esa-sceva/satcom-chunk-collection huggingface dataset:
# LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection
## 2 Methodology
### Bidirectional Characteristic Pyramid  
A feature pyramid network (FPN) [24] is a network structure designed for multi-scale target detection, aiming at extracting features from different layers of convolutional neural networks and enhancing the model's multi-scale detection capability through the flow of information in the upper and lower paths.  
In YOLOv8, compared with the ordinary top-down feature fusion, FPN enhances the performance of the model in dealing with targets of different sizes through multi-scale feature fusion, especially for the detection of small targets, which is significantly improved, as shown in Figure 4, which compares the structure of top-down fusion and FPN in a sketch. The top-down feature enhancement and lateral connectivity make YOLOv8 more robust in complex scenes, while the efficient real-time performance is maintained by optimizing the FPN structure. FPN not only performs well in improving the target detection accuracy but also enables YOLOv8 to achieve better generalization ability and adaptability in the multi-scale target detection task. However, as shown in Figure 4, FPN is a unidirectional feature flow (top-down), which restricts the full fusion of information and prevents simultaneous top-down and bottom-up feature transfer, resulting in a weak capture of details in small targets and complex scenes. In addition, FPN lacks a dynamic weighting mechanism and cannot adjust the weights according to the importance of the features during feature fusion, leading to poor performance in multi-scale target detection and complex scenes, with especially weak performance in small target detection.  
Figure 4: Schematic comparison of top-down fusion and the FPN structure.  
Figure 3: New detection heads.  
In the detection of high-speed falling stones, based on the problem of detecting multiple types of stones falling simultaneously and in complex scenes, the network structure of FPN cannot meet our detection needs, so we introduce the bidirectional feature pyramid network (BiFPN). As shown in Figure 5a, its feature flow is bidirectional (top-down and bottom-up)so that feature maps at different levels can be fused more effectively. It introduces a learnable weighting mechanism that dynamically adjusts the weights according to the actual contributions of the feature maps, improving the efficiency and detection accuracy of feature fusion. Meanwhile, the structure combining our small-target detection head is shown in Figure 5b. BiFPN has fewer parameters and lower computational complexity, which facilitates our subsequent deployment on embedded platforms.
# Analytical Solution and Experimental Validation of the Electromagnetic Field in an IPT System
## 4 Electric and Magnetic Field in the IPT System
### Numerical Integration Method for the Analytical Solution  
The integral of the function \(y\ =\ f(\lambda)\ =\ (\lambda/u)\cdot I_{1}(\lambda a)J_{1}(\lambda\rho)e^{-uh_{1}}\) in Equation (46) needs to be calculated and the integration interval is from \(A\) to \(B\).  
Figure 5: Illustration of the electric field at a sampling point of the double-coil IPT system.  
Since it is difficult to determine the primitive function of the integrand \(f(\lambda)\), the integration method is used to acquire an approximation result. In the numerical integration method, the integration interval is divided into parts, and the sum of their areas is the approximation of the integral of the function \(f(\lambda)\) from \(A\) to \(B\).  
As shown in Figure 6, by defining the area of each small part as \(\Delta s=f(\lambda_{n})\cdot(B-A)/m\),  
\[\int\limits_{A}^{B}f(\lambda)dx\approx\sum\limits_{n=0}^{n=m-1}\frac{B-A}{m}f (\lambda_{n}), \tag{48}\]  
If \(m\) tends to infinity, then  
\[\int\limits_{A}^{B}f(\lambda)dx=\lim\limits_{m\rightarrow\infty}\sum\limits_{n =0}^{\infty}\frac{B-A}{m}f(\lambda_{n}), \tag{49}\]  
In Equation (46), \(A\) is equal to zero. When \(a=\rho=0.1\) and \(\lambda=1000,f(\lambda)\) tends to zero, so the upper limit of the integral, (i.e., \(B\)) can be set to be 1000. To ensure accuracy, the number of tiny intervals was defined as \(m=1\times 10^{5}\).  
The final solutions are obtained by combined analytical derivation and numerical calculations. In the next section, the analytical solution is verified by simulation.
## 3 Kinematic Analysis
### Forward Kinematics of the 6-Dof Redundant Planar Manipulator Robot  
The D-H method is used to characterize the kinematic behavior of the redundant planar manipulator robot. Taking the axes assigned in the geometrical configuration shown in Figure 1, it is possible to obtain the robot joint parameters, which are presented in Table 1.  
The homogeneous transformation matrix of Equation (1) is completely defined based on joint parameters:  
\[\mathbf{A}_{i}^{i-1}(q_{i})=\left[\begin{array}{cccc}\cos(\theta_{i})&-\sin( \theta_{i})\cos(\alpha_{i})&\sin(\theta_{i})\sin(\alpha_{i})&a_{i}\cos(\theta_{ i})\\ \sin(\theta_{i})&\cos(\theta_{i})\cos(\alpha_{i})&-\cos(\theta_{i})\sin( \alpha_{i})&a_{i}\sin(\theta_{i})\\ 0&\sin(\alpha_{i})&\cos(\alpha_{i})&d_{i}\\ 0&0&0&1\end{array}\right] \tag{1}\]  
where: \(i=1,\ldots,6\).  
According to the D-H convention, the product of each transformation matrix that link the coordinate system \(0_{i-1}\) is calculated with the immediately after \(0_{i}\), and the 4x4 T matrix is obtained, which represents the pose (position and orientation) of the last link from the base system.  
\[\mathbf{T}={}^{0}\mathbf{A_{1}}{}^{1}\mathbf{A_{2}}{}^{2}\mathbf{A_{3}}{}^{3} \mathbf{A_{4}}{}^{4}\mathbf{A_{5}}{}^{5}\mathbf{A_{6}} \tag{2}\]  
Then, from \(\mathbf{T}\) the following analytical representation of the position of the last link in the base system (coordinates origin) is obtained:  
\[x=r_{1}\cos(\theta_{1})+r_{2}\cos(\theta_{1}+\theta_{2})+r_{3}\cos(\theta_{1} +\theta_{2}+\theta_{3}) \tag{3}\]  
\[y=r_{1}\sin(\theta_{1})+r_{2}\sin(\theta_{1}+\theta_{2})+r_{3}\sin(\theta_{1} +\theta_{2}+\theta_{3}) \tag{4}\]  
where: \(r_{1}=d_{1}+L_{1}+L_{2}\); \(r_{2}=d_{2}+L_{1}+L_{2}\); \(r_{3}=d_{3}+L_{1}+L_{2}\)  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Joint** & \(\theta_{i}\) & \(d_{i}\) & \(a_{i}\) & \(\alpha_{i}\) \\ \hline
1 & \(\theta_{1}^{*}-90^{\circ}\) & \(L_{0}\) & 0 & \(-90^{\circ}\) \\
2 & \(0^{\circ}\) & \(d_{1}^{*}+L_{1}+L_{2}\) & 0 & \(90^{\circ}\) \\
3 & \(\theta_{2}^{*}\) & 0 & 0 & \(-90^{\circ}\) \\
4 & \(0^{\circ}\) & \(d_{2}^{*}+L_{1}+L_{2}\) & 0 & \(90^{\circ}\) \\
5 & \(\theta_{3}^{*}\) & 0 & 0 & \(-90^{\circ}\) \\
6 & \(0^{\circ}\) & \(d_{3}^{*}+L_{1}+L_{2}\) & 0 & \(0^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 1: Joint parameters of the 6-DoF redundant planar manipulator robot.  
Figure 1: Geometrical configuration of the 6-DoF redundant planar manipulator robot and assignation of the D-H coordinate system (perspective view).
# Big Data Perspective and Challenges in Next Generation Networks
## 2 Major Milestones of Next Generation Networks
### Ultra Dense Networks
#### 2.1.2 Optimal User Association  
As we move towards 5G networks, the heterogeneity of the network will increase very much. The 5G networks will be integrated with heterogeneous network, thus enabling devices of different standard and protocols for mutual operation [18]. Hence, in 5G networks, it is a challenge to determine which standard and spectrum will be used and which user will be associated with it. This phenomenon is called optimal user association. In general, optimal user association depends on Signal-to-Interference plus Noise Ratio (SINR) from every user to base station, the instantaneous load at each base station and the choice of other users in the network. Thus, a simpler approach is required for optimal user association [19; 20]. The existing approach for tackling this is termed as biasing and blanking [2]. _Biasing_ means to push-off users from heavily loaded macro cells to lightly loaded small cells. In this way, users can utilize the maximum number of resources and remaining users in macro cells will also get resources easily. Biasing is thus a way to ensure that everyone gets the resources. _Blanking_ means shutting-off the transmission for the fraction of time while the network is serving the biased small cell users. With this process the SINR of small cell user will increase. Hence, this simplest approach of biasing the small cells and blanking the macro-cells will increase the edge rate by a factor as high as 500 percent. Biasing and blanking is thus a very useful and simple approach for resource allocation in 5G networks [21; 22].
# Homomorphic Encryption Based Privacy-Preservation for IoMT
## 4 Discussion
Furthermore, secret sharing prevents an attacker or the cloud service from determining which VEN cluster was selected for computation operations. Other research literature uses ECDSA ([PERSON], 2018) and blockchain-based decentralized networks ([PERSON], 2018) to secure user identities, but fail to secure the data from being exposed to untrusted parties. Blockchains provide data integrity, and dual signatures using ECDSA authorize edge nodes and cloud services but require all encrypted data to be decrypted and then analyzed. Plaintext data exposes both user and data privacy at both the Cloud and the edge layer. The proposed scheme relies on homomorphic encryption and secret sharing on VENs to protect both user and data privacy without any requirement of decryption of data for further analysis. Data shared in the proposed scheme from IoMT devices to edge nodes are performed over insecure channels. The deployment of nonce values and timestamps in packets sent  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  
and received between the edge and device layer alert the network of possible relay and delay attacks. However, there is a need for a more secure and trusted data communication channel between the device and the physical base station. In future research, we intend to focus on securing device-edge data transmission using a lightweight encryption scheme.
## 5 Result and Discussion
### Effect of the Relative Position of the Cooler Stack
The standing wave component works well in the cooler stack when compared with that in the engine stack because the thermal power that is transported by the \"Dream Pipe Effect\" [3] and corresponds to major loss sources in standing wave thermoacoustic coolers and engines, is proportional to the temperature gradient (as shown in Equation (17) in the previous study [3]). Therefore, it is considered that, given that the temperature gradient was small and \(|Z_{c}/(\rho_{c}a_{c})|\) depended weekly on \(L/L_{loop}\), COP\({}_{c}\) was maximum at \(L/L_{loop}=0.54\), where \(\phi_{c}\) was not close to \(0^{\circ}\) but close to \(60^{\circ}\). It should be noted that when \(\phi_{c}\) is positive, the thermal power pumped by the standing wave component is directed from the cold end to the ambient end of the cooler stack (as shown in Equation (16) in the previous study [3]). To consider the dependence of \(\eta_{tube}\) on \(L/L_{loop}\), the ratio of \(P_{max}\) and \(P_{min}\) is shown as a function of \(L/L_{loop}\) in Figure 4g. Here, \(P_{max}\) and \(P_{min}\) are the maximum and minimum values of the pressure amplitude in the looped tube. As predicted by [PERSON] [20], the traveling wave like field, in which \(P_{max}/P_{min}\) is near unity, transports acoustic power more efficiently than the standing wave like field, in which \(P_{max}/P_{min}\) is much larger than unity. As can be seen from Figure 4c,g, when \(P_{max}/P_{min}\) was small, \(\eta_{tube}\) was large as expected. Therefore, it can be said that when \(L/L_{loop}\) was increased from 0.50 to 0.56, the acoustic field formed in the looped tube was changed from a traveling wave like field to a standing wave like one, and then the efficiency of the tube, \(\eta_{tube}\), was decreased. Figure 4h shows the calculated temperature of the hot heat exchanger on the engine stack, \(T_{H}\). The results indicate that \(T_{H}\) reduced when \(L/L_{loop}\) decreased from 0.56 to 0.50. Nevertheless, this is not a good design choice as COP\({}_{total}\) became close to zero at \(L/L_{loop}=0.50\) as mentioned above.
## 5 Research Methodology and Empirical Findings
### Simulation  
In the simulation system, we will introduce the composition of series, parallel and complex structures to analyze the final results. Reliability analysis plays the role of evaluation and improvement [34]. Companies should consider the orientation of all stakeholders to define the balance efficiency point through value recreated. Decision makers would identify the potential risk by scenario analysis through the analysis of the reliability simulation system [41]. Three simulation architectures, which are series, parallel and multiple complex structures, are shown in Figure 7. In application, the basic structural unit (single, series or parallel) will be integrated to derive various structures in practice [35]. The system can stack up the corresponding environment for discussion by analyzing the application in each situation.  
In the state of the series connection of microservice systems, reliability analysis can be divided into  
_Series Structure [34]_  
\[\mathrm{R_{S}(t)=R_{1}\times R_{2}\times\cdots R_{n}=\prod_{i=1}^{n}R_{i}(t)= \prod_{i=1}^{n}e^{-\sum_{i=1}^{n}\gamma_{i}t}.} \tag{9}\]  
Figure 6: Neural network and weighting.  
Deriving from Equation (4), \(\mathrm{R_{S}(t)}\) stands for the reliability of the series structure. For setting the reliability block diagram, duplicate or time redundancy is checked for its function performing. Because the system may have more than one mean in an item, the redundancy design can help improve the common failure mode [35]. The independent element analysis within the structure will affect the final failure performance of the system.  
_Parallel Structure_[35]  
\[\mathrm{R_{P}(t)}=\sum_{i=m}^{t}\left(\begin{array}{c}n\\ \mathrm{i}\end{array}\right)\mathrm{R^{i}}(1-\mathrm{R(t)})^{n}=1-(1-\mathrm{ R(t)})^{n}=1-\left(1-\mathrm{e^{-\gamma t}}\right)^{n} \tag{10}\]  
For a parallel structure, the probability of a minimum m pass could be represented as \(\mathrm{R_{P}(t)}\) under n times Bernoulli trial. In the observations, the MTTF and the failure ratio can also be used to define parallel reliability. Two services in parallel can be connected in series in their respective systems to expand services (Figure 7).  
_Complex Structure_[34]  
\[\mathrm{R_{C}}=(\mathrm{R_{1}}+\mathrm{R_{2}}-\mathrm{R_{1}}\mathrm{R_{2}}) \mathrm{R_{3}}. \tag{11}\]  
\(\mathrm{R_{C}}\) defines the reliability of the complex architectural system contributed by two different structures. In the complex system shown in Figure 7, we analyze Microservice 1 and 2 as parallel systems connecting to Microservice 3 in series. In various engineering scenarios, the configuration and composition of complex structures will affect the reliability of the overall system. The next issue that manufacturing companies will face is defining the optimal combination with the trade-off between cost and quality.
## 6 Results
### Total RE Installed Capacity vs. Different Policies
#### 6.3.2 Results of the Total RE Installed Capacity vs. Different Policies Analysis for Brazil  
The model equation for the Brazil is described as shown in Table 14:  
\[y=\ 46.774\ +\ 6.523\ x_{1}+12.899\ x_{2}+1.837\ x_{3}. \tag{3}\]  
Brazil does not have any policy in the RD&D and VA category during the period 2000-2017, as reported in the IRENA database [25], thereby it is not included in that model. The coefficients of \(x_{1}\) and \(x_{2}\) are positive and statistically significant, with a \(p\)-value \(\leq\) 0.01. On the other hand, the PS + RI policy (\(x_{3}\)) has a \(p\)-value \(>\) 0.1; therefore, it has no significant relation with the total RE capacity. The correlation factors are 0.942, 0.840, and 0.891, respectively, which depict a direct and strong linear relation between the total RE capacity and the RE policy variables. Moreover, the value of R\({}^{2}\)adj is 0.923, which indicates that 92.3% of the total RE capacity variation is accounted for by the policy variables. In addition, the significance level of F-stats is less than 0.01, which shows a good fit for the model.  
It has been indicated in Table 14 that the first category EI has a larger weight than the others; therefore, it has a positive effect on the total RE capacity in Brazil. This may be due to introducing the PROINFA program, the auctions system, or setting the legal framework for the use of solar PV energy systems and tax exemptions in this country.
## 3 Results and Discussion
### Enzymatic Hydrolysis of Solid Fractions  
In order to observe the GVL assisted treatment efficacy, the cellulose digestion of the treated solid residues as well as the untreated ones was performed, and the results are delineated in Figure 1. As expected, the untreated biomass showed a very low enzymatic saccharification yield of nearly 25%, mainly due to its intact and smooth surface providing less effective enzymatic absorption sites. However, for the treated biomass fractions, the glucose release and the corresponding enzymatic digestibility rose in a gradual way, exhibiting that treatment severity had a positive impact on glucose release. Initially, the yield was not that high (almost 60% for 140-160 \({}^{\circ}\)C); however, it increased in a dramatic fashion (92%) when the thermal conditions went beyond 160 \({}^{\circ}\)C viz (180 \({}^{\circ}\)C:4 h). The lignin was also removed when the treatment conditions were prolonged, so it can be anticipated that, in our case, the biomass web deconstruction, along with the removal of lignin, provided enhanced accessibility for enzymatic penetration. From here, it can be anticipated that the cellulose digestion was drastically improved for the ball-milled pinewood biomass with GVL pretreatment. This treatment operation, due to its biomass dissolution properties, disrupted the biomass native matrix and exposed inner surfaces, which escalated the subsequent enzymatic saccharification. From here, it can be conclusively suggested that this treatment can be one suitable option for lignocellulosic ethanol production.
## 4 Evaluation of Nonlinear Performance Curve Subjected to In-Plane Rocking Behavior
### Evaluation of Mortar-Joint Tensile Crack Strength  
Figure 13 shows the relationship between the mortar-joint tensile crack strength \(P_{r1}\) (shown in Figure 12a), axial force, and stress distribution of solid masonry walls subjected to rocking behavior. In this study, it was assumed that the stress distribution before mortar-joint tensile failure is linear, and the peak strength occurs when the maximum tensile stress reaches the mortar-joint tensile strength \(f_{y\mu}\)[31]. Since the flexural moment and axial force acting on the bottom of a wall are \(P_{r1}\)\(H\) and (\(N_{D}+W\)), respectively, Equation (4) must be satisfied at the peak strength. From Equation (4), the mortar-joint tensile crack strength \(P_{r1}\) is determined by Equation (5). The \(P_{r1}\) of the perforated masonry walls is determined by considering the reduction factor \(\gamma\) in the \(P_{r1}\) of the solid masonry walls, as shown in Equation (6). Since the mortar-joint tensile failure mainly occurs in the bed joints, it is assumed that the factor \(\gamma\) is the ratio of wall length excluding opening length to whole wall length; \(\gamma\) = 0.66 and 0.77 in Specimens M-W and M-D, respectively. The  
Figure 12: Idealized force–deformation relationship of in-plane rocking behavior in masonry walls: (**a**) with peak and (**b**) without peak.  
estimated results of the mortar-joint tensile crack strength in each specimen, based on the above-described procedure, are shown in Table 3 and Figure 17 plotted as red lines, compared to the test results. In Specimens M-N and M-W, where mortar-joint tensile failure occurred, the estimated results show good agreement with the experimental results.  
\[\frac{P_{r1}\cdot H}{(l^{2}\cdot t)/6}-\frac{N_{D}+W}{l\cdot t}=f_{bft} \tag{4}\]  
\[P_{r1}=\bigg{(}f_{bft}+\frac{N_{D}+W}{l\cdot t}\bigg{)}\frac{l^{2}\cdot t}{6H} \tag{5}\]  
\[P_{r1}=\gamma\bigg{(}f_{bft}+\frac{N_{D}+W}{l\cdot t}\bigg{)}\frac{l^{2}\cdot t}{6H} \tag{6}\]  
where  
\(P_{r1}\): Mortar-joint tensile crack strength (kN);  
\(H\): Height from the bottom of the wall to the loading point (=1800 mm, herein);  
\(h\): Wall height (=1390 mm, herein);  
\(l\): Wall length (=1970 mm, herein);  
\(t\): Wall thickness (=200 mm, herein);  
\(N_{D}\): Applied axial force (=62 kN);  
\(W\): Wall self-weight (=10.43, 8.8, and 8.8 kN in Specimens M-N, M-W, and M-D, respectively);  
\(f_{bft}\): Maximum tensile stress of joint mortar (=0.84 N/mm\({}^{2}\) of average adhesive strength in cement and red bricks shown in Figure 5);  
\(\gamma\): Reduction factor (the ratio of wall length excluding opening length to whole wall length, 0.66 and 0.77 in Specimens M-W and M-D, respectively).
# Resonant Laser Ionization and Fine-Structure Study of Silver in an Ablation Plume
## 2 Theory and Motivation
### Doppler Broadening  
Moving atoms experience a Doppler shift of the incident laser radiation. The shifted frequency is given by:  
\[\
u=\
u_{o}(1\pm\frac{\
u}{c}) \tag{1}\]  
where \(\
u_{o}\) is the initial frequency, \(\
u\) is the velocity of the atoms along the laser beam and \(c\) is the speed of light.  
In this experiment, the environment is an ablation plume where there is a very wide velocity distribution attributed to the high temperature of the plume. This contributes to a large Doppler broadening in terms of GHz for each transition line, which is a limiting factor to the resolution of spectroscopic investigations in hot environments.  
In the individual frames of reference of the excitation lasers, the atoms in the trajectory of each excitation laser experience a Doppler effect with respect to the atomic transition lines. When the Doppler-shifted resonant frequencies of these atoms match the frequency of the excitation laser, the atoms will be resonantly excited. In this state, if the complementary excitation laser is tuned at a frequency with the corresponding shift, or has a linewidth sufficient to cover the corresponding shift from its central frequency, subsequent transitions occur and resonant photo-ionization can be achieved over these ranges of frequency values.
## 3 Properties of Different STBCs Signals
### Third STBC Option, \(\alpha=c_{2}\)  
As an analogy, we can express for \(\alpha=c_{2}\)  
\[\begin{split}\mathrm{E}\Big{[}\overline{w}_{q}^{(\alpha=c_{2})}(n) \overline{w}_{q^{\prime}}^{(\alpha,c_{2})}(n+\tau)\Big{]}=\\ \left\{\begin{array}{cc}-2\sigma_{s}^{2}&\text{for}\ (q=0,q^{ \prime}=2,\tau=1),\text{and}\\ &(q=2,q^{\prime}=0,\tau=-1),\\ -\sigma_{s}^{2}&\text{for}\ (q=1,q^{\prime}=2,\tau=2),\text{and}\\ &(q=2,q^{\prime}=1,\tau=-2),\\ 0&\text{otherwise},\end{array}\right.\end{split} \tag{11}\]  
and using (3), (5), and (11) in (6), we write that  
\[U^{(\alpha=c_{2})}(\tau){=}\begin{cases}-\frac{1}{2}\sigma_{s}^{2}\Big{(}h_{ \mathcal{SD}}^{(0,1)}h_{\mathcal{SD}}^{(2,2)}-h_{\mathcal{SD}}^{(2,1)}h_{ \mathcal{SD}}^{(0,2)}\Big{)}\ \text{for}\ \tau=\pm 1,\\ -\frac{1}{4}\sigma_{s}^{2}\Big{(}h_{\mathcal{SD}}^{(1,1)}h_{\mathcal{SD}}^{( 2,2)}-h_{\mathcal{SD}}^{(2,1)}h_{\mathcal{SD}}^{(1,2)}\Big{)}\ \text{for}\ \tau=\pm 2,\\ 0&\text{otherwise},\end{cases} \tag{12}\]
# Flash Flood Early Warning System in Colima, Mexico
## 2 Materials and Methods
### Hardware Development  
Hardware requirements have been defined according to the specific needs of the application, which has resulted in the development of fixed, weather station nodes, and drifter nodes, along with a data acquisition process that uses IoT techniques to retrieve data through the 3G cellular network.  
Power management, the cellular communication method, and other components described in a previous paper [21] remain unchanged. Their initial design effectively handles the extra load and demands of the new hardware now incorporated on the fixed node. The main hardware modules or the fixed node can be seen in Figure 6.