esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Implementation and User Scenario
### Technical Validation and Experimental Results
#### 5.4.7 Practical Implementation Challenges and Ethical Considerations  
Although the AI-MDD-UX framework shows potential for improving user engagement, satisfaction, and personalization, its real-world deployment presents practical and ethical hurdles. Overcoming these challenges is critical to maintaining the system's trustworthiness, scalability, and user-centricity.  
1. **Data Privacy and Security** The framework AI-MDD-UX depends on continuous collection and analysis of user data such as browsing history, behavioral interactions, device context, and feedback, raising concerns about data misuse, unauthorized access, or regulatory violations. To mitigate these risks, future implementations will incorporate privacy-preserving measures, including: (1) end-to-end data-encryption using advanced, lightweight techniques to prevent unauthorized access or leakage during transmission (2) edge-based data processing, where feasible, to handle tasks like interaction tracking, basic UI adaptation, and feedback analysis directly on the user's device. This approach minimizes dependence on centralized servers, reducing risks tied to large-scale data aggregation.
2. **Scalability and Platform Compatibility** The farmwork aims to provide dynamic UI/UX personalization across diverse platforms such as web (React.js), mobile (Flutter), and VR. However, extending its functionality to emerging interfaces (e.g., wearables, voice assistants, and automotive systems) introduces challenges like: (1) adaptive UI design strategies, including modular components, reusable themes, and responsive layouts, to ensure consistency, (2) middleware standard ization to unify interaction data (e.g., gestures, voice inputs) into a common format for AI processing, enabling seamless cross-platform compatibility.
# MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge
###### Abstract  
Advanced communication and information technologies enable smart grids to be more intelligent and automated, although many security issues are emerging. Security situational awareness (SSA) has been envisioned as a potential approach to provide safe services for power systems' operation. However, in the power cloud master station mode, massive heterogeneous power terminals make SSA complicated, and failure information cannot be promptly delivered. Moreover, the dynamic and continuous situational space also increases the challenges of SSA. By taking advantages of edge intelligence, this paper introduces edge computing between terminals and the cloud to address the drawbacks of the traditional power cloud paradigm. Moreover, a deep reinforcement learning algorithm based on the edge computing paradigm of multiagent deep deterministic policy gradient (MADDPG) is proposed. The minimum processing cost under the premise of minimum detection error rate is taken to analyze the smart grids' SSA. Performance evaluations show that the algorithm under this paradigm can achieve faster convergence and the optimal goal, namely the provision of real-time protection for smart grids.  
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Footnote †: journal: Applied Sciences  
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smart grid; situational awareness; edge computing; multi-agent DDPG; deep reinforcement learning
## 2 Principles of WIFFT and DE Algorithms  
### Estimation of Harmonic Parameters by WIFFT  
The WIFFT algorithm is an effective harmonic analysis method. The spectral characteristics of the window function can restrain the spectrum leakage, and the interpolation operation is not only a correction of the weighted process, but can also eliminate the picket-fence effect [23]. In electric energy metering, the Hanning window is often used, as described in [24]:  
\[w(n)=\left\{\begin{array}{ll}0.5-0.5\cos\frac{2\mu\pi}{N},&0\leq n\leq N-1\\ 0,&n<0\text{ or }n>N-1\end{array}\right. \tag{1}\]  
Corresponding to the Hanning window, the formula of interpolation correction is:  
\[\left\{\begin{array}{ll}\beta_{m}=\frac{|X(k_{\text{m}}+1)|}{|X(k_{\text{m}} )|},\delta_{m}=\frac{2\beta_{\text{m}}-1}{1+\beta_{\text{m}}}\\ f_{m}=(k_{\text{m}}+\delta_{\text{m}})\frac{f_{\text{m}}}{N}\\ A_{m}=|X(k_{\text{m}})|\cdot\frac{2\pi\delta_{\text{m}}(1-\delta_{\text{m}}^{ 2})}{\sin(\pi\delta_{\text{m}})}\\ \theta_{m}=\text{angle}[X(k_{\text{m}})]-\delta_{\text{m}}\pi\frac{N-1}{N} \end{array}\right. \tag{2}\]  
In Formula (2),\(f_{\text{m}}\), \(A_{\text{m}}\) and \(\theta_{\text{m}}\) are the frequency, amplitude and phase parameter of \(m\)-th harmonic, respectively. \(k_{\text{m}}\) refers to the number of local maximum spectral line. \(|X(k_{\text{m}})|\) and \(\text{angle}[X(k_{\text{m}})]\) are the amplitude and phase of spectral line \(k_{\text{m}}\) in the weighted signal, and \(\beta_{\text{m}}\) is the spectral peak ratio of two adjacent spectral lines.
# Seamless Indoor-Outdoor Localization Through Transition Detection
## 3 Handover Strategy Based on Three State Density Functions
### RSSI Model  
The RSSI model \(P^{\textit{rssi}}(s)\) takes the WiFi and BLE scan results as the input and returns the probabilities of states. The data to train the RSSI model are obtained from the RSSI data measured while randomly walking in specific areas. The data were measured for about 3 min for each state area, with 5 s of WiFi and BLE scanning intervals.  
Figure 2: One of the experimental office buildings. This building has a pilotis structure, and both sides of the exterior entrances are surrounded by walls and obstacles, making seamless tracking more challenging. There is a double-door entrance connecting the indoor and outdoor spaces, and the transition zone was selected as the area between the inner and outer doors.  
The RSSI scan result of the WiFi and BLE accumulated for each time window of 5 s is defined as a vector \(RSSI_{k}\) of size \(m\),  
\[(RSSI_{k}^{1},RSSI_{k}^{2},\ldots,RSSI_{k}^{m})\in\mathbb{R}^{m}, \tag{3}\]  
where \(RSSI_{k}^{i}\) (\(i=1,\ldots,m\)) is the scalar value of the RSSI corresponding to the \(i\)th access point (including BLE beacons) at the \(k\)th location, which is normalized from (\(-100\) dBm, \(0\) dBm) to (\(0,1\)). The measured RSSI values lower than \(-100\) dBm are considered \(-100\) dBm.  
The autoencoder function \(f(\cdot)\) transforms the normalized \(m\)-dimensional RSSI vector of WiFi and BLE into a dimensionality-reduced \(d\)-dimensional feature vector.  
\[f:(RSSI_{k}^{1},RSSI_{k}^{2},\ldots,RSSI_{k}^{m})\mapsto X_{k}=(r_{k}^{1},r_{k} ^{2},\ldots,r_{k}^{d}),m\gg d \tag{4}\]  
The learning model \(g(\cdot)\) predicts the state for a given set of inputs. For the output layer, the softmax activation function was used to return estimations in the form of probabilities.  
\[g:X_{k}\in\mathbb{R}^{d}\mapsto Y_{k}=(y_{k}^{in},y_{k}^{transition},y_{k}^{ out}) \tag{5}\]  
\[P_{k}^{RSSI}(s)=softmax(Y_{k}) \tag{6}\]
## 3 Results and Discussion  
In Germany, H and LE increases with temperature during spring. However, while LE increases together with GPP during the growing period due to the link between growth and ETP, H reaches its maximum during May when the crop begins to cover the ground and the energy inverted to change temperature stabilizes. The majority of Net radiation is converted into latent heat. After harvest, sensible heat reaches its maximum in August because the crop has been disappeared and does not influence on temperature. Latent heat decreases because there is no crop and no ETP. [PERSON] et al. [11] also showed that during growing season latent heat was the main consumer of net radiation, while sensible heat was dominant during the non-growing season in a rainfed maize crop.  
In Spain, sensible heat reaches its maximum in mid-April when there is no crop and between flooded periods. Latent heat has two cycles that coincides with flooded periods. The first one is related to evaporation and the second one to ETP. Other studies highlighted that latent heat is the main component of available energy in rice crops [12].  
Figure 1 shows the Granger causality tests between H and LE and GPP in both directions (i.e., variable cause GPP and GPP cause variable). In Germany, sensible heat causes GPP at very short term (first three lags). Then, both directions show similar F-test values. In Spain, from lag 2 to 10 GPP causes H more than in the other direction. In both sites, GPP causes Latent heat more than backwards. However, in Germany F-tests are much higher than in Spain especially in the sense GPP causes LE.  
These feedback processes that vegetation exerts on the energy partitioning have been assessed by several authors [13; 14].
# Design, Implementation, and Field Testing
## 1 Introduction
* A mechanism to deliver personalized and discreet alerts to the user on detection of a non-compliant activity in realtime. * A lease-based mechanism that strikes a good balance between meeting the privacy requirement and the usability of the system.
# A Novel Sucrose Isomerase Producing Isomaltulose from _Raoultella terrigena_
## 2 Materials and Methods
### Enzyme Assay  
Recombinant Pal-2 activity was measured in a 1 mL reaction system containing 20% (_w/v_) sucrose and 50 mM disodium hydrogen phosphate-citrate buffer (pH value of 5.5) for 15 min. The assay mixture was pre-incubated at a given temperature for 5 min. Then,the reaction was induced by adding the enzyme and stopped by heating in boiling water for 10 min. Finally, the reaction mixture was centrifuged at 12,000\(\times\)\(g\) for 5 min and the supernatant filtrated through a 0.22 \(\upmu\)m syringe filter. The concentrations of glucose, fructose, sucrose, isomaltulose, and trehalulose were detected by high-performance liquid chromatography (HPLC) method. Waters e2695 system (Waters Corporation, MA, USA), Waters 2414 RI detector, and a HILICpak VG-50 4E column (4.6 mm I.D. \(\times\) 250 mm, Shodex, Japan) were used to analyze samples. The samples were eluted using 88% acetonitrile as a mobile phase at the constant flow rate of 1 mL/min. The HILICpak VG-50 4E column temperature and the RI detector temperature were fixed at 60 \({}^{\circ}\)C and 40 \({}^{\circ}\)C, respectively. The retention times of glucose, fructose, sucrose, isomaltulose, and trehalulose were 8.6, 11.6, 21.2, 22.3, and 23.5 min, respectively. One-unit activity was defined as the amount of enzyme that released 1 \(\upmu\)mol of isomaltulose per minute under the temperature of 40 \({}^{\circ}\)C and a pH value of 5.5.
## 3 Detection Technology of Adverse Geological Bodies
### Borehole Multi-Source Data Fusion Analysis Method
#### 3.2.3 Joint Interpretation of Borehole Multi-Source Data  
There are many correlations between the results of borehole optical imaging, borehole radar and borehole directional acoustic scanning. Cracks, cavities, metal objects, surface interference, etc., can be found in the visible light images, radar images and acoustic scanning images. Through the comprehensive interpretation of the data of borehole optical imaging, borehole radar and borehole directional acoustic scanning section, it can better reflect the geological information such as the shape characteristics of the rock in the test area, the distribution of fractures, joints, fracture zones and veins in the rock mass. The obtained information can be combined with data interpretation to obtain the related rock mass information in the detection area, as shown in Figure 9.  
Data fusion is a method of multivariate statistical joint analysis. For the analysis and research of the data observed in porous media, one technique is to analyze each hole separately, and the other is to analyze the porous media simultaneously. Although there are many uncertain factors in the underground abnormal geological body, there is still a certain correlation between the hole data. If the data are processed separately, it will not only lose a lot of information, but it is also not easy to obtain good analysis results. Through the analysis of the observation data of multiple boreholes, we can study the relationship between the boreholes and reveal the inherent connectivity rules of these boreholes. Different methods of multivariate analysis can also be used to classify and simplify geological anomalies. As shown in Figure 10, there is only one obvious hyperbola at a certain depth on the single-hole radar profile. The electromagnetic wave propagation velocity in the formation can be estimated by using the method mentioned above, and the size of the deviation position of the abnormal body can be calculated.  
Figure 9: Three boreholes position and data fusion diagram.  
Through the comprehensive interpretation of the data of borehole optical imaging, borehole radar and borehole directional acoustic scanning section, it can better reflect the geological information such as the shape characteristics of the rock in the test area, the distribution of fractures, joints, fracture zones and veins in the rock mass.
## 3 Enabling Technologies and Challenges for Futuristic Vehicular Networks
### Network Softwarization
#### 3.1.1 Network Function Virtualization (NFV)  
NFV is a key technological trend to tackle the flexibility and scalability problems associated with traditional hardware-based Network Functions (NFs). In the past, different NFs such as firewalls, Content Delivery Networks (CDNs), Network Address Translation (NAT) were installed as dedicated hardware-based appliances. With this, the implementation of new services and applications were restricted by the deployment of specific hardware-based elements. NFV decouples this network functions from proprietary hardware appliances and runs them as software instances in Virtual Machines (VMs)/containers as Virtual Network Functions (VNFs) [38]. Through NFV, standard network resources such as compute, storage, and network functions can be virtualized and kept on Commercial Off-the-Shelf (COTS) hardware like x86 servers. In addition, multiple VMs/containers, through the proper assignments of virtualized resources, can run on a single server for improving server resource utilization. With the NFV technology, different network functions can be placed in different locations of the networks elements such as data centers, EC servers, etc.  
In the case of a Multiple EC (Multi-EC) platforms-enabled-VN architecture, NFV can potentially bring several benefits, including reduced network cost, less time-to-market for new services, higher resource efficiency, and better scalability. However, each VNF demands specific computation resources based upon the service types. Furthermore, since VNFs can be deployed at multiple locations, it is important to find proper function placement strategies for implementing many hybrid vehicular services over a resource-constrained VN.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 9 Conclusions  
In this paper, the multi-label (LOS, NLOS, and MP) identification for UWB ranging was conducted using three machine learning techniques (SVM, RF, MLP) as a classifier. This was in contrast to the typical binary classification approaches in the literature. The experimental evaluation results based on the F1-score proved that ML-based classifiers could identify the defined three classes with a high score, i.e., 0.69 in the worst-case scenario and 0.92 in the best-case scenario. However, it was unreasonable to single out the best classifier out of the three because their performances depended extensively on the environmental changes and the metrics used to quantify them. Moreover, the insightful results based on the confusion matrix revealed that the MP condition was the most challenging to identify among the three classes. It was also evident in the confusion matrices that the predicted accuracy of the NLOS condition was quite high throughout all the evaluated experiments.  
As future work, the NLOS condition could be further classified into two classes based on the study conducted in [11]. This would lead the defined classes of UWB data to become four, i.e., LOS, MP, soft-NLOS, and hard-NLOS. Indeed, this also means much more data would need to be collected to identify these four conditions in several environments using several different materials and objects. Besides, the experimental evaluation will be conducted in the actual hardware of the microcontroller-based platform instead of a PC.  
Conceptualization, C.L.S.; methodology, C.L.S. and J.D.H.; validation, B.S., J.D.H., M.A., M.H., and U.R.; software, C.L.S., B.S., and M.A.; writing, original draft preparation, C.L.S.; writing, review and editing, B.S., J.D.H., M.A., M.H., and U.R.; visualization, C.L.S., J.D.H., M.A., and M.H.; project administration, M.H. and U.R.; supervision, U.R.; funding acquisition, U.R. All authors read and agreed to the published version of the manuscript.  
We acknowledge support for the article processing charge by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University.  
This work was supported by the Cluster of Excellence Cognitive Interaction Technology \"CITEC\" (EXC 277) at Bielefeld University, funded by the German Research Foundation (DFG). Author [PERSON] was partly supported in this work by the German Academic Exchange Service (DAAD). The authors are responsible for the contents of this publication. We also sincerely thank the anonymous reviewers for their valuable suggestions and feedback.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; nor in the decision to publish the results.
The concatenation of these activation maps forms a tensor where each channel represents the output from a hidden layer of the model. This strategy propagates the watermark across all model layers, better preserving model accuracy and avoiding potential misclassification of real images, as observed in DeepSigns. This approach also simplifies detection, as one only needs to generate samples from the latent space to constitute the trigger set. DICATION is thus independent of selecting a set of real samples (e.g., images) as a trigger set. Moreover, very few parameters need to be stored (only the mean and standard deviation of the latent distribution). In some cases, model accuracy is even improved because our watermarking proposal acts as a regularization term. As we demonstrate, our proposal is highly resistant to all the attacks listed previously (PA, FTA, OWA, WKA, and PIA) and outperforms the DeepSigns white-box dynamic watermarking scheme in terms of both watermark robustness and watermarked model accuracy. **Our main contributions are the following:**  
* **Unified framework**: We present the first unified mathematical framework for white-box DNN watermarking that encompasses all existing schemes and reveals their theoretical inter-connections;
* **Novel dynamic scheme**: We propose DICATION, a new GAN-based dynamic watermarking approach that uses a neural network projection function instead of static linear projections;
* **Latent space innovation**: We introduce the use of latent space triggers (Gaussian noise) instead of training data subsets, eliminating storage requirements and enabling unlimited trigger generation;
* **Superior performance**: DICATION demonstrates significant improvements over state-of-the-art methods in capacity, robustness, and security while maintaining computational efficiency. The rest of this paper is organized as follows. Section 2 presents the background and related work. Section 3 introduces our unified white-box watermarking framework that encompasses the most recent static and dynamic schemes, illustrating their advantages and weaknesses through theoretical unification. Section 4 presents DICATION, our novel, dynamic white-box watermarking scheme that generalizes DeepSigns with significantly improved performance. Section 5 provides the experimental results and a comparative evaluation of our scheme against state-of-the-art solutions. Section 6 discusses the limitations and future work, while Section 7 concludes this paper.
## 3 Data and Methods
### MODIS Products Used in This Study
#### 3.1.4 MOD17A2 GPP/NPP  
The gross primary production (GPP) represents the capacity of the plants in an ecosystem to capture energy and carbon. The net primary production (NPP), the reduction of GPP after autotrophic respiration, is the net carbon stored as new plant material in an ecosystem, which supplies humans with various foods, fuels, fibers and construction materials [21]. Despite the strong theoretical and empirical relationship between surface reflectance and the FPAR, accurate estimates of vegetative productivity (GPP, NPP) depend strongly on the quality of the radiation inputs [59].  
Global productivity can be estimated by combining remote sensing with carbon cycle processing [59]. The MODIS dataset provides the first operational, near-real-time calculation of the global GPP and NPP at a 250-m spatial resolution with daily coverage [10]. The MODIS GPP and NPP products have been validated as being able to capture spatial and temporal GPP and NPP patterns across various biomes and climate regimes, and the products are consistent with the ground flux tower-based GPP and field-observed NPP estimations [21].  
The NPP product is designed to provide an accurate and regular measure of the production activity or growth of terrestrial vegetation. These products will have both theoretical and practical utility. The theoretical use is primarily to define the seasonally dynamic terrestrial surface CO\({}_{2}\) balance for global carbon cycle studies [60, 61]. Previous studies used tower and MODIS GPP/NPP data to model the carbon cycle in forests [11, 17, 50, 62] and determined the relationships between GPP/NPP and LAI, NDVI and climate [10, 62, 63].  
In addition, the NPP/GPP ratio (NG) [64] was found to be constant across a range of CO\({}_{2}\) levels and temperature for herbaceous and woody plants. An assessment of the global patterns in the NG can enhance knowledge about features of the NG and benefit research on global change [21]. In the present study, we also attempt to use the GPP minus the NPP (GN) as a variable of the CO\({}_{2}\) concentration model.  
MODIS global annual estimates of GPP and NPP are within 10.4% and 9.0% [53]. MOD17A2 Monthly GPP/NPP with a resolution of 1 km, downloaded from the University of Montana [65], was used in the present work.