esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Physics-Informed Neural Networks to River Sifting Simulation
###### Abstract  
Water resource pollution, particularly in river channels, presents a grave environmental challenge that necessitates a comprehensive and systematic approach encompassing assessment, forecasting, and effective management. This article provides a comprehensive exploration of the methodology and modeling tools employed to scrutinize the process of river channel pollution due to silting, rooted in the fundamental principles of hydrodynamics and pollutant transport dynamics. The study's methodology seamlessly integrates numerical simulations with state-of-the-art neural network techniques, with a specific focus on the physics-informed neural network (PINN) method. This innovative approach represents a groundbreaking fusion of artificial neural networks (ANNs) and physical equations, offering a more efficient and precise means of modeling a wide array of complex processes and phenomena. The proposed mathematical model, grounded in the Euler equation, has been meticulously implemented using the Ansys Fluent software package, ensuring accuracy and reliability in the computations. In a pivotal phase of the research, a thorough comparative analysis was conducted between the results derived using the PINN method and those obtained using conventional numerical approaches with the Ansys Fluent software package. The outcomes of this analysis revealed the superior performance of the PINN method, characterized by the generation of smoother pressure fluctuation profiles and a significantly reduced computation time, underscoring its potential as a transformative modeling tool. The calculated data originating from this study assume paramount significance in the ongoing battle against river sedimentation. Beyond this immediate application, these findings also serve as a valuable resource for creating predictive materials pertaining to river channel silting, thereby empowering decision-makers and environmental stakeholders with essential information. The utilization of modeling techniques to address pollution concerns in river channels holds the potential to revolutionize risk management and safeguard the integrity of our vital water resources. However, it is imperative to underscore that the effectiveness of such models hinges on ongoing monitoring and frequent data updates, ensuring that they remain aligned with real-world conditions. This research not only contributes to the enhanced understanding and proactive management of river channel pollution due to silting but also underscores the pivotal role of advanced modeling methodologies in the preservation of our invaluable water resources for present and future generations.  
water pollution; artificial neural networks; CFD; Euler equation; PINN Article Application of Physics-Informed Neural Networks to River Sifting Simulation
# Advanced Techniques for Geospatial Referencing in Online Media Repositories
## 5 Discussion
A title card from which OCR was not able to recognize text can be seen in Figure 7. In this specific case, a location reference could not be recognized here. A notable issue was the OCR technology's difficulty with recognizing text presented in large or highly stylized fonts. Title cards, which often showcase artistic typography to capture the viewer's attention, posed a particular challenge. Figure 6: Misidentification by the landmark recognition model, interpreting a person in a white hood (**a**) as the Swedish F 15 Flygmuseum (**b**), showing challenges with AI interpretability and training data biases. Image sources: (**a**) ARD Mediathek; (**b**) Wikimedia Commons. Audio and subtitle processing has emerged as a promising way forward, highlighting the potential of advanced linguistic models. However, it also presents challenges as the contextual subtleties of location information are highly nuanced. The refinement of transcribed texts using GPT-3.5 Turbo marked a notable step in our methodology, enhancing the meaningfulness and correctness of the data. However, this correction did not notably impact the performance of NER tools. This finding suggests that the effectiveness of NER in recognizing location entities may depend on factors beyond textual accuracy. Given the high fidelity of the audio transcriptions, our approach flexibly treated both audio and subtitles as interchangeable, capitalizing on the extensive textual data they provided. This strategy proved beneficial, particularly as the text volume was well-suited to NER tools designed to process large text corpora. Regardless, a notable challenge was the contextual interpretation of location references. A reference to a \"cathedral\" in a video about \"Augsburg\" implicitly pertains to the \"Augsburg Cathedral\". With the contextual linkage, the term is more precise for NER tools to handle effectively. Addressing this, one solution could involve integrating additional context through metadata pre-analyses, potentially enriching the locational specificity of such references. However, this integration must be handled carefully as there is a risk of complicating the analysis by distorting or overlaying non-local place references.
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 5 Taxonomy for Energy-Efficient and Energy-Balanced Routing Protocols
### Single-Hop Communication Method
That is, we grouped the studied single-hop clustering routing protocols based on how the CH is selected and how the nCH nodes choose the cluster to join. The data transmission phase requires no further design since all the nCH nodes transmit their information to their respective CH nodes directly. Likewise, the CH nodes transmit the aggregated data to the BS directly. Accordingly, the studied energy-efficient and energy-balanced single-hop clustering routing protocols are classified based on the cluster formation stages only, as shown in Table 6. In a similar format to Table 2, in Table 6, we firstly classified the studied energy-efficient and energy-balanced single-hop clustering routing protocols based on their network infrastructure as homogeneous and/or heterogeneous WSN. Subsequently, we grouped the various single-hop clustering routing protocols based on the choice of decision variable(s) used in selecting the CH. As represented in Table 6, the decision variables used by these single-hop clustering routing protocols to select the CH are grouped under \"RE\", \"distance\", \"AE\", \"RSS/TP\" and \"predefined\". As explained in Section 5.1.1, these parameters have their pros and cons when used in selecting the CH nodes. Thus, researchers attempt to find a balance in using two or more of these decision variables. Moreover, we grouped these routing protocols that rotate the CH duty in order to avoid early network partitioning. After the CH selection phase, we grouped the routing protocols based on the approach in which the nCH nodes join a cluster. This phase of the cluster formation is equally important in order to evenly distribute the network load. Therefore, the decision variables used by these routing protocols to specify how the nCH nodes join a cluster are grouped under \"RE\", \"distance\", \"RSS/TP\". Also, some authors [190] grouped the SNs based on their location. In doing this, they assume that the SNs are perfectly aware of their location. Although assuming that the SNs are aware of their location might not be practical in reality, we grouped the routing protocols that made this assumption in forming their cluster. In addition, in a similar approach as in Section 5.1.1, we classified the studied energy-efficient and energy-balanced single-hop clustering routing protocols based on the clustering formation into equal or unequal cluster size. Lastly, in Table 6, we strived to classify the single-hop clustering routing protocols based on the optimization solution types or algorithms. Figure 10: Typical example of a small-scale single-hop clustering WSN.
# SCCDNet: A Pixel-Level Crack Segmentation Network
###### Abstract
CrackNet [11] was proposed in 2017 for pixel-level crack detection tasks. Different from the traditional CNNs structure, it removes the pooling layer to avoid the loss of details caused by excessive downsampling. Although CrackNet can achieve better crack detection accuracy, artificially designed filters limit the learning ability of the model. CrackNet-V [30] is improved on the basis of CrackNet. Its model has a deeper structure and fewer parameters and improves the efficiency of crack detection and calculation. However, the data set used by this algorithm only contains specific types of cracks, and it is difficult to guarantee the validity of the model when the types of crack change. [PERSON] et al. [31] proposed a model for pixel-level crack detection called DeepCrack and verified the effectiveness of the model on a dataset containing 537 crack images. Although the above algorithms have achieved good results on their respective datasets, the datasets they use are small and it is difficult to prove the generalization ability of the model. [PERSON] et al. [32] designed the Semantic Damage Detection Network (SDDNet), which greatly improved the computational efficiency of the model. However, the dataset of the model also only has 200 single-type crack images, which have not been published, so the performances of these methods are difficult to make a comparison. According to the above situation, we designed an end-to-end CNNs model for pixel-level crack detection, called the Skip Connected Crack Detection Network (SCCDNet), and its structure is shown in Figure 1. The main contributions of this paper are as follows:  
* We designed an end-to-end crack segmentation network based on the CNNs, which can determine whether each pixel in the image belongs to a crack. In order to improve the segmentation accuracy of the model, the Skip-Squeeze-and-Excitation (SSE) module we designed was introduced into the network, using Squeeze and Excitation to recalibrate the pixels of the feature map. This skip-connection strategy can enhance the gradient correlation between the layers, thereby enhancing the performance of the network. * We designed a dense connection network in the Decoder module to reduce the number of channels of feature maps by considering the crack features learned by each layer of the network; the traditional convolution was replaced by depthwise separable convolution, which reduces the complexity of the model without affecting the segmentation accuracy. * A public dataset with manual annotations was established, including 7169 crack images with a resolution of 448 \(\times\) 448. This dataset includes labeled crack images of different scenes and different shapes, as well as non-crack images which are similar to crack images, which can enhance the generalization ability of the model.
# Hybrid Adaptive Bias OFDM-Based IM/DD Visible Light Communication System
## 2 System Model
### Receiver  
Now, we discuss the demodulation process of the proposed HABO-OFDM scheme. The block diagram structure of the receiver of the proposed HABO-OFDM system is shown in Figure 2.  
It can be obtained that \(x_{n}\) occupies the \((2m+1)\)-th and \((4m+2)\)-th subcarriers, and \(\lfloor y_{n}\rfloor_{c}\) occupies the imaginary parts of the \(4m\) subcarriers in HABO-OFDM scheme. The clipping noise of \(y_{n}\) only falls on the real parts of the \(4m\) subcarriers, and there is no interference to \(x_{n}\). Meanwhile, no clipping noise generated by \(x_{n}\) is superimposed on the imaginary parts of the \(4m\) subcarrier of \(\lfloor y_{n}\rfloor_{c}\) owing to no clipping operation for \(x_{n}\). Therefore, the two sets of signals have no influence on each other during demodulation. Furthermore, the following will prove that the adaptive bias will not cause interference to the transmission signals \(x_{n}\) and \(\lfloor y_{n}\rfloor_{c}\).  
Figure 2: Block diagram structure of the receiver of the proposed HABO-OFDM system.  
Consequently, \(S_{k}\) can be given by  
\[S_{k}=\begin{cases}0,&k=2m+1,\\ 0,&k=4m+2,\\ 4s_{0}+4s_{\frac{N}{8}}\cos(m\tau)+\frac{8}{\sqrt{N}}\sum_{n=1}^{\frac{N}{8}-1}s _{n}\cos(\frac{8 nm\tau}{N}),&k=4m\end{cases}, \tag{14}\]  
where \(m=0,1,\cdot\cdot\cdot,N/8-1\). It can be easily observed that \(S_{k}\) only falls on the real parts of the \(4m\)-th subcarriers, which explains that the adaptive bias \(s_{n}\) does not disturb \(x_{n}\) and \(\left\lfloor y_{n}\right\rfloor_{c}\). Therefore, a relatively simple OFDM receiver structure can be applied to HABO-OFDM scheme.
## 5 Experiment
### Experimental Results and Analysis
#### 5.4.4 Spatial Correlation and Heterogeneity Effects Analysis  
In the selected research scope of the experiment, comprising 90 stations, the geographical features of each station and the spatial characteristics between stations influence the demand for each station as well as the correlation between the demands of different stations. Within the same period, if a station has a higher number of Points of Interest (POIs) or if the POIs are more popular during that period, the demand for shared bicycles at that station tends to be higher. Similarly, if the distance between two stations is shorter, the correlation in demand for shared bicycles between those two stations is higher. The weighted heatmap of the distance adjacency matrix for 40 selected stations on 1 January 2020 is shown in subplot (a) on the left side of Figure 9a. The horizontal and vertical axes represent the chosen 40 stations, and each small square in the figure represents the weight between station \(i\) and station \(j\), which is solely related to the distance between the stations. The darker color indicates a smaller distance between stations, implying a higher correlation in demand for shared bicycles between the two stations.  
When spatial heterogeneity factors are present, the described relationship between a station's own demand and the correlation of demand between stations can change. For instance, considering the scenario of a station disappearing, after the disappearance of a station, the demand for shared bicycles tends to increase for stations closer to the vanished station. In the proposed LGDMGCN model, the dynamic graph generation module dynamically adjusts the weights of the adjacency matrix between stations based on the appearance or disappearance of stations, enabling the model to capture spatial heterogeneity changes. Figure 9b illustrates the weighted heatmap of the distance adjacency matrix on 17 March 2020, where station 7 has been removed. Comparing the two subfigures of (a) and (b), it can be observed that the color of the small squares (7, 6) and (6, 7) has become lighter, indicating a decrease in the weight between stations 6 and 7; meanwhile,  
Figure 8: The metrics of different time granularities.  
the color of (6, 4) has become darker, indicating an increase in the weight between stations 6 and 4. After the removal of station 7, the dynamic graph generation module decreases the weight of that station while increasing the weight of stations closer to it. When spatial heterogeneity factors occur, the model, through such dynamic adjustments, focuses on the changes in heterogeneity of existing stations.
## 4 Attack Surface Analysis
### Compromised Insider Nodes
#### 4.2.3 Network Equipment  
Local Area Networks are particularly vulnerable to cyber attacks targeting network equipment, given their central role in managing communication and data transfer within organizations. Cyber adversaries exploit weaknesses in routers, switches, and similar devices, jeopardizing the integrity and confidentiality of network data. Attack vectorsoften involve exploiting default credentials [69], known software vulnerabilities, and misconfigurations [70] in network devices. Once breached, attackers can engage in various malicious activities, including intercepting and altering network traffic [71], executing man-in-the-middle attacks, or causing network disruptions. Specific attack scenarios, such as man-in-the-middle attacks [72], exemplify the interception and manipulation of device communication, leading to unauthorized access or eavesdropping. Techniques such as ARP spoofing [73] and DNS Spoofing [74] further illustrate this vulnerability. Furthermore, router spoofing [75] involves impersonating a legitimate router to redirect or intercept network traffic, as seen in BGP hacking [76]. Device misconfigurations, such as relying on default credentials or weak passwords, pose serious risks. DNS Spoofing and Cache Poisoning [77] manipulate the Domain Name System, redirecting network traffic or introducing malicious entries into the DNS cache. Firmware exploits [78] target vulnerabilities in the firmware of the device, granting attackers control, as in Firmware Injection [79]. Logical Attacks on Switches [80] exploit vulnerabilities in switch configurations for traffic manipulation or unauthorized access, as demonstrated by MAC Flooding [81]. Routing Table Poisoning [82] manipulates routing tables to redirect traffic or cause network disruptions, exemplified by Routing Information Protocol (RIP) Poisoning [83]. Consequently, there are numerous attacks that can target network equipment, providing several types of unauthorized access to the network. These attacks may enable eavesdropping, traffic manipulation, or injection attacks.
# Estimation of Chlorophyll Fluorescence at Different Scales: A Review
## 2 The Generation of Chlorophyll Fluorescence and Its Spectrum  
Chlorophyll fluorescence is the production of chlorophyll excited by natural sunlight; chlorophyll is the essential pigment in the process of photosynthesis. Chlorophyll fluorescence, heat dissipation, and photosynthesis are important ways to consume the energy absorbed by the leaf (i.e., chlorophyll fluorescence has a direct relationship with photosynthesis).  
Vegetation mostly absorbs the visible light. When vegetation absorbs red light, chlorophyll molecules are excited to the first singlet state; when vegetation absorbs blue light, chlorophyll molecules are excited to the second singlet state (Figure 1). Chlorophyll molecules, which are in an unstable state, need to release the energy to return to a stable state. Heat dissipation is the key way of energy consumption. When chlorophyll molecules in the first singlet state, excluding the heat dissipate, photosynthesis and fluorescence are important approaches to dissipate the energy. From the first singlet state to the ground singlet state, chlorophyll molecules consume energy for chlorophyll fluorescence emission, which has a longer wavelength.  
The chlorophyll fluorescence spectrum ranges from the 640 nm to 850 nm and has two peaks (690 nm and 740 nm). By comparing the vegetation reflectance spectrum (the apparent reflectance) with the fluorescence-filtered vegetation reflectance spectrum (the actual reflectance) simulated by FluorMOD [60], the two convex can be found at 690 nm and 740 nm (Figure 2).
# RFID Based Manufacturing Process of Cloud MES
## 2 The General Framework of the RFID Based Manufacturing Process of Cloud MES  
In industrial production, a cloud MES system introduces cloud platform storage and big data processing technology on the basis of an MES. It is a core part of each enterprise to achieve production synergy, and it is also the key part to promote industrial production being refined, cloud-based, coordinated and intelligent. Combined with the hierarchical position of MES in the enterprise integration model and the introduction of RFID technology, this paper proposes a framework of a manufacturing process of cloud MES system centered on machine tools, as Figure 1 shows.  
As shown in Figure 1, the framework of a manufacturing process via ac loud MES system centered on the processing machine tool and reflects the real-time interconnection and cloudization of the device. The overall structure mainly includes the equipment control layer, the IoT (Internet of Things) layer, the cloud MES layer and the user layer.  
1. Equipment control layer: The equipment in this layer is mainly based on the processing machine tool (or enterprise workshop), and the machine tool (or enterprise workshop) under the cloud MES system being virtualized and abstracted as a node, and as a basic manufacturing unit. Each manufacturing unit needs to be configured with RFID to match the corresponding RFID tags, providing hardware support for real-time tracking and monitoring functions in the cloud MES system.
2. IoT layer: This layer mainly realizes the interconnection of devices between manufacturing units composed of various processing machine tools (or enterprise workshops). The main hardware devices include sensors, industrial Ethernet, fieldbus, switches and wireless network systems.
3. Cloud MES layer: This layer integrates cloud storage, big data technology and an MES system, and is responsible for processing and feeding back the data indexes of the underlying devices uploaded through the IoT layer to realize the cloud-based, virtualized and intelligent management of the manufacturing process by the MES system. It mainly includes a cloud database, a cloud server and a big data processing system module. The cloud database is the integration of the MES database with other databases (such as ERP: Enterprise Resource Planning, SCM: Supply Chain Management, etc.). The cloud server is mainly laid on the public cloud, provided by third-party cloud providers (such as Amazon's AWS, Microsoft's Azure, and the domestic Alibaba Cloud, etc.), The big data processing system module is developed for the specific manufacturing process or as directed by a third-party vendor.  
User layer: This layer mainly realizes the real-time dynamic display for the tracking and monitoring of the manufacturing process by the cloud MES layer. Under any circumstances, the user can keep track of the real-time dynamic of current manufacturing processes by using various methods (such as a web browser, application software client, AR (Augmented Reality), VR (Virtual Reality) intelligent display device, etc.).
## 2 Methodology
### Generating TLS and Coloring
#### 2.2.2 Point Clouds Coloring  
Point clouds coloring for TLS requires matching point clouds with the color digital panoramic image. In this study, point clouds coloring is completed in software Trimble Realworks 10.0.1. The point cloud coloring of TLS is the fitting process of point cloud file and panorama file by RealColor of Trimble Realworks and the fitting process is divided into manual fitting and automatic fitting. The fitting effect of the manual fitting is better comparing with automatic fitting. Thus, the manual fitting is selected to complete Point clouds coloring by matching three key points. The Color Autopano Giga 3.7 is used to generate panorama image with the help of images captured with a digital camera.  
A digital camera installed on a tripod will be used with a wide-angle lens. Because common lenses and wide-angle lenses used are different, wide-angle lenses needs to be calibrated that make point clouds and colorful panoramic image match well (Figure 8).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scanning Parameters**} & \multicolumn{5}{c}{**Precision Grade**} \\ \cline{2-6}  & **Preview** & **Accuracy** & **Accuracy** & **Accuracy** & **Expanded** \\  & **the Scan** & **Level 1** & **Level 2** & **Level 3** & **Scan** \\ \hline Maximum range & 120 m & 120 m & 120 m & 120 m & 340 m \\ Scan time & 1 min & 2 min & 3 min & 10 min & 20 min \\ The spacing of point & 10 m & 15.1 mm & - & - & - & - \\ clouds & 30 m & - & 22.6 mm & 11.3 mm & 5.7 mm & 7.5 mm \\ at different distances & 300 m & - & - & - & - & 75.4 mm \\ The number of & & \(8.7\times 10^{5}\) & \(3.4\times 10^{6}\) & \(1.38\times 10^{8}\) & \(5.55\times 10^{8}\) & \(3.12\times 10^{8}\) \\ cloud point generated & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relationship between TX8 scanning accuracy level and acquired data.  
Figure 8: Coloring TLS with panoramic image. (**a**) is uncolored point clouds. (**b**) is colored point clouds.