esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In contrast to the specimens at low confining pressures, no strain-softening is observed in the stress-strain curves for the specimens at high confining pressures, and the strain distributions are different. Only one or two localized shear bands emerge in the specimen consolidated to confining pressure lower than \(\sigma_{yield}\). The number of localized shear bands increases with increasing confining pressures, and several localized shear bands appear in specimen consolidated to confining pressure greater than \(\sigma_{yield}\). When the specimens fail, only one shear band is fully developed in all specimens. For the other natural clays [18,19,26], one or several types of strain distributions were present in the specimen; however, they were not affected by the confining pressure. [PERSON] et al. [38] conducted mercury intrusion porosimetry (MIP) tests on Zhanjiang clay consolidated to different pressures, and the \(\sigma_{yield}\) of the tested clay samples was 400 kPa. The specific MIP test results are presented in Figure 11 and Table 5. As shown in Table 5, the diameters of the pores (\(d\)) of Zhanjiang clay can be classified into four categories: \(d>\) 10 \(\upmu\)m, 1 \(\upmu\)m \(<\)\(d\) \(<\) 10 \(\upmu\)m, 0.1 \(\upmu\)m \(<\)\(d\) \(<\) 1 \(\upmu\)m, and \(d\) \(<\) 0.1 \(\upmu\)m. When the consolidation pressures are lower than or equal to \(\sigma_{yield}\), the proportion of pores with different diameters does not change significantly (see Figure 11). However, when the consolidation pressure exceeds \(\sigma_{yield}\), the proportion of pores with a diameter between 1 and 10 \(\upmu\)m decreases significantly, whereas the proportion of pores with a pore diameter between 0.1 and 1 \(\upmu\)m increases significantly (see Table 5). The phenomenon above shows that the pore size distribution of Zhanjiang clay changes substantially when the consolidation pressure exceeds \(\sigma_{yield}\). The distribution of pore water pressure in the specimen during the plane strain compression is associated closely with the pore size distribution, whereas the pore water pressure has a significant influence on the formation of the shear band [19,26]. Therefore, the change in pore size distribution might have contributed to the different strain distributions in the specimens of Zhanjiang clay  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of Tests**} &
\begin{tabular}{c} **Confining Pressure (kPa)** \\ \end{tabular} & \(\theta_{M}\) (\({}^{\circ}\)) & \(\theta_{MC}\) (\({}^{\circ}\)) & \(\theta_{R}\) (\({}^{\circ}\)) & \(\theta_{A}\) (\({}^{\circ}\)) \\ \hline T1 & 100 & 57.9 & 56.9 & 54.3 & 55.6 \\ T3 & 300 & 52.1 & 56.9 & 56.2 & 56.6 \\ T5 & 500 & 51.0 & 56.9 & 60.4 & 58.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measured and calculated inclinations of shear bands.
## 3 Results
### Extraction Result Accuracy and Comparison  
Based on the visual interpretation of Google Earth, GF-2, and GF-6 images, a total of 1476 sampling points for validation were obtained for the period 2021-2022. The sampling points were overlain with the abandonment of farmland extraction results (Figure 11), and the extraction results are shown separately in Figure 12b. After counting the extraction results and the true values of the validation set, the overall accuracy of the extraction was calculated as 93.43% with a Kappa coefficient of 0.87.  
Figure 11: Distribution of validation samples.  
Figure 10: Abandoned farmland data extraction results: (**a**) abandoned farmland data extraction results for 2021, (**b**) abandoned farmland data extraction results for 2022, and (**c**) permanently abandoned farmland data extraction results for 2021-2022.  
Additionally, based on the method of this study, the results of the abandoned farmland data extraction using only the GF-6 dataset are shown in Figure 12a. The results using an NDVI time series analysis based on only Landsat data are shown in Figure 12c, and the  
Figure 12: Abandoned farmland data extraction results based on different datasets and methods. (**a**) Extraction results based on NDVI time series analysis using GF-6 data, (**b**) extraction results based on NDVI time series analysis using STARFM-fused data, (**c**) extraction results based on NDVI time series analysis using Landsat 8 data, and (**d**) extraction results based on SVM time series analysis using STARFM-fused data.  
results using an SVM for time series analysis based on the STARFM-fused data are shown in Figure 12d.  
The accuracy of the different methods based on different data is shown in Table 5.
# Structural Performance of R-Type Steel Damper Used in Reinforced Concrete Members
## 4 Phase II Test Results  
### Shear vs. Drift Angle Relationships  
Figure 13 shows the shear force vs. drift angle relationships obtained from the Phase II test for evaluating the applicability of the proposed R-type damper. Figure 14a shows that in Specim M1 without a damper, the RC member resisted the entire external shear force and this resulted in brittle fracture owing to shear and bond cracks. As shown in Table 3, M1 had a maximum load of 171.4 kN at a drift angle of 0.0088 rad in the 1/100 cycle in the positive direction and \(-\)177.8 kN at a drift angle of \(-\)0.01 rad in the \(-\)1/100 cycle in the negative direction. In the 1/75 cycle, the test was terminated as the applied load decreased to approximately 65% of the maximum load. As shown in Figure 15a, the longitudinal reinforcement  
In Specimen M2 with the proposed R-type steel dampers, the yielding and energy dissipation of the steel damper occurred before the RC member was damaged. Hardly any cracks formed in the RC member and ductile behavior was observed for a drift angle of up to 3%. As shown in Figure 15b, the longitudinal reinforcement of M2 remained at a strain of at most 0.0006. The damper yielded in the first 1/400 cycle and the load and drift angle at that time were 52.1 kN and 0.002 rad, respectively. Initially, the vertical member of the steel damper yielded and, immediately after, the lower horizontal member of the steel damper yielded at a drift angle of 0.0025 rad. The upper horizontal member of the steel damper yielded in the 1/200 cycle and the load and drift angle at that time were 68.8 kN and 0.004 rad, respectively.
## 2 Materials and Methods  
### High-Level System Design  
Apart from defining the objective of the work, literature review, and listing the limitations of the current research, our methodology includes proposing a framework that offers solutions for overcoming these limitations and proceeding to evaluate the performance of this framework using standard metrics to showcase the potential of the framework. However, owing to the inhomogeneity of XR applications, a comparative study with a state-of-the-art in a similar experimental setting needs to be performed in future work.  
The Holo-Cloud system is based on the utilization of public cloud services that encompass computational resources, networking, high-availability measures, and security protocols, resulting in the establishment of a reliable, location-agnostic, and platform-independent framework. The primary goal is to facilitate the execution of the XR software core on compatible hardware without necessitating recurrent reprovisioning, reconfiguration, recompilation, and reinstallation of the software executable each time an expansion or shrinkage of the computational infrastructure is required. To actualize this objective, Holo-Cloud proposes the instantiation and configuration of the XR software on a virtualized hardware system, subsequently enabling secure public access for authorized users to the virtualized operating system.  
In this architectural paradigm, the responsibility for providing the hypervisor service, a critical component, lies with the public cloud service provider. The hypervisor service role is to provision and manage on-demand virtual hardware, encompassing the central processing unit (CPU), random access memory (RAM), storage, video graphic acceleration (VGA), and peripheral interfaces allocated to the virtualized operating system. The virtualized operating system, in turn, operates on the hardware provided by the hypervisor, adapting seamlessly to any hardware modification, such as elastic increase or decrease in processing resources or network throughput. Notably, the operating system ensures the continuity of software services to the XR software core. The XR software core remains unaware of any underlying virtual infrastructure modifications.  
In this architectural scheme (Figure 1), the combination of the virtual operating system and the bundled XR software is denoted as a virtual machine. This virtual machine can be encapsulated, relocated, and transitioned from one hypervisor to another. This provides the flexibility to deploy the virtual machine on any public cloud platform with minimum configuration overhead, as almost all the well-known cloud service providers support virtual machine migration from external platforms [43].
# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## 2 Materials and Methods
### Machine Learning and Fuzzy Logic Models for Soil Temperature Estimation
#### 2.2.2 Adaptive Neuro-Fuzzy Inference System (ANFIS)  
The ANFIS is an artificial intelligence method developed by [PERSON] [46] that combines the strengths of ANNs and fuzzy logic systems. The ANFIS automatically optimizes the parameters of the fuzzy inference system through its ability to learn from data. This system is usually based on a Takagi-Sugeno type fuzzy inference structure, and can successfully model linear and non-linear relationships between inputs and outputs. In this study, the ANFIS model is used to model the relationship between the input variables and the target variable (output). Hyperparameters such as the type of MFs (e.g., Gaussian, triangular), the number of MFs, and the learning algorithm were optimized. ANFIS models offer high accuracy and a strong generalization capability, especially for small- and medium-sized data sets. However, the computational effort can increase with increasing model complexity. Detailed information about the ANFIS can be obtained from related reference [47].
## 3 Results
### Data Management  
The software Leica Ski-pro was applied for the treatment of GPS data, which allows calculating coordinates according to the System of Reference chosen, ETR89-UTM. The software Autel Explorer was used to program the flights, which permits designing flight projects or missions.  
For the management of the images that were obtained from the photogrammetric flights, which implies alignation, orientation, scaling and the generation of point-clouds, models, and orthophotographs, Agistoft Metashape was applied.  
One of the advantages of this software is related to the camera self-calibration function, which applies the Exchangeable Image File Format (EXIF) data of the images. This permits solving an important problem in the field of photogrammetry by means of analytic processes [37], as it improves the internal model and physical parameters of the camera, and minimizes the influence of the variables that affect its metric performance [38]. This is particularly indicated for low-cost cameras. The camera self-calibration developed with Agisoft Metashape determines the focal length, the principal point, and the coefficients that are obtained from a polynomial adjustment that is aimed at solving the radial and tangential distortions. The focal length and the coordinates of the principal point can be expressed in millimeter or pixels, while the coefficients are dimensionless parameters. The set of parameters for the camera that was applied for the development of the work are shown in Table 6.  
Figure 6: Location of stadias and targets.  
The technical requirements of hardware were not too demanding, as the models used up to 685 images, with total size was 8.0 GB. The data were processed with a device whose features are listed below:  
* Intel Core i7 3770 K, with CPU at 3.5 GHz, 3901 MHz;
* Four main processors and eight logical processors;
* RAM: 32 GB;
* Hard disk: 2 TB;
* Graphic Card: GeForce GTX560.
# Parallel Load-Bearing and Damping System Design and Test for Satellite Vibration Suppression
## 5 Damping Rod Design and Test  
The relative velocity between two ends of the dampers can be obtained from the FEA results. For eight dampers with the coefficient of 2.8 million N\(\cdot\)s/m, the max relative velocity is \(1.09\times 10^{-2}\) m/s. Then, the max damping force could be calculated from the FEA results, which is 31128 N. The damping rod's stroke and the diameter of damping rod should also be considered. Therefore, a suitable damping rod that could satisfy all the requirements was customized. The outer diameter of the damping rod was 90 mm. Its prototype was tested. The force-velocity curve is shown Figure 7. The star in the figure shows the max damping force under the max velocity is a bit higher than the designed value, which means the damping rod could get better damping effectiveness as well as meet design indicators.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **E\({}_{1}\)/Gpa** & **G\({}_{12}\)/Gpa** & \(\mathbf{v}\) & \(\rho\)/(Kg/m\({}^{3}\)) & \(\mathbf{\sigma_{\text{s}}}\)/Mpa** \\ \hline
2A14 & 72 & 27 & 0.3 & 2780 & 360 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Properties of aluminum alloy.  
Figure 6: PAF model: (**a**) original state; (**b**) viscous damping state.  
Dampers are generally cylinder-piston type. The damping force is calculated as  
\[F=CV^{\alpha},\]  
where \(F\) is the damping force, \(V\) is the velocity of piston rod, \(C\) is the damping coefficient, which is related to the cylinder diameter, the piston diameter, the fluid viscosity etc., and \(\alpha\) is the velocity exponent based on the structure of the damper. Most dampers are non-linear, which means \(\alpha\) does not equal one. The characteristic of a non-linear damper is that the equivalent linear damping coefficient for low speed is larger than that for high speed. This means that the damper can always supply enough damping force even at a low velocity and is not too large to result in structure damage in high velocity. The coefficient-velocity curve of the customized damping rod is shown in Figure 8.  
Furthermore, the damper's elastomeric damping material is semi-fluid. This kind of material has a high viscosity and strong compression performance. In addition, it has good chemical stableness, aging resistance, and excellent thermo-stabilization.
# MicroForest: Lightweight Bottleneck Prediction for Manufacturing Processes on Edge Devices
## 4 Proposed Method
### Building MicroForest
```
0:\(D_{n}\), \(S_{n}\)
0: Root of MicroTree for task \(n\)
1:procedureBuild_MicroTree(\(D_{k},S_{n},d\))
2:if\(d\geq d_{\max}\)or\(\max_{\{f,\theta\}\in S_{i}}IG(D_{k},f,\theta)<\epsilon\)or\(S_{i}=\varnothing\)then
3:return Leaf node with majority class of \(D_{k}\)
4:endif
5:\((f^{*},\theta^{*})\leftarrow\arg\max_{(f,\theta)\in S_{i}}IG(D_{k},f,\theta)\)
6:\(D_{L}\leftarrow\{x\in D_{k}\mid f^{*}(x)\leq\theta^{*}\}\)
7:\(D_{R}\gets D_{k}\setminus D_{L}\)
8:if\(|D_{L}|<K\)or\(|D_{R}|<K\)then
9:return Leaf node with majority class of \(D_{k}\)
10:endif
11: remove \((f^{*},\theta^{*})\) from \(S_{n}\)
12:\(left\leftarrow\textsc{Build\_MicroTree}(D_{L},S_{n},d+1)\)
13:\(right\leftarrow\textsc{Build\_MicroTree}(D_{R},S_{n},d+1)\)
14:return Internal node with children \(left\), \(right\)
15:endprocedure
```  
**Algorithm 1** MicroTree Construction  
MicroForest is an ensemble of multiple MicroTrees constructed as described above. For a manufacturing process with \(N\) distinct tasks, \(M\) MicroTrees are employed to predict bottlenecks, where \(1\leq M\leq N\). Each MicroTree \(MT_{m}\) (\(m\in M\)) is trained independently using the candidate split-rule set \(S_{n}\) and training data \(D_{n}\) extracted by SplitSelector for its assigned tasks. The \(M\) MicroTrees together form the MicroForest, defined as  
\[MF=\{\,MT_{1},MT_{2},\ldots,MT_{M}\}.\]  
At inference time, given the current state of the manufacturing process, each MicroTree \(MT_{m}\) receives the relevant input features and independently predicts whether its associated task(s) will become a bottleneck. This design enables the use of cross-task information in the overall prediction framework while ensuring that no single MicroTree exceeds its capacity.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 5 The Telco Cloud Enabled 5G Applications
### State-of-the-Art Overview
These partners are responsible for overseeing the activities of that specific segment. The illustrative model is shown in the Figure 13. Each of these layers contains several sub-components, each of which may have a different set of stakeholders. These stakeholders are in charge of the operation of that specific component. Figure 12: Telco fog architecture. * User layer: This layer represents all network-connected devices that do not have to be people. * The service provider layer: This layer expresses all of the services that are available to users. Services such as mobility, content delivery networks (CDN), and machine-to-machine (M2M). * The carrier cloud service platform: This layer virtualizes the upper layers, such as Mobile RAN cloud, Mobile core network cloud, and packet data network cloud. * virtual infrastructure layer: This is split into two stages. The first is a cloud infrastructure provider, who offers a clouding environment devoid of physical components. The second is a cloud service provider, which acts as a link between virtual layer and upper layer applications. * The physical layer: It includes the network's hardware entity, which contains cabinets, cooling, and electricity distribution, among other things. The authors of [196] proposed a meta-model for the Telco Cloud system. The model can be used to reenact various configurations, observing the behavior of a framework from an execution and cost standpoint. They discovered that a large scale testing environment for recreation of an entire Telco Cloud climate would be financially costly. However, a limited scope recreation framework cannot detect all potential examples of client behavior. They propose two types of data centers (DC), as illustrated in Figure 14. The first is Remote DC, which is a large facility located relatively in the center of the network, far away from access networks. The second type is Proximal DCs, which are smaller facilities located on the network's edge, close to access networks and end-users. Furthermore, when compared to the traditional model, access networks and cloud infrastructure are quite closely integrated. Because proximal DCs consume the majority of capacity, remote DCs are used for coordination and control. The authors of [200] observed how a Telco Cloud environment can contain network service functions and how a proof-of-concept model can be built on top of the Cloud4 NF platform. This paper clarifies that building a network function is based on over-provisioning to ensure appropriate service levels based on estimated peak traffic and the accepted risk of redundancy model where a single fault can bring the service down. While virtualization has matured as a technology, carrier-grade reliability and performance expectations have also reached feasibility. As a result, the anticipated consolidation of the traditional model and virtualization will result in significant cost savings. Figure 13: Telco cloud architecture.
# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 2 Background and Motivation
### Smart Manufacturing Running Example  
Smart manufacturing integrates manufacturing assets with sensors, computing platforms, communication, control, and simulation using concepts of CPS [15; 16]. Manufacturing processes are highly automated and modern production machines provide vast amounts of sensor data. However, process-awareness of this data might vary as there is usually no BPM system in place to orchestrate or monitor executions [17]. Sensor and process-related data might come from different systems (e.g., IoT applications, manufacturing execution systems (MES), programmable logic controllers (PLC)) at different levels of granularity [18]. Moreover, legacy machines and human shop floor workers might only provide limited amounts of sensor data to allow for monitoring of activity executions.  
In this work, we will base our investigations on examples from the domain of discrete smart manufacturing [19; 20]. Here, we find a sophisticated set of sensors and actuators producing low-level-_raw_-IoT data that can be analyzed for generic patterns and other aspects that may indicate the execution of process activities. Higher level, process-related data from machines and control systems will only be used to validate the correctness of identified patterns in raw IoT data and of analysis steps. The patterns and analysis method can then be transferred to other _smart_ spaces (e.g., smart homes, smart offices, and smart hospitals) that also emit low-level IoT data, but only very limited process-related data. In these smart spaces, there are usually no central BPM systems or other information systems for automated activity monitoring in place and processes are only partially automated [21].
## 4 Communication Technologies for Seamless Connectivity in Smart Farming
### Availability and Challenges of Communication Technologies
#### 4.2.1 Cellular Networks  
Cellular technologies (2G/3G/4G/5G) have been considered as the possible option for long range wireless connectivity. However, using cellular connectivity leads to some limitations related to Low-Power WAN. The high power consumption that makes battery operation not-allowed for long periods of time causes one of these limitations. When itcomes to using cellular modules, people should pay the charge per month to subscribe to the cellular operator or service providers. Thus, such modules are rather more expensive [62]. 5G is the newest technology for the mobile world and IoT communications [63]. However, there are not many explanations on how to combine the high-speed technology of 5G with the low power consumption, which is essential for IoT applications [64]. In fact, 5G has the capability of enabling ultra-high-speed communications through a wide bandwidth [63] and high frequency. This is not suitable for energy constrained IoT devices. Moreover, the Massive Machine-Type Communications (mMTC) and critical Machine-Type Communications (cMTC) are two objectives of 5G to leverage Ultra Reliable and Low Latency Communications (URLLC). The mMTC has been developed for IoT, while the cMTC requirements are too limited. The mMTC 5G necessities are fulfilled using LTE-M and NB-IoT developed by 3 GGP, and there are no other dedicated solutions to be specified for 5G IoT [65].