esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Empirical Insights from Sectorial and Regional Studies
### AI Marketing and SME Performance in Emerging Economies: Evidence from Ghana SMEs  
Abrokwah-Larbi and Awuku-Larbi [100] provide empirical evidence from Ghana on how AI in Marketing (AIM) significantly improves the performance of small businesses in four key dimensions: financial, customer, internal business process, and learning and growth. Drawing on the Resource-Based View (RBV), their study surveyed 225 SMEs and applied structural equation modeling to assess the impact of AIM determinants, including IoT, collaborative decision-making systems, virtual and augmented reality, and personalization. The findings revealed strong positive effects, with AIM explaining up to 61% of variance in learning and growth performance and 58% in internal processes.  
These results reinforce the emphasis of the TOE-DOI framework on organizational and technological readiness, while also showing that even in resource-constrained emerging economies, strategic alignment of AIM capabilities can produce tangible performance benefits. The study also highlights specific barriers such as resource limitations and skill gaps, echoing the challenges identified in Section 4, and suggests that personalization and CDMS are particularly effective levers for SMEs seeking growth and innovation.
## 6 Conclusions  
For a vehicular platoon in the presence of sensing delay and communication delay, this paper deduces a necessary and sufficient internal stability condition and two types of sufficient string stability conditions. In the internal stability analysis, the ETDMs are obtained by revealing the kernel and offspring hypersurfaces of the CTCR paradigm, in which the Dixon resultant is utilized instead of the well-known Sylvester resultant, and then the computational burden is reduced significantly. The strength of our proposed method is exhibited by comparative simulation that the simulation time of our proposed method of computing the ETDMs is just 0.05 min, which is less than that of the Sylvester resultant matrix-based method by two orders of magnitude, and less than that of ACE combined with Kronecker summation by three orders of magnitude. In the string stability analysis, we provide the results for the situation no matter how large the frequency of the leader vehicle's maneuver is. Furthermore, a more practical string stability condition is studied by considering only the region of low frequency, where most of the energy of the spacing errors exists. Based on this, a lower bound of the time headway is deduced for low frequency, which can be used to increase road capacity and decrease fuel consumption. Simulation results show that in different scenarios, the spacing errors propagate along the vehicular platoon without divergence when the time headways are larger than our proposed bounds.  
In this article, we focus on conducting the stability analysis of the homogeneous vehicular platoon with the sensing delay and communication delay. However, vehicular platoons on the real-world roads may be composed of different types of vehicles. Hence, our future work is to analyze the internal and string stability of the heterogeneous vehicular platoon with the sensing delay and communication delay.  
Conceptualization--writing, X.Z.; Writing--editing, Y.S.; Methodology--validation, Z.Z.; Supervision, M.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation (NNSF) of China under Grant 62003054, the Key Research and Development Plan of Shaanxi Province under Grant 2023 YBGY398, and the Fundamental Research Funds for the Central Universities of China under Grant 300102320109.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Vehicle Index**} & \multicolumn{3}{c}{**Tracking Performance**} & \multicolumn{3}{c}{**Ride Comfort**} \\ \cline{2-7}  & **Case 1** & **Case 2** & **Case 3** & **Case 1** & **Case 2** & **Case 3** \\ \hline
1 & 46.4875 & 14.9060 & 23.2915 & 0.5238 & 0.1196 & 0.5120 \\
2 & 35.3689 & 13.6805 & 22.3953 & 0.2825 & 0.0748 & 0.3605 \\
3 & 27.9918 & 12.6983 & 22.0915 & 0.1614 & 0.0557 & 0.2972 \\
4 & 22.9289 & 11.8603 & 22.0800 & 0.0949 & 0.0442 & 0.2593 \\
5 & 19.3520 & 11.1278 & 22.0581 & 0.0568 & 0.0363 & 0.2345 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The tracking performance and ride comfort in the different scenarios.
# Fire Risk Analysis in Large Multi-Compartment Structures Using a Hybrid Multiscale Approach
## 3 Proof of Concept
### Application to a Multi-Story Office Building
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Type of** & **Fuel** & \(m_{0}\) & \(\kappa_{gr}\) & \(\Delta t_{ch}\) & \(p\) & \(HRR_{max}\) & \(A_{f}\) & **Fire Load** \\
**Compartment** & **Components** & **(kg)** & **(W/s\({}^{2}\))** & **(MJ/kg\({}^{2}\))**[20]** & **(MW)** & **(m\({}^{2}\))** & **Density (MJ/m\({}^{2}\))** \\ \hline \multirow{3}{*}{Private office} & Wood pine & 17.5 & 3 & 12.4 & 0.65 & 0.240 & 1 & \\  & PMMA & 15.0 & 15 & 24.2 & 0.85 & 0.760 & 1 & 53.5 \\  & PU foam & 17.5 & 49 & 28.0 & 0.85 & 0.810 & 1 & \\ \hline \multirow{3}{*}{Co-working compartment} & Wood pine & 35.0 & 3 & 12.4 & 0.65 & 0.520 & 2 & \\  & PMMA & 30.0 & 15 & 24.2 & 0.85 & 1.560 & 2 & 53.5 \\ \cline{1-1}  & PU foam & 35.0 & 49 & 28.0 & 0.85 & 1.770 & 2 & \\ \hline Archive room & Wood pine & 200.0 & 3. & 12.4 & 0.65 & 4.000 & 2 & 62.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Nature and initial mass of the fuel components present in the three types of compartments and values of the parameters required to build their design fires. In this table, for each fuel component, \(m_{0}\) is the initial mass of fuel, \(\alpha_{gr}\) is the fire growth rate, \(\Delta t_{ch}\) is the chemical heat of combustion, \(p\) is the fraction of \(m_{0}\) beyond which fire begins to decay, \(HRR_{max}\) is the maximum value of HRR, and \(A_{f}\) is the surface area of fuel (see Section 2.2). Combustion properties of wood, PMMA, and PU foam are available in [20]. Figure 8: Plans of (**a**) the first floor (\(z=0\)) and (**b**) the second floor (\(z=3\) m) of the four-story building. The layouts of the compartments on the third and fourth floors (not shown) are identical to that of the second floor. The fire starts in compartment A08 (red star).
# A LiDAR-Based Backfill Monitoring System
## 3 Methods  
The backfill progress is expressed as the volume of the backfill material divided by the volume of the initial tunnel (Equation (3)). Thus, the key to calculate the filling progress is to find the slurry surface, then reconstruct the mesh model of the intersection body, and estimate the volume of the filling body at every moment. The approach used in calculating the backfill progress is shown in Figure 3. As long as the backfill monitor remains open, these three steps will continue to cycle:  
\[P=\frac{v_{m}}{v_{t}} \tag{3}\]  
Here, \(P\) represents the backfill progress, \(v_{m}\) is the volume of the backfill material, and \(v_{t}\) denotes the initial tunnel volume.  
The following steps are used in this process:  
(1) Slurry surface fitting involves applying the ICP (Iterative Closest Point, which aligns two point clouds by iteratively minimizing the distance between corresponding points) registration algorithm [30] to align the initial tunnel point cloud with the backfilling point cloud, and capturing the point cloud data representing the top surface of the backfill material. After completing step 1, the top surface is located and the point cloud of the surface boundary is obtained (see Section 3.1);  
(2) Generate points of slurry surface, and determine which points belong to the slurry body. After completing step 2, the entire point cloud model of backfill body is obtained (see Section 3.2);  
(3) Reconstruct the point cloud model into a 3D mesh, and compute the volumes of both the slurry body and the original tunnel in order to generate the backfill progress output (see Section 3.3).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Specification** \\ \hline Laser Wavelength & 905 nm \\ Laser Safety & Class 1 (IEC60825-1) \\  & 90 m @ 10\% reflectivity \\ Range & 130 m @ 20\% reflectivity \\  & 260 m @ 80\% reflectivity \\ FOV (Field of View) & 38.4\({}^{\circ}\) (circular) \\ Point Rate & 100,000 points/s \\ Operating Temperature & \(-\)20 \({}^{\circ}\)C to 65 \({}^{\circ}\)C \\ Water and Dust Rating & IP67\({}^{4}\) \\ Supply Voltage Range & 10 to 16 V DC \\ Data Delay & 2 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters for the Livox Mid-40 LiDAR.  
Figure 2: Backfill monitor.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 5 Discussion
### Practical Barriers  
While DTs present significant potential for agricultural applications, several implementation barriers must be addressed. A primary challenge involves the requirement for specialized technical expertise and sustained support systems. The sophisticated nature of these systems, which integrate advanced sensors, machinery, and digital platforms, requires reliable access to technical specialists. This requirement poses particular difficulties for small-scale operations, consistent with historical patterns of slower digital adoption in agriculture sectors.  
Data integration presents another substantial obstacle. Combining heterogeneous data streams from soil sensors, meteorological stations, remote sensing platforms, and historical production records into a unified DT framework requires skills and expertise. The availability of high-speed internet is still a problem in remote areas [118]. Each data source demands specific processing protocols and interpretive methodologies, complicating both initial implementation and ongoing system maintenance. Environmental variability is another uncertainty in predictive models.  
Another major barrier is the need for physical infrastructure. Setting up sensor networks across large agricultural areas requires a high upfront investment and careful planning for proper installation and coverage. The continuous maintenance of monitoring equipment and data transmission systems and a reliable algorithm [119] are essential for ensuring data reliability. These substantial resource requirements may deter adoption without clear demonstrations of system efficacy through robust pilot testing and simulation validation.
# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 4 Results
### Application Scenarios and Tasks of Real-Time Object Detection
Based on our review, it seems that all studies have defaulted the sensor required for real-time object detection to RGB due to its advantages of being of a high resolution, light weight, and low cost and being easy-to-use. Multi- and hyper-spectral cameras can capture spectral information from objects of interest [100; 101], each with unique characteristics. Multi-spectral sensors offer centimeter-level remote sensing data with several spectral bands, typically spanning blue, green, red, red-edge, and near-infrared. In contrast, hyper-spectral sensors provide a multitude of narrow bands, extending from ultraviolet to longwave [21]. The application of multi-spectral and hyper-spectral sensors in non-real-time object detection based on UAV remote sensing has been extensively explored in various domains. For instance, spectral sensors have found utility in precision agriculture [102; 103], landslide monitoring [104], and power line inspection [105]. However, in scenarios where real-time object detection is imperative, our review has revealed a limited number of studies employing spectral sensors. Specifically, only two pieces of literature have been identified that delved into the utilization of spectral sensors for real-time object detection. These studies encompass the detection of tree canopy detection using multi-spectral cameras [74] and the detection of macro plastic in the marine environment using hyper-spectral cameras [96]. Of the articles under our scrutiny, two distinct studies leveraged the potential of thermal infrared cameras to explore their temperature sensitivity. In one instance, the focal point was the detection of photovoltaic panels[84]. In another significant context, the focus shifted towards identifying individuals with elevated body temperatures, which can be potentially indicative of a COVID-19 infection[48]. Furthermore, two additional research studies have reported applications involving the fusion of multiple sensors. In [50], thermal sensors were judiciously deployed for identifying individuals in need of rescue, while an RGB sensor was simultaneously leveraged for precise localization. In another study [60], an RGB camera assumed the role of vessel morphology detection, while thermal infrared technology was adeptly employed to pinpoint heat emissions from engines.
## 3 Results
### Secondary Endpoint  
LCQ was assessed at baseline, day 5 and at the end of treatment (day 10). LCQ scores are shown in Table 3. Significant improvements of the overall score on each domain were recorded in the active group after 5 days as well as at the end of treatment (\(p<0.0001\)). No change in LCQ score was observed in the placebo group at any time during the study (\(p>0.05\)).  
At baseline, a cough severity VAS >9 was recorded in both arms. After 5 days of treatment with the investigational product, the cough severity improved significantly (\(p<0.0001\)) with a VAS score of 0.9 while reaching zero at day 10. On the contrary, the VAS score did not change (\(p=1.0\) day 5 vs. baseline; \(p=0.563\) day 10 vs. baseline) in the placebo arm during the whole study (always >9).  
Figure 2: Cough Clinical Score (CCS). (**a**) Daytime cough CCS; (**b**) Nighttime cough CCS. Black bars: active; grey bars: placebo. ***\(p<0.001\) active vs. placebo.  
At baseline, sleep quality was affected in both groups most of the time due to frequent coughing. Subjects receiving the active product showed a significant reduction of nocturnal cough (\(p<0.001\)) after both 5 and 10 days of treatment. This was associated with a significant improvement of sleep quality assessed by a daily questionnaire filled by subjects using a scoring scale ranging from 0 (no disturbing) to 6 (extremely disturbing). In particular, subjects in the active group showed a mean score of 5.8 at baseline, which significantly decreased to 2.3 after 5 days and was zero at the end of treatment (day 10). On the contrary, in the placebo group, the mean score remained unchanged from baseline to day 5 (5.8) (\(p=0.750\)) and at the end of treatment (5.7) (\(p=0.983\)).  
SF-36 was used to assess the quality of life in subjects treated with either the active treatment or placebo. The study results showed that at the end of the study all items were ameliorated significantly in patients receiving the active treatment, while no significant improvement (\(p=0.268\)) was reported by patients from the placebo arm. These findings corroborate the data from LCQ confirming that the investigational active product is effective in improving the perception of a better quality of life in subjects with a cough.  
All participants (100%) in the active arm reported a positive perception about the treatment and were very happy or happy about the treatment itself.  
Tolerability was rated by the investigator as excellent, good, fair and poor. Both study arms showed an excellent tolerability profile and no side effects were recorded during the entire study. Haematological parameters, biochemistry, and vital signs were not affected by treatments and were within the physiological ranges both at baseline and at the end of the study.
## 1 Introduction
Moreover, kernel density analysis is also an effective spatial statistical method, which calculates the density around each point by constructing a smooth surface and realizes the transformation from discrete objects to continuous fields, and then visualizes the abnormal area to make it more prominent [40]. For these abnormal deformation areas, the reliability of the results can be verified by numerical simulation, comparison of GPS monitoring results, or field investigations of the abnormal area [18; 41; 42]. Numerous studies have shown that SBAS-InSAR technology has high monitoring accuracy [43], providing excellent results for the identification of landslides and other geological disasters [44; 45; 46]. However, most studies have ignored the geometric distortions of the satellite images, including shadows, layover, and foreshortening [45], which are especially pronounced in mountainous areas. These phenomena degrade the quality of the SAR images and result in invalid deformation data. Therefore, how to obtain effective monitoring data and identify geological disasters is of great significance. In order to obtain reliable deformation information and quickly and effectively identify potential geological hazards in Maoxian County, we combined the terrain information of Maoxian County with the satellite parameters of Sentinel-1A ascending data to conduct a visibility analysis. The study area is divided into visible and invisible areas to eliminate the influence of image geometric distortion on monitoring data. Then, the SBAS-InSAR deformation rate in the visible area is extracted, and hot spot analysis is performed to obtain the fast-moving area. In order to highlight the abnormal deformation areas, based on the result of the hot spot analysis, we performed kernel density analysis. Deformation rate, the structural characteristics of the slope, and information from Google images are used to identify different types of potential geological hazards in these abnormal areas. Finally, field investigations are used to verify the types of potential geological disasters and their probability of occurrence.
## 5 Conclusions  
Radiation calibration is needed to carry out irradiance and radiance measurements with micro-spectrometers. This work introduces the method of field radiation calibration for micro-spectrometers, analyzes the effect of different types of connected fibers on the radiation calibration, and analyzes the performance of micro-spectrometers in measuring R\(rs\) in inland waters. The main findings are as follows: (1) Different fiber types mainly affect the calibration coefficients of gain and offset, and the field radiometric calibration coefficients of gain and offset for different fiber types can be found at [[https://github.com/765302995/FRC_Micro-spec](https://github.com/765302995/FRC_Micro-spec)]([https://github.com/765302995/FRC_Micro-spec](https://github.com/765302995/FRC_Micro-spec)) (accessed on 15 November 2022). (2) The MAPD of the micro-spectrometer reached 18.64% and 5.11% for clear water and turbid water, respectively, and the water body R\(rs\) values of unmanned plateau lakes were obtained for the first time using the micro-spectrometer. This article shows that the micro-spectrometer can meet the requirements for field measurements of R\(rs\) of water bodies in inland unmanned areas, and with this breakthrough in the radiation performance of the micro-spectrometer, we can obtain more accurate R\(rs\) measurements of water bodies in unmanned areas.  
Conceptualization J.S. and Q.S.; project administration, Y.Y. and J.L.; writing--original draft preparation, J.S.; writing--review and editing, Q.S.; validation, L.W. and F.Z.; supervision, Q.S.; methodology, J.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China under grant number 2021 YFB3901101.  
Not applicable.  
Figure 7: Micro-spectrometer measurements of unmanned areas of Qarhan and Hoh Xil Salt Lake R\(rs\) and in situ photos.  
**Acknowledgments:** The authors would like to thank [PERSON] from the Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences for help with writing. We are also thankful to all anonymous reviewers for their constructive comments provided on the study. Thanks to [PERSON] and [PERSON] from Shenyang Jianzhu University for their help in obtaining the data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Methodology  
The present study proposes a hybrid framework that employs generative adversarial networks and a Shapley algorithm based on easy data augmentation (HEGS) to address problems with imbalanced data in classification tasks. The EDA method can generate high-quality training sentences with a given sentence pattern by replacing words with their synonyms. Therefore, the sentences generated through EDA lack diverse sentence patterns. To address this problem, GAN-based models can be applied to learn and generate highly diverse sentence patterns based on the probability of each word in a language model. However, EDA generates high-quality sentences that exhibit low diversity, and the GAN-based model can generate sentences that are highly diverse but may deviate from semantic representations. The Shapley algorithm can calculate the similarity of multiple sentences based on the semantic space instead of grammatical structure or linguistic patterns. The present study proposes a HEGS framework that generates high-quality training sentences and improves classification performance. The HEGS framework comprises the five following stages. (1) In initial sentence generation, the EDA processing is applied to create numerous sentences to form an EDA-based augmented data set. (2) In GAN-based model training, the SentiGAN model [7] is used to learn training sentences based on the EDA-based augmented data set. An EDA-based augmented data set is used as a GAN-based model's learning objective. (3) In GAN-based model evaluation, the bilingual evaluation understudy (BLEU) and self-BLEU algorithms are used to evaluate the quality of data generated by the GAN-based model. (4) In final training sentence generation, the final augmented data set created by the GAN-based model is filtered for identical semantic sentences by the Shapley algorithm. (5) In the classification task, the BERT model is applied to train text classification tasks based on the final augmented data set and the original training data. The procedures and processes of the proposed EDA-GAN framework are presented in Figure 1.
# Fault Sensing Using Fractal Dimension and Wavelet
## 6 Experiment and Results
### Experiment Results and Analysis
Therefore, this algorithm is better than the IFBD recognition algorithm regarding fault type recognition. It can be seen from Tables 7 and 8 that the FS method could correctly classify all 10 types of short circuit faults. The initial angle, the fault resistance, and the fault distance had no influence on the recognition result of the FS method. Therefore, the FS method is better than the IFBD recognition algorithm and the DMWC recognition algorithm regarding their comprehensive performances.