esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Direct Under-Sampling Compressive Sensing Method  
### Basic Theory of Compressive Sensing  
Compressive sensing includes three core parts: signal sparse decomposition, uncorrelated signal observation, and signal reconstruction. The sparseness of the signal is a necessary condition for compressive sensing, uncorrelated observation is the key to compressive sensing, and signal reconstruction is the technique of compressively sensing reconstructed signals.  
If a discrete signal \(x\in c^{N}\) is sparse or compressible, it can be represented as a linear combination of a set of standard orthogonal bases  
\[x=\sum_{i=1}^{N}\Psi_{i}\alpha_{i}=\Psi\alpha \tag{1}\]  
In the formula, \(\Psi=[\psi_{1},\psi_{2},\ldots,\psi_{N}]\) is an \(N\times N\) matrix and is considered as the sparse basis of the signal \(x\). If there are \(k(k\ll N)\) non-zero coefficients in \(\alpha\), then the signal \(x\) is called a \(k\)-sparse signal under the sparse basis \(\Psi\). Compressive sensing projects a high-dimensional signal onto low-dimensional spaces through an observation matrix \(\Phi=[\phi_{1},\phi_{2},\ldots,\phi_{M}]^{T}\) using \(M(M\ll N)\) measurements in the following way  
\[y=\Phi x=\Phi\Psi\alpha=\Theta\alpha \tag{2}\]  
where \(y\) is the observation and \(\Theta=\Phi\Psi\) is called the perceptual matrix. According to the \(M\) number of \(y\) observations necessary to calculate \(\alpha\), this can be expressed as an optimization problem in the sense of an \(l_{0}\)-norm  
\[\min\lVert\alpha\rVert_{0}\qquad s.t.\qquad y=\Phi x=\Phi\Psi\alpha=\Theta\alpha \tag{3}\]Equation (3) is an NP-hard problem, so the signal reconstruction solution is transformed into a \(l_{1}\)-norm optimization problem with relaxation constraints. It then becomes  
\[\min\lVert\alpha\rVert_{1}\qquad s.t.\qquad y=\Theta\alpha \tag{4}\]  
This problem becomes a convex optimization problem. The typical reconstruction algorithm can be the greed tracking method [24], convex relaxation method [25], or combination method [26].
# Cluster-Centered Visualization Techniques for Fuzzy Clustering Results to Judge Single Clusters
## 4 Cluster-Centered Visualization Techniques  
For visualization of the clustering results based on the fuzzy \(k\)-means clustering algorithm from Section 2, three new CCV techniques are developed. The first method is \"Compute-Points-Circular\" (CPC), where a cluster prototype is placed in the center of a circle and the other cluster prototypes on the edge of the circle. This method gives an indication of how well a cluster is separated from the other clusters. The second method is \"Angle-2-Clusters\" (A2C), where two cluster centers are placed at specific points and the triangle between each data point and the cluster center is calculated. In the third method, \"Angle-Mapping\" (AM), the data points are considered from the viewpoint of one central cluster, and the angle and radius of each data point are considered. The methods are explained in detail in the following sections and also a visual comparison with other techniques is given.
# Internet of Things for Sensing: A Case Study in the Healthcare System
## 3 S-Band Sensing and Data Processing
### Phase Calibration  
The phase data extracted from wireless CI stream is largely random and inapplicable for detecting Narcolepsy sleep episodes due to the presence of random noise and phase offset between transceiver pair. Thus, to mitigate the impact of random noise from the CI phase data and remove the phase offset, we use linear transformation on raw phase data.  
Let \(\angle WCI^{\wedge}{}_{j}\) represent the measured wireless CI phase data for \(j\)th frequency.  
\[\angle WCI^{\wedge}{}_{j}=\angle WCI_{j}+2\pi\frac{k_{j}}{Z}\tau+\beta+N, \tag{4}\]  
Here, \(\angle WCI\) is the true phase data, \(N\) is the random noise, \(\tau\) is the unsynchronized timing between transceiver, and \(\beta\) is the unknown phase offset. The frequency indices \(k_{j}\) for \(j=\left[1,30\right]\) and FFT size \(Z\) can be obtained from IEEE 802.11n specifications [33]. The unknown terms \(\tau\) and \(\beta\) listed above make it infeasible to obtain useful phase information solely from S-band sensing technique. Thus, to remove the unknown terms, we apply a linear transformation to the raw CI phase data across the frequency bands [34].  
Let the two terms \(L\) and \(M\) represent the slope of the CI phase and phase offset considering overall frequency bandwidth, respectively.  
\[L=\frac{\angle WCI^{\wedge}{}_{30}-\angle WCI^{\wedge}{}_{1}}{k_{30}-k_{1}}. \tag{5}\]  
\[M=\frac{1}{30}\sum_{i=1}^{30}\angle WCI^{\wedge}{}_{j}. \tag{6}\]  
By subtracting the linear term \(Lk_{j}+M\) from raw CI phase data, we get the calibrated phase information \(\angle WCI^{\circ}{}_{j}\)[10], denoted as  
\[\angle WCI^{\circ}{}_{j}=\angle WCI^{\wedge}{}_{j}-Lk_{j}-M. \tag{7}\]
# CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams
## 3 Background
### Target Prediction  
In some scenarios, prior information about the target is given and is incorporated into the entropy map using the target prediction model. This prediction model is based on information that can be obtained in advance. For example, in a rescue mission, it might be useful to take advantage of the fact that people are more likely to be near collapsed buildings or the place where the accident occurred. If, on the other hand, a team of robots is searching for a lost robot, it may be useful to consider that teammate's last known sensor information to determine candidate locations on the map. In this study, the above situations are called context, \(c\). Context is used to estimate the target's location. A Gaussian Mixture Model (GMM) is constructed with a finite number of contexts \(N_{c}\) at time \(k\) using the following equation  
\[p(x_{k}|c_{k})=\sum_{i=1}^{N_{c}}p(x_{k}|c_{k}^{i})p(c_{k}^{i})=\sum_{i=1}^{N_ {c}}\pi_{i}G(x|\mu_{i},\Sigma_{i}) \tag{4}\]  
where \(\pi_{i}\), \(\mu_{i}\) and \(\Sigma_{i}\) are a mixing coefficient, mean vector, and covariance, respectively, for the \(i\)-th distribution. Each component's density is a 2D Gaussian function of the form as following.  
\[G(x|\mu_{i},\Sigma_{i})=\frac{1}{2\pi|\Sigma_{i}|^{1/2}}exp\Big{[}-\frac{1}{2 }(x-\mu_{i})^{/}\Sigma_{i}^{-1}(x-\mu_{i})\Big{]} \tag{5}\]  
A particle filter is employed to implement the prediction model. If one considers the former scenario of searching near buildings after a disaster, a scene may have more than one building. Then, there exist multiple \(\mu\) values representing each collapsed building.
## 4 Backfill Performance Analysis
### Deformation Monitoring  
To evaluate the control effect of WRBM on roof subsidence after backfilling the backfill materials into the goat, five displacement sensors for the roof of the goat were set in the backfilling bodies at a location 450 m from the open-off cut of the backfill panel. Measuring point 1# was 15 m from the tail entry and measuring point 3# was arranged in the middle of the panel. The specific arrangement of the sensors is displayed in Figure 16.  
Based on the monitoring data, measuring points 1# and 3# were selected to monitor and analyze the roof dynamic subsidence. The monitoring results of roof dynamic subsidence are shown in Figure 17.  
Figure 16: Schematic diagram of locations of roof displacement sensors.  
Figure 17: Roof dynamic subsidence curves at the measuring points.  
Figure 17 shows that as the backfill panel advances, the changes in the roof dynamic subsidence of the goat can be divided into three stages: the accelerated subsidence stage (where the backfill panel is 0-88 m from the measuring points), slow subsidence stage (88-188 m), and steady subsidence stage (longer than 188 m). When the distance from measuring points to the backfill panel is more than 188 m, it enters a stage entailing steady deformation. With the advance of the backfill panel, WRBM are gradually compressed and become the main load-bearing carrier to support the overlying strata. The interaction between the compressive deformation of WRBM and the overlying strata begins to be balanced and roof subsidence begins to stabilize. The maximum roof subsidence at measuring point 3# is 568 mm, based on which it is calculated that the compression ratio is 17.81%, similar to the predicted value in the tests. Therefore, the tests provide data to support compressive deformation predictions for WRBM in underground goats.
## 4 Measurements and Methods
### Measurement Method  
The room impulse responses were measured using the Dirac system (Dirac type 7841, v.6.0). An exponential sweep signal was fed to an omnidirectional loudspeaker and recorded by an omnidirectional microphone. Two loudspeaker positions at the front of the classroom were used and, for each loudspeaker position, six microphone positions were used throughout the room. No microphone positions were closer than 2 m to the loudspeaker and none were closer than 1 m to any of the room surfaces.  
The room acoustic parameters measured were reverberation time \(T_{20}\) (s), speech clarity \(C_{50}\) (dB), and sound strength \(G\) (dB). \(C_{50}\) and \(G\) are defined in ISO 3382-1. \(T_{20}\) was evaluated according to ISO 3382-2 using the interval \(-5\) to \(-25\) dB of the decay curve. The sound  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Ceiling and Wall Panels** & \
\begin{tabular}{c} **Mounting Height (Depth of Air** \\ **Cavity) of Suspended Ceiling (mm)** \\ \end{tabular} &
\begin{tabular}{c} **Air Flow Resistivity of** \\ **Ceiling Absorber (kPas/m\({}^{2}\))** \\ \end{tabular} \\ \hline \multirow{2}{*}{1} & 50 mm glasswool ceiling absorber * & 750 & 11.8 \\  & no wall panels & & \\ \hline \multirow{2}{*}{2} & 15 mm glasswool ceiling absorber **, & & \\  & no wall panels & & \\ \hline \multirow{3}{*}{3} & 15 mm glasswool ceiling absorber **, & & \\  & 40 mm glasswool wall absorber *** distributed & & \\ \cline{1-1}  & on two adjacent walls and directly mounted on & & \\ \cline{1-1}  & the walls, see Figure 7. & & \\ \hline \multirow{2}{*}{4} & 15 mm glasswool ceiling absorber ** & 185 & 77.8 \\ \cline{1-1}  & 50 mm glasswool ceiling absorber * & 150 & 11.8 \\ \hline \hline \end{tabular}  
strength \(G\) was measured using a constant sound power source (Nor278, Norsonic). The sound power source was located in the same positions as the loudspeaker.
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 3 Method
### LoRa Emulator
#### 3.4.1 LoRa Modulation  
LoRa is one of the LPWAN modulation techniques to achieve long-range and low power communication by spreading payload symbols across a wide spectrum to increase the energy per symbols and therefore mitigating the effect of narrowband interference. In LoRa the symbols are encoded into cyclic chirp signals using CSS technique, where the starting frequency and the ending frequency of a chirp signal encodes the value of the symbol. The chirp rate is controlled by a parameter called the _spreading factor_ (SF) wherehigher SF stretched the symbol for longer time on air. Higher SFs increase the receiver sensitivity due to the increased energy per symbol, but on the other hand, higher SFs also reduce the data rate and increases the energy consumption of the LoRa IoT device. As such, a trade-off between packet success rate and energy consumption need to be carefully calculated to maintain the sustainability of the system [48].
## 4 Discussion
### Developing Predictive Models to Maintain Optimal LINAC Performance  
Various studies have been conducted to maintain LINACs' optimal condition. Overall, we conducted the downtime prediction using 20-year data records through time series modeling. Table 3 summarizes the characteristics of each study. [PERSON] et al. demonstrated QA feasibility using daily MPC test results on 490 data. It follows that machine performance \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Coefficient** & **STD Error** & **z** & \(\mathbf{p>|z|}\) & **95\% CI (0.025-0.975)** \\ \hline
**ARL1** & 0.9497 & 0.038 & 25.24 & \(<\)0.001 & 0.876 & 1.023 \\ \hline
**MA.L1** & \(-\)0.7114 & 0.041 & \(-\)17.432 & \(<\)0.001 & \(-\)0.791 & \(-\)0.631 \\ \hline
**AR.S.L12** & \(-\)0.6484 & 0.013 & \(-\)51.846 & \(<\)0.001 & \(-\)0.673 & \(-\)0.624 \\ \hline
**AR.S.L24** & \(-\)0.3435 & 0.013 & \(-\)26.597 & \(<\)0.001 & \(-\)0.369 & \(-\)0.318 \\ \hline \hline \(\sigma^{2}\) & 328.3387 & 9.421 & 34.854 & \(<\)0.001 & 309.875 & 346.803 \\ \hline \hline \end{tabular}  
* Note: The model has four main lagged AR and MA variables. The first set of AR and MA variables lagged by 1 time step for ARL1 and MA.L1, respectively. Meanwhile, the second set lagged by 12 and 24 time steps for ARS.L12 and ARS.L24, respectively.  
\end{table} 
Table 1: Results for the seasonal ARIMA model in this study. was checked using previous records for the MLC, gantry, couch, beam, and imager to maintain the optimal LINAC performance. Furthermore, the performance, prediction, and distribution of the beam power variance were calculated using predictive analysis. Compared with other studies, our study used various performance metrics, including the MSE, RMSE, MAPE, CC, and ACF. Additionally, we developed the model based on excellent autocorrelation using ARIMA descriptive time series analysis. The MAPE was calculated as 3%. The metric shows a highly stable prediction with an error rate of less than 5%. Thus, the superiority of the performance test in our study is shown in Table 3. The results of our study show that a predictive maintenance approach using LINAC downtime data is possible. Moreover, by analytically classifying faulty parts, the system can be used clinically for patients at all times when important parts, such as the MLC, have a high percentage of failures. In summary, our study shows that a predictive maintenance approach using LINAC downtime data is feasible. In addition, the presented prediction results indicate that the predicted downtime can be expected for important parts such as MLCs by analytically classifying faulty parts with high failure rates. However, it shows the characteristics of a machine used for a single fraction or hypofractionation (<5 fractions) in brain and spinal tumor treatment in a specific area in a single neurosurgery clinic. Thus, the results do not reflect the characteristics of a general LINAC condition.
## 3 Methods and Materials
### Velocity Model Building  
The principal generation method of the optimal VID model is the application of MVA. Based upon the residual hodograph curvatures occurring on trace gathers after the PreSDM, the Residual MoveOuts (RMO) are calculated, from which the VID initial model is updated using the iteration method [39; 40]. Hence, the resultant VID model is strongly dependent on both the seismic data quality and the processing methodology. In order to solve this problem, within the INGA 2 research projects, the generation of independent velocity fields was applied. Based upon many tests, it was assumed that the initial velocity field for PreSDM can be the Parametric Velocity Model (PVM) presented in Figure 6. The input datasets for model construction were the interval velocities calculated from average velocities recorded in selected wells from the Eastern Carpathians and the Carpathian Foredeep. Considering the regional nature of the velocity field and the varying quality of average velocity records, the static parametric modeling was carried on for layers up to 1000 m thick (Figure 6A). This enabled the interpreters to apply block averaging of velocity values and to eliminate the errors resulting from the varying quality of velocity measurements in wells.  
Modeling applied the anisotropic kriging methodology with the exponential variogram [41] and the preferred interpolation azimuth 120 degree, i.e., consistent with the regional strike of geological structures in the study area [Analysis based upon geological maps provided by the PIG-PIB [42] and on the basic geological map after [PERSON] [38]. Moreover, a regional trend of velocity variation with depth was determined, which was then used as a secondary variable. Such a procedure was necessary due to the relatively small number of wells together with the steeply dipping bedding, which would cause an underestimation of velocities in the deep zone and their overestimation in the shallow zone while ignoring the depth trend of the velocity. For model construction in the depth domain (Figure 6B), the interval velocities were used, obtained from the measurements of average velocities together with shallow velocity models derived from the Seismic Refraction Tomography (SRT) survey. Therefore, datasets provided by the shallow velocity model could be included in the petrophysical modeling [3; 17; 18; 31]. Such an attempt enabled a better representation of the velocity variations in the shallow zone.  
Figure 6: The construction of Parametric Velocity Model (PVM)—visualization. A—3D structural grid; B—Cross-sections through the 3D PVM in the context of the structural grid.
# Fault Diagnosis Method for Aircraft EHA Based on FCNN and MSPSO Hyperparameter Optimization
## 6 Typical Fault Diagnosis of EHA  
In Section 5, 7168 sample sets were obtained by simulation experiments, and each sample set contains six fault data of length 576 and six GADF images of size 64 \(\times\) 64. We divide these sample sets into 5012 training sets, 1435 testing sets, and 721 validation sets in the ratio of 7:2:1. Classification labels zero to six are added to each sample set, where zero represents normal, and one to six represents each of the six typical failure modes mentioned above. To verify the performance of the MSPSO-FCNN under noise interference, Gaussian white noise obeying \(N(0\,,\ 0.5)\) is added to each feature of the original EHA fault data.  
We use Tensorflow 2.0 to train the model, and the development environment is PyCharm Community Edition 2021.3.3. The CPU is Intel(R) Core(TM) i7 9750H 02.6 GHz, the running memory is 16 GB, the graphics card is NVIDIA Geforce GTX 1650, and the operating system is Windows 10.  
Figure 13: GADF images: (**a**) Internal leakage of hydraulic pumps (displacement of actuator); (**b**) Oil mingled with air (velocity of actuator); (**c**) Internal leakage of actuator (rotation speed of motor); (**d**) Friction increases (pressure difference of pump); (**e**) Loss of sensor gain (flow of plunger pump); (**f**) Oil mingled with air (rotation speed of motor); (**g**) Friction increases (outlet pressure of accumulator); (**h**) Internal leakage of hydraulic pumps (pressure difference of pump).  
We adopt the following metrics to evaluate the effectiveness of aircraft EHA troubleshooting [39]:  
1. Accuracy  
The accuracy rate represents the ration of all samples with correct predictions. It is calculated as follows:  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN} \tag{22}\]  
_TP_ denotes the number of positive samples correctly classified, _TN_ denotes the number of negative samples correctly classified, _FP_ denotes the number of positive samples incorrectly classified, and _FN_ denotes the number of negative samples incorrectly classified. In the multiclassification task, each category is considered a positive sample, while all other categories are considered negative samples.
2. Precision and Recall  
Precision is the ratio of correctly predicted positive classes to all items predicted to be positive. And recall is the ratio of correctly predicted positive classes to all items that are actually positive.  
\[P=\frac{TP}{TP+FP} \tag{23}\]  
\[R=\frac{TP}{TP+FN} \tag{24}\]
3. \(F_{1}\)  
\(F_{1}\) is the harmonic mean of precision and recall, which is expressed as follows:  
\[F_{1}=\frac{2 PR}{P+R}=\frac{2 TP}{FP+FN+2 TP} \tag{25}\]
# Primary User Localization Algorithm Based on Compressive Sensing in Cognitive Radio Networks
## 5 Simulation and Analysis of Localization Performance
Being different from RR-WCL, PU-SC  
Figure 5: The localization performance of the PU-CSL and RR-WCL algorithms. (**a**) The root mean square error with the signal-to-noise ratio curve; (**b**) The maximum location error with the signal-to-noise ratio curve. Figure 6: The localization performance of the PU-CSL and RR-WCL algorithms. (**a**) The root mean square error curve with the change of cognitive node number; (**b**) The maximum location error curve with the change of cognitive node number. correlation between source signal and SUs integrally at one time using CS. Therefore, it performs higher localization accuracy due to making more comprehensive consideration of the source localization.
## 1 Introduction
Additionally, a proposition is made to model cognitive abilities and task requirements using a human asset administration shell [13]. Ontology models can also help contextualize key performance indicators (KPIs) [14], identify indirect effects or influences, and analyze relationships within a complex network [15]. They can also assist in visually representing KPI themes, developing dashboards [16], and consolidating KPI-related data [17]. Once this relationship with the decision variable is established, it enables responsive development and optimization. The ontologies, semantic tools, and industry standards proposed in this paper can support the development of systems that enhance operators' resilience, flexibility, and efficiency. The primary contribution of this study is the introduction of a framework known as the Human-Centric Knowledge Graph (HCKG), which models elements related to the human operator, such as monitoring movement, work environment, and collaboration with robots, using ontology and standards. The framework is exemplified through an industrial case study and incorporates graph-based data querying, visualization, and analysis. An instance involving a complex wire harness assembly process illustrates instances of resource allocation and comprehensive support for human-machine collaboration. The key innovations and contributions of this paper include the following:  
* Suggested the expansion of automation standards like ISA-95, AutomationML, or B2 MML to cover human-centric processes and the application of semantic technologies. * Advocated for a Knowledge Graph (KG)-based approach to bolster human-centered and collaborative manufacturing in Industry 5.0. * Showcased a replicable industrial case study to validate the concept. Various graph-based analyses utilizing normal, directed, or hypergraphs will be demonstrated, such as resource allocation assessment, KPI evaluation, or identification of diverse collaboration forms between human and machine agents in the assembly process. This research builds upon a previous conference paper [18], which introduced only a portion of the concept. Initially, Section 2 presents the current state of the field, pinpointingknowledge gaps and motivations. The core contribution of the paper is outlined in Section 3, where the foundational elements of the HCKG design concept are elaborated. Section 4 outlines a wire harness assembly-based case study to trial the human-centered KG-based design concept. Lastly, the contributions and potential avenues for future research are discussed in Section 5.