esa-sceva/satcom-chunk-collection huggingface dataset:
# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 8 Performance Evaluations
### What Happens to the Unpopular Messages?  
Until now, we explored only the performance for popular messages. Epidemic does not differentiate between popular and unpopular messages and thus its performance is exactly the same. Figure 9 presents all three metrics for unpopular messages for Epidemic and Keetchi.  
Very importantly, it can be seen that Keetchi does not ignore unpopular messages. On the contrary, it delivers many of those (around 20-30% across all scenarios), but its overhead does not suffer. Thus, it uses its resources more wisely and focuses its efforts on popular messages. We can see this also while observing the delivery delay of Keetchi--it is really much larger than for Epidemic, meaning that Keetchi only delivers unpopular messages when there is space and time for them.  
Interestingly, with increasing number of nodes, Keetchi delivers more unpopular messages, because of the increased number of communication opportunities. This is different with decreasing cache size, where Keetchi almost stops delivering unpopular data items (Figure 9b). The delay decreases in this case, since Keetchi carries them only for a very short time and drops them again--either they get delivered very quickly or not at all.  
_Take-home message._Keetchi delivers as many unpopular messages, as time and space in the cache permit, and focuses on popular messages.  
Figure 9: Performance comparison for unpopular data items. Mean and 99% confidence intervals of: (**a**) delivery ratio, unpopular messages, more nodes in the network; (**b**) delivery ratio, unpopular messages with decreasing cache size; (**c**) delivery delay, unpopular messages, more nodes in the network; and (**d**) delivery delay, unpopular messages with decreasing cache size.
# Special Issue on Ultrasonic Modeling for Non-Destructive Testing
## 1 Introduction  
This Special Issue of Applied Sciences focuses on advancing modeling methods for the ultrasonic Non-destructive Testing (NDT) of materials. Ultrasonic techniques are employed for non-destructive purposes to evaluate the properties and damage states of structures devised for numerous applications (engineering, building materials, medicine, etc.). The advantages and inconveniences of ultrasonic testing compared to other NDT methods have been widely reviewed [1]. The coexistence of all the different NDT methods can allow the inspection of different kinds of structures and damages [2]. The scope of this Special Issue ranges from ultrasonic wave techniques for classical non-destructive evaluation, the structural health and condition monitoring of structures, existing or novel methods for imaging, ultrasonic characterization, non-linear acoustics, acoustic emission, laser ultrasonics, additive manufacturing, medical applications, and sensors, to, signal and noise analysis.  
The current Special Issue notably aims to explore advances in ultrasonic modeling methods for understanding or predicting NDT inspections. Different authors present novel achievements in the understanding and modeling of ultrasonic waves for NDT applications. High-quality research and review papers on theoretical, practical, and validation aspects were accepted, leading to a collection of 20 published papers. These are briefly reviewed here, classified into different topics. A brief overview of the main developments in the field is recalled in each topic.
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 6 AI-Based Malware Detection
### Opcode and Graph  
The utilization of opcodes as a feature in machine learning models can be a suitable and reliable approach for identifying malware. The successful amalgamation of Windows malware opcodes with machine learning techniques has been demonstrated by many researchers to effectively detect malware. The selected features (opcodes) of each sample (software) were transformed into a graph (Figure 10). The graph created by [PERSON] et al., 2018 [59], depicted nodes as opcodes, with edges representing the affinity of each node (which was computed) in the disassembly of the respective software [59]. Graphs can be transformed into an Eigenspace, thus making it possible for Convolutional Neural Networks (CNNs) to be utilized for the classification of malicious and benign software graphs. [PERSON] conducted an experiment in which the opcode sequences of 1078 piecesof legitimate software and 128 pieces of malicious software were extracted. The analysis of malware using graphs converted from opcodes yielded a detection accuracy of 99.68% and a recall rate of 98.37%. The efficacy of this technique for detecting malicious software is remarkable [60]. Table 4 provides a comparison of different malware detection schemes.
## 2 Materials and Methods
### Grasping with a Simulator  
In this subsection, we explored the effect of ab/d DOF on grasp quality. In general, most grasp quality metrics were proposed based on the locations of the contacts between the hand and the target object, which were hard to measure in a real environment. Hence, we used \"Grasplit\", a publicly available grasp simulator developed by [PERSON] and [PERSON] [35; 36; 37], to evaluate the grasp quality. This simulator has a robotic hand library, including a human hand model with 20 DOFs. Users can import objects for grasp planning and analysis with a built-in simulated annealing algorithm [38]. A grasp quality function was defined as formulations (1) and (2) [37]. A set of desired contact points was predefined of a certain hand model. For the human model used in this experiment, the point number is 18 identified by the  
Figure 4: Setup of the grasp force experiments.  
index _i._\(\hat{n}_{i}\) and \(o_{i}\) are the normal and distance between the desired contact point of the hand model and the object, respectively. \(\alpha\) is a scaling parameter, and \(Q\) is the value of the quality function for a given grasp. All \(Q\) values are higher than zero and a higher value indicates a higher grasp quality. The simulator GraspIt! takes into account factors such as gravity, friction, torque, collision, and so on, which can simulate the physical environment in the real world very realistically, so it is adopted.  
\[\hat{f}\!\!\hat{n}_{i}=\frac{o_{i}}{\alpha}+\left(1-\frac{\hat{n}_{i}\cdot o_{i }}{|o_{i}|}\right) \tag{1}\]  
\[Q=\sum_{\begin{array}{c}\text{all desired}\\ \text{contacts}\end{array}}(1-\hat{f}\!\!\hat{n}_{i}) \tag{2}\]  
In this simulation experiment, the virtual constraint was added to the human hand model, and four different constraint hand models corresponding to the ab/d DOF types defined in Section 1 were used. We imported six disks with a diameter between 110 mm and 160 mm with an interval of 10 mm into the simulation space and used the simulated annealing algorithm to plan the grasp. The thickness of all disks was 10 mm. According to [37], the simulated annealing algorithm starts to converge after 60,000 iterations. Therefore, the number of iterations for each planning was set to 70,000, and the grasp gestures with the highest quality were recorded. For each constraint hand mode, the grasp was planned 10 times for each disk size, and a total of 240 ideal grasping postures were obtained in this experiment.
## 6 Concluding Remarks and Future Perspectives  
Enzyme immobilization is advancing through innovations in nanomaterials, dynamic carrier systems, and artificial intelligence (AI), enhancing biocatalytic efficiency and sustainability. Technologies including metal-organic frameworks (MOFs), magnetic nanoparticles, and 3D-printed scaffolds improve enzyme stability and reusability, even under harsh conditions. AI and machine learning (ML) optimize enzyme-material interactions, immobilization protocols, and the screening of biocompatible nanomaterials. These advancements address challenges like mass transfer, enzyme leakage, and scalability, while enabling the creation of intelligent biocatalysts.  
However, challenges remain, including nanomaterial toxicity, inconsistent synthesis, and environmental impact. The industrial scalability of microfluidics and AI-driven systems requires validation regarding cost-effectiveness and reproducibility. Future research should focus on eco-friendly hybrid materials, such as biodegradable polymer-MOF composites, to reduce ecological risks while maintaining catalytic performance. Optimizing multi-enzyme cascades with AI-driven simulations is also crucial for mimicking natural metabolic pathways.  
Synthetic biology offers potential for engineering enzymes with specialized functions, enhanced through site-specific immobilization. AI-powered lifecycle assessments will be critical for evaluating sustainability. Interdisciplinary collaboration in computational biology, materials science, and green chemistry is essential to connect lab innovations with industry needs. The integration of 3D bioprinting, AI, and bio-based carriers will drive the development of biocatalytic platforms for precision medicine, biofuel production, and environmental detoxification, aligning with global sustainability goals.  
M.T.: writing--original draft. Y.L.: conceptualization, supervision, funding acquisition, project administration, and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was financially funded by Entrusted projects by private enterprises (No. H2020111).  
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
## 5 Discussion  
A framework for identifying technological opportunities for radical inventions (RIs) is established in a novel way. Previous studies did not demonstrate a clear understanding of the conditions under which radical inventions (RIs) form, usually identifying a large number of general technological opportunities before screening them out. This kind of post-screening is costly, with limited guidance for improving the effectiveness of the identification process. The method proposed in this study considers the characteristics of RIs at an early stage, and uses the value of difficulty (VOD) of the problems solved and the value of technological novelty (VON) of new technology applied to directly identify technological opportunities for RIs, without extra costs in processing noisy data. This is helpful for managers and designers to quickly and accurately identify technological opportunities for RIs.  
Patent maps were developed for evaluating the technological novelty of patents. Previous studies have tended to utilize technical details in patent texts. However, after extracting a large number of micro-technical details from the patent text, it is still necessary to summarize this information into macro-technical points, to represent the patent technology, which is not only time-consuming but also misleading. In comparison, using patent filing dates and international patent classification codes (IC), we built a novel patent map to determine the VON of patent technology. This method allows innovation managers to quickly locate novel patents based on the high VON of IC, to determine the novelty of patents classified under the IC. At the same time, this method can help industrial firms to improve the utilization of structured patent data.  
The use of natural language processing, K-means clustering, and complex network analysis in unstructured patent data has been applied to various tasks. Natural language processing pre-processes unstructured patent data into structured data, and combines the analysis results of K-means clustering and complex network analysis to obtain the desired information intelligently. In this study, we used the intelligent computation of VOD for each patent. With the spread of artificial intelligence, theoretical research and the practical application of intelligent technological opportunity analysis using patent data mining are continuously being developed. This will reduce the difficulty of technology development and encourage more firms into radical innovation.
# Frequency-Dependent Acoustic Reflection for Soil Classification in a Controlled Aquatic Environment
## 4 Discussion
### Advantages of the Proposed Approach  
The proposed method is non-invasive by nature compared to physical sampling. Unlike sediment sampling, which requires physical retrieval and disturbs the marine environment, the proposed method is completely non-destructive and can be applied over large areas without ecological impact.  
Compared to backscatter-based seabed classification methods, acoustic spectral analysis offers several key advantages:  
Higher Resolution and Sensitivity: Differentiating soil types based on spectral characteristics rather than just intensity enables a finer-scale classification of seabed material.  
The method is Independent of Water Column Effects: Traditional backscatter-based methods are significantly affected by variations in water properties, leading to inconsistencies. By isolating the energy entering and leaving the sediment, our approach eliminates water column distortions and directly assesses seabed properties.  
Potential for Automation: The method can be integrated with machine learning algorithms for automated classification, reducing human interpretation errors, and increasing efficiency.  
Real-Time Data Processing: Unlike traditional methods that require post-processing of sediment samples, this technique enables real-time seabed characterization, which is particularly beneficial for underwater construction, environmental monitoring, and geological surveys.
## 2 Materials and Methods
### Durability Testing
#### 2.5.1 Accelerated Carbonation  
From the same batches of concrete produced for wall construction, prismatic specimens (dimensions 100 mm \(\times\) 100 mm \(\times\) 400 mm) were prepared for accelerated carbonation tests (in analogy to the standard EN 12390-12:2020 [22]). The specimens were first cured (by wet curing) for 28 days in a controlled atmosphere room (temperature 20 \(\pm\) 2 \({}^{\circ}\)C and relative humidity over 95%) and then moved to a lower relative humidity room (controlled atmosphere 20 \(\pm\) 2 \({}^{\circ}\)C and 60 \(\pm\) 5% RH) for 14 days. After this pre-conditioning period, the specimens were placed in a carbonation chamber, under a CO\({}_{2}\) concentration of 1%, a temperature of 20 \({}^{\circ}\)C, and a relative humidity of 60%. The specimens were kept in the chamber for 19 weeks. After the exposure period, three samples measuring 100 mm \(\times\) 100 mm \(\times\) 50 mm were extracted from each of the large prisms (broken by splitting in a compression testing machine). One surface of each sample was sprayed with phenolphthalein (1%) to identify the depth of the carbonation front.
# Characterization of Light-To-Frequency Converter for Visible Light Communication Systems
## 4 Results and Discussion
However, for the VLC system, such high accuracy measurement is not necessary, because in these systems, time boundaries are wide enough to determine if a symbol is in the on-off state. We experiment with different frequency values \(f_{OOK}\). One thing to note, however, is the LTF frequency estimation for symbol decoding. It is necessary that the condition \(f_{o}\geq f_{OOK}\) should be fulfilled; thus, given the unknown oscillator state of the LTF, when intensity fluctuations occur, there exists a possibility that high state of the square output signal will not be completed. Therefore, we recommend that the LTF output frequency meet the following condition \(f_{o}\geq 4f_{OOK}\), in order to mitigate the frequency estimation problem due to the deviations generated by the LTF output. Regarding the experiment, for each frequency \(f_{OOK}\) value, we can see the link distance between the LED and the LTF converter, which would allow finding an LTF output frequency \(f_{o}\geq 4f_{OOK}\). Figure 7 depicts an experimental estimation of the LTF output frequency \(f_{o}\) versus link distance, for the different values \(f_{OOK}\). We can see that the maximum frequency \(f_{OOK}\) of modulating signal is limited by transmission length, because the light intensity on the LTF is also a function of distance. The minimal link distance was 6.2 cm for a maximum frequency \(f_{OOK}=600\) kHz, without the LTF output frequency operates in saturation mode. Such maximum frequency could be achieved at a greater distance (>6.2 cm), if we consider an optical concentrator in the receiver. For the case of less frequency \(f_{OOK}=1\) kHz, the maximum distance was the 100 cm. Figure 6: Transmitter with on-off keying (OOK) modulator signal and LTF as receiver signal: (**a**) optical power transmission; (**b**) LTF output frequency. In Figure 4, we can appreciate the sensibility of the LTF converter to incident light. For slight variations of transmission reach, a consider change in output frequency is observed. Nevertheless, we consider that having a receiver with high responsivity could limit the visible light communication system, if the application scenarios have light sources different to LED transmitters. Such stimulations may cause a frequency deviation of that mapped in the color-shift keying (CSK) constellation space [23].
# A Spatial 4-DOF Laser Collimation Measurement System
## 3 Error Analysis
### Analysis of Installation Error (Abbe Error) Using the Homogeneous Transformation Matrix Method
 \tag{27}\]  
For \(L\), after the distance, the offset distance of the laser beam in the \(X\)-axis direction is \(2L\alpha\) and the angle offset is \(2\alpha\), while the offset distance in the \(Y\)-axis direction is \(2L\beta\) and the angle offset is \(2\beta\). As shown in the figure, when using plane convex lens L, the laser beam matrix in the X direction can be expressed as follows:  
\[M_{X1}= \begin{bmatrix}1&0\\ -f&1\end{bmatrix}\begin{bmatrix}2L\alpha\\ 2\alpha\end{bmatrix} = \begin{bmatrix}2L\alpha\\ 2(1-Lf)\alpha\end{bmatrix}  \tag{28}\]  
Figure 11: Influence of CMOS translation installation error on the \(X\)-axis. As shown in the figure, after passing through planar convex lens L, the laser beam matrix in the Y direction can be expressed as follows:  
\[M_{Y1}= \begin{bmatrix}1&0\\ -f&1\end{bmatrix}\begin{bmatrix}2L\beta\\ 2\beta\end{bmatrix} = \begin{bmatrix}2L\beta\\ 2(1-Lf)\beta\end{bmatrix}  \tag{29}\]  
For \(L\), after the distance and after the plane convex lens L refraction, the offset distance of the laser beam in the X-axis direction is \(2L\alpha\) and the angle offset is \(2(1-Lf)\alpha\), while the offset distance in the Y-axis direction is \(2L\beta\) and the angle offset is \(2(1-Lf)\beta\). Then, the laser beam matrix in the direction of O1-X1Y1Z1 is calculated as follows:  
\[ \begin{split} P_{CMOS2_{1}}=HP_{CMOS2_{0}}=\begin{bmatrix}1& \alpha&-\beta&\Delta_{X}\\ -\alpha&1&\gamma&\Delta_{Y}\\ \beta&-\gamma&1&\Delta_{Z}\\ 0&0&0&1\end{bmatrix}\begin{bmatrix}2L\alpha\\ 2(1-Lf)\alpha\\ 2L\beta\\ 2(1-Lf)\beta\end{bmatrix}\\ =\begin{bmatrix}2L\alpha+2(1-Lf)\alpha^{2}-2L\beta^{2}+2(1-Lf)\beta\Delta_{X} \\ -2L\alpha^{2}+2(1-Lf)\alpha+L\beta\gamma+2(1-Lf)\beta\Delta_{Y}\\ 2L\alpha\beta-2(1-Lf)\alpha+2L\beta+2(1-Lf)\beta\Delta_{Z}\\ 2(1-Lf)\beta\end{bmatrix}\\ \end{split}  \tag{30}\]  
The pitch angle error in the X direction is \(2L\alpha+2(1-Lf)\alpha^{2}-2L\beta^{2}+2(1-Lf)\beta\triangle_{X}\); the yaw angle error in the Y direction is \(-2L\alpha^{2}+2(1-Lf)\alpha+L\beta\gamma+2(1-Lf)\beta\Delta_{Y}\)  
\[\alpha^{\prime}=2L\alpha+2(1-Lf)\alpha^{2}-2L\beta^{2}+2(1-Lf)\beta\Delta_{X} \tag{31}\]  
\[\beta^{\prime}=-2L\alpha^{2}+2(1-Lf)\alpha+L\beta\gamma+2(1-Lf)\beta\triangle _{Y} \tag{32}\]  
Due to the complexity of error compensation after error analysis, this paper is limited in terms of space and cannot go into detail. The relevant content regarding error compensation will not be discussed in future work.
# Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning
## 4 Experiments and Numerical Evaluation
### Comparison with the State-of-the-Art  
The results of the proposed architectures were compared with the state-of-the-art outcomes. Such comparative results are summarized in Table 5.  
Figure 7: Loss-function and ROC curve for the CNN, LSTM, and CNN-LSTM architectures, the corresponding AUC-ROC metric performances are 0.99464, 0.99679, and 0.99500. **(a)** Loss function for the CNN architecture. **(b)** Loss function for the LSTM architecture. **(c)** Loss function for the CNN-LSTM architecture. **(d)** ROC curve for the CNN architecture. **(e)** ROC curve for the LSTM architecture. **(f)** ROC curve for the CNN-LSTM architecture. In **(d–f)**, the colors indicate different classes and the diagonal dotted line represents the behavior of a random classifier.  
It is worthwhile to notice that our results were obtained using a proprietary database focused on the handwriting recognition task. Furthermore, the results reported in [4,14,19,25,40] used \(26,26,10,12\), and \(12\) different classes, respectively, in contrast to [PERSON] et al. [13] and the results of this study, where \(36\) different classes were used. In this context, it was noticed that \(36\) classes were used by [PERSON] et al. [13]. However, they reported only the results for two sets processed separately, obtaining an accuracy of \(98.69\%\) and \(99.5\%\) for letters (a-z) and digits (0-9), respectively. Table 5 shows the mean of these results as \(99.09\%\).  
In this paper, the letters (a-z) and digits (0-9) were processed and classified as a single dataset representing \(36\) classes. Therefore, the proposed handwriting recognition methodology can recognize a letter or digit from the registered alphanumeric trajectories. The recognition accuracies using CNN, LSTM, and CNN-LSTM architectures were \(99.45\%\), \(99.68\%\), and \(99.55\%\), respectively. The DTW algorithm was also used by [PERSON] et al. [13] and [PERSON] et al. [4], but these last approaches used different methodologies and devices to obtain their corresponding databases. It was observed that the recognition of letters (a-z) in [13] is better than in [4], i.e., \(98.69\%\) and \(95\%\), respectively.