esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Tensor Rank Constrained Image Inpainting Using a Novel Arrangement Scheme
## 3 Methods
### Method 1: The Low Unfolding Matrix Rank-Based Method
First, the image size of 256 \(\times\) 256 \(\times\) 3 is permuted to order-3 tensor with the size of 32 \(\times\) 32 \(\times\) 192 using the basic QA scheme. Then, this order-3 tensor is reshaped into the unfolding matrix of size 1024 \(\times\) 192. (**b**) The singular values of this unfolding matrix. where \(\tau_{\Omega}(X)\) denotes the indicator function:  
\[\tau_{\Omega}(X)=\left\{ \begin{aligned} & 0,\ X\in\Omega\\ &\infty,\ \text{otherwise}\end{aligned} \right.^{\prime}\]  
\(L\) and \(\Lambda\) are the Lagrangian multipliers for variables \(Z\) and \(UV^{H}\), respectively. The regularization parameter \(\beta\) is used to balance the low rankness and sparsity constraints (i.e., TV). The penalty parameters \(\rho_{1}>0\) and \(\rho_{2}>0\) generally affect the convergence of the algorithm. By applying ADMM, each sub-problem is performed at each iteration \(t\) as follows:  
\[X^{t}=\underset{X}{\text{argmin}}\tau_{\Omega}(X)+\frac{\rho_{1}}{2}\Big{\|}M \Phi_{1}X-U^{t-1}V^{(t-1)H}+\Lambda^{t-1}\Big{\|}_{F}^{2} \tag{5}\]  
\[U^{t}=\underset{U}{\text{argmin}}\|U\|_{F}^{2}+\frac{\rho_{1}}{2}\Big{\|}M \Phi_{1}X^{t}-UV^{(t-1)H}+\Lambda^{t-1}\Big{\|}_{F}^{2} \tag{6}\]  
\[V^{t}=\underset{V}{\text{argmin}}\|V\|_{F}^{2}+\frac{\rho_{1}}{2}\Big{\|}M \Phi_{1}X^{t}-U^{t}V^{H}+\Lambda^{t-1}\Big{\|}_{F}^{2} \tag{7}\]  
\[Z^{t}=\underset{Z}{\text{argmin}}\|Z\|_{1}+\frac{\rho_{2}}{2}\Big{\|}DX^{t}-Z +L^{t-1}\Big{\|}_{F}^{2} \tag{8}\]  
\[\Lambda^{t}=\Lambda^{t-1}+M\Phi_{1}X^{t}-U^{t}V^{(t)H} \tag{9}\]  
\[L^{t}=L^{t-1}+DX^{t}-Z^{t} \tag{10}\]  
The initial \(U\) and \(V\) can be determined by solving the following optimization problem using the LMaFit method [47]:  
\[\underset{U,V,X}{\text{min}}\Big{\|}U\ V^{H}-M\Phi_{1}X\Big{\|}_{F}^{2}\ \ \text{ subject to }X(i,\ j)=Y(i,\ j),\ \forall(i,\ j)\in\Omega \tag{11}\]  
The whole algorithm for solving the model (3) is shown in Algorithm 1. ```
Input:\(Y,\ \Omega,\ \rho_{1},\beta,\ \rho_{2}\), maximum number of iteration \(t_{\text{max}}\), convergence condition \(\eta_{tol}\). Initialization: initial \(U^{(0)}\), \(V^{(0)}\) by solving the matrix completion problem (11), \(\Lambda^{(0)}\), \(L^{(0)}\), \(L^{(0)}\), t = 0. ```  
**While \(t<t_{\text{max}}\) and \(\eta<\eta_{\text{max}}\)do**  
The first flexible QA scheme: Turn an image into an order-N tensor \(\Phi_{1}X\), then unfold it. Solve (5)-(10) for \(X^{*}\), where * represents the optimal solution. Update \(\eta_{t+1}=\frac{\left\|X^{t+1}_{t}(:)-X^{t}_{t}(:)\right\|_{F}}{\left\|X^{t}_ {t}(:)\right\|_{F}}\), \(t=t+1\). Endwhile ```
**Output:**\(X^{*}\).
## 4 Methodology
### The 12-Parameter Transformation  
With the change of the origin position, orientation of axes, and scale of coordinate system that exist in the 9-parameter transformation, the 12-parameter transformation also implies skew of coordinate axes [9]. This implies that orthogonal axes of the input coordinate system are not transformed into orthogonal axes in the output coordinate system. The 12-parameter transformation is not often used in common geodetic use and GIS software due to its complexity and the insufficient accuracy improvement it offers compared to other types of transformations. However, the study of [PERSON] et al. [45] shows that using 12-parameter transformation for the coordinate transformation of airborne LiDAR data and terrestrial LiDAR data have 2-3 times greater accuracy than 7-parameter transformation. Also, the transformation is part of different software programs in medical image computing for mapping points in one 3D image volume into their geometrically corresponding points in another, related 3D image volume [8].  
General expression for 12-parameter transformation is [9]  
\[\mathbf{X}_{o}=\mathbf{t}+\mathbf{H}\cdot\mathbf{R}\cdot\mathbf{S}\cdot \mathbf{X}_{i} \tag{72}\]  
where \(\mathbf{X}_{o}\) represents the position vector in the output coordinate system, \(\mathbf{t}\) is the translation vector, \(\mathbf{H}\) is the skew matrix, \(\mathbf{S}\) is the scale matrix, \(\mathbf{R}\) is the rotation matrix, and \(\mathbf{X}_{i}\) is the position vector in the input coordinate system.  
Equation (72), written in matrix form, is  
\[\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}_{o}=\begin{bmatrix}t_{x}\\ t_{y}\\ t_{z}\end{bmatrix}+\begin{bmatrix}1&h_{xy}&h_{xz}\\ 0&1&h_{yz}\\ 0&0&1\end{bmatrix}\cdot\begin{bmatrix}r_{11}&r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{bmatrix}\cdot\begin{bmatrix}\mu_{x}&0&0\\ 0&\mu_{y}&0\\ 0&0&\mu_{z}\end{bmatrix}\cdot\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}_{i} \tag{73}\]
# Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach
## 4 Conclusions and Further Work
This work was carried out under the ICCAT Atlantic-Wide Research Programme for Bluefin Tuna (ICCAT GBYP 10/2020), which is funded by the European Union, several ICCAT CPCs, the ICCAT Secretariat, and other entities (see [[https://www.iccat.int/gbyp/en/overview.asp](https://www.iccat.int/gbyp/en/overview.asp)]([https://www.iccat.int/gbyp/en/overview.asp](https://www.iccat.int/gbyp/en/overview.asp)), accessed on 22 January 2024). This study was conducted according to the guidelines of the Declaration of Helsinki. Ethical review and approval were waived for this study, because the use of underwater stereocameras to measure bluefin tuna dimensions is a non-invasive technique that does not cause them any suffering. The feeding activity is a normal activity in the commercial maintenance of bluefin tuna in cages. Not applicable. The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to the corresponding author. The content of this paper does not necessarily reflect ICCAT's point of view or that of any of the other sponsors, who carry no responsibility. In addition, it does not indicate the Commission's future policy in this area. This project has been possible thanks to the collaboration of Grup Balfego. The autonomous monitoring system was designed and built in collaboration with Zunibal S.L. The authors declare no conflicts of interest. The funders had no role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Author [PERSON] is employed by Company Zunibal SL.
# Research on Routing Algorithm of Construction Robot Cluster Enhanced Ad Hoc Network
## 3 ACER Routing Algorithm
### Dynamic Routing Maintenance  
It is discarded when a node repeatedly receives a routing message in the route establishment time. Otherwise, it is added, and the routing table is updated. The specific time measure is set to \(T\), as shown in Equation (12):  
\[T=\frac{6}{n}\sum_{0}^{n}t_{ij} \tag{12}\]  
where \(t_{ij}\) denotes the time required for any two nodes in the mobile self-assembly network to establish a route, \(n\) is the total number of nodes, and to ensure the accuracy of \(T\), it is necessary to select n as many nodes as possible. Once the routing path is established, all the routing information received by the non-routing path is deleted; when the routing path is established, after \(T\) time, and the routing path still has not established communication, then the routing path is invalid, all the previously established routing paths are disconnected, and the paths are released. Two conditions trigger route maintenance: node movement and adding or removing nodes within the network.  
The routing maintenance steps are as follows:  
1. Node \(i\) sends link confirmation information to neighboring nodes in the routing table and retains the node's routing information if the return information is received within the cycle time. Otherwise, it is deleted, and the routing table is updated;
2. Node \(i\) broadcasts a connection request packet with maximum power across the network. If it receives a reply packet from a node other than the node described in step 1, it updates the routing table by adding the routing information arriving at that node to the routing table.  
If a node moves during the transmission of a packet from source node \(i\) to target node \(j\), causing a topology change that results in a break in an otherwise valid link, node \(m\) at the path break returns a routing error message RERR containing information about the node at the link break to the previous hop node \(l\). At this point, node \(l\) traverses the routing table to seek a new path to the target node \(j\). If there is one, the original routing table contains nodes in the path from node \(i\) to node \(l\) that are updated in the routing table; if not, node \(l\) continues to forward RERR to the next hop node until it finds an available route. If RERR is forwarded to source node \(i\) and no available route is found, the route discovery is re-run.
## 2 Theory
### Differential Absorption Measurement in Transmission
By accurately monitoring the transmitted power for both the online and offline signals, \(\frac{P_{Loff}}{P_{Lon}}\) is a known value, and the measurement provides the differential transmission due to the molecule of interest. By taking the natural logarithm of the differential transmission, one is left with the differential absorption, \(\Big{(}\int_{0}^{R}\alpha(\lambda_{on},r)dr-\int_{0}^{R}\alpha\big{(}\lambda_{ off},r\big{)}dr\Big{)}\), which is related to the total column abundance of the gas between the transmitter and receiver, separated by range \(R\), by:  
\[\int_{0}^{R}N(r)\;dr=\frac{1}{\Delta\sigma(\lambda_{on},\lambda_{off},r)} \bigg{[}\int_{0}^{R}\alpha(\lambda_{on},r)dr-\int_{0}^{R}\alpha\big{(}\lambda_ {off},r\big{)}dr\bigg{]} \tag{9}\]  
where \(\Delta\sigma(\lambda_{on},\lambda_{off},r)\) is the differential cross-section between \(\lambda_{on}\) and \(\lambda_{off}\) as a function of range to range \(R\), determined through laboratory spectroscopy, and the quantity on the right hand side is the measured value as described above. In reality, \(\Delta\sigma\) is a function of atmospheric pressure, temperature, and water vapor concentration in the path of integration, and thus these parameters must also be quantified in order to determine the effective differential cross-section and derive the dry-air mixing ratios of desired GHGs [25]. In the LAnTeRN instrument concept, we use coincident _in situ_ measurements of these meteorological parameters to minimize the atmospheric state uncertainties on determination of \(\Delta\sigma\), and for the retrieval of the GHG mixing ratio from the average number density.
# Microwave Heat Treatment Induced Changes in Forage Hay Digestibility and Cell Microstructure
## 4 Discussion
### Dry Matter Digestibility and Digestibility of Organic Matter in Dry Matter  
The DMD and DOMD contents were affected by the MW treatment, although the effect was not constant across different hays in this study. Lucerne hay showed the highest increase of all hays in the present study, with increases in in DMD content (11%) and DOMD content (14%) at 60 s MW treatment compared with the control. The level of increase in the DMD content of lucerne hay was similar to the finding of [PERSON] et al. (2012) [13]. The study showed that DMD increased by 14.9% when treated for 80 s MW treatment (750 W) compared with the control. [PERSON] et al. (2012) [13] speculated that the increase of DMD content was possibly due to the destruction of the cell microstructure, which is similar to what was observed in the current study (Figures 1 and 2). The cell microstructure destruction from MW treatment might increase nutrient availability and access by enzymes (e.g., CP digestibility). Canola hay showed a 10% and 12% increase in DMD and DOMD content, respectively, at 20 s MW treatment compared with the control. Wheat hay showed a 5% and 8% increase in DMD and DOMD content at 40 s MW treatment compared with the control. The increasing trend of digestibility in this study was supported by [PERSON] et al. (2005) [14], who found a 20% increase in ruminal OM degradability in a nylon bag degradability study of 240 s MW treated wheat straw. Furthermore, a number of researches on concentrate feed (e.g., grains, seed, and seed meal) showed that, MW treatment increase digestibility of concentrate feeds [12,24,25]. However, the improvement of digestibility level in forage hay is comparatively lower than concentrate feeds. This is may be due to the difference in nutrient content, density, and cellular structure [26].
# Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring
## 2 State of the Art
Despite the introduction of various solutions, there is an ongoing search for increasingly effective parking management systems that also ensure the security of implemented functions. The application of wireless data transmission through devices with low power consumption enables increased efficiency in smart parking systems. LoRaWAN is the primary LPWAN technology that can be considered promising and with significant potential for applications in road transport. This also applies to scenarios discussed in [49], where vehicles are out of GSM and Internet network coverage.
## 4 LLC Converter Design Steps
### Quality Factor and Inductance Ratio Selection  
Since the DC gain in resonant converters depends on the operating switching frequency, the conventional state space method cannot be used anymore. Instead, the Fundamental Harmonic Analysis (FHA) model could be applied [64]. The time-domain equations for the LLC resonant converter are also presented in [65] based on Time Interval Analysis (TIA).  
By using the FHA model, the DC source and half-bridge/full-bridge network are replaced by the ideal sinusoidal AC source with fundamental frequency, \(f_{n}\). By applying some simplifications, the LLC converter DC gain can be expressed in terms of the inductance ratio, \(L_{n}\), and quality factor, \(Q\), as follows [4]:  
\[M=\left|\frac{L_{n}f_{n}^{2}}{(L_{n}+1)f_{n}^{2}-1+j[(f_{n}^{2}-1)f_{n}QL_{n}]}\right| \tag{4}\]  
where the quality factor of the series resonant circuit, \(Q\), is defined as follows [4]:  
\[Q=\frac{\sqrt{L_{r}/C_{r}}}{R_{ac}} \tag{5}\]  
Many design procedures have been presented [64; 66; 67]. Among them, we preferred to use the procedure by [PERSON] et al., which allowed us to obtain the circuit parameters without any recursive calculations, starting from the set of converter specifications.  
Inductance ratio, \(L_{n}\), is defined as a ratio of the magnetizing inductance, \(L_{m}\), to the series resonant inductance, \(L_{r}\). A smaller magnetizing inductance will increase the magnetizing current and, consequently, conduction loss. To have a smaller magnetizing inductance that can aid ZVS, a smaller inductance ratio is required.
# Soil and Rockfill Dams Safety Assessment for Henan Province: Monitoring, Analysis and Prediction
###### Abstract  
It is of great significance to explore the spatial and temporal evolution of soil and rockfill dam deformation, ensuring the safety of people's lives and healthy economic development. The spatial and temporal evolution patterns of deformation of 17 large soil and rockfill dams in Henan Province were analyzed by using the PS-InSAR technique and 55 Sentinel-1A images from March 2017 to September 2021. Based on factors such as reservoir water level and rainfall, a series of analyses were conducted on the Xiaolangdi soil and rockfill dam, which has the highest dam height and the most prominent deformation problem. The monitoring results show that all the soil and rockfill dams in Henan Province have different degrees of deformation, and there is a close relationship between dam height and deformation. In addition, the deformation rate of the Xiaolangdi soil and rockfill dam in the past five years presents a \"Stepped Shape\" deformation trend from the top to the bottom of the dam. The deformation of the upper, middle, and lower parts of the dam body reaches 80 mm, 40 mm, and 20 mm, respectively, among which the middle part of the dam crest has the largest deformation. Furthermore, the time series prediction model for sparrow search algorithm Long Short-Term Memory considering the moving average filter (MAF-SSA-LSTM) is proposed to predict and accurately analyze the future deformation of Xiaolangdi soil and rockfill dam with RMSE of 1.526 mm, MAE of 1.447 mm, and MAPE of 2.22%, which proved that the model has high prediction accuracy. It can truly reflect the overall deformation trend of the dam body. The results provide a theoretical basis and decision basis for the census of reservoir safety conditions and deformation history retrieval in Henan Province.  
PS-InSAR; soil and rockfill dam; deformation monitoring; MAF-SSA-LSTM neural network
# An Image Authentication Scheme Using Merkle Tree Mechanisms
###### Abstract  
Research on digital image processing has become quite popular and rapid in recent years, and scholars have proposed various image verification mechanisms. Similarly, blockchain technology has also become very popular in recent years. This paper proposes a new image verification mechanism based on the Merkle tree technique in the blockchain. The Merkle tree root in the blockchain mechanism provides a reliable environment for storage of image features. In image verification, the verification of each image can be performed by the Merkle tree mechanism to obtain the hash value of the Merkle tree node on the path. In addition, the method combines the Inter-Planetary File System (IPFS) to improve the availability of images. The main purpose of this paper is to achieve the goal of image integrity verification. The proposed method can not only verify the integrity of the image but also restore the tampered area in the case of image tampering. Since the proposed method employs the blockchain mechanism, the image verification mechanism does not need third party resources. The verification method is performed by each node in the blockchain network. The experimental results demonstrate that the proposed method successfully achieved the goal of image authentication and tampered area restoration.  
Keywords:image authentication; Merkle tree; hash function; blockchain; distributed storage +
Footnote †: journal:
## 5 Proposed Models
### Architecture Overview
#### 5.3.2 Processing the Large Area Datasets  
Various external factors, weather conditions, and events can significantly impact INT flows. In our weather data category, we integrate four types of data: rainfall, pollution, temperature, and wind speed. These data sources are amalgamated and represented as \(E_{n}\): \(\{E_{0},\ldots,E_{t-1}\}\), with each data point collected at an hourly interval.  
The dataset includes sensor data for weather conditions, specifically capturing rainfall, pollution, temperature, and wind speed. However, these data are tied to specific sensors in various areas, and grid maps for these data points are not provided. Since our predictive analysis focuses on the downtown area, we select the sensor closest to this central region. To standardize the data, we apply Min-Max normalization to rescale the values within a range of [0, 1]. Subsequently, the four types of weather data are concatenated into a single vector comprising four elements, denoted as \(E_{n}\). Instead of utilizing CNN and LSTM models, this vector is introduced into a fully connected (FC) layer, effectively encapsulating the features of weather conditions.  
The time interval for the weather data deviates from that of INT, Milano Today, and Social Pulse, which are recorded at 10-minute intervals. This discrepancy necessitates a distinct structure to process the weather data. The four-element weather vector, prepared during preprocessing, is fed into two FC layers. The initial layer functions as an embedding layer, capturing the intricacies of weather features. We choose an embedding size of 100 for this purpose. The subsequent layer is responsible for reshaping the data to align with the neural network's output dimensions for data fusion. This reshaping layer is configured to output data with dimensions of 512, ensuring compatibility with the overall neural network architecture.
## 2 Engineering Background and in Situ Monitoring Methods  
### Engineering Background  
The deep-buried small-clearance tunnel is designed to be excavated using the drilling and blasting method along three parallel alignments. The distance between adjacent tunnel walls is approximately 11.7 m, with a maximum burial depth of around 2080 m. The terrain traversed by the tunnel is characterized by steep slopes, deeply incised gullies, and highly developed \"V\"-shaped valleys. A partial section of the tunnel is depicted in Figure 1. The primary external forces influencing the tunnel are ice, snow, and cold weathering. The geological structure of the tunnel area is complex, with tectonic movements predominantly exhibiting compressive stress in the north-northeast direction. The lithology in the area is highly varied, with significant metamorphic variation in the local gneiss. The rock formations primarily consist of gneiss (90.2%) and granite (9.8%), with interspersed layers of schist and quartzite.  
The rock mass in this section is relatively fractured, with well-developed joints and a f5-8 fault. The fault is initially speculated to exhibit a left-lateral strike-slip motion, with a fault strike orientation of N35\({}^{\circ}\)-50\({}^{\circ}\) W and a dip direction of 70\({}^{\circ}\)-85\({}^{\circ}\) N. The fault is approximately 120 m wide and intersects the tunnel alignment at a steep angle of around 87\({}^{\circ}\), and the intersection mileage is DK193+135-DK193+255. The fault is predominantly composed of crushed rock and fault breccia, with localized altered rock formations. The location plane of the fault and tunnel is shown in Figure 2.  
Figure 1: Schematic diagram of the partial section of the tunnel.