esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Dimension Spatial Method for Topology Awareness and Multipath Generating
###### Abstract  
Multipath diversity significantly impacts multipath transmission quality. Enough multipath diversity would minimize the negative influence brought by an individual path, thus improving tolerance capability of network congestion and failure. However, multipath diversity is hard to guarantee on overlay networks because of inaccurate awareness of underlay network and multipath generating methods considering little about underlay diversity. In this paper, we design a multi-dimension spatial method for topology awareness and multipath generating. Analyzing that the complicated underlay networks with multiple autonomous systems reduce the accuracy of network positioning for topology awareness, we decompose the underlay networks into multiple dimensions, namely intra and inter autonomous system dimensions. We generate independent views for each autonomous system and merge views by embedding exchange points. Then, we design some spatial mechanisms to evaluate link diversity and to constrain multipath generating. Based on the multi-dimensional view, multipath generating is conducted in inter and intra autonomous system phases. Experiments demonstrate that the proposed method improves topology awareness accuracy and guarantees multipath diversity better and the transmission quality is improved.  
multipath transmission; multipath generating; spatial multipath generating; topology awareness; multipath diversity; network positioning; multi-dimension topology view; underlay diversity +
Footnote †: journal:  
_Article_  
A Multi-Dimension Spatial Method for Topology Awareness and Multipath Generating  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]
## 5 Numerical Results
### Numerical Case
In contrast, the schemes obtained by the other two algorithms are more decentralized, as some resource sites dispatch EVs to all disaster sites. To further understand the impact of the casualty risk function on the degree of coordination at different priority disaster sites, Figure 8b illustrates the trend of the ARWT at both high and low-priority disaster sites. It is obvious that the ARWT of the high-priority site decreased rapidly after a brief rise, from about 43 min to 29 min, a reduction of 32.6%. Meanwhile, after an initial fluctuation, the ARWT of the low-priority site stabilizes at a lower level of 40 min, decreasing by 11.1%. These phenomena indicate that the casualty risk function designed in this paper enables high-priority disaster sites to receive rescue as soon as possible while also considering the low-priority site, effectively reducing the risk of casualties at each disaster site.
# Coordination and Private Information Revelation
## 4 The Analysis
### Full Disclosure Equilibria in the Absence of Coordination Failure  
We begin by examining two scenarios such that the coordination stage exhibits no coordination failure, demonstrating that information revelation is possible in such cases.  
We first consider a scenario where the agents play a pure strategy equilibrium in Stage 2. In particular, depending on whether the \(\theta\) revealed in Stage 1 is'sufficiently high' or not, they coordinate on either \(A\) or \(B\) in the coordination stage.  
**Proposition 1**.: _There exists perfect Bayesian equilibria with no coordination failure in Stage 2 and complete information revelation in Stage 1._  
1. _In this equilibrium, in Stage_ 1_, all_ \(A\) _agents reveal their own types. In Stage_ 2_:_ 1. _In case of non-revelation by the_ \(A\) _agent in Stage_ 1_, agent_ \(B\) _believes that agent_ \(A\) _is of type_ \(\theta_{h}\)_._ 2. _Fix_ \(\theta^{\prime}\)_, where_ \(\theta^{\prime}\in[\theta_{l},\theta_{h}]\)_. The agents coordinate on_ \(A\) _if_ \(\theta\geq\theta^{\prime}\)_, and on_ \(B\) _if_ \(\theta<\theta^{\prime}\)_._
2. _For_ \(\theta^{\prime}=b\)_, the equilibrium so described is efficient._  
Proof.:
1. Clearly, the Stage 2 strategies constitute an equilibrium, so that it is sufficient to examine whether agent A's revelation strategy is optimal or not. Please note that following disclosure of her type, agent A's payoff is at least \(c\) since disclosure ensures coordination in Stage 2. Whereas in case agent A opts not to disclose, then given the belief, agent B plays a completely mixed strategy, adopting \(B\) with probability \(\frac{\theta_{h}+c}{2c}\). It is straightforward to check that for all \(\theta\), it is optimal for agent A to switch to \(B\), so that her payoff is \(\frac{\theta_{h}+c}{2}\), which is less than her payoff from revealing her type (which is at least \(c\)).
2. Please note that there is coordination on \(A\) iff \(\theta\geq b\), which is efficient.  
Please note that the equilibrium involves complete information revelation. This corroborates our central intuition that the issue of information revelation is intimately tied to the possibility of coordination failure.6 Furthermore, there exists an equilibrium which achieves efficiency in the coordination stage.  
Footnote 6: Of course there exist equilibria where the agents coordinate on, say, \(A\) in Stage 2, but there is no information revelation in the first stage.  
Turning to the second scenario, we demonstrate that in case one allows for correlated equilibria in the coordination continuation game, one can again sustain equilibria with full disclosure. Since, in the main text, we restrict attention to Bayesian equilibria, the details for this case have been placed in Appendix A.
## 3 Results and Discussion
### Case Study with ANN Application
The emission was calculated following the recommendations of the National Center for Emissions Management and Balancing IOS-PIB (KOBiZE) in Poland, assuming a value of 719 kgCO\({}_{2}\)/MWh [58]. A summary of the analysis is presented in Table 1. Cumulative values of energy coverage specified in Table 1 were defined by calculating the difference between energy usage and energy production. Thus, the final percentage was a summary of the final calculations using the modeled value of energy production versus the real energy production from measurements from the actual test plant. Installation gain describes the value of overproduced energy that can be sold at market rates, or in the case of negative values, the price that had to be paid to cover the cost of purchasing the missing power from the grid. Figure 7: Visualization of the case study for the month of March. In the analysis, an approximated annual balance was calculated in addition to the three representative months. This was performed assuming that 3 months of the year are favorable for solar energy production, 3 are unfavorable, and 6 represent intermediate conditions:  
\[Annual=I_{mar}\cdot 6+I_{may}\,3+I_{dec}\,3 \tag{3}\]  
This proportion was selected based on historical and prognosis data from the study of the tested PVs. The greatest benefits from energy consumption optimization based on the ANN can be seen in March. The balanced conditions give the widest area for the algorithm and allow for a reduction in external energy consumption by up to 16.47%. This results in an increase in installation gain of almost 8%. At the same time, CO\({}_{2}\) emission is reduced by 63.97 kg per month. Less pronounced improvements can also be noticed in May, with a 4.76% reduction in energy needed to be bought from the grid and 20.69 kg less CO\({}_{2}\) emitted. For December, the differences are negligible--under 0.6% for all the measured parameters. Considering the improvements at the monthly scale, the gain can be considered moderate. However, if we calculate it annually, the conclusions change. The reduction in emissions at a level of 450 kg CO\({}_{2}\) per year cannot be omitted. The same can be said about the increase in the coverage of energy consumption by over 10%. Finally, it must be mentioned that the applied optimization method is very simple. Using more sophisticated methods could potentially allow one to obtain more from the data returned by the ANN.
## 4 Discussion
### Limitations of the Coastline Change Quantification  
The generation of transects along the simplified mainland polygon of Vietnam comes with several constraints when being utilized to quantify coastline change. Masked pixels  
Figure 12: Mean number of valid (cloud-free) observations per pixel from 1986–2021.  
within the MNDWI composites, for example, may interrupt the annual coastline at a transect position. When inland water bodies intersect the transect instead, while only the most seaward intersection point for each year is chosen to quantify coastline change, the coastline intersection point for that year would be selected falsely. This limitation was addressed by excluding outlier points that deviate by more than three standard deviations at each transect. Another issue occurs in very complex and highly dynamic coastal environments. Such intricate coastlines cannot be reasonably represented using generalized transects, because the transect orientation does not correspond to the complex shape of the coast. Furthermore, edge transects of estuaries or headlands are susceptible to false change rate calculations since the actual coast is occasionally omitted in favor of the river shore. The coastline quantification itself is based on linear regression slopes. Linear regressions are generally susceptible to outliers and thus tend to underestimate trends [73]. Furthermore, the standard error is proportionally larger for transects with low mean change rates than for transects with high mean change rates, leading to increased uncertainty in the trend for transects with low mean change rates.  
However, since the change rate quantification as such is difficult to validate (as described in Section 4.1), we compared several local coastline change rates to previously published change rates in the corresponding locations. For example, [PERSON] et al. (2019) [13] published coastline change rates for the Mekong delta covering the time period 1990-2015 using a combination of in situ, remote sensing, and laboratory experiment data. The authors determined mean erosion rates between \(-10\) and \(-30\) m/year on the east coast of the Ca Mau head, which corresponds well to the herein identified erosion hotspot 0a, with an average erosion rate of \(-25\) m/year. At the west coast of the Ca Mau head, [PERSON] et al. (2019) [13] found accretion rates of more than \(+30\) m/year, reflected in erosion hotspot 2b, with an average mean change rate of \(+39\) m/year. [PERSON] (2017) [45] published change rates at Cua Dai beach in the province Quang Nam, which was also detected as an erosion hotspot in this analysis (10a). The authors report erosion rates of up to \(-35\) m/year between 2001 and 2010, while the highest erosion rate identified at hotspot 10a in this analysis amounts to \(-29\) m/year. Although these comparisons cannot substitute for extensive validation, they do show that the herein presented automated calculations of coastline change agree well with the magnitudes of locally determined rates of change.
# Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval
## 4 Experimental Validation and Evaluation
### Experiments in Comprehensive Scenarios  
In this section, we discuss the extensive experiments on Geo-DMP in a more realistic and comprehensive scenario. An approximate square area of 3 km\({}^{2}\) near Zhongguancun, Haidian District is selected as the movement range of nodes. Ten nodes participate in the experiments, whose traces are excerpted from T-drive [116], a dataset that contains real-world trajectories of taxis. Nodes move along their own path repeatedly for two rounds in each experiment execution and their contacts are not planned beforehand. The one-way travel time ranges from 20 to 40 min. We create different conditions for every group of experiments by replacing all the traces of nodes. The movement range and one example set of traces are given in Figure 21.  
First, we evaluate the performance of Geo-DMP when only a single content source is available. Node 1 acts as the user and Node 10 is the RSU, while others are relay nodes. According to the variation of the communication range and whether nodes have knowledge of their own movements in advance, four cases in one group of experiments are given in Table 4. We ran 10 groups of experiments altogether.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Communication Range**}} & \multicolumn{2}{c}{
\begin{tabular}{c} **With Movement Knowledge** \\ **at the Initial State** \\ \end{tabular} } \\ \hline Case A & 300 m & No \\ Case B & 300 m & Yes \\ Case C & 600 m & No \\ Case D & 600 m & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 4: The cases with different conditions.  
Figure 20: Screenshots of the browser in validation of delay tolerant client visualization.
## 5 Detection Methods: Modulation Spectroscopy for Wideband Absorption
### Removing Fringes and Noise Interference  
LAS always suffers interference from electric noise and optical fringes; the latter are caused by multiple reflections upon optical interface, i.e., the so-called \"etalon effect.\" The interference causes low sensitivity and precision in the spectrometers. Since the optical fringes are small and sine-like in a well-designed and -fabricated system, the signal profiles of molecular absorption always exhibit distinct differences to those of optical fringes and electric noise. The WMS-2f signal profile would  
Figure 3: (**a**) broadband spectra of CS\({}_{2}\) around 2176 cm\({}^{-1}\) with 30.5 ppm \(\times\) 5 m; (**b**) simulated WMS-2f signals of Figure 1a with modulation index 0.2 to 2.2, the insert panel describing the definition of spectrum discrimination (SD); (**c**) the SD and normalized WMS-2f signals amplitude under various modulation index [27].  
inherit their difference with an optimized modulation index. So, the molecular absorption can be distinguished from optical fringes and electric noise by the signal profile.  
An empirical mode decomposition (EMD) algorithm has been successfully used in WMS to remove optical fringes [27] and noise [163] by characterization of the signal profile. The procedure for employing EMD to decompose and reconstruct the WMS-2f signal is described in Figure 4a. The detected 2f signal was decomposed into intrinsic mode functions (IMF), which have a specific physical source and are meaningful. The simplest criterion, whether IMF from molecular absorption or interference is the correlation coefficient (R) between an IMF and the reference 2f signal, is shown in Figure 4b,c.  
The components with lower \(R\) most likely come from fringes or noise, while components with larger \(R\) with reference absorption must be the absorption. All these components with higher \(R\) can be added together to reconstruct a WMS-2f free from the interference of fringes and noise. Generally, this method can improve the sensor sensitivity by about 30% [164].
## 5 Conclusions  
This study presents a novel semi-supervised learning method for abstractive summarization. To achieve this, we employed a T5-based model to process texts and utilized an identity mapping constraint and a cycle consistency constraint to exploit the information contained in unlabeled data. The identity mapping constraint ensures that the input and output of the model have a similar representation, whereas the cycle consistency constraint ensures that the input text can be reconstructed from the output summary. Through this approach, we aim to improve the generalization ability of the model by leveraging unlabeled data while requiring only a limited number of labeled examples.  
A key contribution of this study is the successful application of CycleGAN's training process and loss functions to NLP tasks, particularly text summarization. Our method demonstrates significant advantages in addressing the problem of limited annotated data and showcases its potential for wide applicability in a multilingual context, especially when handling Chinese documents. Despite not modifying the model architecture, our approach effectively leverages the strengths of the original T5 model while incorporating the benefits of semi-supervised learning.  
Our proposed method was evaluated on various datasets, and the experimental results demonstrate its effectiveness in generating high-quality summaries with a limited number of labeled examples. In addition, our method employs lightweight models, making it computationally efficient and practical for real-world applications.  
Our approach can be particularly useful in scenarios where obtaining large amounts of labeled data is challenging, such as when working with rare languages or specialized domains.  
It is worth noting that our proposed method can be further improved by using more advanced pre-training techniques or by fine-tuning on larger datasets. Additionally, exploring different loss functions and architectures could also lead to better performance.  
In summary, our study introduces a novel semi-supervised learning approach for abstractive summarization, which leverages the information contained in unlabeled data and requires only a few labeled examples. The proposed approach offers a practical and efficient method for generating high-quality summaries, and the experimental results demonstrate its effectiveness on various datasets.
# Representing and Inferring Massive Network Traffic Condition: A Case Study in Nashville, Tennessee
## 2 Materials and Methods
### CIR Decomposition
Prior research failed to consider the comparison with the simplest random sampling method, thus making the performance less persuasive.
# Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining
## 2 Methodology
### Point Cloud Input  
The point cloud input unit preprocesses the point cloud data to adapt it for the feature extraction methods used in different branches of the network. The architecture adopts a two-branch structure: one branch is dedicated to serialized historical frame data input, while the other handles the current frame data input. For the historical frame data, a series of continuous point cloud frames within a specified time window undergoes standard preprocessing to ensure proper formatting for subsequent processing. These sequence frame data and current frame data are expressed as follows:  
\[X_{t}^{s}=concat(S_{t},S_{t-1},\ldots S_{t-i}),\ X_{t}=S_{t}, \tag{1}\]  
Here, \(S_{t-i}\) represents the point cloud data of the \((t-i)\) frame, and \(concat(.)\) denotes the concatenation operation. Specifically, the superscript \(s\) in \(X_{t}^{s}\) is not a point index but a symbolic indicator distinguishing the concatenated multi-frame point cloud sequence from the single-frame point cloud (\(X_{t}\)). On the right-hand side of the equation, \(concat(S_{t},S_{t-1},\ldots S_{t-i})\), represents the temporal concatenation of \((i+1)\) consecutive full-frame point clouds from time steps \(t\) to \(t-i\). Each \(S_{t-j}\) contains a set of 3D points with  
Figure 1: The overall architecture of the spatiotemporal aggregation network.  
coordinates and intensity attributes. The \(concat(.)\) function stacks these frames along the temporal axis to form a unified spatiotemporal point cloud while preserving the per-frame spatial structure. This representation enables the model to extract temporal cues across multiple observations. Each point cloud frame at a given time (\(t\)) typically consists of \(N_{t}\) points, where each point (\(p_{k}=\left[x_{k},y_{k},z_{k},r_{k}\right]^{T}\)) is characterized by its 3D coordinates and the LiDAR reflectance intensity (\(r_{k}\)). The sequence frame data (\(X_{t}^{s}\)) is constructed by concatenating and aggregating the continuous frames of point cloud data from the \(t\)-th frame (\(S_{t}\)) to the (\(t-i\))-th frame (\(S_{t-i}\)), where \(i\) is typically determined based on a comprehensive evaluation of hardware conditions and model performance. The current frame data (\(X_{t}\)) is represented simply as \(S_{t}\).  
In our current implementation, the frames are evenly spaced within a fixed-length temporal window. This design choice ensures consistent temporal coverage while maintaining computational simplicity and efficiency. Specifically, when using a window of \(N\) frames, they are uniformly sampled from a predefined time span (e.g., 1-2 s), thereby capturing both recent and slightly earlier contextual information.
# Development of User-Participatory Crowdsensing System for Improved Privacy Preservation
## 3 Proposed System
### Process Flow of Proposed System
#### 3.2.2 Sensing Data Provision  
Figure 6 shows the process flow of sensing data provision. (2.1) The data provider \(u_{dp}\) transmits a data provision request including its ID \(K_{pub}(u_{dr})\) to the data processing node. (2.2) The data processing node generates a session key \(K_{seg}\big{(}p_{r},u_{dp}\big{)}\), encrypts the session key using the data provider ID \(K_{pub}(u_{dr})\), and sends it as \(enc(K_{pub}(u_{dr}),K_{seg}\big{(}p_{r},u_{dp}\big{)})\) to the data provider. (2.3) The data provider decrypts the received cipher text to obtain the session key, encrypts the collected sensing data \(d_{i}\) with the session key, and sends it as \(enc(K_{seg}\big{(}p_{r},u_{dp}\big{)},d_{i})\) to the data processing node.
## 2 Materials and Methods
### Incident Solar Irradiance on the Photovoltaic Field
#### 2.2.3 Ground-Reflected Component Modelling
Evidently, it is unlikely that a weather station is close to the location under study. Therefore, it is necessary to use models for the estimation of these parameters. A large number of models can be found in the specialised literature, such as the clear sky models [25], satellite-based models [28], etc. Due to the nature of this study, it is necessary to know the hourly distribution of the incident solar irradiance on a horizontal surface, so the procedure presented in [29] was used. This work considers the meteorological conditions of each location, and it has been used in studies on the design of PV power plants [9], the prediction of solar irradiance [30], etc. A brief description of this procedure is given as follows [29]: (Step 1) Determination of the horizontal beam solar irradiance using the Hottel clear-sky model [10]. (Step 2) Determination of the horizontal diffuse solar irradiance using the Liu and Jordan clear-sky model [10]. (Step 3) Reduction of the values obtained in steps 1 and 2 to the meteorological conditions of the location using Fourier series. The starting data for this procedure are the monthly average of the horizontal beam and the horizontal diffuse solar irradiance. Thesedata are obtained from the \(PVGIS\) database [31] for a period of 10 years. The monthly data used were obtained from the \(PVGIS\)-\(SARAH3\) solar irradiation database [31]. Considering the case of the five operating zones shown in Figure 4a, the incident solar irradiation on the \(PV\) field (\(H_{t}(n)\)) can be calculated by Equation (11) as follows [9]:  
\[H_{t}(n) = \int_{T_{R}(n)}^{T_{S}(n)}I_{t}(n,T,\beta)dT=\int_{T_{R}(n)}^{T_{ H}(n)}I_{t}(n,\beta_{B},T)\ dT+\int_{T_{R}(n)}^{T_{R}(n)}I_{t}(n,-\beta_{\max},T)\ dT+ \tag{11}\] \[\int_{T_{R}(n)}^{T_{R}(n)}I_{t}(n,\theta_{t},T)\ dT+\int_{T_{R}(n )}^{T_{R}(n)}I_{t}(n,\beta_{\max},T)\ dT+\int_{T_{R}(n)}^{T_{S}(n)}I_{t}(n, \beta_{B},T)\ dT\]