esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Watermarking Approaches
### Watermarking Methods
#### 3.2.2 Watermark Embedding via Noise Removal in Diffusion Process  
Watermarking during the diffusion sampling process involves modifying the sampling equations to incorporate watermark information directly into the generated data. This approach ensures that the watermark is seamlessly embedded during sampling, making it highly resilient to removal attempts. [PERSON] et al. (2018) introduce a novel approach for the diffusion model on convex constrained sets. Mirror maps transform data from constrained sets to Euclidean space, enabling efficient diffusion without performance loss. The watermarking scheme modifies the sampling equations to add user-defined tokens that create watermarked outputs, ensuring that only authorized users can verify the watermark, thus providing secure generation with certifiable watermarks. By modifying the sampling equations, these methods embed watermarks directly into the generated output during sampling, ensuring traceability and robustness while preserving the quality of the generated content.
## 5 Classification of Harmonic Emissions Profiles
### Binary Tree Structure Classifier  
The multi-class classification problem of five groups was divided into multiple two-class classifications using a binary tree structure classifier. The structure of the tree classifier was based on a top-down approach, and is displayed in Figure 6.  
The automated classification of emission profiles usually achieves better results if the easier classification problems are treated first [34]. Therefore, the first two-class decision was applied between the class D0 and the classes (D1, D2, D3, D4). The classes (D1, D4) and (D2, D3) were divided in a second two-class decision, and were finally divided into D1 or D4 and D2 or D3 within two additional classification steps [22].
# Location Selection Methods for Urban Terminal Co-Distribution Centers with Air-Land Collaboration
## 4 Algorithm Design
### Construction of a Co-Distribution Center Preselection Set Based on K-Means Clustering  
According to center ground theory and the concept of hexagonal network systems, when end outlets with different service capabilities are arranged, the service scope forms a hexagonal service area in the center ground, covering the whole demand area. Combined with the size of the actual service radius of express delivery outlets, this provides the initial set of distribution areas [23]. Taking the number of proposed outlets as the classification number, the set of co-distribution centers can be selected from the demand points using K-means clustering [24]. According to the model specified in Section 3.4, the set of demand points is J, the target classification number of the clustering algorithm is k, and the preselected set of co-distribution centers is I. Let I_n be the n-th point of this set. The process of selecting the distribution center from the preselected set using K-means clustering is as follows.  
Step 1: Randomly select a point from all customer demand points as the first point of the preselected set of co-distribution centers:  
\[I_{1}=Random(J),\ J=\{1,2,3,\ldots,m\} \tag{15}\]  
Step 2: Calculate the minimum distance of each remaining customer demand point from the preselected set of identified co-distribution centers:  
\[D(J_{l})=\min_{l\in J,k\in I}\sqrt{\left(x_{I_{l}}-x_{I_{k}}\right)^{2}+\left( y_{I_{l}}-y_{I_{k}}\right)^{2}} \tag{16}\]  
Step 3: Calculate the probability that each demand point will be selected as the next clustering center, and select that with the highest probability as a new preselected point for the co-distribution center:  
\[P(J_{l})=\frac{D(J_{l})}{\sum D(J_{l})^{2}} \tag{17}\] \[I_{j+1}=J_{s}|s=\max_{s\in J}P(J_{l})\] (18) \[I=I\cup I_{j+1},\ j=j+1 \tag{19}\]  
Step 4: If j = k, terminate the operation and return I as the final result, i.e., the preselected set of co-distribution centers; otherwise, return to step 2.  
After performing the aforementioned process, the clustering algorithms for different numbers of target categories were evaluated, and the final classification values and preselected set of co-distribution centers were determined [25].
## 2 Methodology
### Entropy Weight Method  
EWM [10; 11] is an objective weighting technique that measures the amount of information contained in each evaluation index by calculating its information entropy. It adjusts the weights based on the variability among data points, thereby minimizing the influence of subjective human judgment and improving the objectivity of the evaluation. The steps for calculating the EWM are as follows:  
1. Original matrix construction: Suppose there are \(m\) data samples and \(n\) evaluation indices. The original data matrix \(X=\left(x_{ij}\right)_{m\times n}\) is constructed, where \(X=\left(x_{ij}\right)_{m\times n}\) represents the value of the \(j^{th}\) index for the \(i^{th}\) sample.
2. The original matrix is normalized to eliminate the effects of differing units and scales. For details on the normalization process, refer to Ref. [17]. The resulting normalized decision matrix is denoted as \(P=\left(p_{ij}\right)_{m\times n}\), and is computed using Equation (3): \[p_{ij}=x_{ij}/\sum_{i=1}^{m}x_{ij}.\] (3)
3. Calculate the information entropy \(e_{j}\) and entropy weight \(w_{j}\) of the \(j\)-th evaluation index, as shown in Equations (4) and (5), respectively: \[e_{j}=-\frac{1}{\ln m}\sum_{i=1}^{m}\left(p_{ij}\ln p_{ij}\right),\] (4) \[w_{j}=\frac{1-e_{j}}{\sum_{j=1}^{m}\left(1-e_{j}\right)}.\] (5)
# Overcoming Challenges in Large-Core SI-POF-Based System-Level Modeling and Simulation
## 3 Component Modeling
### Detector Coupling  
In this section, we describe a model that allows for the computationally efficient simulation of optical coupling between the POF and a detector of arbitrary radius in the presence of misalignments. The model can be used to model spatial filtering at the detector and its dependence on the detector radius. It is based on the same equations used in the connector model for modeling the effects of lateral and longitudinal misalignment [50; 51]. Using Equation (20a,b), it is possible to determine how much power is actually coupled into the detector, and therefore, determine an angle-dependent loss factor as a function of lateral and longitudinal misalignment, where \(r_{0}\) is the lateral shift between the fiber axis and the detector center and \(z_{0}\) is the longitudinal distance of the fiber end to the active area of the photodetector. Accounting for the detector possibly having a radius \(b\) that is different from the fiber core radius \(a\), implies replacing \(a\) with \(b\) in Equation (20b). Thus, we can calculate the power-loss factor as a function of the propagation angle and the two shifts: \(l_{p}(\theta_{i},r_{0},z_{0})\). [50; 51]. The complete model can be represented as:  
\[\mathbf{P_{out}}=\mathbf{M_{det}}\cdot\mathbf{P_{in}} \tag{25}\]  
where \(\mathbf{P_{out}}\) is the vector of output intensities, \(\mathbf{P_{in}}\) is the vector of input intensities that can be functions of time or frequency, and \(\mathbf{M_{det}}\) is a diagonal matrix with diagonal elements \(m_{det}(\theta_{i})\) calculated as:  
\[m_{det}(\theta_{i})=l_{p}(\theta_{i},r_{0},z_{0}) \tag{26}\]
## 5 Post-Evaluation Analysis
### Spoken Term Detection  
Post-evaluation analyses for the STD task were performed based on INL and OOL query comparison and single-word and multi-word query comparison. Since the STD systems cannot retrieve OOV terms, the INV and OOV term comparison cannot be performed.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 5 The Cross-Layer Design Method
### The Optimization-Based Method
#### 5.1.1 Location Information-Free Routing Protocols
The residual energy-based routing protocols have good energy consumption performance, and the depth information-based routing protocols can achieve good propagation delay performance. The performance of [63; 67] are similar and they are both better than [66], since the works in [63; 67] take both the depth information and the residual energy into account. The work in [60] has better performance than the other routing protocols, because in [60] the link quality, the buffer space, and the history of successfully transmission are all taken into account. The performance of [62; 65] which divide the network into different layers based on the residual energy is similar, but the propagation delay in these two protocols is worse than in [63; 66; 67].
## 6 Simulation and Performance Analysis
### Simulation
(According to many experiments, TVFDA needs to run approximately 15 times before stabilizing, when the communication radius of the nodes is 30 m and the nodes are deployed in an area of 120 \(\times\) 120 \(\times\) 120 m\({}^{3}\)). However, at the network layering and clustering stage of PSA, each node needs to broadcast a maximum of four messages, and the communication radii of all the nodes are less than the maximum communication radius. Moreover, the locations of the nodes in the cluster can be obtained by optimizing the node position, that is, each of cluster-in nodes should broadcast once. Therefore, PSA is better than TVFDA in terms of communication energy consumption. Figure 8: Network communication energy consumption comparison. Figure 7: Network deployment energy consumption comparison. Figure 9 shows the number of nodes in the region within the distance \(R_{c}^{\max}\) to the sink node with varying number of nodes in the entire network. As shown, the numbers of nodes in both algorithms increase. However, for the same number of nodes, the number of nodes in the sink node area after PSA deployment is greater than that after TVFDA deployment. PSA does not optimize the cluster head nodes in the entire network and the number of the cluster head node is greater in the region near the sink node, which increases the number of nodes in the region. However, the objective of TVFDA is to promote the uniform distribution of nodes in the network to increase the coverage of the network without considering the number of nodes in the vicinity of the sink node.
# Research on Building Target Detection Based on High-Resolution Optical Remote Sensing Imagery
## 2 Data and Methodology
### Methodology  
This paper focuses on the research of building target detection based on optical remote sensing images. First, since the common components of a typical geometric structure of the vertical top view of a building are right-angle primitives composed of mutually perpendicular line segments, right-angle primitives are obtained using the broken line segment connection method and the right-angle detection method based on voting strategy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data/Resolution** & **Roll Satellite Angle** & **Pitch Satellite Angle** & **Yaw Satellite Angle** \\ \hline JLIGF03B01/1m & \(-25.60\) & \(1.29\) & \(2.98\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Jilin-1 Gaofen 03-1 satellite imagery information.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image** & **Place/Time** & **Size** & **Space Occupied** \\ \hline Image I & & \(700\times 700\) (I) & 407 KB (I) \\ Image II & Port/10 November & \(700\times 700\) (II) & 483 KB (II) \\ Image III & 2020 & \(800\times 800\) (III) & 509 KB (III) \\ Image IV & & \(700\times 700\) (IV) & 422 KB (IV) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Captured experimental image information.  
Figure 1: The original experimental images and the corresponding ground truth maps. (**a**) Original image I, (**b**) Ground truth I, (**c**) Original image II, (**d**) Ground truth II, (**e**) Original image III, (**f**) Ground truth III, (**g**) Original image IV, (**h**) Ground truth IV.  
Then, the inner and outer corners are classified to achieve multiple shapes of building top surface detection, and finally the complete top surface area of the building is obtained by using the image segmentation algorithm based on region growth. However, detection by shape features only can lead to problems such as incomplete detection of the building target facade area and inaccurate results caused by light occlusion. Therefore, the compactness and local contrast-based saliency model of the manifold ranking algorithm is introduced, while considering the low contrast between the building target areas and the background in remote sensing images, and then using the boundary priori saliency detection of the improved manifold ranking algorithm. The bounding box proposal is introduced to remove the environmental noise, and finally the final saliency map is obtained by integrating each detection result and setting the adaptive threshold to obtain the mask map. The rectangular approximation-based and the saliency-based detection results are fused and the interference in the ocean area is removed to obtain the final detection results. In this paper, the unbold notations represent scalars, the lowercase boldface letters represent vectors, and the uppercase boldface letters represent matrices.
## 3 Materials and Methods
### Analysis of Volatile Compound Profile  
Volatile compounds in kefir samples were determined by headspace solid phase microextraction (SPME) coupled with gas chromatography/mass spectrometry (GC/MS) (6890N GC, 5975 MS Agilent, Santa Clara, CA, USA).  
The SPME extraction of volatile compounds was performed using a manual SPME holder with fiber coated with divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR /PDMS; Supelco, Bellefonte, PA, USA), conditioned prior to use. The 5 mL kefir sampleswere transferred into headspace glass vials (20 mL) and hermetically sealed with septa. The vials were kept in water bath (35 \({}^{\circ}\)C) for equilibration (30 min). SPME extraction (40 min) was provided under constant stirring (1000 rpm) with a magnetic stirrer. After sampling, the SPME fiber was withdrawn into the needle, removed from the vial and inserted into the injector (270 \({}^{\circ}\)C) of the GC/MS instrument for 5 min, where the extracted volatiles were thermally desorbed directly to the column.  
The volatile compounds were separated on an HP-5 MS column: 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness, 5%-diphenyl-95%-polydimethylsiloxane (Agilent, Santa Clara, USA). Chromatographic separation was conducted under the following conditions: oven temperature was held for 10 min at 38 \({}^{\circ}\)C, then increased up to 200 \({}^{\circ}\)C (4 \({}^{\circ}\)C/min gradient) and held for 2 min, then raised to 250 \({}^{\circ}\)C at 20 \({}^{\circ}\)C/min, and that final temperature was held for 7 min. The mass-selective detector was operated at 70 eV, and the mass range was 30-350 m/z. The data obtained from GC/MS were processed using MSD ChemStation software (Agilent, Santa Clara, USA). Volatile compounds were identified by comparing their mass spectra with those of the NIST.08 and Wiley 7 th Ed (National Institute of Standards and Technology, USA) libraries and by computing retention indices relative to a series of standard alkanes (C6-C20, Kovats indexes; Sigma-Aldrich, Poznan, Poland). The quantities of volatile compounds were expressed as relative peak areas (peak area of each compound in Table S3/total area) \(\times\) 100.
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 4 Evaluation
### Simulation Results
#### 4.4.2 Evaluation for the Resource Allocation of MTS Scheme  
In this scenario, the distribution of UE population was the same as that of the cell selection scenario. We verified our proposed MTS scheme by comparing to the original max-min scheduling. As shown in Table 3, the max-min scheme can achieve almost optimal radio resource scheduling. In the MTS scheme, we added a threshold to protect the MUEs' QoS in dual connectivity mode. Figure 10 shows that the MTS scheme yielded higher QoS satisfactions from all aspects. Especially, the MTS scheme yielded 5.07% higher QoS satisfaction of the whole system as compared to the max-min scheme.
# Fault ride-Through Control Strategy for Variable Speed Pumped Storage Unit with Full-Size Converter
###### Abstract  
Fault ride-through is a prerequisite for ensuring continuous operation of a variable-speed pumped storage unit with a full-size converter (FSC-VSPU) and providing support for the renewable energy and power grid. This paper proposes low-voltage ride through (LVRT) and high-voltage ride through (HVRT) strategies for FSC-VSPU to address this issue. Firstly, the structure of FSC-VSPU and its control strategy under power generation and pumping conditions are described. Subsequently, the fault characteristics of the FSC-VSPU under different operating conditions are analyzed. More stringent fault ride-through technical requirements than those for wind turbines are proposed. On this basis, the fault ride-through strategies of combining fast power drop, rotor energy storage control, power anti-regulation control, dynamic reactive current control, low power protection, and DC crowbar circuit are proposed. Simulation case studies conducted in PSCAD/EMTDC verify the correctness of the theoretical analysis and the effectiveness of the LVRT and HVRT strategies in this paper.  
variable speed pumped storage unit (VSPSU); full-size converter (FSC); coordination control strategy; fault ride-through +
Footnote †: journal: Applied Sciences
## 4 Numerical Examples
### Example 1: 15-Bar Planar Truss  
The ground structure of this example is shown in Figure 7. A vertical tip load of 10,000 lb is imposed on node 8. The material density is 0.1 lb/in.3 and Young's modulus is specified as \(1.0\times 10^{7}\) Psi. The weight of this truss is minimized with 15 size variables and 15 topology variables. The original cross-sectional area for each bar is 0.5 in.2 and is permitted to vary from 0.1 in.2 to 10.0 in.2. The stress limit is 25,000 Psi for both the tension and compression of all members. The parameters of the GA are set as follows: the population size is 100, evolution generation is 100, and crossover and mutation probabilities are 0.9 and 0.1, respectively.  
Figure 8 illustrates the topology evolution. Two typical configurations are generated in the iterative procedure (Iterations 1 and 4). Figure 9 shows the optimized configurations of this paper, and Figure 10 shows the weight iteration history of this paper. The optimal solutions of variables, structural weight, critical constraint, and structural analyses are listed in Table 2 and compared with the literature. The optimal weight found by this paper is 75.376 kg after seven iterations. In contrast with the 15-bar truss size and layout optimization of previous studies, our solution weighs less than the solutions found by [PERSON] [29], [PERSON] [30] by 5.57% and 0.23%, respectively. The critical constraint is zero, implying the validity of the presented result. Five topology variables were labeled in the process. In particular, the method in this paper converged by 15 structural analyses. Compared with past studies, this method only took 0.19% of the structural analyses in the referred to literature, demonstrating the greatly enhanced efficiency of the proposed method. The optimal results of this classic example and the comparison illustrate the efficacy and the high efficiency of the improved method on truss optimization with mixed variables.  
Figure 7: Ground structure of the 15-bar truss.  
Figure 8: Topology evolution of the 15-bar truss.  
Figure 10: Iteration history for the 15-bar truss.  
Figure 9: Optimization results in this paper.
# Algorithms in Tomography and Related Inverse Problems--A Review
## 5 A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion
In the work by [PERSON], [PERSON] et al. [74], a novel deep learning framework named 3 DInvNet was introduced to address this challenge by reconstructing subsurface 3D permittivity maps from GPR C-scans, with a prior denoising step. The key contributions of this approach, as distinguished from existing deep learning-based GPR detection and 2D reconstruction methods, can be summarized as follows:  
1. A dedicated 3D denoising network, referred to as the \"Denoiser,\" has been meticulously crafted to combat noise interference within GPR C-scans, particularly in the presence of complex and heterogeneous soil environments. This denoiser incorporates a compact 3D convolutional neural network (CNN) architecture, leveraging residual learning principles and a feature attention mechanism to effectively distill the reflection signatures of subsurface objects from noisy C-scans. 2. Following the denoising process, a 3D U-shaped encoder-decoder network, aptly named the \"Inverter,\" is purposefully designed. Its primary function is to translate the denoised C-scans, as predicted by the denoiser, into comprehensive subsurface 3D per mittivity maps. To ensure robust feature extraction across a spectrum of objects with diverse properties, the inverter incorporates multi-scale feature aggregation modules. 3. To achieve optimal performance, a meticulously devised three-step independent learning strategy is employed, facilitating the pre-training and fine-tuning of both the denoiser and inverter components.
# Reduced Complexity Sequential Digital Predistortion Technique for 5G Applications
## 2 Sequential Digital Predistortion Using Under-Sampled Feedback Signals  
### Frequency Domain Analysis of Power Amplifier's Nonlinear Distortions  
Nonlinear distortions such as those exhibited by power amplifiers are commonly characterized by the spectrum regrowth they cause through intermodulation and harmonic distortions. Two-tones along with a simplified polynomial model of the nonlinear PA are often used to illustrate this basic concept. Harmonic distortions can be eliminated through filtering, and hence are not considered problematic when linearizing power amplifiers. Conversely, intermodulation distortions are located too close to the in-band signal to be filtered. Therefore, the synthesis of digital predistortion function is usually based on the observation of the amplifier's output signal over a bandwidth that spans up to five times the bandwidth of the input signal. This serves to guarantee the inclusion of the third and fifth order intermodulation distortions caused by the power amplifier. However, a closer look using a multi-tone signal can clearly illustrate that the in-band frequency range contains significant information about the nonlinearity of the power amplifier. To illustrate this aspect, a five-tone input signal is considered for this analysis. This time domain input signal \(x_{in}(t)\) is given by:  
\[x_{in}(t)=\sum_{i=1}^{5}A_{i}\times\cos(2\pi f_{i}t+\phi_{i}), \tag{1}\]  
where \(A_{i}\), \(f_{i}\), and \(\phi_{i}\) represent the amplitude, frequency and initial phase of the \(i^{th}\) tone.  
The power amplifier's nonlinearity can be modeled by a simple nonlinear polynomial function, such that  
\[x_{out}(t)=\sum_{k=1}^{N}\alpha_{k}x_{in}^{k}(t), \tag{2}\]  
where \(x_{out}(t)\) is the amplifier's output signal, \(N\) is the nonlinearity order, and \(\alpha_{k}\) are the model coefficients.  
The analytical derivation of the amplifier's output signal \(x_{out}\) for a third order nonlinear model, and the analysis of its frequency content show the presence of 145 components including 15 components around DC, 80 components in the in-band region corresponding to the input signal's frequency range (including intermodulation distortions), and 15 and 35 components in the frequency ranges corresponding to the second and third order harmonics, respectively.  
As a numerical example, let us consider the case of a five-tone signal where the tones' frequencies are uniformly spaced between 890 MHz and 910 MHz. Figure 1 depicts the number of terms at each frequency component around the original transmit band. Most importantly, this figure shows that the in-band frequency range has a significantly larger number of terms when compared to the intermodulation range. Therefore, it can be anticipated that the reduction of the observation bandwidth in the feedback path of the digital predistortion system can lead to a fairly accurate representation of the amplifier's nonlinear behavior.