esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Implementation and Discussion of Results
### Ablation Study
#### 6.10.2 Effect of Using Sentiment Analysis Module on the Framework Performance  
In the same context, other experiments are conducted to emphasize the efficiency of using sentiment analysis in our proposed algorithm. Figure 12 shows the performance improvement achieved by adding the sentiment analysis module to our algorithm. The experiments are done for different numbers of episodes. Each number of episodes is done at least 10 times, and the average is taken. In these experiments, the performance is measured as follows. The current day's closing price is compared with the previous day's closing price. If there is a price increase and the algorithm decides to sell, this is considered the correct action. On the other hand, if the algorithm decides to buy, this is considered a wrong action. The performance here is calculated as the percentage of the correct actions relative to the algorithm's total number of actions.
## 5 Discussion
### Hallucination Problems
#### 5.2.2 BIMCoder Model  
While the individual ERNIE and Llama models exhibit some hallucination issues, the BIMCoder model, which combines ERNIE for query preprocessing and key information extraction with SQLCoder for query generation, is designed to mitigate these challenges. The fusion of these two models leverages their respective strengths to reduce the occurrence of hallucination problems, particularly in the context of BIM information retrieval.  
In this model, ERNIE's capabilities in understanding and extracting key information from natural language queries allow it to preprocess and disambiguate complex input. This step ensures that the model has a clearer understanding of the query's intent before SQLCoder is tasked with generating the corresponding SQL query. SQLCoder, with its high exact matching rate and specialized design for structured query generation, helps correct any potential deviations introduced by ERNIE. This combination makes BIMCoder more robust in handling a wide variety of queries while reducing the risk of hallucinations.  
Despite these advancements, hallucination issues can still arise in certain situations. For example, when a query contains highly specialized domain-specific terminology or when the training data does not cover a broad enough range of query types, the model might generate a SQL query that misrepresents the user's intent. This could happen due to limitations in the training data, which may not fully capture the diversity of possible query forms, or due to inherent structural limitations within the models themselves.  
However, by leveraging the complementary strengths of ERNIE and SQLCoder, the BIMCoder model is better equipped to handle ambiguities and edge cases. ERNIE's preprocessing can filter out irrelevant or noisy information, while SQLCoder's precision in query generation ensures that the final output closely matches the original intent. The combination of these models significantly reduces the likelihood of hallucination, making BIMCoder a more reliable tool for natural language-based BIM information retrieval.  
Overall, while hallucination remains a challenge in natural language processing tasks, the BIMCoder model offers a promising solution by combining the best of both ERNIE and SQLCoder. Future work could focus on further enhancing the model's robustness to hallucinations by expanding training datasets, refining the fusion process, and incorporating additional strategies to mitigate errors in query generation.
## 7 Conclusions and Future Works  
This paper proposes a step forward in the approach to predict human behavior in a crowded indoor or outdoor environment and classification of normal and abnormal behavior. Initially, crowd-based data was taken as the input of the system, using some preprocessing tools and techniques. We then reduced the noise and size of the frames and identified the human and nonhuman objects. After that, feature extraction was performed, and the three features were extracted. To minimize the data and to save the computational cost as well as to increase the accuracy of the system, the graph-mining approach was adopted. Finally, in order to predict the behavior of pedestrians, the maximum entropy Markov model was applied. The comparison results show an improvement over the three benchmarked datasets avenue, UMN, and ADOC in mean accuracy and classification rates.  
We will incorporate additional extracting features algorithms from other areas in the future to identify far more complicated behaviors in various contexts, such as emergency service, education, surveillance, and transport system. In addition, we have planned to apply this to remote sensing data and real-time environments.  
Conceptualization, I.A. and Y.Y.G.; methodology, I.A., H.A. and A.J.; software, I.A. and M.G.; validation, I.A., Y.Y.G., S.A.A. and J.P.; formal analysis, M.G., S.A.A. and J.P.; resources, Y.Y.G., M.G., H.A. and J.P.; writing--review and editing, I.A., M.G., S.A.A. and J.P.; funding acquisition, Y.Y.G., S.A.A., H.A. and J.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2023-2018-0-01426) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation). Additionally, it was supported by the Emirates Center for Mobility Research (ECMR) Grant #12R012. In addition, it was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R54), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Sensory Parameters  
Considering the inclusion of seaweeds in the formulation of meat products such as sausages and burgers is quite a challenge, especially when cultural, geographical, and sensory aspects generate rejection or emotions in the consumer that could negatively predispose their purchase decision [51]. A study reported the emotions generated by the combination of seaweed and certain foods, including hamburgers and sausages, where participants (\(n=108\)) did not associate or consider buying seaweed with any of these products [5]. Therefore, in this study, we only considered evaluating _Durvillaea antarctica_ beef burgers preliminarily with a trained panel that could provide us with more objective data regarding the sensory attributes and general acceptability of these products. Each expert was able to significantly differentiate the sensory attributes they assessed. The principal component analysis (PCA) showed a significant correlation between general acceptability and taste concerning dimension 1, which largely explains the variability of the data (77.47%) (Figure 3B). The odor was significantly affected in all treatments containing D. antarctica. Regarding the overall flavor and acceptability, it was better in the control treatments and those with 0.5% of the meal, with a clear decrease in the score of these sensory indices as the concentration of \(D\). _antarctica_ meal increased (Figure 3A). These results are consistent with those recently reported by [PERSON] et al. [21], who investigated the sensory response and general acceptability of a trained panel (\(n=8\)) that tasted beef burgers with 0.5%, 1.0%, 2.5%, and 5.0% Himanthalia elongata meal, observing that the burgers with higher seaweed content were negatively evaluated. Another previous report by [PERSON] and [PERSON] [20] also noted this effect in beef burgers enriched with Himanthalia elongata, which should be related to the overall acceptability. Additionally, the general acceptability of frankfurters made with the edible seaweeds Porphx umbilicalis or Palmaria palmata was low, mainly due to the intense marine flavor [51].
## 5 Vibration Simulation Experiment of DTH Hammer
### Experimental Content
#### 5.2.2 Investigating the Vibration-Damping Performance at Different Levels  
Compared with other drilling processes, pneumatic DTH hammer drilling has more intense shock vibration, up to 40 g. Since the common frequency of DTH hammer drilling is about 9 Hz, in this experiment, five levels of 10 g, 15 g, 20 g, 30 g, and 40 g were explored at an excitation frequency of 9 Hz. This research focuses on the damping effect of the damping device under different vibration levels.  
The device required for this section of the experiment is the same as that used in the previous section. The frequency is set to 9 Hz. The control of different experiments is achieved by adjusting the output level. Then, several groups of experiments were carried out separately for each vibration level. A pulse was selected for each group of experiments to be analyzed after the data were collected using the host computer. The data are as follows (Figure 9):  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Frequency**} & \multirow{2}{*}{**Peak Difference**} & \multirow{2}{*}{**Peak Attenuation**} & \multicolumn{2}{c}{**Fluctuation Rate**} & \multirow{2}{*}{**System Stability**} \\ \cline{3-3} \cline{5-6}  & & & **Rate** & & **Before** & **After** & **Enhancement Rate** \\ \hline
5 Hz & 9.086 & 88.247\% & 1.012 & 0.127 & 87.513\% \\
7 Hz & 9.174 & 87.756\% & 0.866 & 0.124 & 85.626\% \\
9 Hz & 9.450 & 87.825\% & 1.377 & 0.229 & 83.303\% \\
12 Hz & 10.474 & 90.029\% & 1.423 & 0.212 & 85.095\% \\
15 Hz & 8.719 & 88.072\% & 0.856 & 0.201 & 76.600\% \\
20 Hz & 9.947 & 86.443\% & 1.113 & 0.276 & 75.214\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the data before and after damping in six groups of experiments with different frequencies at the same level.  
Figure 8: Vibration curves before and after damping of circuit units of different frequencies at the same level.  
Figure 9: Vibration curves before and after damping of circuit units of different levels at the same frequency.  
To more intuitively understand the damping performance of the damping device at different levels, the four parameters used in the previous section are still used to describe the damping effect. The calculated collated results are shown in Table 2 below.  
The data show that the designed vibration-damping device has a good damping effect at different levels of excitation under the same vibration frequency. By calculating the peak attenuation rate of the vibration signal before and after damping, it can be seen that with the increase in excitation level, the attenuation rate of the damping system on the input peak value decreases, the stability of the system also gradually reduced, and the damping effect decreases. The peak attenuation rate and the enhancement effect on the stability of the system in the vibration range with a frequency of 9 Hz and vibration level of 10-40 g can be guaranteed to be above 86.549% and 84.053%. The vibration-damping performance is good.
# Proposal for a New Bioactive Kinetic Screw in an Implant, Using a Numerical Model
## 1 Introduction  
Growth factors (GFs) are protein molecules that have a role in controlling biological processes, such as cell growth, proliferation, differentiation, and repair. GFs cannot pass through a cell's membrane; they must bind to high-affinity cell receptors to take effect. Many GFs stimulate several cell populations, while others are less versatile and are specific to a particular cell line. Whatever the tissue involved, the healing process always involves a series of molecular, biochemical, and cellular events that can be grouped into three overlapping phases: inflammation, proliferation, and remodelling [1].  
GFs have been used in oral surgery and the most often used and analysed are the bone morphogenetic proteins (BMPs) [2]. More than 20 different BMPs with multiple functions have been detected with an importance in embryogenesis and repair tissues in human adults [3].  
Platelet-derived growth factor (PDGF) [1] can be dismissed during blood clotting in any type of tissue injury that promotes chemotaxis and mitogenesis [4].  
Fibroblast growth factor (FGF), parathyroid hormone (PTH), transforming growth factor-\(\beta\) (TGF-\(\beta\)), vascular endothelial growth factor (VEGF), and insulin-like growth factor (IGF) have been the subject of various studies [5; 6; 7; 8; 9; 10; 11; 12; 13].  
Several options have been described [14] to obtain GFs such as autogenous bone grafts, xenografts, allografts, alloplastic grafts, membranes for guided bone regeneration (GBR), platelet-rich plasma (PRP), inlay and onlay grafting, and distraction osteogenesis. Autogenous bone graft is still considered the gold standard for bone repair, despite some difficulties in its application in clinical practice, related to the morbidity of the procedures and the quantity and quality of available bone.  
Mixing growth factors with implants or osteo-inductive scaffolds can promote new bone formation at the graft site during replacement of the newly formed bone [1].  
The potential of GFs in bone regeneration has been studied by different authors [15; 16; 17; 18], but there is no common sense as to how they work together. Further studies are needed to identify predictable in vivo outcomes. The new biomechanical reuses all the material from the cut and perforation performed by the BKS screw as a homogeneous autogenous transplant, stimulating from the beginning of the inflammation the natural healing and repair factors, allowing for better bone remodelling to be tested.
# Depth Image Completion through Iterative Low-Pass Filtering
## 2 Approach  
This section provides insight into the functionality of the Iterative Low-Pass Filter (ILF) on a complement of depth images. The design concept aims to integrate features acquired from RGB-D scenes, mitigate noise interference, and generate accurate depth data. To achieve this, we employ an iteratively updated process combined with a dynamically adjustable low-pass filter. The RGB information collaboratively contributes to normalizing spatial frequencies, offering insights into the structure and 3D shape of the surface. Initially, we delineate the recovery surface and extract the spatial frequency of the surface structure from RGB images, utilizing watertight boundaries for precise definition. Following this, we extract the spatial features of the target surfaces from RGB images, expediting the overall process by highlighting surface trends. Figure 1 illustrates the Depth Image Completion Process (DICP), which comprises two integral components: (1) the Extraction of Boundaries and Surfaces (EBS) and (2) the ILF Process.
# How Does the Built Environment Affect Drunk-Driving Crashes? A Spatial Heterogeneity Analysis
## 3 Methods
### Semi-Parametric Geographically Weighted Poisson Regression Model  
Not all the correlations of regression parameters in the GWPR model vary over space. For some parameters, they are fixed, or the spatial heterogeneities are exceedingly small and can be ignored. The explanatory variables with geographically varying coefficients are called local variables, while the variables with coefficients that do not change with spatial location are called global variables. SGWPR considers the spatial effects for both global variables and local variables, which can be conducive to exploring the spatial heterogeneity of the factors related to drunk driving crashes. The framework of SGWPR is as follows [36]:  
\[\begin{array}{c}P_{n}+P_{b}=P,\\ ln(\lambda_{i})=\beta_{0}(u_{i},v_{i})+\sum_{j=1}^{P_{n}}\beta_{j}x_{ij}+\sum _{k=1}^{P_{n}}\beta_{k}(u_{i},v_{i})x_{ik}+\varepsilon_{i},\end{array} \tag{4}\]where \(Pa\) is the number of the global variables, \(\beta_{j}\) denotes the coefficient of the global variables \(j\), \(P_{b}\) is the number of the local variables, and \(\beta_{k}\) represents the coefficient of the local variables \(k\).  
The result of the GWPR model is sensitive to the spatial kernel function and bandwidth size. In most research, the adaptive kernel function is more effective compared with the fixed kernel function because it permits the bandwidth to vary spatially [34]. However, the effect of kernel function and bandwidth is not clear for drunk-driving crashes and the related factors. Therefore, the spatial regression models with fixed bandwidth and adaptive bandwidth, as well as both types of commonly used kernel functions, including the Gaussian kernel function and the bi-square kernel function, as shown in Equation (5), were comprehensively tested in this research. The corrected Akaike information criterion (AICc) was used to compare the results and select the best spatial model [37].  
\[\text{Gaussian}:\ W_{ij}=e^{-(\frac{d_{ij}}{\beta})^{2}}, \tag{5}\] \[\text{bi}-\text{square}:\ W_{ij}=\begin{cases}\left[1-\left( \frac{d_{ij}}{b_{i}}\right)^{2}\right]^{2},\ d_{ij}\leq b_{i},\\ 0,\ d_{ij}>b_{i}\end{cases}\]  
where \(W_{ij}\) is the spatial weight function to describe the influence of TAZ \(j\) on TAZ \(i\), \(d_{\bar{ij}}\) is the European distance between TAZ \(i\) and TAZ \(j\), \(b\) is the fixed bandwidth defined by the distance in the Gaussian function, and \(b_{i}\) is adaptive bandwidth defined as the number of the adjacent TAZs around the target TAZ.  
In the GWPR model, the local percent deviance explained _pdev_ instead of local R\({}^{2}\) are calculated to estimate the local goodness-of-fitness, which indicates how well the model fits the data. The local _pdev_ is defined as follows:  
\[pdev_{i}=1-\frac{dev_{i}}{nulldev_{i}}, \tag{6}\]  
where \(\text{dev}_{i}\) is the locally weighted deviance of the fitted model, and \(nulldev_{i}\) is the locally weighted deviance of the null model having only a constant term at location \(i\).
# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 2 Materials and Methods
### Wireless Sensor Network System Design  
With the gradual expansion of greenhouse scale, large-scale greenhouses impose higher demands on wireless sensor network-based environmental monitoring systems. These requirements include improved data accuracy, enhanced network coverage, and increased network reliability. In large-scale greenhouses, the deployment of more extensive wireless sensor networks becomes necessary. However, as the network scale expands, the network structure becomes increasingly complex [2]. In this context, hybrid networks are able to provide a more flexible and scalable network connection and are therefore more suitable for environmental monitoring in large greenhouses such as plant factories [2].  
Figure 1: Experimental site.  
The selection of wireless communication protocols is a critical factor in optimizing the performance of large-scale greenhouse wireless sensor networks. Among various wireless technologies, Wi-Fi, with its high bandwidth and long-range transmission capabilities, meets the demands of large-volume data transmission. In contrast, ZigBee, characterized by low power consumption and multi-hop mesh networking, ensures the reliable communication of low-data-rate sensor nodes, effectively mitigating signal attenuation and interference in complex greenhouse environments. Compared to these, LoRa and NB-IoT suffer from low data rates and high latency, making them unsuitable for high-frequency monitoring applications. Additionally, Bluetooth and Bluetooth Mesh have limited coverage, restricting their applicability in large-scale greenhouse network architectures. Therefore, a Wi-Fi and ZigBee hybrid network solution, with its superior adaptability, reliability, and transmission efficiency, emerges as the optimal choice for greenhouse wireless monitoring systems. This study proposes a hybrid wireless sensor network architecture based on the ZigBee and Wi-Fi communication protocols (Figure 2). This hybrid wireless sensor network consists of multiple clusters, and each cluster consists of a cluster head and multiple sensor nodes. These sensor nodes are responsible for collecting environment data and link quality indicators and executing control commands issued by the cluster head. In addition, the sensor nodes also function as relays within the network. The cluster head is responsible for aggregating the data uploaded by all sensor nodes within the cluster, and transmits these data to the server via the Internet and relays control commands issued by the server back to the sensor nodes. The cluster heads and sensor nodes within each cluster transmit data and commands to each other through the ZigBee communication protocol. Data and commands are exchanged between the cluster head and the server through the Wi-Fi communication protocol.  
Figure 2: Hybrid wireless sensor network architecture diagram.
# Epoxy/Glass Fiber Nanostructured p- and n-Type Thermoelectric Enabled Model Composite Interphases
## 3 Results
### Study of the Nanostructured Interfacial Mechanical Properties  
Apart from the thermoelectric functionality, the interfacial shear strength of the hierarchical GFs was also measured by tow pull-out tests and compared to the interfacial properties of the reference uncoated GF tows. While the influence of the CNTs on the interfacial bonding of the GF with epoxy resin is usually determined by measuring the interfacial shear strength through single fiber fragmentation [2] or pull-out tests [36; 37], the pull out test configuration was deemed as more appropriate for this study and was used to quantify the quality of interfacial bonding between the epoxy matrix and the nanocoated glass fibers which were used in this study.  
Figure 8a shows the results of apparent interfacial shear strength (\(\sigma_{IFSS}\)) measurements, which is indicative of the stress transfer efficiency of the interphase. As can be observed, the uncoated glass fiber tows (REF GF) exhibited the highest interfacial shear strength, i.e., 42.8 \(\pm\) 0.9 MPa. Comparatively, the p-type SWCNTs coated GF model composites exhibited lower interfacial shear strength values at 38.5 \(\pm\) 1.5 MPa, that is a 10.1% decrease in comparison to the uncoated GFs. For the n-type SWCNTs  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**TE Material** & **Electrical Conductivity** & **Seebeck Coefficient (S)** & **Power Factor (\(\sigma\times\mathbf{S^{2}}\))** \\  & **(\(\sigma\)) (S/cm)** & **(\(\mu\)V/K)** & **(\(\mu\)W/m\(\cdot\)K\({}^{2}\))** \\ \hline SWCNTs@SDBS & & & \\ (2 mg/mL \& 5 mg/mL) & 1050 \(\pm\) 2 & \(+23\pm 1\) & 55.5 \\ SWCNTs@SDBS (2 mg/mL \& & & \\
2 mg/mL)\_PEI (5 mg/mL) & 1400 \(\pm\) 4 & \(-31\pm 1.5\) & 134.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electric and thermoelectric values of both p-type and n-type bucky paper films.  
Figure 7: (**a**) schematic illustration of a p-n junction between the model composites created by interconnecting in series the p-type and the n-type model composites by a copper foil at the hot side (125 \({}^{\circ}\)C), while at the cold side (\(\sim\)24 \({}^{\circ}\)C) the copper electrodes were attached with the multimeter for a 2-probe measurement, graphs of (**b**) electrical resistance-electrical conductivity, (**c**) Seebeck coefficient-Power factor and (**d**) \(\Delta V\) vs. \(\Delta T\) curves for the coated p and n-type TE model composites.  
coated GF model composites the interfacial shear strength was about \(30.8\pm 3.4\) MPa, which corresponds to a 28.1% reduction compared to the uncoated GF tows.  
Optical stereoscopic images indicate the failure modes of the model composites after the pull-out testing. Representative fractographic images of the pulled-out tows are shown in Figure 8b.