esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Experimental Terahertz Spectra of 2,4-D  
Figure 1A shows the terahertz time-domain waveforms of reference and 2,4-D, Figure 1B are the terahertz frequency-domain spectra of reference and 2,4-D, and Figure 1C is terahertz absorption spectra of 2,4-D in the range of 0-4 THz. As shown in Figure 1A, the 2,4-D time-domain terahertz waveform had a spectral phase difference and an amplitude difference in comparison with the reference time-domain waveform. The reason was that the optical path of the terahertz wave passing through 2,4-D was larger than N\({}_{2}\). Therefore, the terahertz spectrum of 2,4-D lagged behind the reference spectrum. Beside this, the amplitude of the terahertz spectrum of 2,4-D was lower than that of the reference signal because of the absorption, reflection, and scattering of terahertz by 2,4-D.  
Moreover, compared with the amplitude of reference signal, the frequency-domain amplitude of 2,4-D was lower (Figure 1B). Also, there were three positions with absorption characteristics in the frequency-domain spectrum of 2,4-D at 1.36, 1.60, and 2.38 THz, respectively, among which, the position at 1.36 THz had the most obvious absorption effect. In a previous experiment, the third peak of 2,4-D was detected at 2.67 THz [35], which was different from the results obtained in this work. A THz time-domain spectrometer (Teraview, UK) was used in the previous experiment. Therefore, the different experimental results were probably caused by the difference of instrument components and performance. In Figure 1C, with the increase of frequency, the baseline of the absorption spectrum  
Figure 1: THz spectra of the reference and 2,4-dichlorophenoxyacyacetic acid. (**A**) Time-domain waveforms; (**B**) Frequency-domain spectra; (**C**) Absorption spectrum of 2,4-D.  
rose, which may have been caused by the scattering effects [36]. It can be seen that whether the frequency-domain spectrum or absorption spectrum, the usable frequency range of the sample was in the range of 0.1 to 3 THz. When the THz spectrum was below 0.1 THz, the signal-to-noise ratio (SNR) significantly reduced. Low frequency of the TDS system was mainly limited by diffraction losses and bandwidth of the antenna. When the THz spectrum was greater than 3 THz, the high-frequency oscillations noise was too large.
## 1 Introduction
The geotechnical properties of soil have a direct impact on its deformation characteristics, while soil deformation is closely correlated with the analysis and design of the foundation bearing capacity. Therefore, investigating the bulk modulus, shear modulus, Young's modulus, and Poisson's ratio of stabilized sand represents essential performance parameters for evaluating the efficiency of sand consolidation. [PERSON] et al. [38] conduct measurements and analyses on the microcosmic pattern, mechanical properties, and specific surface area of fiber-reinforced quartz composites. The findings reveal that the modified composites exhibit outstanding mechanical properties and moisture resistance. [PERSON] et al. [22] propose a novel sand-fixing curing agent, a nanosilica/polymer composite material, and conduct a microscale investigation on the mechanical properties and hydration mechanism of the resultant material. The findings demonstrate that the nanosilica/polymer composite material effectively agglomerates the sand particles and enhances the overall strength of the system. [PERSON] et al. [39] implement a geopolymer based on ground granulated blast-furnace slag and fly ash to reinforce sand. The composites are investigated by adjusting the type and ratio of the exciter, water-cement ratio, curing conditions, and slag-fly ash ratio. The results unequivocally demonstrate the remarkable efficacy of the geopolymer, formulated with ground granulated blast-furnace slag and fly ash, in substantially improving the mechanical property of the sand. Despite extensive studies on the reinforcement of sand with polymers at the nanoscale by numerous scholars, there has been limited research on the mechanical behavior of epoxy polymers when they solidify sand using molecular dynamics simulation. Therefore, it is necessary to investigate the mechanical properties of epoxy polymers adsorbed onto sand. Despite the extensive application of epoxy polymers in the solidification of sand, a thorough investigation of the nanoscale mechanisms involved in the epoxy polymerization process for sand solidification is still lacking. This study utilized molecular dynamics simulation to construct molecular models of three different systems, DGEBA, DGEDDS, and AEOR, adsorbed onto sand. The aim was to investigate their adsorption behavior and mechanical properties, including volume modulus and shear modulus. Firstly, molecular models of epoxy polymers and sand crystals were established. Subsequently, molecular dynamics simulation was employed to study the adsorption process of the three epoxy polymers on sand. Finally, various parameters such as the count of hydrogen bonds, adsorption energies, and mechanical properties were computed for each system. Byutilizing molecular dynamics simulation to investigate the molecular behavior of epoxy polymer curing agents on sand, this study offered a comprehensive understanding of the adsorption mechanisms at the atomic scale and conducted an in-depth analysis of the relevant mechanical property parameters of the composite systems.
# Examination of Intermolecular Forces Influencing Headspace Analysis of Biological Samples
## 3 Results
### Effect of Matrix Protein on Headspace Concentration  
After discovering that the HS concentration of octanal decreased significantly in serum, a series of aldehydes were analyzed in water vs. serum in experiment 2. There was sim  
Figure 1: Effect of media solubility on headspace concentration. Analytes are arranged in the order of increasing octanol–water partition coefficients. Headspace responses of analytes were determined in water (blue), serum (red), and 1% intralipid (brown), respectively. Different letters indicate significantly different headspace responses.  
ilarly a highly significant media*volatile effect in experiment 2 (\(p<0.0001\)). Headspace concentrations of aldehydes were consistently lower in serum compared to water, with a more notable reduction seen in unsaturated aldehydes, such as 2-heptenal (Figure 2). Blood proteins were considered to be the primary source of unsaturated aldehyde binding. To investigate likely intermolecular interactions between reactive aldehydes and dissolved solutes in blood serum responsible for this observation, reduced-protein serum was prepared by denaturing and centrifugation. Following protein denaturation and centrifugation, the headspace concentrations of aldehydes from protein-free serum were similar to water solutions (Figure 2).  
In experiment 3, the role of proteins in reducing headspace concentrations of volatiles was further confirmed by adding bovine serum albumin (BSA) to both water and intralipid. Several volatile compounds with varying octanol-water partition coefficients were examined in these new sample matrices. In addition, the effect of peak normalization with the internal standard (acetophenone-d5) was also investigated.  
Both raw and normalized responses were subject to the interaction of volatile and media (\(p<0.0001\)). Headspace concentrations were suppressed by BSA to the similar extent observed in blood serum (Figure 3). HS reduction from serum correlated with the number of unsaturated bonds in the aldehyde molecules. Conjugated 2,4-octadienal showed the greatest reduction when it was analyzed in serum sample. 2-Nonanone showed the greatest reduction in the lipid solution and this reduction became even more significant with the albumin-fortified lipid. Although protein-induced HS variation was minimized by the internal standard, the internal standard (acetophenone-d5, K\({}_{\text{ow}}\) = 1.65) was clearly not subject to the same protein interactions as the aldehydes. Normalization minimized HS variation among matrices best when the partition coefficients of the internal standard and the analyte (e.g., 1-hexanol, K\({}_{\text{ow}}\) = 1.80) were matched.  
Figure 2: Peak responses of aldehydes observed in modified serum samples. The chromatographic peak responses of several aldehydes were compared in water (blue), serum (red), and modified serum via protein denaturation (brown). Different letters indicate significantly different headspace responses.
## 3 Results and Discussion
### The Effect of Bonding Temperature on the Joint Microstructure  
It can be seen from Figure 4 that the eutectic compounds in the ASZ decreased with the bonding temperature increasing from 1040 to 1080 \({}^{\circ}\)C. When bonded at 1080 \({}^{\circ}\)C, the ASZ in the center of the joint disappeared completely and the joint region was only composed of the ISZ and DAZ. However, the eutectic compounds of the ASZ appeared again when the bonding temperature was further increased to 1100 \({}^{\circ}\)C. Considering that the ASZ was formed due to a bonding temperature that was insufficient for the completion of isothermal solidification [20], increasing the bonding temperature from 1080 to 1100 \({}^{\circ}\)C decreased the rate of isothermal solidification. Therefore, it can be inferred that 1080 \({}^{\circ}\)C is a critical bonding temperature for TLP-bonded GH4169 when using BNi-2 solder paste. Below 1080 \({}^{\circ}\)C, increasing the temperature would increase the rate of isothermal solidification. Above 1080 \({}^{\circ}\)C, increasing the temperature would decrease the rate of isothermal solidification. This can be explained with [PERSON]'s second law of diffusion: \(\partial C/\partial t=\mathrm{D}\cdot\partial^{2}C/\partial X^{2}\). As mentioned before, the formation of the ISZ was attributed to the composition changes caused by interdiffusion between the base metal and BNi-2 solder paste. In the joint region, the change of the concentration of an element with time (\(\partial C/\partial t\)) depends on both the diffusion coefficient (D) and the concentration gradient (\(\partial^{2}C/\partial X^{2}\)) of this element. With an increase of bonding temperature, the diffusion coefficient increases, but the concentration gradient decreases. When the effect of temperature on the diffusion coefficient exceeds that on the concentration gradient, increasing the temperature will increase the rate of isothermal solidification. On the contrary, when the effect of temperature on the concentration gradient exceeds that on the diffusion coefficient, then increasing the temperature will decrease the rate of isothermal solidification [21; 22]. Therefore, there should be a critical bonding temperature in the unsteady diffusion process of TLP bonding. For the GH4169/BNi-2 solder paste/GH4169 TLP bonding system the critical bonding temperature was 1080 \({}^{\circ}\)C.  
Figure 3: Backscatter FE-SEM images of the diffusion-affected zone of GH4169.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Zone** & **Ni** & **Cr** & **Fe** & **Nb** & **Mo** & **Ti** & **Si** & **Suggested Phase** \\ \hline E & 22.15 & 24.98 & 11.10 & 22.15 & 17.25 & 1.50 & 0.87 & Cr-Nb-Mo-rich boride \\ F & 26.66 & 32.90 & 10.66 & 13.66 & 13.95 & 1.35 & 0.82 & Cr-Nb-Mo-rich boride \\ G & 52.98 & 25.18 & 16.64 & 1.57 & 2.19 & 1.32 & 0.12 & Ni-Cr rich boride \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chemical composition (wt.%) of different zones marked in Figure 3.
# Shadow Removal for Enhanced Nighttime Driving Scene Generation
## 2 Background
### Shadow Removal  
Early shadow removal methods utilized prior information, such as illumination [43], gradient [44], and region [45], to eliminate shadows. Recently, the trend has shifted towards learning-based approaches [46; 47; 48; 49; 50], which have demonstrated strong performance using paired shadow/shadow-free images. However, due to the challenges in acquiring paired sets, weakly supervised methods [51; 52], unsupervised methods [53; 54; 55], and the combination of physics-based and learning-based approaches have been proposed [56; 57].  
To apply shadow removal techniques in real-world scenarios, such as driving datasets, it is crucial to adopt single-image shadow removal methods that do not require separate shadow mask ground truth, as these methods function effectively as unguided approaches. In [10], a Dual Hierarchical Aggregation Network (DHAN) was proposed to detect and remove shadow regions, and the Shadow Matting GAN was introduced to address data scarcity through data augmentation. The authors of [55] focused on shadow regions using a domain classifier in an unsupervised manner, DC-ShadowNet, while [58] was the first to propose a one-stage, lightweight transformer-based shadow removal technique, called SpA-former. FusionNet, described in [59], approaches the shadow removal problem from the perspective of auto-exposure fusion. Despite these advancements, these approaches still struggle when applied to driving datasets, where the diversity in depth and objects increases the complexity, making it difficult to obtain shadow-free images.  
Therefore, we propose a novel approach that divides a single image into multiple partitions, processes them separately, and then seamlessly stitches them together. Our method notably improves shadow removal performance, particularly in the central regions of driving scenes, where shadows and objects appear smaller due to depth differences.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 8 Future Research Direction
### Resource-Efficient and Lightweight ML Models  
Since IoT devices have limited computing power, it is crucial to design lightweight and energy-efficient IDSs. This involves investigating the effects of reducing computational overhead on detection accuracy and response time for actual deployment in the field. Model compression, pruning, quantization, and knowledge distillation are some of the methods that can be applied to make models simpler without compromising their threat detection abilities.
## 4 Multiple Services Set Identifier (MSSIDs) Technique
### Time Samples of MSSID  
RSS-based fingerprinting approaches have been widely used for indoor positioning. The behavior of RSS signals plays an important role in determining the characteristics and nature of location fingerprints, which are recorded in RM. The RSS is a function of the distance between AP and the user who receives this signal, and it is mainly affected by the multi-path issue [37]. 5000 RSS samples were taken at a specific location from AP with 5 SSIDs under the line-of-sight condition. The behavior of MSSID RSS at a specific location is shown in Figure 3, where 5 SSIDs (SSID\({}_{1}\), SSID\({}_{2}\),..., SSID\({}_{5}\)) are configured on the same AP. Figure 3 clearly shows the effect of the multi-path issue on these signals.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 2 Domain Adaptation Techniques
### Evaluation of Domain Adaptation Techniques

\end{table}
Table 9: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline
**Paper** & **Contribution** & **Advantages** \\ \hline
[273] & Introduces a domain adaptation method based on meta-learning & Can adapt to new domains quickly \\ \hline
[274] & Introduces a multi-task learning framework for domain adaptation & Can improve performance on multiple tasks \\ \hline
[275] & Introduces a reversal gradient technique for domain adaptation & Can better handle data with large domain shifts \\ \hline
[276] & Introduces a domain adaptation method based on self-supervised learning & Can be used with unlabeled data from the target domain \\ \hline \end{tabular}
\end{table} 
Table 9: _Cont._Finally, the examination of publication patterns and surveys (Table 10) shows that domain adaptation research is gaining popularity around the globe. The comprehensive survey by [277; 278] provides a valuable overview of transfer learning, domain adaptation, and multi-source learning methods, emphasizing their interconnectedness. Ref. [279] delves into domain adaptation methods for computer vision, showcasing instance-based, feature-based, decision-based, generative, and meta-learning methods, while addressing challenges and future directions. For recent advances in visual recognition, ref. [280] serves as a resource, discussing key methods and directions in domain adaptation. Furthermore, refs. [281; 282] contributes with a systematic literature review in computer vision domain adaptation, identifying vital research topics [283]. These studies highlight the potential of domain adaptation across diverse domains, including object detection in video [284], face recognition [285], medical image analysis [286], natural language processing [287], robotics [288; 289], 3D vision [290; 291; 292], etc. Additionally, specialized strategies like multi-task learning [293; 294; 295] and transfer learning [296] demonstrate their capability to achieve state-of-the-art performance across various visual recognition tasks and learn domain-invariant representations. The historical trends show an increase in the volume of research articles published over time, highlighting the continued importance and relevance of domain adaptation strategies in the field of computer and robotic vision [297]. In conclusion, the analysis of domain adaptation strategies demonstrates the critical importance of deep learning-based methodologies, the diversity of research articles, and the widespread interest in this area [298]. These findings offer insightful information for academics, professionals, and decision-makers, driving the creation of stronger and more effective domain adaptation methods to handle the difficulties of practical vision applications.
## 3 Proposed Non-Linear Current Control Scheme of Grid Connected Inverter with LCL Filter
### Proposed Observer for Estimation of State Variables of the LCL Filter  
To obtain proper performance using the active damping methods, it is necessary to feedback all the state variables of the LCL filter such as the proposed controller in (45) and (53). This requirement demands the sensors for measuring all state variables, which is not cost effective. However, the need of the mentioned sensors is effectively resolved by using state variable estimation methods. Namely, measuring the grid current (\(i_{g}\)) makes the overall system observable, and the filter capacitor voltage and the inverter current can be effectively estimated. Analysis of the state equations of a single phase LCL filter in static frame, given by (69), shows that measuring \(i_{g}\) makes the system fully observable.  
\[\left[\begin{array}{c}\frac{d{i_{l}}}{dt}\\ \frac{dv_{c}}{dt}\\ \frac{dv_{c}}{dt}\\ \end{array}\right]=\left[\begin{array}{ccc}0&-\frac{1}{L_{1}}&0\\ \frac{1}{c_{f}}&0&-\frac{1}{c_{f}}\\ 0&\frac{1}{L_{2}}&0\\ \end{array}\right]\left[\begin{array}{c}i_{l}\\ v_{c}\\ i_{g}\\ \end{array}\right]+\left[\begin{array}{cc}\frac{1}{L_{1}}&0\\ 0&0\\ 0&-\frac{1}{L_{2}}\\ \end{array}\right]\left[\begin{array}{c}v_{l}\\ v_{g}\\ \end{array}\right] \tag{69}\]  
Accordingly, \(i_{g}\) is measured directly and using a reduced-order Luenberger (ROL) observer, \(v_{c}\) and \(i_{l}\) are estimated. A second-order observer for estimation of \(v_{c}\) and \(i_{l}\) of each phase is utilized in this paper. Details of the Luenberger observer design procedure can be found in [35], which is not elaborated for the sake of brevity. According to the observer design procedure, the bandwidth of the observer should be selected higher than that of the main closed loop-controlled system.  
Based on the discussions presented in the above subsections, the complete block diagram of the proposed controller is as depicted in Figure 5.
# Satellite-Assisted Disrupted Communications: IoT Case Study
## 1 Introduction
Section 3 describes our experimentation methodology, including the configuration of our experiment setup and the concepts of the examined scenarios. In Section 4, we detail certain significant experimental parameters and the scenarios involved, while in Section 5, the results of those scenarios are delineated. In Section 6, we summarize the results of the experiments and identify the limitations and future steps. Finally, in Section 7, we present our concluding remarks.
# A Coal Mine Excavation Tunnels Modeling Method Based on Point Clouds
## 4 Experiments
### Experiment Results
#### 4.2.4 Tunnel Reconstruction Results  
Based on the tunnel centerline, the point cloud normal optimization is performed, and the Poisson modeling algorithm is used to build the tunnel mesh model. As shown in Figure 18, while reflecting the overall outline of the tunnel, the model preserves the undulations of the surrounding rock, making it more realistic and accurate.  
Based on the normal optimization results, different surface reconstruction algorithms were compared for tunnel modeling, as shown in Figure 19. This paper compares the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Average Error (m)**} & \multicolumn{2}{c}{**Standard Deviation (m)**} \\ \cline{2-5}  & **X-Axis** & **Z-Axis** & **X-Axis** & **Z-Axis** \\ \hline mean of points & \(-0.395\) & \(-0.177\) & \(0.413\) & \(0.519\) \\ mean of hull & \(-0.435\) & \(-0.022\) & \(0.654\) & \(0.677\) \\ centroid of hull & \(-0.036\) & \(0.016\) & \(0.388\) & \(0.495\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between the proposed algorithm with other methods and ground truth. The best is indicated in bold.  
Figure 18: Point clouds from four different mines. (**a**) Tunnel a. (**b**) Tunnel b. (**c**) Tunnel c. (**d**) Tunnel d.  
Poisson modeling algorithm [15], ball-pivoting algorithm [34], VCG algorithm [36], and NKSR algorithm [41]. The results indicate that the meshes generated by the ball-pivoting and VCG algorithms contained holes, while the output from NKSR, although smooth, still exhibited slight depressions and lost significant surface texture. In contrast, Poisson modeling produced a complete mesh, demonstrating strong resistance to noise and preserving more surface details, making it the most effective algorithm for tunnel modeling.