)), where \(p_{0}\) is the ambient pressure. (**a**) Driving ratio of \(R=25\%\); (**b**) \(R=75\%\); (**c**) \(R=100\%\).  
lowest averaged lift around \(t_{0}/T=0.5\) is due to the suppression of the separation vortices on the top surface during the down-stroke motion, like the cases of \(R=75\), \(100\%\) with \(t_{0}/T=0.0\) mentioned above.  
For the cases of \(t_{0}/T=0.5\) and \(0.8\), the local steep decreases of the lift in the up-stroke motion (\(t/T=0.18\), \(0.35\)) in the baseline case disappears as displayed in Figure 19b. Figure 20 shows the vortices and the contours of the pressure for the time offset of \(t_{0}/T=0.5\). The vortex shedding from the trailing edge on the bottom surface observed in the baseline case at \(t/T=0.18\) is suppressed similarly to the case of \(R=100\%\). This indicates that the suppression of the flow separation on the top surface in the down-stroke motion is also effective for the suppression of vortex shedding from the trailing edge in the following up-stroke motion; these findings illustrate a similar mechanism to that of the feathering motion produced by insects during flight [15; 16]. However, by this control, the lift is significantly decreased in the down-stroke motion, precipitating the decrease of the time-averaged lift. To conclude this part of the research, the control during the up-stroke motion (\(t/T=0.0\)-\(0.5\)) is effective for lift enhancement.