esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of the Mechanism of Curing Soft Soil
#### 3.4.2 Thermogravimetric Analysis  
To characterize the influence of cement and SWC on soft soil, TG/DTG analysis was conducted on samples with different formulations. Figure 10 shows the TG/DTG curves of seawater-mixed GC, G4, and G7 after curing for 28 days. The analysis was performed with a heating rate of 10 \({}^{\circ}\)C/min, and the test was completed as the temperature increased from 50 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C.  
The causes of weight loss in various temperature ranges in the thermogravimetric curves of soils and cementitious materials have been extensively studied. The weight loss of soil in the range of 60-120 \({}^{\circ}\)C is primarily due to the natural water content in the soft soil. The weight loss at 220-330 \({}^{\circ}\)C mainly results from the decomposition of organic matter. The weight loss at 330-600 \({}^{\circ}\)C is primarily due to the decomposition of kaolin [25]. The weight loss of hydration products in cementitious materials in the range of 50-200 \({}^{\circ}\)C mainly comes from the dehydration of natural water, Aft, and C-S-H [26]. The weight loss at 200-280 \({}^{\circ}\)C is mainly due to the decomposition of C-A-H or C-A-S-H [26]. The weight loss at 270-370 \({}^{\circ}\)C is primarily due to the decomposition of Friedel salt [27]. The weight loss in the range of 400-500 \({}^{\circ}\)C is mainly due to the decomposition of calcium hydroxide [28], and the weight loss at 500-700 \({}^{\circ}\)C is mainly due to the decomposition of carbonate minerals [28].  
Referring to the existing research and combined with Figure 11, it can be observed that the total weight loss of soft soil, GC, and G7 is 3.6%, 12.2%, and 26.9%, respectively. After accounting for the removal of water and organic matter decomposed by heat in soft soil, the total weight loss of stabilized soil G7 is much higher than that of cement soil GC. In the range of 50-200 \({}^{\circ}\)C, which represents the decomposition and dehydration of C-S-H and Aft, the weight loss of stabilized soil G7 is 9.40%, significantly higher than the 4.75% observed for cement soil GC. However, G7 also shows a more pronounced peak in the range of 200-300 \({}^{\circ}\)C, which is attributed to the decomposition and dehydration of C-A-H or C-A-S-H, whereas cement soil exhibits less weight loss in this range. This indicates that SWC generates more cementitious and filler hydration products in stabilized soil. At the weight loss peak representing Ca(OH)\({}_{2}\) at 400-500 \({}^{\circ}\)C, G7 consistently shows higher weight loss than GC. The alkaline component in the DA provides a highly alkaline environment for the stabilized soft soil, stimulates the gelling activity of GGBS and ACP, and promotes the dissolution of active SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) in the soft soil, participating in the hydration reaction to produce more hydration products [18]. This may also explain why stabilized soil exhibits greater weight loss at 50-200 \({}^{\circ}\)C and 200-300 \({}^{\circ}\)C.  
Figure 11: _Cont._
## 6 Conclusions and Prospect  
### Conclusions  
(1) The wet-dry cycling process induced alterations in the expansion and contraction characteristics. As \(N\) increases, \(\delta_{n}\) progressively increased, whereas \(\eta_{a}\) gradually decreased, and the specimen's volume expanded. \(\delta_{r}\) and \(\eta_{r}\) diminished, and the specimen's expansion and shrinkage capabilities declined. The volumetric changes induced by wet-dry cycling are not entirely reversible, and these cycles exert a more significant impact on the expansion properties.  
(2) The wet-dry cycling process induced substantial alterations to the microstructure. Particle rounding increased, stacked aggregate were disrupted, some inter-particle contacts were altered, pores enlarged and interconnected, porosity augmented, and clay mineral content decreased.  
(3) The wet-dry cycling process induced the formation and progression of cracks. The evolution of cracks undergoes stages of initiation stage (\(N\leq 1\)), propagation stage (\(1<N\leq 4\)), and stable stage (\(N>4\)). As \(N\) increases, the cracks gradually expand, with the cracks developing the fastest in the propagation stage and basically stopping in the stable stage.  
(4) The wet-dry cycling process caused a significant reduction in shear strength, primarily attributed to a substantial diminution of cohesion, with a negligible impact on the angle of internal friction.  
(5) The wet-dry cycling process significantly reduced slope stability, and \(F_{v}\) of the slope decreased with an increasing \(N\). Under the influence of wet-dry cycles, slope failures typically evolved from overall or deep sliding to localized or shallow sliding. Measures should be taken to limit the effects of wet-dry cycling on slopes.
## 3 Experimental Results
Most significantly, our results demonstrate that even in the worst PER scenario (\(\simeq\)\(10^{-1}\)), the probability of obtaining a successful bidirectional communication with a SAL lower than 2.5 ms is \(\approx\)86%, whilst in 98.2% of the cases the SAL is below 5.6 ms. Noticeably, even in this very unfavorable condition, the 5G threshold latency value (10 ms) for ITS services [7] is only exceeded with a probability lower than 0.3%. In all other PER cases, our VLC system shows a latency always below the 5G threshold with probabilities larger than 99.95%. Remarkably, in the case of PER = \(10^{-4}\), a safety-critical message could be delivered in less than 2.5 ms with an error probability lower than \(5\times 10^{-4}\). It is important to remark that a significant aspect to be addressed in view of extensive deployment of bidirectional VLC links in ITS is represented by possible effects of unbalance in the quality between the forward and backward channels on the round-trip SAL values. We will provide further theoretical and experimental characterization of such aspects in future works.
# Improving Low-Frequency Panel Absorbers with Two-Dimensional Acoustic Black Hole
## 4 Experimental Validation
### Sound Absorption Measurements  
The sound absorption measurements were performed in a reverberant room that was built according to the ISO 354 standard [34]. The sound absorption performance of uniform HPL 3 mm and a panel with a large ABH (with a = 0.13 m at P22) were investigated. Therefore, 42 units for each case were manufactured and measured in the reverberant room (with volume \(V=195\,\mathrm{m}^{3}\)) of TU Dresden, as shown in Figure 17. Three omnidirectional sound sources and six measurement microphones were used during the measurements. The calculated sound absorption are depicted in Figure 18 in \(1/24\) octave bands.  
Figure 16: Comparison of the simulated and measured normalised acceleration \(\frac{a}{p}\) at 4 points for the panel with ABH of radius \(a=0.13\,\mathrm{m}\) at position P22.  
Figure 17: Measurement setup in the reverberant room.  
It can be seen from the curves that the absorber with the uniform HPL 3 mm panels have absorption peaks at three positions for the selected frequency range. The numerical results have higher vibration amplitudes for those frequency regions as well. However, the levels of absorption are not very high. On the other hand, the effect of including the ABH in the panel can be clearly seen. The sound absorption values are higher than the uniform panel, especially in the 90-170 Hz region, as expected from the numerical simulations.
# A Review on Clustering Techniques: Creating Better User Experience for Online Roadshow
## 3 Discussion
### Functional Purpose
In [49], the authors attempted to investigate the possibility if the clusters generated by _k-means_ and _DBSCAN_ can be used to evaluate the changing relationship between user behaviour and the navigation structure of website. Among the two techniques, _DBSCAN_ is able to identify the changes of the navigation structure of website, whereas _k-means_ failed to detect the changes. Therefore, each clustering technique has its own drawback when applying to different functional purposes. There are two aspects that should be noted when selecting a suitable clustering technique for the functional purpose, namely the processing time and number of clustering groups. The clustering technique is required to provide a shorter processing time when executing web queries categorization, page recommendation, and web personalization. This is because the website has to provide immediate feedback based on the user's navigation behaviour. Since _k-means_, _k-medoids_, and _FCM_ converge faster with shorter processing time, it is widely used for all the functional purposes stated in Table 7. The time complexity for hierarchical clustering and density-based clustering is higher. Therefore, they are not very suitable for functional purposes that require immediate feedback. Instead, they can be applied for user group discovery, user group categorization, and page categorization since the execution time requirement for these processes is less stringent. The second aspect is the knowledge of the number of user groups. The number of clusters needs to be predetermined before using _k-means_, _k-medoids_, _bisecting k-means_, and _FCM_. If the types of the user groups are not known, _CHAMELEON_, _CIRE_, or _DBSCAN_ can be used to perform clustering for user group discovery. However, the researcher has to perform several trial experiments on the weblog to find out a suitable number of clusters.
# An Energy Demodulation Based Fiber Optic Sensing System for Landslide Early-Warning
###### Abstract  
To help reduce the impact of geo-hazards, an innovative landslide early-warning technology based on an energy demodulation-based fiber optic sensing (FOS-LW for short) technology, is introduced in this paper. FOS-LW measures the energy change in a sensing fiber at the segment of micro-bending, which can be caused by landslide movements, and automatically raises an alarm as soon as the measured signal intensity in the fiber reaches a pre-set threshold. Based on the sensing of micro-bending losses in the fiber optics, a two-event sensing algorithm has been developed for the landslide early-warning. The feasibility of the FOS-LW technology is verified through laboratory simulation and field tests. The result shows that FOS-LW has some unique features--such as the graded alarm, real-time responses, remote monitoring, low cost and passive optical network--and can be applied in the early-warning of landslides.  
Keywords:landslides; fiber optic sensing system; early-warning; energy demodulation; event sensor _Article_
## 3 Results
### Changes in Volatile Compounds by GCMS  
The model system detected 31 VOCs; the clustering results (Figure 2A) revealed that the samples with inoculation (M1, M2, M3) clustered into one category, while the control sample (MC) and fresh sample (MF) clustered into another. This result indicates that the VOCs in the _Suanzhayu_ model system with inoculation differed from the control and fresh systems. In all, 43 volatile compounds were detected in the actual system (Figure 2B); including 10 alcohols, 4 alkanes, 17 esters, 4 aldehydes, 2 ketones, 2 acids, and 6 other flavor substances. The AC and A1 samples clustered together, whereas the A2 and A3 samples contained significantly more VOCs, indicating that inoculation with _L. plantarum_ M22 and _L. plantarum_ 3-14-LJ could improve the overall compounds in _Suanzhayu_. Tables S1 and S2 showed that in both the model and actual systems, VOCs in the inoculated groups were higher than in the control group, with the most significant increase in alcohol content. At the same time, inoculation of _L. plantarum_ M22 and 3-14-LJ can significantly increase some important compounds that can provide special flavor to _Suanzhayu_, such as octanal, hexanal, 2-pentylfuran, 1-octen-3-ol, and 3-hexannone. When the VOC content of the three strains was compared, the total ester content and total VOC content of the group inoculated with _Lactobacillus plantarum_ M22 were the highest in both the model and actual system. This indicated that inoculation could significantly increase the content of VOCs in _Suanzhayu_, with _L. plantarum_ M22 having the greatest effect.  
It was found that the content and type of esters produced in the model system were significantly less than that in the actual system (Table 1, Tables S1 and S2). After fermentation in the model system, the total ester content was significantly higher in the groups with inoculation than in the groups without, with M2 exhibiting the highest total ester content. The ester content in A2 was higher than AC in the actual system, while it was lower in A1 and A3. This demonstrated that LAB, particularly _L. plantarum_ M22, can effectively boost  
Figure 2: Hierarchical clustering of volatile compounds in model system (**A**) and actual system (**B**) of _Suanzhayu_. Isoamyl acetate, ethyl hexanoate, and ethyl octanoate contents in model system (**C**) and actual system (**D**) of _Suanzhayu_. Letters “a–d” indicate the significant difference (\(p<0.05\)). For abbreviations, see Table 1.  
ester production in both the model and actual systems. In the model system (Figure 2C), the groups with inoculation produced significantly more isoamyl acetate, ethyl caproate, and ethyl hexanoate than the control group, with M2 having the highest content. In the actual system (Figure 2D), all three esters were detected in the inoculated groups, but no ethyl octanoate was found in the AC group. The A2 group contained the most isoamyl acetate and ethyl hexanoate, followed by AC. Inoculation with LAB, particularly _L. plantarum_ M22, was more favorable for the formation of ester compounds when combined.
# Phenolic Compounds in Trees and Shrubs of Central Europe
## 3 Results
### Phenolic Compounds
#### 3.1.1. Phenolic Acids
Ellagic acid found in Cornelian cherry fruit exhibits immunostimulatory, immunomoddulatory, antimicrobial, antioxidative and anti-cancer action. It inhibits the adverse effect of UVB radiation (Ultraviolet B), protects skin against degradation and exhibits anti-inflammatory action [43; 44; 45]. Ellagic acid is also found in the ester form, bound with glucose, forming hydrolysable tannins (so-called ellagitannins). Salicylic acid, i.e., 2-hydroxybenzoic acid, whose natural source is willow, is another compound of particular interest. Willow bark contains a biologically active substance referred to as salicin [46]. Salicin is a \(\beta\)-glucoside of saligenin [47], which, in vivo, undergoes a two-stage transformation consisting of deglycolisation and oxidation to salicylic acid [48; 49]. Thanks to the rapid development of chemical synthesis in the late 19 th century, this acid has become a direct precursor of other drugs of similar structure, the so-called salicates, and non-steroid anti-inflammatory drugs. They include, e.g., non-acetylated derivatives of salicylic acid such as sodium salicate, methyl salicate, diflunisal, phenyl salicate (salol), choline salicate, ethylene glycol salicate, salicamide, salsalsalate, benolylate  
\begin{table}
\begin{tabular}{c c and diethylamine salicate [50; 51]. In turn, the acetylated derivative of this acid, i.e., aspirin, is an anti-inflammatory, analgesic, antipyretic and antirheumatic drug. To summarise, phenolic acids protect plants against the action of microorganisms and insects, while, in combination with polysaccharides, they make cell walls more rigid. In the human organism, they exhibit diverse biological activity, e.g., scavenging free radicals, chelating metal ions, modifying enzyme activity and protein availability. They prevent cardiovascular disease, cancer and diabetes. Additionally, they protect against photooxidative skin damage [80]. Based on our own conducted research on a representative number of plant material samples of wild-growing trees and shrubs in north-western Poland, it was found that spruce needles had the highest content of phenolic acids, and pine needles the lowest (Table 3). Among the analysed acids, we found in fir benzoic and vanillic, in larch caffeic and coumaric acids, in pine caffeic and ferulic, and, in spruce, 4 hydroxybenzoicic, caffeic and chlorogenic, which were found in the highest
## 2 Methods
### Detection Algorithms
#### 2.4.4. Grayscale Range Detection  
The grayscale range detection algorithm is described in Figure 3c. MS lesions usually appear at a very narrow range of grayscale. An algorithm for MS lesion detection based on this characteristic of \"detection by grayscale\" is very simple to implement, but there is an issue we must address before we can use this method. A relatively large amount of unwanted areas (noise) with the same grayscales appear in the image, but the main difference between the noise and the lesion of the disease is that most of the noise is outside the WM of the brain while the lesion is in this region. We added to the algorithm several steps to screen out the noise. We first check and define grayscale ranges of WM and of lesions in each type of MR images. Then, we define a mask to separate between the pixels that are included in brain WM and those excluded from it. By this action we cut out most of the noise but at the same time we cut out the lesions as well. Because MS lesions can only be found in WM areas, we use the MATLAB function \"mifill\" to fill the holes in the WM areas, which are in fact the lesions themselves. Multiplying the original image by the aforementioned results produces an image that contains WM and any internal part of the brain. Next, the algorithm defines the pixels that are outside the lesion area and term them as \"0\" and those that are inside are termed as \"1\". The last step, as in all detection algorithms, includes filtering according to the size of the object.  
Figure 3: Detection algorithms: (**a**) boundaries detection scheme; (**b**) object shape detection scheme; (**c**) grayscale range detection scheme.
## 5 Results and Discussion
### Assessment of Landscape Diversity
For example, as Figure 6 shows, the classification results of \"grassland\" and \"wetland\" of Suzhou City of Wanbei in the five MCD12Q1 data layers are distinctly different from the GlobeLand30. It can be concluded that the higher the landscape heterogeneity, the larger the diversity indices and the weaker consistency.
## 4 Results and Discussion
### Evaluation and Optimization of Energy Consumption in Simulated LoRa Networks
The network's capacity is reduced, not only due to transmissions in the downlink but also due to the off-period time following these transmissions.
###### Abstract  
Calcium fluoride (CaF\({}_{2}\)) is deposited via vacuum thermal evaporation on borosilicate glass to produce an anti-reflection coating for use in solar modules. [PERSON]'s essential simulation is used to optimize the thickness of the CaF\({}_{2}\) coating on the glass. Experimentally, a 120 \(\pm\) 4 nm-thin CaF\({}_{2}\) film on glass shows an average increase of \(\sim\)4% in transmittance and a decrease of \(\sim\)3.2% in reflectance, respectively, when compared to that of uncoated glass (Un CG), within the wavelength spectrum of approximately 350 to 1100 nm. The electrical PV performance of CaF\({}_{2}\)-coated glass (CaF\({}_{2}\)-CG) was analyzed for conventional and lightweight photovoltaic module applications. An improvement in the short-circuit current (\(J_{sc}\)) from 38.13 to 39.07 mA/cm\({}^{2}\) and an increase of 2.40% in the efficiency (\(\eta\)) was obtained when CaF\({}_{2}\)-CG glass was used instead of Un CG in a conventional module. Furthermore, \(J_{sc}\) enhancement from 35.63 to 36.44 mA/cm\({}^{2}\) and \(\eta\) improvement of 2.32% was observed when a very thin CaF\({}_{2}\)-CG was placed between the polymethyl methacrylate (PMMA) and solar cell in a lightweight module.  
high efficiency; reflection; transmittance; anti-reflection coating; calcium fluoride; conventional PV module; light weight PV module; statistical analysis +
Footnote †: journal: Applied Physics Letters
## 4 Empirical Analysis  
In this section, the impact of the GSCPI on airlines, comprehensive logistics, pipeline, and shipping companies is to be assessed through VARM analysis. The Augmented Dickey-Fuller test was conducted to check the stability of each variable. Then, a Johansson cointegration test was conducted to confirm the loss of information regarding the long-term relationship between variables. After that, the relationship between the logistics industry and the GSCPI is examined through VARM and VECM analysis according to the presence or absence of cointegration between variables. In addition, the IRF is to be additionally performed. Figure 2 shows the flow diagram of the empirical analysis.
## 3 Testbed Deployment
### Gateway and Data Management  
After selecting Crossbow for the WSN technology, their Stargate NetBridge served as the WSN gateway, which is a modified NSLU2 device and is nicknamed the Sluggo gateway. It remained in operation until July 2011 when it had to be replaced with a new Linux-based gateway system (see Figure 4). The need to create a new gateway system stemmed from the shortcomings of the Stargate NetBridge gateway [36,37].  
The new Linux gateway was built using an older, untested and unqualified version of the application XServe, provided by Crossbow. This version of XServe was chosen because it is compatible with open-source Linux operating systems running x86 computer architecture. This not only made the gateway more reliable, but also provided the convenience of having the WSN gateway capable of running on a laptop or PC. XServe was successfully integrated with a compatible PostgreSQL database for collecting and storing WSN data. The Linux gateway was also outfitted with TinyOS, which provided the ability to write, compile, and program wireless more applications. XMesh library files, including Crossbow's applications for wireless motes, were also added to the gateway. The final Linux gateway is operated on an AOpen BB10 mini PC running an Ubuntu 10.04 LTS operating system. A suite of software programs were added to the Linux gateway that perform a variety of tasks including backing up WSN data, tracking IP addresses, and recording internal CPU temperatures of the gateway.  
During the time that the Sluggo gateway was in use, remote connectivity was made using a simple web service manager. The Apache AXIS2/C web service was chosen because it was compatible with Sluggo's architecture and had low computational power and memory requirements. The Sluggo gateway operated as the server while a computer located on University grounds, with the client software installed, requested daily data for downloading.  
Following the move to the new Linux gateway, Dropbox, a file synchronization and backup service, was used in place of AXIS2/C. Dropbox provided a more sophisticated and user-friendly alternative. In addition, the web-based WSN management system, INDAMS (Integrated Network and Data  
Figure 4: Timeline of the ASWP testbed deployment. In the top panel, the solid line shows the number of nodes deployed in the network from April 2010 (phase 1) to April 2014, while the dotted line represents the monthly maximum number of connected nodes within the network. At the bottom, the two bars highlight the time periods of the two network gateways (GW) and the two WSN routing protocols employed by the WSN application (APP).  
Management System for Heterogeneous WSNs) [38], was incorporated into the gateway for data monitoring, providing real-time network status and connectivity information.