esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Field Trail and Soil Properties  
Field experiments with 6 different varieties of soybeans were conducted in the growing seasons of 2020 and 2021 at the test site of the Faculty of Agrobiotechnical Sciences Osjiek, at the Tenja location in the Osjiek-Baranja County, Croatia. Each soybean variety was subjected to 6 different treatments of foliar biofortification (3 treatments with Se, 3 treatments with Zn, and 1 treatment of the combined application of Se and Zn), which together with the control without biofortification makes 7 different treatments, with a completely random arrangement of plots in three replications. Each individual plot had a surface area of 8 m\({}^{2}\). Given that there was a total of 126 plots (6 varieties \(\times\) 7 treatments \(\times\) 3 repetitions), the total experimental area was slightly more than 1000 m\({}^{2}\).  
The soil of field experiment 2020 was alkaline (pH\({}_{\text{H2O}}\) = 8.21; pH\({}_{\text{KCl}}\) = 7.38, according to ISO 10390:2005 [53]), with medium soil organic matter content (SOM = 2.55%, according to ISO 14235:1998 [54]). The soil was very highly supplied with plant-available phosphorus (387.4 mg/kg P\({}_{2}\)O\({}_{5}\) according to AL method [55]), and well supplied with plant available potassium (250.5 mg/kg K\({}_{2}\)O according to AL method [55]). The level of total Zn was at a moderate concentration (61.24 mg/kg) and a low concentration of total Se (0.18 mg/kg) extracted by aqua regia (according to ISO 11466:1995 [56]), as well as a medium availability of Zn (1.63 mg/kg) determined by the EDTA method [57].  
The similar properties of the soil were determined before the experiment in 2021, but with a slightly more alkaline reaction, and lower contents of SOM and plant-available phosphorus: pH\({}_{\text{H2O}}\) = 8.55, pH\({}_{\text{KCl}}\) = 7.70; low soil organic matter content (SOM = 1.90%); high supplied with plant-available phosphorus (263.5 mg/kg P\({}_{2}\)O\({}_{5}\)); well supplied with plant available potassium (233.4 mg/kg K\({}_{2}\)O); a moderate concentration of total Zn (47.8 mg/kg); low concentration of total Se (0.13 mg/kg) extracted by aqua regia; and a low to medium availability of Zn (1.51 mg/kg) determined by the EDTA method.
## 2 Materials and Methods
### Oral Microbiome Assay  
According to the manufacturer protocol, we extracted bacterial genomic DNA from healthy subjects using a QIAmp UCP DNA Micro Kit (Catalog no. 56204, QIAGEN, Hilden, Germany). The metagenomic analysis was performed by Omics Sciences and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Thailand. This method obtained 16s RNA from a total of 36 metagenomic DNA samples (pre and post samples from G1 (\(n=16\)); pre and post samples from G2 (\(n=20\))) covering the variable regions V3-V4 were generated using forward and reverse primers (\(5^{\prime}\)-TCGTGCAGCGCTCAGATGTGTATAAGAGACAG CCTACGGGNGGCWGGCAG-\(3^{\prime}\) and \(5^{\prime}\)-GTCTCGTGGCCTGGGAGATGTGTATAAGAGACA GGACTACHVGGGTATCTAATCC-\(3^{\prime}\)) on the Illumina MiSeq device (5200 Illumina Way, San Diego, CA, USA). MOTHURv.1.43.0 software (The University of Michigan, State Street, Ann Arbor, MI 48109, United States) was used in sequence filtration and in the analysis of sequenced data [48]. After checking the quality of pair-end reads with the information in FASTQ files using DADA2, the poor-quality reads were filtered out as per the default QIIME \(2^{\text{TM}}\) threshold values (minimum quality score = 25, minimum/maximum length = 200/1000, no ambiguous bases allowed, and no mismatches allowed in the primer sequence) [49].  
From the total of 36 samples, we obtained 1,013,094 non-chimeric 16S rRNA gene sequence reads. The resulting high-quality sequences were clustered into de novo operational taxonomic units (OTUs) with 97% identity and assigned taxonomy for each OUT sequence. The resulting QIIME artifacts data were visualized and analyzed using QIIME 2 View. Applying microbiomes composition analyses, we found the substantially prevalent bacterial taxonomic classifications at the species level.
# A Review of Oil Spill Remote Sensing
## 5 Detection of Oil on the Sea Bottom
### Chemical Analysis  
Hydrocarbons in the water column and possibly over sunken oil can sometimes be detected using chemical analysis such as mass spectrometry or fluorometer [65].
# Path Difference Optimization of 5G Millimeter Wave Communication Networks in Malaysia
## 5 Link Margin Estimation  
In 5G cellular networks, accurate path loss estimation helps to determine the number of cells required to cover a given area, assess the loss due to the transmission medium, calculate the actual power required for transmission, and optimize overall system performance. Path loss is defined as the power loss or attenuation of a propagating signal in the transmission medium. In the literature, various path loss models have been introduced to estimate path loss based on specific environmental and transmission medium conditions.  
In this paper, we consider the effects of rainfall in urban and highway scenarios, as well as in free-space transmission media, to estimate the path loss. Due to high population density and data requirements, we specifically examine path loss in urban \((PL^{U})\) and highway \((PL^{H})\) scenarios. According to the free-space path loss model, the LOS and NLOS path losses for urban and highway scenarios are estimated as follows [33].  
LOS Path loss:  
\[PL^{U}=38.77+16.7*log10(R)+18.2*log10(f) \tag{15}\]  
\[PL^{H}=32.4+20*log10(R)+20*log10(f) \tag{16}\]  
NLOS Path loss:  
\[PL^{U/H}=36.85+30*log10(R)+18.9*log10(f) \tag{17}\]  
The frequent heavy rainfall in Malaysia influences the path loss. The modified LOS and NLOS path loss expressions for the urban and highway scenarios are as follows:  
LOS Path loss:  
\[PL^{U}_{R}=38.77+16.7*log10(R)+18.2*log10(f)+\alpha \tag{18}\]  
\[PL^{H}_{R}=32.4+20*log10(R)+20*log10(f)+\alpha \tag{19}\]  
NLOS Path loss:  
\[PL^{U/H}=36.85+30*log10(R)+18.9*log10(f)+\alpha \tag{20}\]  
where \(\alpha\) is the path attenuation due to rainfall, which is provided by Equation (3), \(R\) is the path difference between transmitter and receiver in \(m\), and \(f\) is the operating frequency in GHz.  
The link margin due to heavy rainfall is  
\[LM=\frac{G_{t}G_{r}P_{t}}{KT_{s}\Gamma(PL)_{0}(PL)_{R}(E_{b}/N_{0})}, \tag{21}\]  
where \(G_{t}\) and \(G_{r}\) are the respective gains of the transmitter and receiver antenna, \(P_{t}\) is the transmitter power, \(K\) is [PERSON]'s constant, \((PL)_{0}\) is the free-space path loss, \(T_{s}\) is the noise temperature, \((PL)_{R}\) is the path loss due to rainfall, \(\Gamma\) is the data rate, and \(E_{b}/N_{0}\) is the ratio of the energy per bit to the spectral density of the noise.
## 3 Result Analysis and Discussion  
In this part, the suggested system will be assessed, scrutinized, and juxtaposed against conventional Flip-F-OFDM and established methods such as HSF Flip-OFDM [42], ACO-OFDM [19; 37; 43], JM-DCO-F-OFDM [38], and DCO-OFDM [14; 19; 43] considering bandwidth efficiency, signal-to-noise ratio (SNR), bit error rate (BER), peak-average power ratio (PAPR), and computational complexity. This evaluation will be conducted through simulation verification and mathematical analysis.
# Decentralized Consensus Protocols on \(SO(4)^{N}\) and \(Tso(4)^{N}\) with Reshaping
## 3 Consensus on \(SO(4)^{n}\) with Reshaping
### Illustrative Examples
#### 3.3.4 Nine-Agent Ring Lattice  
Similarly to the previous sections, the undirected, nine-agent ring lattice in Figure 1c is assessed with the same initial conditions as in the nine-agent ring graph simulation. The lattice allows communication with \(m=2\) nearest neighbors on either side of each agent.  
Figure 6a shows that the base case protocol results in principal angles associated with the relative attitudes converging to separation angles \(2\pi p/9\) for \(p=1,2\). Figure 6b shows that Case 1 reshaping successfully destabilizes the non-consensus equilibria and converges to zero relative principal rotation angles, thus showing that consensus is achieved, which is different from the result for the nine-agent ring graph. In general, the addition of connections or increasing the value of \(m\) for the ring lattice has the effect of decreasing the stability of the non-consensus equilibria. For a ring lattice with \(m>1\), it has been shown [21] that there is a sequence of ring lattice networks with connectivity approaching 68.09% (i.e., the maximum node degree is \(0.6809(N-1)\)), for which the \(p=1\) balanced  
Figure 4: Simulations on \(SO(4)^{5}\) for (**a**) Base case protocol on a 5-agent ring graph and (**b**) Case 1 reshaping protocol with \(\arg\max_{\theta\in[0,180]}f(\theta)=50^{\circ}\) on a 5-agent ring graph. Initial conditions are chosen in the region of attraction of the \(p=1\) non-consensus equilibria. Right plot demonstrates consensus, while left plot does not.  
equilibrium (in which the minimum separation is \(2\pi p/N\)) is still stable. Figure 6c shows that Case 2 reshaping also results in consensus, as well as a faster convergence time with respect to Case 1 in this example. This communication topology further demonstrates the reshaping method's ability to ensure the almost global stability of the consensus subspace.
## 3 Methodology
### UWB-BLE Beacons  
One of the most well-known use cases of UWB is beacons, or \"reflectors\", small pieces of hardware that act as data transmitters. Beacons emit radio wave signals to locate a smartphone within a specific radius. In this way, they function as signal emitters and smart devices as receivers. For such actions to take place, the user must download the application and authorize communication with a mobile phone [71].  
Using two different wireless networks--BLE and UWB--creates an improved spatial awareness solution. The BLE part provides an energy-efficient method for detecting the presence of nearby devices through initial passive proximity detection and then activates UWB ranging [72]. When a nearby object is detected, the BLE method launches the UWB and then generates data, which are used to ascertain both the location and the direction of the object. In turn, the UWB part provides precise location based on broadband coverage.
## 4 Examples of Engineering Applications
### Construction Quality Grading  
To evaluate the construction quality of concrete structures in hydraulic tunnels reasonably, it is necessary to divide the evaluation results into appropriate grades. At present, China has not issued relevant specifications for concrete structures in hydraulic tunnels. This paper refers to the Technical Procedure for Detection and Evaluation of Defects in Hydraulic Concrete Buildings (DL/T5251-2010) [29], the Code for Design of Hydraulic Tunnels (SL279-2016) [30], and the research achievements of relevant scholars [27; 31]. The relevant design requirements for tunnel support and lining, tunnel grouting, seepage prevention and drainage, and the quality grade classification of hydraulic tunnel concrete structure construction by relevant scholars can be consulted. Therefore, the construction quality condition of the concrete structures in hydraulic tunnels is categorized into five grades: Grade I indicates that the quality of tunnel construction is excellent, with few or no quality problems, and only normal inspection of the construction process is required; Grade II indicates that the quality of tunnel construction is good, with relatively small problems, and the construction process needs to be strengthened to prevent; Grade III indicates that the quality of tunnel construction is qualified, with relatively more problems, and the construction process needs to formulate the appropriate monitoring and management system; Grade IV indicates that the quality of tunnel construction is basically qualified, with more problems but still within the permissible range, but the construction needs to be strictly prevented from proceeding, and the quality problems can be corrected using appropriate means; Grade V means that the construction quality of the tunnel is unqualified--there are many problems, and the construction process must put forward targeted quality control and corrective measures for the quality problems to avoid quality and safety incidents.
# Performance of the 5 th Generation Indoor Wireless Technologies-Empirical Study
## 2 Materials and Methods
### Evaluation
#### 2.2.2 Evaluation Setup and Tools  
The detailed measurements and extensive results were carried out in 5 GTN in comparison with Wi-Fi 6 and 5G NR performance in indoor facilities. The evaluation was made by making throughput and delay measurements with different wireless connection techniques. The 5G measurements were made by using Nokia 5G small cell base station with 60 MHz channel bandwidth. Wi-Fi 6 measurements were carried out with the Aruba 510 Series Wi-Fi 6 indoor access point (AP-515) [5] with 160 MHz channel bandwidth.  
The measurements were made by anchoring each base station in turn to the other end of the office building and performing throughput and delay measurements at the specified measurement points. The Figure 2 shows the location of the base station and measurement points in the floor plan of the office building.  
The measurement device was a Samsung S10 5G (2019) Android phone that had Keysight Nemo Handy [32] installed. Nemo Handy is an application that enables measuring wireless diagnostics information from the air interface. It provides several integrated tools for measuring the performance of telecommunication networks. In our test campaign,we used the iPerf3 software to measure throughput and the Ping tool to measure bidirectional delay. The measurement results recorded by Nemo Handy were analyzed on a laptop with Nemo Outdoor software [33].  
Packet Inter Net Groper (PING) is a very simple transmission control protocol/Internet protocol (TCP/IP) utility [34]. Here, it is used to measure round trip time (RTT) between a  
Figure 1: Architecture of the evaluation setup.  
Figure 2: The measurement points presented on a map of VTT’s office building.  
client and a server connected via 5G or Wi-Fi 6. Ping determines the RTT by sending one or more IP packets to the destination host and measuring the time it takes from sending the data packet to receiving an acknowledgment for that packet. iPerf3, on the other hand, is a tool for measuring the peak throughput of IP networks [35]. iPerf3 provides extensive support for setting parameters related to protocols, timings, and buffers.  
Table 1 lists all the parameters that were used in the Ping and iPerf3 measurements. To eliminate instantaneous minimum and maximum values, Ping and iPerf3 measurements are repeated numerous times at each measurement point, and the results were averaged. During the Ping run, the RTT was measured with a 32-byte packet 600 times every 100 ms, and iPerf3 measured the throughput per second for 5 min. As noted in [36], the lack of packet order control of the user datagram protocol (UDP) results in the fact where throughput of TCP traffic is less than the UDP throughput. Thus, iPerf3 measurements of this evaluation were performed with the UDP protocol, which easily filled the 5G and Wi-Fi 6 links due to the lack of flow control. The evaluation of other protocols, such as the general-purpose (QUIC) communication protocol was left outside the scope of this paper.
## 9 Conclusions  
The damage potential for MS-AS sequences was comprehensively conducted by examining the correlation between the intensities of MS-AS sequences and the corresponding structural damage. The following general conclusions have been drawn:  
1. The correlations between \(IM\)MS and \(DI_{1}\)MS-AS, \(IM\)AS and \(DI_{1}\)MS-AS, \(IM_{1}\)MS-AS and \(DI_{1}\)MS-AS, and \(IM_{2}\)MS-AS and \(DI_{2}\)MS-AS were examined in the logarithm space for the SDOF systems with varying periods. The results show that the IM-DI pair in terms of \(IM_{2}\)MS-AS and \(DI_{2}\)MS-AS has the highest correlation among the considered four IM-DI pairs. In other words, the proposed IM, which is defined as \(IM\)AS/\(IM\)MS, has a better capability to predict damage potential caused by earthquake sequences in comparison with \(IM\)MS, \(IM\)AS, and \(IM_{1}\)MS-AS.
2. A total of 22 classic IMs were considered as the candidates to define the intensities of MS-AS sequences. Amongst these IMs, \(I_{A}\), \(\
u_{\rm rs}\), and \(PGD\) are the most optimal ones to formulate \(IM_{2}\)MS-AS for the acceleration-related, velocity-related, and displacement-related IM groups, respectively, due to the high correlations with \(DI_{2}\)MS-AS. There is no single IM to best formulate \(IM_{2}\)MS-AS in the entire structural period range. \(I_{A,2}\)MS-AS and \(\
u_{\rm rs,2}\)MS-AS are the best IMs before and after a transition period \(T_{c}\), separately.
3. A comprehensive parametric study was conducted by considering various types of hysteretic models and different yield reduction factors. The results show that the effects of varying hysteretic models and yielding reduction factors are limited on the correlation between \(IM_{2}\)MS-AS and \(DI_{2}\)MS-AS. This result reveals that the obtained conclusion in this study is in a general prospect.
4. A step-wise equation consisting of linear and nonlinear parts was regressed as the median prediction of \(\rho\) between the best \(IM_{2}\)MS-AS and the caused \(DI_{2}\)MS-AS. Moreover, through regression analysis, another step-wise function was developed to denote the standard deviation of the \(\rho\) values along with \(T\). The empirical equations can fit the obtained data reasonably well, providing an opportunity for the further studies to conduct a quick prediction of damage potential for a specific MS-AS earthquake sequence.  
Conceptualization, Z.Z. and X.Y.; methodology, X.Y.; validation, Z.Z.; formal analysis, Z.Z. and X.Y.; data curation, Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, X.Y.; supervision, X.Y. and D.L.; funding acquisition, X.Y. and D.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Scientific Research Fund of the Institute of Engineering Mechanics, China Earthquake Administration (grant no. 2018D08), the National Natural Science Foundation of China (grant no. 51778198; 51678209). The authors declare no conflict of interest.  
Figure 17: Linear regression between optimum IMs and engineering demand parameters (EDPs).
## 3 Testing Results and Analysis
### CBR Test  
The CBR test was based on ASTM D1883 and D4429 and the indoor test was based on ASTM D1883. Each group consisted of specimens that were compacted 10 times, 25 times, and 56 times. The relationships between CBR and dry density were obtained. The CBR corresponding to the 95% \(\gamma_{\text{dmax}}\) of the compaction test was the true CBR. The test specimens were divided into 0%, 4%, and 8% added cement. Figure 20a presents the CBRs corresponding to a 95% maximum dry density. The CBRs of specimens with 0%, 4%, and 8% added cement were 15.6%, 93%, and 123%, respectively. The graph presents the relationship between dry density and CBR for three compaction patterns. When the number of compactions and cement content increased, the CBRs also increased. Thus, a concurrent increase in compaction energy and added cement led to a considerable increase in soil bearing capacity. Figure 20b indicates that when the number of compactions and amount of added cement increased, the swelling ratios decreased. In 1994, [PERSON] et al. [3] suggested that that swelling of mudstone in Taiwan is related to clay minerals; specifically, the montmorillonite content of mudstone in Renwu is low.
###### Abstract
Since [PERSON] early reports on highly discriminative boronate-based receptors for monosaccharides [19], new promising imprinting approaches to design glycan or glycoprotein specific probes have been reported [20; 21; 22; 23; 24; 25; 26; 27]. Indeed, the design of molecularly imprinted polymers (MIPs) and their use as artificial recognition elements have been shown to be successful for targeting glycans such as SA and glycosamino-glycans (GAGs) [15]. [PERSON] et al. have reported, in a series of publications, an orientation controlled imprinting procedure using boronate anchored glycans [25]. This has resulted in glycan specific nanoprobes showing high selectivity for cell surface glycosulations. Using an innovative multilayered core-shell architecture comprising quantum dot cores, nanoprobes were developed for selective targeting and imaging of hyaluron and sialylated glycosylation sites on human keratinocytes [22]. Based on a ternary complex imprinting procedure, we recently reported on fluorescently reporting SA imprinted nanoprobes for combined detection of both \(\alpha\)-2,3 and \(\alpha\)-2,6 SA on cancer cells. We demonstrated an affinity for various cell lines such as DU145, PC-3, Jurkat, and B-CLL and further verified the binding specificity by enzymatic cleavage of fluorescent SA-MIP nanoprobes for detection of SA on cancer cells [20; 21]. In the present study, we have performed screening of SA expression on a collection of breast cancer cell lines by using SA-MIPs and lectins, and analyzed SA-MIP targeting to the cells by flow cytometry and confocal fluorescence microscopy. The breast cancer cell lines were also analyzed using antibodies for the expression of the epithelial EpCAM and CD44, which is overexpressed in several cell types including cancer stem cells. We show that the SA-MIPs recognize breast cancer cells with low CD44 and high EpCAM expression, indicating that SA-MIPs can be used as an additional tool for detecting EpCAM positive breast cancer cells. The fluorescence microscopy images revealed the staining pattern of both lectins and SA-MIPs. Importantly, we show a novel use for SA-derivatives by pre-treating the SA-MIPs with these inhibitors before cell staining. Thereby, we could demonstrate a concentration-dependent reduction in binding of the SA-MIPs to breast cancer cells, indicating the specificity of the SA-MIP interaction.
## 2 Materials and Methods
### Ichoa
The updated distance computation now includes a fifth term, corresponding to the Balancer's influence, as shown in Equation (25). Similarly, the position updates from each elite agent are extended to include the contribution from the Balancer in Equation (26). Finally, the new position of the agent is determined by averaging the effects of all five elite chimps, resulting in the revised population update rule presented in Equation (27). This extension allows the algorithm to dynamically adapt its search behavior by blending directional guidance from both historical best agents and adaptive exploration driven by the Balancer's hybrid mechanism. \[\left\{ \begin{aligned} & d_{Attacker}=|c_{1}.X_{Attacker}-m_{1}.X|\\ & d_{Barrier}=|c_{2}.X_{Barrier}-m_{2}.X|\\ & d_{Chaser}=|c_{3}.X_{Chaser}-m_{3}.X|\\ & d_{Driver}=|c_{4}.X_{Driver}-m_{4}.X|\\ & d_{Balancer}=|c_{5}.X_{Balancer}-m_{5}.X|\\ \end{aligned} \right., \tag{25}\]\[X\left(t+1\right)=\frac{X_{1}+X_{2}+X_{3}+X_{4}+X_{5}}{5} \tag{27}\]  
Integrating the balancer into the CHOA framework introduces a dynamic self-regulation mechanism that enhances the optimizer's adaptability to complex, high-dimensional search landscapes. This capability is particularly crucial for tuning the hyperparameters of deep hybrid models such as the proposed Transformer-GAN-AE architecture, where the performance is highly sensitive to initialization choices, learning rates, latent dimensions, and architectural configurations. The Balancer enables the algorithm to maintain diversity during early stages of the search while reinforcing exploitation near promising solutions, without requiring predefined switching thresholds. As a result, the improved ChOA can more effectively navigate the intricate and multimodal hyperparameter space, offering more stable convergence behavior, superior model generalization, and higher-quality configurations for robust intrusion detection in Edge and IIoT environments.