esa-sceva/satcom-chunk-collection huggingface dataset:
# InSAR-CTPIM-Based 3D Deformation Prediction in Coal Mining Areas of the Baisha Reservoir, China
## 4 Real Experiments
### Analysis of 3D-CTPIM Deformation Prediction Results  
According to the method described in Section 2.1, high-coherence points from 12 periods of time-series deformation monitoring results from 13 November 2015 to 19 November 2016 for the same settling funnel in the red rectangular box were used as the observations data to solve the InSAR-3D-CTPIM prediction parameters, and the results were \(q=0.74\), \(d=0.43\), \(\tan\beta=1.08\), \(\theta_{0}=\)81.57\({}^{\circ}\), \(S_{1}=15.68\) m, \(S_{2}=16.82\) m, \(n=0.21\), and \(c=7.36\); after obtaining the unknown parameters of the InSAR-3D-CTPIM model, the vertical, east-west and north-south deformation of the mine can be predicted according to Equations (4) and (5), respectively. Figure 9 shows the four-phase 3D time-series deformation prediction results on 23 May, 1 December 2016, 18 January, 7 March 2017, respectively (the prediction area is shown in the red rectangular box in Figure 8). It can be seen from Figure 9 that the cumulative deformation of the area shows a gradual increase over time to stabilization, which is in line with the mining deformation evolution law of the mine; the maximum displacements in the vertical direction, east-west direction and north-south direction are 181 mm, 46 mm and 40 mm, respectively. Figure 10a-d shows the InSAR deformation monitoring results for the periods of 1 December 2016, 18 January, 23 February, and 7 March 2017, and 300 high-coherence points are selected for comparing the deformation predicted by the InSAR-3D-CTPIM model with that monitored by SBAS-InSAR. The differences in the predicted deformation of the InSAR-3D-CTPIM model and the SBAS-InSAR monitoring results are compared, and the bars of the differences are shown in Figure 10e-h, which show that most of the differences in the deformation are centered at \([-20,\,20]\) mm, and that the predicted deformation is basically in agreement with that of the InSAR monitoring results.  
Figure 9: Three-dimensional deformation prediction results obtained from InSAR-3D-CTPIM model (reference time: 13 November 2015).
## 3 Results and Discussion
### Optimization of Red Pigment Production by M. ruler OMNRC45
The production of the red pigment increased gradually with a rise in the fermentation temperature from 20 \({}^{\circ}\)C to 25 \({}^{\circ}\)C and then to 30 \({}^{\circ}\)C. Subsequently, pigment production decreased as the incubation temperature increased from 30 \({}^{\circ}\)C to 35 \({}^{\circ}\)C and then to 40 \({}^{\circ}\)C. Similar findings were reported by [PERSON] et al. [43] on the optimum temperature for pigment production by _M. purpureus_ MMK2, _M. ruber_ KCTC6122, and _M. purpureus_ P-57. More recently, [PERSON] and [PERSON] [44] reported that optimum mycelium growth and pigment production by _M. anguineus_ and _M. purpureus_ MTCC410 were observed at a fermentation temperature of 30 \({}^{\circ}\)C. However, these results were slightly different from those reported by [PERSON] et al. [45], who found that the maximum pigment yield was obtained between 32 \({}^{\circ}\)C and 35 \({}^{\circ}\)C.  
It is well known that the initial pH of the culture medium has a significant effect on enzyme and pigment production by _Monascus_ spp. [46]. Accordingly, an experiment was designed to study the effect of different pH values on biomass and red pigment production by _M. ruber_ OMNRC45. When the fungal isolate was cultured in a liquid medium under different initial pH values (4.5-8.5), pH 6.5 was found to be optimal for pigment production, as shown in Figure 4b. Evidently, an alkaline pH (8.5) strongly inhibited both biomass and extracellular pigment production. [PERSON] et al. [47] reported that pH values ranging from 5.5 to 6.5 were ideal for the production of pigments by _Monascus_ spp., while [PERSON] et al. [48] reported (for _M. purpureus_) a wider optimal pH range (4.5-7.5) for the same.
## 3 Description of Control Schemes
### Integrating UKF with MPC
Moreover, in step 4, the process noise \(Q^{k}\) is added in the covariance matrix to check the disturbance rejection of the observer. \[\widetilde{\widetilde{x}}^{k}=\sum_{i=0}^{2n}W_{i}^{m}\widetilde{x}_{i}^{k} \tag{25a}\] \[\widetilde{\mathbf{P}}^{k}=\sum_{i=0}^{2n}W_{i}^{c}\left(\widetilde{ \widetilde{x}_{i}}^{k}-\widetilde{\widetilde{x}}^{k}\right)\left(\widetilde{ \widetilde{x}_{i}}^{k}-\widetilde{\widetilde{x}}^{k}\right)^{T}+Q^{k} \tag{25b}\]  
Step 5: Measurement update  
Equation (26a) passes the sigma points through a function \(h\) that excludes the unobservable states, such as speed and angle from the observable states. In Equation (26a), the \(\gamma_{i}^{k}\) is the vector output of the estimated speed and angle. The average of the vectors is calculated by multiplying Equation (26a) with the weighted mean, as shown in Equation (26b). Similarly, in Equation (26c,d), the covariances are estimated on the same pattern in the measurement step as calculated in the prediction step (step 4). However, the output vectors are utilized instead of the state vectors. Measurement noise \(R^{k}\) is added in the measurement covariance matrix. \[\gamma_{i}^{k}=h(\chi_{i}^{k}),i=1,\ldots,2n \tag{26a}\] \[\widetilde{\widetilde{y}}^{k}=\sum_{i=0}^{2n}W_{i}^{m}\gamma_{i}^{k}\] (26b) \[\mathbf{P}_{yy}^{k}=\sum_{i=0}^{2n}W_{i}^{c}\left(\gamma_{i}^{k}-\widetilde{ \widetilde{y}}^{k}\right)\left(\gamma_{i}^{k}-\widetilde{\widetilde{y}}^{k} \right)^{T}+R^{k}\] (26c) \[W_{0}^{m}=\frac{\lambda}{n+\lambda} \tag{23a}\]0 (26d) \[W_{0}^{m}=\frac{\lambda}{n+\lambda} \tag{23a}\]1 (26e) \[W_{0}^{m}=\frac{\lambda}{n+\lambda} \tag{23a}\]2 (26f) \[W_{0}^{m}=\frac{\lambda}{n+\lambda} \tag{23a}\]3  
The iterative process of UKF is similar to the EKF algorithm until the optimized value is achieved except for the sigma point evaluation process. Equation (26e) estimates the Kalman gain while Equation (26f,g) calculate the estimated mean and covariance, respectively [34]. Equation (24f) is the final output of the UKF block in which two states, estimated speed, and angle, are extracted, and the speed is fed back to the PI controller, which proportionally converts it into reference current \(i_{q}\) for the comparison with the measured current, as shown in \[W_{0}^{m}=\frac{\lambda}{n+\lambda} \tag{23a}\]4 The block diagram of UKF is illustrated in \[W_{0}^{m}=\frac{\lambda}{n+\lambda} \tag{23a}\]5  
\[W_{0}^{m}=\frac{\lambda}{n+\lambda} \tag{23a}\]6 UKF nonlinear modeling for PMSM.
###### Abstract  
A novel reflective metasurface (RMS) is proposed in this paper. The MS measures 128 \(\times\) 128 \(\times\) 2.794 mm\({}^{3}\) and consists of a six-layer vertically stacked structure, with a liquid crystal (LC) cavity in the middle layer. A dual fan-shaped direct current (DC) bias circuit is designed to minimize the interaction between the radio frequency (RF) signal and the DC source, allowing control of the LC dielectric constant via bias voltage. This enables multiband operation to improve communication capacity and quality for x-band devices. The polarization conversion (PC) structure employs an orthogonal anisotropic design, utilizing logarithmic functions to create two pairs of bowtie microstrip patches for linear-to-circular polarization conversion (LCPC). Simulation results show that for _x_-polarized incident waves, with an LC dielectric constant of \(\varepsilon_{\mathrm{r}}\) = 2.8, left- and right-handed circularly polarized (LHCP and RHCP) waves are achieved in the frequency ranges of 8.15-8.46 GHz and 9.84-12.52 GHz, respectively. For \(\varepsilon_{\mathrm{r}}\) = 3.9, LHCP and RHCP are achieved in 9-9.11 GHz and 9.86-11.81 GHz, respectively, and for \(\varepsilon_{\mathrm{r}}\) = 4.6, they are in 8.96-9.11 GHz and 9.95-11.51 GHz. In the case of _y_-polarized incident waves, the MS reflects the reverse CP waves within the same frequency ranges. Measured results show that at \(\varepsilon_{\mathrm{r}}\) = 2.8, the axial ratio (AR) is below 3 dB in the frequency ranges 8.16-8.46 GHz and 9.86-12.48 GHz, with 3 dB AR relative bandwidth (ARBW) of 3.61% and 23.46%, respectively. For \(\varepsilon_{\mathrm{r}}\) = 4.6, the AR \(<\) 3 dB in the frequency range of 9.78-11.34 GHz, with a 3 dB ARBW of 14.77%. Finally, the measured and simulated results are compared to validate the proposed design, which can be applied to various applications within the corresponding operating frequency band.  
reflective metasurface (RMS); multi-band; linear-to-circular polarization conversion (LCPC); liquid crystal (LC); polarization conversion rate (PCR) 2025158499
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Path Establishment (Route Discovery)
#### 3.5.2 Reactive (Demand-Driven)  
In contrast with proactive routing protocols, reactive protocols compute and establish routing paths only when it is necessary (on demand). This strategy is adopted in order to decrease the burden on the network and reduce energy consumption resulting in maintaining unnecessary information. When a sensor needs to establish a path, it triggers a process of route discovery and waits for the response. This process may take time to establish a routing path between nodes, which causes the packet delivery delay. The time-driven protocol TEEN presented previously is a reactive routing protocol. Many other reactive routing protocols are used in WSNs among them, we quote:  
* **Ad Hoc On-Demand Distance Vector (AODV)**  
AODV is a reactive protocol used in WSNs [59]. It uses a route request (RREQ) message sent by the source node. When a routing path is required, the destination responds by transmitting a route reply (RREP) message. Authors in [60] show that the AODV protocol reduces control traffic significantly by originating path requests on-demand but establishing routing paths between nodes may take time.
## 4 Experiment and Results  
The objective of this study is to identify the optimal process parameters for converter steelmaking with reduced phosphorus content at the endpoint while minimizing the production cost per tonne of steel, utilizing an optimization algorithm. The original dataset comprises 14 process parameters recorded throughout the steelmaking process, including molten iron composition (C, Si, P content), operational parameters (oxygen blowing volume, temperature), and flux proportions (line, dolomite, etc.). During preprocessing, heats failing to meet molten steel quality standards, lacking critical parameters (e.g., endpoint phosphorus content), or exhibiting statistical outliers were removed, resulting in 4000 validated data groups retained for subsequent analysis. A subset of the processed sample data is presented in Table 1. The preprocessed sample data that meet the specified requirements is utilized to construct a regression model that establishes the mapping relationship between process parameters (e.g., lime addition amount, oxygen blowing volume) and the endpoint phosphorus content of molten steel as well as the production cost per ton of steel. A dual-objective optimization problem is formulated by defining the objective function, and an improved multi-objective whale optimization algorithm is employed to optimize the process parameters. The parameter adjustment is specifically implemented through the following mechanisms.  
To ensure physical feasibility, the optimization process integrates domain-specific constraints derived from metallurgical thermodynamics. For instance, lime dissolution exhibits nonlinear saturation when the addition exceeds 80 kg/ton (Section 2), and temperature thresholds govern reaction kinetics. These constraints are explicitly enforced via Equation (6), ensuring that optimized parameters align with industrial practice rather than arbitrary combinations. Parameter encoding and search space definition involve encoding the process parameters as 12-dimensional decision variables, with upper and lower bounds determined based on historical data ranges. Dynamic weight allocation adaptively adjusts the optimization weights of phosphorus content and cost according to the distribution state of the current solution set. A hybrid search strategy is designed based on social hierarchy cooperation, deeply integrating the hierarchical population structure of the MOGW with the spiral contraction mechanism of the MOWOA to achieve collaborative optimization of process parameters in high-dimensional space. Constraint handling imposes an exponential penalty on solutions that violate process boundaries, ensuring that parameter adjustments  
Figure 2: Improved multi-objective whale optimization algorithm flowchart.  
comply with industrial feasibility. Finally, the optimal parameter combination satisfying production conditions is selected from the Pareto solution set.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Nomadic Mobility Model (NCM)
#### 6.3.4 Expected Transmission Count  
Figure 15 shows the ETX metric in a mobile network environment using the NCM mobility model, emphasizing variations in node count and the inclusion of attacker nodes. It compares scenarios with and without the Hello Flood attack, with mobile nodes representing unaffected nodes and attacker nodes indicating those under attack. The node count ranges from 10 to 60, and the percentage of mobile nodes and attacker nodes spans from 10% to 40%. Across different node counts, there is a consistent increase in ETX values, reflecting a higher transmission count required for successful packet delivery as the network size grows. This trend persists regardless of changes in the percentage of attacker nodes, highlighting the significant impact of network size on transmission efficiency. Additionally, the presence of attacker nodes is associated with higher ETX values, indicating a decrease in overall link quality and reliability. Notably, in scenarios without an attack, ETX remains stable, whereas in attacked scenarios, it rises, reaching its peak at 60 nodes, with 40% of  
Figure 14: Throughput without and with attack using NCM.  
them being both malicious and mobile. These peak ETX values signify the highest transmission count requirement under such conditions, underscoring the substantial influence of network density and attacker presence on ETX values.  
The NCM model and the Hello Flood attack work together to boost the ETX through various mechanisms. Firstly, the NCM model's structured movement patterns can cause congestion in some network areas when many nodes move at once. This can cause packet collisions and longer transmission times, which directly lead to higher ETX values. Secondly, the Hello Flood attack inundates the network with excessive traffic, saturating network channels and exacerbating interference, thus causing further delays in packet transmission and increasing ETX. Additionally, the movement patterns of nodes in the NCM model may introduce routing instabilities, leading to suboptimal routing decisions and increased packet loss. When combined with the disruption caused by the Hello Flood attack on routing protocols and increased packet loss, the overall impact on ETX is amplified. Moreover, the increased network activity from both the NCM model and the Hello Flood attack can lead to contention for limited network resources, such as bandwidth and processing power, resulting in higher transmission delays and contributing to an increase in ETX. Additionally, the Hello Flood attack may introduce additional processing overhead and network congestion, further delaying packet transmission and increasing ETX values. Overall, the collaboration between the NCM mobility model and the Hello Flood attack results in a notable increase in ETX, driven by congestion, routing instability, resource depletion, and increased network activity.
# Self-Supervised Shear Wave Noise Adaptive Subtraction in Ocean Bottom Node Data
###### Abstract  
Ocean Bottom Node (OBN) acquisition is a technique for marine seismic survey that has gained increased attention in recent years. The removal of shear wave noise from the vertical component of receivers plays a crucial role in the subsequent processing and interpretation of OBN data. Previous solutions suffer from noise residue or signal impairment for complex noise and signal overlap scenarios. In this work, we present and explore a self-supervised deep learning approach to attenuate shear wave noise in OBN data. It applies a deep neural network (DNN) to perform adaptive subtraction and comprises two steps to remove the noise associated with the two horizontal components of receivers, respectively. The two horizontal components are considered as noise reference and are sequentially fed into the DNN, and the DNN predicts the actual leaked noise from the contaminated vertical components data. The self-supervised method achieves improvements in the signal-to-noise ratio (SNR) on a set of synthetic data. The implementation of our method on field data demonstrates that it effectively attenuates the shear wave noise and preserves the valid signal.  
OBN; shear wave noise attenuation; self-supervised; deep learning; adaptive subtraction Article  
Self-Supervised Shear Wave Noise Adaptive Subtraction in Ocean Bottom Node Data.
## 4 Experiments
### Experiment Settings
#### 4.1.4 Evaluation Metrics  
We evaluate the performance of the model in our experiments by using the four metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Final Displacement Error (FDE), and Average Displacement Error (ADE). RMSE is targeted to measure the stability of prediction accuracy and MAE is to evaluate the prediction ability of a model, while ADE and FDE represent the average Euclidean errors between the predicted positions and true ones.  
\[RMSE =\sqrt{\frac{1}{n}\sum_{k=1}^{n}(y_{k}-\widehat{y}_{k})^{2}} \tag{16}\] \[MAE =\frac{1}{n}\sum_{k=1}^{n}\|y_{k}-\widehat{y}_{k}\|_{1}\] \[ADE =\frac{1}{n}\sum_{k=1}^{n}\|y_{k}-\widehat{y}_{k}\|_{2}\] \[FDE =\frac{1}{n_{T}}\sum_{i=1}^{n_{T}}\|y_{i}^{T}-\widehat{y}_{i}^{T} \|_{2}\]  
Note that \(n\) is the total number of predicted track points, and \(n_{T}\) is the total number of trajectories; \(y_{k}\) and \(\widehat{y}_{k}\) denote the real position and the corresponding predicted result, respectively, for the \(k\)-th track point; \(y_{i}^{T}\) and \(\widehat{y}_{i}^{T}\) represent the real position and the predicted position for the last track point of each trajectory; \(\|\cdot\|_{1}\) is one norm, and \(\|\cdot\|_{2}\) denotes Euclidean distance. The smaller the value of the metrics, the more accurate the model's prediction. Note that \(y_{k}\) and \(\widehat{y}_{k}\) are the real and predicted data, respectively, normalized by Min-Max Normalization method for longitude and latitude. The Min-Max Normalization method can be formulated in the following part:  
\[x^{*}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{17}\]  
where \(x\in X\), \(x_{max}\), \(x_{min}\) denote the maximum and minimum value in \(X\), respectively, \(x\) is the original data, and \(x^{*}\) is the normalized data.
# Drowsiness Detection in Drivers Using Facial Feature Analysis
## 3 Methods
### Metrics  
The following metrics were used to evaluate the performance of the drowsiness detection system:  
1. Confusion matrix--a table summarizing the True Positives, True Negatives, False Positives, and False Negatives. The matrix provides insights into specific types of errors made by the system and helps fine-tune thresholds;  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Duration (min)** & **Corresponding CE Value** \\ \hline
0 & 60 \\ \hline
5 & 51 \\ \hline
10 & 43 \\ \hline
15 & 34 \\ \hline
20 & 25 \\ \hline
25 & 17 \\ \hline
30 & 08 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Duration versus CE threshold.  
2. Accuracy--the proportion of correctly classified instances (drowsy and non-drowsy) out of the total number of instances. Accuracy provides an overall measure of the system's effectiveness in detecting drowsiness;
3. Sensitivity (recall)--sensitivity indicates the ability to correctly identify drowsiness when it occurs. It is the ratio of True Positives (correctly detected drowsy frames) to the total number of actual drowsy frames. Sensitivity ensures the system minimizes the risk of missing drowsy cases, which is critical for safety applications;
4. The accuracy and sensitivity are expressed in Equations (21) and (22) as follows: \[\text{Accuracy}=\frac{\sum\text{TP}+\text{TN}}{\sum\text{TP}+\text{FP}+\text{ TN}+\text{FN}}\] (21) \[\text{Sensitivity}=\frac{\sum\text{TP}}{\sum\text{TP}+\text{FN}}\] (22) where TP, TN, FP, and FN represent True Positive, True Negative, False Positive, and False Negative respectively. For real-time detection, our primary metrics of interest were sensitivity and accuracy;
5. Dlib Error Rate--Dlib's facial landmark detector was tested on a dataset of frontal as well as augmented face images with annotated ground-truth landmarks. The error rate was calculated as the average Euclidean distance between Dlib-detected landmarks and the corresponding ground-truth landmarks. For frontal images, detection of the face was high; however, Dlib maintained moderate accuracy but showed limitations for extreme angles, high face rotations, poor lighting conditions and small face sizes inside the camera which restricts the facial landmark detector from being masked onto the face.
# Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation
## 3 Overall System Architecture
### Indoor-Environment Infrastructure  
The indoor environment used for the experiments was the second floor of the engineering college at An-Najah National University, which covers an area of approximately 23 \(\times\) 23 m\({}^{2}\). Figure 6 shows a 2D map of the study area. There are eight APs distributed in the area, as illustrated in Figure 6 with red symbols. The type of APs deployed is TP-link, and all the APs were installed at an approximate height of 2.7 m.  
Track with Predetermined Checkpoints  
The robot can follow a predetermined path marked by a line, with the addition of checkpoints at which the robot must stop in a specific order. The robot uses the sensors to detect the line and checkpoints and adjusts its movement based on the feedback received from the sensors. Infrared sensors are used to detect the contrast between the line and the ground. The robot is designed to follow the line by moving towards the center of the line; when a sensor crosses the line, the robot adjusts its movement to bring the robot to the center of the line. In addition to following the predetermined path, the infrared sensors are used by the controller to detect when there is a checkpoint mark on the line, where it stops and performs its desired task. A plus (+) sign along the path is used to indicate a stopping point, and an algorithm using the existing infrared sensors can detect those checkpoint marks as described in the next section.  
Overall, the robot can follow a predefined path marked with checkpoints, making it useful for repetitive tasks such as the case described in this research.  
The robot is set to transmit the selected WiFi-beacon parameters and the robot's position to the server. The robot surveys the floor at a speed of about one checkpoint per minute, at about 1 cm/s on average. Most of the time is spent on collecting the required samples, whereas a fraction of the time (\(\sim\)5%) is spent on the actual movement. The robot moved along a tracking line with a length of 82 m and TPs 0.45 m apart for this study. The main goal is to use the collected WiFi samples to train an AI model to estimate the exact location of any handheld device in 3D space using WiFi-beacon frames. The actual AI modelling, training, and validation are the subject of a forthcoming study.  
Figure 6: Map showing locations of the access points (APs) and transceiver points (TPs).