esa-sceva/satcom-chunk-collection huggingface dataset:
# A Digital Twin Architecture to Optimize Productivity within Controlled Environment Agriculture
## 4 Results and Discussion  
Latency tests were performed to verify the implementation of the DT architecture. In this article, a latency test refers to the assessment of the time taken for a message to travel in between two actors. It is worth noting that the minimization of the latency was not within the scope of this work and does not constitute a priority for CEA production systems since these are not hard real-time ones. Latency was assessed as a mean to certify the achievement of the communications indicated in Figure 6.  
Figure 7 illustrates the latency taken within each communication. To emulate a feasible optimization scenario, a single data point was transmitted within each communication. The only exception was constituted by the 'download' communication in which one million data points were downloaded from the cloud. The communication sequence illustrated in Figure 6 was repeated 300 times to evaluate the latency variability through the random error formulation [35].  
Figure 6: Sequence diagram showing the communications between the actors of the DT architecture. The name of each communication is indicated in parentheses.  
The latency data demonstrate that it was possible to achieve the bidirectional communication typical of DT architectures [17], i.e., from the physical domain to the cyber one and vice versa (Figure 1). Considering the selected simulators, a DT architecture potentially able to optimize the productivity was built.  
From an industrial perspective, the proposed DT architecture constitutes a scalable architecture that may be applied to industrial systems by replacing the Arduino controller with the utilized PLCs (Programmable Logic Controllers). From an educational perspective, the illustrated low-cost prototype may be replicated from educational institutions for the generation of automation and agricultural hands-on laboratories, and for the investigation of approaches for the optimization of the productivity in CEA production systems.  
Even if a functional DT architecture was built, several works are still missing before certifying its ability to optimize the productivity. In particular, a case study should be implemented and productivity should be optimized through the developed architecture. Different challenges will occur as the aforementioned definition of heuristics for the identification of close-to-optimal climate control strategies and crop treatments in acceptable computation time.
# An Overview of Indoor Positioning and Mapping Technology Standards
## 4 Our Research on IPMN Standards
### Standards for Indoor Map Data
#### 4.2.3 GIS Model for Indoor Spatial Data  
Although GIS models and spatial analysis are widely used in outdoor settings, there are some limitations for indoor GIS due to complicated and isomeric structures of indoor elements. This specification frames the indoor GIS model, which is somewhat different from the existing GIS model in terms of data organization, management, and representation. Figure 9 shows the basic indoor GIS model, which mainly consists of three parts. Firstly, indoor spatial data are organized and managed according to the content model, each core element is grouped or classified into logical layers. Secondly, the geometry representation should support 2D, 2.5D, and 3D coordinates, and furthermore, time or dynamic properties of elements should be considered for spatiotemporal analysis. Indoor spatial data can be stored, transferred or exchanged with MDR, Indoor GML, and other indoor map data formats. Lastly, topological relationships can be derived from the geometry data or cached for immediate pathway finding. From an overall view, this GIS model for indoor spatial data conforms with those for outdoor data, however, the existing commercial or open-source GIS software hardly supports indoor spatial data.  
Figure 8: Examples of element geometry extraction. (**a**) original floor plan, (**b**) door geometry extraction, (**c**) vertical pathway geometry extraction.  
Figure 7: Procedure of digitalizing indoor map from existed floor plan.
# Experimental Study on Permeability Evolution of Sandstone during Triaxial Compression Damage
## 3 Analysis of Test Results
### Stress-Strain Curve and Damage Characterization
During this phase, the stress-axial strain curve maintains a linear trend, with the upper stress limit signifying the onset of rock fracturing. 3. Stage III (stable crack expansion stage): The deviatoric stress-axial strain curve maintains an approximate linear pattern. However, the deviatoric stress-circumferential strain curve begins to exhibit nonlinear behavior as new cracks emerge within the specimen and gradually propagate in a stable fashion. The upper stress limit aligns with the crack damage stress. 4. Stage IV (cracks non-stationary expansion stage): The microfracture increases rapidly, and progressive damage occurs within the rock sample. As the volumetric strain enters an expansion phase, the stress-strain curve demonstrates pronounced nonlinearity. The upper stress value in this context corresponds to the peak strength of the sandstone \(\sigma_{c}\). 5. Stage V (post-peak strain and damage stage): At the point where the deviatoric stress reaches its maximum, the internal structure undergoes complete disruption. Fissures rapidly propagate, culminating in the formation of a macroscopic fracture surface. The overall deformation is primarily characterized by shear-slip damage along this fracture surface. Subsequently, stress diminishes progressively with the augmentation of strain. Based on the findings presented by [PERSON] et al. [19] and [PERSON] et al. [25], it is known that the fracture modes of rocks are single shear, conjugate shear, shear-tension combination, and cleavage. The damage patterns of sandstone under various combinations of confining pressures and seepage pressures were obtained, as shown in Figures 5-8. Most of them are monoclinic shear damage throughout the whole sandstone, while it is tensile splitting at uniaxial compression. In summary, it can be deduced that the damage pattern exhibited by sandstone is notably characterized by brittleness, with minimal evidence of volumetric expansion. The shear damage surface appears sleek, while an abundance of granular fragments is observable. Figure 5: Failure modes of drying specimens under different confining pressures. Figure 6: Failure modes of saturation specimens under different confining pressures.
## 2 Challenges in Address-Based Communication Architecture  
The conventional host-centric IP-based architecture faces several challenges, especially in the domain of resource-constrained IoTs. Some of the major challenges in address-based architecture are summarized as follows.  
1. **Scalability:** The current WSNs deployment utilizes the conventional address-based solution(s) which may induce the efficiency and scalability issues. For instance, extracting the PH value of soil from a single sensor in the agricultural field requires the address and type information of each sensor (i.e., the mapping of address with the content). Therefore, managing the enterprise level deployment of nodes becomes a significantly complex as well as time-consuming operation.
2. **Data Unavailability:** Considering the low battery and low memory constraints of WSN nodes, the Data unavailability issue may arise. To cope with data unavailability problems, an in-network caching-like solution is required. Unfortunately, the conventional IP architecture provides no support for in-network caching [18].
3. **End-to-End connectivity:** In most IoT applications, the consumers are interested in updated Data rather than the location of the producer of that specific Data item [19]. For instance, a user requires the temperature information of certain area (e.g., XYZ) is Interested in the temperature value irrespective of the identity of the temperature-sensing node at that location.
4. **Mobility Support:** There is a lack of native mobility support in IP and to overcome mobility constraint(s), IP utilizes several additional protocols to support the mobility of nodes in dynamic environments.
## 4 Experimental Outcomes
### Performance Module  
The summary of the experimental results of all eight CNN models considered in this study in terms of AUC with 5-fold cross-validation is provided in Table 2 for the three conceived experimental configurations, i.e., without augmentation (_No Aug_), with basic augmentation (_Basic Aug_), and with advanced augmentation (_Adv Aug_), respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{3}{*}{**Architecture**} & \multicolumn{3}{c}{**Area under the Curve (AUC)**} \\ \cline{2-4}  & **No Aug** & **Basic Aug** & **Adv Aug** \\  & **(None, Ori,** & **(None, Ori,** & **(None, Ori,** \\  & **Oro, Ost)** & **Oro, Ost)** & **Oro, Ost)** \\ \hline \multirow{3}{*}{MobileNet-v2} & \(91.9\pm 1.1\) & \(92.4\pm 0.9\) & \(93.6\pm 1.2\) \\  & \(97.4\pm 0.4\) & \(98.0\pm 0.6\) & \(97.6\pm
###### Abstract  
In this paper, an improved end-to-end autoencoder based on reinforcement learning by using Decision Tree for optical transceivers is proposed and experimentally demonstrated. Transmitters and receivers are considered as an asymmetrical autoencoder combining a deep neural network and the Adaboost algorithm. Experimental results show that 48 Gb/s with 7% hard-decision forward error correction (HD-FEC) threshold under 65 km standard single mode fiber (SSMF) is achieved with proposed scheme. Moreover, we further experimentally study the Tree depth and the number of Decision Tree, which are the two main factors affecting the bit error rate performance. Experimental research afterwards showed that the effect from the number of Decision Tree as 30 on bit error rate (BER) flattens out under 48 Gb/s for the fiber range from 25 km and 75 km SSMF, and the influence of Tree depth on BER appears to be a gentle point when Tree Depth is 5, which is defined as the optimal depth point for aforementioned fiber range. Compared to the autoencoder based on a Fully-Connected Neural Network, our algorithm uses addition operations instead of multiplication operations, which can reduce computational complexity from \(10^{8}\) to \(10^{7}\) in multiplication and \(10^{6}\) to \(10^{8}\) in addition on the training phase.  
deep learning; machine learning; Adaboost algorithm; neural networks; optical fiber communication +
Footnote †: journal:  
Article
# Boat-to-Grid Electrical Energy Storage Potentials around the
## 1 Introduction
### The Growing Importance of Energy Storage Markets  
According to a forecast by the U.S. Department of Energy [41], it is expected that by 2030, the energy storage markets including stationary and transportation will increase to 2.5-4 TWh from 2020's 800 GWh. A major driving force behind this growth is the rising penetration of electrified powertrains, because of which today, mobility accounts for the greatest demand for energy storage, about five to ten times more than stationary energy storage by energy capacity. The compound effects of fast decreasing battery storage prices, the development of electrified transportation and the growing share of variable renewable energy sources of energy in electric energy generation have resulted in an unprecedented growth in the use of energy storage all around the world. Stationary energy storage is widely regarded now as an effective solution for demand management and enhancing the resilience and reliability of the grid in the context of grid-tied renewable electricity. Parallel to this, the surge in the numbers of EVs, also thanks to various government incentives and the manufacturers' relentless efforts to boost production, is predicted to be a lasting global trend too [41].
# A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems
## 2 OFDM Systems
### Subcarrier Allocation  
The total number of \(N\) subcarriers is uniformly allocated among \(K\) users and thus, the share of each user is \(V=N/K\). With the knowledge of \(\{\tilde{H}_{k,m}^{n},\forall n,m,k\}\), BS can allocate subcarriers to match the user channel condition. In this paper, we assume that the subcarrier \(n\) is always allocated to the user \(k\), which has the largest \(\sum\limits_{m=1}^{M}\left|\tilde{H}_{k,m}^{n}\right|^{2}\) unless the quota of this user is full. In this case, the subcarrier is allocated among remaining users.
# Detection of Reflected XSS Vulnerabilities Based on Paths-Attention Method
## 3 Vulnerability Detection Method Based on Paths-Attention
### The Composition of the Paths-Attention Vulnerability Detection Method
#### 3.2.3 Attention Mechanism  
The biggest technical challenge of the PATS model is to combine the training model with attention mechanism. Through experiments, it was found that using the above method to map code snippets into vectors did not achieve ideal detection results in XSS vulnerability classifiers. However, inspired by neural translation models in natural language processing, it was discovered that not all syntax paths are equally important. An algorithm is needed to highlight more distinctive syntax paths, so attention mechanism is introduced.  
The aggregated code vector \(\mathbf{v}\in\mathbb{R}^{\text{d}}\) represents the entire code snippet. \(\mathbf{v}\) is determined by the combination of the contextual vectors \(\{\mathbf{c}_{1},\cdots,\mathbf{c}_{\text{n}}\}\) and their attention weights. The specific process is shown in Formula (8):  
\[\text{code vector }\mathbf{v}=\sum_{i=1}^{n}\alpha_{i}\widetilde{\mathbf{c}_{i}} \tag{8}\]  
At each forward propagation step, a \(\widetilde{\mathbf{c}_{i}}\) is obtained, and the attention vector for the current step is calculated based on \(\widetilde{\mathbf{c}_{i}}\). Then, the backpropagation algorithm is used to update the weights of each neuron. When the gradients of the model converge, the final code vector is obtained by combining the current \(\alpha_{i}\) and \(\widetilde{\mathbf{c}_{i}}\). Once the model converges, the aggregated semantic vector of the code snippet is outputted.  
The algorithm for extracting the semantics of the entire code is as follows: Algorithm 1: Algorithmic process of neural network attention mechanism.  
Input: Code snippet D  
Output: Vector V  
Process:  
Step 1: Convert code snippets to AST;  
Step 2: Traverse the AST to parse the syntax path, store the value of each node in a matrix called \"vocab value\", and store the path in \"vocab path\";  
Step 3: Embed the set of discrete vector groups into a continuous space and combine them into a single vector \(\mathbf{c}_{i}\);  
Step 4: Calculate the dot product of weight matrix \(\mathbf{W}\) and \(\mathbf{c}_{i}\), and map the result to \((-1,1)\) through activation function \(\tanh\). The result is denoted as \(\widetilde{\mathbf{c}_{i}}\);  
Step 5: Compute the dot product between \(\widetilde{\mathbf{c}_{i}}\) and the global attention vector \(\alpha\), then normalize to obtain the weight \(\alpha_{i}\) for each \(\widetilde{\mathbf{c}_{i}}\);  
Step 6: Add up the weighted values \(\alpha_{i}\) multiplied by their corresponding \(\widetilde{\mathbf{c}_{i}}\) to obtain the aggregated vector \(\mathbf{v}\).  
**Algorithm 1**Neural Network Attention Mechanism
# Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data
## 2 Background and Related Works
### Flooding Disaster Knowledge Graphs  
Several studies have already proposed flood disaster knowledge graphs to solve the issue for heterogeneous data using ontology engineering [17; 18; 19; 20]. In [20], the authors proposed a data management and utilization framework for urban flooding. To manage heterogeneous data, the relationship between data was defined in terms of time (e.g., before, after, during, equal, meet, overlap, and disjoint), space, and semantics (e.g., is-a, part-of, equivalent-of, synonymy-of, and relevant-of). The influence index of the factors influencing the flood disaster was calculated using the proposed framework. In [15], the authors approached the solution of data management and sharing problems in disaster response management. They designed a flood disaster response domain ontology using interlocking institutional worlds to solve this problem. Finally, [19] developed an information-centric flood ontology for structural and easy access to critical information, such as disasters. The developed ontology can be used in cyber infrastructure systems for natural disaster preparedness, monitoring, response, and recovery. Furthermore, the proposed methodology can be easily integrated with the domain knowledge of the expert system and used in voice-enabled intelligent applications through a web-based information platform. As mentioned above, most of the previous studies related to flooding disaster knowledge graphs constructed knowledge graphs based on scenarios or case studies. Therefore, although the common main purpose is to solve the issues of heterogeneous data, the knowledge graph is defined with different terms and relationship definitions, which imposes the limitation of difficult reuse or integration. Fortunately, there are recent several studies resulting from the beAWARE project within the European Horizon 2020 program, which aims to integrate and harmonize heterogeneous data from various sources such as sensors, social media, general public data, and data from the first responders for real-time crisis management due to climate-related natural disasters. Those results provide useful ontologies that can refer to the area of knowledge-based disaster management [27; 28]. However, not only the integration of data from various sources and formats but also the issue of standardization and interoperability are still ongoing because various organizations still use different terms and data fields despite data having the same meaning.
## 5 Conclusions  
To more accurately model the influence of surrounding pedestrians and space on individual pedestrian behavior and generate diverse predicted future trajectories, this paper proposes a social and spatial attentive GAN. The social attention module uses comprehensive motion features, including a pedestrian's relative distance, speed, and velocity angle, as key factors in social interactions, whereas the spatial attention module utilizes spatial features with semantic segmentation information. We also propose a method for utilizing the CARLA simulator to generate multi-trajectory datasets and better evaluate multimodal prediction performance. The effectiveness of our proposed model in modeling complex pedestrian interactions is demonstrated through experiments conducted on both single and simulated multi-trajectory datasets.  
Similar to prior studies, our work is based on simulating pedestrian interactions through a fixed architecture network. As indicated in experiments, complex models can underperform in some simple scenarios, such as linear walking. Therefore, to further improve prediction accuracy, future research can explore networks with adaptive interaction modeling capacity.  
Conceptualization, P.C.; methodology, D.D.; software, D.D.; formal analysis, L.L.; investigation, L.L.; writing--original draft preparation, L.L.; writing--review and editing, P.C.; project administration, P.C.; funding acquisition, P.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by grants from the National Natural Science Foundation of China (No. 62163014).  
**Data Availability Statement:** The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.  
Figure 6: Visualization of multi-trajectory prediction by our model in the Plaza and the Crossroad scenarios. The red line in each scenario represents the trajectory of a pedestrian (enclosed in a red box) generated by the CARLA simulator. The green lines indicate the manually labeled possible paths. The purple envelope diagrams indicate the ranges of the 25 predicted trajectories.
# A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery
## 3 Methods
### Evaluation Criteria  
#### 3.3.1 Coordinate Deviation of Homonymous Points  
The coordinate deviation for homonymous matching points was calculated to estimate the registration accuracy between adjacent bands using the following Equation:  
\[\left\{\begin{array}{l}m_{X}=\sqrt{\frac{\sum_{i=1}^{N}(X_{b}-X_{w})_{i}^{2 }}{N}}\\ m_{Y}=\sqrt{\frac{\sum_{i=1}^{N}(Y_{b}-Y_{w})_{i}^{2}}{N}}\\ m_{A}=\sqrt{m_{X}^{2}+m_{Y}^{2}}\end{array}\right., \tag{16}\]  
Figure 8: Schematic diagram of matching points outside Delaunay triangulation.  
where \((X_{b},Y_{b})\) are the coordinates of a matching point in the reference band; \((X_{w},Y_{w})\) are the coordinates of a matching point in the band to be registered; \(m_{X}\), \(m_{Y}\), and \(m_{A}\) are the accuracy values in the X and Y direction and the total accuracy of the registered image, respectively; and N is the number of matching points used in the accuracy assessment.  
Overall, 20 evenly distributed homonymous points were manually selected for the accuracy assessment of non-adjacent bands. Equation (16) was used to evaluate the registration accuracy for non-adjacent bands.
# The Value of Fractal Analysis in Ultrasound Imaging: Exploring Intricate Patterns
## 3 Results
### Skin and Wound Healing  
High frequency ultrasound (HFU), combined with fractal analysis, offers a sophisticated approach to evaluating the healing process of pressure sores. By extracting parameters that relate to the echographic structure and attenuation properties of tissue, researchers have developed a quantifiable healing function that facilitates monitoring the progression of wound generation and healing [31]. This quantitative method provides a powerful tool for the early detection and continuous monitoring of pressure sores, which is particularly advantageous for patients with impaired mobility. The integration of HFU and fractal analysis allows for a detailed assessment of skin and tissue changes at the microscopic level. Fractal analysis, a method used to evaluate complex geometric patterns, can detect subtle variations in tissue structure that traditional imaging techniques might miss. This capability is crucial in tracking the intricate process of wound healing, which involves multiple phases, including inflammation, tissue formation and remodeling [31]. [PERSON] et al. [31] reported that HFU was used to analyze skin changes in pregnant women, revealing significant variations in skin thickness and structure between hypertensive and nonhypertensive pregnancies based on skin structure changes detected by HFU, thus offering a predictive value for potential complications [31]. The combination of HFU and fractal analysis represents a promising advancement in the field of wound care. This method not only enhances the early detection and monitoring of pressure sores, but also offers a detailed understanding of the healing process, ultimately improving patient outcomes and quality of life [31].