esa-sceva/satcom-chunk-collection huggingface dataset:
# Bounce: A High Performance Satellite-Based Blockchain System
## 4 Slot Protocol  
In this section, we describe the protocol for each slot \(i\) with reference to Figure 2. Then we review failures and responses to failures. Finally, we describe a design to support one higher level use case: payment.  
Figure 2: There may be several Sending Stations for some slot \(i\) and several Merkle Roots per Sending Station. For the sake of figure simplicity, we show one of each. Some time before the beginning of the slot, each Sending Station creates a Merkle tree which it sends (1) to \(f+1\) Broadcast Ground Stations. After reconstructing the Merkle tree and verifying the root, each Broadcast Ground Station signs the root of that Merkle tree and sends the signed root (1’) back to the Sending Station as well as to User nodes. User nodes are owned by parties of the _Bounce_ infrastructure, but that are interested in the transactions of the blockchain. The Sending Station creates a multisignature using the Ground Station signatures of that Merkle Tree root. A possibly disjoint set of \(f+1\) Broadcast Ground Stations send (2’) to the Sending Station the Commit Record for the previous slot \(i-1\) that they have received (2) from the satellite for slot \(i-1\). The Sending Station sends (3) a Sending Station Message consisting of a slot number, a reset number, an array of one or more multisigned Merkle Tree roots, and the multisigned Commit Record from the previous slot to the satellite for slot \(i\). The satellite Bounce Unit tests for the validity of each Sending Station message and then (4) sends out a Commit Record for slot \(i\), which may be positive or negative. The Broadcast Ground Stations make this Commit Record widespread (5) to the user community including, possibly, to Accumulator Nodes whose job it is to keep track of account balances, as described in Section 4.3.
## 2 Background and Literature Review
### Explainable Artificial Intelligence  
The primary objective of Explainable Artificial Intelligence (XAI) is to provide descriptive insights into machine learning models, enabling justification, control, improvement, and discovery [14]. Due to the increasing complexity and widespread adoption of deep learning models, the demand for interpretability has received significant attention [25]. It is crucial to unlock the black box of AI and demonstrate its reliability, robustness, and interpretability [26]. XAI [27; 28] has become an important area of AI research and development.  
In Ref. [27], interpretable techniques are categorized into two main types: model-based explanation and post hoc explanation. Model-based explanations focus on the creation of interpretable models, while post hoc explanations aim to describe black-box models [13]. Interpretable models refer to linear regression [29] or decision tree [30], while typical black-box models are deep learning [31].  
For black-box models, we primarily rely on other interpretable techniques for post hoc explanations. Common methods such as LIME [32] and SHAP [33] are applicable across various machine learning models. LIME utilizes linear approximation to construct interpretable surrogate models for explaining individual predictions, although these surrogate models may not always be accurate. On the other hand, SHAP, grounded in Shapley value theory, offers robust measures of feature contributions suitable for local and global explanations, albeit with high computational complexity. With the advancement of deep learning, researchers have proposed additional interpretable techniques tailored to deep learning models. Common interpretable methods are mainly applied to the image domain, such as occlusion maps, salience maps, class activation maps, and attention maps [34; 35; 36]. Based on these techniques, a variety of interpretable model variants have been developed, such as Grad-CAM [37]. This method generates highlighted regions in images that correspond to specific categories by using gradient information from convolutional neural networks. It can also be extended to one-dimensional models. However, it often emphasizes large areas and may overlook local details, which can affect the precision and sensitivity of localizing small targets.  
Table 1 provides a comparative analysis of three classical models based on explanation scope, computational complexity, theoretical basis, and limitations.  
In summary, we require an interpretable method that is free from surrogate model construction, has low computational complexity, and effectively handles detail features. This paper proposes a method to achieve these goals by introducing adaptive masks. Adaptive masks directly operate on earlier convolutional layers with more features to ensure the processing of detail features. Importantly, the feature weights represented by adaptive masks are obtained automatically as the model trains, eliminating the need for surrogate model construction and complex subsequent computations.
## 2 Results and Discussion  
### Separation of Chiral Mono-Terpenes in MDGC-MS  
A chromatogram of all 15 chiral monoterpenes and isotopes is found in Figure 1. All of the compounds showed good resolution. The elution order for linalool oxide and rose oxide isomers (standards were only available as isomer mixtures) were confirmed from previous methods investigating chiral compounds using the same column configuration as this method [20].  
Figure 1: Separation of chiral monoterpenes and deuterium isotopes mixture using MDGC-MS. Numbers referred to the compounds: (1) d\({}_{3}\)-(S)-(–)-limonene, (2) (S)-(–)-limonene, (3) d\({}_{3}\)-(R)-(+)-limonene, (4) (R)-(+)-limonene, (5) (–)-(2S,4R)-_cis_-rose oxide, (6) (+)-(2R,4S)-_cis_-rose oxide, (7) (2R,5R)-(+)-_trans_-linalo oxide, (8) (2R,5S)-(–)-_cis_-linalo oxide, (9) (–)-(2R,4R)-_trans_-rose oxide, (10) (+)-(2S,4S)-_trans_-rose oxide, (11) (2S,5S)-(–)-_trans_-linalool oxide, (12) (2S,5R)-(+)-_cis_-linalool oxide, (13) d\({}_{3}\)-(R)-(–)-linalool, (14) (R)-(–)-linalool, (15) d\({}_{3}\)-(S)-(+)-linalool, (16) (S)-(+)-linalool, (17) d\({}_{3}\)-(–)-\(\alpha\)-terpineol, (18) (–)-\(\alpha\)-terpineol, (19) d\({}_{3}\)-(+)-\(\alpha\)-terpineol, (20) (+)-\(\alpha\)-terpineol, (21) (R)-(+)-\(\beta\)-citronellol.
# HBIM: Background, Current Trends, and Future Prospects
## 4 Data Collection and Modeling
### Software and Data Formats
#### 4.3.2 Data Formats and Interoperability  
The interoperability of HBIM among models and software relies on the effective export and import of both graphical and non-graphical data, as well as the ability to work with this data regardless of the software used. After defining the level of development as previously described, the next step is to disseminate and export the data in a unified format. Thisformat must contain and preserve all graphical and non-graphical information without alteration, ensuring that it remains readable at all times.  
Formats that can import and export all information at once can be categorized as either proprietary or non-proprietary file formats. Proprietary formats are not readable by other software, even though they typically contain similar information created by the same BIM modeling software developers. The most used and well-known example of a non-proprietary format is the IFC format, which allows for readability across various software platforms ([PERSON], 2017). It includes the same information as proprietary formats but offers open-access data, facilitating a more democratic use of HBIM technology and enhancing interoperability between software systems ([PERSON], 2017). In contrast, the transferability of proprietary formats to other software often depends on separate agreements.  
The IFC standard continues to evolve, with the introduction of the IfcAlignment entity in version 4.1 and further enhancements in IFC 4.3, which incorporates specialized domains to address both land and maritime infrastructure specifications. These updates are designed to facilitate high-level semantic representation for infrastructure projects, aiding software developers in achieving greater precision in project contexts ([PERSON] et al., 2018). However, there remains a significant gap in the development of IFC standards tailored to heritage structures. [PERSON] et al. (2018) proposed a workflow to enhance HBIM interoperability using experimental IFC classifications, addressing the challenge that IFC formats for HBIM cannot directly align with ISO standards by introducing semantic fields and classifications. Similarly, [PERSON] et al. (2019) applied IFC standards in a case study of the Ducal Palace of Guimaraes (Portugal), successfully demonstrating interoperability, although the application was limited to open-access BIM software.  
Another non-proprietary format is the Construction-Operation Building Information Exchange (COBie), which focuses on asset data rather than graphical data. While the IFC format is more equitable in its approach, COBie serves a different purpose. As mentioned earlier, these non-proprietary formats are increasingly favored for use with HBIM models ([PERSON] et al., 2018). This trend is evident in the fact that most BIM software developers have recently incorporated the capability to work with these open-access formats alongside their proprietary formats.
# Detecting Dynamic Communities in Vehicle Movements Using Ant Colony Optimization
## 2 Methods  
The EACO framework is displayed in Figure 1. First, the OD points of vehicle movements were mapped onto road network road segments using a nearest-neighbor matching method [27]. Second, at each snapshot, a spatially embedded graph \(G=(V,E,W)\) was constructed based on road segments and OD pairs, where \(V\) is the set of vertices (each road segment is a vertex), \(E\) is the set of edges (two road segments connected by at least one OD pair form an edge), and \(W\) is the set of weights (the weight of an edge is the number of OD pairs between two vertices). Third, ACOScan was upgraded to identify statistically significant spatial communities at each snapshot. Finally, different rules defined based on the Jaccard coefficient were employed to find the evolution of the significant spatial communities. The main notations used in this study are listed in Table 1.  
Figure 1: Framework of the proposed method.
## 4 Conclusions  
In this paper, a 3D object copyright protection method based on ghost imaging encryption and cellular automata transform (CAT) watermarking algorithm is proposed. The watermark is first encrypted by ghost imaging and then embedded in the light-field image by CAT algorithm. The watermark is extracted by CAT inverse algorithm and decrypted by using the generated scatter intensity in ghost imaging as the key, and the decrypted watermarked image is of high quality. Through a series of experimental operations, the experimental results show that the imperceptibility of the watermark embedding algorithm is higher than that of the traditional DCT and DWT algorithms, and it is more robust and has better resistance to added Gaussian noise, etc. The ghost imaging encryption process before embedding improves the security of the carrier image in use and plays an important role in identity authentication and copyright protection.  
Conceptualization, M.W., M.C. and C.Y.; methodology, M.W. and C.Y.; software, M.C. and M.W.; validation, M.W., M.C. and C.Y.; formal analysis, M.W., M.C. and C.Y.; investigation, M.W. and M.C.; resources, M.W., M.C. and C.Y.; data curation, M.W., M.C. and C.Y.; writing--original draft preparation, M.W. and M.C.; writing--review and editing, M.W., M.C. and C.Y.; visualization, M.W. and M.C.; supervision, M.W., M.C. and C.Y.; project administration, M.W., M.C., C.Y. and J.L.; funding acquisition, C.Y. and J.L. All authors have read and agreed to the published version of the manuscript.  
The funding from Sichuan University (2020 SCUNNG205); National Natural Science Foundation of China (61775151, 61975138); Natural Science Foundation of Guangdong Province (2018A030070009, 2021A1515011091); Educational Commission of Guangdong Province (2018 KTSCX143, 2020 ZDZX3056).  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Attack**} & \multicolumn{2}{c}{**Our Method**} & \multicolumn{2}{c}{**Method [36]**} & \multicolumn{2}{c}{**Method [37]**} \\ \cline{2-7}  & **NC** & **BER** & **NC** & **BER** & **NC** & **BER** \\ \hline Gaussian noise (v = 0.1) & 0.9610 & 0.0162 & 0.9513 & 0.0185 & 0.9605 & 0.0184 \\ white noise & 0.9602 & 0.0184 & 0.9510 & 0.0195 & 0.9593 & 0.0189 \\ JPEG compression (QF50) & 0.9510 & 0.0194 & 0.9502 & 0.0204 & 0.9418 & 0.0264 \\ Gaussian filtering & 0.9692 & 0.0153 & 0.9508 & 0.0197 & 0.9684 & 0.0178 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Robustness comparison against attacks.  
**Acknowledgments:** The authors are grateful for the reviewers' careful, unbiased and constructive suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Electric Power Systems
### IEEE 24-Bus System  
This power system is composed of 24 nodes and 38 interconnections between transmission lines and transformers [29], with total active and reactive power demands in the peak load case of 2850 MW and 580 MVAr, respectively. The configuration of the IEEE 24-bus system is depicted in Figure 3. In addition, all its parametric information is reported in Tables 2-4. The bases for the per-unit simulations of this power system are taken as 100 MW and 220 kV, respectively. It is worth mentioning that, for simulation purposes, the slack source is assumed to be at node 13 with a controlled voltage output of 1.080 pu. Finally, the voltage regulation bounds of this power system are set within \(\pm 10\%\) in all nodes of the network.  
Figure 2: General structure for implementing optimization models in GAMS [41].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Node** & \(\mathbf{P_{g}^{\text{min}}}\) & \(\mathbf{P_{g}^{\text{min}}}\) & \(\mathbf{Q_{g}^{\text{min}}}\) & \(\mathbf{Q_{g}^{\text{max}}}\) & **Node** & \(\mathbf{P_{g}^{\text{min}}}\) & \(\mathbf{P_{g}^{\text{min}}}\) & \(\mathbf{Q_{g}^{\text{min}}}\) & \(\mathbf{Q_{g}^{\text{max}}}\) \\ \hline
1 & 0.500 & 2.500 & \(-0.750\) & 0.750 & 16 & 0.150 & 1.350 & \(-0.500\) & 0.750 \\
2 & 0.250 & 1.850 & \(-0.900\) & 0.700 & 16 & 0.150 & 1.350 & \(-0.500\) & 0.750 \\
7 & 0.400 & 3.000 & \(-0.800\) & 1.200 & 18 & 0.500 & 4.000 & \(-1.850\) & 2.000 \\
13 & 0.000 & 6.000 & \(-5.000\) & 5.000 & 21 & 0.450 & 4.500 & \(-1.500\) & 1.500 \\
14 & 0.000 & 0.000 & \(-1.500\) & 1.500 & 22 & 0.250 & 2.800 & \(-1.000\) & 1.000 \\
15 & 0.250 & 1.500 & \(-0.750\) & 0.800 & 23 & 0.750 & 6.000 & \(-1.650\) & 1.750 \\ \hline \hline \end{tabular} All data is per unit.  
\end{table}
Table 2: Active and reactive power capabilities in the conventional sources.  
Figure 3: Electrical configuration of the IEEE 24-node test system.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Node** & \(\mathbf{P_{d}}\) & \(\mathbf{Q_{d}}\) & **Node** & \(\mathbf{P_{d}}\) & **Node** & \(\mathbf{P_{d}}\) & **Node** & \(\mathbf{P_{d}}\) & **Node** & \(\mathbf{P_{d}}\) & **\(\mathbf{Q_{d}}\)** \\ \hline
1 & 1.080 & 0.220 & 7 & 1.250 & 0.250 & 13 & 2.650 & 0.540 & 19 & 1.810 & 0.370 \\
2 & 0.970 & 0.200 & 8 & 1.710 & 0.350 & 14 & 1.940 & 0.390 & 20 & 1.280 & 0.260 \\
3 & 1.800 & 0.370 & 9 & 1.750 & 0.360 & 15 & 3.170 & 0.640 & 21 & 0.000 & 0.000 \\
4 & 0.740 & 0.150 & 10 & 1.950 & 0.400 & 16 & 1.000 & 0.200 & 22 & 0.000 & 0.000 \\
5 & 0.710 & 0.140 & 11 & 0.000 & 0.000 & 17 & 0.000 & 0.000 & 23 & 0.000 & 0.000 \\
6 & 1.360 & 0.280 & 12 & 0.000 & 0.000 & 18 & 3.330 & 0.680 & 24 & 0.000 & 0.000 \\ \hline \hline \end{tabular} All data is per unit.  
\end{table}
Table 3: Power consumption in the demand nodes for the IEEE 24-bus system.
## 3 Materials and Methods
### Mapping in Dense Vegetation by the Fusion of FPCS Components from Different Data Sources Using GEE
#### 3.3.2 Application of PCA  
All the bands were mean-centered, i.e., subtracting the corresponding band mean from the band values before deriving the covariance matrix and eigen analysis. Finally, FPCS was applied separately to these three datasets (ASTER, Sentinel-2, and Landsat-8) in the raw and stretched (using DS) form to obtain PC images. The results were obtained by applying FPCS to both the stretched and raw data; for all the three datasets, 34 PCs were  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Bands** & **Spectral Range** & **Resolution** & **Acquisition Time** \\ \hline \multirow{4}{*}{Landsat-8 OLI [59]} & & B5 (NIR) & 0.82 to 0.88 μm & \multirow{4}{*}{30 m} & \multirow{4}{*}{Mosaicked median-pixel data acquired from 2015 to 2020} \\ \cline{3-4}  & & B6 (SWIR 1) & 1.57 to 1.65 μm & & \\ \cline{3-4}  & & B7 (SWIR 2) & 2.11 to 2.29 μm & & \\ \hline \multirow{4}{*}{Landsat-8 TIRS [59]} & & B10 (TIR 1) & 10.6–11.19 & \multirow{4}{*}{100 m resampled to 30 m} & \multirow{4}{*}{2020} \\ \cline{3-4}  & & B11 (TIR 2) & 11.50–12.51 & & \\ \hline \multirow{4}{*}{ASTER [5]} & & B1 (Green) & 0.520–0.600 μm & \multirow{4}{*}{15 m} & \multirow{4}{*}{Mosaicked median-pixel data acquired from 2015 to 2020} \\ \cline{3-4}  & & B2 (Red) & 0.630–0.690 μm & & \\ \cline{3-4}  & & B3N (NIR) & 0.78 to 0.86 μm & & \\ \cline{3-4}  & & B10 (TIR 1) & 8.125–8.475 μm & & \\ \cline{3-4}  & & B11 (TIR 2) & 8.475–8.825 μm & & \\ \cline{3-4}  & & B12 (TIR 3) & 8.925–9.275 μm & & \\ \hline \multirow{4}{*}{Sentinel-2 MSI [60]} & & B2 (Blue) & 0.490 μm & \multirow{4}{*}{10 m} & \multirow{4}{*}{Mosaicked median-pixel data acquired from 2015 to 2020} \\ \cline{3-4}  & & B3 (Green) & 0.560 μm & & \\ \cline{3-4}  & & B4 (Red) & 0.665 μm & & \\ \cline{3-4}  & & B8 (NIR) & 0.842 μm & & \\ \cline{3-4}  & & B11 (SWIR 1) & 1.57 to 1.65 μm & & \\ \cline{3-4}  & & B12 (SWIR 2) & 2.11 to 2.29 μm & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relevant near infrared (NIR) and shortwave infrared (SWIR) bands of each dataset.  
Figure 3: Schematic chart of the applied methodology.  
generated, i.e., 10 from the Landsat-8 OLI and the Landsat-8 TIRS, 12 from the ASTER, and 12 from the Sentinel-2 data sources with the same spatial resolution, by using nearest neighbor resampling.
# Hyaluronic Acid-Based Wound Dressing with Antimicrobial Properties for Wound Healing Application
## 1 Introduction
HA is widely used in wound dressing applications under different morphologies from films [12] and hydrogels [13; 14] to fibres [15; 16] and non-woven fabrics [17] and foams [18; 19] (Scheme 1). HA-based hydrogel wound dressings stand out due to their ability to be a suitable solution for all four stages of wound healing, as well as being non-irritant and non-reactive with biological tissue and permeable to metabolites. Especially used in ophthalmological applications, HA film-like dressings show desirable properties as they permit the transmission of water vapour, O\({}_{2}\) and CO\({}_{2}\). Moreover, they are thin and elastic, flexible and transparent, and are used generally in combination with hydrogels. However, films are usually not suitable for highly exudating wounds. Being originally used to decrease the severity of abdominal adhesions, by being placed between injured tissues, HA films found fortune as tear film substitutions as a potential remedy for wounds associated with dry eye syndrome [20]. On many occasions, wound healing has to be enhanced through foam-like structures in order to allow an easier spread and make sure little residue is left, making them easily applicable for all body areas, including hair-bearing areas. Generally, HA films compose a hydrophobic outer layer protecting the wound from liquid but allowing gaseous exchange and water vapour. Foam wound dressing characteristics also include the absorption of wound drainage. In this review, an overview of the different HA-based wound dressings having antimicrobial activity for wound healing is provided. In particular, focusing attention to the antimicrobial activity obtained from the bacteriostatic effect of the HA itself or from the incorporation of synthetic or natural antimicrobial agents in the HA-based wound dressing.
# Empowering a Broadband Communications Course with a Unified Module on 5G and Fixed 5G Networks +
## 2 Approach and Learning Objectives
### Methodology
#### 2.1.2 Section B: Service Categories of 5G/F5G and 6G Networks
The main learning objectives of this section were (a) the introduction of the students to the fixed generations taxonomy, the definition of which is assigned to the currently formed ETSI Industry Specification Group (ISG), (b) the understanding that the five generations of wireless networks and five generations of fixed networks are two sides of the same  
Figure 2: Classification of services based on three key requirements (latency, bit rate, and fiber density/connectivity) [22]. eFBB: Enhanced Fiber Broadband, FFC: Full Fiber Connection, GRE: Guaranteed Reliable Experience, FFBC: Full Fiber Broadband Connection, GRFB: Guaranteed Reliable Fiber Broadband, GRFFE: Guaranteed Reliable Full Fiber Experience, and FFGRB: Full Fiber Guaranteed Reliable Broadband. coin, (c) the more profound understanding of the main limitations and novelties of each generation, as well as (d) the more profound understanding of the leading service categories, their classification based on their requirements, and the understanding that these are the main driving forces behind the network evolution.
## 4 Experimental Results and Discussion
### Packet Loss Ratio (PLR)  
The packet loss ratio (PLR) of the proposed method and other existing methods are shown in Figure 10. For 100 nodes, the proposed CERP obtained 0% PLR (i.e., there was no loss), and the existing methods, cross-layer-based adaptive threshold and cross-layer fuzzy logic approaches, achieved high PLR values of 2.58% and 4.54%, respectively. It shows that the proposed CERP is more effective for data transmission. The PLR increases when the number of nodes increases. The graph indicated that the introduced method significantly decreased packet loss compared to other energy-efficient algorithms.
## 2 Related Work
### AUV-Aid Approaches  
In these approaches, AUVs act as data mules to deliver underwater data. [PERSON] et al. illustrated the feasibility of leveraging AUVs for underwater data collection [10]. [PERSON] et al. demonstrated the increase of the network lifetime via AUV gathering data from sensor nodes [23].  
Research on AUV-aid data gathering has focused primarily on how to reduce the length of AUV's traveling path [24]. [PERSON] et al. take the Value of Information (VoI) that value of sensing data decreases over time into account and propose a Greedy and Adaptive AUV Path-planning (GAAP) algorithm [25]. Moreover, the cooperation among AUVs is also studied in 3D underwater environments [26]. However, these works are not using MI communications to improve the reliability and throughput.  
In this paper, we leverage the advantages of different wireless underwater transmission modes and the mobility of AUVs to provide a cyber interconnection scheme that enables distributed and efficient data delivery from underwater sensors to the surface stations.