esa-sceva/satcom-chunk-collection huggingface dataset:
# Wavelet-Based Estimation of Damping from Multi-Sensor, Multi-Impact Data
## 2 Problem Formulation
### Model of Observation  
One of the most commonly used methods for extracting unknown parameters from Equation (2) involves recording the impulse response of vibrating systems. This is typically performed by applying an impact input to the system, such as striking the vibrating structure with a hammer and subsequently recording its response. However, in practical scenarios, determining the unknown parameters \(A_{j}\) can be challenging. The inconsistency in the impact force makes it difficult to obtain reliable measurements, especially since no sensor is available to monitor the impact amplitude. Additionally, the presence of noise in the sensors can further diminish the accuracy of the impulse response measurements. Furthermore, there is also uncertainty regarding the exact timing of the impact, which may vary with each recording.  
To address these challenges, for a set of \(K\) signal recordings modeled by Equation (2), the measurement model can be expressed as follows:  
\[\text{Signal}^{k}(t)=B^{k}x(t-\tau_{0}^{k})+\epsilon^{k}(t). \tag{4}\]  
Here, \(k\) is an index that ranges from 1 to \(K\), representing each individual signal recording in the set. In this revised model, the additive noise due to observation inaccuracies is represented by a random variable \(\epsilon^{k}(t)\), which is assumed to follow a white noise distribution with zero mean and variance \(\sigma_{e}^{2}\) (\(\epsilon^{k}(t)\sim N(0,\sigma_{e}^{2})\)). \(B^{k}\) signifies the amplitude of the impact. The moment of impact is modeled by a time-shift \(\tau_{0}^{k}\), which is considered to be uniformly distributed (\(\tau_{0}^{k}\sim U(0,T_{f})\)), with \(T_{f}\) being the final recording time.  
The measurement model presented in Equation (4) is designed to account for the inherent variabilities and uncertainties that are typically encountered in real-world applications. Specifically, the model incorporates impact amplitude variability using \(B^{k}\), imprecision in the time of impact by \(\tau_{0}^{k}\), and a noise component \(\epsilon^{k}(t)\). By including these factors, the model provides a more realistic framework for understanding and analyzing impulse response recordings in vibrating systems.  
One crucial aspect to consider when discussing real-world applications is the nature of the signal acquired by sensors, particularly when using an IMU. When leveraging an IMU, the recorded signal is that of acceleration rather than position. While this may initially seem like a significant difference, in the frequency domain, its primary effect is on the amplitude. However, this amplitude shift does not affect the frequency or damping components of the signal, which are critical for our analysis. By modeling the system in terms of acceleration, the extraction methodologies will stay consistent. Therefore, while the IMU provides acceleration data, our approaches and analytical strategies remain effective.
# Integrating IP Mobility Management Protocols and MANET: A Survey
## 1 Introduction
It presents, analyzes, and discusses existing mobility management solutions for integrated MANET networks over the last two decades. The surveyed proposals are all placed into three main categories based on the adopted gateway discovery mechanism: Proactive gateway discovery-based mobility management solutions, reactive gateway discovery-based mobility management solutions, and hybrid gateway discovery-based mobility management solutions. A comparative analysis is also provided for each category using a table form. The rest of the paper is divided into four sections. Section 2 provides an overview of traditional mobility management solutions. Section 3 highlights common MANET routing protocols, especially those that have been commonly used in the surveyed solutions. A review of existing mobility management solutions for integrated MANET, along with the comparative study is presented in Section 4.
# Petri Net Modeling for Ising Model Formulation in Quantum Annealing
## 2 Preliminaries
### Combinatorial Optimization Problems  
Combinatorial optimization is a branch of operations research and computer science; it is utilized frequently in a wide range of areas, such as artificial intelligence, bioinformatics, and VLSI circuit design [21; 22].  
A combinatorial optimization problem can be denoted by \(CP=(X,\Sigma,Cstr,f)\), where \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) is a set of variables, \(\Sigma=\{\Sigma_{1},\Sigma_{2},\ldots,\Sigma_{n}\}\) is a set of types for each variable, \(Cstr\) is a set of constraints, and \(f:\Sigma_{1}\times\Sigma_{2}\ldots\times\Sigma_{n}\rightarrow\mathbb{R}\) is an objective function such that \(\Sigma\) includes discrete types, such as integers, binary numbers, and Boolean values.  
Here, \(\mathbb{R}\) is the set of real numbers. The combinatorial optimization problem requires the optimum solution to be obtained with respect to \(f\), out of all the feasible solutions, where feasibility means that the values assigned to the variables satisfy all of the constraints in _Cstr_.  
Many combinatorial optimization problems are NP-hard; that is, no polynomial-time deterministic algorithms are known, to date, to solve such problems [5]. Sophisticated solvers, based on the branch-and-bound and branch-and-cut methods, can efficiently search for feasible solutions; however, they are still computationally insufficient for large-size instances of NP-hard problems.  
Related to a given combinatorial optimization problem, we sometimes consider corresponding decision problemsin quantum annealing, for which rather than requiring just a yes or no for the existence of a feasible solution, we also need to obtain a feasible solution for the decision problem. This type of decision problem is called the search variant as compared to the yes-no type decision variant [5]. For example, we solve a search variant decision problem to obtain a viable schedule to complete all tasks by a given deadline in scheduling problems.  
It should be noted that NP-hard decision problems, both search and decision variants, are equally difficult to solve for the corresponding optimization problem from the viewpoint of computational complexity. However, a search variant can be implemented more easily than an optimization problem for quantum annealing. Iterative solving of search variants of the decision problem leads to the optimum solution by varying the parameter related to the optimization, that is, the deadline for the scheduling problem. We use this technique in the job shop scheduling example in Section 4.  
In the following sections, we treat four NP-hard combinatorial optimization problems: minimum vertex cover, graph partitioning, traveling salesman, and job-shop scheduling problems to enable readers to comprehensively understand our method and to show the usefulness of our Petri net modeling-based approach for quantum annealing.
# Extracting Sentence Embeddings from Pretrained Transformer Models
## 5 Conclusions
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **T0** & **T4** & **BERT** & **BERT + Avg.** & **Avg.** & **B2S** & **B2S-100** & **RE** \\ \cline{2-9}
**Layer** & \multicolumn{3}{c}{**Last**} & \multicolumn{3}{c}{**First + Last**} & \multicolumn{3}{c}{**-**} \\ \hline avg. & 67.3 & 73.3 & 72.2 & 70.8 & 63.6 & 72.0 & 72.0 & 23.3 \\ + normalize & 68.2 & 73.8 & 81.7 & 73.1 & 65.7 & 76.4 & 76.3 & 20.4 \\ + quantile-u & 69.8 & 72.4 & 74.1 & 73.0 & 66.8 & 75.2 & 72.9 & 22.3 \\ + quantile-u\({}^{W}\) & 69.7 & 73.5 & 81.6 & 74.7 & 68.6 & 75.7 & 75.1 & 21.7 \\ + whiten & 33.8 & 36.1 & 32.3 & 31.7 & 26.3 & 22.9 & 24.8 & 21.2 \\ + whiten\({}^{W}\) & 47.7 & 49.2 & 58.3 & 61.8 & 56.3 & 58.8 & 58.3 & 24.0 \\ + zscore & 67.9 & 73.2 & 72.7 & 71.3 & 63.7 & 72.3 & 72.1 & 22.8 \\ \hline idf\({}^{W}_{t}\) & 67.1 & 67.0 & 71.9 & 70.4 & 63.0 & 69.9 & 69.9 & 29.2 \\ + normalize & 68.4 & 69.4 & 81.8 & 73.0 & 65.2 & **81.4** & **81.4** & **36.6** \\ + quantile-u\({}^{W}\) & 70.3 & 71.6 & 73.8 & 72.5 & 65.9 & 74.4 & 72.2 & 30.7 \\ + quantile-u\({}^{W}\) & 70.8 & 72.5 & 80.6 & 74.0 & 67.1 & 74.8 & 80.7 & 32.1 \\ + whiten & 32.7 & 34.8 & 33.0 & 31.7 & 23.7 & 27.3 & 24.1 & 20.5 \\ + whiten\({}^{W}\) & 47.8 & 51.9 & 64.5 & 66.1 & 61.9 & 67.6 & 68.5 & 30.0 \\ + zscore & 69.8 & 70.0 & 72.2 & 70.4 & 62.7 & 70.1 & 70.2 & 29.7 \\ \hline idf\({}^{I}_{t}\) & 55.1 & 75.3 & 72.0 & 70.5 & 59.3 & 72.0 & 72.0 & 26.1 \\ + normalize & 63.2 & 75.6 & 81.2 & 76.9 & 60.9 & 76.4 & 76.3 & 24.8 \\ + quantile-u & 68.8 & 78.5 & 74.6 & 76.1 & 59.1 & 75.2 & 72.9 & 26.1 \\ + quantile-u\({}^{W}\) & **71.2** & 78.7 & 81.3 & 77.0 & 61.0 & 75.7 & 75.1 & 25.8 \\ + whiten & 30.6 & 35.7 & 30.8 & 29.8 & 25.0 & 22.9 & 24.8 & 21.3 \\ + whiten\({}^{W}\) & 46.8 & 46.1 & 62.7 & 63.0 & 52.9 & 58.8 & 58.3 & 26.7 \\ + zscore & 68.6 & 79.1 & 73.3 & 72.4 & 59.0 & 72.3 & 72.1 & 25.8 \\ \hline -biases & 66.5 & 70.4 & 82.6 & 73.0 & 71.7 & 70.5 & 71.9 & 22.8 \\ + normalize & 67.0 & 70.5 & **82.9** & 74.8 & 73.8 & 75.2 & 80.9 & 22.6 \\ + quantile-u & 68.8 & 80.3 & 80.7 & 73.8 & 72.3 & 73.5 & 73.8 & 23.8 \\ + quantile-u\({}^{W}\) & 69.8 & **80.6** & 82.6 & 76.5 & **74.2** & 75.6 & 72.7 & 22.9 \\ + whiten\({}^{W}\) & 48.4 & 46.0 & 64.5 & 64.4 & 54.9 & 57.1 & 53.6 & 23.9 \\ + whiten & 31.6 & 36.3 & 33.2 & 29.3 & 22.7 & 23.3 & 23.9 & 22.0 \\ + zscore & 67.6 & 79.8 & 82.1 & 72.6 & 71.4 & 72.6 & 72.6 & 23.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: _Cont._
## 4 Routing Protocols Using Ant-Like Mobile Agents
### Mobile Agent Routing (MAR)  
[PERSON] and [PERSON] proposed an algorithm called Mobile Agent Routing (MAR) [43]. In MAR, each data packet is considered as an agent that explores the network to collect routing information. Like ARA, MAR protocol has a certain number of ant-like agents, and they constantly explore the network to collect the routing information [43]. The ant agent of MAR has a history list with fixed length. The list contains the intermediate nodes the ant agent has visited. When the ant agent arrives at a node, the node looks at the history list, and if it finds better routing information than one in its own routing table, the node copies the information from the list to the routing table. If the node has better information than one in the history list of the ant agent, it also copies it to the history list of the ant agent. Then the node lets the ant agent move to a randomly selected neighbor node. If the nodes in the network come and leave frequently, the number of ant agents is increased. When the network becomes stable, the number of ant agent is decreased. In order to assess the stability of the network, each node sends _hello messages_ to its neighbors. If the node finds no reply from a certain node, it regards the node as having left the network, and deletes its entry from the routing table.
# The Spotting Distribution of Wildfires
## 2 A Transport Model for Firebrand Transport and Combustion
If one examines too far downwind, one will not find spotfires from an impulse release, since there is a finite combustion lifetime for each firebrand. It is useful at this point to introduce the _combustion operator_ and its inverse, where the combustion operator \(C(m,t)\) tells us how much mass (in kilograms) remains after \(t\) time units, starting from \(\text{mass}=\ m\). The inverse combustion operator \(C^{-1}(m,t)\) is then defined as usual by  
\[C^{-1}(C(m,t),t)=m. \tag{3}\]  
Another important concept is the _landing time_, which we denote by \(t^{*}(x)\). The landing time has units of seconds, and quantifies how long it took a firebrand released at \(x=0\) to reach the ground at location \(x\). Provided windspeeds vary monotonically with height, each downwind location will have a unique landing time, hence this quantity is well defined. For example, if we have constant windspeed \(w\) and falling speed \(v\), then \(t^{*}(x)=\frac{x}{w}\), as can easily be computed. In order to apply the ignition operator, it is necessary to determine the total landed mass at given location. This involves integrating the asymptotic landing distribution against \(m\), incorporating the inverse combustion operator evaluated at the landing time into the distribution for the integration. We delay the presentation of this complicated term until Section 2.3. Ignition, if it occurs, is extremely rapid in tinder-dry burning conditions (observed experimentally for example in the Porter Lake experiments [42]), which corresponds to scenarios we are interested in exploring. In this context we assume that masses ignite instantly upon landing. Hence to determine the spotting distribution, we can apply the ignition operator to the total landed mass, where, as in the previous paragraph, the landing distribution's mass component is evaluated in terms of the inverse combustion operator at the landing time. The exact formula is given in Equation (23). One can then augment our earlier flow chart to include spotting,  
\[\underbrace{\phi(z,m)}_{\text{Launching distribution}}\quad\underbrace{ \rightarrow}_{\text{Transport and combustion}}\quad\underbrace{\mathbb{L}(x,m)}_{ \text{Landing distribution}}\quad\underbrace{\rightarrow}_{\text{Ignition}}\quad \underbrace{\mathbb{S}(x)}_{\text{Spotting distribution}} \tag{4}\]  
In turn, this flowchart defines a _map_\(\mathbb{T}:\phi\rightarrow\mathbb{S}\). This is a map between distributions, which allows for abstract mathematical explorations of the spotting process.
## 4 Results
### GBDT Model Construction and Accuracy Analysis
#### 4.3.2 Variable Importance Analysis  
In this study, a total of 26 variables were derived from optical imagery, terrain data, and auxiliary datasets. Using the gradient boosting decision tree regression algorithm, the contribution rates of each variable to the AGB estimation model were ranked based on their importance. The results, as presented in Figure 7, indicate that the three most influential variables are \"TCG\", \"RENDVI\", and \"VDVI\". This highlights the significant role of the tasseled cap transformation index and vegetation indices in capturing critical spatial variations in AGB. Among these, the contribution rate of the tasseled cap transformation index far surpasses the other variables, underscoring its pivotal role in the biomass estimation model. This phenomenon can likely be attributed to the fact that the tasseled cap transformation incorporates critical structural and compositional information. It effectively reduces the influence of soil brightness in the imagery while enhancing the greenness component. This effect becomes increasingly pronounced with greater vegetation coverage, as the transformation accentuates the spectral differences associated with varying levels of vegetation density.
## 3 Performance Analysis Methods
### Expressions for Path Loss and Average Path Loss
#### 3.3 Expressions for Channel Capacity  
Channel capacity, as an important metric for performance analysis of communication systems, is the maximum data transmission rate that can be achieved by the communication system. In short-distance and free space optical communication using IM/DD, the average and peak intensity of the transmitted signal is limited [50]. Thus, most of the current research mainly focuses on the bounds of the system capacity, i.e., the emphasis is on deriving the lower/upper bounds [51; 52]. In [52], a closed-form expression of channel capacity for VLC systems with high SNR is derived by neglecting the gap between the exact and lower limit values of channel capacity:\[C=\frac{B}{2\ln 2}\ln\left(1+\frac{e\sum\limits_{j=1}^{2}\text{SNR}_{j}}{2\pi} \right), \tag{20}\]  
where \(B\) is the bandwidth; \(e\) is the natural constant and \(\text{SNR}_{j}\) denotes the average received electrical signal-to-noise ratio (SNR) from the \(j^{th}\) Tx. For the proposed V2I-VLC system, since the received optical power at Rx can be expressed by (15), \(\text{SNR}_{j}\) can be expressed as follows:  
\[\text{SNR}_{j}=\frac{\left(\eta P_{t,j}h_{j}\right)^{2}}{\sigma_{o}\,^{2}}. \tag{21}\]  
Substituting (2) and (21) into (20), the channel capacity of the proposed V2I-VLC system can be obtained as follows:  
\[C=\frac{B}{2\ln 2}\ln\left[1+\frac{e\left(\eta P_{t,j}h_{a}h^{PL,\alpha_{p}} \right)^{2}}{\pi\sigma_{o}\,^{2}}\right]. \tag{22}\]
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 3 Assessment of Processing Time and Memory Requirements
### Acquisition of a Pilot Channel with Assistance
#### 3.3.1 Coherent Integration Only with Serial Implementation  
The implementation is exactly the same as when there is no assistance, shown in Figure 6. The timing diagram is also the same, shown in Figure 7, the only difference is that the part that repeats is repeated \(N_{\rm SCB}\) times instead of \(N_{\rm S}\) times. The processing time for one frequency bin is thus,  
\[\begin{array}{l}T_{\rm FB}=KN_{\rm FFT}N_{\rm S}N_{\rm SCB}+2(N_{\rm FFT}+L )-(N_{\rm P}+N_{\rm Z})\\ =KN_{\rm S}N_{\rm SCB}(2N_{\rm P}+N_{\rm Z})+3N_{\rm P}+N_{\rm Z}+2L\\ =N_{\rm P}(2 KN_{\rm S}N_{\rm SCB}+3)+N_{\rm Z}(KN_{\rm S}N_{\rm SCB}+1)+2L. \end{array} \tag{20}\]  
Therefore, the processing time is approximately divided by \(N_{\rm S}/N_{\rm SCB}\) compared to the case without assistance, as expected. Note that if the assistance is perfect, i.e., \(N_{\rm SCB}=1\), the processing time becomes the same as the one for the parallel implementation without assistance (Equation (9)), as expected.
## 3 Results and Discussion
### Evaluation and Discussion  
A comparative analysis was also conducted on the workload of the existing 152 fire stations and the 150 optimal fire station locations obtained in this study. In our former research, we obtained the distribution of all incidents in Shanghai in 2022. Based on the sites of 150 stations in the model, the counts of incidents in protected units of each station could be measured. Figure 9 shows the annual incidents in different jurisdictions and the counts of fire stations for different incident ranges after the re-planning. Compared to Figure 2, the number of incidents exceeding 1000 throughout the year was reduced to 0. Additionally, the number of fire stations in areas with less than 200 incidents per year was reduced to 19. Most fire station jurisdictions encompassed an area below 50 km\({}^{2}\). There were no coverage areas with excessively small or extremely large areas, resulting in a more balanced distribution of fire resources.  
Figure 8: (a) Response coverage rate and (b) average travel time at different numbers of fire stations.  
Figure 9: (a) The annual incidents in different jurisdictions and (b) the counts of fire stations for different incident ranges after the re-planning.  
To assess the level of uniformity in the incident distribution of the station placement scheme, the unevenness (\(U\)) of the station deployment load was defined as  
\[U=\sqrt{\frac{\sum_{i=1}^{N}(x_{i}-\overline{x})^{2}}{n}} \tag{9}\]  
where \(x_{i}\) is the annual number of incidents in the jurisdiction of fire station \(i\) and \(\overline{x}\) is the average annual number of incidents in the fire station's jurisdiction.  
The unevenness (\(U\)) represents the degree of unevenness in the fire station deployment load, where a higher \(U\) value indicates a higher level of imbalance and less optimal fire station placement. The unevenness of the existing fire station deployment in Shanghai was \(U=327.1\), while the unevenness of the proposed scheme with 150 fire stations was \(U=200.3\). This indicates a significant improvement in the level of imbalance in fire station deployment.  
It should be noted that this paper was intended to apply a risk assessment-based model for fire station planning. Given the urbanization characteristics of Shanghai, the risk assessment system only selected typical parameters for which there are accurate and available data. Factors such as topography and spatial roads could be considered to develop a more comprehensive model in different regions.
# Generative Adversarial Networks for Zero-Shot Remote Sensing Scene Classification
## 2 Methods
### Overall Framework  
In this section, we present the overall framework of our CSPWGAN model. As shown in Figure 1, our model is composed of two parts, including the Class Semantic Feature Representation Module and the Feature Generation Module.  
**Class Semantic Feature Representation Module.** Typical zero-shot task datasets usually contain class attribute vectors [9]. Since there are no class attribute vectors in the remote sensing image dataset, we used the natural language pre-training model to generate the word vectors we need.  
**Feature Generation Module.** We used a generative model to train a generator that can generate class image features by class semantic features, which can convert the zero-shot remote sensing scene classification problem into a traditional classification problem and avoid the hubness problem [31]. In addition, using generative adversarial networks can prevent the class imbalance problem under the GZSL setting. Meanwhile, we considered that remote sensing images have inter-class similarity and intra-class diversity. We used the classification loss, semantic regression module, and class-prototype loss to constrain the generator. The classification loss was used to preserve inter-class discrimination. We used semantic regression module to ensure that the image features generated by the generator can represent the semantic features. To ensure intra-class diversity of the synthesized image features and avoid generating too homogeneous image features, we introduced class-prototype loss to constrain the generator. The key to solving this problem is to obtain a high-performance generator, and multiple losses to jointly constrain the generator to obtain better results.