esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Solution of the Deterministic Planning Problem
#### 4.1.2 Results for the Cases with Reliability Requirements
the additional infrastructure to cope with the additional energy is small, in contrast to Case 1, where the entire demand of the household must be met during a fault in the power grid, and greater capacity in microgrid assets, is required. In summary, the average savings that the different microgrids would accrue for supporting critical load during fault hours, at a specific protection level, such as 90%, and for 8 h of grid service interruption, are equivalent to 15.2% for one-client microgrids and 20.5% on average for microgrids of 10, 20 and 100 clients, compared to Case 1. This could represent a more economical alternative for the community, while at the same time it could be affected by the loss of comfort.
# Performance Analysis of Latency-Aware Data Management in Industrial IoT Networks +
## 6 Large-Scale Simulations  
The testbed environment gives us an important ability to test the methods on real conditions and derive useful indications. However, at the same time, it does not allow us to perform larger scale, or variable experiments, easily and fast. For this reason, we developed a simulation model based on the system modeling presented in Section 3. The simulation environment we use is MATLAB. We verify that the simulations are meaningful via validation, by comparing the results obtained with the simulation model to those of the testbed experiments, and then, we extend our performance evaluation through simulations.
## 5 Conclusions  
This study introduces the concept of a Heterogeneous encoder for the first time, integrating two different types of encoders to fully leverage their respective advantages. Additionally, this study designs a Demodulate Bridge to filter out redundant information in feature maps with low computational cost. In the decoder, we explore the ability of different convolution combinations to retain information about organs of various sizes. Based on the characteristics of the dataset labels, we classify medical datasets into multi-scale organ information datasets and single-scale organ information datasets. The experimental results demonstrate that MINTFormer maintains excellent performance across all types of datasets, indicating its great potential for further optimization and enhancement in deep learning applications for complex medical image segmentation tasks.  
However, MINTFormer still has certain limitations. Efficiency analysis indicates that while MINTFormer achieves outstanding results with large-scale models, its performance remains inferior to that of other state-of-the-art models when used in medium or small-scale architectures. The reliance on large models imposes higher hardware requirements in clinical settings, thereby limiting MINTFormer's generalizability. Therefore, future work will focus on lightweight optimizations to enhance its adaptability across diverse scenarios.  
Additionally, to expand MINTFormer's application scope, we have collected a substantial dataset of pancreatic cancer tissue slides. Upon completing the annotation process, we will evaluate MINTFormer's potential for pancreatic cancer detection.  
Conceptualization, X.Q., C.D.; methodology, X.Q., C.D.; software, X.Q., C.D.; validation, X.Q.; formal analysis, C.D.; investigation, X.Q.; resources, X.Q.; data curation, X.Q.; writing--original draft preparation, C.D.; writing--review and editing, X.Q.; visualization, X.Q.; supervision, X.Q.; project administration, X.Q.; funding acquisition, X.Q. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by STI2030-Major Projects (2021 ZD0201900), Guangxi Key R&D Project (Grant no. AA22068057).  
Not applicable.  
Data Availability Statement: Data available in a publicly accessible repository. This data can be found here: [[https://github.com/fdiskdc/mitformer](https://github.com/fdiskdc/mitformer)]([https://github.com/fdiskdc/mitformer](https://github.com/fdiskdc/mitformer)) (accessed on 11 June 2025).  
Special thanks to [PERSON] (School of Artificial Intelligence, Guangxi Minzu University, Nanning, Guangxi, 530300, People's Republic of China, e-mail: [EMAIL_ADDRESS]) and [PERSON] (School of Foreign Languages, Nanning Normal University, Nanning, Guangxi, 530100, People's Republic of China, e-mail: [EMAIL_ADDRESS]) for guidance in this research.  
The authors declare no conflicts of interest.
## 3 Results and Discussion  
### Equilibrium Phases  
The results of the thermodynamic modeling are presented in Figure 3 and indicate a duplex structure of 50% austenite and 50% delta ferrite at the sintering temperature of 1270 \({}^{\circ}\)C. This is in general very favorable for sintering and quite intentional, as diffusion in the bcc lattice is higher than in the fcc lattice and thus sintering activity can be increased to achieve a high density after sintering. Below the sintering temperature, the austenite content increases until an almost full austenitic microstructure is present at 975 \({}^{\circ}\)C. During further cooling, the embrittling sigma phase and small amounts of chromium nitride form in equilibrium. The formation of brittle sigma phase is a problem known in particular from duplex steels. However, the sigma phase generally forms only after long dwell times at high temperatures, so rapid cooling in the process can usually prevent its precipitation.  
Figure 3: Equilibrium calculation of different phase fractions as a function of temperature.
###### Abstract  
Determining the route from the starting point to the destination is one of the first tasks performed when planning a ship's voyage. Before the computer age, routes were plotted manually by seafarers based on maps. Nowadays, algorithms are used for this purpose, which make it possible to reach any port in the world. In scientific publications, one can mostly find algorithms that generate global routes based on historical weather and traffic data on major sea lanes. Such routes do not take into account the current hydrometeorological conditions in the area where the ship is currently located, so that disturbances generated by environmental forces can increase energy consumption. A solution to the problem can be local routing based on the currently prevailing hydrometeorological conditions. With this approach, it is possible to respond to dynamically changing sea conditions, determine the route along which the impact of environmental forces on the hull will be least severe and minimize fuel and energy consumption. This paper presents an algorithm that determines the local passage route of an offshore ship using the example of a vessel moving to an anchorage to drop anchor. The algorithm defines a grid of points between the start point (the vessel's current position) and the end point (the anchor position), and then determines the transition weights between each grid point based on the vessel's capability plots. Finally, a modified Dijkstra algorithm determines the route where the sum of the transition weights will be as small as possible. During the tests, it was found that the time needed to find the passage route depended on the chosen grid density of the waypoints and was as follows: for a 6 \(\times\) 6 grid--0.05 s, for an \(11\times 11\) grid--0.36 s, for a \(16\times 16\) grid--0.47 s and for a \(21\times 21\) grid--0.85 s. It was also found that the algorithm identified a route where the impact of environmental forces on the ship's hull was 13% less than the direct route to the destination, resulting in a 7.5% reduction in energy consumption. The operation of the algorithm for determining the passage route was demonstrated in the anchor design tool developed in the Unity3D environment.  
dynamic positioning system; route planning; [PERSON]'s algorithm; Unity3D
## 3 Methodology
### Encoder  
As shown in Figure 3, we used Res2 Net-50 [27] to construct the encoder, used for local feature extraction, and pretrained it first on the ImageNet [28] dataset. Given the aligned original input images T1 and T2, both of size \(3\times 256\times 256\), T1 and T2 passed through 5 stages (En1, En2, En3, En4, and En5, respectively) to obtain the multiscale feature representations, respectively. Here, the multiscale feature maps output after 5 stages of encoder are expressed as \(X_{A}^{i}\) and \(X_{B}^{i}\), \(i\) means the \(i\)th layer, and \(i\in\{1,2,3,4,5\}\). Their sizes were \(1/4\), \(1/4\), \(1/8\), \(1/16\), and \(1/32\) of the original size, respectively, and the channels were \(64\), \(256\), \(512\), \(1024\), and \(2048\). The \(X_{A}^{i}\) and \(X_{B}^{i}\) obtained from each level were fused to output the corresponding \(f_{i}\) through a simple but effective CAC module, and the specific output sizes of each stage are shown in Table 1. Owing to local correlation and translational invariance, traditional CNNs can effectively model local fine-grained information using prior information [29].  
Figure 2: Overview of DETDNet.
## 3 Results and Analysis of the Ground Test  
Due to the non-standard design of earth observation systems and wireless communication systems, the performance of products from different manufacturers varies greatly. Therefore, it is necessary to verify whether their performance meets the needs of real-time monitoring of natural disasters. The performance verification work is divided into two steps, namely the ground test and the flight test. The ground test involves simulating the flight environment through ground carriers to assess the performance of the orthographic stabilization platform and the transmission capability of the airborne mobile communication equipment.
## 1 Introduction
Fenton reaction conditions do not involve high dosages of chemicals, extreme temperature, and pressure. According to Equation (1), ferrous ions catalyze the decomposition of hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) in aqueous media to produce hydroxyl radicals for the degradation of organic materials [19]. \[Fe^{2+}+H_{2}\mathrm{O}_{2}\to Fe^{3+}+OH^{-}+OH^{\bullet} \tag{1}\]  
It follows that the Fenton process approaches outstanding performance in decomposition of the lignin content of natural fibers [20], which can generate microfibril flax fibers with reduced diameters that possess desirable properties for filter-based applications. Reducing fiber diameter is critical for enhancing performance in textile applications, since it leads to finer, smoother fabrics with improved softness and texture [21]. For the case of filter media, fibers with reduced diameter have a greater surface area, which yields improved entrapment of particles and contaminants [22]. For the case of polymer composites, the increased surface area of the fibers improves the fiber-matrix interaction, which leads to better stress transfer and mechanical performance of the composite [23]. Whereas the conventional Fenton process offers an effective chemical treatment method for natural fibers, there are sparse studies on the effect of Fenton treatment on the diameter of FFs for variable conditions [24; 25]. This research aims to develop a sustainable treatment process for natural fibers that is based on the Fenton AOR process to isolate micron-sized flax fibers to improve their physiochemical properties (e.g., fiber size and chemical functionality). Structural modification of fibers by the Fenton AOR process is proposed as an alternative modification method to improve separation efficiency of the filtration products based on alteration of surface chemistry and fiber diameter. In contrast to the conventional Fenton oxidation process, the modified Fenton treatment AOR is conducted on the surface of solid fibers under heterogeneous conditions. Consequently, the Fenton AOR process at solid surfaces may result in a reduced rate of chemical consumption and sludge generation, thereby improving the sustainability of the process [24]. To demonstrate the effectiveness of the modified Fenton AOR, treated flax fibers were characterized by several complementary methods: thermogravimetric analysis (TGA), spectroscopy (FT-IR and NMR), and microscopy (SEM). The results for the treated flax fibers were compared with a conventional alkali treatment process, where SEM was used to examine the surface of the raw and treated flax fibers. In turn, the Fenton AOR process reported herein is anticipated to contribute to a more sustainable route for the treatment of flax biomass to yield fiber products with improved properties for filter-based applications.
## 2 Materials and Methods
### Model Scenarios  
We tested three machine learning-based models against a physical model using the generated dataset. Each model's ability to capture both standard and near-critical point phase behavior was evaluated, as near-critical conditions introduce particular challenges for modeling (see Figure 1). P is pressure, T is temperature, zi is overall composition, xi is liquid fraction, yi is vapor fraction, and Ki is equilibrium ratio.  
\begin{table}
\begin{tabular}{l l l l} \hline  & **Pc (atm)** & **Tc (K)** & \(\mathbf{\omega}\) \\ \hline carbon dioxide (CO2) & 72.8 & 304.2 & 0.225 \\ \hline n-butane (C4) & 37.5 & 425.2 & 0.193 \\ \hline n-decane (C10) & 20.8 & 617.6 & 0.49 \\ \hline interaction coefficients & carbon dioxide (CO2) & n-butane (C4) & n-decane (C10) \\ \hline carbon dioxide (CO2) & 0 & 0.115 & 0.115 \\ \hline n-butane (C4) & 0.115 & 0 & 0.012 \\ \hline n-decane (C10) & 0.115 & 0.012 & 0 \\ \hline \end{tabular}
\end{table}
Table 1: Physical model inputs used to generate ML input dataset.  
1. Direct Model:In this scenario, a neural network was trained to directly predict the liquid phase composition \(x_{i}\) and vapor phase composition \(y_{i}\) for each component. The neural network was trained on the full dataset, with the inputs being temperature, pressure, and overall composition. The outputs were the mole fractions of the components in the liquid and vapor phases at equilibrium. Special attention was given to the model's performance near the critical point, where rapid changes in composition occur.  
2. Hybrid Model 1--neural network for Ki and Rachford-Rice:The first hybrid approach combined machine learning with physical modeling. The neural network predicted the equilibrium coefficients \(K_{i}=y_{i}/x_{i}\) for each component, where \(y_{i}\) and \(x_{i}\) represent the vapor and liquid mole fractions, respectively. Once the \(K_{1}\) values were predicted, the one single Rachford-Rice iteration was used to compute the phase compositions and generate tie-lines in the compositional space. This method retained physical consistency while benefiting from the speed of machine learning. The performance around the critical point was specifically monitored due to the high sensitivity of the \(K_{i}\) values in these conditions.  
3. Hybrid Model 2--neural network for logarithmic compositions:The second hybrid approach added another layer of transformation to the model outputs. Here, the neural network predicted \(\log(x_{i})\) and \(\log(y_{i})\), the logarithmic mole fractions of the components in the liquid and vapor phases. These outputs were exponentiated to recover \(x_{i}\) and \(y_{i}\), which were then used to compute the equilibrium coefficient \(K_{i}\). As in Hybrid Model 1, the Rachford-Rice iteration was applied to ensure consistency and generate accurate tie-lines in the compositional space. This method was also tested rigorously near the critical point, where minor differences in the predicted compositions can lead to significant errors.
## 7 Experimentation and Analysis
### Algorithm Performance Validation
#### 7.1.2 Algorithm Convergence Speed Comparison  
Figure 9 illustrates the optimization graphs for the 12 test functions, and based on the average curve change over 30 consecutive rounds of experiments with 200 iterations per round, the GLSOAG converges ahead of the BSA on test function F8 but at a comparable rate of convergence to the PSO and SOA, gaining an advantage in the early part of the iteration but then iterating at a sub-optimal rate of convergence. In addition, the convergence speeds on the remaining test functions all take the lead by a large margin early in the iteration.
# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
## 3 Proposed Methods: The SecureLLM Framework
This entails balancing noise injections harmoniously so that the privacy guarantees do not unduly degrade utility, in addition to coordinating the cryptographic schemes with the schedule cycles of decentralized aggregators to optimize latency. Additionally, comprehensive cross-layer vulnerability assessments and rigorous penetration testing will be performed in future work to proactively identify and address integration vulnerabilities. By addressing these effects of interactions directly and through a holistic approach to security evaluation, SecureLLM will be able to provide robust, useful, and long-lasting security guarantees throughout the entire LLM lifecycle. To address composite noise, cumulative delay, and unforeseen vulnerabilities that may arise from layering lightweight cryptography, decentralized fine-tuning, and differentially private inference, SecureLLM adopts a multi-pronged strategy. First, a systematic cross-layer parameter tuning mechanism is proposed to calibrate the noise injection levels, encryption schedules, and aggregation frequencies across the different layers. Adaptive algorithms continuously monitor performance metrics (e.g., throughput, latency, and error rates) and security parameters (e.g., privacy loss and anomaly detection scores), dynamically adjusting these settings to mitigate the effects of compounded noise and delay. Second, the system includes complete vulnerability assessment processes, including automatic penetration testing and anomaly detection, integrated to find and patch unforeseen vulnerabilities caused by inter-layer interactions. Finally, SecureLLM makes use of end-to-end cryptographic logging and audit trails to offer complete transparency throughout all layers so that any integration issues can be quickly diagnosed and fixed. All these controls ensure that the integrated system maintains its intended security guarantees without a quantifiable loss of performance. Each organization or data provider (labeled as a \"local node\") locally encrypts its gradients using lightweight symmetric ciphers (e.g., AES-GCM) and shares only encrypted updates with an MPC-based aggregator. The aggregator, operating under multi-party computation protocols, never decrypts individual gradients but combines them securely into a global model update. Key management for data encryption is handled via ephemeral key exchanges (as further described below). During inference, the model injects calibrated noise into the outputs (Section 3.3) to preserve user-level privacy. This layered pipeline prevents any single party from accessing raw data or unencrypted gradients while also thwarting membership inference attacks at inference time. Recent work on secure multi-party computation and robust aggregation provides practical methods for detecting malicious gradient injections and Sybil attacks [34,35].
# Developing Standard BIM Execution Plans for Complex Construction Projects
## 2 Methodology
* Empirical Validation Through Industry Survey: The survey data provide a means to validate the proposed BEP framework. By comparing the theoretical insights gained from document analysis and scientometric analysis with empirical data, this study can refine and adjust the framework to better align with industry practices and expectations. 1. Enhancing Reliability and Applicability Combining scientometric analysis with industry surveys enhances the reliability and applicability of the findings in several ways: (1) **Robust Data Foundation:** Scientometric analysis offers a robust, data-driven foundation by highlighting influential studies and key themes. This ensures that the proposed framework is supported by the most relevant and impactful research in the field. (2) **Empirical Validation:** Industry surveys provide empirical validation, ensuring that the theoretical framework is relevant and applicable. This dual approach bridges the gap between theory and practice, making the findings more reliable and actionable. (3) **Comprehensive Understanding:** By integrating both quantitative and qualitative data, this study achieves a comprehensive understanding of BEPs. The scientometric analysis provided a macro-level view of research trends, while the surveys offered micro-level insights into practical challenges and needs. 4. **Addressing Practical Challenges:** The empirical data from industry professionals highlight specific areas where BEPs currently fall short. This direct feedback informed the refinement of the framework, ensuring that it addressed real-world challenges and enhanced project outcomes. * **Integration of Emerging Technologies:** Provisions for integrating emerging technologies such as IoT, digital twins, and advanced data management systems should be incorporated into the proposed BEP framework. It should be ensured that the framework remains relevant as the industry evolves technologically.
# Joint Multi-Scenario-Based Earthquake and Tsunami Hazard Assessment for Alexandria, Egypt
## 2 Seismicity, a Seismotectonic Framework, and the Impact of Tsunamigenic Sources on Alexandria
Historical records, such as the devastating tsunamis of 365 CE and 1303 CE, highlight the potential for destructive tsunamis to impact the Egyptian coast. Studies have shown that large, shallow earthquakes along the Hellenic Arc, particularly those associated with normal and thrust faulting, can trigger significant tsunamis. While the historical record provides valuable insights, the relatively short timeframe limits our understanding of the long-term seismic and tsunami hazard potential of the region. \begin{table}
\begin{tabular}{c c c c c} \hline
**Date (CE)** & **Lat\({}^{\circ}\)** & **Lon\({}^{\circ}\)** & **Imax at Alexandria** & **Magnitude (Mw)** \\ \hline
320 & 31.20 & 29.90 & VI & 6.0 \\ \hline
21/06/365 & 35.06 & 24.94 & - & 8.3 \({}^{*}\) \\ \hline
04/796 & 32.00 & 31.00 & VI & 6.0 \({}^{*  
Figure 3: _Cont._  
Figure 2: Main tectonic boundaries in the Eastern Mediterranean region, illustrating the Hellenic Arc, Cyprus Arc, Rosetta fault (RF), Temsah fault (TF), and Hinge Zone fault which indicated by the black lines. The small yellow circles indicate the main cities. The yellow arrows indicate present-day stress in and around Egypt and are derived from Ali and Badreldin [31]. Detailed mapping of the fault is derived from [24; 36].
# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
## 3 Results
### Crack Detection Limit Analysis  
To assess the performance limitations of SegNet in crack analysis, this study employed a single original image, which was progressively downscaled to various resolutions for semantic segmentation. As illustrated in Figure 7, the first row displays the original image reduced to 224 \(\times\) 224 pixels. The second row presents the image downscaled to 425 \(\times\) 425 pixels, subsequently divided into 224 \(\times\) 224 pixel tiles. The third row follows the same procedure with an image size of 850 \(\times\) 850 pixels. The fourth and fifth rows show the image downscaled to 1700 \(\times\) 1700 pixels and segmented into 224 \(\times\) 224 pixel patches.  
In Figure 7, blue boxes delineate the coverage areas corresponding to the subsequent row's images, while yellow boxes highlight regions where cracks were no longer detected following downsampling. The results indicate that when the resolution is reduced from 1700 \(\times\) 1700 to 850 \(\times\) 850 pixels, SegNet fails to detect four crack regions. Further reduction to 425 \(\times\) 425 pixels results in the loss of one additional crack region. When the resolution is further downscaled to 224 \(\times\) 224 pixels, two more crack regions remain undetected. Upon examination of the yellow-marked areas and their corresponding downsampled versions, it was observed that cracks narrower than two pixels tend to become undetectable by SegNet.