esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Reliability Evaluation Method of a Single Failure Criterion
### System Reliability Estimation Based on Multivariate Copulas
#### 2.2.2 Failure Criterion II: Failure of Any Two Adjacent Components  
Failure criterion II refers to the series-parallel system shown in Figure 3. It assumes that failure of any two adjacent components will cause the whole structure to fail.  
Figure 2: Multiple-component series system.  
For a sub-parallel system composed of two components, the nonlinear correlation between the components in each subsystem is considered. In the series system consisting of each sub-parallel system, each sub-parallel system is considered to be independent of each other. The performance function of each component is defined as follows in Equation (9):  
\[Z_{ij}(\mathbf{X})=Z_{ij}(X_{1},X_{2},\ldots,X_{n}),i=1,2,\ldots,n-1,\ j=1,2, \ldots,n. \tag{9}\]  
where \(Z_{ij}\) represents the performance function of the \(j\)th component in the \(i\)th sub-parallel system.  
According to the relationship between the components and the system shown in Figure 3, with Equations (1) and (7), the system reliability of failure criterion II (failure of any two components) can be obtained based on the bivariate copula function, as follows:  
\[\begin{array}{ll}P_{\hat{f}_{1}}&=P[Z_{11}(\mathbf{X})\leq 0,Z_{12}(\mathbf{X}) \leq 0]\cup P[Z_{22}(\mathbf{X})\leq 0,Z_{23}(\mathbf{X})\leq 0]\cup\cdots\cup P \big{\{}Z_{n-1,n-1}(\mathbf{X})\leq 0,Z_{n-1,n}(\mathbf{X})\leq 0\big{\}}\\ &=1-(1-P[Z_{11}(\mathbf{X})\leq 0,Z_{12}(\mathbf{X})\leq 0])\cap\cdots\cap \big{(}1-P\big{\{}Z_{n-1,n-1}(\mathbf{X})\leq 0,Z_{n-1,n}(\mathbf{X})\leq 0 \big{\}}\big{)}\\ &=1-\prod\limits_{i=1}^{n-1}\big{(}1-P\big{\{}Z_{i,j-1}(\mathbf{X})\leq 0,Z_{ ij}(\mathbf{X})\leq 0\big{\}}\big{)}=1-\prod\limits_{i=1}^{n-1}\big{(}1-C\Big{(}P_{ \hat{f}_{i,j-1}},P_{\hat{f}_{i,j}}\Big{)}\big{\}},\ j=i+1.\end{array} \tag{10}\]  
where \(C(\cdot)\) represents the binary copula function and \(P_{\hat{f}_{1}}\) represents the system failure probability of failure criterion II.
# CoFi: Coding-Assisted File Distribution over a Wireless LAN
## 6 Reverse Engineering of CoFi and 802.11
### Asymptotic Throughput of CoFi and 802.11  
We consider a wireless LAN topology with one AP and \(K\) clients, denoted as \(C_{1},C_{2},\ldots,C_{K}\). The average reception rate of each link (AP\(\to C_{i}\)) equals \(p_{i}\). The maximum link bandwidth supported by the underlying PHY is \(R\). We assume each client requests a large file from the AP and the AP serves all clients in a round-robin manner.  
For the 802.11 MAC, we assume the AP adopts persistent retransmission without backoff, thus analyzing an asymptotic upper bound of its throughput. For each client \(C_{i}\), the average number of transmissions needed to deliver a unit packet is \(x_{i}=1/p_{i}\). Since the airtime of each packet is constant, the interval that AP serves \(C_{i}\) is:  
\[f_{i}=\frac{1}{\sum_{j=1}^{K}x_{j}} \tag{2}\]  
which is independent of the client's identity. Within this interval, the total units of packets that are successfully delivered is \(K\). Therefore, the total network throughput is:  
Figure 15: The per-packet delay of the network running 802.11 protocol and CoFi protocol with/without MAC caching in a dynamic topology.  
\[T=\frac{K}{\sum_{j=1}^{K}x_{j}} \tag{3}\]  
Since each client only receives one packet intended for it during each interval, its effective throughput is:  
\[T_{i}=R\cdot\frac{T}{K}=\frac{R}{\sum_{j=1}^{K}x_{j}} \tag{4}\]  
which depends on the link quality of all other clients.  
For CoFi without caching, in each round a client is served only once. Therefore, its effective throughput only depends its own link quality. Specifically, the throughput of a client \(C_{i}\) is:  
\[T_{i}^{\prime}=(R/K)\cdot p_{i} \tag{5}\]
## 4 Experiment and Analysis of Experimental Results
### Comparison Analysis between Simulated and Measured Vibration Signals under Fault Condition  
Figure 16 draws the waveform and frequency spectrum of the simulated and measured vibration signal of the planetary gearbox with one tooth broken on the sun gear. Similarly, the waveform error between the simulated signal and the measured signal is 1.1361%. The gear meshing frequencies \(f_{m},2f_{m}\), and \(3f_{m}\) can be observed in Figure 16c,d. However, it can be seen that due to the tooth fault, the sidebands are abundant and the spectral structure turns out to be more complex than that of the healthy condition. These sidebands appear at the following locations: \(f_{m}-f_{rs}-f_{c}\), \(f_{m}-f_{rs}\), \(f_{m}+f_{rs}\), \(f_{m}-f_{rs}+f_{c}\), which can help diagnose the fault through the spectrum. These results are consistent with the results of the theoretical analysis in Section 3.2. It should be mentioned that there exist some difference between simulation signal and experimental signal pertaining to the aspect of amplitude; this is inevitable because the given parameters of the simulation signal are hard to perfectly match the actual condition of the experimental planetary gearbox. However, the amplitude difference does not affect the verification of the proposed model, which focuses on the frequency features. In general, the simulated signal's characteristics are consistent with the measured signal, which proves the correctness of the proposed model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Theoretical Value** & **Simulated Value** & **Measured Value** & \(e\) \\ \hline
240 Hz & 240 Hz & 239.94 Hz & 0.025\% \\ \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of the simulation and measured results for meshing frequency.  
Figure 16: _Cont._  
Figure 15: Comparison of simulated and measured signal under healthy condition: (**a**) simulated signal; (**b**) measured signal; (**c**) frequency spectrum of simulated signal; (**d**) frequency spectrum of measured signal.
## 2 Background and Related Work
### System Development Tools  
Two network information collection tools, Ganglia [17; 18; 16] and Network Weather Service (NWS) [19; 20; 21], are employed in this study.  
(1) Ganglia is a resource monitoring system that monitors the grid nodes of a system, and transmits data with XML format. With Ganglia, users can determine current cluster conditions through Web pages. Ganglia, adopting TCP/IP as its communication protocol and supporting the unicast/multicast transferring mode, comprises the Gangia Monitoring Daemon (gmond), Ganglia Meta Daemon (gmetad) and Ganglia PHP Web Frontend, in which the gmond installed in user nodes employs a simple listen/announce method to share its own information, including CPU capability, memory capability, disk storage, network status and process information, with servers and other nodes through XDR (XML-Data reduced), where XDR is a trimmed and improved version of the XML-Data schema syntax. The gmetad, installed in a server as a query conducting mechanism, can collect other nodes' gmond and other servers' gmetad information, and stores the information in an indexed round-robin database that not only holds data, but also displays the time sequence data to the system's manager. The Ganglia PHP Web Frontend is a web-based sub-system that dynamically queries all nodes and shows the result to users.  
(2) NWS is a distributed system that employs different kinds of sensors to periodically gather nodes' current status and the network status of a grid system. For example, the CPU sensor checks CPU availability, and the network sensor monitors small-message round-trip time, large-message throughput, TCP socket connection/disconnection time, and the efficiency of TCP/IP protocol (e.g., bandwidth and latency).
## 4 Discussion
### Implications for Management  
To enhance the carbon sequestration capacity of Inner Mongolia, this study proposes the following management suggestions based on the analysis of landscape patterns and ecosystem functions:  
1. Enhancing landscape connectivity  
Landscape connectivity is crucial for regional carbon sequestration capacity. Reducing landscape fragmentation (lowering SPLIT values and increasing JII values) and constructing ecological corridors can effectively improve the connections between patches and promote the flow of matter and energy. This strategy is not only applicable to forests and grasslands but also to urban green space systems, achieving dual benefits of carbon sequestration and regional climate regulation by optimizing green space layout [32; 33]. Especially in the central and western regions, priority should be given to repairing severely fragmented ecological patches and incorporating isolated landscape units into the ecological network to enhance overall regional connectivity [36].
2. Regionally differentiated management  
Differentiated management strategies should be adopted based on the different natural conditions and NPP distribution in the eastern and central western regions in the study area. In the high-value areas of the eastern region, the focus is on maintaining the stability of the existing ecosystem and further improving vegetation coverage through moderate policies of returning farmland to forests and grasslands. In the central and western regions, due to limitations in water and soil conditions, priority should be given to restoring vegetation types with strong drought tolerance, such as desert shrubs and drought-tolerant herbaceous plants, and improving the suitability of vegetation growth environment through micro water collection technology and soil improvement measures [26; 34]. Especially for arid and nutrient-deficient regions exhibiting slow ecological recovery [23], water resource allocation and drought prevention measures should be tailored based on the dynamic response of NPP to climate change. Moreover, promoting the cultivation of stress-resistant tree and grass species in vulnerable areas is essential for bolstering the climate adaptability of ecosystems [37].
3. Optimizing patch size and spatial structure Patch layout should be optimized for different land use types to enhance ecosystem functionality. Specifically, large patches should be concentrated as much as possible and maintain good connectivity with the surrounding landscape, while small patches should enhance the overall carbon sequestration capacity of the ecosystem through landscape integration or optimization of edge complexity [35]. For example, in farmland areas, it is recommended to promote agricultural landscape diversification measures (such as vegetation restoration on embankments and ecological buffer zone construction) to reduce the impact of agricultural production on landscape fragmentation and enhance farmland carbon sequestration capacity.
## 4 PP-NFTSMC-ESO Controller Design
Substituting Equations (32)-(35) into (31) results in  
\[\dot{V}_{2}=\dot{V}_{1}+\sigma\dot{\sigma}=-k_{0}z_{1}^{2}+z_{1}\widetilde{x}_{ 2}+l_{2}\omega_{0}^{2}\widetilde{x}_{1}\sigma+z_{1}z_{2}-2|z_{1}z_{2}|\tanh^{2} \Bigl{(}\frac{\sigma}{\tau}\Bigr{)}-k_{3}\sigma^{2}-\eta|\sigma| \tag{36}\]  
According to Lemma 1, it follows that  
\[\dot{V}_{2}\leq-k_{0}z_{1}^{2}+z_{1}\widetilde{x}_{2}+l_{2}\omega_{0}^{2} \widetilde{x}_{1}\sigma-k_{3}\sigma^{2}-\eta|\sigma| \tag{37}\]  
Applying Cauchy-Schwarz inequality, (37) can be transformed to  
\[ \begin{array}{ll}\dot{V}_{2}&\leq-k_{0}z_{1}^{2}+\frac{1}{2}\omega_{0}^{2}z_ {1}^{2}+\frac{1}{2\omega_{0}^{2}}\widetilde{x}_{2}^{2}+\frac{1}{2}\bigl{(}l_{ 2}\omega_{0}^{2}\widetilde{x}_{1}\bigr{)}^{2}+\frac{1}{2}\sigma^{2}-k_{3} \sigma^{2}+\frac{1}{2}\sigma^{2}+\frac{1}{2}\eta^{2}\\ &\leq-k_{0}z_{1}^{2}+\frac{1}{2}\omega_{0}^{2}z_{1}^{2}-k_{3}\sigma^{2}+ \sigma^{2}+\frac{1}{2\omega_{0}^{2}}\widetilde{x}_{2}^{2}+\frac{1}{2}\bigl{(}l _{2}\omega_{0}^{2}\widetilde{x}_{1}\bigr{)}^{2}+\frac{1}{2}\eta^{2}\\ &=-\Bigl{(}k_{0}-\frac{1}{2}\omega_{0}^{2}\Bigr{)}z_{1}^{2}-(k_{3}-1)\sigma^{ 2}+\Omega\\ &\leq-\gamma V_{2}+\Omega\end{array}  \tag{38}\]  
where \(\gamma=\min\Bigl{\{}2r\Bigl{(}k_{0}-\frac{1}{2}\omega_{0}^{2}\Bigr{)},\;2(k_{ 3}-1)\Bigr{\}}\), and \(\Omega=\frac{1}{2\omega_{0}^{2}}\widetilde{x}_{2}^{2}+\frac{1}{2}l_{2}^{2} \omega_{0}^{4}\widetilde{x}_{1}^{2}+\frac{1}{2}\eta^{2}\). As \(\Omega\) is a function of the estimation errors, which will converge to a small value after a finite time \(t_{1}\) when selected a large enough \(\omega_{0}\). Therefore, if letting \(k_{0}>\frac{1}{2}\omega_{0}^{2}\), \(k_{3}>1\), and \(T=t-t_{1}\), we have  
\[V_{2}\leq V_{2}(t_{1})\exp(-\gamma T)+\frac{\Omega}{\gamma}[1-\exp(-\gamma T) ],\;\forall t>t_{1} \tag{39}\]  
**Theorem 2**: _For the deep-sea electric oil-filled joint actuator (5), under Assumption 1, with the ESO (15), the proposed control law (32)-(35) guarantees that the closed-loop system is stable and the output position tracking error converges to a small neighborhood of the origin by appropriately choosing the observer parameter \(l_{1},l_{2},l_{3},\,\omega_{0}\), and the controller parameters \(k_{0},\,k_{1},\,k_{2},\,k_{3}\), and \(\eta\)._
## 3 Numerical Model
### Mesh Independence Study  
A mesh independence study was performed to determine the effect of mesh quality on numerical results. Only free-triangular elements were used to create the mesh for these numerical simulations, as shown in Figure 4. The meshing size steps were set differently for the fin and the PCM domains, with the fin being much smaller and not experiencing phase change. Five mesh sizes were used to study the effect of varying the average element sizes in both the fin and PCM domains, as shown in Table 2.  
Every mesh study simulation had a time step of 30 s and simulation time of 7200 s, using the model with 12 finned tubes, a flow rate of 9 LPM, a discharging temperature range of 65 to 21 \({}^{\circ}\)C, and a mushy-zone temperature range of \(\Delta T\) = 4 K. A defined temperature on the inner side of the copper HTF interface was used at this stage instead of a convective boundary condition and was ramped down using the function made from the experimental data as mentioned previously.  
Figure 5 shows the system's equivalent power; the difference in the studied mesh made a negligible difference. Though negligible, a mesh was selected, as outlined in, Section 3.6 following the mushy-zone range study.
## 6 Dependability for MCAs in 6G  
One of the primary focus of 6G networks is MCAs. These applications usually require dependable services in terms of latency and error rates, and due to their nature, this must be equivalent to wired networks. The requirements of MCAs are closely related to those of Ultra-Reliable Low-Latency Communications (URLLC) with a target latency of 0.1 ms and a block error rate (BLER) of \(10^{-9}\). Although these KPI values are not applicable to all use cases, they do have practical relevance in a couple of them. As examples, we could mention autonomous driving, remote surgery, and augmented reality [68]. Needless to say, MCAs also mandate high-security communications and resource efficiency. Current 5G networks' approaches for meeting the requirements of MCAs based on tweaking the system design is not scalable, nor efficient. Future 6G networks need to make use of application-domain information in order to predict actual resource requirements. Furthermore, 6G networks need to introduce new parameters that will not only help with characterizing resource needs but will also ease dependability analysis [17].  
Due to its performance, edge computing is gaining traction as a viable solution for meeting the requirements of MCAs. The drivers behind the adoption of edge computing in MCAs use cases are the amount of data being transferred between end devices and edge servers, and time taken for data processing at the edge server. Due to the proximity of the edge server to the source of data, the network requirements mentioned at the beginning of this subsection could be met, even in the scenario of a massive amount of data. Furthermore, edge intelligitization eases meeting these requirements, as it is capable of offering micro-interaction with end devices, bringing management much closer to them, thereby reducing the communications overhead due to data fetching and controlling [34].
###### Abstract  
The information obtained from direct data (geological mapping and boreholes) and indirect techniques (reflection seismology, time-domain electromagnetics and magnetometry) is combined to analyse the northern limit of the Bailen basin (southeastern Spain). This Triassic-Neogene basin is confined by a graben-type structure, limited by two normal faults in the SW-NE direction (the Banos de la Encina-La Carolina fault and Guarroman fault). The movement of these faults was complex, with different pulses occurring over time. Therefore, the subsidence of the basin and the sedimentary filling of the graben were different, giving rise to lateral changes in the facies and thicknesses. This study focuses on the Banos de la Encina fault, chosen as the experimental site to analyse the effectiveness and accuracy of these geophysical techniques to reveal the basement structure and geometry. Seismic reflection allows to detect two faults that caused the subsidence of the eastern sector of the graben. The TDEM method made it possible to calculate the depth of the Palaeozoic basement, as well as reveal the presence of the two aforementioned faults. Magnetic total field data highlight variations in the basement depth that can be used to infer previously unknown fractures, in this case, in the NW-SE direction.  
fault detection; seismic reflection; time-domain electromagnetics; magnetometry; Spain
# TalkRoBots: A Middleware for Robotic Systems in Industry 4.0 +
## 3 TalkRoBots Middleware Presentation
### Communication
#### 3.2.4 Message Transmission Modes  
As presented previously, TalkRoBots uses three different modes, which will be detailed in this section.  
1. **Interpter Mode:** In the case where a robot is not able to receive a command directly, messages will be received field-by-field in order to prevent compatibility problems if a robotic language cannot manipulate strings. In this case, two functions have to be implemented on the robot, in the robotic language, to define how to handle receiving and sending messages, when communicating with the TalkRoBots middleware. Figure 4 presents an example of an exchange between two robots in the _Interpter Mode_. In this figure, Robot1 needs to share its position with Robot3. To do so, it sends a message to its TalkRoBots middleware. The latter prepares an object _Message_ with these data. Then, the message is serialized in JavaScript Object Notation (JSON) format and encrypted using Transport Layer Security (TLS). The security mechanisms will be described in the next Section 3.3. When the message is received on the middleware of Robot3, it is verified and deserialized. Finally, it is sent field-by-field to Robot3.  
Figure 3: Message structure.  
2. **Direct Mode:** If a message targeting a robot is a _Command_ or a _Request_, the configuration file defined in Figure A3 is used to translate the message into the appropriate command using robotic language. This command is transmitted to the robot, and it is executed immediately. However, if the command is not referenced in the configuration file or if the file itself does not exist, the message is transmitted using the default mode (i.e., the _Interpreter_ mode). Figure 5 shows an example of a _Command_ message sent by a user to a robot. The first steps are the same as those described for the previous Figure 4. Once the message reached the middleware of the Robot3, we differentiate two cases. The first one corresponds to the case where the _Subtype_ is properly defined in the configuration file so it can be transmitted using the _Direct_ mode. The second one, used by default, relies on the _Interpreter_ mode.
3. **ROS Mode:** In the same manner as for the \"direct mode\", a command/request message is associated through the configuration file to a ROS topic. A Topic message type is retrieved automatically and the TalkRoBots message is transformed into a corresponding ROS message and published in the right topic.
## 3 Derrick Model and Calculation Parameters under Complex Load  
### Numerical Model and Validation  
To study the strength and reliability of the derrick under complex load, the JJ315/45-K-type derrick was used as an example and modeled. The numerical model of the derrick is shown in Figure 3. The opening size of the upper part stationary is 2.3 m, the opening size of the lower part stationary is 8.3 m, the width of the upper side stationary is 2.3 m, the width of the lower side stationary is 2.6 m, and the height from the top to the setback floor is 45 m. The section types of the derrick beam are primarily divided into I-beam, channel, rectangular and pipe. The derrick's material is Q345 with a density of 7850 kg/m\({}^{3}\), an elastic modulus of 206 GPa, a Poisson's ratio of 0.28 and a yield strength of 345 MPa. The mesh parameter of the derrick model is 1 m, and the boundary condition is that all other degrees of freedom of the bottom four key points are constrained except for the axial rotational freedom of the pin.  
The derrick works under gravity, hook load, stand load and wind load at the same time. The gravity is applied as acceleration to the derrick and the gravities of the crown block, traveling block and racking board are applied on the corresponding node in the form of concentrated force. The hook load is the average applied at the top four nodes of the  
Figure 3: The numerical model of derrick. (**a**) The oblique view of derrick. (**b**) The side view of derrick.  
derrick. The stand load is applied horizontally at the racking board. The wind load of each section is applied as the average pressure at the nodes of the sections.  
To ensure the accuracy and reliability of the numerical simulation, the model was verified with the experimental testing results. The above numerical model was verified by stress monitoring, which was monitored from an offshore platform derrick under hook load in a South China Sea oil field by the Safety Environment Quality Supervision and Testing Research Institute. The strain sensor was set at the position of the column in section III-2, and the numerical results of the corresponding position were compared with the testing results. As shown in Figure 4, with the increase in hook load, the 20 measured values are consistent with the numerical results. The maximum error does not exceed 5.2%, indicating that the numerical simulation model is accurate and reliable.