esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### ADH and ALDH Activities  
Ethanol was metabolized through a two-step process in which ADH oxidizes ethanol to acetaldehyde, which was further oxidized to acetate by ALDH. Hence, ADH and ALDH are the principal enzymes responsible for the metabolism of ethanol in humans [14]. The effects of UL, GTL, and BTL on ADH and ALDH activities are shown in Figure 4. When the concentration of the samples was 25 \(\upmu\)g/mL, all of them promoted ADH activity with the following order: GTL \(>\) UL \(>\) BTL (Figure 4A), while only UL and BTL could promote ALDH activity over a small range (Figure 4B). This boost in ALDH activity can mitigate the adverse effects of alcohol consumption by minimizing tissue exposure to acetaldehyde [28]. Both UL and GTL increased ADH and ALDH activities, while BTL increased ADH activity and decreased ALDH activity. The processing of green tea significantly improved ADH and ALDH activities compared with UL, while BTL promoted ADH activity, which is beneficial to the development of tea products derived from _P. chinense_ leaves. Furthermore, the ADH activity showed a high positive correlation with the content of (\(-\))-epicatechin (\(R\) = 0.945, \(p\) < 0.01) and afzelin (\(R\) = 0.938, \(p\) < 0.01) content as well as a high negative correlation with the content of kaempfer-3-\(O\)-rutinoside (\(R\) = 0.851, \(p\) < 0.05) and pinocombrin-7-\(O\)-(3\({}^{\prime\prime}\)-\(O\)-galloy-4\({}^{\prime\prime}\),6\({}^{\prime\prime}\)-hexahydrodiphenyl)-glucoside (\(R\) = 0.818, \(p\) < 0.05), whereas the ALDH activity showed a high positive correlation with the content of (\(-\))-epicatechin  
Figure 3: The DPPH (**A**), ABTS\({}^{+}\) (**B**) radical scavenging values, ferric reducing power (FRAP, (**C**)) and (**D**) the IC\({}_{50}\) values of VE, _P. chinense_ leaves (UL), _P. chinense_ leaves green tea (GTL), and _P. chinense_ leaves black tea (BTL).  
(\(R=0.945\), \(p<0.05\)), (+)-catechin (\(R=0.912\), \(p<0.01\)), and afzelin (\(R=0.948\), \(p<0.01\)). The effect of polyphenol on ADH and ALDH activities includes two mechanisms: non-covalent binding [29] and ionic action [30]. Studies have reported that (\(-\))-epicatechin, (+)-catechin, and pinochemrin-7-O-[3''-O-galloyl-4'',6''-hexahydroylphenyl]-\(\beta\)-D-glucose may be the active ingredients in _P. chinense_ for the observed hepatotoxicity effects [7; 31].
# VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron
## 4 Experiments and Discussions
### Datasets  
The CASIA-B dataset [47] is a large multi-view gait database, which contains 124 subjects. The gait sequences were captured from 11 viewing angles, with different clothings and carrying conditions. Ten sequences were recorded for every subject, six of which acquired under normal walking, two of which were acquired with coats on, and the remaining two were acquired with bags.  
The OU-ISIR dataset D [48] consists of 185 individuals with 370 gait sequences, recorded from a lateral view. The dataset comprises two subsets, namely DBlow and DBhigh. Each of the subsets contain 100 individuals with high fluctuation (DB\({}_{high}\)) and small fluctuation (DB\({}_{low}\)). Normalized autocorrelation (NAC) was used to compute the gait fluctuations in every gait sequence.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Layers** & **Configurations** \\ \hline \multirow{8}{*}{} & Conv 1-1 & 3 \(\times\) 3 conv, stride = 1, padding = 1 \\ \cline{2-3}  & Conv 1-2 & 3 \(\times\) 3 conv, stride = 1, padding = 1 \\ \cline{2-3}  & Max-Pooling & 2 \(\times\) 2, stride = 2 \\ \cline{2-3}  & Conv 2-1 & 3 \(\times\) 3 conv, stride = 1, padding = 1 \\ \cline{2-3}  & Conv 2-2 & 3 \(\times\) 3 conv, stride = 1, padding = 1 \\ \cline{2-3}  & Max-Pooling & 2 \(\times\) 2, stride = 2 \\ \cline{2-3}  & Conv 3-1 & 3 \(\times\) 3 conv, stride = 1, padding = 1 \\ \cline{2-3}  & Conv 3-2 & 3 \(\times\) 3 conv, stride = 1, padding = 1 \\ \cline{2-3}  & Conv 3-3 & 3 \(\times\) 3 conv, stride = 1, padding = 1 \\ \cline{2-3}  & Max-Pooling & 2 \(\times\) 2, stride = 2 \\ \cline{2-3}  & Max-Pooling & 2 \(\times\) 2, stride = 2 \\ \cline{2-3}  & Conv 5-1 & 3 \(\times\) 3 conv, stride = 1, padding = 1 \\ \cline{2-3}  & Conv 5-2 & 3 \(\times\) 3 conv, stride = 1, padding = 1 \\ \cline{2-3}  & Conv 5-3 & 3 \(\times\) 3 conv, stride = 1, padding = 1 \\ \cline{2-3}  & Max-Pooling & 2 \(\times\) 2, stride = 2 \\ \hline \multirow{8}{*}{} & Fully Connected & 512 \\ \cline{2-3}  & Batch Normalization & - \\ \cline{2-3}  & Leaky ReLU & - \\ \cline{2-3}  & Dropout & 0.3 \\ \cline{1-1} \cline{2-3}  & Fully Connected & 512 \\ \cline{1-1} \cline{2-3}  & Batch Normalization & - \\ \cline{1-1} \cline{2-3}  & Leaky ReLU & - \\ \cline{1-1} \cline{2-3}  & Dropout & 0.3 \\ \cline{1-1} \cline{2-3}  & SoftMax & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Layer-wise architecture of the proposed VGG16-MLP model.  
The OU-LP dataset [49] is a relatively large dataset that comprises 4016 individuals (1 to 94 years old). The dataset was categorized into two subsets where two sequences were recorded for every individual in sequence A, while one sequence was recorded for every individual in sequence B. The gait sequences were captured from four viewing angles, namely \(55^{\circ}\), \(65^{\circ}\), \(75^{\circ}\), and \(85^{\circ}\). In this work, sequence A with 3916 individuals is used for performance evaluation. A summary of the datasets is presented in Table 2.
## 4 Discussion  
### Correlation between Melt Pool and Microstructure  
In Figure 7, the experimental results from the previous sections are summarized in the form of average values to display the principal relationships between beam power, melt pool, and resulting microstructure. Area energy, line energy, beam return time, and lateral velocity are constant to isolate the effect of the beam power. To discuss these results, it is important to bear in mind that aluminum evaporation mainly depends on the peak temperature of the melt pool [12], the area of the melt pool [13], and the time in the liquid state [13], also called melt pool lifetime. It should be noted that these melt pool properties are not independent of each other. For instance, a hotter melt pool is also larger and stays longer in the liquid state. The melt pool lifetime also determines the time for entrapped gas, introduced by the powder or process, to rise and escape from the melt pool and hence the final porosity. Lastly, the nearly lamellar area is closely linked to the aluminum evaporation as well since the overall Al loss influences the Al-enriched zone of the first few melt lines.  
The microstructural properties from the experiments can be divided into two regions. From 180 to 900 W, the aluminum evaporation increases, while the nearly lamellar area and the gas porosity decrease with increasing beam powers. This indicates, that the peak melt pool temperature gets higher and the melt pool becomes broader and longer-lasting. These findings fit well with the measured and calculated lateral melt pool extension, which is also increasing. After a minimum around 900 W, the gas porosity and nearly lamellar area steadily rise, whereas the aluminum evaporation declines. At the same time, the lateral melt pool extension is still slightly increasing. Consequently, the melt pool has to have a lower peak temperature and stay shorter in the liquid state to account for the reduced aluminum evaporation and increased gas porosity. To explain these opposing trends of the evolution of the melt pool and the resulting microstructure, a closer assessment of the beam diameter and energy input via the electron beam is conducted in the following section.  
Contrary to the other results discussed, the melt pool depth does not show a strong impact on the beam power. Therefore, the melt pool depth is less sensitive to the beam diameter than all other features. The penetration depth for 60 kV electrons in titanium is approximately 10 \(\upmu\)m [18]. Therefore, most of the melt pool depth is molten via heat conduction [19], and the influence of the broader electron beam for increasing beam powers is not as crucial as for the lateral melt pool extension or the aluminum evaporation.  
Figure 6: Porosity (**a**) and aluminum content (**b**) over the beam power for both powder batches. The porosity and the aluminum content decrease with increasing beam power up to 900 W. Subsequently, both values rise for higher beam power. The dotted lines indicate the powder properties: the gas porosity inside the powder (**a**) and the measured aluminum content (**b**) of the powder.
###### Abstract  
Monitoring air quality in livestock farming facilities is crucial for ensuring the health and well-being of both animals and workers. As livestock farming can contribute to the emission of various gaseous and particulate pollutants, there is a pressing need for advanced air quality monitoring systems to manage and mitigate these emissions effectively. This study introduces a multi-sensor air quality monitoring system designed specifically for livestock farming environments. Utilizing open-source tools and low-cost sensors, the system can measure multiple air quality parameters simultaneously. The system architecture is based on SOLID principles to ensure robustness, scalability, and ease of maintenance. Understanding a trend of evolution of air quality monitoring from single-parameter measurements to a more holistic approach through the integration of multiple sensors, a multi-sensor platform is proposed in this work. This shift towards multi-sensor systems is driven by the recognition that a comprehensive understanding of air quality requires consideration of diverse pollutants and environmental factors. The aim of this study is to construct a multi-sensor air quality monitoring system with the use of open-source tools and low-cost sensors as a tool for Precision Livestock Farming (PLF). Analysis of the data collected by the multi-sensor device reveals some insights into the environmental conditions in the monitored barn. Time-series and correlation analyses revealed significant interactions between key environmental parameters, such as strong positive correlations between ammonia and hydrogen sulfide, and between total volatile organic compounds and carbon dioxide. These relationships highlight the critical impact of these odorants on air quality, emphasizing the need for effective barn environmental controls to manage these factors.  
multi-sensor; air quality; Precision Livestock Farming (PLF)
## 4 Experiment Results
### Complexity Analysis  
Here we evaluate the computational complexity of our method. For comparison, we consider the problem of a reconstructing image from \(K\)-frame raw spike data of size \(H\times W\). We first discuss the complexity of each pixel in the reconstruction image. In coarse-grained reconstruction, for each pixel, the CSR method only needs to update the fixed time interval \(T\), the number \(n\) of ISI in the set \(T\), the number \(N\) of spikes in the region \(R\), and the predetermined threshold \(\theta\) when a spike generates at that pixel. For the TFI and TFP methods utilized in CSR, the TFI only needs to find the nearest spike to reconstruct the image; thus, the best and the worst case complexity of the TFI are \(O(1)\) and \(O(logK)\), respectively. The complexity of the TFP is \(O(w)\), where \(w\) is the time window utilized in TFP. Therefore, the total complexity of the reconstruction methods utilized in CSR is \(O(logK)\). Note that the CSR method needs extra steps to distinquish whether a pixel belongs to the motion region or not by Equation (6). However, it only takes constant time for each pixel. It does not affect the asymptotic time complexity. Therefore, the time complexity of the CSR method is \(O(HWlogK)\) for each frame.  
Different from coarse-grained reconstruction, the randomized correspondence algorithm [33], as the patch matching method, is added to fine-grained reconstruction in the dynamic region reconstruction. It takes at most \(logR\) steps to find the most similar patch in each of the adjacent frames \(f_{t}(x,\,y)_{t=t_{m}\pm 5i}\), where \(i=\{1,2,3,\ldots N\},N=10\), where \(R\) is the region of fine-grained distinction. Thus, the total complexity of the randomized correspondence algorithm is \(O(RlogR)\). Fine-grained distinction also takes constant time for each pixel, so it does not affect the asymptotic time complexity. Therefore, the total complexity of the CFSR method is \(O(HW\!Rlog(R))\) for each frame, and it takes at most \(O(HW\!K\!Rlog(R))\) time to construct a K-frame video from raw spikes.  
In comparison, the SNM method in [24] takes at least \(O\big{(}H^{3}W^{3}\big{)}\) time to implement a graph cut for each frame; thus, it takes at least \(O\big{(}H^{3}W^{3}K\big{)}\) time to reconstruct a K-frame video [25]. Therefore, our method achieves a significantly lower time complexity than the state-of-the-art methods.
# Design of Broadband High-Frequency Multi-Throw RF-MEMS Switches
This switch achieved an isolation of 26 dB below 20 GHz and had an actuation voltage of 5.6 V. Recognizing the increasing demands for higher frequencies in communication systems, in 2022, [PERSON] et al. ventured into the realm of THz MEMS switches. They presented a design spanning a remarkable frequency range from 140 to 750 GHz [21]. Although this design has an insertion loss nearing 3 dB and an isolation of 12 dB, its ability to cover such high frequencies is noteworthy. However, the actuation voltage of 55 V might present challenges for specific applications, especially those aiming for miniaturization in compact communication systems. That same year, [PERSON] et al. examined a monolayer molybdenum disulfide switch tailored for 6G communication systems, operating from DC to 480 GHz and achieving isolation of 13 dB [22]; however, high-frequency signal isolation still requires enhancement. [PERSON] et al. designed an SP4T switch with an impressive isolation reaching 62 dB but only operated at 14 GHz [23]. In 2023, [PERSON] proposed a broadband terahertz MEMS ohmic switch for 6G communication operating at 150 GHz, achieving 20 dB of isolation and an actuation voltage of 20 V, which remains relatively high for integrated devices [24]. In the same year, [PERSON] et al. showcased an SPMT switch design with a multi-contact cantilever structure, operating at an actuation voltage of 3.5 V but limited to frequencies below 10 GHz [25]. From these studies, it becomes evident that optimization in terms of actuation voltage, insertion loss, and isolation remains a challenge for high-frequency switches. Most studies still focus on single-throw switches at high frequencies, while multi-throw switch research in these bands is limited. As systems become increasingly compact, the demand for multi-throw switches grows, posing a pressing issue to address. To address this research gap, we present an innovative triple-pole triple-throw (3P3T) RF MEMS switch designed for multi-band coverage in more complex and compact systems. Simulated using the Finite Element Method (FEM), our switch demonstrates superior performance characteristics, including wide bandwidth, low actuation voltage, excellent S-parameters, and rapid response times, making it an ideal candidate for 6G applications, particularly in satellite communications, military radar, and the Internet of Things.
# Underwater Electromagnetic Sensor Networks, Part II: Localization and Network Simulations
## 2 Parametrization of the Communication Channel
We conclude that the normal distribution is a reasonable approximation, so that we can assume \(X\sim\mathcal{N}(0,\sigma^{2})\), with \(\sigma^{2}=0.56\,\mathrm{dB}\). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Distances (m)** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **15** & **20** & **25** \\ \hline Path loss (dB) & 70 & 77 & 83 & 88 & 93 & 97 & 102 & 106 & 110 & 114 & 117 & 125 & 142 & 156 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulated measurements to adjust parametric model (1) as characterized in [9]. Figure 3: Measurements and path loss models. A final observation from Figure 2 relevant to the wireless channel simulation is that we can dismiss slow fading. Once we have fit all of the parameters, we can use the parametric model for estimating the distance between nodes and develop localization algorithms, as explained in the following section.
## 6 Application Areas
### Application Area 1: Production of Crops and Foods  
Numerous food companies are leveraging BCT to enhance trust, transparency, and traceability. The distributed and immutable nature of the blockchain's ledger allows for comprehensive tracking of the supply chain, spanning from the farm to the end consumer [40]. This capability is particularly valuable in tracking the origin and preparation of crops and meals, contributing to the assurance of their safety and quality. The implementation of smart contracts within the supply chain can automate various processes, while BCT ensures the verification of crop and food quality [41]. The automation facilitated by blockchain technology has the potential to streamline tasks, ultimately improving productivity in the food supply chain. Moreover, BCT has the capacity to reduce instances of fraud and food waste, thereby enhancing both food security and overall quality.
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 5 Experimental Verification
### Identification of Cable Damage
#### 5.2.2 Damage Degree Identification  
The results of the damage identification are shown in Figure 24.  
As can be seen from Figure 24, the damage identification index is essentially the same as the preset value, which indicates that the proposed damage identification method is feasible.
# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## Methodology
### Mapping Algorithm
For the mapping of Trees, all pixels for which after the extraction of Built were greater than \(-16.0\) dB at three consecutive dates of imaging as stated above, and with b6 greater than or equal to 0.5 were put into this category. Trees exhibited the second highest backscatter, and as its temporal backscatter profile is also far away from that of Water, there was no need to utilize the MNDWI. Finally, all pixels which do not meet the classification criteria outlined above, were classified as Others, a class which as earlier mentioned, contains roads, barren, grasslands and other low biomass plants, and in the final land-cover map, could include misclassified pixels from Rice, Water, Built and Trees. Results obtained from this algorithm were validated via accuracy tests.
# Numerical Simulation Analysis of Concrete-Filled Circular CFRP-Steel Middle Long Columns
## 4 Finite Element Calculation
### The Selection of Unit Type  
In ANSYS finite element analysis, the selection of element types is based on material characteristics and structural configurations. The SOLID65 element is widely adopted for nonlinear simulation of concrete materials due to its unique constitutive model incorporating tensile cracking and compressive crushing capabilities. The SOLID45 element, as a three-dimensional solid element, is suitable for simulating the mechanical behavior of steel tubes. Considering the thin-walled characteristics of CFRP materials (with thickness dimension significantly smaller than in-plane dimensions), shell elements are more appropriate for modeling. This element selection scheme accurately represents the mechanical properties of each constituent material, ensuring the reliability of numerical simulations.  
SOLID65 was used for the concrete units, and MISO was adopted as a reinforcement criterion. The stress was set to not release after concrete cracking. SOLID 45 was used for the steel pipe unit. SHELL181 was used for the CFRP unit, and the BKIN was adopted for CFRP. This element can not only simulate the lateral bending deformation of concrete-filled circular CFRP columns but also establish different sections for delamination so as to simulate different layers of CFRP [31].
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 2 Materials and Methods
### TCP Sockets
#### 2.3.1 Data Sending by TCP  
TCP is a protocol that ensures the reliable transmission by executing a data flow management. Therefore, there are three principal functions inside the Linux kernel that intervene in the data sending process and flow management [17].  
1. \(\mathrm{tcp\_sendmsg}\): which copies the user space data to the Linux kernel space, then it is assigned to the \"socket buffer\" and divided in smaller packets.
2. \(\mathrm{tcp\_send\_skb}\): which organizes the data in \"socket buffer\" for the transmission queue, and decides whether the transmission can take place or not.
3. \(\mathrm{tcp\_transmit\_skb}\): which builds the TCP header and sends the packets to the network layer.
###### Abstract  
With the accumulation of the ionospheric radio occultation (IRO) data observed by Global Navigation Satellite System (GNSS) occultation sounder (GNOS) onboard FengYun-3C (FY3C) satellite, it is possible to use GNOS IRO data for ionospheric climatology research. Therefore, this work aims to validate the feasibility of FY3C/GNOS IRO products in climatology research by comparison with that of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), laying the foundation for its application in climatology study. Since previous verification works of FY3C/GNOS were done by comparison with ionosondes, this work matched NmF2/hmF2 of FY3C/GNOS and COSMIC into data pairs to verify the profile-level accuracy of FY3C/GNOS IRO data. The statistical results show that the overall correlation coefficients of both NmF2 and hmF2 are above 0.9, the overall bias and std of NmF2 differences between FY3C/GNOS and COSMIC are \(-\)2.19% and 17.48%, respectively, and the bias and std of hmF2 differences are \(-\)3.29 and 18.01 km, respectively, indicating a high profile-level precision consistency between FY3C/GNOS and COSMIC. In ionospheric climatology comparison, we divided NmF2/hmF2 of FY3C/GNOS into four seasons, then presented the season median NmF2/hmF2 in \(5^{\circ}\times 10^{\circ}\) grids and compared them with that of COSMIC. The results show that the ionospheric climatological characteristics of FY3C/GNOS and COSMIC are highly matched, both showing the typical climatological features such as equatorial ionosphere anomaly (EIA), winter anomaly, semiannual anomaly, Weddell Sea anomaly (WSA) and so on, though minor discrepancies do exist like the differences in magnitude of longitude peak structures and WSA, which verifies the reliability of FY3C/GNOS IRO products in ionospheric climatology research.  
Keywords:ionospheric climatology; radio occultation; validation; FY3C/GNOS; COSMIC