esa-sceva/satcom-chunk-collection huggingface dataset:
# Causality in Control Systems Based on Data-Driven Oscillation Identification
## 2 Proposed Methodology
### Median Ensemble Empirical Mode Decomposition  
Median Ensemble Empirical Mode Decomposition (MEEMD) is a variation of the EEMD algorithm that uses the median operator instead of the mean operator to ensemble noisy Intrinsic Mode Function (IMF) trials [23]. The use of this algorithm is a practical extension of the classic EMD and justified choice with real-world applications. The EMD method was developed so that data can be examined in an adaptive time-frequency-amplitude space for nonlinear and non-stationary signals [24]. It decomposes the input signal into a few Intrinsic Mode Functions and a residue. The given equation will be as follows:  
\[I(n)=\sum_{m=1}^{M}IMF_{m}(n)+Res_{M}(n), \tag{3}\]  
where \(I(n)\) is is the multi-component signal. \(IMF_{m}(n)\) is the \(m\)th intrinsic mode function, and \(Res_{M}(n)\) represents the residue corresponding to \(M\) intrinsic modes. The proposed median EEMD (MEEMD) algorithm, defines median operator as:  
\[\text{median}=\begin{cases}IMF(t)[(N+1)/2]&\text{- when $k$ is odd},\\ \frac{IMF(t)[(N/2)]+IMF(t)[(N/2)+1]}{2}&\text{- when $k$ is even},\end{cases} \tag{4}\]  
where \(IMF(t)\) denotes the ordered \(IMF\) list at time instant \(t\), which is obtained from \(N\) independent noise realizations. Consider a real-valued signal \(x(t)\) and a predefined noise amplitude \(\epsilon\), the used MEEMD is outlined in Algorithm 1.  
1. Generate the ensemble \(y_{n}(t)=x(t)+\epsilon\omega_{n}(t)\) for \(n=1,\ldots,N\), where \(\omega_{n}(t)\sim N(0,1)\); 2. Decompose every member of \(y_{n}(t)\) into \(M_{n}\) IMFs using the standard EMD, to yield the set \((d_{m}^{n}(t))_{m=1}^{M_{n}}\); 3. Assemble same-index IMFs across the ensemble using the median operator to obtain the final IMFs within MEEMD; for instance, the \(m\)th IMF is computed as \(d_{m}(t)=median(d_{m}^{1}(t),d_{m}^{2}(t),\ldots,d_{m}^{N}(t))\).  
**Algorithm 1** Algorithm of MEEMD  
Nevertheless, EMD, and thus its derivative MEEMD has limitations. In particular the occurrence of dependencies between the length of analyzed data and the number of received IMFs resulting from such a decomposition process.
# Wear of 17-4 PH Stainless Steel Patterned Surfaces Fabricated Using Selective Laser Melting
###### Abstract  
The recent developments in additive manufacturing (AM) are providing unprecedented opportunities in various fields, including the fabrication of advanced materials for tribological applications. The present work describes the results of an exploratory study focused on the analysis of 17-4 PH steel surfaces obtained using selective laser melting (SLM). In particular, the study includes the analysis of baseline (as-produced) and textured steel surfaces. Surface texturing comprises hexagonal prism structures (with or without dimples) arranged in a honeycomb pattern with 50 \(\upmu\)m or 100 \(\upmu\)m gap spacing. Starting from the minimum printing size enabled by the 3D printing platform, various textures are prepared by scaling up the characteristic dimensions of the prisms up to 500%. The obtained surface patterns are characterized (qualitatively and quantitatively) using a non-contact computerized numerical control (CNC) measuring system. The coefficient of friction (_COF_) was investigated using a Ball-on-Disk configuration using bearing steel balls as counterparts. For a fixed sliding speed, different contact loads and sliding radii were considered, while the tests were carried out in either dry or lubricant-impregnated conditions. The results of wear tests in both dry and lubricated conditions indicated that the baseline samples are provided with lower _COF_ compared to the textured ones. For the latter, neither the gap spacing nor the presence of dimples led to significant variations in the _COF_. However, in lubricated conditions, the values of the _COF_ for baseline and textured surfaces were closer and much smaller. In particular, the results provide clear indications regarding reducing the gap between prisms, which had a beneficial effect on the _COF_ in lubricated conditions. Similarly, sensitivity to dimples was quite remarkable, with a reduction in the _COF_ of about 30% when the larger gap spacing between the prisms was used.  
additive manufacturing; functional surfaces; tribology; 17-4 PH steel +
Footnote †: journal: Applied Sciences
# A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China
## 5 Progress of CO2 Storage in China
### Development Status of CO2 Geological Storage in China  
Currently, geological storage is still the dominant method of CO2 storage [167]. The principle of CO2 geological storage is an imitation of the mechanism of fossil fuel storage in nature. The geological storage of CO2 can be achieved by transporting CO2 to an  
Figure 5: CO2 storage classifications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Storage Type** & **Advantage** & **Disadvantage** \\ \hline \multirow{8}{*}{**Geologicalstorage**} & Deep saline aquifers & \begin{tabular}{c} Widely distributed \\ Large storage potential \\ Good storage condition \\ Infrastructure facilities \\ Experienced storage \\ \end{tabular} & \begin{tabular}{c} Uncertain sealing groundwater \\ resources pollution \\ Large, depleted field is required \\ \end{tabular} \\  & \begin{tabular}{c} Un-mineable coal seams \\ \end{tabular} & \begin{tabular}{c} No more investment cost \\ \end{tabular} & \begin{tabular}{c} Specific geological structures with lateral \\ connection and vertical isolation with \\ surrounding strata are required \\ \end{tabular} \\  & Salt caverns & \begin{tabular}{c} Low cost \\ Widely distributed \\ Permanent storage \\ Negligible leakage risk \\ Environmentally safe \\ \end{tabular} & \begin{tabular}{c} Suitable rock salt structure is required for \\ salt cavern construction \\ \end{tabular} \\  & \begin{tabular}{c} \\ Mineralogical carbon \\ storage \\ \end{tabular} & \begin{tabular}{c} Negligible leakage risk \\ Environmentally safe \\ \end{tabular} & \
\begin{tabular}{c} Slow reaction kinetics \\ \end{tabular} \\ \hline \multirow{2}{*}{**Ocean storage**} & \multicolumn{2}{c}{Huge storage potential} &
\begin{tabular}{c} Seawater’s pH value variation, \\ Marine ecological destruction \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Advantages and disadvantages of CO2 storage methods [58; 94; 163; 164; 165; 166; 167; 168; 169].  
appropriate location through pipelines or vehicles and injecting it into the ground with specific geological conditions [94; 170] and specific depths [170; 171]. Currently, the available geological storage of CO2 includes deep saline aquifers, depleted oil and gas reservoirs, un-mineable coal seams, salt caverns, and mineralogical carbon storage.
# Physiological Driver Monitoring Using Capacitively Coupled and Radar Sensors
## 4 Discussion  
It was shown, in a study on 5 subjects in highway and city driving conditions, that the proposed capacitive ECG system can provide accurate beat detection, heart rate and individual R-R intervals, as well as full ECG waveforms, through clothing. By applying the proposed motion artefact algorithms, a real-world performance was achieved better than the previous state of the art. If desired for a specific application, the accuracy can be further improved by using a stricter classifier for the motion artefact algorithms, at the expense of discarding a higher fraction of the data. Further work is needed to permit capacitive ECG measurement also when thicker clothing (e.g., winter jacket) is worn.  
Capacitive respiration measurement was also demonstrated, but with a lower coverage than for ECG. In future work, its performance is expected to benefit from signal quality algorithms and a multi-electrode array approach similar to ECG.  
Proof-of-concept experiments have shown that the proposed radar sensor can provide respiration and, through the application of the proposed motion reduction algorithms, average heart rate during driving. Radar-based measurement of individual beat to beat intervals for heart rate variability remains a challenge during driving conditions. Further work on motion reduction algorithms as well as radar integration into the vehicle is needed to address those challenges.  
Figure 10: Comparison between the vital signs extracted with the radar and the reference driving at 120 km/h. The top two traces show an example of the respiration from radar and from the reference belt, showing a clear correlation. The third and fourth traces show an example of the heartbeat signal from the radar and from the reference ECG. The bottom trace shows the accelerometer signals, which show significant vibration amplitudes.  
It is expected that in the end a sensor fusion of both methods will provide the most robust results.  
Conceptualization, methodology, investigation, analysis of the capacitive sensor experiments: I.D.C., A.P., T.T. Conceptualization, methodology, investigation, analysis of the radar sensor experiments: M.M., T.T. Writing--original draft: M.M., T.T. Writing--review and editing: all authors. Supervision: R.P., C.V.H., T.T.  
This research received no external funding.  
Authors wish to thank [PERSON] for his assistance in the investigation approval procedure and the ANWB driving school in Eindhoven, the Netherlands, for their support in the execution of the experiments.  
The authors declare no conflict of interest.
# Energy Harvesting over Rician Fading Channel:
## 4 Numerical Results and Discussion
### Effect of Various Parameters on SER  
The purpose of the following simulations is to confirm the correctness of the SER formulas provided in the analysis. First, Figure 9 presents the effect of the hardware impairment level on the SER performance. In this simulation, the time-switching factor is chosen as \(\alpha=0.5\) with the same reason as in Section 4.1, the transmission rate is fixed at 1 bps/Hz, and the ratio \(P/N_{0}\) varies in the range from 0 dB to 40 dB. From the results, it is showed that SER decreases to the asymptotic value when the ratio \(P_{s}/N_{0}\) increases. The Monte Carlo simulation curves overlap with the corresponding analysis curves. This confirms the validity of our analysis. When the hardware impairment level goes higher, the SER also has a larger value, as expected. Furthermore, the SER performance of the QPSK scheme is better than the one of the BPSK scheme in the same simulation condition. This can be explained because the QPSK modulation scheme can transmit two bits in one symbol while the BPSK scheme can only transmit one bit per symbol. Hence, if we have the same constraint on the transmission rate for both methods, the required SNR for maintaining good communication would be smaller for the QPSK method. As a result, the outage probability and the SER for QPSK modulation would be smaller than the ones of BPSK.  
In a similar way, the influence of the time-switching factor on the symbol-error-rate at the destination node is illustrated in Figure 10. The simulation parameters are \(\kappa=0.1\), \(R=1\) bps/Hz, and \(P/N_{0}\) varies from 0 dB to 40 dB. Again, the simulation curves match perfectly with the corresponding analysis curves. The SER tends to approach its asymptotic value, and QPSK modulation still provides the better SER performance than BPSK for both values of \(\alpha\). Note that, for this simulation, the asymptotic values do not depend on the time-switching factor \(\alpha\). However, \(\alpha\) surely has an effect on the immediate value of SER. In fact, the SER performance should be better with the value of \(\alpha\) that is in the middle of its range. For example, in Figure 10, the SER value for \(\alpha=0.5\) is less than the one with \(\alpha=0.2\). The explanation is the same as the case of Figure 8.  
Figure 8: Achievable throughput versus \(\alpha\).
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 3 Simulation Platform Design
### Upstream Network  
The upstream network is modeled at the fundamental frequency by a three-phase source with nominal voltage \(U_{s}\) and a short-circuit impedance \(Z_{s}=R_{s}+jX_{s}\), serving as a simplified (Thevenin) representation of the network upstream of the PCC. \(Z_{s}\) is derived from the short-circuit power \(S_{sc}\) using \(Z_{s}=U_{s}^{2}/S_{sc}\). \(S_{sc}\) is either provided by the distribution network operator or calculated as detailed in [14].  
Reference values of \(Z_{s}\) for assessing PQDs at the LV level, as per IEC/TR 60725 [16], indicate typical short-circuit power levels derived from extensive surveys in European LV networks (230 V, 50 Hz). For example, devices with rated currents \(\leq\) 16 A and \(\leq\)75 A have reference \(Z_{s}\) values of 0.24 + \(j\)0.15 \(\Omega\) and 0.15 \(+\)\(j\)0.15 \(\Omega\), corresponding to short-circuit powers of 0.57 MVA and 0.76 MVA, respectively.
## 4 The Digital-Twin-Based Detection and Protection Framework
### The Detection and Protection Method in the Framework
#### 4.3.2 SH Detection and Protection Method
Input: \(Pos_{DT}\), \(LSA_{DT}\)_and_\(RIB_{DT}\)  
Output: if_Sinkhole_exist  
Start  
1. For each piece of data \(<ID_{orb}^{i}\parallel ID_{sat}^{i}\), \(ID_{orb}^{j}\parallel ID_{sat}^{j},DLY^{ij}>\) in \(RIB_{DT}\)
2. if \(ID_{orb}^{i}=ID_{orb}^{j}\):
3. if abs(\(ID_{sat}^{i}-ID_{sat}^{j}\)) = 1 or abs(\(ID_{sat}^{i}-ID_{sat}^{j}\))= \(M-1\):
4. Not SH item, continue
5. else:
6. \(L_{n}=\text{Min}\left(\left|ID_{sat}^{i}-ID_{sat}^{j}\right|,M-\left|ID_{sat}^ {j}-ID_{sat}^{j}\right|\right)\)
7. if \(DLY^{ij}\leq L_{n}\times\overline{DLY_{iol}}\) :
8. SH exists, break
9. else: Not SH item, continue
10. elif abs(\(ID_{orb}^{i}-ID_{orb}^{j}\)) = 1 and \(ID_{sat}^{j}=ID_{sat}^{j}\):
11. if \(LAT^{i}\) and \(LAT^{j}\geq\text{Polar region boundary latitude}\):
12. if \(DLY^{ij}\leq 2\times\overline{DLY_{iol}}+\overline{DLY_{anol}}\):
13. SH exists, break
14. else: Not SH item, continue
15. else: Not SH item, continue
16. else:
17. if \(LAT^{i}\) and \(LAT^{j}\geq\text{Polar region boundary latitude}\):
18. \(L_{n}=\left|ID_{sat}^{i}-ID_{sat}^{j}\right|+2\), \(L_{o}=\left|ID_{orb}^{i}-ID_{orb}^{j}\right|\)
19. else: \(L_{n}=\left|ID_{sat}^{i}-ID_{sat}^{j}\right|\), \(L_{o}=\left|ID_{orb}^{i}-ID_{orb}^{j}\right|\)
20. if \(DLY^{ij}\leq L_{n}\times\overline{DLY_{anol}}+L_{o}\times\overline{DLY_{anol}}\):
21. SH exists, break
22. else: Not SH item, continue
23. Detect the next piece of data
24. return if_Sinkhole_exist End ```
**Algorithm 1** SH detection algorithm  
After the SH detection process, if an RIB that can cause a SH is detected, the ground station will use the satellite position information \(Pos_{DT}\) and link-state information \(LSA_{DT}\) to generate the correct RIB. Then the ground station generates a correct routing table for the satellite corresponding to the wrong RIB and uploads the routing table to the satellite. If there are no RIBs with errors, the ground station and satellite network require no additional operation.
## 4 Experiments and Results
### Assessment for Real World Data  
Finally, we conducted a set of experiments on a real-world dataset reported in Reference [13]. This work shows the results of different working teams grouped by task. We focused on the work regarding _Task 5_ regarding the analysis of hate speech against women and immigrants on a corpus of labeled tweets in Spanish and English. The labels were encoded as three binary values described as follows:  
* **HS--a** binary value indicating the presence or absence of hate speech against one of the given target people (women or immigrants).
* **TR--a** binary value indicating if the target of the hate speech is a generic group of people (0) or a specific individual (1).
* **AG--a** binary value indicating if the hate speech present in the tweet is aggressive (1) or not (0).  
For our purposes, we filtered the data to retain only those tweets in Spanish and labeled with HS = 1. Then, we selected manually those tweets containing expressions focused on women (most labeled with HS = 1,TR = 1). As result a set of instances containing misogynistic tweets were obtained. On the other hand, instances containing non-misogynistic tweets were obtained simply by filtering tweets tagged with HS = 0. In total, a set of 1774 tweets (with a comparable number of classes) was obtained. On this data, we executed 100 times our model attempting to recognize the presence or absence of misogynistic manifestations. We resort again to the metrics of accuracy, precision, recall, and F1-score to quantify the recognition ability. In Figure 5, the approximation to the density function for the executed experiments is shown.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Statistic** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Q\({}_{1}\) & 0.87 & 0.86 & 0.92 & 0.89 \\ Q\({}_{2}\) & 0.88 & 0.87 & 0.93 & 0.89 \\ Q\({}_{3}\) & 0.88 & 0.88 & 0.93 & 0.90 \\ IQR & 0.01 & 0.01 & 0.01 & 0.01 \\ CI & [0.84, 0.91] & [0.83, 0.90] & [0.89, 0.96] & [0.87, 0.92] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quartile analysis for the assessment of _learning ability_ of the model.  
Figure 4: Approximation to the density function of the values corresponding to accuracy, precision, recall, and F1-Score exhibited by the model during the assessment of learning ability.  
As in the above assessment, the model exhibited a successful performance. The quartile analysis in Table 3 shows a close variability to that of learning assessment. It means that the recognition ability of our method (on unknown data) is statistically comparable to its ability exhibited during the learning process. To formalize this assumption, we finally conducted a hypothesis test that allowed us to show that there is not a significant difference between what we have called learning ability and recognition ability (\(p\)-value > 0.05).
## 4 Methodology and Testing Process
### LoRA Fine-Tuning  
The training is based on Stable Diffusion V1.5, using the Adam8 bit [40] optimizer with decoupled weight decay. The training consists of 22 k steps with a batch size of 1. The initial learning rate is \(1\times 10^{-4}\), and a single Tesla T4 GPU is used. The training is planned for 20 epochs, and a total of 5 LoRA fine-tuned models are recorded. The change in the loss function value (Loss) was also recorded. The loss function is an important metric for evaluating the training effectiveness of the model. Generally, as the number of training iterations increases, the loss function tends to converge, indicating that the model training has converged [24]. Figure 4 shows the trend of the loss function value changing with the increase in training iterations during the training process. It can be observed that, as the number of training iterations increases, the loss function value gradually decreases, indicating effective training [41]. However, if the training loss decreases too much, it may indicate overfitting. Therefore, it is necessary to monitor the model's performance using a validation set and test each set of fine-tuned model weights to ensure generalization. Finally,  
Figure 3: Main and sub-corridor style nursing units floor plan dataset (portion).  
the model needs to be evaluated on an independent test set to ensure its effectiveness in practical applications [16].  
Without changing the main hyperparameters, the effects of each fine-tuned model were tested by varying the LoRA weight values (Table 4). The test results show that epochs 6 to 10 can produce relatively excellent LoRA models, characterized by well-defined functional partitions, clear boundaries, and accurate color values for functional blocks. Model 5, which is the tenth generation model, can be selected for subsequent generation experiments. The LoRA model weight value should be set in the range of 1 to 1.2.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 2 Kalman Filtering GNSS/INS Fusion Navigation Basic Principles
### Basic Principles of Kalman Filtering
#### 2.1.1 Loosely Coupled  
Loosely coupled [22] processing, during the state prediction process, relies solely on the sensor's output data as observation values to update the predicted values. This approach is logically straightforward and practical, allowing for the fusion of multiple sensors to enhance accuracy. For instance, using an IMU as the source of raw data for prediction computation, while utilizing the GNSS positioning results as observation valuesfor error correction updates. The mainstream practice usually involves employing the IMU as the prediction data sensor and other sensors as observation sensors.  
Loosely coupled systems are generally divided into open-loop and closed-loop systems. Due to the large errors in the open-loop system where the attitude of the IMU is not corrected by the Kalman filter, this experiment adopts a closed-loop system. In the closed-loop system, the attitude of the IMU is corrected by the Kalman filter. The architecture of the loosely coupled closed-loop system is illustrated in Figure 1.
# Silicon 3D Microdosimeters for Advanced Quality Assurance in Particle Therapy
## 4 Application of SOI Mushroom Microdosimeters
### RBE Derivation and Cell Survival Predictions
#### 4.2.2 Cell Survival Prediction in Proton and Heavy Ion Therapy  
Figure 16a,b show a comparison of the survival fraction (SF) for MIA PaCa-2 cells (marker) as reported in [26] for each linear energy transfer (LET) of \({}^{16}\)O and \({}^{20}\)Ne ions irradiated at NIRS (QST)-HIMAC scanning beam delivery. Solid lines correspond to the SF calculated based on the modified MK model using Equations (2)-(5), where the microdosimetric quantity \(\overline{y_{D}}\) was measured with the trenched planar Mushroom microdosimeter  
Figure 14: RBE\({}_{10}\) distribution obtained by the free-standing Mushroom microdosimeter embedded in Polyimide in response to 290 MeV/u SOBP \({}^{12}\)C ions at HIMAC in comparison with the TEPC data [33].  
Figure 15: (**a**) RBE\({}_{10}\) distribution obtained with the MicroPlus Mushroom microdosimeters versus depth in water for the 400 MeV/u \({}^{16}\)O pristine BP, (**b**) zoomed-in view of RBE\({}_{10}\) distribution [32].  
and the MK model parameters obtained for this cell line. It can be seen that for doses up to 3 Gy, good agreement between the cell SF and calculated SF based on measurements with SOI microdosimeters and MKM was observed for all types of ions. This good agreement emphasized that the SOI 3D Mushroom microdosimeter and the MKM model can be very useful tool to predict the survival of cells for these heavy ion irradiations.  
Figure 16(a) shows the derived \(\text{RBE}_{\text{D}}\) distribution obtained with the MicroPlus probe for 2 Gy delivered at the middle of the 50 mm proton SOBP at iThemba Labs proton beam facility, South Africa, compared with \(\text{RBE}_{\text{D}}\) values obtained from Chinese hamster ovary (CHO-K1) SF cells measured in vitro in a PMMA phantom (Figure 17(b)) as well as the physical dose measured at the same position using a MarkusTM ionisation chamber. A 250 kVp x-ray was used as a reference radiation. \(\text{RBE}_{\text{D}}\) values were found to increase from 1.21-2.32 along the SOBP. The RBE calculated from the SOI-microdosimeter and MKM method was found to be in good agreement with that obtained from in vitro data within experimental uncertainty. The percentage difference was less than 8% for corresponding water equivalent depths [34].  
Figure 17: (**a**) Comparison of RBE values obtained from in vitro CHO-K1 cell survival curves and those values obtained using the modified MKM with CHO cell parameters and lineal energy measured with the SOI 3D Mushroom microdosimeter for 2 Gy (\(\text{RBE}_{\text{D}}\)) delivered at the middle of the SOBP. A 250 kVp X-ray was used as a reference radiation. (**b**) CHO-K1 cell survival curves measured in vitro along the proton SOBP in a PMMA phantom [34].  
The results presented provide good justification of the utility of 3D SOI Mushroom microdosimetry for RBE and cell survival prediction in proton therapy.