esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding +
## 4 Numerical Evaluations  
In this section, we evaluate the throughput (i.e., the expected rank arriving at the node) and the decoding time of multi-phase recoding. We consider a multi-hop line network where all the links share the same packet loss rate \(p=10\%\), \(20\%\), or \(30\%\). The feedback packets in the evaluation of decoding time have the same packet loss rate as the BNC packets. We choose a batch size \(M=4\) or \(8\) in the evaluation, with \(t_{\text{avg}}=M\) at each node.  
Figure 5: An example flow of the protocol with two-phase systematic recoding. The hyphen in the feedback BID–phase–rank triple means that the value will not be used by the previous node.  
We focus on comparison of multi-phase and single-phase recodings, as it is known that in these lossy networks, BNC outperforms traditional end-to-end transmission schemes. For end-to-end transmission schemes, a packet can reach the destination only if it is not lost at any of the links. Therefore, we can abstract the network as a single-hop one with loss rate \(p^{n}\), where \(n\) is the number of hops in the original topology. Then, we can approximate the (normalized) throughput by \((1-p)^{n}\) and the decoding time by \(F/(1-p)^{n}\). As a numerical example, for \(n=9\), \(p=10\%\) and \(F=1000\), the throughput is about \(0.39\) and the decoding time is about \(2581\), which are much worse than that achieved by single-phase recoding shown in the remainder of this section.
# Comparative Study Between Active AMD and ABS Devices by Using \(\mu\)-Synthesis Robust Control
## 2 Materials and Methods
### Active Vibration Control Devices
#### 2.5.1 Control with AMD System  
One of the first versions of active structural vibration control approaches was the improvement of TMD systems to actively adjustable mass-damper (ATMD) systems. This system is also known as the Active Mass Driver (AMD). This type of system primarily aims to counteract a structure's lateral movements by positioning a steel pendulum or mass at its top, as shown in Figure 3. Between the structure and the TMD system, an actuator applied a calculated force in real time [26; 27; 28; 29; 30; 31; 32]. In the general case of the AMD, the matrices [M], [C], and [K] were of dimension (n + 1), and the displacement vector {x} had (n + 1) entries.  
In our scenario, adding the AMD to the last floor produced the following matrix:  
\[M=\left[\begin{array}{cc}M_{nxm}&0_{nx1}\\ 0_{1 xn}&m_{a}\end{array}\right] \tag{29}\]  
\[C=\begin{bmatrix}C_{n-1 xn-1}&0_{n-1x1}&0_{n-1x1}\\ 0_{1 xn-1}&C_{nxm}+c_{a}&-c_{a}\\ 0_{1 xn-1}&-c_{a}&c_{a}\end{bmatrix} \tag{30}\]  
\[K=\begin{bmatrix}K_{n-1 xn-1}&0_{n-1x1}&0_{n-1x1}\\ 0_{1 xn-1}&K_{nxm+c_{a}}&-k_{a}\\ 0_{1 xn-1}&-k_{a}&k_{a}\end{bmatrix} \tag{31}\]  
with \(m_{a},k_{a}\), and \(c_{a}\) as the mass, stiffness, and damping of the added AMD mass, respectively. Vector B represents the location of the device control and the seismic influence.  
\[B=\left\{0,0\ \ldots,-1,1\right\}_{1x(n+1)}^{T} \tag{32}\]
# A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities
## 4 Mobility Management Using Follow-Me Cloud-Cloudlet Approach
### Follow-Me Cloud-Cloudlet Phase  
The phase mainly implements the cooperation of cloud-cloudlet \(CL_{d\to d}\). We now have Then, we have \(TIM=(\underbrace{1,1,\cdots,1}_{m^{\prime\prime}}\underbrace{10,0,\cdots,0}_{n-m ^{\prime\prime}})\), where \(m\leq m^{\prime\prime}\). After the new route path is re-routed from the new serving data center of \(c_{d}\) to the new cloudlet \(l_{d}\) which is determined by FMCC, \(n-m^{\prime\prime}\) un-transmitted packets are transmitted from \(c_{d}\) to UE through F-AP\({}_{d}\) of \(l_{d}\) by using the new re-calculated route path. The detailed procedure is given.  
**S1.**: When a new route path from DC of \(c_{d}\) to DC of \(l_{d}\) is re-calculated in route calculation module of FMCC, FMCC generates the OpenFlow _flow-mod_ message to DCG\({}_{d}\) of \(c_{d}\) to LMA\({}_{d}\) of \(l_{d}\), the new re-calculated route from DCG\({}_{d}\) of \(c_{d}\) to LMA\({}_{d}\) of \(l_{d}\) is then constructed.
**S2.**: All \(k\)-th un-transmitted packets from the final TIM message, for all if \(b_{k}=0\) and \(m+1\leq k\leq n\) are sequentially transmitted by using the new re-calculated route from DCG\({}_{d}\) of \(c_{d}\) to LMA\({}_{d}\) of \(l_{d}\). Finally, all packets are successfully received by UE such that \(TIM=TIM\_UE=(\underbrace{1,1,\cdots,1}_{n})\).  
As shown in Figure 10, since \(TIM=(1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 0,\ 0)\), then 9-th and 10-th packets are transmitted from CN of \(c_{d}\) and finally all packets are received, where \(TIM\_UE=(1,1,1,1,1,1,1,1,1,1)\).  
Figure 9: An example of _follow me cloud_ phase.
# Temporal Continuity Expression for Network Topology of Space Information Systems
## 3 Calculation of Time-Related Adjacency Matrix
### Calculation of the Relative Angular Velocity Factor of the Node  
The relative angular velocity relationship of \(S_{11}\) with the three nodes in one system period is shown in Figure 4.  
Since the relative angular velocities are all continuously derivable, and none of the relative angular velocities exceeds the set threshold, the relative angular velocity factor of the node corresponds to a single Fourier series. In this paper, the fourth-order Fourier series is used for fitting, i.e., \(\dot{\theta}_{S_{1}S_{2}}(t)=a_{0}+\frac{n}{\sum}(a_{n}\cos(n\cdot\omega\cdot t )+b_{n}\sin(n\cdot\omega\cdot t))\), \(n=4\).  
Table 4 shows the fourth-order fitting Fourier series coefficients corresponding to the relative angular velocity factor.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Satellite Pairs** & **Expression** \\ \hline \(S_{28}\) and \(S_{11}\) & \(\sigma_{2}(t)=\frac{10,650-d_{S_{11}5_{28}}(t)}{10,602-230}=\frac{10,650-d_{S_{1 1}5_{28}}(t)}{830}\) \\ \(S_{34}\) and \(S_{11}\) & \(\sigma_{2}(t)=\frac{14,820-d_{S_{11}5_{28}}(t)}{14,820-10,1099}=\frac{14,820-d_{ S_{11}5_{28}}(t)}{12,881}\) \\ \(S_{47}\) and \(S_{11}\) & \(\sigma_{2}(t)=\frac{15,120-d_{S_{11}5_{28}}(t)}{15,120-10,970}=\frac{15,120-d_{ S_{11}5_{28}}(t)}{4150}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The expression of the link distance intensity factor.  
Figure 4: The relative angular velocity relationship of \(S_{11}\) with the three nodes in one system period. (**a**) \(S_{28}\) and \(S_{11}\); (**b**) \(S_{34}\) and \(S_{11}\); (**c**) \(S_{47}\) and \(S_{11}\).  
Figure 3: The distance relationship of \(S_{11}\) with the three nodes in one system period. (**a**) \(S_{28}\) and \(S_{11}\); (**b**) \(S_{34}\) and \(S_{11}\); (**c**) \(S_{47}\) and \(S_{11}\).  
Therefore, the expressions of the relative angular velocity relationship of \(S_{11}\) with the three nodes are listed in Table 5.  
Based on the expressions of \(\sigma_{1}(t)\), \(\sigma_{2}(t)\) and \(\sigma_{3}(t)\), the elements of the adjacency matrix can be calculated as shown in Table 6.  
Similarly, the expressions of all the elements in the adjacency can be calculated, and the network topology, denoted as \(A\triangleq\left\{\delta_{ij}(t)\right\}\), can be obtained. That is the temporal continuity expression for the network topology.
## 2 Models and Definitions
### Network Reliability  
In UWSNs, network reliability reflects the effect on the network service quality when a node does not function because of serious environmental and external factors or a lack of energy. From node deployment, network reliability can be expressed by the redundancy of a network node. In this study, network reliability can be expressed by the number of nodes within a certain range of the sink node and the average number of their neighbor nodes. If the two indicators are great, the network reliability is also great.
# Numerical Investigation of Excitation of Various Lamb Waves Modes in Thin Plastic Films
###### Abstract  
Ultrasonic-guided waves are widely used for the non-destructive testing and material characterization of plates and thin films. In the case of thin plastic polyvinyl chloride (PVC), films up to 3.2 MHz with only two Lamb wave modes, antisymmetrical A\({}_{0}\) and symmetrical S\({}_{0}\), may propagate. At frequencies lower that 240 kHz, the velocity of the A\({}_{0}\) mode becomes slower than the ultrasonic velocity in air which makes excitation and reception of such mode complicated. For excitation of both modes, we propose instead a single air-coupled ultrasonic transducer to use linear air-coupled arrays, which can be electronically readjusted to optimally excite and receive the A\({}_{0}\) and S\({}_{0}\) guided wave modes. The objective of this article was the numerical investigation of feasibility to excite different types of ultrasonic-guided waves, such as S\({}_{0}\) and A\({}_{0}\) modes in thin plastic films with the same electronically readjusted linear phased array. Three-dimensional and two-dimensional simulations of A\({}_{0}\) and S\({}_{0}\) Lamb wave modes using a single ultrasonic transducer and a linear phased array were performed. The obtained results clearly demonstrate feasibility to excite efficiently different guided wave modes in thin plastic films with readjusted phased array.  
air-coupled ultrasonic; Lamb waves; finite element modeling; plastic films Article  
M 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Stress-Strain Analysis of the Rock Mass and Passive Bolts Using the Finite Difference Method
### Stress-Strain Analysis in a Bolted Region  
In Figure 6, a bolted region is discretized into \(n\) annuluses with a uniform thickness \(\Delta r\), starting from the bolted-unbolted interface. The \(i\)th annulus is bounded by two circles of \(r=r_{i-1}\) and \(r=r_{i}\). The radial stress \(\sigma_{r(0)}\), tangential stress \(\sigma_{\theta(0)}\), and radial displacement \(u_{r(0)}\) at the outer boundary of the bolted region (\(r=r_{(0)}=R+l_{b}\)) equal \(\sigma_{r(R+lb)}\), \(\sigma_{\theta(R+lb)}\), and \(u_{r(R+lb)}\), respectively. The number of the rock mass annuluses \(n\) and inner radius \(r_{i}\) of each annulus can be respectively expressed as:  
\[n\ =\ l_{b}/\Delta r, \tag{22}\]  
\[r_{(i)}\ =\ r_{(i-1)}-\Delta r. \tag{23}\]  
For the mechanical analysis of a bolted region, first, the elastoplastic state of the rock mass in the \(i\)th annulus is verified using Equation (4) (Mohr-Coulomb (M-C) rock mass) or Equation (5) (Hoek-Brown (H-B) rock mass); then, the stresses and displacements in an elastic annulus can be analyzed using the equations in Section 3.2.1; the stresses and displacements in a plastic annulus can be analyzed using the equations in Section 3.2.2; the behaviors of the passive bolts can be analyzed using the equations in Section 3.2.3; and the radius of the plastic region can be obtained in this process.
## 5 Results and Discussion  
To evaluate the effectiveness of the proposed FM-ITC-Darea strategy, benchmark schemes were established, including no control, the SPSC-LCC integrated control strategy based on the simple proportional speed controller (SPSC) algorithm, and the FM-ITC strategy using multi-agent reinforcement learning for static control areas. These benchmarks illustrate the advantages of the FM-ITC-Darea strategy with dynamic control areas. Below, the basic experimental setup is introduced, and the training process of FM-ITC-Darea is analyzed.
# Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review
## 5 Identified Challenges and Solution Directions
### RQ1. What Are the Identified Challenges in the MSA Domain?
#### 5.1.1 Service Discovery  
In a distributed architecture such as microservice architectures, the discovery of microservices is one of the primary challenges. The challenges for service discovery relate to the design, implementation, and quality concerns. At the design level, designing the service discovery is considered to be a challenge due to the multiple various service discovery mechanisms such as client-side, server-side, and hybrid service discovery. The proper decision needs to be made based on the various different requirements and quality concerns. Often multiple different design alternatives can be identified, and it is not easy to derive a feasible alternative. Implementing service discovery is directly dependent on system size and selected design so the most important criteria for the implementation are high availability and scalability. A misguided design selection and subsequent development will affect the system's availability and scalability. During the operation time or run-time, discovering the proper services requires the corresponding orchestration which needs to be aligned with the required quality of service parameters. One important quality factor is the latency of the discovered and triggered service.
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 5 Simulation and Results
### Probability of Successful Prediction  
When the prediction entities fail to predict the processing delay or uplink occurrence, the UE avoids transmitting an empty packet at the prescheduled time, thus preventing battery consumption. However, the radio resources reserved for NPUSCH are not used and reduce the cell capacity. To generalize the unused radio resource, we compare the radio resources of the legacy NB-IoT procedure with those of the proposed PBESM:  
\[P_{ul}^{(i,j)}.N_{legacy}^{(i,j)}\geq P_{ul}^{(i,j)}\Big{\{}P_{s}^{(i,j)}.N_{ PBESM}^{(i,j)}+\left(1-P_{s}^{(i,j)}\right)N_{legacy}^{(i,j)}\Big{\}}+\left(1-P_{ ul}^{(i,j)}\right)\left(1-P_{s}^{(i,j)}\right).N_{NPUSCH}^{(i,j)} \tag{6}\]  
where:  
* \(P_{s}^{(i,j)}\) is the target probability of successful prediction for the uplink packet occurrence,
* \(P_{ul}^{(i,j)}\) is the probability of uplink packet occurrence to the downlink packet in the NB-IoT device, and
* \(N_{X}^{(i,j)}\) the number of the radio resource units for procedure \(X\) including repeating transmission. The number of radio resources used in the legacy NB-IoT and proposed scheme are, respectively, \[N_{legacy}^{(i,j)}=N_{NPRACH}^{(i)}+N_{NPDCCH\_RAR}^{(i)}+N_{NPDSCH\_RAR}^{(i)} +N_{NPUSCH\_MSG3}^{(i,j)}+N_{NPDCCH}^{(i,j)}+\ N_{NPUSCH}^{(i,j)}\] (7) \[N_{PBESM}^{(i,j)}=P_{o}^{(i,j)}.N_{NPUSCH}^{(i,j)}+\left(1-P_{o}^{(i,j)} \right).\left(N_{NPUSCH}^{(i,j)}+N_{legacy}^{(i,j)}\right).\] (8) Then, Equation (6) becomes \[P_{o}^{(i,j)}\geq\frac{N_{NPUSCH}^{(i,j)}}{N_{legacy}^{(i,j)}}\times\varphi^{ (i,j)}\] (9) where \(\varphi^{(i,j)}=\left(1-\frac{1}{P_{u}^{(i,j)}}\right).\left(1-\frac{1}{P_{s} ^{(i,j)}}\right)+1\geq 1\).  
Equation (9) shows that the lower boundary for the target probability is proportional to the ratio between the number of resources for NPUSCH and the legacy procedure. In addition, the target probability should increase with the uplink packet size.
## 3 Design of RoiSeg  
In this section, we will describe the designing process of RoiSeg. As shown in Figure 1, RoiSeg consists of three modules, namely, ROI-central-point generation and feature extraction, automatic training-sample generation, and an online sample classifier. The purpose of RoiSeg is to classify the foreground through the ROI central points. In the first module, the frame difference and canny edge detection are used to transform the background modeling of each pixel into an ROI-central-point-based classification problem, which greatly reduces the amount of data operation. We then extract the features of the ROI central points and provide them to the automatic training-sample generation. In the second module, the characteristics extracted from the ROI central points are made into training samples using ROI-central-point-based sample clustering and a proposed trajectory-based class classifier. In the third module, we explore the training samples and find that they are class imbalanced. A K-means oversampling method is proposed to solve the class imbalance problem and a means to update training samples online is employed to compensate for the weaknesses of the Bayesian classifier.
# An Orthogonal Wheel Odometer for Positioning in a Relative Coordinate System on a Floating Ground
## 4 Discussion
Additionally, the orthogonal wheel structure is equipped with a sliding structure so that the positioning system can easily ignore the irregular movement of the system caused by the floating ground. The experimental results show that the errors of the orthogonal wheel positioning system are within the acceptable range (\(\pm\)3 cm) under the conditions of linear motion, curvilinear motion and floating environment, and the expected effect of the centimeter-level positioning system is achieved. The experimental results highlight the potential application of the positioning system in a complex environment. In the case of subsequent use of other positioning calibrations, the accuracy may be even higher.
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
## 3 Methods and Materials
### Experiment Three Application--MAUI ROS Live  
The third MLS application, named MAUI ROS Live, is built upon all components of the MAUI ROS application from phase two. Phase three has two primary design objectives.  
The first is to exchange telemetry data between ScanEagle and CASSMIR. The second is to send control messages from a third party acting as ground control for ScanEagle. We installed MAUI ROS within the ScanEagle, CASSMIR, and ground control virtual environments during this phase. At runtime, MAUI ROS prompts the user to select which profile to run. The profiles ScanEagle, CASSMIR, or ground control. Each profile comes with specific functions enabled or disabled for overall use case functionality. The ScanEagle virtual environment creates the MLS group, and the CASSMIR and ground control virtual environments represent the second and third members of the MLS group. Figure 5, shows the architecture setup for the MAUI ROS Live application.  
For the first objective, MAUI ROS Live replaces the generic plaintext topic messages with actual ScanEagle and CASSMIR ROS topic data. The UxS unique topic data types of interest are the odometer data from ScanEagle and the GPS data from CASSMIR. Unlike MAUI ROS, where the topic messages are of data type **string**, the odometer and GPS data are more complex data types containing multiple parameters. To handle these complex data types, we created the **odom_to_string** and **gps_to_str** functions that convert each parameter within the unique data type to a string, and these newly concatenated strings are converted into a single string. This conversion allows MAUI ROS Live to utilize all prior functions to encrypt, decrypt, and send and receive the ScanEagle and CASSMIR data over the IP network. Since the odometer and GPS data have been concatenated, the received message must be parsed to display the data in their original form. To address this, we created the **parse_string** function which was also added to the **convert** class.  
For the second objective, MAUI ROS Live adds the ability for the ScanEagle to receive commands from a third member acting as ground control. This functionality is accomplished by creating a third thread that allows for another TCP connection between ScanEagle and the ground control client as shown in Figure 6. The ground station prompts a user to pass a control command to adjust the ScanEagle turn rate. The turn rate must be a number between \(-30\) and \(30\). The negative numbers represent a left turn, and the positive numbers represent a right turn. These control commands are sent utilizing all prior functions over the IP network. When the ScanEagle receives the control command, it also utilizes all prior functions. Once the message is converted into a data type **string** it is then  
Figure 5: Depicts architecture used to test the MLS MAUI ROS Live Application.  
converted to a **float** data type and published to the **turn_rate** topic to control the ScanEagle within the simulated environment.
## 3 Results and Discussion
### Sensitivity Analysis  
As illustrated in Figure 7, the sensitivity analysis results showed that \(r_{d,th}\) and \(r_{d,sim}\) converged at 10 m traversed distance. The 20% difference of \(r_{d,th}\) from \(r_{d,sim}\) can be attributed to the different variables considered to determine each parameter. \(r_{d,th}\) used inrencing speed, travel velocity, and capture width to theoretically calculate the gaps between consecutive processed frames related to the detection rate, as illustrated in Section 2.1.  
On the other hand, \(r_{d,sim}\) determined the detection rate based on the number of unique plants within the processed frames, as influenced by traversed distance, hill spacing, inferencing speed, travel velocity, and capture width, as described in Section 2.2, Section 2.3, Section 2.4. Results showed that simulation better approximated the detection rate than theoretical approaches at less than 10 m traversed distance, 0.2 m hill spacing, 2.5 m/s travel velocity, 2.4 _fps_, and 0.5 m frame capture width.  
These results infer that at very short distances, \(r_{d,sim}\) approximates the detection rate more accurately than \(r_{d,sim}\). However, for long traversed distances, the influence of hill spacing on the detection rate was no longer significant and \(r_{d,th}\) can simply be used to calculate the detection rate.  
Figure 6: Laboratory setup composed of Jetson Nano 4 GB, Logitech StreamCam Plus, and variable speed conveyor belt with artificial potted plants.