esa-sceva/satcom-chunk-collection huggingface dataset:
# Instantaneous Reactive Power Theory in the Geometric Algebra Framework
## 3 The New Approach to Multi-Phase Systems
### Instantaneous Power Current and Instantaneous Reactive Current in Multi-Phase Systems  
After defining the power terms in a multi-phase system, in this section the current components are obtained. From the definition of the instantaneous power multivector (44), it is possible to determine, for a given voltage \(\mathbf{u}\), the current vector \(\mathbf{i}\).  
**Theorem 5**.: _The current vector \(\mathbf{i}\) of a multi-phase system supplied with a voltage \(\mathbf{u}\) is obtained from the instantaneous power multivector according to expression (64):_  
\[\mathbf{i}=\frac{\mathbf{u}^{\intercal}\mathbf{s}}{\mathbf{u}^{\intercal} \mathbf{u}}=\frac{\mathbf{u}\mathbf{s}}{\mathbf{u}^{\intercal}\mathbf{u}} \tag{64}\]  
In fact, if the left side of (44) is multiplied by \(\mathbf{u}^{-1}\), it gives:  
\[\mathbf{u}^{-1}\mathbf{u}\mathbf{i}=\mathbf{u}^{-1}\mathbf{s} \tag{65}\]  
and from (17) it follows that (66):  
\[\mathbf{i}=\mathbf{u}^{-1}\mathbf{s}=\frac{\mathbf{u}^{\intercal}\mathbf{s}}{ \mathbf{u}^{\intercal}\mathbf{u}}=\frac{\mathbf{u}\mathbf{s}(t)}{\left\| \mathbf{u}\right\|^{2}}=\frac{\mathbf{u}\mathbf{s}}{u^{2}} \tag{66}\]  
since it is always verified that:  
\[\mathbf{u}^{\intercal}=\mathbf{u} \tag{67}\]  
**Theorem 6**.: _The current \(\mathbf{i}\) is decomposed into two terms, the instantaneous power current \(\mathbf{i_{p}}\) and the instantaneous reactive current, \(\mathbf{i_{q}}\):_  
\[\mathbf{i}=\mathbf{i_{p}}+\mathbf{i_{q}}=\frac{\mathbf{u}p}{\left\|\mathbf{u} \right\|^{2}}+\frac{\mathbf{u}\mathbf{q}}{\left\|\mathbf{u}\right\|^{2}} \tag{68}\]  
_Furthermore, both components are orthogonal and verify the relationship (69):_  
\[\left\|\mathbf{i}\right\|^{2}=i^{2}=\left\|\mathbf{i_{p}}\right\|^{2}+\left\| \mathbf{i_{q}}\right\|^{2}=i_{p}^{2}+i_{q}^{2} \tag{69}\]  
The proof is identical to that presented for Theorem 3.  
Both current components, instantaneous power current and instantaneous reactive current, verify the relationship (70):  
\[i^{2}=i_{p}^{2}+i_{q}^{2} \tag{70}\]  
where:  
\[\left\|\mathbf{i_{p}}\right\|^{2}=i_{p}^{2}=\frac{p^{2}}{u^{2}} \tag{71}\]  
and:  
\[\left\|\mathbf{i_{q}}\right\|^{2}=i_{q}^{2}=\frac{q^{2}}{u^{2}} \tag{72}\]Finally, from (70), the partition of the instantaneous apparent power for multi-phase systems is:  
\[\left\|\mathbf{s}\right\|^{2}=u^{2}i^{2}=u^{2}i_{p}^{2}+u^{2}i_{q}^{2}=p^{2}+q^{2} \tag{73}\]  
where:  
\[\begin{array}{l}p=u{i_{p}}\\ q=u{i_{q}}\end{array} \tag{74}\]  
according to (71) and (72).
## 3 Experiment  
### Data Sources  
In our study, we concentrated on the Estaillades Carbonate images from the Digital Rocks Super Resolution Dataset 1 (DRSRD1) [19]. Estaillades carbonate is a mono-mineralic, calcitic rock that features two types of pores: larger intergranular macropores and smaller intragranular micropores. Our analysis utilized high-resolution micro-CT images of a 7 mm diameter Estaillades sample, scanned using micro-CT scanner. These images prominently capture the intergranular macropores, providing a clear view of the macroscopic pore structures within the carbonate.  
The micro-CT images in our dataset have a resolution of 3.8 and 3.1 microns, allowing us to examine the complex pore structures of the Estaillades carbonate in great detail. This dataset also includes a three-phase segmentation of the rock sample, distinguishing pore voxels, voxels with unresolved porosity, and solid voxels. Such detailed segmentation is crucial for our analysis, particularly for evaluating the porosity and permeability characteristics of the carbonate rock.
## 3 Challenges in Big Data for Machine Learning
### Data Quality and Preprocessing  
Data quality is a critical concern in big data environments, and it directly impacts the performance and reliability of ML models. Big data are often characterized by their \"messiness\"--datasets can be incomplete, noisy, inconsistent, and riddled with errors. The presence of outliers, missing values, and duplicated data can lead to biased or inaccurate models if not properly addressed. Moreover, in many cases, big data sources are heterogeneous, combining structured data (such as relational databases) with unstructured data (such as text, images, and videos), each requiring different preprocessing techniques [211; 212; 213; 214].  
The process of cleaning and preparing big data for ML involves several complex steps, including data integration, transformation, normalization, and feature extraction. Given the scale of big data, these preprocessing steps can be computationally intensive and time-consuming. Automated data cleaning techniques often struggle with the diversity and complexity of real-world datasets, necessitating human intervention to ensure data integrity. Furthermore, data are often generated in real time, requiring continuous preprocessing to maintain data quality. This dynamic nature of big data presents an additional challenge, as models need to be continually retrained to adapt to new data, which may change in structure and distribution over time [215; 216; 217; 218].
## 1 Introduction  
Reactivation of pre-existing immature and/or unfavorably oriented faults is an important issue in earthquake seismology, particularly in cases of seismicity induced by fluid injection or extraction in applications such as enhanced geothermal systems, shale gas fracking, nonconventional oil/gas production, and sequestration of CO\({}_{2}\) and other fluid wastes into deep formations [1; 2]. The conditions for shear rupturing along favorably oriented (easiest to be reactivated under the present-day regional stress environment) and well-developed faults (weakened by fault gauges and smooth fault surfaces) provides the lower limiting bound to overpressures, which is a key parameter in the design and management of injection applications. In geothermally active areas, earthquakes that occurred on unfavorably oriented faults are considered to be evidence of over-pressurized fluids and have attracted increasing attention (e.g., [3; 4; 5; 6]). At the same time, immature and/or unfavorably oriented faults, especially those distributed in sedimentary formations, are also important because such faults are locked and show very low levels of background seismicity, but might be reactivated due to man-made local pore pressure and stress changes [2]. Much more attention should be paid to such faults in geomechanical analyses and risk assessments of related applications.  
Acoustic emission (AE) events have been observed during frictional sliding on pre-cut faults in laboratory rock specimens. In addition to rock blocks, the roughness of the fault surface, geometric features (bend and orientation) of the fault, gouge, and loading speed have been investigated [7; 8; 9]. Variations in \(b\)-value during stress buildup and release were observed in laboratory-created fault zones [10]. In a very recent study, the temporal evolution of the seismic \(b\)-value during stable and unstable frictional sliding experiments on layers of glass beads in a double-direct shear configuration was investigated [11]. Thus far, AE activity related to the stick-slip of unfavorably oriented faults has not been systematically investigated in the laboratory. The authors, therefore, carried out AE studies using rock specimens containing artificially pre-cut faults as well as naturally created faults in well-controlled laboratory conditions [12]. In the present study, stick-slip tests were performed on a rough fault pre-cut at 45\({}^{\circ}\) in granitic specimens. Such a fault can be considered to be an analogue of unfavorably orientated immature faults.
## 3 Results
### Stage 3  
#### 3.3.1 Define Desired Behavior  
The zeta \((\sigma)\) parameter, also known as the damping coefficient, is a value used in dynamic system analysis to describe a system's damped response. It indicates the amount of damping present in the system and affects its behavior in terms of stability, response time and damping. The zeta value varies between 0 and 1, where 0 represents a sub-damped system, 1 indicates a critically damped system and values above 1 represent over-damped systems. Another important parameter in the analysis of dynamic systems is natural frequency. The natural frequency, represented by the letter omega \((w_{n})\), is a measure of the speed at which a system will vibrate or oscillate in the absence of damping or external forces. For the calculation of the establishment time \((t_{sa})\), a commonly used criterion is to consider that the response has reached the stable state when it is within 2% of the final value. In this case, the set time can be approximated using the following Formula (29).  
In the pole assignment, a desired behavior of the controlled system is sought, so poles are chosen that generate the type of response expected from the system. In this case, it is sought to obtain a slightly damped behavior, so poles with a value greater than 1 are chosen. In addition, the system is intended to have an establishment time of approximately 320 min and a settlement time of about 125 min for the third operation. To calculate the natural frequency of the system, Equations (28) and (29) are used to obtain the desired pole values.  
\[\sigma=1.1 \tag{28}\]  
\[w_{n}=4/(\sigma*t_{sa})=0.0291 \tag{29}\]  
Equation (30) shows the desired poles of the system, which are obtained from the desired natural frequency \((w_{n})\) and damping ratio \((\sigma)\). The poles are represented as complex conjugate values, since the system is second-order, plus a cancellation of poles and zeros is performed to use the second-order system. In this case, low damping behavior is desired, so the damping ratio is set to 0.1 and the natural frequency is calculated from the desired settlement time and establishment time values.  
\[s_{1,2}=-w_{n}*\sigma+/-w_{n}\Big{(}\sqrt{\sigma^{2}-1}\Big{)} \tag{30}\]  
Replacing the values results in the two desired poles being the following; see Equation (31).  
\[s_{1}=-0.0187,s_{2}=-0.0453, \tag{31}\]In the case of Equation (32), a specific pole \((s_{3})\) is being manually assigned to achieve a desired behavior in the system. To get that desired pole, you take a zero from the open-loop transfer matrix and cancel it with the desired pole. This technique is called pole-zero cancellation. Pole-zero cancellation involves adding a zero in the controller transfer function to cancel an unwanted pole in the system transfer function. In this case, a pole in the open-loop transfer matrix is canceled with a zero in the controller transfer function, resulting in the desired pole in the closed-system transfer function.  
\[s_{3}=-0.0128 \tag{32}\]  
With the desired poles defined, you can perform pole assignment to obtain the driver gain that ensures the desired system behavior.
## 2 Calculation Model and Basic Assumptions
### Stochastic Medium Theory  
For the calculation method of surface settlement caused by soil loss, the Peck formula cannot consider the influence of the tunnel cross-section shape on settlement trough [18]. At present, it is suitable for tunnels with large buried depth, but not for ultra-shallow buried tunnels. Therefore, this paper aimed to use stochastic medium theory to calculate the ground settlement caused by soil loss.  
The stochastic medium theory was proposed by [PERSON] [33] through a test in a sandbox, and it was then applied by [PERSON] [34] to predict the ground surface displacement caused by tunnel construction and mining engineering. The stochastic medium theory regards rock and soil as a stochastic medium and the stratum displacement caused by underground engineering excavation as a stochastic process. Therefore, ground subsidence can be regarded as the sum of deformation caused by the complete collapse of several units.  
As shown in Figure 2, the settlement of the upper soil (\(x,y,z\)) caused by the complete collapse of a homogeneous infinitesimal excavation unit under undrained consolidation condition can be written as follows [35]:  
\[w=\frac{(\tan\beta)^{2}}{\left(\eta-z\right)^{2}}\exp\left\{-\frac{\pi(\tan\beta) ^{2}}{\left(\eta-z\right)^{2}}\Big{[}(x-\xi)^{2}+(y-\zeta)^{2}\Big{]}\right\}d \xi d\zeta d\eta, \tag{6}\]  
where \(\eta\) is the distance between the excavation unit and the surface, and \(\beta\) is the main influence angle of the upper stratum.
# Generic Digital Twin Architecture for Industrial Energy Systems
## 4 Proof-of-Concept: Digital Twin Instantiation
### Simulation Service API  
The Simulation Service is realized as a microservice that communicates over REST-ful service endpoints with other services or agents. The stored information inside the Shared Knowledge base is made available by calling HTTP methods on the endpoints. In the same way, information can be inserted into the knowledge base, and the Simulation Service can be invoked. Currently, information about the simulation model and the service status can be retrieved, models and data can be uploaded, and the model can be trained. Also, predictions can be made by the Simulation Service, calling an HTTP method on a special endpoint. A full list of the implemented service endpoints, as well as the required parameters, can be found in Table 2. The Simulation Service adds information about a new model instance to the Shared Knowledge base automatically, whenever a new model is created or successfully trained, or a new hyper-parameter for the model is set. In the current implementation, only Matlab simulation models are supported by the Simulation Service.  
\begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Endpoint** & **Description** & **HTTP** & **URL** & **Parameters** \\ \hline service & Returns information about the simulation service as JSON & \multicolumn{2}{l}{‘GET’} & \multicolumn{1}{l}{/servicestate} & none \\ \hline model & Returns information about the current model within the simulation service as JSON & \multicolumn{2}{l}{‘GET’} & \multicolumn{1}{l}{/model/} & none \\ \hline train & Trains the current model and returns information of the current model together with a summary of the model performance. & \multicolumn{1}{l}{‘PUT’} & \multicolumn{1}{l}{/train} & \multicolumn{1}{l}{data\_path = path to the training data;} \\ \hline predict & Returns prediction of the input data from the selected model instance as JSON & \multicolumn{1}{l}{‘GET’} & \multicolumn{1}{l}{/predict} & \multicolumn{1}{l}{model = modelLocation returned by calling model-endpoint or after a training invocation;} \\ \hline uploaddata & Uploads new data to the simulation service which is used for training or prediction & \multicolumn{1}{l}{‘POST/upload/data/} & \multicolumn{1}{l}{file = data stored in a file in arbitrary format} \\ \hline uploadmodel & Uploads a new model for the Matlab based Simulation Service instance. & \multicolumn{1}{l}{‘POST/upload/model/} & \multicolumn{1}{l}{file = code stored in a Matlab file} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hypertext Transfer Protocol (HTTP) endpoint description for the implemented Simulation Service Application Programming Interface (API).
# Multi-Device to Multi-Device (MD2 MD) Content-Centric Networking Based on Multi-RAT Device
## 3 Proposed Method  
This section proposes a method for solving the previously described problem encountered in an MD2 MD CCN environment. First, a method for transmitting and receiving device information using Bluetooth Beacon is proposed. Furthermore, application scenarios for the proposed method in terms of content delivery and network configuration between mobile devices are proposed.
## 3 Research of the Path Tracking Control Strategy
### Design of Vehicle Attitude Control Law  
It can be seen from the above analysis that the deviation of the actual driving direction angle \(\theta\) from the ideal driving direction angle \(\theta_{d}\) will cause the instability of the closed-loop system in the initial stage of control. Hence, it is necessary to design the vehicle body attitude control law to make \(\theta\) track \(\theta_{d}\) as soon as possible.  
Make \(\theta_{e}=\theta-\theta_{d}\), where,\(\theta_{e}\) refers to the angle deviation value, take the sliding mode function \(s_{3}=\theta_{e}\), then  
\[\dot{s_{3}}=\dot{\theta_{e}}=w-\dot{\theta_{d}} \tag{20}\]  
Compared with the traditional exponential approach control law:  
\[\dot{s_{3}}=-ks_{3}-\varepsilon\text{sgn}(s_{3}) \tag{21}\]  
An approach law combining exponential and power is designed in this paper:  
\[\dot{s_{3}}=-k\big{|}s_{3}\big{|}s_{3}-\varepsilon\big{|}s_{3}\big{|}s_{4} \text{sgn}(s_{3}) \tag{22}\]  
where, \(\varepsilon\), \(a\), \(b\), \(k\) refer to the positive design parameters respectively, and meet the requirements of \(0<\varepsilon<1\), \(0<a<1\), \(b>1\), \(k>0\). Compared with the control law (21), the \(|s|^{d}\) term in the control law (22) improves the smoothness of the approach. The \(k|s|^{b}\) term ensures that the approach speed is faster when it is far away from the sliding mode surface, and the \(k|s|^{b}\) term is smaller when it is near the sliding mode surface, thereby reducing clutter.  
When the trajectory of the vehicle body attitude system is outside the sliding mode surface, take Lyapunov function \(V=s^{2}/2\) derivative, and substitute Formula (21) into it to get \(\dot{v}=s_{3}\dot{s_{3}}=-k\big{|}s_{3}\big{|}s_{3}^{2}-\varepsilon s_{3} \big{|}s_{3}\big{|}a<0\). Thus, \(\theta\) can be converged to \(\theta_{d}\).  
Therefore, the actual vehicle body attitude control law can be obtained as follows:  
\[w=\dot{\theta_{d}}-k\big{|}s_{3}\big{|}s_{3}-\varepsilon\big{|}s_{3}\big{|}s _{3}\big{|}s\text{sgn}(s_{3}) \tag{23}\]
# Improving the Manipulability of a Redundant Arm Using Decoupled Hybrid Visual Servoing
## 2 Methodology
### Robot Kinematics with Task Priority
By using DHVS the object is less likely to be lost and the method is more robust against calibration than the PBVS method since two out of six camera velocities will be created directly from the image space. The 3D calculation of the visual features is used to regulate the errors of rotations and translation in the \(Z\)-axis. As a result, feasible trajectories for the robot will be generated, and image singularities caused by these four components in the interaction matrix will be eliminated. In Algorithm 1, the pseudocode of the proposed approach has been illustrated. ```
1 Inputs: e, e\({}_{p}\);
2 Outputs: q;
3 Initialization;
4 Define desire points;
5 Modify task as Eye-in-Hand;
6 whileNot convergeddo
7 fori=1 toi=features.size()do
8 ComputeL\({}_{xy}\),L\({}_{r}\),L\({}_{Pr}\) andL\({}_{pxy}\)
9 end for
10 forj=1 toj=camera-velocity.size()do
11 compute camera velocities\({\bf v}_{cam}=({\bf v}_{xy},{\bf v}_{r})\);
12 use adaptive gain;
13 IBVS update visual features;
14\({\bf v}_{xy}={\bf L}_{xy}^{+}\{-k(\|e\|)\,{\bf e}-{\bf L}_{r}{\bf v}_{r}\}\);
15 PBVS update visual features;
16\({\bf v}_{r}={\bf L}_{Pr}^{+}\{-k(\|e\|){\bf e}_{p}-{\bf L}_{pxy}{\bf v}_{xy}\}\);
17
18 end for
19 fork=1 tok=joint-velocity.size()do
20 calculate joint velocities\(\dot{\bf q}\);
21 define manipulability as secondary task;
22 use damped least square method;
23 command robot;
24\(\dot{\bf q}={\bf f}^{\ddagger}{\bf g}_{c}^{\sigma}{\bf v}_{cam}^{c}+({\bf I}-{ \bf J}^{\ddagger\lambda}{\bf J}){\bf q}_{0}\);
25
26 end for
27
28 end for
```  
**Algorithm 1**Decoupled hybrid visual servoing. The inputs of the algorithm are the feature errors in the image screen and their counterparts in the 3D space. The output of the DHVS algorithm will be the vector of joint velocities. In line 8 of Algorithm 1, the decoupled matrices will be determined from (8) and (9). Camera velocity would be estimated in lines 14 and 16 using the calculated decoupled matrices in line 8. Adaptive representation of the controller gains was used in this calculation to increase the VS task speed. Eventually, the robot joint velocities will be measured and commanded to the robot velocity controller in line 23 of Algorithm 1. Not to mention that the controller would use manipulability as a secondary task in measuring the joint velocities, and DLS inverse would be used instead of pseudoinverse to convert EE velocities to the joint velocities. Using DLS would assist the controller in reducing the impact of discontinuities and limiting the impact of singularities.
## 3 Data-Processing Methodology
### Metrics Extraction  
Metrics (feature) extraction is the process of reducing a data stream to a set of informative and nonredundant metrics. The extracted metrics are used as inputs to models and support the model's interpretability.  
To analyze the variability of physiological metrics over time, we employ a moving window approach, where a window of a user-specified width and step moves through the input signal, time-stamping the position of the window and computing the median or average of the values in the window. In the discussed case, we are using a window width of 5 min and a step of 5 min (i.e., no overlap between neighboring windows).  
Table 1 lists some of the HRV metrics that can be extracted from IBI time series.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **Unit** & **Description** \\ \hline _Time Domain_ & & \\ IBI & ms & Median IBI \\ SDNN & ms & Standard deviation of interbeat intervals \\ RMSSD & ms & Root mean square of successive differences in interbeat intervals \\ PNN50 & \% & Percentage of successive interbeat intervals that differ more than 50 ms \\ TI & - & HRV triangular index = the integral of the HRV histogram divided by the height of the histogram \\ \hline \hline \end{tabular}
\end{table}
Table 1: Heart rate variability metrics that can be extracted from IBI time series [21,23].  
Figure 5: Processing of data acquired from wearables integrating heart rate and activity monitors. FIFO = first-in, first-out; QC = quality control.  
To calculate HRV metrics in the frequency domain, we resample the IBI stream to 5 Hz using the piecewise cubic-Hermite-interpolating polynomial method [24]. If there is a gap in the signal greater than 15 s, we pick the longest continuous segment to resample in the 5 min window. If there are more than three gaps of this length, we do not use this window. After interpolation, the signal is filtered to the low-frequency (LF: 0.04 to 0.15 Hz) and high-frequency regions (HF: 0.15 to 1.0 Hz; we increased the upper limit to 1.0 Hz vs. the typically used 0.40 Hz to account for physically active persons). We compute a value representing the LF and HF power for 60 s and 30 s windows, respectively, using the following formula: value = In (variance (data window)). Values less than 2.5 and more than 9.0 are eliminated, and the median values are calculated to represent the 5 min window [24].  
For respiration rate, SpO2, and step count, all of which are reported by the S+ app every 1 min, we calculate the average of values in the 5 min window; we require at least two valid data points for the calculation. The window width and the required number of datapoints are configurable.  
For each metric, we define the valid data fraction, VDF (%), as the ratio of the number of valid 5 min windows to the total number of 5 min windows in the monitoring period.
## 3 Experimental Design and Implementation
### Experimental Data
#### 3.1.4 Data Representativeness and Validation  
The dataset's morning peak focus aligns with critical signal optimization needs, as this period experiences maximum traffic pressure and demonstrates the most pronounced directional flow imbalances requiring coordinated signal control. The 10 time periods (each spanning 2 h) provide sufficient temporal coverage to capture typical weekday traffic patterns while maintaining consistency. The dataset contains 7252 valid vehicles, enabling statistically reliable parameter estimation for network-level traffic analysis, though penetration rates vary across road segments. The data quality after preprocessing ensures robust parameter extraction, with successful path identification for all processed vehicles and comprehensive coverage of all major network movements.