esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Steady-State Thermal Analysis of Internal Helical Gears Pair
### Analysis and Calculation of Convective Heat Transfer Coefficient  
(1) Convection heat transfer coefficient of addendum  
The convective heat transfer between the cylindrical surface of the addendum and the lubricant is approximated as the convective heat transfer of the lubricant flowing over a slender plate [26]. The convective heat transfer coefficient is given by the following equation:  
\[h_{c}=0.664P_{c}^{0.333}\lambda\left(\frac{\omega}{\eta}\right)^{0.5} \tag{32}\]  
\(p_{c}\) represents the Prandtl number of the lubricant, \(P_{c}=\rho_{1}c_{1}v_{1}/\lambda\), \(p_{1}\), \(c_{1}\)\(v_{1}\lambda\) are the specific gravity, heat capacity, dynamic viscosity and thermal conductivity of the lubricant, respectively. \(\omega\) is the angular velocity of the gears and \(\eta\) is the dynamic viscosity of the lubricant.  
Figure 2: Schematic diagram of internal gear pair transmission.  
2. Convective heat transfer coefficient of meshing tooth surface The convective heat transfer coefficient between the meshing tooth surface and the lubricating oil is determined by the following equation: \[h_{d}=0.228R_{e}{}^{0.731}P_{c}{}^{0.333}\lambda/L_{d}\] (33)  
In the above equation, \(L_{d}\) represents the pitch circle diameter of the gear, \(R_{e}\) is the Reynolds number, as determined by Equation \(R_{e}=\omega r_{k}^{2}/v_{0}\), and \(r_{k}\) is the radius of any meshing point on the tooth surface.
3. Flow heat transfer coefficient end face The end-face flow heat transfer in an internal helical gears pair can be analyzed analogously to the heat transfer problem of a rotating disk. The flow of lubricating oil over the surface of the disk can be classified into three states: laminar flow, transitional flow, and turbulent flow. The calculation of the heat transfer coefficient is strongly dependent on the lubricant's flow state, which can be effectively characterized by the Reynolds number [27].  
The flow state of the lubricant varies with the Reynolds number, which in turn affects the convective heat transfer coefficient, as given by:  
\[\left\{\begin{array}{l}h_{e}=0.308\lambda(m+2)^{0.5}P_{r}{}^{0.5}\left(\frac {\omega}{\eta}\right)^{0.5},\ R_{e}\leq 2\times 10^{5},\ \text{laminar flow}\\ h_{e}=10\times 10^{-20}\lambda\left(\frac{\omega}{\eta}\right)^{4}R_{e}{}^{7},\ 2\times 10^{5}\leq R_{e}\leq 2.5\times 10^{5},\ \text{Transition layer flow}\\ h_{e}=0.0197\lambda(m+2.6)^{0.2}\left(\frac{\omega}{\eta}\right)^{0.8}R_{c}{}^ {0.6},\ R_{e}>2.5\times 10^{5},\ \text{Turbulent flow}\end{array}\right. \tag{34}\]  
In the equation above, \(\lambda\) represents the thermal conductivity of the lubricant, \(P_{r}\) is the Prandtl number of the lubricant-air mixture, \(\eta\) is the kinematic viscosity of the lubricant-air mixture, and m is an exponential constant that characterizes the change in surface temperature of the disk in the radial direction. For the purposes of this study, a quadratic distribution is assumed, i.e., \(m=2\). \(R_{c}\) denotes any radius on the surface of the disk.
# Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol
## 4 Proposed Methodology  
This paper suggests an energy consumption management methodology based on the LEACH protocol to solve the energy overloading issue. This protocol also functions with the concept of around. The period between attaining a new clustering and data collection was repeatedly carried out with this new clustering known as around. The suggested clustering protocol follows the same two phases as in the existing LEACH protocol. The first is the setup phase, in which the establishment process of clusters and the selection of CHs (cluster head) are exerted, and the steady-state phase deals with the data transmission but with specific enhancements. Each node determines whether to become a cluster head for the current round during the setup phase. If the number of cluster heads is decreased, then every cluster head would occupy a wider area, which will cause a problem as certain cluster members will be far from their cluster heads and use a lot more energy for communication [20]. The exceeding count of cluster heads will heighten the entire network's energy consumption and expurgate network life due to the connection among the CH. The BS requires much more power than ordinary procedures. The CH is chosen according to the threshold value with specific energy-dependent characteristics, but in the original LEACH protocol, the CH is selected randomly without energy usage. Therefore, to achieve minimal energy consumption, it is crucial to select the optimal cluster and cluster head [21].
# An Energy Efficient Routing Approach for Cloud-Assisted Green Industrial IoT Networks
## 2 Design Issues and Routing Challenges in Green Industrial IoT Networks
### Wireless Links and Communication Patterns  
Harsh environment in industries are prone to high RF interference due to presence of several equipment, human, and obstacles. This adverse condition may result in wireless link instability and path disruption, which severely impact on data transmission of IIS. Furthermore, this situation leads to increased packet error rate and delay. Based on the application characteristic, the range of link delay may vary from a few seconds to minutes. On the other hand, different wireless communication technologies are used in the IIoT system; for energy efficiency, routing overhead should be reduced.  
The underlying routing protocol is the key player for path selection should address this instability and maintain reliable communication path.  
The heterogeneous nature of industrial networks and applications indicates the presence of different communication patterns, such as point-to-point, point-to-multipoint, and multipoint-to-point. The presence of different communication patterns imposes challenges to data collection and distribution in an industrial environment [19]. Reliable data delivery is an important aspect in IEMS because the data can be transferred for different purposes, such as real-time monitoring, remote control, and emergency response.
# Dynamic Shortest Paths Methods for the Time-Dependent TSP
## 4 Computational Experiments
### Computational Results  
#### 4.2.1 Comparisons between Formulations  
We begin by comparing the performance of the different formulations, namely, the arc-based formulation (TDTSP-A), both with and without path-based pricing, and its path-based counterpart (TDTSP-P). The increased size of the instances (see above) has immediate consequences regarding the computational performance (see Table 1 for the solution statistics for the large instances): Formulation (TDTSP-A) spends an average of about 13 min solving the root relaxations of the problem instances. Enabling column generation decreases this time to about 55 s, whereas the path-based formulation (TDTSP-P) averages at 17 s. While the difference in running times is less pronounced for the small instances, it is clear that column generation is necessary in order to solve larger TDTSP instances.  
Despite the fact that the path-based formulation solves the initial relaxation the fastest, it explores only seven nodes on average during its execution, compared to 29 nodes explored by the arc-based formulation. Consequently, the average remaining gaps are 0.57 and 0.55, respectively. The gap here is defined as \((p-d)/d\), with \(p\) being the objective function value of the best-known feasible solution, and \(d\) the best-known lower bound.
# A Traceable High-Accuracy Velocity Measurement by Electro-Optic Dual-Comb Interferometry
## 5 Conclusions  
In order to achieve a wide range and high accuracy in the field of velocity measurement, an electro-optic dual-comb Doppler velocimeter was proposed and studied in this work. In principle, the proposed velocimeter which was derived is feasible and can further improve velocity measurement accuracy. Experimentally, we locked the repetition frequencies of the two optical combs in the microwave frequency domain and beat them to make the detection. In this way, we verified its high accuracy in the range of 100-300 mm/s. The measured range of 4.69 mm/s to 15 km/s was theoretically derived. The proposed velocimeter combines the merits of high precision, high accuracy, and wide range. In addition, since the repetition frequency used for the measurement is traceable to a rubidium clock, its potentially superior traceability can be utilized in velocity metrology. It is of great meaning and necessity because it helps to provide an available velocimeter with high stability and an extremely compact configuration, making a potential contribution to the velocimeter in practical engineering applications.  
[PERSON] designed the experiment. [PERSON] and [PERSON] performed the experiment. [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] wrote the original manuscript. [PERSON] and [PERSON] did the review and editing.  
This research was funded by the Research Project of Tianjin Education Commission (No.JWK1616).  
We thank [PERSON] and [PERSON] for their help in performing the experiment.  
The authors declare no conflict of interest.
## 2 Bundle Adjustment for Motion Estimation Using HFR Cameras
### Jacobian Matrix for Stereo HFR Cameras  
Stereo vision is widely used for non-contact measurement, which is suitable for the physical evaluation of an explosion. In this section, we use the synchronized stereo HFR camera's captured motion. For each fragment captured by these stereo HFR cameras, during its movement, the formation of the Jacobian matrix is shown in Figure 2. Figure 2 presents the HFR cameras with 540 FPS of the first 20 samples (e.g., for the first 37 ms). In Figure 2, each 4 \(\times\) 18 matrix block of yellow and purple presents the Jacobian matrix of both cameras at time stamp \(j\), and the matrix block in yellow are the \(\mathcal{J}_{e}^{i,j}\). The matrix block in light blue is \(\left[\mathcal{J}_{pw}^{1j},\mathcal{J}_{pw}^{2j}\right]^{T}\) at time stamp \(j\). The matrix block in purple from Figure 2 are zeros. Both the Jacobian matrix and Hessian Matrix are sparse matrices. Therefore, we use the Shur Complement to solve Equation (5). Equation (5) can be specified as:  
\[\left[\begin{array}{cc}\mathcal{J}_{C}^{T}\mathcal{J}_{C}+\lambda D_{C}& \mathcal{J}_{C}^{T}\mathcal{J}_{PW}\\ \mathcal{J}_{PW}^{T}\mathcal{J}_{C}&\mathcal{J}_{PW}^{T}\mathcal{J}_{PW}+ \lambda D_{PW}\end{array}\right]\left[\begin{array}{cc}\Delta\sigma_{C}\\ \Delta\sigma_{PW}\end{array}\right]=-\left[\begin{array}{cc}\mathcal{J}_{PW}^ {T}F\\ \mathcal{J}_{PW}^{T}F\end{array}\right] \tag{10}\]  
Since the dimension of the cameras is related less to the position, \(\mathcal{J}_{pw}\) is the diagonal block matrix which makes the \(\mathcal{J}_{pw}^{T}\mathcal{J}_{pw}+\lambda D_{pw}\) full rank. We use the Shur complement in the following:  
\[(C-EP^{-1}E^{T})\Delta\sigma_{C}=-g_{C}+EP^{-1}g_{PW} \tag{11}\]  
\[\Delta\sigma_{PW}=P^{-1}\Big{(}-g_{PW}-E^{T}\Delta\sigma_{C}\Big{)} \tag{12}\]  
Equation (11) efficiently reduces the complexity of the calculation. From Figure 2 we can see that since matrix \(P\) is a block diagonal matrix, its inversion is relatively simple. The  
Figure 2: **(a)** Jacobian matrix for stereo high frame-rate (HFR) cameras; **(b)** related Hessian Matrix of these two HFR cameras.  
original Equation (5) can be easily solved by Equations (11) and (12), especially since the number of captured images is large.
# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
## 3 Experiments
### Results and Analysis
#### 3.4.1 HO-3D Dataset
Specifically, on the HO-3D dataset, NICE-SLAM achieves an ADD-S of only 25.68, while SDF-2-SDF performs slightly better, reaching 34.88, but both fall short of our method's performance by half.
## 3 Experiment
### Result and Discussion  
As shown in Figure 7, data were acquired on a single optical fiber time transfer device by sampling for 3000 s. Before the security encryption scheme, the standard deviation of a single optical fiber time transfer system was 1.1 \(\times\) 10\({}^{-11}\). The peak-to-peak value of jitter was 104 ps. Additionally, the stability TDEV was 9.2 \(\times\) 10\({}^{-12}\) at 1 s. After introducing the security encryption scheme, the standard deviation of a single device was 1.7 \(\times\) 10\({}^{-11}\). The peak-to-peak value of jitter was 120 ps, and the stability TDEV was 10.7 \(\times\) 10\({}^{-12}\) at 1 s. According to the test data, it was found that there was no significant difference in indicators when the security encryption method was introduced into a single device to ensure system security. A single secure optical time transfer system was validated on a 50 km optical link, and the encryption algorithm data were collected, as shown in Table 2. Before encryption, the plaintext data of the link delay from the local to the remote site through 50 km optical fiber were 249,746,186 ps. The 256-bit SM2 algorithm was used to encrypt the public key  
Figure 6: Comparison of delay jitter data of single equipment.  
Figure 7: TDEV comparison chart of single equipment.  
and the generated random number K to obtain the ciphertext C_out. After being encoded and modulated to the laser, it was sent to the remote site equipment through the optical fiber link. After decoding, the remote device used the corresponding private key d\({}_{n}\) to decrypt. It was found that the data after encryption were the same as those with plaintext before encryption. According to the calculation results of the algorithm test, the encryption time was about 130 us. Additionally, the decryption time was about 150 us.
## 3 Underwater Acoustics Channel Statistics
### Cluster Amplitude and Coefficient of Variation  
Each path cluster may contain multiple rays, which appear as different taps in the CIR. Here, the amplitude of the path cluster is not the maximum peak of the path cluster because the phase of the arrival rays is time variable for the same delay, causing large fluctuations in the maximum peak. Instead, the amplitude of each path cluster is characterized by energy averaging. For this purpose, we introduced a threshold \(T_{m}\) and calculated the cluster amplitude \(A_{k}\) of the \(k\)th path cluster as follows [11]:  
\[A_{k}(n)=\sqrt{\frac{1}{M_{k}}\sum\limits_{m_{k}=1}^{M_{k}}h^{2}(n,\{m_{k}| \frac{|h(n,m_{k})|}{\max(|h(n,m_{k})|)}\geq T_{m}\})}, \tag{1}\]  
Equation (1) calculates the RMS of several adjacent taps, satisfying \(|h(n,m_{k})|/\max(|h(n,m_{k})|)\geq T_{m}\) in the \(k\)th path cluster. \(A_{k}(n)\) is the amplitude of the \(k\)th path cluster at the \(n\)th sampling time. \(M_{k}\) is the total number of taps satisfying the threshold condition in the \(k\)th path cluster, and \(m_{k}\) is the tap index of the \(k\)th path cluster. The time interval between two adjacent taps must exceed the time resolution.  
There are some path clusters in the channels of the shadow and direct-arrival zones with extraordinarily small amplitudes. Path clusters with amplitudes below a threshold can be ignored because their energies negligibly contribute to the CIR. These path clusters can be treated as noise in communication systems. The selection criterion of a channel path cluster is given by  
\[\frac{A_{k}(n)}{\max\{A_{k}(n)|k=1,2,\ldots,K\}}\geq T_{c}, \tag{2}\]  
where \(K\) is the total number of path clusters, and \(T_{c}\) is the threshold of the ratio between the \(k\)th path cluster in a channel and the maximum energy cluster in a channel. The \(T_{c}\) in the shadow and direct-arrival zones was initially set to empirical values of 0.1 and 0.05, respectively.  
After determining the number of path clusters in the channel, we evaluated the arrival paths of these clusters based on the simulation results. The path clusters with different arrival paths were separated for statistical analysis post-processing.  
As mentioned above, the randomness degrees of the different clusters are quantified by their coefficients of variation, defined as  
\[D_{k}=\frac{\sqrt{\frac{1}{N}\sum\limits_{n=1}^{N}\left(A_{k}(n)-\overline{A _{k}}\right)^{2}}}{\overline{A_{k}}}. \tag{3}\]  
Figure 5: Flowchart of the frequency domain correlation (BPF = bandpass filter; DDC = digital down conversion; LPF = lowpass filter; FFT = fast Fourier transform; IFFT = inverse FFT).  
In Equation (3), \(A_{k}(n)\) represents the amplitude of the \(k\)th path cluster in the channel obtained at the \(n\)th sampling time, and \(\overline{A_{k}}=1/N\sum\limits_{n=1}^{N}A_{k}(n)\). Equation (3) computes the standard-to-mean deviation of the amplitude of \(k\)th path cluster over \(N\) CIRs.
## 3 Results and Discussion
### Validation of the Developed Algorithm
#### 3.1.1 Validation of the Delamination Model
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Size of** **Delamination**} & \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Analysis Type**} & \multicolumn{6}{c}{**Frequency Mode**} \\ \cline{5-10}  & & & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multirow{5}{*}{Intact} & [PERSON] et al. [39] & Experiment & 21 & 61 & 140 & 193.5 & 228 & 399 \\  & [PERSON] et al. [39] & HSDT & 23.68 & 64.31 & 152.06 & 199.4 & 230.28 & 404.51 \\  & & \% Error & 12.76 & 5.43 & 8.61 & 3.03 & 1 & 1.38 \\ \cline{2-10}  & & Degenerated & 22.79 & 61.52 & 142.05 & 190.1 & 226.34 & 389.89 \\  & & \% Error & 8.54 & 0.85 & 1.46 & 1.76 & 0.73 & 2.28 \\  & & Layerwise & 23.53 & 63.53 & 146.7 & 189.1 & 223.45 & 392.56 \\  & & \% Error & 12.07 & 4.15 & 4.79 & 2.25 & 2 & 1.61 \\ \hline \multirow{5}{*}{0.25a} & [PERSON] et al. [39] & Experiment & 20.4 & 55.5 & 142 & 185.5 & 221 & 394 \\  & & HSDT & 23.4 & 61.67 & 145.68 & 187.8 & 225.46 & 399.31 \\  & & \% Error & 14.71 & 11.12 & 2.59 & 1.23 & 2.02 & 1.35 \\ \cline{2-10}  & & \multirow{2}{*}{Present} & Degenerated & 22.70 & 61.47 & 141.66 & 181.58 & 212.60 & 380.47 \\  & & \% Error & 11.26 & 10.76 & 0.24 & 2.11 & 3.80 & 3.44 \\ \hline \multirow{5}{*}{0.5a} & [PERSON] et al. [39] & Experiment & 20 & 48.5 & 135 & 182 & 201.5 & 380 \\  & & HSDT & 22.09 & 54.91 & 137.44 & 175.5 & 211.69 & 371.3 \\  & & \% Error & 10.45 & 13.22 & 1.81 & 3.57 & 5.06 & 2.29 \\ \cline{2-10}  & & \multirow{2}{*}{Present} & Degenerated & 22.03 & 61.00 & 133.81 & 168.52 & 193.72 & 341.91 \\  & & \% Error & 10.17 & 25.78 & 0.88 & 7.41 & 3.86 & 10.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Natural frequency of four layer square cantilever laminated composite plate for different size of delamination.
## 3 Results and Discussion
This visualization provides a comprehensive understanding  
Figure 6: Transmittance plot for a metamaterial beam with internal resonator coupling (\(k=k_{r}\)) comprising eight resonators, which equates to four unit cells. Figure 7: Analysis of the influence of internal coupling stiffness \(\kappa\) on the metastructure’s bandgap frequencies in Equation (33), showing the consistent edge of the first bandgap and the emergence of narrow higher-frequency gaps within certain \(\kappa\) ranges. of how internal coupling affects the bandgap frequencies, offering insights into the precise tuning of the metastructure's vibrational properties. It can be seen that the emergence of additional bandgaps occurs within certain ranges of \(\kappa\), demonstrating the metastructure's sensitivity to variations in internal coupling. The plot serves as a detailed map for predicting the dynamic behavior of the metastructure under varying conditions of internal coupling, which is critical for applications requiring targeted vibration isolation frequencies. The results reveal that metastructures with internally coupled resonators retain the primary bandgap found in conventional metastructures but also introduce an additional, thinner bandgap at a higher frequency. This secondary bandgap remains separate from the primary one, making it challenging to use internal coupling to merge both bandgaps for vibration isolation in continuous and distributed metastructures. This difficulty arises because the second bandgap's nature is linked to a 180-degree phase change in resonators with identical natural frequencies (\(\omega_{r}\)). It would be beneficial to investigate the impact of varying \(\omega_{r}\) in different unit cells. Despite these challenges, it is noteworthy that in lumped systems, metastructures with internally coupled resonators significantly widen the bandgap compared to conventional configurations.
###### Abstract  
Forest fires are a fatal threat to environmental degradation. Wireless sensor networks (WSNs) are regarded as a promising candidate for forest fire monitoring and detection since they enable real-time monitoring and early detection of fire threats in an efficient way. However, compared to conventional surveillance systems, WSNs operate under a set of unique resource constraints, including limitations with respect to transmission range, energy supply and computational capability. Considering that long transmission distance is inevitable in harsh geographical features such as woodland and shrubland, energy-efficient designs of WSNs are crucial for effective forest fire monitoring and detection systems. In this paper, we propose a novel framework that harnesses the benefits of WSNs for forest fire monitoring and detection. The framework employs random deployment, clustered hierarchy network architecture and environmentally aware protocols. The goal is to accurately detect a fire threat as early as possible while maintaining a reasonable energy consumption level. ns-2-based simulation validates that the proposed framework outperforms the conventional schemes in terms of detection delay and energy consumption.  
wireless sensor networks; forest surveillance system; forest fire detection system; energy efficient; fire reaction delay +
Footnote †: journal:  
_Future Internet_**2018**, _10_, 102; doi:10.3390/610100102
# Data Exchange in Cluster Structure for Longevity of IoT
## 2 Related Work
### Routing Protocols  
As IoT succeeds WSNs, things in IoT are similar to the sensor nodes in WSNs. Since sensor nodes are equipped with limited resources, effective operation methods have been researched to support the longevity of IoT networks. The highest proportion of energy in sensor nodes is consumed by the wireless communication module. Therefore, to reduce the energy consumed by the communication module, studies on routing protocols in WSNs have been conducted. According to the network architecture, the routing protocols are classified into planar structure and tree hierarchical structure.  
In a planar structure, all the sensor nodes perform the same role. According to the time of formation of routing paths, a table-driven method or on-demand method can be employed. The table-drivenmethod calculates the routing paths before the data transmissions are requested. On the other hand, the on-demand method configures the routing paths when the data transmissions are issued. When compared to the hierarchical tree structure, the planar structure requires a larger routing table and has less scalability.  
In a hierarchical tree structure, the sensor nodes are divided into clusters composed of a header node and member nodes. It has a group-based architecture. When member nodes in clusters require data transmissions, communication is undertaken via the header node. Hence, the size of the routing table decreases [5].  
If the planar structure method is applied to the IoT environment, since each thing has a routing table to support all the things, a significant amount of storage resources is exhausted. Moreover, even if the tree structure method can manage the routing tables effectively, it requires the participation of all the things for data collection. This approach causes an increase in the network traffic, and thus, the energy consumed by the things increases significantly, the network bandwidth is wasted, and the unnecessary time delay is inevitable.