esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual Partial Reversible Data Hiding Using Enhanced Hamming Code
## 1 Introduction
Unlike halftone-based methods [25], it supports general grayscale images while guaranteeing full reconstructability, making it suitable for secure high-capacity RDH applications. The key contributions of this paper are summarized as follows: (1) The proposed scheme achieves a high embedding capacity of up to 1.5 bpp by applying enhanced Hamming codes to virtual pixels constructed from two cover images, offering a significant improvement over prior dual-image RDH methods. (2) Despite the high embedding rate, our method maintains excellent stego image quality with PSNR values of 48-49 dB, demonstrating a balanced tradeoff between capacity and imperceptibility. (3) The proposed method's robustness against statistical steganalysis is validated through RS analysis, confirming its resistance to detection and ensuring security in data hiding applications. (4) The reversibility of both cover images is guaranteed without additional side information, making the proposed scheme applicable to sensitive fields such as medical imaging and secure communications. (5) The proposed method is computationally efficient, relying on simple bitwise operations and lookup tables, enabling practical implementation in real-time systems. The rest of this paper is organized as follows: Section 2 reviews the Hamming code used as a single error-correcting code along with the RDH method; Section 3 presents the proposed partial RDH method based on dual images; Section 4 presents the experimental results; and Section 5 presents the conclusions.
## 5 Conclusions and Future Work  
Learning is an important aspect of education. Determining how to learn has generally attracted more attention than what to learn in recent learning systems. However, what to learn is more critical than how to learn because a learning direction aligns learning with interests, ensuring learning is more effective and efficient. For this purpose, we designed a novel course decision assistant that provides services of course suggestion and course degree prediction. The contributed technical services and experimental findings are concluded as follows.  
* In terms of course suggestion, the relationship patterns between user personalities and course degrees are mined from learning logs. From the mined patterns, the potential high-performance courses are provided as a suggestion list.
* In terms of course degree prediction, the unknown degrees are estimated by the prediction algorithm. In this algorithm, the user similarities are calculated to filter users. By user-filtering, the relevant users can be gathered to provide useful information for predicting unknown degrees.
* In terms of experimental findings, a number of objective and subjective evaluations were conducted to verify the validity of the evaluation. For objective evaluations, the results showed that the prediction results are strongly related to the ground truth data, showing that the proposed prediction algorithm is reliable for the users. For subjective evaluations, the experimental results revealed that most testers wanted an intelligent system that can suggest courses and predict course degrees. After using the proposed system, more than 94% of testers assigned satisfactory ratings to the proposed system, showing that our proposed system can provide useful information for selecting courses.  
Figure 6: Summary of the ratings of the three levels of questions.  
Although the proposed idea has been applied in a real system, a set of issues remains to be investigated for improvements.  
* First, for course suggestion, in addition to personality, other information could be considered, e.g., teacher, teaching type, and so on.
* Second, for course degree prediction, although the prediction results show the prediction algorithm is effective, other prediction methods, such as course filtering, will be tested further. From a course point of view, the unknown degree can be derived by similar courses.
* Third, this study was just the first step of the proposed idea that was realized in a single department. In the future, it will be applied across different departments.  
Conceptualization, J.-H.S. and Y.-W.L.; Data curation, L.-N.C.; Methodology, J.-H.S. and Y.-W.L.; Validation, Y.-W.L. and L.-N.C.; Writing--original draft, J.-H.S.; Writing--review & editing, J.-H.S.  
This research was funded by Ministry of Science and Technology, Taiwan, R.O.C. grant numbers MOST 107-2221-E-230-010 and MOST 107-2511-H-230-003-MY2.  
The authors declare no conflicts of interest.
## 2 Background and Related Work
### Quantifying Clinical Protocol Compliance
This work also considered the clinicians as compliant with a guideline if they stated why the guideline was not being followed. Those individual measurements need to be aggregated as a next step and reported using either the _activity_ perspective, i.e., the compliance is reported per activity [11; 46], or the _patient_ perspective. In this case, there are also several possibilities. For instance, reference [12] defines overall adherence to a prescribed distress screening protocol as calculated based on documentation in the EHR that screening adherence and an appropriate clinical response had occurred. Only when both the screening and response took place, overall adherence per patient was considered \"yes\". The authors calculated the overall adherence rate as the number of overall adherent patients per number of all patients. Some authors, such as [PERSON] et al. [43], introduced a scoring system. Each protocol execution could receive a score between 0 and 5, where score 4 or 5 allowed one deviation from the protocol and meant excellent protocol execution. The adherence was reflected by the percentages of the protocol executions with each of the six scores. Another approach was used in [38]. The authors defined a number of indicators operationalizing the guidelines' requirements that served as measures of guideline adherence. The presence of an indicator was given a score, resulting in a point score for a protocol. Similarly, [PERSON] et al. [44] calculated the number of steps that were compliant with reporting the percentages of executions with zero, one, or two non-compliant activities. Other authors decided to calculate a non-compliance rating (NCR) as the number of recommendations not acted upon [47]. To summarize, in the current literature, the following aspects are considered in the assessment of compliance, in particular in the healthcare context:  
* Measurement based on: objective data (from hospital information systems) vs. subjective data (from questionnaires),
* Main actor: health provider vs. patient (or his family),
* Perspective: protocol level vs. activity/action level,
* Medical subject: continuous protocol vs. protocols with steps,
* Approach to deviations: deviations allowed after explanation vs. no deviations allowed.
## 1 Introduction
A MANET can operate either as a closed network, where a legitimate node can be easily separated from unauthorised nodes, or as a highly dynamic network, exhibiting considerable intermittent nodal connectivity so making it very challenging to distinguish malicious from legitimate nodes. Furthermore, network devices can vary from small energy-constrained computing devices with limited hardware capability though to powerful personal computers. 2. There is the diversity of feasible wormhole attacks, _i.e._, PM, HM, I-B and O-B channels. Each wormhole type has its distinct characteristic providing the opportunity to launch attacks in many different modes, with each mode imposing its own set of challenges for any detection mechanism. In addition, cognisance of the incidences of erroneous wormhole identification, so called False Positive (FP) must be considered in any proposed detection paradigm. These reasons provided the motivation to investigate a generic wormhole detection methodology with the aim of achieving both consistently high detection rates under a range of network conditions, and having the capability to manage all wormhole variants. Before introducing the new detection algorithm, a review of some of the existing prevention and detection strategies for MANET wormholes is presented.
## 6 Calculation of Parameters for Mudslide Mitigation  
### Total Mudslide Volume  
The design and mitigation measures for Dashiling Gully considered a 100-year return period (\(p=1\%\)) rainfall event with an intensity of 125 mm/h. The total mudslide volume (\(Q\)) can be determined through either computational or empirical methods. While empirical methods offer higher accuracy, they often lack the necessary data and can only provide  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mudslide Hazard Level** & **Deposition Depth (H, m)** & **Logical Relation** & **HV (m2/s)** \\ \hline high risk & H \(\geq\) 2.5 & OR & HV \(\geq\) 2.5 \\ \hline medium risk & 0.5 \(\leq\) H \(<\) 2.5 & AND & 0.5 \(\leq\) HV \(<\) 2.5 \\ \hline low risk & 0.0 \(<\) H \(<\) 2.5 & AND & HV \(<\) 0.5 \\ \hline \hline \end{tabular} Note: HV represents the product of deposition depth and movement velocity.  
\end{table}
Table 2: Mudslide hazard classification based on deposition depth (H) and the product of maximum deposition depth and maximum velocity (HV).  
Figure 7: Mudslide hazard zone map.  
rough estimates. Therefore, a computational method based on the mudslide duration (T) and the peak discharge (\(Qc\)) was employed, following the formula provided in Appendix I [32] of the \"Mudslide Disaster Prevention and Control Engineering Survey Specification\" (DT/T0220-2006) [30]. The typical peak duration of a mudslide ranges from 20 to 60 min, with 60 min selected for this calculation.  
The total mudslide volume is calculated using Equation (13):  
\[Q=KTQc \tag{13}\]  
When \(F<5\) km\({}^{2}\), \(K\) is equal to 0.202. The calculated total mudslide volume for the main gully section is presented in Table 3.
## 5 Experimental and Analysis
### Results and Discussion
#### 5.2.2 Sea State Degree: 1\(-\)2  
There is a light breeze and smooth wavelet on the sea surface at a sea state of 1-2, and the measurement noise is non-Gaussian distributed, \(v_{k}\sim 1.875N(0,R)+0.04N(0.357*R)\), \(R=diag[0.4,0.3,0.3,0.4]\). Figures 7 and 8 demonstrate that the CKF algorithm diverges in both the \(XYZ\) axis and yaw angle estimation when the noise distribution is non-Gaussian. This indicates that CKF is only suitable for Gaussian noise environments. Furthermore, in the estimation of \(Y\) and \(Z\) axis displacement, particle degradation gradually occurs in PF after a certain period of time. The importance density function of CPF cannot always be approximated by a Gaussian distribution, as evidenced by the small divergence observed at sea state levels 1-2. Hence, the proposed algorithm shows improved noise immunity by incorporating a mixed Gaussian model. Overall, the results suggest that the algorithm presented in this paper outperforms CKF, PF, and CPF under mixed noise conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Degree** & **Wind** & **Wind Speed (m/s)** & **Wave** & **Wave Height (m)** \\ \hline
0 & Calm & 0\(-\)0.2 & Calm glassy & / \\
1 & Light air & 0.3\(-\)1.5 & Calm rippled & 0\(-\)0.1 \\
2 & Light breeze & 1.6\(-\)3.3 & Smooth wavelet & 0.1\(-\)0.5 \\
3 & Gentle breeze & 3.4\(-\)5.4 & Slight & 0.5\(-\)1.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: State of the sea.  
Figure 5: \(XYZ\)-axis and yaw angular error at sea state of 0. (**a**) \(X\)-axis position estimation error; (**b**) \(Y\)-axis position estimation error; (**c**) \(Z\)-axis position estimation error; (**d**) Yaw angular position estimation error.  
Figure 6: \(XYZ\)-axis and yaw angular velocity error at sea state of 0. (**a**) \(X\)-axis velocity estimation error; (**b**) \(Y\)-axis velocity estimation error; (**c**) \(Z\)-axis velocity estimation error; (**d**) Yaw angular velocity estimation error.  
Figure 8: \(XYZ\)-axis and yaw angular velocity error at sea state of 1–2. (**a**) \(X\)-axis velocity estimation error; (**b**) \(Y\)-axis velocity estimation error; (**c**) \(Z\)-axis velocity estimation error; (**d**) Yaw angular velocity estimation error.  
Figure 7: \(XYZ\)-axis and yaw angular error at sea state of 1–2. (**a**) \(X\)-axis position estimation error; (**b**) \(Y\)-axis position estimation error; (**c**) \(Z\)-axis position estimation error; (**d**) Yaw angular position estimation error.
# A Throughput and Energy Efficiency Scheme for Unlicensed Massive Machine Type Communications +
## 3 Throughput Optimization
### Extreme Cases  
In this section, we evaluate the two extreme cases in terms of maximum number of transmissions and their effect on the throughput. To do so, we consider having no transmission \(m=0\) and having a very high number of retransmissions \(m\to\infty\) in the network. Considering the zero transmission case (A detailed analysis of the model studied in this paper while \(m=0\) can be found in our previous work [18].), we reach the following for the throughput:  
\[T =\log(1+\beta)\left(1-\epsilon^{\frac{1}{m+1}}\right)\] \[=\log(1+\beta)\left(1-P_{\text{out}}\right)\] \[=\log(1+\beta)e^{-k\lambda\beta^{2/\kappa}}. \tag{10}\]  
Moving on to the \(m\to\infty\) case, by replacing \(\epsilon=P_{\text{out}}^{(1+m)}\) in (7), we reach the below equation for the average number of retransmissions for the extreme cases (\(1+\bar{m}_{e}\)):  
\[1+\bar{m}_{e}=\frac{P_{\text{out}}^{(1+m)}(1+m)(P_{\text{out}}-1)-P_{\text{out }}^{(1+m)}+1}{(P_{\text{out}}-1)(P_{\text{out}}^{(1+m)}-1)}, \tag{11}\]  
now by inserting (11) in (3), we can achieve the throughput equation in this case. After some mathematical manipulation, it is proven that while \(m\to\infty\), we reach the same throughput equation as in (10). Hence, for both extreme cases of \(m=0\) and \(m\to\infty\), the system behavior remains the same in terms of throughput. We later use these results in the numerical results section of this paper and discuss what they show and why they are important. More details about these two extreme cases can also be found in [52].
# High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis
## 6 Results and Discussion
### Comparison with Four Popular Methods
#### 6.2.2 Affinity Propagation  
The affinity propagation (AP) algorithm, known for its power in clustering data, operates by considering similarities between pairs of data points and simultaneously evaluating all data points as potential exemplars (cluster centers). It employs an iterative technique to recursively search for clusters, where real-valued data are exchanged between data points until a set of high-quality exemplars and their associated clusters are established. In our experimental analysis, we implemented the AP algorithm to cluster higher-dimensional datasets. The algorithm yielded a total of 976 clusters for dataset 1 and 1525 clusters for dataset 2. In addition, we assess the performance using the silhouette coefficient. Employing affinity propagation, dataset 1 achieves a maximum coefficient value of 0.195, whereas dataset 2 attains a coefficient value of 0.0745.
## 4 Outer Dispatcher Methodology and Modelling
### Step 2--Computation of Potential Intermodal Trips  
This step computes the potential intermodal trips determining the most suitable combination of RP and PT legs using the MaaS Dispatcher and PT Journey Planner. Firstly, the RP vehicles and expected PUDO times are determined, considering the congestion of the RP fleet at that precise time window. Secondly, the departure choices at an origin stop to a set of destination stops are determined, taking into account the concrete departures and arrivals in the timetable. The Outer Dispatcher then aggregates these intermediate results to estimate the feasible assignments for each pending request with its subset of candidate stops. The approach is then as follows:  
* For **Pure-RP.** These trips are resolved with a unique request to MaaS Dispatcher with the original time-windows constraints.
* For **Pure-PT.** These trips are resolved with a unique request to PT Journey Planner with the original time-windows constraints.
* For **RP + PT or RP + PT + RP.** First, an initial arrival time at the stop is estimated, considering an estimation of the first RP leg and the congestion of the RP fleet. Afterwards, an appropriate PT departure leaving after the arrival at the stop is computed. This step assumes that the candidate stops that are close to the original destination will be accessed by foot (RP + PT trips). However, for those that are farther away, an appropriate arrival time will be estimated using a last leg of RP. Finally, the first RP leg using the restricted time windows to access the origin stop is determined.
* For **PT + RP.** Similar to RP + PT + RP case, it assumes that the initial access to the PT stop is performed by foot.
# Guard Band Protection Scheme to Facilitate Coexistence of 5G Base Stations and Radar Altimeters
## 3 Numerical Results
### Suburban Areas  
In the suburban environment (Figure 9), all curves using a 150 MHz guard band remain below the red-dotted line. The maximum received interference is \(-15.0214\) dB, \(-13.8884\) dB, and \(-11.7434\) dB for the 4-by-4, 8-by-8, and 16-by-16 antenna arrays, respectively. This indicates that the proposed guard band protection scheme effectively mitigates interference to acceptable levels in suburban environments. In contrast, in rural environments, a 5G base station equipped with a 16-by-16 antenna array presents a significant interference risk, even with a 150 MHz guard band. This discrepancy arises because signal propagation in suburban areas experiences higher path loss compared to rural areas. Conversely, when using a 50 MHz guard band, all curves surpass the threshold. Specifically, the maximum received interference is \(-2.4096\) dB, 6.2689 dB, and 12.6532 dB for the 4-by-4, 8-by-8, and 16-by-16 antenna arrays, respectively. In these antenna configurations, the probabilities of exceeding the threshold are 99.4%, 98.1%, and 95.6%, respectively. Additionally, in the absence of a guard band, the maximum received interference reaches 21.6080 dB, 26.8996 dB, and 34.0605 dB with the 4-by-4, 8-by-8, and 16-by-16 antenna arrays, respectively. The probabilities of meeting the threshold are 74.7%, 59.8%, and 41.5%, respectively. These values closely align with the peak values without protection, as summarised in Table 4.
## 3 Measurement, Control, and Communication Architecture
### Software and Communication
#### 3.2.4 System Data Flow  
The complete communication process within the platform is structured into four primary stages, ensuring robust interaction between sensing, control, and supervision layers:  
1. The Arduino(r) continuously acquires voltage, current, and wind speed measurements, structuring the information into UART frames and transmitting them to the Raspberry Pi(r) via serial communication;
2. Upon receiving the UART data, the Raspberry Pi(r) parses the frames, formats the content into JavaScriptObjectNotationstructures, and broadcasts it through a WebSocket server (port 8081), enabling real-time data availability to external clients;
3. The MATLAB(r) app, acting as a WebSocket client, subscribes to the data stream, dynamically updates the graphical user interface, and enables the operator to send control commands based on system conditions;
4. Commands sent by the user are transmitted from the MATLAB(r) app to the Raspberry Pi(r) via WebSocket (port 8080), which are then forwarded through UART to the Arduino(r). These commands trigger control actions such as modifying the PWM signal to the boost converter or toggling operating modes.  
This layered communication architecture promotes modularity, fault isolation, and system scalability, laying the groundwork for future enhancements such as multi-turbine supervision, implementation of adaptive MPPT algorithms, or seamless integration with cloud-based SCADA and analytics platforms.
# The Cube Surface Light Field for Interactive Free-Viewpoint Rendering
## 5 Results and Discussion
### Rendering Quality  
To evaluate the quality of images rendered by our method, we use the cube surface light field with a resolution of \(64\times 64\times 256\times 256\) and compare the images rendered by several light field interpolation methods with the images rendered by the path tracing. Partial images rendered with a resolution of \(2048\times 2048\) pixels are shown in Figure 9.  
As can be seen from Figure 9, the rendering results by the cube surface light field can reconstruct the global illumination effect of the scene. Especially, a more accurate global illumination effect can be rendered when a more appropriate interpolation method is used. For example, the NNI rendering produces aliasing artifacts, while the CBSI rendering produces smoother images. It also proves from another aspect that our method can be used to accelerate the path tracing rendering. This is because our method records the light rays generated by the path tracing rendering in advance, and only a light ray query is needed in the rendering stage.  
To further illustrate the quality of rendered images, we use the non-reference image evaluation indexes, BRISQUE [36] and NIQE [37] to evaluate all images in viewpoint A and report the results in Table 2. The lower the values of BRISQUE and NIQE are, the better the image quality is. As can be seen from Table 2, the best BRISQUE and NIQE are achieved by the path tracing rendering and the CBSI rendering, respectively. Moreover, the CBSI rendering gets the second higher BRISQUE score. This is because each pixel is represented by no more than 16 rays in the cube surface light field rendering while more than 100 rays are blended for each pixel in the path tracing rendering which makes the rendered images smoother. Different interpolation rendering can produce different visual effects, indicating that the more correlated light rays are selected, the more accurate the results are for the under-sampled light rays.  
We compare the images rendered by our method with the reference images rendered by the path tracing when 1000 rays are sampled from each pixel and report the SSIM and PSNR in Figure 10. The CBSI rendering and the NNI rendering get the highest SSIM and PSNR, respectively. This is because the CBSI rendering produces smoother images that are closer to the images rendered by the path tracing rendering. However, the CBSI rendering will blur the edges in the rendered images, making its PSNR lower than others.  
Figure 9: The comparison of novel views rendered by different methods (each column) at different viewpoints A, B and C (each row).
# A 3D Vision-Based Weld Seam Extraction Method for Arbitrarily Placed Spatial Planar Weldments
## 2 Improved RANSAC for Plane Segmentation
### Improved RANSAC  
Before extracting weld seams from the registered point cloud, we employ RANSAC for single-plane segmentation. As a widely utilized method in geometric feature extraction and shape detection, RANSAC has proved its effectiveness in multiple fields such as SLAM [15], Geographic detection in nature [16] or urban areas [17; 18], and so on. As outlined in the algorithm proposed in [19], vanilla RANSAC iteratively fits a plane from a point cloud over \(k\) iterations. Each iteration entails four sequential steps (referred to as steps (1) to (4) in Figure 2) to obtain a candidate plane, maximizing the count of inliers--points with distances to the candidate plane smaller than a preset threshold.  
The iteration number \(k\) is defined as  
\[k=\frac{\log(1-p)}{\log(1-\omega^{3})}, \tag{6}\]  
where \(\omega\) is the inlier ratio, i.e., is the number of inliers divided by the number of points, and the constant variable \(p\) is normally set as 0.99 [20].  
Despite its effectiveness in various applications, vanilla RANSAC encounters challenges when applied to plane segmentation tasks in spatial planar weldments. To address  
Figure 2: Flowchart of vanilla RANSAC for single plane segmentation.  
these issues, we propose several rules tailored to the different steps of the vanilla RANSAC, and the refined algorithm is simply called \"improved RANSAC\" in this paper.