esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in the Synthesis of Graphene-Based Nanomaterials for Controlled Drug Delivery
## 2 Ways of Controlled Drug Delivery
### Endogenous Stimuli-Responsive Drug Delivery  
_pH-stimulated drug delivery._ Owing to rapid breeding of tumor cells, veins near tumor cells cannot provide enough oxygen and nutrients for cell proliferation. Thus, anaerobic glycolysis occurs during cellular metabolism and produces a considerable amount of lactic acid, considerably decreasing the tumor pH value to below-normal levels. Therefore, the pH differences inside the body can be used to improve targeted drug delivery [68]. For example, HA-, RC-, and LA- functionalized GO were demonstrated exhibiting a pH sensitive anticancer drug release behavior [50; 51; 52].  
_Enzyme-stimuli drug delivery._ Enzymes play an important role in cell regulation, and their type, activity, and concentration vary in different organelles and parts. Therefore, various activities and concentrations of enzymes can be used to deliver drugs to tumor tissues [69]. A common enzyme-controlled delivery mechanism includes the following processes: (i) degradation of peptide chains by proteases; (ii) degradation of polysaccharides by glycosylaces; and, (iii) hydrolysis of phospholipid-containing block copolymers by lipases. Notably, the first process facilitates drug delivery in organelles. For instance, [PERSON] et al., developed a sequentially-functionalized GO as cellular protease-mediated programmed co-delivery system integrating membrane-related proteinsand intracellular-functioning small-molecule drugs, this GO nanocarrier could efficiently release TRAIL to the sites of action through an enzyme stimuli manner [54].  
_Redox-stimulated drug delivery._ The concentration of glutathione (GSH) in cells ranges from 2 to 10 mM, while its concentration in blood is 2-10 uM. Disulfide bonds in the blood and extracellular micro-oxidation environments are extremely stable. In cells, disulfide bonds undergo oxidation-reduction reactions with glutathione and can thus promote drug release through nanocarriers [70]. The concentration of glutathione in tumor cells is four times higher than in normal cells. Therefore, the amount of drug released in the tumor site is larger than those released in other sites and the release rate is faster. For example, [PERSON] et al., demonstrated that the GO-Ag nanocomposite could efficiently release loaded Dox under GSH regulated environment [56]. In addition, oxidation responsiveness was also explored for triggered drug release in inflammatory tissues, which are characterized by an accumulation of reactive oxygen species (superoxide radicals, hydrogen peroxide, hydroxyl radicals, singlet oxygen). For instance, [PERSON] et al., reported that thioketal-based nanoparticles formulated with a reactive oxygen species-sensitive polymer could deliver small interfering RNAs to the sites of intestinal inflammation, thus realizing therapeutic levels of gene silencing after oral administration [71].
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 2 Likelihood-Based Modulation Recognition Methods
### Average Likelihood Ratio Test (ALRT)  
When the parameter \(\mathbf{u}_{i}\) is a random variable with a known distribution, ALRT constructs the likelihood function by taking the statistical average over the unknown parameters:  
\[\Gamma_{\text{ALRT}}(H_{i})=\prod\
olimits_{n=0}^{N-1}E_{\mathbf{u}_{i}}[ \Gamma(r_{n}|H_{i},\mathbf{u}_{i})], \tag{5}\]  
here, \(E_{\mathbf{u}_{i}}[\cdot]\) represents the expectation operation over the random parameter \(\mathbf{u}_{i}\). For example, when the carrier phase \(\theta_{c}\) (uniformly distributed in \([-\pi,\pi]\)) is unknown, ALRT integrates over the phase:  
\[\Gamma_{\text{ALRT}}=\frac{1}{2\pi}\int_{-\pi}^{\pi}\prod\
olimits_{n=0}^{N-1 }p(r_{n}|H_{i},\theta_{c})d\theta_{c}. \tag{6}\]  
Assume that the symbols are independent and uniformly distributed, the ALRT expression for M-PSK signals in [16] is simplified as:  
\[\Gamma(\mathbf{R}|H_{i})=E_{s_{n}^{k}|N-1}\left[I_{0}\left(\sum\limits_{n=0}^ {N-1}\frac{2\left|r_{n}^{*}s_{n}^{k}\right|}{\sigma^{2}}\right)\times\text{ exp}\left(-\sum\limits_{n=0}^{N-1}\frac{\left|s_{n}^{k}\right|^{2}}{\sigma^{2}} \right)\right], \tag{7}\]  
Figure 2: Basic structure of likelihood-based modulation recognition methods.  
where \(I_{0}(\cdot)\) is the zeroth-order modified Bessel function, and \(s_{n}^{k,i}\) is the theoretical value of the \(k\)-th candidate symbol under the modulation hypothesis \(H_{i}\). The ALRT method addresses parameter uncertainty by statistically averaging received signals under various modulation assumptions, theoretically offering high recognition accuracy. However, its computational complexity increases sharply with the dimensionality of parameters, posing a significant challenge in practical applications. This complexity is exacerbated in VLC due to the effects of LED nonlinearity and multipath scattering, further limiting the practical applicability of the ALRT method.
## 4 Sensor Integration Platform
### Unmanned Vehicle  
Unmanned vehicles are very useful in an area where an earthquake has damaged critical infrastructures or areas with toxic gas or polluted air which is unsafe and unhealthy [79]. These vehicles can reduce the threat of unknown environments to rescuers and emergency workers. Common unmanned devices include drones and mobile robots [19], but drones are the most used in disaster areas [79]. An unmanned aerial vehicle (UAV), a drone, can monitor a large area in a short period of time. Therefore, drones have become an increasingly popular tool for use in earthquake response and recovery efforts, including search and rescue operations, damage assessment, mapping, monitoring, infrastructure inspection, delivering aid, etc. [80]. They were widely used to provide long-term light in dark evenings and communication networks in search and rescue sites during the 2023 Turkiye MS 7.8 earthquake. A UAV also can create 3D maps of its surroundings using lasers, which are very useful for mountain environments.  
Medical robots are very popular nowadays due to increasing accuracy. They can be divided into urban search and rescue robots, evacuation robots, and on-site diagnosis robots based on their functions. An urban search and rescue (USAR) robot, represented by the serpentine robot, conducts a preliminary exploration of the disaster site and identifies human survivors by examining the video (with audio) [81]. This instrument transmits the location of survivors to a centralized cloud server. It also monitors the relevant air quality in the selected area to determine whether it is safe for rescuers to enter the area [18]. An evacuation robot can be utilized to remove survivors from debris. An on-site diagnosis robot can judge the condition of the injuries of a survivor according to the skin condition of the buried person [82]. However, earthquake rescue robots are not popular due to their high cost, and they can only be brought to a disaster area by an emergency communication  
Figure 3: Three types of earthquake emergency vehicles.  
agency after the disaster. Therefore, it is difficult to use mobile robots dedicated to disaster reduction to carry out disaster reduction activities in the initial stage of a disaster.
# Contactless Real-Time Eye Gaze-Mapping System Based on Simple Siamese Networks
## 3 Eye Gaze-Mapping System
### Gaze-Matching Network  
The gaze-mapping system has difficulty generalizing because each person has different gaze habits, such as looking to the side, and various head angles. In addition, few-shot learning was suitable because the GGM dataset did not have many training images. The Siamese network, which used two inputs and returned the similarity between two vectors, is shown in Figure 4.  
To perform the feature extraction from the left and right eyes, the Siamese network was applied to the gaze location and gaze vector. Because the difference in the scale of each input was quite large, each input values were normalized. However, for depth, the maximum and minimum values could not be specified, so an additional single-channel, fully connected layer was added. The gaze vector and gaze location could then be shifted to a similar scale.  
There were three sub-modules in the gaze-mapping module (refer to Figure 5).  
* The head-pose estimator extracted the head-pose vector using information collected from the Jetson TX2, such as bounding box, landmarks, etc.
* The gaze-matching network extracted the gaze area within the image using information from various modules, including the head-pose vector, gaze vector, bounding box, and landmarks.
* The logging and visualization modules recorded the events occurring in each module in the database, visualized the logged data, and transmitted the images. As a result, the matching-point coordinates were obtained in every loop, and the gaze-matching network was inferred by the region-estimation loop during the logging process.
# Routing Path Assignment for Joint Load Balancing and Fast Failure Recovery in IP Network
###### Abstract  
Distributed link-state routing protocols, including Open Shortest Path First (OSPF) and Intermediate System-Intermediate System (IS-IS), have successfully provided robust shortest path routing for IP networks. However, shortest path routing is inflexible and sometimes results in congestion on some critical links. By separating the control plane and the data plane, the centralized control of Software Defined Networking (SDN)-based approach possesses flexible routing capabilities. Fibbing is an approach that can achieve centralized control over a network running distributed routing protocols. In a Fibbing-controlled IP network, the controller cleverly generates fake protocol messages to manipulate routers to steer the flow of the desired paths. However, introducing fake nodes destroys the structure of the loop-free property of Loop-Free Alternate (LFA) that is used to achieve fast failure recovery in IP networks. This paper addresses this issue and presents a solution to provision routing paths so a Fibbing network can still apply LFA in the network. The proposed network jointly considers load-balanced and fast failure recovery. We formulate the problem as an integer linear programming problem. The numerical results reveal that the proposed method can provide 100% survivability against any single node or single link failure.  
load balancing: fast failure recovery; Fibbing; IP network routing +
Footnote †: journal: Applied  
**Keywords:** load balancing; fast failure recovery; Fibbing; IP network routing
## 3 Materials and Methods  
### Privacy-Preservation with Homomorphic Encryption  
To address potential privacy issues in wearable devices, we propose a cloud-based framework that relies on homomorphic encryption as a mechanism to guarantee the privacy and integrity of a person's personal health information when wearable devices are used for remote health monitoring. Encryption takes place on the wearable device, and the encrypted form of the collected information (ciphertext) is sent to a cloud service for processing and/or storage. Data processing via AI-based systems can be performed in the cloud computing environment directly on homomorphically encrypted data without the need to decrypt the data or access the decryption key. Inspired by the work of [PERSON] et al. [15], a variant of the MORE (Matrix Operation for Randomization or Encryption) encryption scheme [25] is used to perform privacy-preserving computations within neural network models. The scheme encrypts plaintexts \(m_{1}\in\mathbb{R}\) and \(m_{2}\in\mathbb{R}\) in ciphertexts \(\mathbf{C_{1}}=\mathbf{SM_{1}S^{-1}}\) and \(\mathbf{C_{2}}=\mathbf{SM_{2}S^{-1}}\), where \(\mathbf{M_{1}}= \begin{pmatrix}m_{1}&0\\ 0&r_{1}\end{pmatrix} \) and \(\mathbf{M_{2}}= \begin{pmatrix}m_{2}&0\\ 0&r_{2}\end{pmatrix} \) is constructed using a random parameter \(r_{1}\in\mathbb{R}\) and \(r_{2}\in\mathbb{R}\), respectively, and \(\mathbf{S}\in\mathbb{R}^{2\times 2}\) defines the symmetric secret key. Here the matrix-based secret key \(\mathbf{S}\) is chosen to be invertible. Decryption is done by computing \(\mathbf{K_{1}}=\mathbf{S^{-1}C_{1}S}\) and \(\mathbf{K_{2}}=\mathbf{S^{-1}C_{2}S}\). Finally, the plaintexts \(m_{1}\) and \(m_{2}\) is extracted as \(m_{1}\leftarrow\mathbf{K_{1}}_{(1,1)}\) and \(m_{2}\leftarrow\mathbf{K_{2}}_{(1,1)}\), respectively. The homomorphic properties of this scheme can be identified easily using linear algebra. Encryption of \(m_{1}+m_{2}\) is computed as  
\[\mathbf{C_{1}}+\mathbf{C_{2}}=\mathbf{SM_{1}S^{-1}}+\mathbf{SM_{2}S^{-1}}= \mathbf{S}(\mathbf{M_{1}}+\mathbf{M_{2}})\mathbf{S^{-1}} \tag{1}\]  
and \(m_{1}\times m_{2}\) is computed as  
\[\mathbf{C_{1}}\times\mathbf{C_{2}}=\mathbf{SM_{1}S^{-1}S}\mathbf{M_{2}S^{-1}} =\mathbf{S}(\mathbf{M_{1}M_{2}})\mathbf{S^{-1}}. \tag{2}\]  
Similarly, it can be shown that this is also true for subtraction and division. Moreover, non-linear functions that can be evaluated directly on a matrix (e.g., logarithm, exponentiation, square root) can also be evaluated directly on a ciphertext. Other non-linear functions can still be constructed by approximating them with a finite polynomial series, leading to a sequence of elementary algebraic operations. This approach allows one to perform an arbitrary number of operations, with arbitrary complexity, without loss of precision. This is the main feature that enables the implementation of complex learning-based algorithms on encrypted data. MORE is a symmetric-key algorithm where a key generated according to Algoritm 1 is used for both encryption (Algorithm 2) and decryption (Algorithm 3).
# Structuring Reference Architectures for the Industrial Internet of Things
## 5 Considered Reference Architectures
### Industrial Internet Reference Architecture  
The Industrial Internet Reference Architecture (IIRA) published by the IIC aims at a comprehensive model of the industrial internet, independent of specific domains and industries. The wide scope results in a broad coverage of topics whereas concrete implementation guidelines are only partly provided.  
The main IIRA categorization is based on the aforementioned ISO/IEC 42010, introducing the four viewpoints Business, Usage, Functional and Implementation. Nevertheless, IIRA lacks an explicit set of addressed concerns. While major concerns can be extracted by analyzing the viewpoint descriptions, a specific allocation of concerns to viewpoints is not given. This results in a certain vagueness of requirements for IIoT implementations. Therefore, various IIoT architectures can be compliant to IIRA requirements while interoperability or data exchange is not possible as a matter of heterogeneous implemented patterns.  
IIRA groups its guidelines related to interoperability and data exchange in the Functional Viewpoint, further separated into multiple domains. While the main aspects of IIoT are discussed as parts of the Functional Viewpoint, key aspects like connectivity [37] and security [38] are discussed in separate documents. Figure 5 presents the relations between these central classifications by showing their overlaps of concerns. The Venn diagrams organize the framed concerns regarding the coverage of the certain classifications. The grouping takes place according to the relations as provided by the knowledge graph. Visualizing the data in form of Venn diagrams and co-occurrence matrices (Section 7) hides the complexity of the graph and allows any interested user to directly work with the information. Further views and configurations are possible at the website and available for any user. One can see the extent of the Functional Layer as the key part of the IIRA as it also relates to nearly all concerns from the others. Even though the visualization only supply _quantitative_ comparison of concerns, the content is intuitively plotted in order to make it easy to understand. A quantitative presentation means in this context that only the bare number of concerns are compared. The impact and relevance of each single one is explicitly not taken into account. An additional _qualitative_ analysis, also regarding the importance of specific concerns, would require a deep understanding of each individual use case and therefore cannot be achieved with a generic approach like presented here. Consequently, the main concepts and most important insights are outlined by the Functional Viewpoint section of the IIRA.
## 3 Results
### Spatiotemporal Demand Prediction  
The spatiotemporal grid prediction approach (Section 2.3) is implemented to predict vehicle counts at shelters and lodging facilities across the study area. Using a spatial prediction grid and the GAM model, vehicle counts were visualized using GIS tools, identifying hotspots of facility demand. Figures 13 and 14. Show heat maps for vehicle distribution patterns across the month. Day 1 saw peak vehicle counts, reflecting high initial demand. Day 15 showed reduced counts, coinciding with Hurricane Sally's landfall, while Day 30 revealed partial recovery. Higher vehicle intensity near training zones indicates potential model biases or regional dynamics affecting predictions.  
Figures 15 and 16 are spatial heat maps showing the vehicle counts at the lodging facilities. The predicted vehicle distribution shows stable patterns over time, with most regions having low to moderate vehicle counts. Higher activity was concentrated around training facilities, with lower intensities at test areas, indicating model bias influenced by training data. This highlights the need for improved generalization to better predict vehicle dynamics in test regions.  
These spatial heatmaps show the predicted demand across the entire study area, extending beyond the known facility locations. The model was trained on data from existing shelters and lodging facilities, and the heatmaps aim to highlight potential demand hotspots at both known and unknown locations. The concentration of demand near training zones partly reflects true evacuation patterns, as lodging facilities are typically located along primary evacuation routes. Because the GAM model interpolates over spatial coordinates, the predicted hotspots may not exactly coincide with existing facilities (in our case the hotspots correctly capture demand in larger facilities). Areas of high predicted demand without existing facilities highlight potential service gaps and can inform emergency planners about areas where additional resources may be needed, for example, for deciding on locations of new facilities.  
Figure 14: Shelter generalized additive model predictions across space and on days (**a**) 1; (**b**) 15; (**c**) 30.  
Figure 16: Lodging facility generalized additive model predictions across space and on days: **(a)** 1; **(b)** 15; **(c)** 30.  
Figure 15: Lodging facility generalized additive model predictions across space and time.
# Optimization of Multimodal Transport Paths Considering a Low-Carbon Economy Under Uncertain Demand
###### Abstract  
Aiming at the uncertainty in cargo demand in the transportation process, the multimodal transportation path optimization problem is studied from the perspective of a low-carbon economy, and the robust optimization modeling method is introduced. Firstly, a robust optimization model for multimodal transportation is built using the multimodal transportation path optimization model under demand certainty, and the total transportation cost is then calculated by taking into account not just only the cost of transportation and trans-shipment but, additionally, the price of waiting because of schedule restrictions on trains and airplanes. Secondly, carbon emissions are added into the model as a constraint or cost by converting four different low-carbon policies. Then, the simulated annealing mechanism is introduced to improve the ACO algorithm. Finally, solomon calculus is used for the solution. The outcomes demonstrate that the improved annealing ant colony hybrid algorithm simulation can essentially improve the multimodal transportation path optimization problem with uncertain demand and promote multimodal transportation emission reduction. Among the four carbon emission policies, the mandatory carbon emission policy means are tough, and the greatest impact comes from reducing emissions and using less energy. Energy conservation and emission reduction have the second-best impact, while the three policy tools of carbon taxes, carbon trading and carbon payment are more modest.  
low-carbon economy; multimodal transport; robust optimization; ant colony algorithm; path optimization
## 1 Introduction
In addition, the backfilling time is slower in deep mines compared to shallow mines due to higher compressibility and the need for the gangue filling material to settle and compact completely [20]. Backfilling mining is important for the safety and stability of coal mines, and each method has its own set of applications and advantages based on the mining conditions and the sort of support required. However, solid waste backfilling, compared to other methods, can be produced in situ alongside coal mining activities and does not need to transport the gangue to the ground. This method is called \"mining-separating-backfilling + X\". It is a great technique for achieving safe and efficient mining and integrated environmental development, is the most effective and economical way to backfill the goat, and has been used in numerous underground coal mines [21; 22; 23]. Advanced equipment such as a coal-gangue separation robot system for the intelligent separation of coal and gangue has been studied by some scholars, such as [PERSON] et al. [24]. In China, this technology has been applied in many coal mines, including the Tangshan Mine of the Kailuan Group. Based on the geological and mining conditions of the Tangshan coal mine, the efficiency of solid backfilling on strata and ground subsidence is systematically analyzed and presented in this paper. In addition, this research adopts the methods of physical analog modeling, numerical simulation, and field measurement in correlation with a theoretical analysis. First, a non-contact full-field displacement and strain measuring system, MatchID-2D software (v.2021.1), was implemented to analyze the displacement and deformation, as well as the stress changes, in the overlying strata during mining and backfilling in the physical analog model. Second, for the 3D numerical modeling, FLAC3D software (v.5.00) was adopted to investigate the mechanical mechanisms of strata and ground subsidence and the backfill body movement induced by underground mining. Through these simulations, the whole process of overlying strata movement, ground subsidence, and the stress changes induced by underground mining can be reproduced, and the effect of solid backfill on strata and ground subsidence can be revealed. However, field measurements are still the most effective way to study strata and ground subsidence because the data from field measurements are accurate, provide direction for mining operations, and support their design. Attention has been given to the solid backfill method used to date, and the research results have important practical significance for future prospects and theoretical guidelines for engineering designs in solid backfilling mining.
## 3 Proposed Approach and Methodology
### System Architecture  
The system comprises three main components:  
* _VLC-Enabled Infrastructure:_ Traffic lights and roadside units equipped with VLC modules to enable real-time data transmission between vehicles, infrastructure, and pedestrians.
* _AI Agents:_ Independent DRL-based agents are deployed at each intersection, with a centralized global agent coordinating the local ones. This structure ensures scalability and real-time adaptability to traffic dynamics.  
* _Data Integration Platform:_ A platform that collects, processes, and integrates real-time data from VLC-enabled devices, including vehicle positions, speeds, and pedestrian requests.