esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search
## 4 Simulation Setup and Results
### Discussion  
The experimental results highlight the strengths and weaknesses of the MOILP and MOTS algorithms compared to other VM hosting algorithms. Both MOILP and MOTS consistently outperform others in VM hosting efficiency, resource utilization, and power consumption, with some trade-offs in execution time:  
* VM hosting efficiency: MOILP and MOTS host significantly more VMs, with MOTS hosting 32% more VMs than FFD and 11% more than FF at high loads. MOTS is nearly as efficient as MOILP, with only a 0.25% difference in hosted VMs. MOACO and MOGA perform better than other algorithms but still fall short of MOILP and MOTS.
* Residual resource wastage: MOILP and MOTS exhibit the lowest resource wastage, even as VM demand increases, while FF, FFD, and WF show the highest wastage due to their limited hosting capacity.
* Power consumption: MOILP and MOTS consume slightly more power than MOACO and MOGA due to higher VM acceptance rates. For example, MOTS consumes up to 6% more power than FFD but only 1-2% more than MOACO and MOGA, reflecting better resource utilization despite the increased energy use.
* Execution time: MOILP is five times slower than other VMP solutions, while MOTS offers the best balance between efficiency and execution time.  
Figure 15: Average percentage of storage usage among active PMs.  
In summary, MOILP and MOTS are the top-performing algorithms, excelling in hosting efficiency, resource utilization, and power consumption. While MOILP is time-consuming, MOTS strikes the best balance between performance and computational cost. Both algorithms, though slightly more power-hungry, offer superior VM hosting capacity and resource optimization, making them ideal for environments where these factors are prioritized.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 7 Future Directions and Recommendations
### Feedback Mechanisms for Continuous Improvement  
Effective deployment of Large Language Models (LLMs) in maritime operations necessitates robust feedback mechanisms that allow continuous learning and improvement. Systematic feedback from end users, such as crew members, operators, and maritime managers, is essential for refining model outputs and ensuring alignment with operational realities. This feedback can help address issues such as inaccuracies, contextual misunderstandings, and usability challenges.  
One approach involves implementing real-time feedback loops during operational use. For instance, users can provide feedback directly through model interfaces, flagging outputs that are incorrect, unclear, or require additional context. Such feedback can then be logged and analyzed to identify patterns of errors or areas where the model's performance needs enhancement. Aggregated feedback from multiple users over time can provide insights into recurring issues, which can inform model retraining and fine-tuning.  
Additionally, structured feedback sessions can be organized as part of routine maritime operations. During these sessions, users can review model outputs, discuss their effectiveness, and propose improvements. Feedback collected during simulations, training exercises, or live operations can be particularly valuable in identifying practical gaps in the model's application. This structured approach ensures that feedback is not only reactive but also proactive, capturing a wide range of use cases and potential edge cases.  
To ensure that feedback mechanisms are actionable, it is important to integrate feedback management systems that categorize and prioritize user input. Such systems can employ natural language processing (NLP) techniques to analyze feedback data, clustering them by themes or common issues. For example, frequently flagged issues related to navigation recommendations or regulatory compliance can be escalated for immediate review, while less critical feedback can be used for long-term optimization.  
Incorporating feedback into LLM development workflows also requires establishing clear protocols for retraining and validating models. Feedback should be incorporated into new training datasets, ensuring that model updates address identified weaknesses while maintaining performance in other areas. Furthermore, integrating Explainable AI (XAI) techniques can enhance the feedback process by enabling users to understand how models generate specific outputs, making it easier to identify inaccuracies or suggest refinements.  
By systematically gathering and utilizing feedback, LLMs can evolve to better meet the complex demands of maritime operations. These mechanisms not only enhance the accuracy and reliability of model outputs but also build trust among users, fostering greater acceptance and integration of LLMs in the maritime industry.
## 3 Results and Discussion  
### Microstructural Analysis of the Specimens in the as-Built Condition  
The representative OM micrographs of the microstructural features of the single tracks in the as-built condition, at the interface between the depositions and the AISI 316L substrate, are depicted in Figure 2. It can be seen that the single tracks exhibited a microstructure primarily composed of a lathy martensitic matrix, with a high amount of lathy \(\delta\)-ferrite at the boundary between the steel plate and the depositions. The \(\delta\)-ferrite was mainly localized at the interface because of the highest heat dissipation through the substrate. This is in agreement with other research [9, 41] that proved that the microstructure of L-DED 17-4 PH steel is a result of the high heating and cooling rates experienced during the AM process (higher than those achieved during conventional ones), which prevent the formation of austenite [33, 42, 43]. Specifically, in the L-DED deposition of tracks on a metallic substrate, the highest cooling and solidification rates are found across the substrate/track interface, gradually decreasing with the distance from the substrate. Hence, in 17-4 PH single-track depositions, the high G\(\cdot\)R ratios (G = thermal gradient (\({}^{\circ}\)C/mm), R = growing speed (mm/s)) at the interface are expected to generate a fine martensitic matrix, but also a high amount of retained \(\delta\)-ferrite  
The X-ray diffraction pattern recorded on the single tracks in the as-built condition is shown in Figure 3. Considering the identified peaks, the presence of martensite (\(\alpha\)/) and austenite (\(\gamma\)) was confirmed; however, it should be noted that the austenite peaks can be attributed to the austenitic microstructure of the substrate due to the limited area of the deposited tracks. The \(\delta\)-ferrite peaks were not identified because of the overlap with the crystallographic features of the \(\alpha\)/-martensite, as already observed by other authors [43, 44, 45]. No other peaks related to the presence of strengthening precipitates were detected.  
Figure 2: Representative OM micrograph of the microstructural features of the single tracks in the as-built condition, at the interface between the depositions and the AISI 316L substrate.
## 2 Related Work
### Image Inpainting

Table 1: Overview of methods for image inpainting and light-field occlusion removal, detailing their key features and limitations. semantically coherent structures for larger missing areas. Recent advancements in deep learning algorithms, along with the availability of extensive datasets of single RGB images, have enabled the effective filling of these masked regions without needing information beyond the mask. Reference [11] introduced a specialized convolutional layer structure called Partial Convolution, which plays a significant role by encoding contextual features and avoiding artifacts from invalid pixels within the masked region through masked and re-normalized convolution. Building on this, ref. [48] introduced Recurrent Feature Reasoning (RFR), which is a two-stage model based on hierarchical vector-quantized variational auto-encoder for reconstructing large continuous holes by recurrently inpainting parts of the image and averaging the generated feature groups when they contain no invalid pixels. Another method, developed by [49], involves Learnable Bidirectional Attention Maps (LBAMs). This method replaces Partial Convolution with attention mechanisms in both the encoder and decoder, allowing the decoder to focus on filling only the masked regions. Unlike Partial Convolution, LBM uses soft attention maps and differentiable mask updates, which provide greater flexibility and stability during model training. Despite these improvements, single-image inpainting techniques often require large datasets for training and can struggle with reconstructing complex occlusions solely based on learned semantics. In the context of light-field imaging, single-image inpainting has been used to address light-field completion, which involves filling entire light-field views with consistent information. Instead of directly addressing the 4-D manifold, refs. [50; 61] applied single-image inpainting to the central view (CV) image and propagated the information to the remaining views. Their approach used the single-image inpainting method for the CV image with a given inpainting mask, focusing on transferring the inpainted information to other views within the light field. Other methods have incorporated additional information such as edges and segmentation masks to improve the inpainting results.
## 3 Parametric Study for Determining the Optimal Sampling Strategy
### Sampling from a Domain  
This sampling strategy refers to data referring to continuous probing tests (e.g., the Cone Penetration Test or Standard Penetration Test). The length of the sampling domain is always measured from the soil surface, whilst arithmetic mean values are used for the various soil properties in the Finite Element Method (FEM) analysis (see Section 2).  
Since each (finite) element (see Figure 1) has edge 0.1 length units (in this respect, meters), sampling is considered to take place every 0.1 m (along the vertical direction). The minimum and maximum sampling domain length considered were 0.1 m (rather referring to a single point) and 3.4 m, respectively. It is noted that for all cases examined in this section, the optimal sampling distance was found again to be at x/\(H\) = 0. Thus, for space economy, the analysis below generally refers to the x/\(H\) = 0 case.
# A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy
## 3 Methodology
### RGB-to-J Component of CIECAM02 Model Conversion  
There are numerous color space models available in the image processing field, such as HSV, HSI, CIELAB, CIE L*U*V, CIECAM97s, and many more for the better visualization of image features. This work adopted an enhanced version of the CIECAM97s model, called CIECAM02, due to its strength in grayscale with high rates of brightness. It also has the ability to measure color differences in diverse ranges of viewing coordinates and to provide uniform color spaces. The CIECAM02 color model was developed by the Commission International Eclairage (CIE) in 2002 to overcome the challenges of the CIECAM97s model. The CIE is responsible for releasing color appearance models for color management systems. The CIECAM02 has the potential to anticipate color appearances, especially  
Figure 4: Structure of the proposed fundus image enhancement scheme.  
chromatic adaption in dynamic ranges of viewing conditions. It has six different color appearance modules, such as brightness \(Q\), lightness \(J\), colorfulness \(M\), chroma \(C\), saturation \(S\), and hue \(H\)[27].  
Although our objective was to enhance the contrast of fundus images, the first step of the proposed scheme was the conversion of an input RGB fundus image into lightness, denoted as \(J\), an attribute of the CIECAM02 model. This respective \(J\) component contains gray information with an eminent level of brightness. Initially, an RGB image was separated into red, green, and blue channels, and then stored each channel value in a vector. Afterwards, tristimulus values were computed in order to get a \(J\) component of the CIECAM02 model. Equation (1) illustrates the tristimulus value calculation process:  
\[\left(\begin{array}{c}X\\ Y\\ Z\end{array}\right)=\left[\begin{array}{ccc}0.41\,0.35\,0.18\\ 0.21\,0.71\,0.07\\ 0.01\,0.11\,0.95\end{array}\right]\cdot\left[\begin{array}{c}R^{\prime}\\ G^{\prime}\\ B^{\prime}\end{array}\right]. \tag{1}\]  
In the above Equation (1), tristimulus values, denoted as \(XYZ\), were obtained by the multiplication of \(R^{{}^{\prime}}\), \(G^{{}^{\prime}}\), \(B^{{}^{\prime}}\) values (i.e., stored in a vector) with chromaticity values, which were taken from Reference [28]. Next, the computed \(XYZ\) values were then used in Equations (2) and (3) to get the \(J\) module of the CIECAM02 model as follows:  
\[F_{XYZ}(x,y,z)=srgb2 xymz\,(f_{y}\,(x,y,z)), \tag{2}\]  
\[F_{J}(x,y,z)=xyz2 cam\,(f_{XYZ}\,(x,y,z)). \tag{3}\]  
In Equation (2), \(srgb2 xymz\) is a standard red, green, and blue colorimetry, used here as a function of the vectorized tristimulus values. Finally, the \(J\) attribute was achieved using an \(xyz2 cam\) function in Equation (3), applied to acquired tristimulus values.
# 6G to Take the Digital Divide by Storm: Key Technologies and Trends to Bridge the Gap
## 3 The Technologies for Bridging the Service-Delivery Divide
### Non-Terrestrial Networks  
Service continuity and scalability are two prerogatives of ubiquitous connectivity that can be fostered, in 5G and beyond systems, with the integration of terrestrial and non-terrestrial networks (NTN) [12]. NTNs encompass spaceborne and airborne systems. Satellites may be either geostationary (i.e., GEO) or orbit around the Earth at low or medium orbits (i.e., LEO and MEO). Among LEO satellites, a growing interest has risen towards CubeSats, a new class of miniaturized satellites known for their very small dimension and low cost, hence enabling their deployment in mega-constellations to provide global connectivity and large throughput. Equally interesting are Unmanned Aerial Vehicles (UAVs) that may be deployed in swarms to provide an on-demand aerial infrastructure where needed [13]. Based on these features, NTNs can come into play to mitigate the digital gap, by ensuring connectivity to a massive number of Internet of Everything (IoE) devices where terrestrial networks can fail, therefore in disadvantaged areas, in emergency scenarios, and highly crowded environments.  
Figure 2: The evolution of the Digital Divide.
## 4 WiFi 6/6E: IEEE 802.11 ax
### Target Wakeup Time (TWT)  
Target wakeup time (TWT) was a technique adopted from the 802.11 ah standard, and was introduced to conserve the power of energy-constrained IoT devices/sensors deployed at smart homes. With TWT, devices can negotiate wake up and transmission times and stay in sleep mode for the remainder of the time. Since TWT also helps minimize channel contention, WiFi 6 can support a massive number of indoor IoT devices without affecting general users.  
With the aforementioned design changes, WiFi 6 was able to support high data rates, low latency, and highly reliable connectivity to cater for 6G applications in indoor environments. Many network equipment vendors such as Cisco, tp-link, and Netgear are already shipping WiFi 6 routers and other equipment [40; 41; 42]. On the other hand, consumer device manufacturers such as HP, Dell, Samsung, and Apple have integrated the WiFi 6 support in their latest devices [43; 44]. Furthermore, the adoption rate of WiFi 6E is also increasing around the globe [45]. Most countries have adopted the 5925-6425 MHz range while considering extending it to adjust bands as well.
## 4 Analysis of Sound Radiation Characteristics for Reinforced Laminated Cylindrical Shell Structures
### Effects of Reinforcement Length  
This section will explore the influence of reinforcement length on the radiated noise of laminated cylindrical shell structures. Based on the cylindrical shell model with 16 circumferential rib reinforcements, with other parameters unchanged, cylindrical shell structures with reinforcement lengths of 28 mm, 30 mm, and 32 mm are selected for radiated noise calculation, and the results are shown in Figure 16.  
From the graph, it can be observed that with the increase in the length of circumferential rib reinforcements, the sound power level curve shifts towards higher frequencies. Compared to the results for reinforcement lengths of 28 mm and 30 mm, the sound power level curve for a reinforcement length of 32 mm shifts towards higher frequencies, resulting in fewer radiated peaks at lower frequencies. From the perspective of the overall sound power level, the total sound power levels for reinforcement lengths of 28 mm, 30 mm, and 32 mm in the reinforced cylindrical shell are 80.14 dB, 79.17 dB, and 80.28 dB, respectively, indicating that the best performance is achieved with a reinforcement length of 30 mm. The results indicate that with the linear increase in the reinforcement length, the sound power level of the cylindrical shell structure does not exhibit a linear change. This may be attributed to the fact that the reinforcing ribs connect the inner and outer shells, playing a role in fixing the inner and outer shells and transmitting vibrations. Therefore, with the increase in the reinforcement length, the stiffness of the cylindrical shell increases, and the cavity between the inner and outer shells also enlarges. At this point, the excitation force is located between the ribs, leading to enhanced shell-to-shell coupling. When the reinforcement length increases to a certain value, shell-to-shell coupling dominates, which in turn enhances the vibration of the cylindrical shell and increases the structural sound radiation. Therefore, the length of the reinforcement should be reasonably selected within the corresponding range of total sound power levels.
###### Abstract  
In the present work, a TiO\({}_{2}\)/zeolite photocatalyst was synthesized by dispersing TiO\({}_{2}\) nanoparticles obtained through the sol-gel method onto the surface of natural zeolite derived from inginimbrite residue. The zeolite was obtained from an inginimbrite rediscument collected from a quarry in Arequipa City, Peru. The research focused on the effect of zeolite on the TiO\({}_{2}\) nanoparticles. The synthesized photocatalysts were characterized using various techniques, including field-emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray (EDS), X-ray diffraction (XRD), diffuse reflectance spectroscopy (DRS), and Brunauer-Emmett-Teller surface area analysis (BET). The results revealed that the TiO\({}_{2}\)/zeolite samples displayed high crystallinity, with TiO\({}_{2}\) being present in three phases and zeolite being present in the analclime phase. Furthermore, these samples exhibited a band gap of 3.14 eV and a high surface area compared to that of bare TiO\({}_{2}\). Finally, the photocatalytic activity of the TiO\({}_{2}\)/zeolite composite obtained was evaluated toward the decomposition of 10 ppm and 20 ppm of methyl orange (MO) dye. The TiO\({}_{2}\)/zeolite samples demonstrated improved photocatalytic activity compared to that of pristine TiO\({}_{2}\) under the same experimental conditions. This enhancement is primarily attributed to the increased specific surface area of the TiO\({}_{2}\)/zeolite samples, making them promising materials for future efficient and sustainable photocatalytic applications.  
titanium dioxide; analclime zeolite; ignimbrite; photocatalysts; nanomaterial +
Footnote †: journal: Applied Sciences
## 3 Numerical Simulations Based on Matlab/Simulink for the Proposed PMSM-SCS Using DTC Strategy
### Numerical Simulation for PMSM-SCS Based on DTC Strategy, Using Constant Flux
In Figures 6-10 (updated) the performance of the FO-type controllers is shown, having a gradually increasing complexity, and it can be observed that there is improvement in the main parameters of interest in this case: response time and stabilization time. Also, for each figure, the following magnitudes are presented: electromagnetic torque, stator currents and fluxes in the reference frame \(\alpha\)-\(\beta\). It can be observed that, in accordance with the performance improvement of the control systems in question for the proposed reference variations, at the moments of the occurrence of jumps in the reference, a slight increase in the electromagnetic torque appears, while the shapes of the currents and fluxes remain, sensitively, the same shape. A comparison of the evolution of the PMSM rotor speed for the sensorless control system based on the DTC strategy using PI, PI-EOA, FOPI, TID and FO-Lead-Lag speed controllers at constant flux is shown in \[H_{FOPI}(s)=\frac{6.2s^{1.1}+24}{s^{1.1}} \tag{53}\]4 It can be seen that the use of FO-type controllers improves the performance of the control system, so that the best performance in the comparison shown is provided by FO-Lead-Lag speed controllers, followed by TID and FOPI speed controllers. \[H_{FOPI}(s)=\frac{6.2s^{1.1}+24}{s^{1.1}} \tag{53}\]5 Parameter evolution of the PMSM-SCS based on DTC strategy using FO-Lead-Lag controller at constant flux. \[H_{FOPI}(s)=\frac{6.2s^{1.1}+24}{s^{1.1}} \tag{53}\]6 Parameter evolution of the PMSM-SCS based on DTC strategy using TID controller at constant flux.
## 3 Test Results and Analysis
### XRD Analysis  
An XRD test was carried out on sample A after it was stored at 60 \({}^{\circ}\)C for 4 h and 180 \({}^{\circ}\)C for 10 days, and the composition changes were compared and analyzed. The results are shown in Figure 6.  
Figure 4: The apparent morphology of TeO\({}_{2}\) before and after different temperature tests.  
Figure 5: The apparent morphology of fluororubber before and after different temperature tests.  
Figure 3: The apparent morphology of magnesium powder before and after different temperature tests.  
It can be seen from the XRD pattern of Figure 6 that the position of the characteristic diffraction peak of sample A is mainly concentrated at 25\({}^{\circ}\)-70\({}^{\circ}\), the agent stored at 180 \({}^{\circ}\)C for 10 days corresponds to the characteristic peak positions of samples stored at 60 \({}^{\circ}\)C for 4 h, and the diffraction peak shapes are similar. The order of the strongest peak and the second strongest peak changed, and the peak intensity changed, this phenomenon is related to the flatness of pressed sheet in XRD test, and there are defects in the crystal, but these are not the factors that affect the ignition ability of the composition. The characteristic peak diffraction angles of magnesium are mainly at 32\({}^{\circ}\), 34\({}^{\circ}\), 36\({}^{\circ}\), 47\({}^{\circ}\), 57\({}^{\circ}\), 62\({}^{\circ}\), and 68\({}^{\circ}\), and the characteristic peak diffraction angles of TeO\({}_{2}\) are mainly at 26\({}^{\circ}\), 29\({}^{\circ}\), 37\({}^{\circ}\), 48\({}^{\circ}\), and 55\({}^{\circ}\). After storage at 180 \({}^{\circ}\)C for 10 days, Mg(OH)\({}_{2}\) was found in the mixture (diffraction angles of about 18\({}^{\circ}\) and 38\({}^{\circ}\)); this was due to the reaction between magnesium and moisture in the air during the transfer process of the sample after the high-temperature test. Apart from this, no other new products were found.