esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Blockchain Technology for Enhancing Digital Twins' Security and Transparency
## 6 Discussion  
The results provided by this study demonstrated the viability of integrating blockchain technology with digital twins to enhance security, transparency, and performance in industrial applications. The implemented framework leveraged blockchain's tamper-proof nature to address critical vulnerabilities in digital twin systems, such as data integrity and unauthorized access. Security tests validated the robustness of the architecture proposed, effectively preventing unauthorized communication and ensuring data consistency. Moreover, the proposed case study illustrated the practical applications of this framework, showcasing how it could be deployed in real-world scenarios. For instance, fraudulent data transmissions were effectively intercepted, ensuring the reliability of the information used for decision-making in real time. Performance benchmarks also highlighted the scalability and adaptability of the system, particularly with the utilization of the QBFT consensus protocol, which demonstrated superior throughput and resilience under high transaction loads. Despite these advantages, challenges such as interoperability with existing digital twin solutions and the computational overhead of blockchain integration remain areas for further exploration. Future research could investigate lightweight blockchain models or Layer 2 scaling solutions to optimize system performance.  
Conceptualization, A.F. and S.V.; methodology, A.F. and S.V.; software, A.F. and S.V.; validation, A.F. and S.V.; formal analysis, A.F. and S.V.; writing--original draft preparation, A.F. and S.V.; writing--review and editing, A.F. and S.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
[1]**Data Availability Statement**: Source code is available at the following link: [[https://github.com/[PERSON]/Blockchain_Integration_AiWatch](https://github.com/[PERSON]/Blockchain_Integration_AiWatch)]([https://github.com/[PERSON]/Blockchain_Integration_AiWatch](https://github.com/[PERSON]/Blockchain_Integration_AiWatch)), accessed on 9 January 2025.  
[2]**Conflicts of Interest**: The authors declare no conflicts of interest.
###### Abstract  
(1) Background: Mercury is a threat for the aquatic environment. Nonetheless, the entrance of Hg into food webs is not fully understood. Macrophyres are both central for Hg entry in food webs and are seen as good candidates for biomonitoring and bioremediation; (2) Methods: We review the knowledge gained on the uptake and effects of inorganic Hg (IIHg) and methyl-Hg (MMHg) in the macrophyte _Elodea nuttallii_ found in temperate freshwaters; (3) Results: _E. nuttallii_ bioaccumulates Hfg and MMHg, but Hfg shows a higher affinity to cell walls. At the individual level, Hfg reduced chlorophyll, while MMHg increased anthocyanin. Transcriptomics and metabolomics in shoots revealed that MMHg regulated a higher number of genes than Hfg. Proteomics and metabolomics in cytosol revealed that Hfg had more effect than MMHg; (4) Conclusions: MMHg and IHg show different cellular toxicity pathways. MMHg's main impact appears on the non-soluble compartment, while Hfg's main impact happens on the soluble compartment. This is congruent with the higher affinity of IHg with dissolved OM (DOM) or cell walls. _E. nuttallii_ is promising for biomonitoring, as its uptake and molecular responses reflect exposure to IHg and MMHg. More generally, multi-omics approaches identify cellular toxicity pathways and the early impact of sublethal pollution.  
bioaccumulation; biomarker; biomonitoring; macrophyte +
Footnote †: journal: Applied Sciences
## 3 Step-Climbing Ability
### Step Climbing Height Analysis  
The height that the robot can climb steps depends on the effective radius \(R_{e}\) of the front wheels and the value of the height that the front swing arm can lift [21]. The robot climbs the low step height, as shown in Figure 6.  
\[h(\theta_{1})=R+L_{1}\text{sin}\theta_{1}-R_{2}\text{cos}\theta_{1} \tag{4}\]  
\[R_{e}=\frac{x}{2\text{sin}\beta\text{cos}\beta} \tag{5}\]  
\[\text{sin}\beta=\frac{x}{\sqrt{x^{2}+y^{2}}}\ \ \text{cos}\beta=\frac{h}{\sqrt{x^{2 }+y^{2}}} \tag{6}\]  
\[x=L_{1}\text{cos}\theta_{1}+R_{2}\text{sin}\theta_{1} \tag{7}\]  
\[R_{e}(\theta_{1})=\frac{L_{1}^{2}+R^{2}+R_{2}^{2}-2 RR_{2}\text{cos}\theta_{1} +2L_{1}R\text{cos}\theta_{1}}{2L_{1}\text{sin}\theta_{1}+2R-2R_{2}\text{cos} \theta_{1}} \tag{8}\]  
Figure 5: Step climbing gait planning for mobile robots. (**a**) Sketch of gait planning for a mobile robot climbing a low step. (**b**) Sketch of gait planning for a mobile robot climbing a high step.  
With the height of the climbing low step \(h(\theta_{1})\) as the objective function and the angle \(\theta_{1}\) between the centerline of the front swing arm and the horizontal plane as the variable, the height model of the climbing step is as follows.  
\[\left\{\begin{array}{cc}MAX\ Z=h(\theta_{1})\\ ST&h(\theta_{1})-R_{e}(\theta_{1})\leq 0\\ 0\leq\theta_{1}\leq\pi/2\end{array}\right. \tag{9}\]  
The height of the steps that the robot can climb when climbing high steps is determined by the effective radius of the main body and the front swing arm, as shown in Figure 7.  
As for the analysis of climbing high steps, the height and effective radius of climbing high steps are as follows.  
\[\delta=\theta_{2}-\gamma=arcsinR-R_{2}/L_{2} \tag{10}\]  
\[h=R+L\text{sin}(\theta_{2}-\delta)+L_{1}\text{sin}(\theta_{2}-\delta-\theta_{ 1})-R_{2}\text{cos}(\theta_{2}-\delta-\theta_{1}) \tag{11}\]  
\[x=L\text{cos}(\theta_{2}-\delta)+L_{1}\text{cos}(\theta_{2}-\delta-\theta_{ 1})+R_{2}\text{sin}(\theta_{2}-\delta-\theta_{1}) \tag{12}\]  
\[\begin{array}{cc}\left[R+L\text{sin}(\theta_{2}-\delta)+L_{1}\text{sin}( \theta_{2}-\delta-\theta_{1})-R_{2}\text{cos}(\theta_{2}-\delta-\theta_{1}) \right]^{2}+\\ R_{e}(\theta_{1},\theta_{2})=\frac{\left[L\text{cos}(\theta_{2}-\delta)+L_{1} \text{cos}(\theta_{2}-\delta-\theta_{1})+R_{2}\text{sin}(\theta_{2}-\delta- \theta_{1})\right]^{2}}{2\left[L_{1}\text{cos}(\theta_{2}-\delta-\theta_{1})+L \text{cos}(\theta_{2}-\delta)+R_{2}\text{sin}(\theta_{2}-\delta-\theta_{1}) \right]}\end{array} \tag{13}\]  
Taking the height of the climbing step \(h(\theta_{1},\theta_{2})\) as the objective function, we use the angle \(\theta_{1}\) (between the centerline of the front swing arm and the centerline of the middle  
Figure 6: Diagram of robot climbing low step height.  
Figure 7: Diagram of the robot climbing a high step height.  
the angle \(\theta_{3}\) between the centerline of the rear swing arm and the x-direction as variables. The overturning height model is as follows.  
\[\left\{\begin{array}{c}MAX\ Z=H(\theta_{1},\theta_{3})\\ -\frac{\pi}{2}<\theta_{1}<\frac{\pi}{2}\\ 0\leq\theta_{3}\leq\pi\end{array}\right. \tag{19}\]
# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
## 3 Classification of Phishing Detection Strategies
### Heuristic Approaches  
Heuristic methods detect phishing based on behavioral analysis and pattern recognition rather than rigid rules. These approaches consider attributes such as the following:  
* URL Heuristics: Anomalous URL lengths, presence of IP addresses instead of domain names, and suspicious subdomains;
* Content Analysis: Abnormal HTML structure, deceptive anchor texts, and inconsistencies in page layout [53];
* Sender Verification: Mismatched \"From\" and \"Reply-To\" addresses;
* Example of a Heuristic Scoring Algorithm: \[H(x)=\sum_{i=1}^{m}w_{i}\,*\,f_{i}(x)\] \[D(x)=\left\{\begin{array}{ll}1&\text{if }H(x)\geq\theta\\ 0&\text{Otherwise}\end{array}\right.\] where the following variables are used: \[H(x)\] is the heuristic score for email/website x. \[w_{i}\] represents the weight assigned to feature i. \[f_{i}(x)\] is the heuristic feature function (e.g., URL length and HTML tags). \[m\] is the total number of features analyzed. \[\theta\] is the predefined threshold determining classification as phishing (1) or benign (0).  
Challenges include the following: while heuristics are more flexible than rule-based systems, they often suffer from high false positive rates because legitimate emails/websites may unintentionally trigger heuristic rules.
## 4 Discussion
### Measurements on the VFs
Various measurement protocols (e.g., traction testing, shear rheometry, linear skin rheometry, indentation testing) have been used to estimate the elastic modulus of porcine VFs [1]. Using the traction testing approach on six porcine VFs, an elastic modulus of 16.3 (\(\pm\) 2.9) kPa was found [72]. Besides, [PERSON] et al. [46] measured an elastic modulus of 17.86 (\(\pm\) 7.91) kPa in the low strain region using a traction testing approach based on digital image correlation (DIC). Both methods are also static methods, which measure the properties ex vivo, so the results were comparable with ours. In Figure 6c, we see that the estimated elastic modulus in the present study was in the same range. On the other hand, a similar DIC-approach found a elastic modulus of 40.5 (\(\pm\) 15.7) kPa [73]. [PERSON] et al. [73] explained the inconsistency with the different measurement methods, which led to an underestimation of the elastic modulus in mechanical measurements. The indentation testing method reported lower values between 1.6 and 5.7 kPa [1]. Those values were below the estimated values in our work. This could be a consequence of the highly different measurement methods. Therefore, no conclusion can be drawn about the frequency dependency of the elastic modulus. Therefore, further research with vibration VFs would be necessary.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 6 Lessons Learned, Open Issues, and Research Challenges
### Lessons Learned
#### 6.1.7 Topology Prediction Accuracy and Localization Error  
In position-based forwarding, location error is an important issue which decreases the routing protocol performance significantly. In FANETs, UAVs usually exchange hello packets with one-hop or two-hop neighbors to update their mobility (position and velocity) and neighbor list [21; 22]. The hello interval defines how frequently UAVs broadcast their location information with their neighbors. A low hello interval gives an up-to-date position, and it also produces higher control overhead. In contrast, the high hello interval reduces the control overhead, but it reduces the location accuracy. Similarly, delay in hello broadcasting also causes UAVs to record the inaccurate position of their neighboring UAVs. Thus, the hello interval should be controlled according to the degree of mobility changes in FANETs, which can be estimated by predictive link duration [23]. This is because predictive link duration is the function of relative velocity, relative distance, transmission power, and flying directions in 3D space [29].
## 1 Introduction
### Context and Existing Limitations  
Despite the promise of LLMs and VLMs, adopting these technologies within hospital environments poses unique challenges. Privacy regulations [4] (e.g., HIPAA in the U.S. or Data 3 Laws in Korea) demand that patient data remain strictly confidential and within internal, controlled networks. Proprietary solutions, such as closed-source, external LLM services, are not only costly but often violate these stringent requirements by sending sensitive data off-premises.  
Although recent multimodal AI frameworks like HAIM [5] and MONAI [6] have gained traction for clinical decision support, they largely focus on traditional machine learning or specific imaging tasks (e.g., using XGBoost, DenseNet, or BERT-like text encoders). These approaches typically rely on smaller or specialized models that may not require the significant computational resources of modern LLMs and VLMs. Furthermore, such frameworks generally lack robust, hospital-wide deployment architectures capable of securely orchestrating multiple concurrent models at scale in an offline environment.  
In contrast, current state-of-the-art open-source frameworks designed for LLM deployments, such as OpenLLM, H2O LLM Studio, or RayLLM, still suffer from critical shortcomings in healthcare scenarios. These include inadequate privacy compliance, limited customization options, difficulty in scaling to hospital-wide deployments, and insufficient orchestration tools for managing multimodal workflows within an internal network. As a result, hospitals remain in need of a comprehensive, on-premises solution that can flexibly integrate large-scale LLM/VLM deployments into existing infrastructure while maintaining data security, scalability, and adaptability to evolving clinical needs [7; 8].
## 1 Introduction
The proposed method is designed to extract segmentation masks for all objects in panoramic images by leveraging the iterative execution of SAM and post-processing algorithms. This design allows for the accurate detection of both large and small objects, even under the challenging environmental conditions of indoor construction sites. Subsequently, a label assignment algorithm is developed to assign labels predicted by a target model to the detected object masks. For evaluation, plane segmentation was selected as the target task among various indoor construction segmentation applications, and Panoplane360 [19] was adopted as the target model. The proposed method was applied to the target model, and both quantitative and qualitative comparisons were conducted to verify improvements in segmentation quality. The purpose of this study is to explore and enhance object segmentation techniques using panoramic images in indoor construction environments. Accurate segmentation of construction elements (e.g., walls, columns, and MEP components) can assist in various applications, such as progress monitoring, safety inspections, and spatial planning. This is particularly useful during the interior finishing and quality management stages of building construction. The contributions of the proposed method are as follows:  
1. This study improves panoramic segmentation performance in complex indoor construction sites by leveraging SAM, a foundation model for perspective image segmentation. 2. To enable accurate object extraction in indoor construction images, filtering algorithms were developed for iterative SAM execution, along with a labeling algorithm that assigns semantic labels to objects based on predictions from a target model. 3. The method is validated through quantitative and qualitative evaluations on real construction site datasets, confirming its applicability and practical value in real environments. This paper is structured as follows: Section 2 discusses panoramic image segmentation, and the challenges associated with its application in indoor construction environments. It then examines plane segmentation, its significance, and the limitations of Panoplane360, followed by an introduction to SAM and its automatic mask generation model. Section 3 details the proposed methodology, dividing it into the object extraction stage and the label extraction and labeling stage, along with a description of the developed algorithms at each stage. Section 4 describes the experimental setup, including the dataset and evaluation methods, and presents the experimental results and analysis. Finally, Section 5 summarizes the study, discusses the significance of the findings, and addresses research limitations and future directions.
## 6 Conclusions and Future Research
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**City** & **BT(0.7; 0.4)** & **BT(0.6; 0.3)** & **BT(0.6; 0.4)** & **BT(0.6; 0.5)** & **BT(0.7; 0.3)** & **BT(0.7; 0.5)** & **BT(0.8; 0.3)** & **BT(0.8; 0.4)** & **BT(0.8; 0.5)** \\ \hline Aalborg & 3 & 3 & 3 & 3 & 3 & 3 & 3 & 3 \\ \hline Amsterdam & 21 & 19 & 20 & 20 & 22 & 20 & 21 & 22 & 23 \\ \hline Ankara & 55 & 55 & 55 & 55 & 55 & 55 & 56 & 56 & 56 \\ \hline Antalya & 41 & 41 & 41 & 41 & 43 & 41 & 44 & 42 \\ \hline Antwerpen & 22 & 22 & 23 & 23 & 19 & 23 & 18 & 20 & 21 \\ \hline Athens & 79 & 79 & 79 & 79 & 79 & 79 & 79 & 79 & 79 \\ \hline Barcelona & 66 & 66 & 67 & 67 & 66 & 67 & 66 & 67 & 67 \\ \hline Belfast & 18 & 18 & 18 & 16 & 20 & 18 & 22 & 21 & 19 \\ \hline Belgrade & 74 & 75 & 75 & 74 & 75 & 74 & 74 & 74 & 74 \\ \hline Berlin & 39 & 39 & 39 & 39 & 39 & 39 & 40 & 40 & 39 \\ \hline Bialystok & 14 & 15 & 16 & 18 & 14 & 15 & 13 & 13 & 13 \\ \hline Bologna & 48 & 46 & 46 & 46 & 48 & 47 & 48 & 48 & 48 \\ \hline Bordeaux & 32 & 31 & 32 & 33 & 31 & 32 & 32 & 32 & 33 \\ \hline Braga & 47 & 48 & 50 & 51 & 46 & 48 & 41 & 42 & 44 \\ \hline Bratislava & 64 & 65 & 65 & 64 & 64 & 61 & 62 & 60 & 60 \\ \hline Bruxelles & 49 & 49 & 49 & 49 & 49 & 50 & 51 & 50 & 50 \\ \hline Bucharest & 77 & 77 & 77 & 77 & 77 & 77 & 77 & 77 \\ \hline Budapest & 65 & 64 & 63 & 62 & 65 & 64 & 67 & 66 & 65 \\ \hline Burgas & 50 & 50 & 48 & 48 & 51 & 49 & 52 & 52 & 51 \\ \hline Cardiff & 8 & 8 & 8 & 8 & 8 & 8 & 8 & 8 \\ \hline Cluj-Napoca & 44 & 44 & 42 & 42 & 45 & 43 & 46 & 45 & 45 \\ \hline Copenhagen & 13 & 12 & 10 & 10 & 13 & 11 & 15 & 15 & 15 \\ \hline Diyarbakir & 60 & 58 & 58 & 58 & 60 & 60 & 61 & 62 & 63 \\ \hline Dortmund & 24 & 27 & 24 & 24 & 25 & 24 & 24 & 24 & 24 \\ \hline Dublin & 16 & 16 & 15 & 14 & 16 & 16 & 17 & 17 & 16 \\ \hline Essen & 38 & 38 & 38 & 38 & 38 & 38 & 38 & 38 & 38 \\ \hline Gdarsk & 31 & 33 & 34 & 32 & 32 & 31 & 31 & 31 & 31 \\ \hline Geneve & 7 & 7 & 7 & 7 & 7 & 7 & 7 & 7 & 7 \\ \hline Glasgow & 35 & 34 & 31 & 30 & 36 & 34 & 36 & 36 & 35 \\ \hline Graz & 9 & 9 & 9 & 9 & 9 & 9 & 12 & 11 & 9 \\ \hline Groningen & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 \\ \hline Hamburg & 15 & 14 & 14 & 13 & 15 & 14 & 14 & 14 & 14 \\ \hline Helsinki & 11 & 13 & 13 & 15 & 11 & 13 & 9 & 9 & 10 \\ \hline Heraklion & 76 & 76 & 76 & 76 & 76 & 76 & 76 & 76 & 76 \\ \hline Istanbul & 75 & 74 & 74 & 75 & 74 & 75 & 75 & 75 & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## 5 Biological Functions of RAC1B
### Adhesion and Cell-Cell Interactions  
RAC1B overexpression has been shown to decrease the adhesive properties of CRC cells by enhancing DVL3-mediated Wnt pathway signaling and induction of Wnt target genes specifically involved in decreasing the adhesive properties of CRC cells [20]. As mentioned above (see Section 4.4), this ability of RAC1B was dependent on a functional NLS and hence its nuclear localization.  
RAC1B activation controls cell-cell interactions, i.e., the assembly of the adherens junctions by mediating laminin-5-integrin-induced signaling by PI3-kinase in HT29 cells [36]. However, in contrast to constitutively active RAC1, RAC1B was unable to induce the disassembly of E-cadherin complexes from junctions in human keratinocytes [37], which may be due to the inability of RAC1B to bind and activate PAK [8].
## 5 Conclusions  
Path loss and signal power in urban and rural environments can be affected by various factors such as distance, obstacles, interference, antenna height, and frequency band. The actual amount of loss will depend on the specific deployment and environmental factors. In this paper, mmWave frequency band, large-scale path-loss models and UMi, UMa, and RMa scenarios are considered to estimate the path loss and signal power. PL and RP are estimated for 28 GHz, 38 GHz, 60 GHz, and 75 GHz using various path-loss models. From the results, it is predicted that the path loss is lower and the signal power is higher in an urban micro scenario than the in urban macro and rural macro scenarios, and out of all four models used, the 5 GCM model achieves lower path loss and higher signal power in all environments. In the future, we want to estimate and compare the path loss of urban micro, macro, and rural macro environments using optimization algorithms like GA, PSO, and GWO.  
**Author Contributions:** Conceptualization, C.S.; methodology, C.S.; software, C.S.; validation, M.R. and L.L.C.; writing--original draft preparation, C.S. and M.R.; reviewing, editing, and supervision, M.R., A.W., A.F.O. and M.H.J.; funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript.  
Figure 17: Received power of an Urban Macro environment at 75 GHz.  
Figure 18: Received power of a Rural Macro environment.  
**Funding:** This work is supported and funded by the Fundamental Research Grant Scheme-\(FRGS/1/\) 2021/_ICT09/MMIU/02/1_, Ministry of Higher Education, Malaysia.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare that there is no conflict of interest in this paper.
## 2 Materials and Methods
### Analytical Methods  
Powder X-ray diffraction (PXRD) was used to analyze the educts. The parameters of the PXRD measurements were identical to the work of [PERSON] et al. [6] (PANalytical X\({}^{\prime}\)Pert\({}^{3}\) Powder with PIXcel 1D detector; 45 kV, 40 mA; apertures: 0.04 rad Soller and fixed slits (0.125\({}^{\circ}\) and 0.25\({}^{\circ}\))). Diffractograms range from 10\({}^{\circ}\) to 75\({}^{\circ}\) 2\(\Theta\) with 49.725 s per step and step size 0.013\({}^{\circ}\) 2\(\Theta\)[6]. For qualitative phase analysis, the ICDD (International Centre for Diffraction Data) database [25] implemented within the HighScore Plus suite [26] and, for quantitative phase analysis, the Rietveld method [24] with the software package Profex-BGMN [27; 28] were applied. The amorphous content was determined by adding crystalline TiO\({}_{2}\) (Kronos 2900-TiO\({}_{2}\); rutile, supplied by KRONOS TITAN GmbH) of known weight (~10 wt.%).  
The degree of carbonation of the samples was investigated depending on experimental time. The decrease of rollastonite accompanied by the increasing of calcium carbonate and amorphous contents had to be quantified. Different approaches were applied to identify and evaluate educts and products quantitatively. The weight fractions of rollastonite, calcite, aragonite, and X-ray amorphous material were determined with the Rietveld method [24]. To quantify the fraction of CaCO\({}_{3}\), heat flow coupled with thermogravimetric analysis (DSC-TG; STA 449 F3 Jupiter NETZSCH) were carried out. The gas release during the heating regime was additionally recorded with MS (QMS 403D Aeolos NETZSCH). The atom mass units were counted from 1 to 100 u. The thermoanalytical measurements were carried out in the temperature interval from 296 K (23 \({}^{\circ}\)C) to 1273 K (1000 \({}^{\circ}\)C). The heating rate was 10 K per minute. Approximately 15-25 mg of sample material was used per measurement [6].  
The products were milled before the PXRD and DSC-TG/MS measurements were carried out. An internal standard (rutile) was added for PXRD measurements.  
The amount of X-ray amorphous content of CaCO\({}_{3}\) in the product could be analyzed as well with DSC-TG/IR/MS.  
The content of carbonate (crystalline and amorphous) was determined by the loss of CO\({}_{2}\) which is given in Equation (2):  
\[\text{CaCO}_{3}[\text{wt.%}]=\frac{\text{mass loss CO}_{2}\ [\text{wt.%}]}{ \left(\frac{\text{mass CO}_{2}\ [\text{g/mol}]}{\text{mass CaCO}_{3}\ [\text{g/mol}]}\right)} \tag{2}\]