esa-sceva/satcom-chunk-collection huggingface dataset:
# A Self-Recovery Fragile Image Watermarking with Variable Watermark Capacity
###### Abstract  
Currently, the watermark capacity of most self-recovery fragile image watermarking schemes is fixed. That means for smooth regions and texture regions, the length of watermark information is always the same. However, it is impractical since more recovery information is needed for the recovery of texture regions. In this paper, a self-recovery fragile image watermarking with variable watermark capacity is proposed. Based on the characteristic of singular value decomposition (SVD), a new block classification method is introduced. The image blocks are classified into smooth blocks and texture blocks. For smooth blocks, the average pixel values are adopted as the recovery information to recover the tampered blocks, while for texture blocks, the quantized and coded DCT coefficients are adopted as the recovery information. After encrypted by binary pseudo-random sequence, the recovery watermark of each block is embedded into its mapping block. In the detection side, the three-level detection mechanism is applied to detect and locate the tampered regions. The experimental results prove that the proposed method achieves good tamper detection results, and the recovered image has better image quality than other self-recovery fragile watermarking methods.  
Keywords:fragile watermarking; tamper detection and self-recovery; variable watermark capacity; singular value decomposition (SVD); discrete cosine transform (DCT) +
Footnote †: journal: Applied Sciences
## Results and Discussion
### pH Analysis  
The determination of the concentration of hydrogen ions (pH measurement) in fish products can be used to assess the degradation progress [19]. The typical pH of live fish varies between 7.0 and 7.3. After death, the pH is generally between 6.0 and 6.8 and in some species, is below 6.0, because  
Figure 4: (**a**) Bacterial growth results, shown as total viable counts (TVC) in nutrient agar (Nb) (**\(\blacksquare\)**); (TVC) in iron agar solid medium (IR) (**\(\blacktriangle\)**), and H2S-reducing bacteria (**\(\blacksquare\)**) in ice-stored GrA for 16 days (CFU: colony-forming units). Each point is a mean value of two analyses, with vertical bars indicating standard deviation; (**b**) Torrymeter values of whole GrA during ice storage; (**c**) pH values of GrA samples against storage time on ice. Each point represents the mean values of three analyses, with vertical bars indicating standard deviation.  
of high glycogen concentrations. The results of the pH measurements during spoilage showed that after rigor mortis, the pH increased from 6.4 to 6.7 (Figure 4c), throughout the experiment duration. However, pH measurements were not statistically significant (\(p\) = 0.21) and may not be suitable for GrA, in contrast to other species, because of individual initial differences, fluctuations of the values, and the low correlation with the sensory results [21; 22].
## 4 Results and Discussion
### Evaluation of the YOLOv11n-seg Model Performance for Material Shape Detection  
To evaluate the system's ability to detect material shapes (sharp-edged vs. rounded), an industrial line-scan camera and the lightweight YOLOv11n-seg model were employed. The model is optimized explicitly for low-power hardware, such as Raspberry Pi and edge devices. In this project, the model was deployed on a laptop equipped with an NVIDIA RTX 3500 Ada Generation GPU with 12 GB of memory.  
* YOLO Version: Ultralytes 8.3.53
* Software Environment: Python 3.11.7, PyTorch 2.5.1 + CUDA 12.1
* Number of Layers: 265.
* Parameters: 2,834,958.
* Computational Load: 10.2 GFLOPs.  
The dataset consisted of 519 labeled real-world images from the industrial line, containing a total of 1105 objects:  
* 237 images with 461 sharp objects.
* 176 images with 644 rounded objects.  
The model was trained for 100 epochs and achieved stable convergence. Key performance metrics are summarized in Table 2.  
Model processing speed:  
* Pre-processing time: 0.5 ms.
* Inference time: 4.9 ms.
* Post-processing time: 1.3 ms.
* Total per image: \(\sim\)6.7 ms.  
To demonstrate the model's performance in real-world conditions, the inference results on multiple samples were captured and are presented. Figure 20 shows the detection outputs on both the RGB and grayscale images. This dual-mode testing was essential to evaluate the model's adaptability in various camera settings (color and monochrome line-scan imaging).  
Additionally, Table 3 presents a summary of all the training and evaluation metrics, highlighting consistency across epochs and balanced performance across classes.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Metric** & **Sharp Objects** & **Smooth Objects** & **Average** \\ \hline Box Precision (P) & 0.864 & 0.873 & 0.868 \\ \hline Box Recall (R) & 0.837 & 0.809 & 0.823 \\ \hline [EMAIL_ADDRESS] & 0.908 & 0.921 & 0.914 \\ \hline [EMAIL_ADDRESS]:0.95 & 0.706 & 0.652 & 0.679 \\ \hline Mask Precision (P) & 0.882 & 0.873 & 0.878 \\ \hline Mask Recall (R) & 0.850 & 0.801 & 0.826 \\ \hline mAP Mask & 0.658 & 0.604 & 0.631 \\ \hline \end{tabular}
\end{table}
Table 2: The YOLOv11n-seg model’s performance metrics.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Class** & **Images** & **Instances** & **Box (P)** & **Box (R)** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & **Mask (P)** \\ \hline All & 519 & 1105 & 0.868 & 0.823 & 0.914 & 0.679 & 0.878 \\ \hline Sharp & 237 & 461 & 0.864 & 0.837 & 0.908 & 0.706 & 0.882 \\ \hline Smooth & 176 & 644 & 0.873 & 0.809 & 0.921 & 0.652 & 0.873 \\ \hline \end{tabular}
\end{table}
Table 3: The final performance results of the YOLOv11n-seg model.  
The YOLOv11n-seg model demonstrated robust and high-precision classification of small industrial particles. Its ability to distinguish between sharp and smooth objects in RGB and grayscale images highlights the generalization capacity of the model across different vision systems. This significantly reduces the risk of conveyor belt damage and improves the automated material sorting quality in BECs systems.
# Numerical Simulation of Hot Jet Detonation with Different Ignition Positions
## 2 Numerical Model and Methods  
### Physical Model  
Figure 1 presents the physical model of the detonation chamber analyzed in this paper. As shown in the Figure, the chamber is 100 mm in diameter and 1800 mm in length, and the left wall of the detonation chamber is closed. Six obstacles are arranged inside the detonation chamber which the blocking ratio is 0.35 [33]. The distance between the first obstacle and the left wall of the detonation chamber is 380 mm. The previous three obstacles are equidistant, the distance between adjacent obstacles is 175 mm. The distance between the last three obstacles is 250 mm. Four monitoring points (P1-P4) are set to monitor the changing of parameter. The monitoring point is 100 mm behind the obstacle. The hot jet tube with the 32 mm inner diameter and the 200 mm length is aligned perpendicular to the centerline of the detonation chamber. The distance between the hot jet tube and the left wall of the detonation chamber is 114 mm, the ignition is located in the hot jet tube. As shown in Figure 1b, top ignition or wall ignition will be investigated. Flame accelerates in hot jet tubes first, and then jet flame propagates into detonation chamber and produces a detonation wave in it.
## 3 The Main Parameters and Indices of MOT
### Application Requirement Analysis
#### 3.3.3 Increasing the Measurement Bandwidth  
When a gravimeter from a research team is used as an example, please refer to Figure 4[70] for the timing diagram of the gravity measurement. The gravity measurements should be conducted from left to right. The figure indicates the time required for each stage and the total time (\(T_{a}\)).  
In Figure 4, it is evident that only 60% of the total time is dedicated to atomic interference. Additionally, there is a substantial measurement dead zone in each cycle of the atomic gravimeter. This dead zone arises from the time spent on atomic cooling and confinement, speed selection, state preparation, normalized final state detection, and other stages. The existence of this dead zone reduces the measurement efficiency, limits the improvement of the measurement bandwidth, and makes eliminating some resonance noise challenging. As the use of atomic gravimeter dynamic measurements continues to progress, the issue of measurement bandwidth has been increasingly discussed [71].  
In atomic gyroscopes, while the development of nuclear magnetic resonance gyroscopes has matured, the theoretical accuracy of atomic interference gyroscopes has increased, making them more suitable for applications in strategic weapons and equipment. However, atomic interference gyroscopes currently face challenges related to their narrow measurement bandwidth and limited range, which restrict their application range [72].
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 4 System Model
### Patient Block Agent
#### 4.5.1 Classifier and Queue Manager
```
1:Input:\(b(s(i))=\{b(s)_{1},b(s)_{2},\ldots,b(s)_{n}\}\)
2:\(e(s(i))=\{e(s)_{1},e(s)_{2},\ldots,e(s)_{n}\}\)
3:Output: Three Classes
4:Begin
5://TS-DNN algorithm using DRL
6:Initialize all b(s(i)), and e(s(i)) for \(Q(S,a)\)
7:Repeat (For Each Episode)
8:Choose \(a\) from \(S\) by policy derived from Q
9:Input Layer
10:for\(\forall BAN_{i}\)do
11: Take action \(a\) and observe \(r\) and \(s\)
12:\(Q(S,a)\to Q(S,a)+\tau[r+\gamma Max_{a},Q(S^{\prime},a^{\prime})-Q(s,a)]\)
13:endfor
14:for\(\forall S_{i}\in S\), do
15: Learn \(\leftarrow\tau,\Gamma,\mu,\zeta\)
16: Compute \(F(f_{i})\)
17: Assign \(F(f_{i})\to weight\) function
18:endfor
19:Collect \(F(f_{i})\) from all neurons
20:Compute \(O\)
21:Return (\(classes\))
22:End
```  
**Algorithm 1** TS-DNN. We classify the fault data by considering training set values. For the currently transmitted packet, we compute the relative difference factor (\(RDF\)), which shows the incorrect sensor readings. The notation \(RDF\) is calculated by,  
\[S(sr(c),(p))=\frac{Sr(c*p)}{|c|\times|p|}=\sum_{i=1}^{n}\frac{i(c*p)}{\sqrt{ \sum_{i=1}^{n}C_{i}^{2}}*\sqrt{\sum_{i=1}^{n}P_{i}^{2}}} \tag{10}\]  
where \(Sr(c*p)\) denotes the sensor readings for current and previous packets. After the classification, each class of the packets is put into a separate queue, and it is organized by the queue manager so that we nearly used three queues: emergency queue (EQ), periodical queue (PQ), and faulty queue (FQ). The FQ packets are used to find the \(RDF\) and send the alarm message to the particular sensor via PBA. To process all medical packets without any packet drop, we proposed Reyni entropy \(\mathrm{Re}\), which runs all packets and changes the processing of one queue into another queue by computing the input parameters. \(\mathrm{Re}\) is the order of \(\delta\), where \(\delta\geq 0\) and \(\delta\
eq 1\) are defined as,  
\[H_{\alpha}(x)=\frac{1}{1-\alpha}log\left(\sum_{i=1}^{N}P_{i}^{\alpha}\right) \tag{11}\]  
where \(x\) is the discrete random variable with the possible result as \(1,2\ldots n\) and corresponding probabilities are \(p_{i}=\frac{1}{n}\) for all \(i\ldots n\). Then, all the Renyi entropies of the distribution are equal: \(H_{\alpha}(x)=logn\), and \(P_{i}^{\alpha}\) represents the input parameters current values and \(\alpha\) is the weight value, which ranges between 0 and 1. Finally, the packets are transmitted to the physician, ambulance, caregiver and pharmacy to perform further actions.
## 3 Data-Driven Solutions Applied for the Detection of NTLs
### Supervised Methods
#### 3.1.3 Decision Tree  
Decision Tree (DT) is a classifier that uses a flowchart-like graph, starting with an initial node and then \"branching out\". New samples are classified based on rules formed during the training of the DT. Essentially, DT is an algorithm that employs IF-ELSE-THEN rules. The main disadvantage of this model is that it can overfit by correctly classifying samples, creating an infinite number of branches [33; 40; 41; 42]. DT is used for classification and regression models and largely depends on the training dataset. It is also very sensitive to the problem of class imbalance [4; 16].  
CART, combined CART, and RF algorithms for big data analysis have been used for loss detection. It has been found that the CART technique leads to better results (93%), due to the ability to achieve fast classification and computational expansion in parallel [44]. Other algorithms studied, such as Adaboost and DT C4.5, have achieved performance rates of 88%, 82%, and 73% [33].
# Infrared Nanosecond Laser Texturing of Cu-Doped Bioresorbable Calcium Phosphate Glasses
## 2 Materials and Methods  
### Glass Preparation and Laser Modification  
The undoped and Cu-doped calcium phosphate glasses used in this work, with composition (in mol%) ((50 P\({}_{2}\)O\({}_{5}\)-25 CaO-8 MgO-11.5 Na\({}_{2}\)O-2.5 B\({}_{2}\)O\({}_{3}\)-3 SiO\({}_{2}\))x-(CuO), with x = 1 when y = 0 and x = 0.99 when y = 1), were synthesized via a conventional melt-quenching technique using high-purity (99+%) biocompatible chemicals. The latter were weighed and mixed within a dry box to minimize the glass hydroxyl ions (OH\({}^{-}\)) content. The batched chemicals were melted in a quartz crucible at a temperature of 1200 \({}^{\circ}\)C for 1 h under controlled atmosphere; the melt was cast into a preheated 8 mm diameter steel mold, then annealed at a temperature around the glass transition temperature, \(T_{g}\) = 443 \({}^{\circ}\)C, for 5 h to relieve internal stresses, and finally cooled down slowly to room temperature. The Cu-doped calcium phosphate glass was blue in color, and the absorption spectra of the glass samples were obtained using a dual-beam scanning spectrophotometer (UV-2600, Shimadzu, Columbia, MD, USA).  
A ns IR (Nd:YVO\({}_{4}\)) Master Oscillator Power Amplifier (MOPA) fiber laser system (Datalogic Arex 20 MW, Bologna, Italy), with a central emission wavelength at 1064 nm, was used in this study. The laser is an industrial-grade system consisting of a galvo-scan system coupled with an F-theta lens at the output to facilitate the realization of complex designs on a two-dimensional plane. The effective focal length of the F-theta lens is 160 mm,providing a focal spot size of \(\sim\)60 \(\upmu\)m. A schematic diagram of the adopted laser system is reported in Figure 1a, while the Gaussian nature of the laser beam is shown in Figure 1b,c. For the current study, the pulse width delivered by the laser system was fixed at 4 ns at a pulse repetition rate of 20 kHz. The average laser power and scan speed were varied as the parameters of the present study. The modification characteristics of the generated surface features were studied by writing single scan lines for each selected parameter combination. The Gaussian beam profile was acquired using a camera (Pixelink CMOS USB 3.0, Ottawa, ON, Canada) and was further analyzed using the ImageJ software.
# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
###### Abstract  
Wind power has emerged as a crucial substitute for conventional fossil fuels. The combination of advanced technologies such as the internet of things (IoT) and machine learning (ML) has given rise to a new generation of energy systems that are intelligent, reliable, and efficient. The wind energy sector utilizes IoT devices to gather vital data, subsequently converting them into practical insights. The aforementioned information aids among others in the enhancement of wind turbine efficiency, precise anticipation of energy production, optimization of maintenance approaches, and detection of potential risks. In this context, the main goal of this work is to combine the IoT with ML in the wind energy sector by processing weather data acquired from sensors to predict wind power generation. To this end, three different regression models are evaluated. The models under comparison include Linear Regression, Random Forest, and Lasso Regression, which were evaluated using metrics such as coefficient of determination (R), adjusted R; mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Moreover, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were taken into consideration as well. After examining a dataset from IoT devices that included weather data, the models provided substantial insights regarding their capabilities and responses to preprocessing, as well as each model's reaction in terms of statistical performance deviation indicators. Ultimately, the data analysis and the results from metrics and criteria show that Random Forest regression is more suitable for weather condition datasets than the other two regression models. Both the advantages and shortcomings of the three regression models indicate that their integration with IoT devices will facilitate successful energy prediction.  
In the IEEE International Conference on Intelligent Transportation Systems (IWT), IEEE, 2015, pp. 1-5.  
2024 14 10276 10.3390/app142210276
# Heuristics for a Two-Stage Assembly-Type Flow Shop with Limited Waiting Time Constraints
## 3 Heuristic Algorithms
### Genetic Algorithm
#### 3.3.5 Crossover  
The crossover operation aims to generate better offspring by exchanging the information of the selected parents. In this study, six types of crossover operation are considered: one-point order crossover (OX1), two-point order crossover (OX2), similar job crossover (SJX), two-point similar job crossover (SJX2), similar block crossover (SBX), and two-point similar block crossover (SBX2). Like the selection operation, the proposed GA uses the best of these six methods. For a description of these crossovers, let \(parent_{1}\) and \(parent_{2}\) be the selected parents to generate _offspring_.  
* Procedure: OX1 _Step 0_. Let \(point_{1}\) be a cutoff point randomly selected from \(parent_{1}\). _Step 1_. Give the front part (from the first to \(point_{1}\)) of \(parent_{1}\) to _offspring_. _Step 2_. Remove the jobs, that are given to _offspring_ in \(Step\) 1, from \(parent_{2}\). _Step 3_. Sequence jobs remaining in \(parent_{2}\) into the unoccupied positions of _offspring_ from left to right in the order that they appear in \(parent_{2}\).
* Procedure: OX2 _Step 0_. Let \(point_{1}\) and \(point_{2}\) be cutoff points randomly selected from \(parent_{1}\), respectively. _Step 1_. Give the middle part (between \(point_{1}\) and \(point_{2}\)) of \(parent_{1}\) to _offspring_. _Step 2_. Remove the jobs, that are given to offspring in \(Step\) 1, from \(parent_{2}\). _Step 3_. Sequence jobs remaining in \(parent_{2}\) into the unoccupied positions of _offspring_ from left to right in the order that they appear in \(parent_{2}\).
* Procedure: SIX _Step 0_. Give the jobs in the same position in both parents to _offspring_. _Step 1_. Let \(point_{1}\) be a cutoff point randomly selected from \(parent_{1}\). _Step 2_. Give the front part (from the first to \(point_{1}\)) of \(parent_{1}\) to _offspring_. _Step 3_. Remove the jobs, that are already given to _offspring_, from \(parent_{2}\). _Step 4_. Sequence jobs remaining in \(parent_{2}\) into the unoccupied positions of _offspring_ from left to right in the order that they appear in \(parent_{2}\).  
SJX2 modified from SIX selects two cutoff points and gives the middle part between the two points of \(parent_{1}\) to _offspring_, as in OX2. Additionally, SBX and SBX2 are very similar to SJX and SJX2, respectively. The only difference is that, in \(Step\) 0 of SJX (and SJX2), two adjacent jobs are defined as a block and not an individual job but blocks in the same position in both parents are given to _offspring_. The detailed procedures of SJX2, SBX, and SBX2 are omitted because the rest of the steps are the same.
## 3 Results and Analysis
### Indentation Force and AE Events in the Indentation Process
The AE accumulative energy of the second indentation process was higher than that of the first indentation process. Combining with Figures 5 and 7, it can be seen that the step formation of AE cumulative energy curve corresponds to the penetration force drop, and the rock sample can be damaged greatly in the test. Therefore, the relationship between AE cumulative energy and penetration force can be used to predict rock failure. The variation law of cumulative energy corresponds to that of penetration force. AE amplitude is an important parameter of acoustic emission, which is related to the strength of the event. Its value is not limited by the AE threshold and can truly reflect the AE source signal, so it can roughly reflect the real situation of micro-crack activity in rock. Figure 8 shows the relationship between AE amplitude and penetration depth. It can be seen that the AE amplitude increases with the increase of penetration depth in the early stage, and then becomes stable gradually, but there are also some sudden signals, and the amplitude value of acoustic emission is at a high level. When the specimen is destroyed, the value of AE amplitude is at the maximum value of the whole stage, which indicates that there are a lot of cracks in the rock during this stage, and the main fracture surface is gradually connected and merged until the specimen is destroyed. Figure 6: Penetration forces and AE events with different water contents: (**a**) \(0.11\%\); (**b**) \(0.22\%\); (**c**) \(0.32\%\); and (**d**) \(0.43\%\). One is the first indentation process; and two is the second indentation process. Figure 7: Typical AE cumulative energy–penetration depth curve.