esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Tbps and Kilometer-Range Transmission of Terahertz Signals
## 4 High-Sensitivity SIMO Space-Ground Communication Architecture  
With technological advancements and the integration of satellite communications, the internet, and mobile wireless networks, the Space-Terrestrial Integrated Network (STIN) has emerged as a strategic framework [112; 113]. Satellite-ground communication constitutes the core of STIN, driven by three fundamental requirements: global coverage, capability enhancement, and service evolution.  
While microwave bands remain the most widely used spectrum for satellite communications, they suffer from inherent limitations--namely, broad beamwidth and high power consumption--making them unsuitable for high-rate, large-bandwidth transmissions over long distances. FSO communication employs near-infrared optical carriers to establish terrestrial links within Earth's atmosphere, but its long-range performance is severely constrained by atmospheric absorption, scattering, and turbulence, rendering it more suitable for inter-satellite links than satellite-to-ground scenarios demanding high data rates [114; 115].  
THz wireless communication emerges as a promising alternative to FSO for satellite-to-ground applications. Operating in the 0.1-10 THz range, it offers superior environmental robustness while preserving advantages of FSO-like bandwidth capacity [116; 117; 118].
## 3 Results
### Disaster Management
#### 3.1.4 Planning  
In terms of building and construction, drones are revolutionising the way rural landscapes are visualised and analysed. [PERSON] et al. [103] illustrate how aerial drone photography can generate detailed 3D models of villages, enabling decision-makers and stakeholders to gain a comprehensive understanding of local conditions from multiple vantage points. Also, drones are gaining recognition for their precision in identifying small solar home systems, a task where satellite imagery falls short due to resolution limitations [106]. Research evaluates drone imagery's effectiveness, considering both accuracy and cost, thereby advancing sustainable development goals like universal electrification [106]. [PERSON], [PERSON] et al. [104] demonstrated that drone-based photogrammetry using point cloud data is highly effective for monitoring construction progress. Similarly, [PERSON] et al. [105] found drone-based photogrammetry invaluable for reconstructing 3D models of bridges. Drone regulations in the construction industry, as studied by [PERSON] et al. [108], highlight the need for a comprehensive regulatory framework. Additionally, drones combined with AI technology, offer a sustainable approach to detecting thermal losses in buildings, enhancing safety and efficiency in various climatic conditions [107].  
Heritage  
In heritage conservation, drones are emerging as key tools for documentation and risk assessment. [PERSON] [110] highlights their use in creating digital archives of heritage ruins through combined laser scanning and aerial surveying. As [PERSON] et al. [109] note, drones offer accessible and affordable means to digitally capture heritage sites, safeguarding their exterior features and layouts against potential risks and aiding in their preservation.
# An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication
## 3 Proposed Methodology for Robust Communication through Machine Learning Algorithm  
The VANET's dynamic condition was developed in this section to select the suitable routing of each node. Furthermore, allocating the bandwidth of each node was obtained for accurate routing. In VANET, the optimization approach SVM-HHO-VANET was developed to select a route based on clustering to provide precise bandwidth allocation for proper routing. Here, the proposed methodology was halved into two phases. Security-based modified SVM was used to find DDoS attacks early on. In the next stage, the HHO optimization approach for the VANET dynamic scenario was used to achieve effective bandwidth allocation. This research included cluster selection, accurate cluster node communication, detecting DDoS attacks, route prediction, bandwidth allocation between the cluster nodes, distance, velocity, speed, bandwidth, number of nearby vehicles, and throughput parameters for QoS analysis. Additionally, evaluation metrics such as energy consumption, delay, and drop were considered for QoS performance analysis. In this work, several nodes in the vehicle were analyzed based on distance, speed, velocity, and bandwidth. The location of the vehicle was determined in each iteration, according to fitness. When the node's position was less than 0.5 and the nearby vehicle was greater than or equal to 1, the best location was updated. If the present iteration was higher than the maximum iteration, the target vehicle's position was considered the best location. To achieve a maximum optimal solution, the DF approach was integrated with WOA.  
Furthermore, the dragonfly solution was initialized and determined the bandwidth by QoS value according to the objective function. The maximum iteration was utilized until the process continued to enhance the bandwidth. Finally, energy consumption, delay, and drop were considered based on the QoS parameter for accurate routing. The proposed approach was accurate in allocating bandwidth compared with other existing techniques. This study is robust and fast in predicting DDoS attacks and allocating the bandwidth using the H-WDFOA technique, in the best position after validating for stability in vehicular networks.
# Biology and Regulation of Staphylococcusococcal Biofilm
## 3 Biosynthesis of PIA/dPNAG and Its Regulation  
In 1987, [PERSON] published a paper on the phenotypic variation of _S. epidermidis_ slime production in vitro and in vivo [38]. Today, we know that the \"slime\" they described was the excopolsaccharide PIA (polysaccharide intercellular adhesin), whose chemical structure was first described in _S. epidermidis_ in 1996 [83]. Later, PIA was also referred to as \(\updelta\)-(1,6)-N-acetylglucosamineamine (PNAG) [84]. The more chemical-sounding name PNAG is not really a correct description of the glucosamine polymer as it ignores the fact that N-deacetylation takes place at certain intervals, which is essential for biofilm formation. PIA represents a linear homoglycan of at least 130 beta-1,6-linked 2-deoxy-2-amino-D-glucopyranosyl residues which are from 80 to 85% N-acetylated. The rest are non-N-acetylated and positively charged. Since a correct chemical description was cumbersome, the name PIA was chosen in the initial description of the structure [83]. PIA is a polymer of partially de-N-acetylated \(\updelta\)-1,6-linked \(N\)-acetylglucosamineamine (dPNAG). In the scientific community, the terms PIA and PNAG are both used. As this is confusing for the non-specialist reader, we propose to use the term PIA/dPNAG.
## 5 Data Analysis and Result  
### Reliability and Validity  
To test the consistency of the hypotheses, we utilized SPSS 29 for data analysis. The data analysis consisted of two steps. First, as the variable questions in the questionnaire come from different literature and scales, we used exploratory factor analysis (EFA) and Cronbach's alpha calculation to complete the reliability and validity test of the questionnaire data. The next step is to calculate the comprehensive reliability (CR), average variance extracted (AVE), and KMO. In the end, multiple linear regression was utilized to assess the connection between factors in each stage and the overall co-creation experience.  
We tested that the values of each variable are greater than the universal level of 0.60, which indicates that the questionnaire has a certain degree of credibility. The next step was to use SPSS's exploratory factor analysis (EFA) with varimax rotation to determine which factor had the highest load in each item, confirming that all variables in Table 2 are independent. The results showed that the questions in the dependent and independent variables in the questionnaire had the highest loadings. The detailed factor composites and Cronbach coefficients are shown in Table 3. The CR, AVE, and KMO values are greater than 0.70, 0.50, and 0.70, respectively, which proves the internal consistency and validity of the questionnaire. The specific data are shown in Table 3.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Constructs** & **Measured Item** & **Factor Loading** & **Sources** \\ \hline \multirow{3}{*}{\begin{tabular}{l} Immersive \\ \(\alpha=0.889\) \\ \end{tabular} } & Q30 I am focused on the destination’s AR game. & 0.870 & \\  & Q31 Time seems to pass quickly during my AR gaming experience. & 0.897 & \\  & Q32 I became completely immersed in the AR game. & 0.916 & \\  & Q33 I am addicted to this AR game. & 0.751 & \\ \hline \multicolumn{3}{l}{Post visitation} \\ \hline \multirow{3}{*}{\begin{tabular}{l} Generate memories \\ \(\alpha=0.844\) \\ \end{tabular} } & Q34 The AR game experience has created memorable impressions for me. & 0.788 & \\  & Q35 It reminds me of an unforgettable memory from the past. & 0.818 & [34] \\  & Q36 I will remember what happens in the AR gaming experience. & 0.815 & \\ \hline \multirow{3}{*}{\
\begin{tabular}{l} Emotional \\ Resonance \\ \(\alpha=0.894\) \\ \end{tabular} } & Q37 I have developed an emotional connection to the cultural heritage site. & 0.826 & \\  & Q38 I draw inspiration from the cultural heritage site. & 0.821 & \\  & Q39 I feel a strong sense of belonging to this cultural heritage site. & 0.883 & \\  & Q40 This AR game experience gives me a feeling of happiness. & 0.820 & \\ \hline \multirow{3}{*}{
\begin{tabular}{l} Overall co-creation experience \\ \(\alpha=0.754\) \\ \end{tabular} } & Q41 I am satisfied with interacting with other visitors through AR games. & \\  & Q42 I consider the overall experience with AR games to be & 0.719 & \\  & unforgettable. & 0.731 & \\  & Q43 I am fully engaged from start to finish in the AR gaming experience. & \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._
# Data Augmentation Methods for Enhancing Robustness in Text Classification Tasks
## 4 Evaluation Experiments
### Experiment Results
#### 4.2.2 Robustness Results  
The results presented in this section demonstrate the effectiveness of our proposed data augmentation methods in improving the robustness of the models under attack by TextFooler [6] and PWWS [9]. Figures 5 and 6 show the improvement in the accuracy under attack and the decrease in the attack success rate, respectively, for the AG-News dataset [20]. _Cognate3_, _Antonym2_, and _Antipode2_ exhibit notable increases in the accuracy under attack and decreases in the attack success rate under both attacks. In particular, _Antipode2_ demonstrates a significant increase in the accuracy under attack (2.80% and 8.60% in Figure 5) and a decrease in the attack success rate (\(-3.00\%\) and \(-9.34\%\) in Figure 6) in both attacks.  
Figures 7 and 8 show the improvement in the accuracy under attack and the decrease in the attack success rate for the TREC dataset [21; 22]. _Cognate2_, _Antonym1_, _Antonym2_, _Antipode1_, and CheckList [7] all exhibit increases in the accuracy under attack and decreases in the attack success rate under both attacks. Specifically, _Antonym1_ and _Antonym2_ show solid results, with increases in the accuracy under attack (1.07% and 3.80%; 1.67% and 1.27% in Figure 7) and decreases in the attack success rate (\(-1.19\%\) and \(-4.08\%\); \(-1.70\%\) and \(-1.27\%\) in Figure 8) in both attacks.  
Figures 9 and 10 show the improvement in the accuracy under attack and the decrease in the attack success rate, respectively, for the SUBJ dataset [23]. _Antonym2_ and _Antipode1_ demonstrate notable increases in the accuracy under attack (1.13% and 2.33%; 1.00% and 2.53% in Figure 9) and decreases in the attack success rate (\(-1.20\%\) and \(-2.48\%\); \(-1.02\%\) and \(-2.61\%\) in Figure 10) under both attacks.  
Figures 11 and 12 show the improvement in the accuracy under attack and the decrease in the attack success rate for the SMS-Spam dataset [24]. All data augmentation methods exhibit increases in the accuracy under attack and decrease in the attack success rate under both attacks. _Cognate1_ and _Antipode2_ show solid results, with _Antipode2_ demonstrating an impressive increase in the accuracy under attack (16.93% and 11.47% in Figure 11) and a decrease in the attack success rate (\(-16.68\%\) and \(-11.15\%\) in Figure 12) in both attacks. The SMS-Spam dataset's limited semantics and lexical diversity [24] may contribute to the observed results.  
Figure 10: SUBJ Attack Success Rate Decrease.  
Figure 9: SUBJ Accuracy Improvement Under Attack.  
Our proposed data augmentation methods, particularly the Antonym-based and Antipode-based variations, demonstrate superior performance in terms of the robustness improvement compared to the current methods EDA [10] and CheckList [7].
# An Algorithm for Part Input Sequencing of Flexible Manufacturing Systems with Machine Disruption
## 1 Introduction  
Disruption events occur unpredictably in supply chain (SC) and production environments. Unexpected events in real-world manufacturing environments include resource-related events and operation-related events [1]. Disruption happens in various fields in supply chain and production environments. Supply disruption, production disruption, and transportation disruption are examples of disruption forms [2]. With the increase in SC activities and global business activities, the impact of disruption could be substantial [3]. Because of the uncertainty in disruption event occurrence and the seriousness of disruption impact, disruption handling is an important issue. Manufacturing systems should be flexible so as to absorb disturbance on a short horizon [1].  
Flexible manufacturing systems (FMSs) produce a middle volume and a wide variety of part types [4, 5]. The systems aim to achieve efficiency of mass production systems and flexibility of job shops. FMSs possess not only computer numerical control machines but also automated material handling devices. These devices include automated guided vehicles, rail-guided vehicles, robots, and so forth. Researchers categorize different types of FMSs mainly as flexible flow systems and general flexible machining systems [6]. It is very complicated to manage FMS production. An FMS with capacity constraint may not produce orders in time, resulting in some parts having to be sent to a job shop [7].  
Supply chain engineering is a very important issue in the area of production research. Because of the serious impact of disruption in supply chains and in production systems, research efforts are placed on disruption handling in supply chains and in production systems. The mitigation of disruption risk can be made proactive or reactive. Therefore, there are two types of disruption handling approaches for production scheduling, that is, proactive scheduling and reactive scheduling [8]. Proactive scheduling takes into account unexpected disruption to build protection when schedules are generated. Reactive scheduling adjusts schedules when unexpected disruption events happen. Dynamic systems can be managed by applying advanced information technology such as Radio Frequency Identification (RFID). Therefore, the application of advanced information technology makesit possible to obtain and process information to handle disruption reactively. This paper applies reactive scheduling to handle machine disruption in FMS part input sequencing. The paper proposes an algorithm to provide a solution for part input sequencing of FMSs with machine breakage.  
The remainder of the paper is described in the following. Section 2 presents related works. In Section 3, an algorithm for part input sequencing of FMSs to handle machine breakage is proposed. Evaluation with the analyses of the results of the proposed algorithm is described in Section 4. Conclusions, managerial implications, and further works are summarized finally in Section 5.
# A Novel Method to Determine the Optimal Location for a Cellular Tower by Using LiDAR Data
## 4 Results and Discussions
### Determination of the Strength of the Signal
#### 4.1.1 Location of Existing Towers and Cell phone Users  
For the study, a project area of the RGIPT campus was taken, which lies in between (latitude (26.265788), longitude (81.504372)) and (latitude (26.263355), longitude (81.515723)). Adjoining the campus, there is a railway line.  
The project area of the RGIPT campus is surrounded by the Jio and Voda towers, as shown in Figure 8. Except for the Jio tower, the rest of the towers are far from the RGIPT location. The main aim of this research is to find an optimal location for a cell phone tower. This optimal location will provide adequate signal strength to the user's location. The cell phone will catch the signal strength from the cell phone tower that is near to the location. The nearest tower will give better signal strength.  
Although there are two types of towers near the RGIPT location, one is a Jio tower and the other is a Vodafone tower. The closest to the RGIPT location is \"Jio tower A\". \"Jio tower A\" is at location (latitude 26.263130\({}^{\circ}\), longitude 81.515706\({}^{\circ}\)). \"Jio tower A\" (nearest tower) is considered for the further processing of deploying the tower for the RGIPT location. \"Jio tower A\" plays an important role in the cellular signal strength of RGIPT.  
Figure 7: Result Flowchart.  
From the project area, the 1500 points were taken as a user's location. At these points, signal strength due to the nearest cell phone tower was recorded by a mobile application named (Network cell info lite), shown in Figure 9. The cell phone tower is not limited to only one network. It can be for multiple networks.  
An existing signal strength map from Jio tower A for the RGIPT campus made in ArcGIS is shown in Figure 10b. This signal strength map is calculated for 1500 locations of the RGIPT campus.  
Figure 8: Tower locations near the RGIPT campus.  
Figure 9: Mobile app for signal strength collection, Network cell info lite.
## 3 Results
### Complexity Analysis
#### 3.1.6 Overall Classification Flow  
* Description: The overall classification involves processing each frame independently for static signs and using sliding window algorithms for dynamic signs.
* Complexity: Given \(M\) static images and \(N\) frames in dynamic videos, the overall complexity is \(O(M)+O(N)\). Since the algorithms are linear regarding the number of inputs, the combined complexity remains manageable even for larger datasets.
## 3 Results and Discussion
### CO\({}_{2}\) Benefits of GCI Ehicles and Life Cycle Assessments
The manufacturing of vehicles and batteries and the end of life (EoL) are considered in this study using Gabi software [33]. As shown in the figure, despite having a significant carbon burden from the beginning of its manufacturing stage, BEV appears to have the lowest accumulated lifetime GHG emission levels when set at 17.3 t CO\({}_{2\text{eq}}\). On the other hand, ICE passenger vehicles display the highest levels of GHG emissions in the base case as a result of the combination between the fossil source and lower thermodynamic efficiency. Based on the LCA results, the conventional ICEVs produce more than 14-10 t CO\({}_{2\text{eq}}\) in their lifetime compared to BEV. In case of GCI hybrids, the GHG emission gaps can be reduced to 2-4 t CO\({}_{2\text{eq}}\). In the world, electrical power is generated by power plants that use different primary energy sources. These primary energy sources are all of natural origin; they include fossil fuels (coal, petroleum, natural gas) and low-carbon energy sources (nuclear, renewables) that release a lot or a little CO\({}_{2}\) to the atmosphere. The GHG emissions of BEV and PHEV depend on the rate of de-carbonization and its effects on the carbon intensity of the electricity that is generated, and a differentiated technology deployment pathway can be developed. In some cases, where the grid is sufficiently de-carbonized and where charging infrastructures are widely available, a direct transition to BEV could be a feasible option; however, in other cases, an advanced GCI hybrid technology can be the transitionary technology to bridge the gap in the interim period.
# Biosynthesis of Polyhydroxyalkanoates from Defatted _Chlorella_ Biomass as an Inexpensive Substrate
## 3 Results and Discussion
### Characterization of PHA Polymers  
#### 3.2.1 FTIR Analysis  
The FTIR spectra of PHA polymers accumulated by _B. megaterium_ ALA2, _C. necator_ KCTC 2649, and _H. mediterranei_ DSM 1411 are shown in Figure 4. PHA polymers produced by the different bacterial strains showed characteristic bands of ester bonding present in PHB homopolymer at 1734 and 1185 cm\({}^{-1}\)[27]. The FTIR spectrum of PHA polymers produced by _H. mediterranei_ DSM 1411 showed additional peaks between 1450 and 1000 cm\({}^{-1}\) corresponding to CH\({}_{2}\) wagging, CH\({}_{3}\) bending, C-O, C-C and C-O-C stretching, confirming the P (3 HB-_co_-3 HV) nature of the sample [28]. [PERSON] et al. [29] reported the same bands for P (3 HB-_co_-3 HV) produced from ricotta cheese exhausted whey.  
Figure 4: The FTIR spectra of PHA produced by **(a)**_Bacillus megaterium_ ALA2, **(b)**_Cupriavidus_ _near_ KCTC 2649, and **(c)**_Halofrax mediterranei_ DSM 1411 using 0.3 N HCl DCB hydrolysate as the carbon source.  
Figure 3: Time profile of OD at 600 nm, DCW, PHA accumulation, carotenoid production, and substrate utilization by _Halofrax mediterranei_ DSM 1411 using 0.3 N HCl DCB hydrolysate as the carbon source in a flask culture. Data are shown as mean values \(\pm\) standard deviation (\(n=3\)).
# Crustal Structure of Hainan Island and Surrounding Seabed Based on High-Resolution Airborne Gravity
## 5 Discussion
### Geological Implication of High Gravity Anomaly in Central Hainan Island  
Our gravity anomaly decomposition results reveal a series of NE-trending linear anomalies in the middle-to-lower crust of the study area. The gravity Moho inversion results also highlight several interesting features. We contend that these anomalous belts have recorded the evolutionary history of Hainan Island. Located at the intersection of three continental plates (Figure 1), Hainan Island has a complex tectonic history, and three fault systems in the EW, NE, and NW directions remain. The influence of the Tethyan tectonic system and the Paleo-Pacific tectonic system on Hainan Island since the Phanerozoic era is still an important issue in the region's tectonic evolution. Currently, research is mainly conducted using petrology and geochemistry methods. The results still allow for multiple interpretations, though many issues remain unresolved.  
Our analysis clearly identifies NE-trending fault structures in D5 (Figure 4) and D6 (Figure 5), demonstrating two key implications: these fault structures are more developed in the middle crust, and the NW-trending subduction of the paleo-Pacific plate predominantly shaped the deep geological structures of Hainan Island. Previous studies have extensively investigated the granites of Hainan Island. There are currently two theories: One view is that the granite of Hainan Island from the Paleozoic era to the Mesozoic era is the post-collisional products linked to the northwestward subduction of the paleo-Pacific plate. They thought that the granites on Hainan Island share formation characteristics with those in the Pearl River Mouth Basin, collectively forming an arc-shaped granite belt [32; 53]. An alternative view proposes that these granites originated in the Tethyan tectonic system and exhibit genetic similarities to those exposed in the Indochina Peninsula. Proponents of the latter argue that the granite on Hainan Island represents products of the eastward extension of the Tethys Ocean [6; 18; 55; 56]. Our results predominantly support the first interpretation: Hainan Island became incorporated into the Paleo-Pacific subduction system during the late Paleozoic era, and numerous NE-trending faults developed throughout this subduction phase.