esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 2 Related Work
### Dynamic Decomposition and Hierarchical Optimization  
To address the challenges of high-dimensional task conflicts and dynamic scheduling in the complex task decomposition of satellite networks, researchers have proposed a dynamic decomposition and hierarchical optimization strategy that reduces the complexity of the problem through decoupling in the spatiotemporal dimension [21; 22; 23; 24; 25; 26].  
For instance, [PERSON] et al. [21] dynamically decomposes the target area into candidate observation bands and optimizes the combination using the adaptive genetic algorithm. Moreover, [PERSON] et al. [24] adopts a hierarchical structure of task allocation merging and utilizes dynamic programming to reduce task conflicts. [PERSON] et al. [25] proposed a dynamic task decomposition and resource allocation optimization method based on the MILP algorithm, which improves the efficiency and real-time performance of complex task scheduling and resource mapping in satellite networks through spatiotemporal decoupling and multi-level optimization strategies. Also, [PERSON] et al. [26] utilized a lookahead-based variant of HEFT (HEFT-LA) to dynamically adjust task scheduling in satellite networks, improving task execution efficiency by anticipating future resource demands. However, such methods still face the problems of insufficient task conflict handling and scheduling flexibility, and require exploration of more precise resource allocation and scheduling strategies.
## 2 Pharmaceuticals and Personal Care Products (PPCPs) in the Ecosystems
### Microbial Remediation in Culture
#### 2.1.3 Phycoremediation
High PPCPs concentration inhibit algae growth [196; 197] but low concentrations can allow bioaccumulation (**b**) or metabolism (**c**) and therefore, the increase in biomass [180]. **b**. PPCP bioadsorption and bioabsorption associated to glucosil-conjugation bioaccumulation of PPCP or PPCP toxic derivatives [230]. Increase of ROS defense reactions [231]. **d**. Increase in the production of algal biomass useful by acclimatization in the production of biodiesel and biogas [182]. **c**. Bioabsorption and intracellular degradation by cytochrome P450 and associated enzymes [223] and extracellular degradation mediated by lacases [102; 103]. **e**. Biomass usable in food and feed industry [227], biofetrilizants [232], and pharmaceutical industry [233]. Microalgae such as _Desmodesnus subspicatus_, _Chlamydomonas mexicana_ and _Scenedesmus obliquus_ can bioaccumulate emerging contaminants along with growth nutrients [223]. The accumulation of PPCPs in microalgae cells can induce the generation of reactive oxygen species (ROS) that are related to defense and adaptation mechanisms [231]. Moreover, Nordic microalgae isolated from nature can remove lipophilic active pharmaceutical ingredients, equally or more efficiently than the investigated culture collection strains. While _Coelastrella_ sp. and _Coelastrum astroideum_ were most efficient in accumulating certain compounds in their biomass, two algae species, _Chlorella vulgaris_ and _Chlorella saccharophilia_, were not only highly efficient in removing all 19 pharmaceuticals but also only small amounts of these compounds accumulated in their biomass allowing for its further use [222]. _Nanmochlor_ sp. mediated photo- and bio-degradation were the primary pathways for the removal of 17\(\alpha\)-, 17\(\beta\)-estradiol and salicylic acid but the incomplete removal of all steroidal hormones demonstrates that there is still an endocrine disrupting potential in the ecosystem [108]. Recently, the use of an unidentified microalgae consortium, isolated from ponds enriched with emerging contaminants, has improved the results of phycoremediation [236].
# Data Augmentation Methods for Enhancing Robustness in Text Classification Tasks
## 7 Conclusions  
In this study, we introduced three novel data augmentation methods for improving the robustness of text classification models. We conducted evaluation experiments on four text classification datasets and found that, in addition to an increased robustness, our augmented datasets improve the prediction models' performance. We also compared our methods to two existing data augmentation methods. We found that one of our proposed methods performs similarly in terms of a performance enhancement while demonstrating superior results in terms of a robustness enhancement. Our empirical results demonstrated the effectiveness of our proposed data augmentation methods.  
Conceptualization, H.T.; software, H.T.; validation, H.T.; formal analysis, H.T.; investigation, H.T.; data curation, H.T.; writing-original draft, H.T.; writing-review and supervision, S.K. and Y.M.; funding acquisition, Y.M.; project administration, Y.M.; All authors have read and agreed to the published version of the manuscript.  
This research has been supported by KAKENHI (20K11830) Japan.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://github.com/tang777777/MDPI_codes](https://github.com/tang777777/MDPI_codes)]([https://github.com/tang777777/MDPI_codes](https://github.com/tang777777/MDPI_codes)) (accessed on 14 December 2022).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Accuracy under Attack**} & \multicolumn{2}{c}{**Attack Success Rate**} \\
**Models** & **Accuracy** & **F1-Score** & **TextFooler** & **PWWS** & **TextFooler** & **PWWS** \\ \hline Original & 99.00\% & 97.68\% & 72.40\% & 79.40\% & 26.87\% & 19.80\% \\ \hline Cognate1 & 99.33\% & 98.41\% & 87.80\% & 90.27\% & 11.61\% & 9.13\% \\ Cognate2 & 99.40\% & 98.58\% & 83.33\% & 86.93\% & 16.16\% & 12.54\% \\ Cognate3 & 99.33\% & 98.42\% & 83.73\% & 87.87\% & 15.71\% & 11.54\% \\ \hline Antonym1 & 99.20\% & 98.13\% & 83.67\% & 86.13\% & 15.66\% & 13.17\% \\ Antonym2 & 99.27\% & 98.28\% & 83.47\% & 86.73\% & 15.92\% & 12.62\% \\ Antonym3 & 99.27\% & 98.27\% & 85.60\% & 89.20\% & 13.77\% & 10.14\% \\ \hline Antipode1 & 99.33\% & 98.43\% & 85.80\% & 88.07\% & 13.62\% & 11.34\% \\ Antipode2 & 99.47\% & 98.73\% & 89.33\% & 90.87\% & 10.19\% & 8.65\% \\ Antipode3 & 99.13\% & 97.98\% & 77.93\% & 83.40\% & 21.38\% & 15.87\% \\ \hline EDA [10] & 99.27\% & 98.28\% & 81.20\% & 86.40\% & 18.21\% & 12.96\% \\ CheckList [7] & 99.53\% & 98.90\% & 84.93\% & 87.67\% & 14.67\% & 11.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: SMS-Spam Performance and Robustness Results.  
Figure A1: Antipode3 Run2 Confusion Matrix on AG-News.  
Figure A4: Antonym2 Run3 Confusion Matrix on TREC.
## 2 Basic Theories  
To achieve efficiency optimization, the efficiency calculation model and optimal algorithm model should be established first. In this Section, the basic meshing coordinate system, tooth contact analysis, and loaded tooth contact analysis were carried out to obtain relevant parameters that would be useful for efficiency calculation. The PSOGSA optimal algorithm and optimized variables were also determined. The SBG design and modeling here is based on the local synthesis method proposed by Prof. [PERSON], which could optimize the meshing behavior by controlling the second order contact parameters [23; 24]. Even though this paper takes the local synthesis method as an example, it should be pointed out that other design methods, such as the ease-off flank modification method, could also combine with the PSOGSA algorithm to maximize efficiency. It should also be added that the present method focuses on tooth surface frictional power loss and the windage power loss has been ignored.
# Levy Statistics and the Glassy Behavior of Light in Random Fiber Lasers
## 2 Materials and Methods
### Fiber Bragg Grating-Based Random Fiber Lasers  
As mentioned above, FBG-based RFLs were first introduced in 2009 [44; 45]. Currently, the FBG fabrication methods, characterization, and management constitute a well-developed field, and further information on this subject is deferred to ref. [68].  
Typically, a writing setup based, for instance, on CW UV radiation or femtosecond sources at 800 nm, is employed to inscribe an FBG into a conventional or core-doped optical fiber. The case of interest here exploits an active core single-mode fiber, using trivalent erbium ions, Er\({}^{3+}\), which can be excited at 980 nm or 1480 nm, and emits in the 1540-1560 nm spectral region. Instead of inscribing the FBGs in an evenly spaced way, thus leading to conventional resonators, the erbium-based FBGs are randomly spaced and play the role of random scatterers, leading to RFL emission. Generally, the fiber is placed on a movable stage which is randomly displaced, thus providing the randomness in the FBG writing process. Several tens to hundreds of gratings can be inscribed along several tens of cm of fiber length.  
In the next section, we will describe the fabrication and characterization of the Er-RFL system with a specially-designed FBG [45]. We comment that this system has been lately used as an experimental platform to study complex photonic phenomena, such as the observation of unconventional Levy-like statistics of output intensity values and the demonstration of the nontrivial replica-symmetry-breaking regime, which marks the signature of the phase transition from a photonic paramagnetic to a photonic spin-glass phase.
## 5 Results
### Round-Trip Time (RTT)
#### 5.1.3 Factory  
The evaluated factory environment presents a scenario with significant interference in the 2.4 GHz band, stemming from multiple industrial sources. This environment is classified as an indoor non-LoS setting, which naturally contributes to higher levels of interference compared to typical residential environments. However, the performance of the latest Wi-Fi standards remains robust in this more challenging setting, as shown in Figure 7. Specifically, WiFi6_2.420 and WiFi4_2.420 technologies achieve values of \(RTT_{max}\) below 20 ms and _mdev_ around 1.5 ms. Additionally, it is evident that in the 2.4 GHz band, increasing the channel bandwidth significantly elevates the RTT, as shown with WiFi7_2.480 and WiFi7_2.4160. This effect is primarily due to higher susceptibility to interference, increased contention in the shared spectrum, and the inherent limitations of lower-frequency bands, which exacerbate congestion and retransmissions, ultimately impacting latency stability. In contrast, the other evaluated technologies reach \(RTT_{max}\) values less than 5 ms and _mdev_ values below 0.5 ms, providing highly satisfactory results despite additional interference typical of industrial environments.  
The results of the analysis indicate that the WiFi6E_680 and WiFi6E_6160 configurations provide the most optimal performance in terms of latency for short to medium distances, regardless of the environment evaluated and without taking LoS into account. This is primarily because they operate in the 6 GHz band, which remains largely unsaturated due to the limited number of devices currently capable of utilising this portion of the spectrum.  
Figure 6: Round-trip time results, including minimum, maximum, and mean (in red), for multiple Wi-Fi standards in a residential environment.  
Figure 7: RTT measurements (minimum, maximum, and mean values in red) obtained in an industrial/factory scenario for the evaluated Wi-Fi technologies.
# Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques
## 3 Proposed Methodology
### Enhanced Data Preprocessing
Each time slot represents a discrete 5 min interval, specifically from 8:00 AM to 8:55 AM, covering the entire hour prior to 9:00 AM, aligned with the prediction target. Each vector \(s^{L}_{7,l}\) is then encoded into a 316-dimensional feature vector, as illustrated in Figure 3b. On the other hand, \(S^{S}\) captures the most recent short-term segment, containing the latest and most immediate data points leading up to the current time. This segment ensures that the model has access to the latest information. Each data point in both \(S^{L}\) and \(S^{S}\) is comprehensively encoded. Detailed descriptions of the symbol definitions and specific dimensions are provided in Table 1.  
This PS plays a pivotal role in structuring the data for the subsequent application of positional encoding, therefore enhancing the overall effectiveness of the SLIT model \begin{table}
\begin{tabular}{l l l} \hline \hline
**Attributes** & **Dimensions** & **Description** \\ \hline \multirow{3}{*}{Holiday} & \multirow{3}{*}{3} & Weekday \\  & & Weekend \\  & & National holiday \\ \hline Day of the week & 7 & Monday to Sunday \\ \hline Month & 12 & January to December \\ \hline \multirow{3}{*}{Peak} & \multirow{3}{*}{4} & Morning-peak \\  & & Noon-peak \\  & & Night-peak \\  & & Off-peak \\ \hline Time slot & 288 & \# of time slots in each day (1 time slot = 5 min) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Temporal attributes. Figure 3: Periodic segments. in traffic prediction. The algorithm, detailed in Algorithm A1, through its structured presentation of the segmentation process, enhances both clarity and reproducibility while perfectly aligning with the SLIT model's innovative approach to traffic data analysis. Following the original Transformer model [26], Positional Encoding (PE) is applied to the periodic segments \(S\) to enhance temporal context understanding. PE utilizes sinusoidal functions to encode each position within \(S\) in \(\mathbb{R}^{(D^{\prime}\times T^{\prime})\times m}\), where \(D^{\prime}=8\) and \(T^{\prime}=12\) denote the number of segments and the number of time points per segment, respectively. This results in 96 unique positions, each associated with \(m=316\) dimensions of features, making up the tensor dimensions of \(96\times 316\). This encoding embeds each position with a pair of sine and cosine functions, whose frequencies are dependent on the position within \(S\) and scaled logarithmically by the dimension index. A detailed pseudocode for this PE process is described in Algorithm A1. Following PE, the encoded tensor, \(\hat{S}\), is then split into short-term and long short-term encoded segments, represented as \(\hat{S}^{S}\) (an \(12\times 316\) tensor) and \(\hat{S}^{L}\) (an \(84\times 316\) tensor), respectively. These encoded segments are subsequently inputted into the SLIED module. This process not only aims to enhance SLIT's capability for predicting travel time across different time frames but also contributes to improving data quality.
# DSVN: A Flexible and Secure Data-Sharing Model for VANET Based on Blockchain
## 7 Conclusions  
This paper studies a safe and efficient data-sharing model for VANET. This model provides a data-sharing platform with privacy-preserving and authorized access by CP-ABE, blockchain, and IPFS. It has no single point of failure and can undo user-level attributes. Experiments show that our scheme has certain advantages compared with other schemes in data encryption and data decryption at the user end. In the scheme, the revocation overhead of a single attribute of a user is relatively low. The transaction processing delay of the model is short and has certain concurrency capabilities. The model is proved to be IND-CPA safe in the game under the CDH assumption. In future work, we will optimize the on-chain information storage. We are considering adopting an editable blockchain to reduce old blocks with invalid information. In addition, we will increase the concurrency performance of our model by improving the consensus mechanism.  
Figure 10: Cost of transmission.  
**Author Contributions:** Conceptualization, X.C.; methodology, X.C., Y.C. and K.F.; software, X.C. and X.W.; validation, Y.C.; formal analysis, Y.C.; investigation, X.W.; resources, X.Z. and K.F.; data curation, X.C.; writing--original draft preparation, X.C.; writing--review and editing, Y.C.; visualization, X.W.; supervision, K.F.; project administration, Y.C.; funding acquisition, X.Z. and K.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Key Research and Development Project of Hunan Province grant number 2020 NK2033 and the APC was funded by project 2020 NK2033.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial Features
## 3 AutoStageMix
### Identifying a Transition Point
#### 3.3.5 NTRA-C and None  
Identifying INTRA-C in the original footage is equivalent to detecting shot boundaries. If an FBT or INTER-C occurs near an INTRA-C, the necessary temporal gap between transitions may not be maintained, potentially reducing viewer satisfaction. To prevent this, it is essential to detect INTRA-C in the original videos and avoid placing other transitions nearby.  
[PERSON] et al. examined whether a non-similar scene appears within five frames surrounding the scene where FBT occurs [1]. However, this approach does not guarantee a sufficient time interval between transitions. [PERSON] et al. used Adobe's commercial software, Premiere Pro, to detect shot boundaries [2], but relying on commercial tools contradicts the object of this research, which aims to generate cross-edited videos automatically. [PERSON] et al. initially detected shot boundaries by comparing histogram differences between consecutive frames and subsequently filtered out false positives using the SURF algorithm [19]. However, SURF [25] is known to be computationally expensive.  
Therefore, we propose a shot boundary detection method that leverages the intensity histograms of each frame along with the bounding box data obtained in Section 3.2. Specifically, histogram correlations were computed for each RGB channel between consecutive frames. If the correlation for any channel fell below 0.7, the frame was flagged as a potential boundary. Next, the intersection over union (IoU) was calculated between the largest bounding boxes in the frames before and after the potential boundary. If the IoU was less than 0.5, the boundary was confirmed as an actual shot boundary.  
For each channel \(c\in\{R,G,B\}\) in frame \(k\) and frame \(k+1\), the histogram correlation coefficient \(r_{c}\) is computed as follows:  
\[r_{c}=\frac{\sum_{i=0}^{L-1}(h_{c}^{i}(k)-\mu_{c(k)})\times\left(h_{c}^{i}(k+1) -\mu_{c(k+1)}\right)}{\sqrt{\sum_{i=0}^{L-1}\left(h_{c}^{i}(k)-\mu_{c(k)} \right)^{2}\times\sum_{i=0}  
Let \(S\) be the minimum correlation coefficient across the three color channels:  
\[S=\min(r_{R},r_{G},r_{B}). \tag{6}\]  
If \(S<0.7\), frame \(k\) is considered a potential shot boundary.  
Next, if this condition is met, let the largest bounding boxes in frame \(k\) and frame \(k+1\) be \(b_{k}\) and \(b_{k+1}\). The IoU of these two bounding boxes is calculated as follows:  
\[\text{IoU}(b_{k},b_{k+1})=\frac{b_{k}\cap b_{k+1}}{b_{k}\cup b_{k+1}}. \tag{7}\]  
If \(\text{IoU}(\text{b}_{k},\text{b}_{k+1})<0.5\), the frame is finally determined to be a shot boundary.
## 2 Study Area and Datasets
### Datasets  
Three sources of RS satellite data were utilized (S1, S2, and L8/9). Moreover, the proposed Pa-PCA-Ca ensemble structure is a supervised classification technique, meaning that it needed Ground Truth (GT) data for model calibration and validation.
# Protection of Digital Elevation Model--One Approach
## 4 Discussion
### Discussion of the Feasibility of the Proposed Model  
The management of DEM data in a military network.  
DEM data can be managed by using the digital certificate status check service. A digital certificate can have a revoked or suspended status. The status of the digital certificate is checked on the sender's side, so if the certificate is revoked or suspended, the sender cannot send data to the recipient. On the other hand, if the recipient finds that the certificate is not valid during the verification of the digital signature, it rejects such data because there is no longer any guarantee that it comes from the trusting sender.  
The model enables secure transmission of the digital securities to every end entity. This characteristic can be used to download the encryption key from the distribution center for key management.  
Message protection from source to end user. The model allows data to be transmitted to the end source without a third-party having insight into the content of the message. Data security is not affected by the transmission path nor the number of devices participating in the transmission. The model assures the recipient that they will receive data that other entities have not had access to, has not been altered, and came from a trusted source. On the other hand, the sender is sure that they sent the data to the authentic recipient and that no one other than the recipient will see the data.  
The model does not depend on the applied network technology. This means that the model does not depend on the size of the network, the topology of the network, the role of the entities in the network, and the method of communication between the network nodes.  
Independence from the position of end entities and network nodes. The model enables the fulfillment of security questions in stationary and dynamic environments of entities and network elements. If the entities that exchange data move and (or) if the network nodes change their position, as long as there is network connectivity, the model will ensure authenticity, integrity, confidentiality, and non-repudiation.
###### Abstract
This imbalance causes the model to be biased towards learning a larger number of categories during the training process, thus affecting the ability to recognize a smaller but important number of categories (e.g., severely damaged and collapsed houses). Secondly, current approaches to assessing building damage rely on semantic segmentation models that are standard in computer vision. However, these methods fail to consider the specific characteristics of building damage, resulting in suboptimal assessment outcomes. For instance, after a building collapses, debris, tiles, and other objects may be scattered around, and the collapse direction can be random. Directly applying existing semantic segmentation models may result in incomplete feature extraction and misclassification. Furthermore, there is a significant resolution difference between UAV images and satellite remote sensing images. Consequently, traditional convolutional neural networks may struggle to effectively capture the complete information of a house, leading to decreased accuracy in house classification [19]. Therefore, the objective of this study is to design an enhanced deep learning model that incorporates multi-directional convolution, data augmentation strategies, and deep and shallow feature fusion. The methodology presented in this paper is designed for post-earthquake building damage assessment utilizing UAV and satellite remote sensing imagery. The primary contributions of this study can be summarized as follows:  
1. A novel data augmentation module (DAM) is presented, different from the commonly used data augmentation methods such as rotation and size scaling, by integrating oversampling techniques and label polygon dilation techniques, which can improve the situation where the model weights are biased towards a large number of categories. 2. A building damage attention module (BDAM) is proposed to enhance the accuracy of severely damaged and collapsed categories by considering the randomness of the collapse direction in collapsed buildings following earthquakes, as well as the heterogeneity in texture features in damaged houses and the ground. 3. A multilevel feature adaptive fusion module (MFAF) is introduced to search for optimal parameters on feature maps of different scales, focusing on extracting contour integrity information among houses of different sizes and enhancing the model's sensitivity to diverse house sizes.