esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The hybrid multi-laser module presented in this work demonstrated significant potential for generating multiple high-performance microwave signals, specifically tailored to meet the demands of the THORMUX project. This innovative module integrates advanced optical technologies to achieve precise and reliable signal generation, a crucial requirement for microwave photonics systems.  
Figure 10: (**a**) Electrical spectrum demonstrating tunability across the frequency range. By precisely adjusting the emission wavelength of laser 3, the local oscillator signal within the Ka-band was finely tuned from 29 GHz to 31 GHz and 32 GHz. The V-band signal is also displayed. (**b**) Measurement of the electrical linewidth of the LO signal at 32 GHz.  
The InP/Si\({}_{3}\)N\({}_{4}\) hybrid integrated lasers exhibit exceptional promise as optical carrier light sources for RF photonics-based multiplexer and demultiplexer systems. Previous studies [17; 20] have characterized this type of hybrid integrated lasers, but this work represents, to the best of our knowledge, the first implementation of an integrated hybrid multi-laser module consisting of four single-gain lasers and one dual-gain laser. This module allows multiple RF channels to be handled simultaneously.  
The lasers characterized in this work exhibit high output power, with a maximum of 23 mW for laser 2, and an extensive wavelength tuning range spanning the entire C-band. However, this tuning process is not continuous, as mode hops are observed in the wavelength maps. Additionally, the lasers demonstrated narrow optical linewidths of less than 40 kHz in free-running mode. These narrow optical linewidths translate directly into narrow electrical linewidths, below 80 kHz, for local oscillator signals generated through optical heterodyning. This marks a significant improvement over previous optical heterodyne systems that relied on monolithic InP DFB lasers [24; 25], showcasing the hybrid module's great performance.  
The results obtained for dual local oscillator signal generation further confirm the system's effectiveness at target frequencies, demonstrating its suitability for applications requiring reliable signal generation across multiple frequency bands. The capability to generate high-quality RF signals over diverse frequencies underscores the module's adaptability for various advanced communication and sensing technologies.  
An additional advantage of the lasers lies in their ability to exhibit repeatability, a critical factor for practical deployment. Once the initial operating parameters for generating an RF signal at a specific frequency are established, the system can consistently reproduce that frequency whenever those parameters are restored. This reliability holds true even after multiple adjustments to the parameters, ensuring consistent performance over time.
# Lightweight Neural Network Optimization for Rubber Ring Defect Detection
## 1 Introduction
[PERSON] et al. [22] proposed ShuffleNet using point-by-point group convolution and channel shuffling to greatly reduce the computational cost while maintaining accuracy. [PERSON] et al. [23] proposed MobileNets, which use deep separable convolution instead of normal convolution and greatly reduce the computational effort of neural networks. While universal lightweight networks have contributed to network size reduction, there exists a considerable scope for enhanced optimization in neural networks tailored to particular scenarios. Unlike other industrial parts, the unique shape of rubber O-rings results in a notably regular distribution of defects. Standard convolution processes do not consider the targeted detection areas; they represent an inefficient use of resources in the detection of defects in rubber O-rings. Notably, defects in rubber O-rings are confined to the surface, and the blank space in the center of the images does not require detection (Figure 2). However, standard convolution processes involve convolving each pixel in an image. To mitigate this inefficiency, we have introduced preprocessing techniques, including image inversion, which have demonstrated considerable effectiveness. Figure 1: Examples of surface defects of rubber seal rings. The position of the surface defect is annotated by the red rectangle, each belonging to one of the following categories: (**a**) cracks, (**b**) chips, (**c**) impurities, and (**d**) wear. Meanwhile, [PERSON] et al. [24] discovered significant redundancy in the intermediate convolutional layers computed by CNN-based neural networks. As a result, they proposed GhostNet as a replacement for convolutional computation that uses partial linear transformations to minimize computational cost. However, replacing all the convolutional modules with Ghost modules in GhostNet does not exactly support their findings, as only some of the convolutional layers have similar linear relationships between different feature maps. Hence, a YOLO (You Only Look Once) network optimization method based on the Ghost module is proposed to identify the most suitable replacement layers, and ordinary convolutional modules are replaced with Ghost modules, in combination with a digital image processing technique, which greatly improves the efficiency. Additionally, improving efficiency means reducing training time and lowering computational resource consumption. A formula has been proposed for assessing this network. Our methodology facilitates the identification of the most optimal positions for replacing convolutional layers. The optimized network thus achieved effectively balances efficiency with accuracy. Compared to the original network, the network size decreases by 30.5% and the computational cost decreases by 23.1%, whereas the average precision only decreases by 1.8%.
## 4 Validation
### Findings and Observations  
* Efficiency Improvements: The integration of an enhanced AHP with a systematic grouping approach significantly reduced the time required for requirements analysis and prioritization. On average, the time savings amounted to approximately 30%, compared to traditional methods, as demonstrated across multiple case studies.
* Accuracy in Requirement Classification: The advanced grouping model proved particularly effective in categorizing requirements into groups that were more nuanced than the conventional tripartite division (inessential, desirable, and mandatory). This nuanced categorization allowed for a more precise assessment of each requirement's impact, leading to more informed decision-making processes.
* Enhanced Design Quality: The application of the framework led to observable improvements in design quality and system functionality. By accurately prioritizing critical requirements, development teams were able to allocate resources more effectively, enhancing the robustness and performance of the final IoT solutions.  
The complexity of integrating AHP within large-scale IoT projects remains a challenge, particularly when dealing with highly interconnected and dynamic environments. Additionally, the dependency on expert input for initial requirement grouping may introduce a degree of subjectivity, which could affect the replicability of the process across different teams or projects.
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
## 6 Verifying of DCSS Protocol
### Improved Version of DCSS Protocol  
Some changes were made in the modelling of the protocol to address the security threats discovered in the previous version and an improved version of the DCSS protocol is presented. To improve this protocol the \(UE\) send ID (\(FID\)), hash \(hFID\) and to \(SPAAA\) in message 1 and to \(SS\) in message 3, \(SPAAA\) sends ID (\(SPID\)) and hash \(hSPID\) to \(UE\) in message 2 and \(SS\) sends ID (\(SSID\)) and its hash \(hSSID\) in message 4. This prevents the attacker from using the information it has on \(UE\) and impersonating the \(UE\) when trying to start sessions with other entities.  
After changes were made, the protocol was modelled and run in ProVerif again, there was no attack found as shown in Figure 6, hence the protocol is secure. The ProVerif results indicate that the secrecy of Ack_1, Ack_2, Fid, d1, ChT, ShT holds as shown in Table 4. The authentication events in form of non-injective and injective agreements hold that is query []; event (endSS()) event(beginUE()) is true.  
Figure 5: DCSS Protocol Attack Trace.
# Pre and Postprocessing for JPEG to Handle Large Monochrome Images
## 3 Experimental Results
### Test 4  
The standard JPEG method and the proposed method are applied to the high quality image shown in Figure 3d to show the advantages of the proposed method in this test, and the simulation results are given in Table 3.  
Here, two simulations are considered, these are:  
* When Q for the proposed method is high (=88), then the proposed method is +3.7 dB higher than JPEG with the same CR value.
* When Q for the proposed method is low (=20), then the proposed method is +2 dB higher than JPEG and the CR value for the proposed method is more than four times that for JPEG.  
Table 3 shows these two simulations, showing that the proposed method is more efficient for high CR and high quality images. Generally, the standard JPEG has CR \(<\) 64 (with PSNR = 29 as in the image in Figure 3d in Test-2 while the proposed method has CR \(<\) 300 (with PSNR = 28 as in the image in Figure 3d in Test-4.  
The curves shown in Figure 5 represent the relationship between the PSNR and CR for JPEG and the proposed algorithm measured for the image given in Figure 3d and used in Test-4. The curves show that, for this large image, the new approach achieves better compression ratio than ordinary JPEG. Figure 6 shows the result at CR (=74) with magnification for the original image, image of JPEG method, and our method image, respectively. It is obvious that the features of the reconstructed image using our proposed method show less blocking effects and blurring artifacts in comparison with the JPEG method for the same image size.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image (d)** & **Simulation A** & **Simulation B** \\ \hline Original size & 1.97 M & 1.97 M \\ PSNR of JPEG & 26.45 & 26.14 \\ PSNR of proposed method & 30.17 & 28.15 \\ SSIM of JPEG & 0.7797 & 0.7708 \\ SSIM of proposed method & 0.8736 & 0.8147 \\ Size using JPEG & 27.8 k & 26.6 k \\ Size using proposed & 28.1 k & 6.47 k \\ CR using JPEG & 71 & 74 \\ CR using proposed & 70 & 304 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of Test 3.  
Figure 5: PSNR vs. CR for JPEG only and the proposed algorithm.  
Figure 6: Simulation results for the same CR (=74), and the image magnification for the same part. (**a**) original Image, (**b**) zoom of selected of a, (**c**) JPEG method result, (**d**) zoom of selected part of \(c\), (**e**) our method result, (**f**) zoom of selected part of \(e\).
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Color (OC)
#### 2.5.2 Summary and Future Direction  
The future of OC using RS methods heavily relies on our ability to plan beyond a single sensor mission and to provide long-term, high-quality, traceable satellite reflectance measurements. These capabilities along with coupling between missions as well as calibration and validation exercises could result in having more reliable multi-decadal datasets. Along with the parameters derived from OC, many satellite-derived variables, including photosynthetic-active radiations, OSW, rainfall, and OSS could be combined to provide better opportunities for studying OC.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 2 Principles of Grounding Systems
### Regulatory Standards and Guidelines
#### 2.2.2 Regulations for Earthing Arrangements  
IEC 60364-5-54, which is concerned with earthing of low-voltage electrical installations, provides guidance on these aspects, such as minimum sizes of earthing conductors and other parameters such as impedance. This standard is formulated in order that earthing systems can adequately deal with fault currents without excessive rise in voltage or damage to any connected equipment. Table 1 shows the IEC 60364-5-54 Regulations for Earthing Arrangements.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Clause** & **Regulation Aspect** & **Description** \\ \hline
54.1 & General Requirements & Specifies the need for protective earthing and equipotential bonding to ensure electrical safety. \\ \hline
54.2 & Types of Earthing Systems & Defines TN, TT, and IT earthing systems and their respective grounding methods. \\ \hline
54.3 & Earth Electrodes & Specifies types (rod, plate, strip, foundation earth) and installation guidelines. \\ \hline
54.4 & Protective Conductors & Defines cross-sectional area requirements and materials (copper, aluminum, galvanized steel). \\ \hline
54.5 & Equipotential Bonding & Requires bonding of exposed conductive parts and extraneous conductive parts to minimize touch voltages. \\ \hline
54.6 & Earthing Resistance & Establishes limits for ground resistance to ensure safe fault current dissipation. \\ \hline
54.7 & Connection Methods & Specifies bolted, welded, or exothermic welding for secure electrical continuity. \\ \hline
54.8 & Lightning Protection Integration & Coordination with IEC 62305 for integrating grounding systems with lightning protection. \\ \hline
54.9 & Testing and Verification & Mandates periodic ground resistance testing and documentation. \\ \hline \end{tabular}
\end{table}
Table 1: IEC 60364-5-54 Regulations for Earthing Arrangements [2].
## 3 Results
### Time Spectrum  
In experimental parameter modulation studies, the laser intensity (\(\mathbf{a_{0}}\)) and beam waist radius (\(\mathbf{\omega_{0}}\)) exhibit significant synergistic modulation effects on the energy distribution and temporal characteristics of time-resolved spectra. Figure 9 shows our amplification diagram of the peak value of the time spectrum. When both parameters are within low ranges (e.g., \(\mathbf{a_{0}}=1\), \(\mathbf{\omega_{0}}=1\)), the energy distribution of the temporal spectrum displays broad and flat features, with a low-intensity main peak accompanied by multiple secondary peaks, reflecting a weakly coupled interaction between electrons and the laser field.  
As \(\mathbf{a_{0}}\) and \(\omega_{0}\) are independently increased, observations reveal that under a fixed laser intensity (\(\mathbf{a_{0}}\)), varying \(\mathbf{\omega_{0}}\) from 1 to 9 causes energy changes spanning up to five orders of magnitude (minimum: zero orders). Conversely, under a fixed \(\mathbf{\omega_{0}}\), increasing \(\mathbf{a_{0}}\) from 1 to 9 leads to energy variations of up to eight orders of magnitude (minimum: three orders). These results demonstrate that a gradual increase in \(\mathbf{a_{0}}\) induces significant increases, by orders of magnitude, in the peak energy of temporal spectra, with its dominant regulatory role far exceeding that of \(\mathbf{\omega_{0}}\). The intense laser field significantly elevates the electron acceleration gradient, forcing radiation energy to concentrate within a narrower temporal window. This manifests as a sharp increase in the main peak intensity and a marked compression of the pulse width. Under fixed values of \(\mathbf{a_{0}}\), enlarging \(\mathbf{\omega_{0}}\) reduces the number of secondary peaks in the temporal spectra, transforming the main peak profile from an asymmetric tailed shape to a nearly symmetric Gaussian-like structure.  
Notably, the simultaneous increase in \(\mathbf{a_{0}}\) and \(\mathbf{\omega_{0}}\) further enhances the optimization of temporal spectra. When both parameters are raised in tandem, the temporal spectra not only achieve extremely high peak energy but also exhibit a significantly reduced full width at half maximum (FWHM), indicating ultrafast, high-intensity energy concentration in the temporal domain.  
Figure 8: Under the condition when \(\mathbf{\omega_{0}}=9\). Comparison and translation of the electronic trajectory diagrams, where \(\mathbf{a_{0}}\) is 1, 5, and 9, respectively.
# A Review of Transformer-Based Approaches for Image Captioning
## 7 Challenges and Future Directions
### Better Multimodal Alignment  
Image captioning is one of various tasks that have shown the power of models incorporating different modalities. In this case the models entail the vision and the language domains. A key challenge in this regard is coming up with better ways to improve the cross-modal representations of the different domains viz. vision and language domains. [PERSON] et al. [38] pointed out the lack of grounding, i.e., the lack of explicitly labeled alignments between image regions and words in corresponding texts. Closely related to this is the ambiguity of the visual representations in the semantic embedding space. The ambiguous and overlapping region features in the embedding space are illustrated in Figure 12. [PERSON] et al. [38] proposed the use of object tags for a better alignment. In [112]_pivotal keywords_, which take the style into account, are proposed for better alignment. The use of large-scale text-image-pair datasets for vision-language pre-training using contrastive and masked language modeling losses also considerably assists in improving the multimodal alignment. There is, however, a need for more research on better ways of achieving the alignment  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **In** & **Near** & **Out** & **Overall** \\ \hline OSCAR [38] & 84.8 & 82.1 & 73.8 & 80.9 \\ VIVO [40] & 89.0 & 87.8 & 80.1 & 86.6 \\ VinVL [39] & 98.0 & 95.2 & 78.0 & 92.5 \\ LEMON [52] & 112.8 & 115.5 & 110.1 & 114.3 \\ SimVLM [103] & 113.7 & 110.9 & 115.2 & 112.2 \\ CoCa [110] & - & - & - & 120.6 \\ GIT [111] & 122.4 & 123.9 & 122.0 & 123.4 \\ GIT2 [111] & 124.2 & 125.5 & 122.3 & 124.8 \\ UniversalCap [112] & 118.9 & 120.6 & 114.3 & 119.3 \\ PaLI [55] & - & - & - & 124.4 \\ BLIP-2 [113] & 123.7 & 120.2 & 124.8 & 121.6 \\ \hline \hline \end{tabular}
\end{table}
Table 10: CIDEr scores for VLP models on the Nocaps test split dataset.  
between region features and words as this will result in clearer and more precise generated captions for images.
## 1 Introduction
### Research Contributions  
The results of this study are categorized into two main areas. First, a specific methodology for the implementation of DTs in production and service systems was developed. This methodology is supported by modular architecture and an adaptable framework, facilitating its deployment in various industrial contexts beyond waste management. This versatility opens possibilities for extending the methodology to fields such as advanced manufacturing, wastewater treatment, and precision agriculture. However, implementing it in new sectors presents risks, such as the need for highly skilled personnel, the initial investment in technological infrastructure, and the management of large data volumes, which may pose barriers to adoption. Second, the methodology was validated in a practical case study at a composting facility in Cajamarca, Colombia, yielding quantifiable results: a 10% increase in process efficiency and a significant reduction in variability between processed batches, achieving an increase of 1200 kg in monthly compost production. Given the complexity and variability of organic waste transformation processes, rural composting facilities present an ideal context for testing adaptable and low-cost DT solutions. Focusing on a single case allows for in-depth validation under real-world conditions, enabling precise control and iterative development aligned with the goals of this research. These results confirm the effectiveness of the proposed model, highlighting its potential to enhance operational efficiency and promote sustainability across various sectors.
## 2 Modeling and Computational Details
### Nanoparticle Flow Modeling  
It is widely accepted to ignore the axial conduction for fluid flows in a channel when the Pe value is greater than 100 [37]. Furthermore, pressure work and viscous dissipation were neglected in the current study due to the low resulted value of the Brinkman number of the investigated cases (Br = \(\mu\text{u}_{\text{m}}^{2}/\text{q}_{\text{w}}\text{R}<0.01\)). The following single-phase, axisymmetric governing equations of continuity (Equation (1)), momentum (Equations (2) and (3)), and energy (Equation (4)) were adopted in the current study.  
\[\frac{1}{r}\frac{\partial(vr)}{\partial r}+\frac{\partial u}{\partial z}=0 \tag{1}\]  
\[\rho_{nf}\bigg{(}u\frac{\partial u}{\partial z}+v\frac{\partial u}{\partial r }\bigg{)}=-\frac{\partial p}{\partial z}+\mu_{nf}\bigg{(}\frac{\partial^{2}u }{\partial z^{2}}+\frac{\partial^{2}u}{\partial r^{2}}+\frac{1}{r}\frac{ \partial u}{\partial r}\bigg{)} \tag{2}\]  
\[\rho_{nf}\bigg{(}u\frac{\partial v}{\partial z}+v\frac{\partial v}{\partial r }\bigg{)}=-\frac{\partial p}{\partial z}+\mu_{nf}\bigg{(}\frac{\partial^{2}v }{\partial z^{2}}+\frac{\partial^{2}v}{\partial r^{2}}+\frac{1}{r}\frac{ \partial v}{\partial r}\bigg{)}-\frac{v}{r^{2}} \tag{3}\]  
\[\rho_{nf}C_{Pnf}\bigg{(}u\frac{\partial T}{\partial z}+v\frac{\partial T}{ \partial r}\bigg{)}=k_{nf}\bigg{(}\frac{\partial^{2}T}{\partial z^{2}}+\frac{ \partial^{2}T}{\partial r^{2}}+\frac{1}{r}\frac{\partial T}{\partial r}\bigg{)} \tag{4}\]  
The assumption of single-phase simulation was adopted because air velocity was more than one million times the Al\({}_{2}\)O\({}_{3}\) nanoparticles' relative velocities. Moreover, the resulted Stokes number (St) was much less than one, which confirmed that the particle will follow the air flow very closely [38]. Therefore, it was valid to assume that Al\({}_{2}\)O\({}_{3}\) nanoparticles and air were in homogenous and stable states and could be treated as a new fluid with new thermophysical properties like those listed in Table 1. The properties of the resulting  
Figure 1: Schematic of the simulated pipe with boundary and axisymmetric details.  
air-Al2O3 nanofluid were calculated based on Equations (5)-(9) [4; 34]. These include the density (\(\rho_{\rm nf}\)), specific heat (C\({}_{\rm Pnf}\)), thermal conductivity (\(\rm{k_{nf}}\)), viscosity (\(\rm{\upmu_{nf}}\)), and thermal expansion coefficient (\(\rm{\upbeta_{nf}}\)) as a function of volume fraction (\(\rm{\upphi}\)) of the Al2O3 nanoparticles to the air. These values were considered constant during the CFD simulation, apart from \(\rho_{\rm nf}\) values, which were estimated based on the Boussinesq approximation.  
\[\rho_{nf}=(1-\phi)\rho_{f}+\phi\,\rho_{s} \tag{5}\]  
\[C_{Pnf}=(1-\phi)C_{pf}+\phi\,C_{ps} \tag{6}\]  
\[k_{nf}=k_{f}\frac{k_{s}+2k_{f}-2\phi\Big{(}k_{f}-k_{s}\Big{)}}{k_{s}+2k_{f}+ \phi\Big{(}k_{f}-k_{s}\Big{)}} \tag{7}\]  
\[\mu_{nf}=\frac{\mu_{f}}{(1-\phi)^{2.5}} \tag{8}\]  
\[\beta_{nf}=\frac{\phi\,\rho_{s}\beta_{s}}{\phi\,\rho_{s}+(1-\phi)\rho_{f}}+ \frac{(1-\phi)\rho_{f}\beta_{f}}{(1-\phi)\rho_{f}+\phi\,\rho_{s}} \tag{9}\]