esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Beef and Dairy Cattle
The second genomic region that overlapped the most among studies was found on BTA17 (from 17,836,577 to 18,018,432 bp), which is associated with the _MAML3_ gene (mascermind-like transcriptional coactivator-3) and also linked with terrain-use indices [34; 50]. Other common genomic regions among studies were located on BTA3 (from 89,165,719 to 89,209,186 bp; _C8B_ gene--complement C8 beta chain, associated with temperament [52; 53]), BTA8 (from 59,723,669 to 59,785,504 bp; _RUC2_ gene--RUN and SH3 domain containing-2, associated with terrain-use indices [34; 50]), BTA11 (from 74,117,511 to 74,124,989 bp; _POMC_ gene--proopiomelancortin, associated with temperament and pen score [48; 92]), BTA21 (from 47,398,496 to 47,734,138 bp; _MIPOL1_ gene--mirror-image polydactyly-1, associated with temperament and distance travelled from water [50; 53]), and BTA26 (from 37,562,768 to 37,602,774 bp; _SLC18A2_ gene--solute carrier family-18 member-A2). The _SLC18A2_ gene has been associated with temperament, pen score, and milking speed behavior indicator traits [48; 54]. The functional annotation using 383 cattle candidate genes did not render significant terms after the Bonferroni multiple-test correction. However, biologically important terms related to behavior responses were identified, such as neuronal action potential, regulation of blood pressure, and steroid hormone-mediated signaling biological processes. Various metabolic pathways associated with behavior identified in this study are aldosterone regulation, olfaction, sensory transduction, glutamatergic synapse, and vasopressin-regulated water reabsorption (Table S4). The GO terms for biological processes, molecular functions, cellular components, and metabolic pathways are shown in Table S4. To identify potential causal mutations within candidate genes associated with behavioral traits in cattle, the allele frequencies of SNPs located in exon regions for each gene were compared between more temperamental breeds (Group 1) and more docile breeds (Group 2). Using a whole-genome sequence database, 292 genes were identified containing 6098 SNPs within exon regions. After adjustments for subspecies bias _(Bos taurus taurus_ versus _Bos taurus indicus_) and false discovery rate (FDR), 1423 SNPs located across 255 genes significantly different in allele frequencies between the docile and temperamental cattle groups. The significant genes are represented by colored dots in Figure 3, and all genes with their respective SNPs, \(p\)-values, and statistical power are presented in Table S5A. Furthermore, 64 genes had a ratio of significant to non-significant SNPs greater than 50 percent. These genes with higher proportion of significant SNPs are represented by triangle dots in Figure 3. Additionally, 106 genes had statistical differences between their allele frequencies between more docile and more temperamental breed groups considering a power of [PERSON]'s exact test higher than 80% (Table S5B).
## 4 Signal Processing Algorithms for RIS Channel Estimation  
Based on different parameter acquisition schemes and mathematical modeling, we divide channel estimation algorithms into five aspects: conventional-based, optimization-based, CS-based, deep-learning-based, and composition-based algorithms.
# CoSiWiNeT: A Clock Synchronization Algorithm for Wide Area IIoT Network
## 3 WANs: Time Data Measurement and Analysis
### Summary: Network Time Data Measurement  
All the measured network traces by their network types, and communication medium have been summarized in Table 1.  
The lowest timing request error rate (TRER) are 0 for 'Trace 4' and 0.00075 for 'Trace 3' of WAN measurements. 'Trace 1' and 'Trace 2' have comparatively higher TRER. Typically, heterogeneous WANs comprising of multiple segments of wired and wireless networks from public and private domains are non-deterministic and susceptible to various noise  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Trace Nr**} & \multirow{2}{*}{\
\begin{tabular}{c} **Timing** \\ **Requests** \\ \end{tabular} } & \multicolumn{2}{c}{**Erroneous**} & \multirow{2}{*}{**TRER \({}^{*}\)**} & \multirow{2}{*}{
\begin{tabular}{c} **Network** \\ **Condition** **\({}^{**}\)** \\ \end{tabular} } \\  & & & & \\ \hline
1 & 5000 & 14 & 0.0028 & Fair \\ \hline
2 & 1160 & 2 & 0.0017 & Fair \\ \hline
3 & 4000 & 3 & 0.00075 & Fair (Low) \\ \hline
4 & 7500 & 0 & 0.0000 & Good \\ \hline \hline \end{tabular}  
* * Timing request error rate (TRER) = \(\frac{\text{Timing requests with errors}}{\text{Total no. of timing requests}}\); ** Good: TRER = 0; Fair: 0.05 > TRER > 0; Poor: TRER > 0.05.  
\end{table}
Table 1: Measured network traces.  
Figure 4: Trace 2: Time data measurement.  
sources, often missing and delaying packets. Thus, a finite TRER includes one or more failures in exchanging timing requests and responses and indicates a possible deterioration of the network. A criteria to define network conditions (good, fair, or poor) based on that network's TRER value were developed. Using this criterion, traces 1 and 2 were defined as from a 'Fair' network, trace 3 from a 'Fair (Low)' network, and trace 4 from a 'Good' network.
## 3 Results and Discussion
### Practical Applications and Cost Analyses
#### 3.3.2 Cost Comparison  
BJ200 seamless expansion joint binder products are relatively mature, especially SSEA-3 epoxy asphalt binder as a new bridge seamless expansion joint material, compared with BJ200, given its strong resistance to shear and fracture resistance, thermosetting, good resistance to water loss, and other advantages. At the same time, construction continuous operation epoxy asphalt binder mixing temperature is also relatively low compared to BJ200, and it can reduce energy loss and the cost of construction. By taking the expansion joint size 600 \(\times\) 80 \(\times\) 1000 mm as an example, Table 6 lists the cost analysis of the BJ200 seamless expansion joint and the epoxy asphalt seamless expansion joint.  
Table 6 shows that, in the bridge seamless expansion joint design service life of 20 years, epoxy asphalt seamless expansion joint cost is lower than the BJ200 seamless expansion joint. Given that using every linear meter of construction of epoxy asphalt seamless expansion joints over the construction of BJ200 seamless expansion joints leads to total investment savings of USD 88.12, it can be seen that the epoxy asphalt seamless expansion joints are more economical.
# Distributed Satellite Relay Cooperative Communication with Optimized Signal Space Dimension
## 3 Results
### Analysis of MIMO Channel Characteristics
Since the elements of the matrix \(\mathbf{A}_{1}\) and \(\mathbf{A}_{2}\) satisfy the complex Gaussian i.i.d., the high probability satisfies that the rank of the matrix \(\mathbf{P}\) is \(2M\). Therefore, we can draw the conclusions that (18) has solutions and exist \(\mathbf{q}\) by exploiting the rank-nullity theorem. The node selects the optimal precoding vector within the signal space dimension, so that the aligned pairs of different users remain independent of each other during transmission. Further, the general formula of formula (4) can be obtained. The signal is aligned to three directions in the signal space dimension, as shown in Figure 5.  
The relay receives the superimposed signals transmitted in different alignment directions and performs decoding and detection. There is no mutual interference between superimposed signals, and simple linear independent decoding can be performed \(\left(\mathbf{u}^{[R,i]}\right)^{H}\mathbf{u}^{[R,i]}=0\), \(i\
eq j\). By applying the physical network coding modulation-demodulation mapping principle, the relay encodes the decoded symbols. It must be emphasized that the intersection subspaces of information exchange pairs do not need to be orthogonal to each other.
# Practical Inner Product Encryption with Constant Private Key +
## 5 Efficiency Analysis and Implementation Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **Private Key Length** & **Number of Pairings for Decryption** & **Group Order** \\ \hline
[1] & \((2\ell+1)|\mathbb{G}|\) & \(2\ell+1\) & Composite \\
[3] & \((\ell+3)|\mathbb{G}|\) & \(\ell+3\) & Prime \\
[16]-1 & \((\ell+1)|\mathbb{G}|\) & 2 & Prime \\
[16]-2 & \((\ell+6)|\mathbb{G}|+(\ell-1)|\mathbb{Z}_{p}|\) & 9 & Prime \\
[22] & \((2\ell+3)|\mathbb{G}|\) & \(2\ell+3\) & Prime \\
[24]-1 & \((4\ell+1)|\mathbb{G}|\) & 9 & Prime \\
[24]-2 & \(9|\mathbb{G}|\) & 9 & Prime \\
[24]-3 & \(11|\mathbb{G}|\) & 11 & Prime \\
[23] & \((4\ell+2)|\mathbb{G}|\) & \(4\ell+2\) & Prime \\
[25] & \((4\ell+2)|\mathbb{G}|\) & \(4\ell+2\) & Prime \\
[26]-1 & \((15\ell+5)|\mathbb{G}|\) & \(15\ell+5\) & Prime \\
[26]-2 & \((21\ell+9)|\mathbb{G}|\) & \(21\ell+9\) & Prime \\
[27] & \(6\ell|\mathbb{G}|\) & \(6\ell\) & Prime \\
[28] & \(\ell|\mathbb{G}|\) & \(\ell\) & Composite \\
[29] & \(3|\mathbb{G}|\) & 3 & Prime \\
[30] & \((4\ell+2)|\mathbb{G}|\) & \(4\ell+4\) & Prime \\
[15]-1 & \((2\ell+1)|\mathbb{G}|+(\ell-1)|\mathbb{Z}_{p}|\) & 3 & Prime \\
[15]-2 & \(5|\mathbb{G}|\) & 3 & Prime \\
[5] & \(2m|\mathbb{G}|\) & \(2m\) & Prime \\
[36] & \((4\ell+5)|\mathbb{G}|\) & \(4\ell+5\) & Prime \\
[6]-1 & \(5|\mathbb{G}|\) & 5 & Prime \\
[6]-2 & \(7|\mathbb{G}|\) & 7 & Prime \\
[7] & \((\ell+1)|\mathbb{G}|\) & \(\ell+1\) & Composite \\ \hline Ours & \(1|\mathbb{G}|+1|\mathbb{Z}_{p}|\) & 1 & Prime \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of our scheme’s efficiency with that of other schemes. The vector length for an IPE scheme is denoted by \(\ell\); the bit lengths of the representations for an element in \(\mathbb{Z}_{p}\) and \(\mathbb{G}\) are denoted by \(|\mathbb{Z}_{p}|\) and \(|\mathbb{G}|\), respectively; the leakage resilience parameter is denoted by \(m\). 128 bytes, respectively. In the case of the BN254 group, the size of the base field order is 256 bits and the embedding degree is 12. For a bilinear map \(e:\mathbb{G}_{1}\times\mathbb{G}_{2}\rightarrow\mathbb{G}_{\mathbb{T}}\) over the BN254 group, the bit lengths of elements in \(\mathbb{G}_{1}\), \(\mathbb{G}_{2}\), and \(\mathbb{G}_{\mathbb{T}}\) are 64, 128, and 384 bytes, respectively. For the length of predicate and attribute vectors, we chose \(\ell=100\). As evident in Table 3, the encryption and decryption algorithms of our scheme were highly efficient. For decryption and encryption, only 10 and 20 ms was required, respectively. Our encryption algorithm was 5, 8.5, and 13 times faster than that in [15, 16, 29], respectively, and our decryption algorithm was 10, 14, and 14 times faster than that in [15, 16, 29], respectively. Moreover, our private key length was 86, 2.6, and 4.3 times shorter than that in [15, 16, 29], respectively. However, as a trade-off, the length of the ciphertext in our scheme was the largest among these schemes.
# Imaging Diagnostics of Inside of a Building Wall Using Millimeter-Wave Reflectometer
## 3 Practical Application
### Signal Processing  
The overview flowchart of the signal processing algorithm is shown in Figure 5. The procedures were as follows: (i) Fixing the measurement positions, including the initial position. (ii) Performing reflectometric measurement; the received signal usually fluctuates due to the effect of multiple reflections between circuit elements (voltage standing wave ratio: VSWR > 1). This disturbs the evaluation of the signals. We measured a reference signal for a normal sample beforehand and input this into a processing algorithm in order to cancel those fluctuation components. This is rather similar to the calibration work of the VNA. (iii) Analysis of the reflectometer signal; the difference between the maximum and minimum values of the signal for the desired analysis frequency interval is measured, and the adhesive state is quantitively evaluated. The values are classified according to the \"non-adhesive indication value\" collected beforehand, with or without adhesive. (iv) Performing the above-mentioned processing for every measurement position. (v) After the measurement of the target area is completed, the adhesive area to the measured area, that is, the adhesion rate, is calculated.
## 4 Results
### Statistical Analysis
#### 4.1.3 \(t\)-Test on Heart Rate  
We performed an independent samples \(t\)-test on the variables including resting heart rate, average heart rate during exercise, maximum heart rate, and perceived exercise. The offline group showed a higher average heart rate during exercise than the AR group, and there was no difference between the groups in resting heart rate and maximum heart rate. There was no difference between the groups in terms of perceived exercise, and both  
Figure 6: This graph represents the average score of the IGAOS survey results of the experimental and control groups. groups perceived the physical activity program as being at a medium- to high-intensity level, which is equivalent to'slightly difficult'. Table 4 shows the heart rate and RPE results. The average resting heart rate of the experimental group was 75.2 beats per minute (bpm), and the resting heart rate of stable adults over 20 years of age is usually 60 bpm to 100 bpm. The average resting heart rate of the comparison group was 79.7 bpm, which was higher than that of the experimental group. There was no gender difference between the two groups. The average heart rate during exercise of the experimental group was 113.25 bpm, which was slightly lower than the comparison group at 128.8 bpm. The maximum heart rate during exercise was 150.4 bpm in the experimental group, and in the comparison group, it was 154.6 bpm. For people in their 20s, the maximum heart rate is 200 beats, and an exercise that makes you slightly out of breath is 60% of the maximum heart rate or 120 beats. Moderate intensity is 64-76% of the maximum heart rate, and the average age of participants was 19.5 years old, similar to a 20-year-old. Based on the maximum heart rate of 200 bpm during exercise, the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Group** & **N** & **M \(\pm\) SD** & **T** & \(\mathbf{p}\) \\ \hline \multirow{2}{*}{Resting heart rate} & AR & 10 & 75.2 \(\pm\) 7.55 & \multirow{2}{*}{\(-\)1.202} & \multirow{2}{*}{0.245} \\  & Offline & 10 & 79.7 \(\pm\) 9.11 & & \\ \hline Heart rate during & AR & 10 & 113.2 \(\pm\) 15.03 & \multirow{2}{*}{\(-\)2.222} & \multirow{2}{*}{0.039 \({}^{*}\)} \\ exercise & Offline & 10 & 128.8 \(\pm\) 16.23 & & \\ \hline \multirow{2}{*}{Maximal heart rate} & AR & 10 & 150.4 \(\pm\) 16.58 & \multirow{2}{*}{\(-\)0.584} & \multirow{2}{*}{0.566} \\  & Offline & 10 & 154.6 \(\pm\) 15.55 & & \\ \hline Rating of perceived & AR & 10 & 12.9 \(\pm\) 1.37 & \multirow{2}{*}{0.139} & \multirow{2}{*}{0.891} \\ exertion & Offline & 10 & 12.8 \(\pm\) 1.81 & & \\ \hline \hline \end{tabular} \(r_{p}<0.05\).  
\end{table} 
Table 4: The results of the independence samples \(t\)-test for heart rate and RPE.
## 3 Propulsion  
The method of propulsion critically affects the maximum achievable efficiency of the rotor. Historically, the most commonly utilised method was drag, by utilising a sail faced normal to the wind, relying on the drag factor (\(C_{d}\)) to produce a force in the direction of the prevailing wind. This method proved inefficient as the force and rotation of the sail correspond to the wind direction; therefore, the relative velocity of the wind is reduced as rotor speed increases (Table 1).  
\begin{table}
\begin{tabular}{c c} \hline \  
Reducing efficiency further is the drag of the returning sail into the wind, which was often shielded from the oncoming wind. Unshielded designs rely on curved blade shapes which have a lower drag coefficient when returning into the wind and are advantageous as they work in any wind direction. These differential drag rotors can be seen in use today on cup anemometers and ventilation cowls. However, they are inefficient power producers as their tip speed ratio cannot exceed one [4].  
An alternative method of propulsion is the use of aerodynamic lift (Table 1), which was utilised without precise theoretical explanation for over 700 years in windmills then later in vintage aircraft. Today, due to its difficult mathematical analysis, aerodynamics has become a subject of its own. Multiple theories have emerged of increasing complexity explaining how lift force is generated and predicted. Aerodynamic force is the integrated effect of the pressure and skin friction caused by the flow of air over the aerofoil surface [7]. Attributed to the resultant force caused by the redirection of air over the aerofoil known as downwash [8]. Most importantly for wind turbine rotors, aerodynamic lift can be generated at a narrow corridor of varying angles normal to the wind direction. This indicates no decrease in relative wind velocity at any rotor speed (Table 1).  
For a lift driven rotor (Table 1) the relative velocity at which air strikes the blade (W) is a function of the blade velocity at the radius under consideration and approximately two thirds of the wind velocity (Betz theory Section 2) [4]. The relative airflow arrives at the blade with an angle of incidence (\(\beta\)) dependant on these velocities. The angle between the blade and the incidence angle is known as the angle of attack (\(\alpha\)).
## 4 Relay Based QoS-Aware Routing Protocol for I2O WBAN  
### Motivation  
One of the major challenges in I2O WBANs is maximizing the WBAN lifetime [21]. To date, there are several routing protocols that have been reported in literature such as single or two-relay WBAN techniques [22]. However, those routing protocols are not very energy efficient and less likely to support long-term healthcare monitoring tasks. In [23], the authors stated that the energy consumption of implants is directly related to the transmission distance and therefore, energy efficient routing protocols are an effective approach in minimizing the overall length of communication paths. Moreover, by deploying an incremental relaying strategy, the complexity and energy consumption are transferred from the implant device to the on-body relay, which is a device that can be easily replaced and recharged, in contrast to the in-body sensor nodes [10; 34]. QoS requirements in WBANs vary between applications [35]. A practical approach is to focus on data transmission models that are used in different applications and map the requirements of these onto a set of QoS metrics [35]. Figure 10 demonstrates the multiple relay-based routing protocols proposed in [21; 22]; Figure 10a, single relay based scenario; Figure 10b-d, two-relay based selective routing techniques.  
Figure 8: Link margin versus distance for 16 PSK at multiple levels of data rates and powers.  
Figure 9: Link margin versus distance for 16 QAM at multiple levels of data rates and powers.  
In this section, mathematical formulas for important QoS metrics in terms of network lifetime, network throughput and end to end delay are given, along with the related constraint functions. Details of our proposed incremental relaying strategy is given and compared with the two-relay protocol. The results show that the incremental routing protocol outperforms the two-relay based protocol in terms of network lifetime, throughput. However, the latter protocol could support high traffic load conditions when compared with the former.
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 4 Performance Analysis and Simulation Results
### The Localization Performance
#### 4.2.1 Test1: the Effect of Range Error  
According to Equation (5), we assume that the range error is white Gaussian noise with the variance \(\sigma^{2}\) increasing from 1 m to 5 m. Compare the RMSE (Root Mean Square Error) of four different algorithms and the result is shown in Figure 5.  
From the simulation result, we can see that MDS-MAP and MSL algorithms have a large localization error and the localization performance of the new proposed DRL algorithm is better than the other three approaches. The simulation result also demonstrates that the proposed Double Rate Scheme can increase the ranging accuracy in multi-hop networks which will improve the localization performance as well. The proposed optimal selection scheme of reference nodes can increase the accuracy further which means that DRL has the least localization error of the four algorithms.  
Figure 4: The relationship between code-width and transmission range.  
Figure 5: Comparison of different localization algorithms with range error.
## 3 Results  
### Correlating and Determining the Orthomosaic Treatment Approach  
As in the results, there was no difference between the treatments performed; all images and the yield variables (FB, DB, and LAI) were evaluated together, that is, independent of the agroforestry systems. The average, maximum, minimum, median, and standard deviation data are described (Table 4).  
The NGRDI, MGRVI, and VARI indexes showed the highest positive and significant (\(p<0.05\)) correlations for fresh mass (0.8 for NGRDI, 0.8 for MGRVI, and 0.8 for VARI), dry mass (0.7 for NGRDI, 0.7 for MGRVI, and 0.7 for VARI), and LAI (0.7 for NGRDI 0.7 for MGRDI, and 0.7 for VARI), respectively. The SCI, SI, and * HUE indexes showed the highest negative and significant (\(p<0.05\)) correlations for fresh biomass (0.8 for SCI, 0.7 for SI, and 0.7 for * HUE), dry mass (0.7 for SCI, 0.7 for SI, and 0.7 for * HUE), and LAI (0.7 for SCI, 0.6 for SI, and 0.5 for * HUE), respectively, while BGI, GLI, RGB, and BI showed non-significant occurrences (\(p>0.05\)) (Figure 4). The indexes SCI and MGRVI showed significant behavior (\(p<0.05\)) identical to NGRDI with correlations of 0.8, 0.7, and 0.7 for FB, DB, and LAI, respectively, with SCI values in modulo equal to the values of NGRDI. It was decided to remove both in the estimation process, keeping only the NGRDI (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Treatment** & **Mean** & **Maximum** & **Minimum** & **Median** & **Standard Deviation** \\ \hline \multirow{3}{*}{Fresh biomass (kg ha\({}^{-1}\))} & Cotton & 16,185.38 & 32,650.67 & 1068.67 & 18,324.13 & 2327.29 \\  & Caatinga & 14,537.66 & 33,775.73 & 988.53 & 15,451.33 & 2214.05 \\  & Cowpea & 14,569.63 & 31,284.93 & 1527.87 & 15,461.83 & 1671.66 \\  & Corn & 16,260.14 & 38,802.67 & 1556.27 & 13,969.97 & 3393.82 \\ \hline \multirow{3}{*}{Dry biomass (kg ha\({}^{-1}\))} & Cotton & 5050.21 & 12,007.62 & 193.60 & 4968.13 & 1057.59 \\  & Caatinga & 4675.15 & 12,568.19 & 130.21 & 5024.50 & 1065.31 \\ \cline{1-1}  & Cowpea & 5116.42 & 19,795.12 & 239.20 & 4065.86 & 2077.48 \\ \cline{1-1}  & Corn & 5241.47 & 14,414.41 & 275.34 & 4318.56 & 982.65 \\ \hline \multirow{3}{*}{Leaf area index (m\({}^{2}\) m\({}^{-2}\))} & Cotton & 1.23 & 2.55 & 0.29 & 1.25 & 0.28 \\ \cline{1-1}  & Caatinga & 1.24 & 2.25 & 0.33 & 1.26 & 0.15 \\ \cline{1-1}  & Cowpea & 1.32 & 2.88 & 0.31 & 1.31 & 0.26 \\ \cline{1-1}  & Corn & 1.27 & 3.56 & 0.35 & 1.33 & 0.38 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Descriptive analysis of biomass and leaf area index of buffel grass and bushveld signal grass for each treatment.  
The approach to treating the orthomosaics by removing components (tree and soil) that contributed the most to improving correlation values was the removal of trees (Figure 4) and was used in generating the predictive models.