esa-sceva/satcom-chunk-collection huggingface dataset:
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 2 Model and Algorithm
### Training Environment
#### 2.3.2 Five-Axis Thrust Calculation  
In Figure 5a, divert nozzles produce lateral acceleration by thrusting at the spacecraft's center of mass, typically in the range of hundreds to thousands of newtons. This allows for rapid orbital adjustments without attitude changes. Nozzles 0 and 2 act on the \(z\)-axis, while nozzles 1 and 3 act on the \(y\)-axis.  
Figure 5b illustrates attitude control nozzles at the spacecraft's tail, exerting tens to hundreds of newtons. Nozzles 4 and 7 induce clockwise rolling, while nozzles 5 and 6 induce counterclockwise rolling. Nozzles 4 and 5 pitch up, nozzles 6 and 7 pitch down, nozzle 8 causes left yaw, and nozzle 9 causes right yaw.  
The equations for the synthesis of thrust in the five-axis direction are as follows:  
\[{F_{y}}={F_{3}}-{F_{1}} \tag{2}\]  
\(z\)-axis thrust:  
\[{F_{z}}={F_{2}}-{F_{0}} \tag{3}\]  
yaw-axis thrust:  
\[{F_{yaw}}={F_{9}}-{F_{8}} \tag{4}\]  
pitch-axis Thrust:  
\[{F_{pitch}}=({F_{6}}+{F_{7}})-({F_{4}}+{F_{5}}) \tag{5}\]  
roll-axis Thrust:  
\[{F_{roll}}=({F_{6}}+{F_{5}})-({F_{4}}+{F_{7}}) \tag{6}\]  
Figure 5: Synthesis method of divert control thrust and attitude control thrust. (**a**) Divert control thrust synthesis; (**b**) attitude control thrust synthesis.  
The thrust generated by each nozzle can be calculated according to the thrust calculation equation. The thrust can be expressed as the product of the thrust coefficient, \(C_{f}\), the pressure, \(P_{c}\), and the throat area, \(A_{t}\).  
\[F=C_{f}P_{c}A_{t} \tag{7}\]
## 3 DAB LCC Resonant Converter TPS Control Validation  
### Experimental Hardware Setups  
Figure 18 shows the experimental setup of the DAB LCC resonant converter TPS wireless charging control system. The hardware setup consists of a wireless charging transformer, DAB DC/DC converter built with 900 V, 23 A silicon carbide (SiC) MOSFET C3M0120090D inverter and rectifier bridges, a 1000 \(\mu\)F capacitor on the DC source side, a 2200 \(\mu\)F capacitor on DC load side. A variable resistance load connected to the rectifier DC output emulates the battery charging condition.  
DAB LCC resonant converter operates at 30 kHz resonant frequency. SiC MOSFET C3M0120090D has on-resistance Rds = 120 m\(\Omega\), source-to-drain capacitance 40 pF, and SiC MOSFET body-diode forward voltage 4.8 V, body-diode reverse recovery charge Qrr = 115 nF, and body-diode reverse recover time 24 ns.  
The LCC resonant compensation circuit consists of high-frequency ferrite core inductors and 630 V polypropylene high-frequency high voltage film capacitors. The current and voltage sensor circuit is built with a LEM LA 55-P current transducer and a LEM LV 25-P voltage transducer. PLECS RT box is used as a real-time controller for fast prototyping and experimental validation of the proposed TPS control scheme.  
Figure 17: Inverter/rectifier voltage/current, MOSFET gating signals, diode currents PLECS circuit simulation results for Vo = 30 V, Io = 0.1 A (R\({}_{\text{dc}}\) = 300 \(\Omega\), emulate CV mode) with \(\beta\)p (deg) = 47.065\({}^{\circ}\), \(\beta\)s (deg) = 46.47\({}^{\circ}\), \(\delta\) (deg) = 110\({}^{\circ}\).  
In the experiment test, the input DC power supply voltage is set to 30 V. The inverter-to-rectifier phase shift angle is set to \(\delta\) (deg) = 90\({}^{\circ}\) and rectifier phase shift \(\beta\)s is regulated to meet R\({}_{\text{ac}}\) = 38 \(\Omega\) in normal TPS close loop operation. When rectifier R\({}_{\text{ac}}\) feedback close loop control is disabled, the rectifier maximum phase shift angle \(\beta\)s = 0.5 (\(\beta\)s (deg) = 180\({}^{\circ}\)) is applied. The measurement results are also obtained for different inverter-to-rectifier phase shift angle \(\delta\) settings to validate the conclusion obtained from the TPS open loop control circuit simulation.
## 3 Model  
The material point model of the slope is shown in Figure 3, where Figure 3A represents the pure soil slope model, and Figure 3B represents the SRM slope model. The following simplifications have been made when modeling SRM slope, considering that SRM slopes are primarily found in reservoir areas [14], where most of the stones have been washed by river water and are primarily pebbles, thus simplifying the shape of the stones to round [4]. For both slopes, the number of material points is 84,052. For the SRM slope, the number of material points representing stones and soil is 19,077 and 64,975, respectively, and the stone content is 22.7%.  
Figure 3: (**A**) Geometric parameters of pure soil slope model; (**B**) Geometric parameters of SRM slope model. (84,052 material points in total, 64,975 MPs for soil, 19,077 MPs for stone).  
In the simulation, essential boundary conditions are applied on both sides, and the bottom of the model and other surfaces are free. The mechanical parameters of the material are shown in Table 1. The parameters are mainly from [9]. Young's modulus of the rock is 400 times that of the soil, the ratio of the internal friction angle to the dilatancy angle is less than twice, and the ratio of the internal friction force is 90 times. The rock will not reach the yield state in the actual simulation process.  
To make the simulation more stable, this paper sets a small-time step, 0.01 ms, in the simulation. The simulation process lasted for 22 s and was divided into two stages. 0-2 s was the first stage, and a stable PIC momentum mapping format [41] and elastic constitutive model were adopted. The gravity is linearly loaded until g = 9.8 m/s\({}^{2}\), and then the momentum is set to 0 to obtain the initial state of the slope. 2-22 s is the second stage, using a FLIP momentum mapping scheme with good momentum conservation characteristics [42].
# Smartwatch-Based Monitoring and Alert System for Factory Operators Using Public Cloud Services
## 3 Materials and Methods
### The Implementation of the Proposed Solution  
#### 3.2.1 Configuring AWS Cognito Service  
AWS Cognito was selected for this system due to its robust user management and authentication features, which integrate effortlessly with the AWS ecosystem and web applications. Utilizing user pools, Cognito enables the creation and administration of a user directory, providing registration and login functionalities tailored to different user roles. Once the user pool is configured, Cognito simplifies user account creation, confirmation, and management. By assigning users to specific groups within the user pool, we efficiently control access permissions to the web application. Upon login, Cognito generates access tokens (JSON Web Tokens) containing user scope and group affiliations, which are then leveraged in authentication flows and across the application.  
We set up the user pool by following these configuration steps:  
* **Authentication Methods:** We defined authentication options, allowing users to log in using either their username or email address.
* **User Creation:** During account creation, users are required to provide their name, which is stored and later used for tracking employee-related information.
* **User Group Creation:** A user group was established within the user pool to manage accounts with administrator privileges in the web application.
* **App Client Configuration:** We configured an \"App Client\" to generate a client ID, which is used by the server to interact with the Cognito API. This enables user authentication, as well as the ability to add, modify, and delete user profiles.
# Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining
## 2 Related Concepts
### Mathematical Description of Multi-Objective Optimization of Maximal Frequent Itemset Mining  
The maximal frequent items were used as the optimization objective. Then, two optimization objects, namely _support_ and the number of items in an itemset, were noted. According to Property 1, _support_ and the number of items in an itemset are two contradictory optimization objectives. The constraint was that the _support_ was higher than the given threshold. Given the gene codes of solution individual \(X\) = (\(x_{1}\), \(x_{2}\),..., \(x_{m}\)) and big data transaction set \(D\), the mathematical descriptions of the multi-objective optimization problem are:  
\[Maximize\ P(X) \tag{3}\]  
\[Maximize\ \sum_{i=1}^{m}x_{i} \tag{4}\]  
\[s.t.\ \ \ \ \begin{array}{l}P(X)\geq min\_support\\ x_{i}\in\{0,1\}(\ i=1\ \sim m)\end{array} \tag{5}\]  
where \(P(X)\) is the probability of occurrence of an itemset, which is determined by the individual gene encoded in \(D\), which is known as _support_. \(m\) is the quantity of the frequent 1-itemset with _support_ higher than the threshold. \(X_{i}\in\{0,1\}\) (\(i=1,2,\ldots,m\)) is a decision variable that is used to determine whether the item \(i\) belongs to the itemset \(X\), where 0 indicates no and 1 indicates yes. _Min_support_ is the support threshold that is used to determine whether it belongs to the frequent itemset. If \(P(X)\) is not lower than the threshold, then it is a frequent itemset; otherwise, it is not a frequent itemset.
### Background and Literature Review
#### 1.2.2 Content Replication Based on Push Protocols  
Content replication refers to the duplication of data or content to multiple locations to ensure its availability and consistency. In CDNs, to replicate the content over edge servers, two important protocols are considered: UDP (User Datagram Protocol) is a quick, erratic, and unordered method of data transport, whereas TCP (Transmission Control Protocol) is dependable and ordered. Where speed is important and delivery order is not essential, such as in live streaming, UDP may be utilized for video equipment. TCP, on the other hand, is more appropriate for video equipment where dependability is crucial, such as video on demand. The choice between TCP and UDP for video content delivery depends on the specific requirements of the application and the trade-off between reliability and speed. Therefore, different protocol-based application layers to push segmentation of adaptive video streaming in CDN infrastructure are proposed [13]. Their purpose was to reduce resource usage of CDN components. The optimal push protocol is selected to reduce the overhead of surrogate servers and reduce usage of the network resources. [PERSON] et al. [14] investigated the mechanism of Server Push by employing QUIC as a primary transport protocol for multicast-assisted adaptive bitrate streaming. The proposed approach gave a significant solution that reduced the amount of established video streaming sessions and reduced the wastage of network bandwidth.  
[PERSON] et al. [15] discussed the advantage of HTTP/2, which is a popular protocol that provides several improvements over its predecessor HTTP/1.1 such as multiplexing, server push, and header compression. They designed three modular HTTP/2 server push-based strategies leveraging Multipath TCP to improve the delivery of video content in lossy environments. Their goal is to improve start-up delays and overall performance. The combination of HTTP/2 and MPTCP can provide a powerful and flexible architecture for adaptive bitrate video delivery, enabling high-quality video playback even in challenging network conditions.  
[PERSON] et al. [16] compared application layer protocols for video transmission. They take advantage of WebSocket to support adaptive bitrate streaming, where the server can send different qualities of the video stream based on the client's available bandwidth. One potential challenge with WebSockets over a CDN is the need to ensure that the WebSocket connection is established and maintained across all CDN edge locations. However, modern CDNs provide sophisticated WebSocket routing and load balancing mechanisms, enabling WebSocket connections to be efficiently and reliably established across the CDN. Server-Sent Events (SSE) [17] over a CDN can provide a powerful and efficient solution for real-time content delivery, enabling fast and responsive updates to be delivered to clients, regardless of their location or available bandwidth. Data transmission between original server and edge servers based on strategy protocols are summarized in Table 1[13; 14; 15; 16; 17].
## 3 Dynamic Analysis of EDL Trajectory  
The online released Mars Science Laboratory (MSL) EDL trajectory [22] is chosen as the trajectory of the benchmark. The Chinese Jiamusi deep space TT&C (tracking, telemetry, and command) station is selected as the signal receiving station.  
The computed range rate, its derivatives, and the corresponding Doppler dynamics at X-band are shown in Figures 3-5.  
Figure 4: Computed first-order derivative values of Doppler for MSL: (**a**) First-order derivatives of range rate; (**b**) Doppler rate (X-band).  
Figure 5: Computed second-order derivative values of Doppler for MSL: (**a**) Second-order derivatives of range-rate; (**b**) Second-order Doppler rate (X-band).  
Figure 3: Computed Doppler values for Mars Science Laboratory (MSL): (**a**) Range-rate; (**b**) Doppler frequency (X-band).  
It could be seen from Figure 3 that the Doppler frequency curve (also the range-rate curve) drifts linearly with time at the beginning, which corresponds to the cruise flight of MSL, approaching Mars. At around 100 s, the spacecraft got into the Mars atmosphere for aerobraking, and the Doppler frequency increased rapidly and smoothly. The maximum of the Doppler rate is around 3400 Hz/s. After aerobraking, the spacecraft got closer to the surface of Mars. At around 350 s, there is a sharp jump of the Doppler rate, which could also be seen in Doppler frequency and second-order Doppler rate curve. The maximum value of the second-order Doppler rate curve is around 450 Hz/s\({}^{2}\). The total duration of EDL lasts about 5 minutes, and the variation of Doppler frequency is about 160 kHz.
## 4 Outage Probability Analysis  
Besides BER, outage probability is another important performance index of coherent THz wireless links. The outage probability is the chance that the instantaneous capacity is insufficient to meet the necessary data rate [30]. Thus, the outage probability of a given coherent THz link can be expressed as  
\[P_{\text{outage}}(\Lambda)=\Pr(\text{Y}<\Lambda)=\int_{0}^{\Lambda}f_{\text{ Y}}(\text{Y})d\text{Y} \tag{35}\]  
where \(\Pr(\cdot)\) represents the probability of outage occurrence, \(\Lambda\) is the predefined outage threshold, and \(f_{\text{Y}}(\text{Y})\) is the PDF of the instantaneous SNR. With our derived composite PDF models, the outage probability under a specific transmission condition can be calculated. For weak turbulence, substituting the relationship \(\text{Y}=\gamma h\) and Equation (12) into Equation (35), one obtains the outage probability for LN channels as  
\[\begin{split} P_{\text{out,LN}}=&\frac{1}{2}\Bigg{[} \exp(\mu_{c})\operatorname{erfc}\Bigg{(}\frac{\ln\Big{(}\frac{\Lambda}{A_{0}h _{1}\bar{\gamma}}\Big{)}+\mu_{b}}{\mu_{c}}\Bigg{)}-\exp\bigg{(}\mu_{a}-\gamma ^{2}\mu_{b}+\frac{\gamma^{4}\mu_{c}^{2}}{4}\bigg{)}\\ &\times\operatorname{erf}\Bigg{(}\frac{\gamma^{2}\mu_{c}}{2}- \frac{\ln\Big{(}\frac{\Lambda}{A_{0}h_{1}\bar{\gamma}}\Big{)}+\mu_{b}}{\mu_{c} }\Bigg{)}\Bigg{]}.\end{split} \tag{36}\]
## 3 Related Work
### Formal Methods  
Formal methods are mathematics-based techniques used for describing properties of software and hardware systems [15]. The Z notation is a model-centered approach usually used for specifying the behavior of programs by abstract data types and has standard set operators. The Z allows organizing asystem into its smaller components using a powerful data structure named schema. The schema defines a way in which state of a system can be specified and further refined by describing details of a system [16]. A schema has two parts, one for variables definitions and another for defining the properties of these variables.  
To the best of our knowledge, the only effort to model wireless sensor and actuator networks has been reported in [17]. They used Colored Petri Nets (CPNs) to model and validate some components of sensor and actuator networks. However, it is unclear that the CPN approach can be used to model more sophisticated WSANs algorithms like PCR. Recently, some works in the literature [18; 19; 20] have advocated the use of formal methods in _ad hoc_ and sensor network protocol validation. For example, [PERSON] _et al._ have proposed a formal specification based conformance testing methodology to validate the routing protocols in MANET, especially DSR [18], while the authors in [19] used Real-Time Maude for modeling, performance estimation and model checking of the state-of-the-art OGDC algorithm for sensor networks. A comprehensive survey of formal and informal techniques for protocol validation in _ad hoc_ and sensor networks is presented in [20]. Some other similar work on a formal specification of MANET routing protocols can be found in [21; 22; 23].  
Our work is different from the formal method-based approaches discussed above and others in that: (i) we model WSAN as a dynamic graph and transform PCR into a corresponding formal specification using Z notation. This formal specification is analyzed and validated using the Z Eves tool, and (ii) we simulate the specification to analyze the effectiveness and efficiency of PCR.
## 3 Engineering Example
### Results  
(1) When the response displacement method is adopted and the peak acceleration of SL-2 is 0.15 g, during the lateral seismic analysis, the rate of the diameter change  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Working Condition**} & \multicolumn{8}{c}{**Joint Position**} \\ \cline{2-9}  & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Section 1 & 0.312 & 0.482 & 0.391 & 0.386 & 0.529 & 0.393 \\ Section 2 & 0.302 & 0.460 & 0.434 & 0.384 & 0.525 & 0.373 \\ Section 3 & 0.261 & 0.411 & 0.359 & 0.298 & 0.473 & 0.348 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Joint stretching value of lining segment (Time-History Analysis Method).  
Figure 23: Rate curve of diameter change of lining segment (Time-History Analysis Method).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Working**} & \multicolumn{8}{c}{**Rate of Diameter Change at the Positions at which Monitoring Sites are Located (\%)**} \\ \cline{2-9}  & **1–9** & **2–10** & **3–11** & **4–12** & **5–13** & **6–14** & **7–15** & **8–16** \\ \hline Section 1 & 1.076 & 1.060 & 1.076 & 1.336 & 1.228 & 1.156 & 1.148 & 1.112 \\ Section 2 & 1.008 & 1.204 & 1.244 & 1.300 & 1.200 & 1.080 & 1.044 & 0.984 \\ Section 3 & 0.872 & 0.908 & 1.048 & 1.248 & 0.864 & 1.068 & 0.956 & 0.880 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Rate of diameter change of lining segment (Time-History Analysis Method).  
Figure 24: Joint-stretching value curve of lining segment (Time-History Analysis Method).  
varies between 1.846% and 1.914%, and the maximum joint-stretching value between sections varies between 0.680 and 1.159 mm; during the longitudinal seismic analysis, the longitudinal misalignment varies between 0.836-0.999 mm, and the joint-stretching value varies between 5.093 and 6.274 mm. Under different seismic actions, the maximum value of the longitudinal deformation of the structure always appears at the position at which the abrupt change from soft to hard strata occurs, proving that this is the weak position of the longitudinal seismic resistance of the shield tunnel structure; thus, it should be given priority in the design.  
(2) The calculation results of the dynamic time-history analysis indicate that the maximum bending moment, maximum shear, and maximum axial force of the shield tunnel are 222.5 kN\(\cdot\)m, 1448.3 kN, and 3456.3 kN, respectively. The strength requirement of the lining segment can be satisfied by proper reinforcement. Under the action of an SL-2-level ground motion, the maximum rate of diameter change for the shield tunnel is 1.336%, and the maximum joint-stretching value is 0.529 mm. The geological condition has a certain influence on the internal force and deformation of lining segments. When the silt layer of the shield tunnel lining segments is thicker and the geological condition is relatively poor, the internal force, the rate of diameter change, and joint-stretching value of the joints are larger.
## 3 Results and Discussions
### Comparison of the Performance of the Models  
An illustration of detection using SSD MobileDet Edge TPU is shown in Figure 10. The detection using the model is shown on the left, while the ground truth is shown on the right. As we can see, the model was able to identify a tree trunk in the image, despite its distance from the camera and its modest size.  
Table 2 shows the performance of the SSD model with different models as backbones on the GPU and the Raspberry Pi. From the table, it can be seen that the best performance in terms of accuracy on PC was achieved by the SSD model with MobileDet Edge TPU and on the RaspberryPi with MobileNet Edge TPU. The degradation in accuracy when switching from a PC to a Raspberry Pi was less with the MobileNet Edge TPU than with the other models. Although the SSD with MobileNet-V2 model and the MobileDet model performed better than SSD with MobileNet Edge TPU on PC, the accuracy on the RaspberryPi decreases. MobileNet-V1 achieved the worst results on PC and the Raspberry Pi.  
It is interesting to note that for all models, the accuracy of the model running on the Raspberry Pi with TPU is superior to the accuracy of the model running on the Raspberry Pi without TPU.  
Figure 10: Detection results using SSD MobileDet Edge TPU.  
Figure 9: mAP of models during training (threshold = 50). Top left: MobileNet-V1, top right: MobileNet-V2, bottom left: MobileNet Edge TPU and bottom right: MobileDet Edge TPU.  
On the Raspberry Pi with TPU, the models all have virtually the same inference time, with the SSD achieving the fastest inference time with MobileNet-V1 as the backbone.  
The inference time for the models created for the TPU is longer than MobileNet-V1 and V2 on the RaspberryPi without TPU, but it is the same on the RaspberryPi with TPU.  
Figure 11 shows the mean average accuracy versus latency of the trained models on the RaspberryPi without and with TPU. As can be seen, the models created for the TPU perform better on the TPU, however the inference time is longer than MobileNet-V1. Depending on the application, the model should be chosen because there is a trade-off between accuracy and latency.
# Influence of Soil Particle Size on the Engineering Properties and Microstructure of a Red Clay
###### Abstract  
Particle size exerts a significant influence on the mechanical behavior of soil. However, insufficient research has been carried out on red clay formations, which are widespread in some Chinese provinces. Here, using unconfined compressive strength (UCS) tests, wetting-drying (WD) tests, and low-temperature nitrogen adsorption tests, we examined the relationship between the particle size and a number of mechanical and microstructural characteristics of a red clay outcropping at a construction site in China. Our results suggest that, depending on the surface area, porosity, particle size, and dry density, the failure mechanism in UCS tests will be different. That is, as the particle size increases, the failure mode of soil changes from split failure into shear failure. In addition, as the dry density increases, the UCS of the soil sample is significantly improved, and its total porosity and fractal dimension decrease. We also evaluated a dependence on particle size and the number of WD cycles on the distribution and size of cracks in WD tests. We conclude by suggesting that particle size, dry density, and WD behavior should all be taken into account in roadbed designs in red clay formations.  
red clay; particle size effect; specific surface area; pore; engineering characteristic +
Footnote †: journal: Applied Sciences