esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions
## 2 Related Work
### Scene Detection in Autonomous Driving Scenarios  
In autonomous driving, scene detection is important for detecting dynamic environments, ensuring safety, and making real-time driving decisions. Traditional scene detection methods involve object detection, semantic segmentation, and instance segmentation for recognizing road structures, obstacles, and traffic participants.  
Deep learning-based approaches, especially CNN and transformer-based architectures, have significantly improved scene detection accuracy. Models such as faster DGCB-Net [15] have been widely used to detect objects and to segment scenes in autonomous driving datasets such as the KITTI and nuScenes datasets. However, these models often struggle with generalization in unseen driving scenarios due to variations in weather, lighting, and occlusions.  
Recent advancements in VLM have led to their being endowed with multimodal scene understanding, where textual descriptions complement visual features to enhance detection and interpretation. Methods based on large-scale pre-trained VLM, such as BLIP-2 and LLaVA, have been developed to generate natural language scene descriptions that provide contextual insights beyond mere object recognition. Using such methods, autonomous systems can identify relationships between objects, predict potential hazards, and enhance decision-making.  
In addition, self-supervised learning approaches have been proposed to improve scene detection in complex driving conditions. Contrastive learning techniques, such as those used in SimCLR [16], can enable models to learn robust feature representations from large-scale unlabeled datasets. With these techniques, the scene detection performance can be improved by leveraging diverse driving environments and conditions.  
This paper proposes the integration of VLM-based contextual scene descriptions and optimizes BLIP-2's Q-Former module for enhancing scene detection in autonomous driving, and it discusses its adaptation for real-world road environments. By optimizing prompting strategies and employing domain-specific pre-training, we aim to improve the model'scapability to generate structured scene descriptions that could enhance interpretability and situational awareness in autonomous driving scenarios.
# Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints
## 4 Computational Results
### Results and Analyses
Notably, the cumulative energy consumption of this bus platoon amounts to 45.37 kWh, which falls below the effective battery capacity calculated as 30 \(\times\) 3 \(\times\) 0.7 = 63 kWh, indicating the correctness of the energy consumption constraints and charging decisions. Analogously, other trip sequences are confirmed to be valid. In terms of cost, the total cost of a trip sequence is generally determined by the nodes covered by the sequence and the number of assigned module bus platoons. If a trip sequence is assigned only one module unit, its cost is calculated by summing the arc costs that constitute the sequence. On the other hand, if the trip sequence is assigned multiple module buses, each cost item needs to be multiplied by the corresponding quantity to calculate the total cost of the sequence. It is evident that the cost can vary when different platoons are assigned to the same sequence. For example, in Table 3, the cost of the first sequence carrying three module buses is $155.23. However, if the same sequence is assigned one MB unit, the cost would be $155.23/3. Additionally, we have provided the costs of the trip sequences with the assigned platoons in the last column of Table 3, and the sum of these costs perfectly aligns with the objective function value obtained by our model, validating the accuracy of cost calculation.
## 6 Open Challenges, Future Prospects and Research Directions
### Capacity-Approaching Probabilistic Amplitude Shaping  
Future research in capacity-approaching PAS will aim to further optimize PAS implementation, enhance adaptability, and reduce computational complexity. The work will focus on ensuring efficient use of the communication channel by aligning the signal's amplitude distribution with the channel's capacity.  
Optimization of PAS Implementation: Future research will focus on optimizing PAS implementation in practical systems, ensuring that it can be deployed effectively in real-world FSO networks. This will involve developing efficient algorithms for shaping and decoding that minimize computational complexity while maximizing performance.  
Enhancing Adaptability: Efforts will also be directed at enhancing the adaptability of PAS to different channel conditions. This adaptability is crucial for FSO systems, where channel conditions can change rapidly due to weather and other environmental factors. Researchers will work on creating PAS schemes that can dynamically adjust the signal shaping based on real-time feedback from the channel, ensuring optimal performance under all conditions.  
Reducing Computational Complexity: Reducing the computational complexity of PAS is another critical area of focus. While PAS offers significant benefits in terms of SE and robustness, its implementation can be computationally intensive. Future research will aim to streamline the algorithms used for PAS, making them more efficient and practical for deployment in FSO systems. This could involve leveraging advanced hardware, such as Field-Programmable Gate Arrays and Graphics Processing Units, to accelerate the necessary computations.
# Development of Predictive Equations for Thermal Conductivity of Compost Bedding
## 4 Conclusions  
Studies were conducted to evaluate the effect on thermal conductivity (\(k\)) with the variation in moisture content (MC), the degree of compaction (DC\({}_{\rm o}\)), particle size (PS), and the depth of bedding material from different compost-bedded pack (CBP) barns. Based on the results, the following conclusions are made:  
1. A strong dependence of thermal conductivity on the moisture content and degree of compaction (static and dynamic) was observed. Thus, a growing trend in thermal conductivity (\(k\)) was observed with increasing moisture content and degree of compaction for all bed materials tested. The polynomial regression models developed in this study presented an excellent model fit, with R\({}^{2}\) greater than 91%.
2. Overall, thermal conductivity increased with increasing particle size (PS), indicating that it is strongly dependent on PS, and;
3. In general, the values of thermal conductivity reduced along the evaluated depths.  
Conceptualization: F.A.D., J.L.T., G.B.D. and R.A.B.; data acquisition: F.A.D.; data analysis: F.A.D. and T.J.F.; design of methodology: F.A.D.; writing and editing: F.A.D., C.E.A.O., R.R.A., P.F.P.F., R.A.B., J.M.B., L.L. and M.B. All authors have read and agreed to the published version of the manuscript.  
This research received external funding of the Brazilian National Research Council--CNPq (n. 407052/2018-6) and FAPEMIG (APQ-00853-17).  
Not applicable.  
The authors would like to thank the Brazilian Organizations (CNPq, FAPEMIG, and CAPES) and the University of Kentucky, whose support was greatly appreciated.  
The authors declare no conflict of interest.  
Figure 13: Estimated thermal conductivity (\(k_{est}\), W m\({}^{-1}\) K\({}^{-1}\)) of bed compost materials versus measured (\(k_{meas}\), W m\({}^{-1}\) K\({}^{-1}\)) values when applying dynamic compaction (DC) for each depth evaluated: (**a**) H1, (**b**) H2, and (**c**) H3.
# Design and Application of a Smart Lighting System Based on Distributed Wireless Sensor Networks
## 1 Introduction
Illuminance value of daylight is measured by ambient light sensors inside the sensing modules equipped in the luminaires closest to windows and then broadcasted to the whole network. Occupant presence is detected by occupancy sensors, that is, passive infrared (PIR) and micro-Doppler sensors. In addition, an algorithm is designed to fuse outputs of these two types of sensors in order to improve the efficiency and accuracy of occupant detection. A distributed WSN is constructed, where one node corresponds to one sensing module. The decentralized control is adopted to decrease communication traffic and delays, and underillumination is resolved by dimming the luminaire of not only a node whose occupancy sensor interrupt is triggered but also its neighboring nodes. Using the proposed smart lighting system, energy savings can be achieved in real-application. The contributions of this paper are as follows:  
1. A smart lighting system for real-application is developed. The system is based on a distributed WSN consisting of sensing modules colocated with luminaries. Combined with the neighbor-aided dimming method, decentralized control provides sufficient illumination in a timely manner. 2. The performance of the proposed system is verified by experiments in practical environments. The proposed system is evaluated in two different scenarios, a metro station and an office room. The factors influencing energy savings are analyzed, and the results show that the energy saving in a metro station is inversely proportional to the people flow density and the energy saving in an office room was influenced by weather, desired illuminance level, location of worktables, and the number of staff in the room. Based on the results, the proposed system can facilitate the smart lighting system design. The remainder of this paper is organized as follows. Section 2 introduces recent related work in the field. Section 3 describes the proposed system, network architectures, and the neighbor-aided illumination control method. Section 4 presents evaluation of the proposed system in two different scenarios. Section 5 provides discussion.
# Impact of Deep Eutectic Solvents on Extraction of Polyphenols from Grape Seeds and Skin
## 1 Introduction
Concerning the rapid growth of the wine industry, there was a need to perform comprehensive research on biologically active compounds of the grape that would include different varieties cultivated in this area. To the best of our knowledge, most of the studies explored the composition and bioactivity of seeds and/or skins obtained from international varieties [18; 19; 20]. Investigations of typical Serbian grapes have been undertaken using only a few autochthonous varieties [21; 22; 23; 24]. Keeping in mind all of the mentioned above, the main aim of the present study was to determine total phenolic content and antioxidant capacity of grape seed and skin extracts obtained from ten grape varieties (both international and autochthonous varieties were included). Moreover, the present study was designed as an opportunity to broaden previous investigations of extraction efficiency of NADES [10; 25; 26] Thus, the complementary aim was to exploit deep eutectic solvents (choline chloride: glucose and choline chloride: citric acid) as solvents for extracting valuable phenolic compounds from the grape. Overall results showed that acid based NADES outperformed acidified aqueous ethanol, concerning both seeds and skin. There were no significant differences between widely used international and autochthonous varieties. Additionally, seeds and skin obtained from some Serbian traditional grapes were outstanding with markedly high antioxidant potential.
# A Multi-Objective Evaluation Method for Smart Highway Operation and Management
## 4 Methods for Assigning Weights to Evaluation Indicators
### Entropy Weight Method (EWM)  
The EWM [56] is an objective weighting method that utilizes the concept of entropy to determine the weights of indicators. The entropy weight method is recognized for its reliability and objectivity in result calculation, ensuring stability and consistency. The entropy value allows for the assessment of the dispersion level of a particular indicator. Entropy is inversely proportional to information: when there is a significant difference in the data of a certain indicator, its effective information is greater, resulting in a smaller entropy value and a larger weight for that indicator; conversely, when the difference is smaller, the entropy value is larger, and the weight is smaller. The introduction of the entropy weight method has the potential to reduce the subjectivity of AHP weighting and the fluctuation of weights due to data changes. This improvement would lead to a more objective assessment of indicator differences and importance, resulting in enhanced calculation accuracy [57]. The calculation process is as follows.  
Step 1: Data normalization.  
Assuming that there are \(n\) evaluation objects and \(m\) evaluation indicators contained in the evaluation matrix, then \(x_{ij}\) is the value of the \(j\)-th indicator for the \(i\)-th sample \((i=1,2,\cdots,n;j=1,2,\cdots,m)\). Since the measurement units for various indicators are not uniform, it is necessary to normalize them before calculating the comprehensive weights. This involves transforming the absolute values of the indicators into relative values.  
\[\text{Positive indicators}:\ x_{ij}^{\prime}=\frac{x_{ij}-min\big{\{}x_{1j}, \ldots,x_{nj}\big{\}}}{max\big{\{}x_{1j},\ldots,x_{nj}\big{\}}-min\big{\{}x_{1 j},\ldots,x_{nj}\big{\}}} \tag{13}\]  
\[\text{Negative indicators}:\ x_{ij}^{\prime}=\frac{max\big{\{}x_{1j},\ldots,x_{nj }\big{\}}-x_{ij}}{max\big{\{}x_{1j},\ldots,x_{nj}\big{\}}-min\big{\{}x_{1j}, \ldots,x_{nj}\big{\}}} \tag{14}\]  
For ease of calculation, the normalized data is still denoted as \(x_{ij}\).  
Step 2: Calculate the correlation coefficient by determining the proportion of the \(i\)-th solution's indicator value under the \(j\)-th indicator, as follows:  
\[p_{ij}=\frac{x_{ij}}{\sum_{i=1}^{m}x_{ij}}. \tag{15}\]  
Consequently, it is possible to establish a weight matrix for the indicator data.  
Step 3: Calculate the information entropy value \(\text{e}_{j}\) and redundancy \(\text{g}_{j}\) for the \(j\)-th indicator using the following formulas:  
\[e_{j}=-k\sum\limits_{i=1}^{n}p_{ij}lnp_{ij}; \tag{16}\]\[g_{j}=1-e_{j}. \tag{17}\]  
Step 4: Calculate the weight \(w_{j}\) for the \(j\)-th indicator using the following formula:  
\[w_{j}=\frac{g_{j}}{\sum_{j=1}^{m}g_{j}}. \tag{18}\]
## 4 Case Studies
### The Impacts of the PMV Index and the Adjustable Heating Duration on Heating Systems  
The impacts of the PMV index on heating systems are compared and analyzed in Table 8. Since the PMV index is not suitable for evaluating the heating loads of the industrial area and there is no abandoned WT and PV power in the commercial area, only the abandoned WT and PV power in the residential area and the office area are listed in Table 8. Taking the residential area as an example, the heating loads curves and indoor temperature curves under different PMV indices are shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Capacity of Each** & **Abandoned WT and PV power** & **Abandoned WT and PV power** \\
**Tie Line/(MW)** & **in Residential Area/(MW)** & **in Industrial Area/(MW)** \\ \hline
5 & 12.73 & 18.37 \\
8 & 1.58 & 11.28 \\
10 & 0 & 7.54 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Abandoned WT and PV power in functional areas under different tie line capacities.  
Figure 10: Impacts of the PMV index on heating systems.  
Figure 10a shows the output curve of WT and PV power of the residential area in a specific scenario. Figure 10b,c show the heating loads power curves and the indoor temperature curves of the heating building under different PMV index in this scene, respectively. The dotted lines in Figure 10c are the allowed range of indoor temperature fluctuations under the corresponding PMV index. It can be seen from Table 8 that as the PMV index becomes stricter, the abandoned WT and PV power in the residential area increases and there is abandoned WT and PV power in the office area when the PMV index is 0.2. This is because the stricter the PMV limit is, the smaller the range of indoor temperature fluctuations, and the smaller the elasticity of the heating loads on the amplitude axis is, resulting in a reduction in the virtual 'heat storage' capacity of the heating systems. Therefore, in the case of large fluctuations in renewable energy, it cannot work well to stabilize and consume renewable energy. In the residential area, the output of WT and PV is large during 8 a.m.-5 p.m. and abandoned WT and PV power appears during this period. When the PMV index is widened, the heating loads during this period can be increased and the indoor temperature is increased to the upper limit within the allowed range, thus allowing consumption of renewable energy. In summary, the restriction of the PMV index could be relaxed to reduce the abandonment rate of WT and PV.  
As for the industrial area, some industries could adjust their working time to alter the heating duration \(T^{\prime}\) across a wider time range. The impacts of \(T^{\prime}\) on the consumption of renewable energy are compared and analyzed in Table 9. It can be seen from Table 9 that when \(T^{\prime}\) is increased, the abandoned WT and PV power of the industrial area decreases. Therefore, if heating supply could be adjusted at a larger time scale, more renewable energy could be consumed.
## 3 Results and Discussion
### Anaerobic Digestion
The viability of submerged vegetation biomass in anaerobic digestion was investigated by [PERSON] [44]. They tested the biomass of brown algae _Macrocystis pyrifera_, _Durvillaea antarctica_, and a substrate based on the mixture thereof. Their study demonstrated comparable biogas yields, reaching 180.4 \(\pm\) 1.5 cm\({}^{3}\)/gTS regardless of the substrate tested. The application of the mixture of species tested resulted in diminished biogas production effectiveness that was approximately 158 cm\({}^{3}\)/gTS. The methane content of the gaseous metabolites of anaerobic bacteria ranged from 60.0% to 70.0%. [PERSON] et al. [45] also analyzed biogas productivity from the biomass of macrophytobenthic plants. They noted the highest technological effects during the fermentation of _Laminaria digitata_ brown algae, belonging to the order _Laminaries_, which yielded a rate of methane production of 500 cm\({}^{3}\)CH\({}_{4}\)/gVS. The use of _Macrocystis_ sp. enabled producing 390-410 m\({}^{3}\) cm\({}^{3}\)CH\({}_{4}\)/gVS, _Gracilaria_ sp. produced 280-400 m\({}^{3}\) cm\({}^{3}\)CH\({}_{4}\)/gVS, and _Laminaria_ sp. produced 260-280 m\({}^{3}\) cm\({}^{3}\)CH\({}_{4}\)/gVS. The lowest final technological effects were  
Figure 6: The course of anaerobic digestion in S2 depending on the experimental variant (**(a)** variant 1; **(b)** variant 2). determined upon the fermentation of green algae class _Ultra_ sp., whose methane production was barely 200 cm\({}^{3}\)CH\({}_{4}\)/gVS. An investigation conducted by [PERSON] et al. [46] proved that the effects of fermenting macroalgae harvested from the Puck Bay, Poland, were directly influenced by the organic load rate of the anaerobic digester. The highest methane production per organic matter load fed to the digester, i.e., 240 cm\({}^{3}\)CH\({}_{4}\)/gVS, was noted at OLRs ranging from 1.0 kg VS/m\({}^{3}\cdot\)d to 2.0 kg VS/m\({}^{3}\cdot\)d. Higher OLRs values had a direct effect on diminished methane production.
## 4 Discussions
### Performances of Susceptibility Modeling
Similar to the findings of the SVM model's outperformance in this study, [PERSON] et al. [34] also reported that SVM performed the best among several other ML approaches for debris flow susceptibility along with the China-Pakistan Karakoram highway. However, the effectiveness of the RF model over SVM in different studies has also been discussed in [14; 34]. For example, [PERSON] and [PERSON] [77] compared SVM and RF with several other machine learning techniques (MLTs) in Saudi Arabia and found that the RF model was the best performer in the region for LSM. Similar to this finding, [PERSON] and [PERSON] [14] observed a slight difference between the prediction rates of RF and SVM when applied to the north-west (NW) Peloponnese, Greece, and concluded the better performance of the RF model compared to SVM. Similar results with a minute difference in both of these models are observed by [PERSON] et al. [33] in Shexian County, China. With the varying nature of the accuracy and performance of SVM and RF in different studies, we infer that comparative to other MLTs, the performance results of SVM and RF are quite close, with minimal differences (Figures 9 and 10), and that both of these models can be considered as equivalent in providing reliable results in the area. The similarity in AUC values (0.967) of RF and GBM was due to the ensemble natures of these models, with decent interpretability. This finding is in agreement with the results from recent studies [24; 78]. Considering the adhering similarity in prediction abilities, normally GBM offers a relatively higher number of sensitive parametrizations than RF, which makes it difficult to implement in a larger area [32]. Therefore, with the slight difference in POA values (about 1.24%) in this study between these two models, RF was found to be more reliable than GBM. Although maxENT has been successfully applied to species distribution modeling, a comparison of its performance with other ML models has not been fully investigated, particularly for LSM. However, some efforts have been made to compare the performance of this model [11; 79; 80; 81]. For example, [PERSON] [11] compared the performance of the maxENT with the LR model by producing landslide susceptibility maps of Boeun, Korea and found the effectiveness of maxENT over LR. Similar to the findings of [PERSON] [11], [PERSON] et al. [81] found that this model performed better than LR in Deba Valley, Northern Spain. In contrast to the other outperforming models, LR yields the lowest accuracy, and our findings regarding the relatively poor performance of LR are also supported by other related studies [15; 32].