esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Tarifit Language Background
### Writing Systems and Orthographic Variation  
Tarifit can be written using multiple script systems: Tifinagh (the traditional Berber script), Latin script, Berber Latin script, or Arabic letters, reflecting the diverse literacy practices and historical influences within the community [3]. Table 2 illustrates this orthographic diversity through a simple greeting, demonstrating how the same linguistic content can appear in multiple written forms depending on the context and community practices.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Region/Country** & **Speakers** & **Primary Contact Languages** \\ \hline Northern Morocco & 3,000,000 & Arabic, French \\ Belgium & 700,000 & Dutch, French \\ Netherlands & 600,000 & Dutch \\ France & 300,000 & French \\ Spain & 220,000 & Spanish, Catalan \\ Other Europe & 180,000 & Various \\ \hline Total & 5,000,000 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tarifit speaker distribution by region.  
Tifinagh represents the traditional indigenous script, increasingly promoted in educational and cultural contexts as part of Berber language revitalization efforts. The Latin-based orthographies are most common in digital contexts and educational materials, while Arabic script usage reflects the broader Arabic literacy in Morocco. This orthographic diversity poses significant challenges for computational processing, as the same linguistic content may appear in multiple scripts depending on the writer's background, intended audience, and platform.  
For the purposes of this study, we focus exclusively on Latin script representation of Tarifit, as it is the most prevalent form in digital communication and online resources from which our dataset is constructed. This methodological choice allows for consistent preprocessing and evaluation while avoiding the additional complexity of cross-script normalization, though we acknowledge that a comprehensive Tarifit NLP system would ultimately need to handle all script variants. The lack of standardized orthographic conventions across different Tarifit-speaking communities further complicates automated text processing, creating additional preprocessing challenges for machine translation systems, particularly when training data or evaluation metrics must account for multiple valid representations of the same linguistic content.
## 4 Experiment Results and Analysis
### Experimental Settings
#### 4.2.2 Hyper-Parameter Settings  
In the process of training, the proposed SEM-RCNN model is pre-trained on the MS-COCO dataset, firstly. Then, the training step of SEM-RCNN contains two steps. The head is frozen and trained with an epoch of 150 and a learning rate of 0.0001. After that, the whole network is trained with an epoch of 150 and a learning rate of 0.001. The final model parameters have the lowest loss function obtained on the validation set. There are 98,793,356 trainable parameters of SEM-RCNN in total. The intersection over union (IoU) is the ratio of intersection and concatenation of the prediction box and true box. A result can be considered a correct detection only if the IoU value of the prediction box is greater than the set threshold, so the IoU threshold is a critical hyperparameter that may highly affect the performance of the proposed model. To systematically choose the IoU threshold,  
Figure 11: The EMs images in EMDS-6. The color images above are original images and the binary images below are the corresponding GT images.  
we test the detection performance of different IoU settings using SEM-RCNN based on SE-ResNet-101. The IoU threshold is set from 0.1 to 0.9 with the stride of 0.1, and the average detection indices based on 5-fold cross validation are shown in Table 1.  
By reviewing Table 1, we find that the set of IoU thresholds is critical for the detection performance of the proposed model. The highest mAP, Precision, Recall, and F1-score can be obtained when the IoU threshold is set as 0.3. Though the mean IoU is higher when the threshold is set as 0.4, 0.8, and 0.9, the other indices (such as Precision) are not satisfactory. Therefore, by considering the aggregate detection performance, the IoU threshold is set as 0.3, which performs accurately and balanced.
## 2 Contributions of Proto-MAML to the State-of-the-Art  
Proto-MAML applied to QA represents an innovative approach within the field of ML for the analysis, detection, and remediation of vulnerabilities in source code. This model is distinguished by its interpretative capacity and its design, which addresses the critical demands of the SSDLC and DevVecOps, offering novel elements when compared to existing methodologies, as outlined below.  
1. _Dynamic Prototypes and Real-Time Adaptability:_ Proto-MAML leverages Prototypical Networks to create dynamic representations that are tailored to each task, allowing for seamless adaptation to evolving code patterns without extensive re-training. This capability sets it apart from traditional static methods, which rely on frequent updates to remain effective. Furthermore, Proto-MAML dynamically adjusts to changes in codebases and emerging threats, ensuring continuous protection while maintaining computational efficiency with optimized complexity.
2. _Few-Shot Learning (FSL):_ The FSL capabilities of Proto-MAML ensure high accuracy even when limited labeled data are available. This ability to generalize from minimal data addresses a fundamental challenge in security vulnerability detection, particularly for less-common programming languages or newly identified vulnerabilities.
3. _Contextual and Predictive Insights:_ Through integrating BERT, Proto-MAML enhances interpretability by generating contextualized explanations and predictive remediation strategies. This combination not only identifies vulnerabilities but also anticipates potential issues, providing actionable recommendations that assist development teams in proactively mitigating risks.
4. _Resource Optimization and Efficiency:_ Proto-MAML optimizes computational resources by reducing the parameters required for task-specific adaptation, facilitated by Prototypical Networks. This efficiency ensures seamless integration into CI/CD pipelines, enabling real-time security assessments without introducing delays while maintaining rapid development cycles and prioritizing security.
5. _Actionable Interpretability:_ Proto-MAML bridges the gap between technical detection and practical remediation by delivering clear outputs in natural language. This approach enhances collaboration between development, security, and operations teams, ensuring that the proposed solutions are comprehensible and can be applied immediately.
## 2 The Applied Methodology  
### State Analysis and Equivalent Circuits  
Analysis of the OLMEH-AR started with computer simulation (PSIM v. 9.1). Derivation of the computer model of the magnetic core in PSIM v9.1 software is a tedious trial-and-error process; however, the computer simulation model proved to be an indispensable tool in the undertaken study.  
Examination of the simulated waveforms of the OLMEH-AR in Figure 2 reveals that its operation period comprises two symmetrical half cycles. The positive half-cycle commences when the secondary current becomes positive, \(i_{2}(t)>0\) and comprises four states. Note that during the negative half-cycle, a similar order of events occurs (with \(M_{2}\) interchanging its function with \(M_{1}\) and \(D_{2}\) interchanging its function with \(D_{1}\)). Therefore, it is sufficient to analyze only the positive half-cycle.  
State 1 [0, \(t_{1}\)], see Figure 3a, commences at \(t=0\). Here, both active switches conduct and effectively short the secondary winding.  
State 2 [\(t_{1}\), \(t_{2}\)], see Figure 3b, commences at \(t=t_{1}\), when, by the controller's command, the \(M_{1}\) switch is turned off but \(M_{2}\) remains on. Here, the rectifier diode \(D_{1}\) turns on and allows the current to charge the battery. Since a constant battery voltage is applied to the secondary winding, the flux density in the core starts ramping up linearly, while causing the magnetizing current to rise in a nonlinear fashion.  
State 3 [\(t_{2}\), \(t_{1}+t_{\rm pw}\)], see Figure 3c, commences at \(t=t_{2}\) when the rectifier currents drop to zero, \(i_{\rm in}=i_{\rm b}=0\). Here, the anti-parallel diode of the \(M_{1}\) switch starts conducting and shorts the secondary winding.  
Figure 2: Typical simulated waveforms of an OLMEH-AR under CVL.  
State 4 [\(t_{1}+t_{\text{pw}}\), \(T/2\)], see Figure 3d, commences when, by the controller's command, switch \(M_{1}\) is turned on again and helps its anti-parallel diode to keep the winding shorted. Under short circuit conditions, the flux density in the core remains constant until the end of the positive half cycle.  
The OLMEH-AR model based on a simplified cantilever model with a saturable magnetizing inductance, \(L_{m}\), placed on the secondary winding, while neglecting the leakage inductance, is shown in Figure 4a. The ideal transformer \(1:N\) acknowledges the fact that the line conductor acts as a single-turn primary winding, while the secondary one consists of the \(N\) turn wound on the OLMEH's core. The simplified equivalent circuit of the short-circuit (SC) state (States 1, 3, and 4) is shown in Figure 4b, and the simplified model of State 2, the only charging state (CS) that allows current to the load, is presented in Figure 4c.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 2 Methodology and Implementation
### Intrusion Detection
#### 2.1.8 Comparison of the Models  
Table 2 provides a comprehensive analysis of five prominent object recognition models: Inception, Xception, AlexNet, VGG16, and the YOLO family. Each model is evaluated based on key parameters, including its architectural design, object detection speed, accuracy,  
Figure 9: Architecture of YOLOv8 [28].  
training dataset requirements, and hardware compatibility. Among these, YOLOv8 emerges as a highly favorable choice for this research.  
YOLOv8 distinguishes itself by delivering a one-shot detection architecture that smoothly incorporates object localization and classification in a single pass. This innovation, paired with its quick detecting speed, making it particularly ideal for real-time applications. Moreover, YOLOv8 achieves this performance without losing accuracy, making it an ideal candidate for precision-critical applications.  
While models such as Inception, Xception, and VGG16 display great detection accuracy, they frequently require significant training datasets [29; 30; 31]. In comparison, YOLOv8's small dataset needs are a significant advantage, especially given its ability to retain high accuracy. Additionally, YOLOv8's support for GPU acceleration considerably boosts its performance, particularly in real-time processing contexts.  
YOLOv8 offers an optimal mix between speed and accuracy, establishing itself as the premier model for applications demanding real-time, high-precision object detection. Its unique design and hardware compatibility further boost its adaptability across a wide range of application situations.  
YOLOv8 is the principal model used for object identification in the SFSS. The algorithm is trained on a bespoke dataset that includes photos of common agricultural invaders, such as animals, and human trespassers. The dataset was carefully built to incorporate multiple environmental variables to ensure strong model performance in real-world scenarios. YOLOv8 analyzes incoming photos from the ESP32 sensors in real time, recognizing possible threats with excellent accuracy. Once an incursion is detected, the model provides a confidence score for the discovered entity, categorizing it as either an animal or a person. The system then prompts a warning that is delivered to the farmer using a cloud-based notification system and triggers an alarm that is present to frighten away the incursion-causing agent.
# Monitoring of Liquid Viscosity for Viscous Dampers through a Wireless Impedance Measurement System
## 3 Experimental Set Up
In order to analyze the peak frequency of the impedance real part curve to more visually monitor the change of damping fluid viscosity, the sweep band of the excitation signal is set to 70-100 kHz with a 6 Hz step in the subsequent experiment.
# Applications of Nanomaterial Coatings in Solid-Phase Microextraction (SPME)
## 4 Advances in the Adsorbent Type Used in the Extraction Phase of the SPME Fiber
### SPME Based on Nanoparticles
#### 4.1.1 Metal Nanoparticles in SPME
After testing the technique on environmental water samples that had been tampered with, they came to the conclusion that it worked well [59]. When compared to common macroscale materials like alumina, silver nanoparticles (Ag NPs) have garnered interest as sorbents in SPME because of their improved intrinsic features, such as greater chemical activity and ultrafine grain size. These nanoparticles can be chemically modified with particular reagents to create highly selective coatings for SPME fibers, which will enable the more effective and specific preconcentration and extraction of metal ions [52].
# A 3D Vision-Based Weld Seam Extraction Method for Arbitrarily Placed Spatial Planar Weldments
## 2 Improved RANSAC for Plane Segmentation
### Improved RANSAC
#### 2.2.2 Optimization for Candidate Plane Validation  
The second challenge, termed \"invalid candidate plane\", arises after the plane model calculation step (step 2) of the algorithm depicted in Figure 2. Occasionally, invalid candidate planes may emerge despite the sampled points passing Rule 1, as illustrated in Figure 3b. In this scenario, while the sampled points possess similar normal vectors, the candidate plane's normal vector exhibits a significant disparity.  
Rule 2. Candidate plane validation.  
Figure 3: Situations of invalid segmentation: (**a**) invalid sampling; (**b**) invalid candidate plane; (**c**) closely spaced planes; (**d**) erroneously included inliers.  
For any candidate plane calculated using the sampled points, its normal vector must be close to those of the sampled points. That is to say, candidate plane \(S\) is valid only when  
\[\text{arccos}\bigg{(}\frac{|\mathbf{n}_{i}\cdot\mathbf{n}_{S}|}{\|\mathbf{n}_{i}\|\|\mathbf{n}_{S} \|}\bigg{)}<\sigma_{a},\ \ i\in\{1,2,3\}, \tag{8}\]  
where \(\mathbf{n}_{S}\) is the normal vector of \(S\).
## 3 Experimental Methods  
To verify the feasibility of the operating principle, we performed an experiment in which we used a photo-crosslinkable polymer liquid crystal (PCLC) film from which the SPG and PPGs were made [23]. Anisotropic patterns can be imprinted in the PCLC films  
Figure 2: Structure (left) and function (right) of (**a**) SPG, (**b**) PPG\({}_{(+)}\), and (**c**) PPG\({}_{(-)}\).  
by recording a polarization hologram from LCP and RCP light for which one beam has a parabolic-shaped wavefront. Figure 3 shows the experimental setup for fabricating the PPGs. As light source, we used a diode-pumped solid-state (DPSS) laser (0355-05-01-0020-700, Cobolt AB) (Solna, Sweden) with an operating wavelength of 355 nm. The SoPs of the two interfering beams were converted to LCP and RCP using quarter-wave and half-wave plates (QWP and HWP). A cylindrical lens was inserted in one of the optical paths before the sample plane to change the wavefront into a parabolic shape. The PPG\({}_{(+)}\) and PPG\({}_{(-)}\) were respectively fabricated by interchanging the handedness of the circular polarization between the parabolic and plane wavefront beams. The focal length of the cylindrical lens is 1 m. Fabrication details of the polarization gratings out of PCLC film are described in our previous papers [20,24]. We fabricated PPGs with a grating pitch of 3.73 \(\upmu\)m and a diffraction efficiency of 97.3%. We configured the SPG by eliminating the cylindrical lens (Figure 3). The effective diameter of the aperture of the fabricated polarization gratings is approximately 6 mm.  
We next assembled an optical setup for demonstrating the OV characterization using a SPG and two PPGs [Figure 4a]. In this setup, a beam emitted from the Nd:YAG laser (operating at 532 nm) was first expanded in diameter and reflected onto a SLM (SLM-200; Santec Inc.) (Aichi, Japan). The amplitude and phase distributions of the reflected light were both modulated using a hologram projected onto the SLM; an OV is thus generated as diffracted light. One diffraction spot from the SLM is filtered by a pinhole and then directed onto the system of SPG and PPGs. The LCP and RCP components divided by the SPG are cylindrically focused by the PPG\({}_{(+)}\) and PPG\({}_{(-)}\), respectively. A beam profile of each spot is imaged using a CMOS camera. In this experiment, we generated a LP OV with \(\ell=4\). We also demonstrated the feasibility of PV characterization by generating a PV using the SLM and \(q\)-plate (QP) [Figure 4b]. The QP is a space-variant HWP, which converts a linearly polarized beam into a PV [25, 26, 27]. With this setup, we generated PVs with \(p=1\) and \(p=2\) using two kinds of QPs made of a liquid crystal film.  
Figure 3: Optical setup for fabricating SPG and PPGs from PPLC by photoalignment.
## 8 Conclusions  
The highly integrated information and real-time data sharing of networked ATM has become a necessary means to ensure the safety of air traffic. With the application of artificial intelligence and other information network technologies in the ATM, while improving the performance and efficiency of the existing system, various vulnerabilities in the information network have seriously affected the safe operation of the ATM-CPS. Therefore, the information security assurance of the networked ATM must consider mining its security risks and system vulnerabilities on the basis of cyber-physical integration, establishing a systematic assurance system, and realizing an all-round information security assurance of the networked ATM system. To summarize, the research of this paper has application value and prospects in the future.  
**Author Contributions:** Conceptualization, X.L. and R.D.; methodology, X.L.; validation, R.D., Q.W. and L.Z.; investigation, X.L.; resources, R.D.; writing--original draft preparation, X.L.; writing--review and editing, R.D.; visualization, X.L.; supervision, Q.W.; project administration, L.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the joint funds of National Natural Science Foundation of China and Civil Aviation Administration of China (U2133203), the National Natural Science Foundation of China (62172418), the Natural Science Foundation of Tianjin, China (21)[CZD]Z00830), the Scientific Research Project of Tianjin Municipal Education Commission (2019K117), and the Fundamental Research Funds for the Central Universities of China (ZXH2012P004, 3120201026).  
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Advances in Three Dimensional Bioprinting for Wound Healing: A Comprehensive Review
## 4 3D Bioprinting Techniques
### Other Techniques
#### 4.4.6 Cell Laden Spheroids  
Two bioprinting approaches have been devised to manufacture cell-loaded spheroids using 3D bioprinting: direct cell extrusion and sacrificial bioink printing. Direct cell extrusion 3D bioprinter allows the production of complex 3D structures resembling living tissues and organs in vitro. These structures are obtained by the precise and flexible printing of complex products such as cell-containing biosimilars, ECM and cytokines [236].  
Bioprinters create repeatable and systematic biological structures using additive manufacturing and layer-by-layer deposition processes. Large numbers of spheroids may be created in vitro in this manner. Spheroids are created by extruding droplets containing cell-loaded bioinks. Bioinks with cell adhesion capabilities are essential in the spheroid generation process. Bioinks with cell-adhesive characteristics promote stable cell attachment and proliferation in an environment comparable to ECM. As a result, inside the droplet, a spheroid is entirely filled with cell forms [237; 238; 239; 240; 241].  
Cell aggregation and intercellular adhesion are stimulated by a drop of cell-repellent bioink, resulting in the creation of a small spheroid. However, in bioprinting, numerous bioink parameters such as viscosity, shear thinning, viscoelasticity, biodegradation, crosslinking, and cellular adhesion must be chosen. These characteristics are crucial for achieving the required structures [242; 243; 244].  
It is proposed to use a sacrificial bioink rather than a hydrogel to fabricate precise 3D patterned spheroids. After printing the polymer barrier and matrix biomaterial as a supporting medium, the cell-loaded sacrificial bioink was pierced into the matrix biomaterial. The matrix biomaterials were selectively cross-linked, and the sacrificial bioink that surrounded the cells was dissolved, resulting in holes sealed within the matrix biomaterial. Imprinted cells inside the holes were able to form spheres in situ because of ongoing culture, and the accurate 3D placement of the spheres was achieved in a single simultaneous procedure. Spheroid size control and correct spheroid localization were also established. Although sacrificial bioink 3D bioprinting allows for exact spheroid production at a specific location, its practical use is restricted since it is more expensive, difficult to handle, and time-consuming than other procedures [240; 245].