esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia
## 5 Natural Resource Extraction
### Above-Ground Mining Operations
#### 5.2.1 Example Case-Study: Latrobe Valley Open-Cut Coal Mines (Victoria)  
In the Latrobe Valley, Victoria (for location, see Figure 2A), InSAR has been used to measure ground displacements at the Hazelwood and Loy Yang open-cut coal mines. At the Hazelwood mine, InSAR analysis by [43] revealed subsidence rates of \(>\)\(-60\) mm/yr attributed to mining activities and associated groundwater extraction related to mine dewatering. Following a precipitation event, cracks were observed within the mine and nearby roads, resulting in the 7-month closure of a nearby major road. InSAR observations showed that this freeway had undergone a tilt of \(>\)0.5 mm/m [43].  
At the Loy Yang mine, we use ALOS PALSAR data acquired between 2007-2011 to map LoS velocities (Figure 7A). At the edges of the mine, LoS velocities of up to \(-40\) mm/yr are observed (Figure 7A), the majority of these displacements occurring at a constant rate within the analysed time period (see example time-series in Figure 7B). However, the coverage of coherent InSAR pixels directly coincident with the open-cut operations is limited by coherence loss over time as a result of rapid and regular changes of the surface (Section 2 Figure 7).  
Other challenges in applying InSAR data to monitor the stability of open-pit mines include shadowing resulting from steep slopes (e.g., the lack of coherent pixels on the west-facing slope of Loy Yang mine in Figure 7A) and rapid or large magnitude displacements resulting in incoherence (as described in Section 2). To overcome this, both SAR backscatter offset tracking and interferometry can be applied to capture large magnitude (10-100s cm) and small magnitude (sub-cm) scale displacements, respectively. Coupled with SAR backscatter change detection to monitor surface changes (such as shifting-waste piles) from a single dataset, it is possible to capture the full extent of the deformation field across a mine site and identify potential hazards in areas with no other monitoring infrastructure.
# Evaluation and Characterization of Alginate Extracted from Brown Seaweed Collected in the Red Sea
## 2 Materials and Methods
### Extraction of Sodium Alginate  
Sodium alginate was extracted from the selected seaweeds by the modified method based on the study of [PERSON] et al. (2018). About 100 g of powdered samples were immersed in 2% formaldehyde for 24 h, in an airtight conical flask. Then, the solution was filtered out using a Glass Funnel and Whitman qualitative filter paper (70 mm), while the soaked seaweeds were washed with distilled water two to three times. The HCl solution (0.2 M) was added and maintained at room temperature for 24 h. The solution was separated by filtration, and the residue was washed with distilled water two to three times. The alginate was extracted with the addition of sodium carbonate (2% \(m\):\(v\)) to the residue previously obtained, and this process occurred overnight. Then, the extract was filtered through a gauze cloth bag and the filtrate was bleached with 2.5% (\(v\):\(v\)) sodium hypochlorite. The extracted sodium alginate was precipitated from the solution by ethanol, then washed with acetone, and dried overnight at 60 \({}^{\circ}\)C. The alginate yield was measured as follows:  
\[\text{Yield of sodium alginate }(\%)=\frac{\text{Weight of alginate }}{\text{Weight of seaweed dried biomass }}*100\]
the supply of fodder and energy concentrates to livestock. However, ruminal fermentation of forages and concentrates produces high amounts of methane (CH4). Therefore, considerable efforts have been devoted to finding alternative sources of forage that contribute to satisfying the nutritional requirements of ruminants and that contribute to the reduction in methane production in the rumen [1]. The CH4 is produced from carbon dioxide and hydrogen as a by-product in rumen fermentation. Agriculture accounts for about 47% to 56% of total anthropogenic methane emissions [2]. It is known that from the agricultural sector, dairy, bovine, caprine, and ovine livestock contribute substantially to the increase in CH4 production using the continuous process of ruminal fermentation. Hence, livestock activity contributes to the emission of greenhouse gases (GHG), contributing almost 30% of the total anthropogenic methane emissions into the atmosphere [3]. It has recently been recognized that due to their secondary metabolite content, some plants, leaves, fruits, and roots reduce methane production in the rumen [4]. In vitro studies have shown that secondary metabolites such as tannins have antimethanogenic activity, either directly by inhibiting methanogens or indirectly by attacking protozoa [5]. Plants with bioactive compounds (saponins and tannins) to modify fermentation and rumen inhibition of methanogenesis are one option and are generally safe, cheap, and readily available [6]. In this way, some plants, such as LLL and PP, can be used as alternative sources of forage in ruminant feeding. Moreover, the LLL is a highly available legume species commonly used as forage for ruminant feeding in North and South America, Asia, Australia, Africa, and the Pacific Ocean Island [7]. Additionally, PP has recently been introduced into diets and supplements to maintain ruminant body condition during dry periods [8]. Although numerous studies on the effects of plants with a high tannin content, such as LLL, on rumen fermentation have found reductions in enteric methane production, the mechanisms of how the tannin content of these plants reduces methane production in vitro are still unclear. It is hypothesized that the incorporation of LLL and PP into ruminant diets will decrease methane production in vitro and alter the rumen microbial population. Therefore, the objective of this study was to evaluate the effects of incorporating LLL and PP in ruminant diets on gas and methane production, rumen fermentation patterns, and the amount of methanogens during in vitro fermentation.
## 4 Proposed Work
### Secure Data Sharing  
As shown in Figure 5 the Phase 2 secure data sharing protocol in IoT communication involves multiple steps. Initially, IoT device 1 stores data (images, files, etc.) in the decentralized interplanetary file system (IPFS) and retrieves a unique Content ID (CID). The CID is then encrypted using the ASCON algorithm with the shared key from Phase 1 to ensure confidentiality. The encrypted CID is stored on the blockchain, enabling authentication and transaction traceability. IoT device 2 retrieves the encrypted CID, decrypts it using ASCON, and uses the decrypted CID to retrieve data from IPFS. This process ensures secure, confidential, and traceable data sharing between IoT devices, guaranteeing data integrity and security.  
Figure 4: Sequence diagram of the mutual authentication phase.
# An Improvement of the Load Transfer Method for Energy Piles Under Thermo-Mechanical Loads
## 3 Results and Discussion
### Results and Discussion of Kunshan Test
#### 3.2.1 Case 1: 25% of the Ultimate Load and Coupled Heating  
A comparison of the calculated results for the load of 420 kN and simultaneous heating shows that the results of the iterative method of unbalanced force elimination are in general agreement with the results of the test. After the superimposed temperature effect with the load, the axial force and lateral resistance of the pile are redistributed, and it can be seen from Figure 9b that the temperature change has less effect on the lateral resistance of the upper part of the pile, but it can still be found that the negative resistance of the upper part of the pile due to the temperature is offset by the positive resistance of the upper load, so  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**(a) Properties of Soil** & & & & & & \\ \hline
**Soil Properties** & **Fill** & **Silty Clay 1** & **Silty Clay 2** & **Silty Clay 3** & **Silt with Sand** & **Silty Sand** \\ \hline Depth, \(h\) (m) & 0–3.8 & 3.8–4.8 & 4.8–6.8 & 6.8–10.6 & 10.6–15.6 & 15.6–21.8 \\ Density, \(\rho\) (kg/m\({}^{3}\)) & 1800 & 1830 & 1770 & 1940 & 1840 & 1860 \\ Cohesion, \(c\) (KPa) & — & 20.5 & 12.0 & 40.5 & 7.5 & 5.9 \\ Friction angle, \(\varphi\) (\({}^{\circ}\)) & 11.6 & 12.8 & 10.0 & 15.0 & 25.1 & 27.1 \\ Elastic modulus, \(E\) (MPa) & 28 & 25 & 15 & 80 & 100 & 140 \\ CPT tip resistance (MPa) & 0.79 & 0.77 & 0.48 & 2.38 & 5.11 & 7.73 \\ \hline \hline
**(b) Properties of Piles** & & & & & & \\ \hline
**Numbering of Piles** & **Type of Piles** & **Pile Length** & **Pile Diameter** & **Strength** & & \\  & & **(m)** & **(mm)** & **(grade)** & **Form of Buried Pipe** \\ \hline F1 & Bored piles & 17 & 600 & C30 & Double Spiral \\ \hline \hline \end{tabular}
\end{table}
Table 2: The physical and mechanical properties of soil and piles.  
Figure 8: Schematic diagram of energy pile (F1) sensor arrangement and buried pipe.  
that the lateral resistance of the upper part of the pile gradually decreases, while the lower part of the pile is the opposite. The results of the iterative method of unbalanced force elimination also reflect this phenomenon well, and both the test results and the calculated results of the lateral resistance distribution curve (Figure 9b) can be found to show that the neutral surface of the pile is about 11.1 m. Therefore, the above analysis shows that the iterative method of unbalanced force removal at the top of the pile for energy piles established in this paper can be used to study the effect of thermo-mechanical coupling on its load transfer characteristics and performance.
# Adaptive Thermal Control of Cell Groups to Extend Cycle Life of Lithium-Ion Battery Packs
## 3 Results
At this point, in order to restore capacity balance within the pack, we stopped cooling for CG1 as it had the highest capacity and resumed cooling for only CG2 as it had the lowest capacity. The capacity measurement at the 85 th cycle revealed that CG2\({}^{\prime}\)s degradation rate had slowed to the original rate of about 3 mAh/cycle, while CG1\({}^{\prime}\)s degradation rate had accelerated to about 8 mAh/cycle. As Figure 4 shows, both CG2 and CG3 had capacities within 0.03 Ah at the 85 th cycle, hence cooling was also resumed for CG3, while CG1 still had significantly higher capacity so cooling for CG1 was not resumed. By the 100 th cycle, all three cell groups were brought back into balance (within 0.03 Ah). The cells retained about 93% of their original capacity, based on using the 2.9-4.0 V as the voltage range for determining capacity. The reason for the large capacity fade after only 100 cycles was due to the high charge and discharge rate employed, in addition to uncooled cell groups (depending on the cycle number) during cycling. Typical current LIBs tend to last at least 500 cycles before dropping below 80% state of health (SOH), but these are estimated based on typical average charge and discharge rates of 1C. Even though we are not utilizing the full SOC range in this experiment, the severe capacity degradation in only 100 cycles observed in our experiments may be attributed to the extremely high discharge and very high charge rates employed here. Since we are applying thermal control based on feedback from capacity measurements, it is appropriate to denote our protocol as \"adaptive thermal control\".
###### Abstract  
Background: This study investigated selenium nanoparticles' protective effects (SE-NPs) against carbon tetrachloride (CCI\({}_{4}\))-induced hepatic injury in rats. Methods: Rats were divided into four groups (\(n=8\)). Group 1 rats received the vehicle solution only. Group 2 received a single intraperitoneal injection of 1 mL/kg CCl\({}_{4}\) in liquid paraffin (1:1 \(r/v\)). Group 3 was treated with SE-NPs (2.5 mg/kg) twice a week for three weeks before receiving CCl\({}_{4}\) challenge. Oxidative stress, liver function, liver histopathology and serum lipid levels were evaluated. Results: Plasma concentrations of aspartate transaminase (AST), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), urea, creatinine, malondialdehydehyde (MDA) and the toxicity marker, lactate dehydrogenase (LDH), were significantly elevated in rats treated with CCl\({}_{4}\) compared to the controls. CCl\({}_{4}\) also caused a significant decline in liver glutathione (GSH) concentration. SE-NP pretreatment significantly improved the level of AST, urea, creatinine, MDA, LDH, and GSH in the CCl\({}_{4}\)-injected rats towards the control levels. Conclusions: SE-NPs restored both liver function and hepatic structure in CCl\({}_{4}\) treated rats. SE-NPs exhibit an ability to counter markers of liver injury induced by CCl\({}_{4}\) and restore oxidative stability to lipid profiles and liver structure and function.  
Keywords:CCI\({}_{4}\); hepatotoxicity; oxidative stress; liver functions; selenium nanoparticles +
Footnote †: journal:  
Selenium is an essential element for living organisms. It affects the activity of glutathione peroxidase and helps protect cells and tissues from oxidative damage. It is used as a dietary supplement [11] and supports immune responses to prevent various diseases [12; 13]. Cancer and immune dysfunction are associated with modest selenium deficiency and altered expression and single nucleotide polymorphisms of some selenoproteins [11; 14]. The impact of selenium on health and disease, dietary selenium requirements for support of cognitive and immune functions, and cancer prevention at the molecular level is hot in contemporary research [15]. Cells of the immune system, especially T lymphocytes and macrophages, express most of the 25 genes encoding human selenoproteins, with GPx isoenzymes, GPx1 and GPx4, exhibiting the highest expression levels in both cell types [16]. Selenium derivatives are detoxifying agents toward mercury, cadmium, lead, and many other elements [12]. The study on selenium interacting with other toxic elements in vivo is a hot-spot of contemporary research [17]. The suitability of selenium nanoparticles (SE-NPs) in such a study is attributed to their chemical stability, biocompatibility, and low toxicity [18]. Thus, selenium may be useful against hepatotoxicity as demonstrated in this study.
# Shedding of GPP130 by PC7 and Furin: Potential Implication in Lung Cancer Progression
## 2 Results
### Comparative Growth of A549 Cells Overexpression GPP130 or CASC4 Constructs  
As observed in all our previous experiments, overexpression of WT GPP130 enhanced proliferation by an average of \(\sim\)40% (Figure 7). In contrast, compared to WT GPP130, the absence of shedding at both S1 and S2 (GPP130 R70A + R270A) resulted in a proliferation like that obtained with an empty vector (Figure 7), suggesting that complete abrogation of shedding prevents the proliferation induced by GPP130. Notably, the GPP-4 REL mutant did not significantly enhance proliferation above that observed with WT GPP130 (Figure 7). This is likely because in A549 cells, overexpression of GPP130-4 REL results in a very effective S1 cleavage, which is subsequently cleaved with the predominant formation of the non-proliferative S2 fragment aa 278-696 (Figure 6B), Indeed, Western blot analysis reveals the presence of only the S2 fragment when GPP-4 REL is overexpressed in A549 cells (Supplementary Figure S1). This contrasts with the overexpression of GPP130-4 REL in HEK293 cells that leads to the predominant generation of the S1 fragment without significant cleavage at S2 (Figure 3C). In our previous study, we observed that efficient shedding of CASC4 (variant CASC4-5 REL) enhances triple-negative breast cancer MDA-MB-231 cells migration and invasion [4]. However, the proliferative effect of this variant was not studied. In the present study, we compared the proliferative activity of CASC4 overexpression and its efficient shedding (CASC4-5 REL) in lung cancer A549 cells. The data show that CASC4 significantly enhances the proliferation of A549 cells compared to EV (Figure 7). We also observed a trend revealing that CASC4-induced proliferation is -24% higher compared to that resulting from GPP130 overexpression (Figure 7). However, CASC4-5 REL only slightly enhances proliferation above that observed with WT CASC4, as opposed to a more robust increase in migration/invasion previously observed [4]. It should be noted that CASC4 is only cleaved once at Arg66\(\ll\)[4], whereas GPP130 exhibits two shedding sites (S1 and S2). Finally, the combination of GPP130-4 REL and CASC4-5 REL led to a proliferation phenotype like that of CASC4-5 REL alone, supporting the above data on the lack of added pro-proliferative activity of GPP130-4 REL compared to WT GPP130.  
Figure 7: Shedding of GPP130 and CASC4 enhances the proliferation of A549 cells. Incucyte analysis of cellular proliferation of A549 cells overexpressing pIRES-empty vector (EV) or WT GPP130, GPP130-R70A-R277A, GPP130-4 REL, GPP130-C6A-5 REL, WT CASC4, CASC4-5 REL, GPP130-4 REL + CASC4-5 REL. The data are normalized to day 0 and proliferation is followed for 96 h. The statistically significant results (* \(p<0.05\), ** \(p<0.005\)) at the 96 h timepoint are depicted as separate bar graphs [\(n=3\)], ns means not significant.
# Design of NO\({}_{2}\) Photoacoustic Detection System Based on Finite Element Simulation
###### Abstract  
Based on photoacoustic spectroscopy and finite element simulation technology, a simulation model of sound field excitation in a cylindrical resonant photoacoustic cell was established. The finite element simulation method was used to analyze the acoustic mode and sound pressure distribution of the cavity structure of the photoacoustic cell. The effects of the geometric parameters of the resonator and the buffer cavity on the performance of the photoacoustic cell were compared. The frequency response characteristics of the photoacoustic cell and the effects of the air intake and the air outlet were studied. Based on the simulation results, a cylindrical resonant photoacoustic cell was designed, and a photoacoustic sensor for NO\({}_{2}\) detection was built. NO\({}_{2}\) with a volume fraction of \(10^{-5}\) was taken as the sample gas through frequency scanning to obtain the frequency response curve of the system. The resonant frequency is 1730 Hz, and the cell constant is about 542.3 (Pa\({}^{\circ}\)cm)/W. The quality factor is 10.05. By linear fitting the calibration curve of the sensor, the fitting slope is 0.012 uV/ppb, and R\({}^{2}\) is 0.998. Atmospheric NO\({}_{2}\) detection was carried out for two consecutive weeks, whose findings are in good agreement with the data released by a Huainan environmental monitoring site. The experimental results show that the system can detect NO\({}_{2}\) in the atmosphere with high sensitivity.  
photoacoustic spectroscopy; finite element simulation; blue diode lasers; NO\({}_{2}\) detection +
Footnote †: journal: applied sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Design of NO\({}_{2}\) Photoacoustic Detection System Based on Finite Element Simulation. _Appl. Sci._**2024**, 14, 11343. [[https://doi.org/10.3390/app142311343](https://doi.org/10.3390/app142311343)]([https://doi.org/10.3390/app142311343](https://doi.org/10.3390/app142311343))  
10.3390/app142311343
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 4 Optimized-AODV Protocol Topologies and Testbed Design
### How the Optimizations Were Achieved  
This section discusses how we improved the proposed O-AODV protocol's efficiency in comparison to its predecessor version.
## 5 Analysis and Results
### Sequence of Operations  
In this study, the proposed algorithm's work is suitable for IoT blockchain, where a slight time delay (wait to collect data from all sites) is required. The working principle of this algorithm can be understood easily by this flow chart.  
The flow chart shown in Figure 6 is used to understand the process of this consensus algorithm in the case of validating nodes (\(v=5\)) and integral multiple (\(m=3\)). Data receiving and voting processes are running in parallel. If the master node is selected, then data are transmitted to it, which will be further sent to validating nodes after selecting and removing duplicates from the data. Data collection by BCIoT nodes and master transmission to validating nodes is an about 30-s long process as we have seen this time delay in RMS. The master node also removes duplicate values by comparing data values. Hash for Genesis block in this consensus algorithm is created and stored in the ledger of all the blockchain nodes on the initial stages. The master node will broadcast the block to all BCIoT nodes and increase the rating of the first validator. The voting process is started to select the next master. This process runs in parallel with the previous validation process, but in this case, any prior validator or master could not be selected again. They have to wait for one turn more.
# Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees
## 2 Materials and Methods
### Windowing and Look-Back Filtering  
The selected features were extracted from small windows (150 ms) to simulate the real life scenario where signals arrive in batches. We windowed each trial of the data with overlaps of 50 ms. Instead of randomly associating each individual time window to either the training or test sets, however, we assigned all windows from a given trial to one or the other. This allows for specific processing of consecutive windows as would be the case in real-life scenarios where the user must first train and then use the device. A novelty of this investigation is that not only are the features extracted from the 'current' window employed; a combination of present and past windows is also considered.  
Figure 1: Hand gestures proposed by [PERSON] et al. in [10].  
For each feature set (classic and TD-PSD) we applied the classification algorithms to the feature vectors computed from a combination of the current and previous windows as follows:  
* Present (P): Feature vector extracted from only the current EMG window \(n\): \[\vec{v}_{n}=\begin{bmatrix}v_{n}^{1}\\ \vdots\\ v_{n}^{K}\end{bmatrix}\] (1) For the classic features we have \(K=5\) and \[\vec{v}_{n}=\begin{bmatrix}\text{MAV}\\ \text{RMS}\\ \text{ZC}\\ \text{SSC}\\ \text{WL}\end{bmatrix}\] (2) While for the TD-PSD features we have \(K=6\) and \[\vec{v}_{n}=\begin{bmatrix}\log m_{0}\\ \log(m_{0}-m_{2})\\ \log(m_{0}-m_{4})\\ \log S\\ \log F_{\text{irr}}\\ \log R_{\text{WL}}\end{bmatrix}\] (3)
* Log version of Present (PL): Logarithmically scaled version of the previous vector, i.e., \(\log\left(x^{2}\right)\): \[\vec{l}_{n}=\begin{bmatrix}log[(v_{n}^{1})^{2}]\\ \vdots\\ log[(v_{n}^{K})^{2}]\end{bmatrix}\] (4)
* Present-Past (PPa): Feature vector from the current window combined with that of the previous window. Values of the feature vector of the previous window are concatenated with the current one as follows: \[\vec{b}_{n}=\begin{bmatrix}\vec{v}_{n}\\ \vec{v}_{n-1}\end{bmatrix}\] (5)
* Log version of Past and Present (PPaL): Logarithmically scaled \(\log\left(x^{2}\right)\) version of the previous vector: \[\vec{l}_{n}=\begin{bmatrix}log[(b_{n}^{1})^{2}]\\ \vdots\\ log[(b_{n}^{2K})^{2}]\end{bmatrix}\] (6)  
Whereas various techniques reported here have been used previously in other combinations, to our knowledge combining log transformation with look-back (PPaL) is new.