esa-sceva/satcom-chunk-collection huggingface dataset:
# Postharvest Evaluations of Blackcurrant Fruits with Chitosan and Ultraviolet A Treatments
## 3 Results and Discussion
### Asorbic Acid Changes During Storage  
Ascorbic acid content in 'Tofino' blackcurrants ranged from 60 to 98 ug/100 mg on average during storage (Supplementary Table S2). The range of ascorbic acid was not significantly different among treatments during storage but was lower than previously published blackcurrant ascorbic acid content varying between 161.58 and 284.46 mg/100 g detected by the HPLC method [38]. It was also reported that the blackcurrant ascorbic acid content varied by genetic background, growing season, and location, and the range could be 50 to 400 mg/100 mL [39]. In this research, ascorbic acid content was quantified by the colorimetric method. This method's sensitivity to detect ascorbic acids might also contribute to the differences in ascorbic acid content from other methods. For postharvest storage, the ascorbic acid content depended on the light intensity and fruit maturity as well [40; 41]. Many factors might have contributed to the results of this study. In summary, a large variation of ascorbic acid content was observed during 'Tofino' postharvest storage, and postharvest treatments did not influence blackcurrant ascorbic acid contents.
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 5 Proof of Concept
### Scenario I--Manage Incoming Traffic Using Different Strategies  
This scenario investigated how the different strategies could play a role in managing incoming traffic. It used the topology adopted (see Figure 5), and explored the strategies for controlling inbound traffic explained in Section 4.  
Figure 6 presents the execution of Workloads 2 and 3 using the standard BGP behavior. Due to the BGP's destination-based forwarding paradigm, all 200 Mbps of the network traffic generated from the _50 Networks_ to the _Server_ is strangled into one of the \"best\" next hops. Hence, the selected next hop to the _Server_ is the path [200,400], and then the network traffic was forwarded to AS 200. Once the maximum available bandwidth to this path is 100 Mbps, the results in Figure 6 reflect that limitation. Hence, even if the workload is balanced or unbalanced, the maximum bandwidth reaching the _Server_ is 100 Mbps. For convenience, the unbalanced workload will be used as a reference for analyzing the results for the other control applications.  
The results of the reactive _Random_ load balancer control application (see Section 4.1) to Workloads 2 and 3 is depicted in Figure 7. Practically, both workloads were distributed equally to the valid and available paths [200,400] and [300,400]. Compared against the reference of BGP behavior (uses just one \"best\" path per prefix), the _Random_ optimized and used all of the 200 Mbps available bandwidth to reach the _Server_. This doubled the utilization of the inbound links of AS 400 for both workloads (balanced or unbalanced) when compared against the BGP reference.  
Figure 6: BGP behavior for network traffic to the _server_ 192.168.0.1/24 in AS 400.  
Considering the Round-Robin load balancer (RR), Figure 8 presents the results. The RR reached the full bandwidth utilization only for the workload that generated the balanced network traffic. The unbalanced traffic was not well distributed and did not explore the full potential of the available and valid paths of the topology. The unbalanced workload produced a more similar result to the BGP behavior (BGP-unbalanced workload) than when the balanced workload was used.  
The Round-Robin load balancer with the threshold (RRT) was also used. The threshold selected was 95% of the bandwidth for each available path. If this threshold was crossed, then the inbound control application used just the \"best\" next hop selected from the BGP. The results are shown in Figure 9. As expected, with the balanced workload, the RRT reached the full bandwidth utilization for the prefix 172.16.0.0/24 of the AS 400. These results corroborated with that found for the RR control application. However, for the unbalanced workload, the RRT was close to the 200 Mbps of bandwidth, and was a better result than RR.  
Figure 8: Round-Robin load balancer.  
Figure 7: Random load balancer results.  
Figure 9: Round-Robin load balancer with the threshold.
## 4 Results
### HMSAM Effects
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Construct**} & \multirow{2}{*}{**Condition**} & \multicolumn{2}{c}{**Pre-Test**} & \multicolumn{2}{c}{**Post-Test**} \\ \cline{3-6}  & & **Mean** & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline \multirow{3}{*}{Utility} & Copilot & 5.78 & 0.99 & 5.48 & 1.14 \\ \cline{2-6}  & Video & 5.72 & 0.94 & 5.92 & 0.87 \\ \cline{2-6}  & Total & 5.75 & 0.96 & 5.70 & 1.03 \\ \hline \multirow{3}{*}{Enjoyment (Joy)} & Copilot & 4.76 & 1.24 & 4.70 & 1.25 \\ \cline{2-6}  & Video & 5.19 & 1.25 & 5.26 & 1.22 \\ \cline{2-6}  & Total & 4.98 & 1.25 & 4.98 & 1.26 \\ \hline \multirow{3}{*}{Ease of Use} & Copilot & 5.47 & 0.96 & 5.58 & 1.19 \\ \cline{2-6}  & Video & 5.68 & 0.95 & 5.90 & 1.05 \\ \cline{2-6}  & Total & 5.58 & 0.96 & 5.74 & 1.12 \\ \hline \multirow{3}{*}{Behavioral Intention of Use (BIU)} & Copilot & 5.54 & 1.16 & 5.41 & 1.25 \\ \cline{2-6}  & Video & 5.64 & 1.00 & 5.93 & 1.03 \\ \cline{2-6}  & Total & 5.59 & 1.08 & 5.67 & 1.16 \\ \hline \multirow{3}{*}{Control (Ctrl)} & Copilot & 5.24 & 0.93 & 5.11 & 1.05 \\ \cline{2-6}  & Video & 5.46 & 0.89 & 5.43 & 1.16 \\ \cline{2-6}  & Total & 5.35 & 0.91 & 5.27 & 1.11 \\ \hline \multirow{3}{*}{Focalized Immersion (FI)} & Copilot & 5.13 & 1.02 & 5.12 & 1.04 \\ \cline{2-6}  & Video & 5.41 & 1.13 & 5.54 & 1.22 \\ \cline{2-6}  & Total & 5.28 & 1.08 & 5.33 & 1.15 \\ \hline \multirow{3}{*}{Temporal Dissociation (TD)} & Copilot & 4.41 & 1.27 & 4.60 & 1.39 \\ \cline{2-6}  & Video & 5.01 & 1.32 & 5.00 & 1.52 \\ \cline{2-6}  & Total & 4.72 & 1.32 & 4.80 & 1.46 \\ \hline \multirow{3}{*}{Curiosity (Cur)} & Copilot & 4.83 & 1.33 & 4.69 & 1.34 \\ \cline{2-6}  & Video & 5.17 & 1.33 & 5.35 & 1.26 \\ \cline{2-6}  & Total & 5.00 & 1.34 & 5.03 & 1.33 \\ \hline \end{tabular}
\end{table} 
Table 3: Descriptive statistics of HMSAM pre-test and post-test perceptions per condition. supported by medium effect sizes in key dimensions (e.g., \(\eta^{2}\) = 0.06 for curiosity, \(\eta^{2}\) = 0.08 for intention to use), suggesting that the findings are not solely due to random variation. Future studies should implement appropriate \(p\)-value correction techniques to ensure more conservative statistical inference and control for inflated Type I error risk when testing multiple constructs.
# Hydrochemical Characteristics and Suitability Assessment of Groundwater Quality for Irrigation
## 2 Materials and Methods  
### Study Area  
The Srem region is located in the Vojvodina province, northern Serbia, in the southern part of the Pannonian-Carpathian Basin, around 45\({}^{\circ}\) northern latitude and between 19\({}^{\circ}\) and 20\({}^{\circ}\) eastern longitude. It encompasses around 3486 square kilometers, with 66% of land use devoted to agriculture. Several different types of soil are present, but predominant soil type is chernozem. Vojvodina's geomorphological units, formed in the Pleistocene and Holocene epochs, include high areas, sandstones, loess plains, loess terraces, alluvial terraces, and alluvial plains. Agriculture in this region, focusing on cereals, fruits, and grapes, heavily relies on groundwater, constituting 49% of total water use. According to [16] in the Srem region, the total irrigated area is 3655 ha, of which plough land and gardens constitute 2484 ha, orchards 1152 ha, vineyards 11 ha, meadows and pastures 3 ha, and other perennial plantations 5 ha. The terrain is slightly inclined, and the representative wells on which the monitoring of water quality was carried out are located near the settlements of Nikinci, Lacarak, and Sid. The wells, at depths of 20-30 m, primarily consist of fluvial and alluvial sediments from the youngest Quaternary period [17]. Figure 1 shows the location of Serbia, the Srem region, and groundwater monitoring wells of shallow aquifers.
## 2 Description of the Study Site  
Figure 1a shows a map of the San Francisco Bay Area, and the small shaded boxes indicate the locations of toll plazas. The dates indicate when the changes in toll plaza staffing and the number of different types of lanes were made. The seven days following the staffing changes were excluded from the analysis to allow time for drivers to adjust to the new toll operation.  
The Antioch (ANT) Bridge is comprised of three lanes, two of which operated as dedicated FasTrak lanes, while the remaining lane was a cash lane prior to 1 May 2014. One of the existing FasTrak lanes was converted to a cash lane during carpool operating hours (5:00 a.m. to 10:00 a.m. and 3:00 p.m. to 7:00 p.m.), but operated as a dedicated FasTrak lane at other times of the day. Staffing at the ANT was increased by four hours on weekdays--the cash lane requires a toll booth officer to be stationed at a booth, while FasTrak lanes do not. The travel times before and after the staffing/lane changes remain unaffected.  
Benicia (BEN) Bridge comprises 17 lanes. Lanes 10-17 are \"open road tolling\" [14] lanes which use electronic collection to gather FasTrak information. Drivers can pass without the need to slow  
Figure 1: (a) Toll plazas in the San Francisco Bay Area. (b) and (c) Average travel times near bridges during weekdays before and after changes in toll plaza staffing.  
down, since there are no toll booths. Lanes 1-9 operate as cash lanes and FasTrak lanes at various times of the day. Cash lane operation hours at the BEN were reduced beginning 1 May 2014, resulting in significant delays (see Figure 1b) to the extent that staffing had to be readjusted two weeks later, as discussed in detail later in this paper.  
Staffing at the Richmond San Rafael Bridge (RSRB) was increased to better accommodate cash lane demand. When the capacity for the FasTrak lanes is greater than the FasTrak demand, and the demand for the cash lane exceeds cash lane capacity, converting FasTrak lanes to cash lanes can reduce overall delay. Marked increase in travel times near the toll plazas were observed on two different days (see Figure 1c) following the increase in staffing. Although the factors that caused the marked increase in travel time remain elusive, this observation shed light on developing an analytical method. The data from the RSRB and the BEN were used to estimate the parameters of the proposed proxy measure [15].  
No changes in staffing were made at the San Francisco Oakland Bay Bridge (SFOBB), and the changes in San Mateo (SM) and Dumbarton (DUM) bridges did not result in changes in travel times. Staffing at Carquinez (CAR) Bridge was reduced, but the travel time remained unaffected.
# A Novel Deep Learning Stack for APT Detection
## 4 Deep Learning Detection Stack
### Historical Appearance  
The purpose of the third layer is to detect if an outlier has appeared earlier in the network. Since APT can stay in inertia-state for a long period of time to avoid detection, historical appearance verification is important for the process. The third layer is kept up to date by re-training the neural network with the outliers that have passed through the detection process.  
The third layer detects using Recurrent Neural Network--Long Short-Term Memory (RNN-LSTM) units whether an outlier has appeared before. While RNN can learn temporal patterns and values that are estimated from current and past values, its memory function is short. RNN-LSTM adds a cell to RNN enabling longer memory that has the ability to learn these patterns from longer sequences of data, thus improving performance and allowing better time-series estimation [20].  
IP/TCP packet is constructed from 27 unique fields, thus each field is treated as a unique feature and all 27 features are passed through neural network, while comparing to earlier features. The neural network is trained with earlier outliers, which allows the neural network to compare 27 features simultaneously and predict earlier appearance.  
We can think features as signal fragments, that is previously found outliers create some type of continuous signals and these signals can be visualised afterwards. One approach to identify new outliers in signals is to detect change points in signals [13]. However, we cannot assume that a new outlier, in any of the 27 signals, creates a detectable change point, as signals do not necessarily behave regularly in our case.  
[PERSON] et al. stated that anomalous events do not have patterns and they occur frequently in a network intentionally or unintentionally [20]. From this perspective, we can consider known outliers as normal and new outliers as anomalies, while seeking those from outlier data flow. [PERSON] et al. tested RNN-LSTM for anomaly detection in IP bearer network by gathering data from 31,000 ports with higher than 1 Gb/s traffic. After training the system for one week, they managed to get good anomaly detection rates in a test environment of 824 ports. They considered three types of anomalies: (i) warning, one anomaly dot; (ii) issue, two continuous anomaly dots; and (iii) alarm, more continuous anomaly dots. However, they stated that fixed the one-week monitoring period caused many false positives, as traffic in port level is not so periodical. Another concern was computing usage while using RNN-LSTM in each port of test environment, as it uses a lot of computing power [16], thus we have to consider one or more sequential RNN-LSTMs, not parallel.
## 3 Model Inversion and Discussion
### The Influence of Smoothness on Inversion Results  
Two simple models were used to test the influence of smooth individual vectors on the inversion results. Figure 3a is the cross-section through the x-axis of a 2-D magnetic dyke model. The dyke was buried at a depth of 50 m and extended to 150 m. Figure 3b is the cross-section through the x-axis of a 2-D magnetic dyke model at a dip angle of 135\({}^{\circ}\).  
The norm \(p\) of the model objective function is 2, and only the positive constraint \((m\geq 0\,SI)\) is imposed on the model, without the upper limit of magnetic susceptibility. The magnetic anomaly distribution of the model is shown in Figure 4.  
Figure 5 is the magnetic anomaly curve when the magnetic susceptibility \(\chi\) was 0.01 \(SI\). In order to ensure the accuracy of the simulation, the size of the forward model element was \(5\times 5\) m. During inversion, too many mesh sections will aggravate the ill-posedness of inversion. Therefore, the horizontal grid spacing was set as 10 m, and the grid spacing in the depth direction was a sparse grid that increased proportionally, and Algorithm 2 was used for inversion.  
In order to compare the conventional adaptive differential evolution and the improved one, the smooth and non-smooth inversion results are shown in Figure 6. It is not difficult to observe that the proposed algorithm was capable of obtaining better results than the  
Figure 4: The tested models used for studying the effect of smooth vector strategy. (**a**) Model 1, (**b**) Model 2.  
Figure 3: The cross-section through the x-axis of the 2D models. (**a**) Model 1, (**b**) Model 2.  
original algorithm. Figure 6 shows that the continuity of the model was poor, the inversion results were divergent, and the background noise was strong without considering the smoothness. When \(p_{f}=2\), the recovery of the model was better than \(p_{f}=1\). It can also be found from the above two figures that the magnetic parameters recovered by the inversion were 0.01 \(S1\) lower than the theoretical values without adding upper bound constraint. The main reason for this phenomenon is that \(l_{2}\) norm inversion tends to obtain the smoothest model. For the abnormal body with a simple shape, the result can roughly reflect the dip information of the geologies. However, when the shape of the abnormal body tends to be complex, it can seldom depict the edge information of the model. In the following study, we set \(p_{f}=2\) to smooth the individuals in the population by default.  
Figure 5: Anormal magnetic field \(\Delta\)T of Model 1 and 2. (a) Model 1, (b) Model 2.  
Figure 6: The smooth and non-smooth inversion results of Model 1 and Model 2. (a) The smooth inversion of Model 1
## 2 Status of RIS and LC-RIS
### Advantages of LC-RIS vs. RIS with Other Methods  
First, from traditional mirror-based IRS to the emerging RIS, technological evolution represents a leap from uncontrollable to controllable (reconfigurable). Second, from p-i-n diode or MEMS-based switches to LC-enabled reconfigurable techniques, RIS is undergoing a revolution from digitally controlled steps (bringing undesired phase quantization effects) to continuously tuned beamforming (analogue control with ultra-high spatial resolution), i.e., the long-lasting challenges of discrete phase-shift levels posed by traditional switch-based technologies can be fully addressed by LC phase-shifting network-based antenna array beam steering. The achievable tuning resolution (spatial) is infinitely fine in theory. Table 1 presents a comparison of the potential performance of LC-based RIS and other mechanisms.  
Figure 2: Methods to construct RIS.  
Lower insertion loss (in the microwave and millimeter-wavelength ranges) [29] is another strength of LC against semiconductors and MEMS. In terms of ferrites and ferroelectrics, high power consumption and high insertion loss remain unaddressed; hence, they are not the optimal solutions for RIS.  
Concerning the entire costs, LC-filled units are relatively easy to manufacture and have been widely used in display technology for decades. As a result, they are readily available at a low cost, making them an attractive option for RIS applications. Contrarily, metamaterials require more complex and expensive manufacturing processes, and their properties are fixed once they are fabricated. This means that they are not as flexible or adaptable as LCs.  
Furthermore, LCs are compatible with a wider range of frequencies than many metamaterials. This makes them more versatile and allows them to be used in a broader range of ultra-wideband applications, e.g., by placing LC-RIS surfaces in strategic locations, such as buildings, vehicles, or outdoor environments, they can be used to reflect, refrac, or absorb electromagnetic waves to enhance signal strength and mitigate interference. This can help to improve the overall performance of wireless communication systems in terms of data rates, reliability, and latency.  
Another area where LC-RIS can be applied in 6G is in enabling intelligent signal processing. By controlling the phase of reflected waves, LC-RIS can be used to create complex electromagnetic wave patterns that can be used to encode information. This can be used to create new types of communication systems that are more efficient and reliable than existing systems.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 3 System Model and Problem Description
### System Model
#### 3.1.2 Mobile Device Model
This focus on appropriate placement enhances service responsiveness to task requests from mobile devices but also increases the complexity of identifying the optimal solution. The model proposed in this paper demonstrates versatility, making it suitable for a variety of mobile scenarios. Figure 2: Edge node placement system model for mobile devices in mining environment.
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 6 Conclusions and Outlook  
Various conducting polymers are of interest for biosensor applications because of their excellent electrical, chemical, and physical properties. Researchers have utilized these properties to actively optimize the performance of these biosensors by controlling the structure of the conducting polymer in nanoscale such as 0D, 1D, 2D, and 3D. In the case of the 0D structure, most researches were conducted to generate uniformly sized conductive polymer nanoparticles using template-based methods (microemulsion and hard template) and then applied to the biosensor electrodes by attaching inorganic components or bio-receptors to the surface. In the case of 1D structure, the tube or fiber structure-based textiles were manufactured using methods such as electrospinning, electrodeposition, and hard template and applied them as a flexible biosensor electrode by attaching various receptors. Using simple electrodeposition, spin coating, and screen printing methods, the 2D structure manufactured a flexible, large-area film and used it as a conductive polymer film-based sensor electrode. Finally, in order to expand the active surface area of the nanomaterials to the analyte, various manufacturing methods were modified to generate the 3D-conducting polymer nanostructure, and applied as a highly reactive biosensor electrode material. Insights gained from this field will promote the development of flexible, wearable sensors that could have far-reaching consequences in the healthcare industry.  
The manuscript was written through contributions of all authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by Korea government (NRF-2018R1C1B5041562).  
The authors declare no conflict of interest.
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 6 Experimental Results
### Scenario 2 (UIMTS to WiMAX)  
Figures 14-16 depict the outcome of handover_delay, delivery ratio, and bandwidth utilization for the service request 50, 100, 150, 200, and 250 in FVHDA and VHDA protocols. The performance of the two protocols is compared. From that comparison, we may infer that FVHDA outperforms VHDA by 6.14% in handover_delay, 3.24% in delivery ratio, and 3.32% in bandwidth utilization.  
Figure 14: Service Request vs. Handover_Delay.  
Figure 13: Service Request vs. Bw_Utilization.  
Figure 15: Service Request vs. Delivery Ratio.
## 2 Related Work
Moreover, the proposed solution makes use of the reinforcement learning algorithm and chooses optimal data forwarders and achieves network reliability. In [37], the authors proposed an ant-based QoS-aware routing protocol for heterogeneous WSNs, which aims to incorporate multimedia and scalar nodes. Furthermore, the routing decision of the proposed solution is based on control, scalar and multimedia traffic and achieves better outcomes in terms of delivery ratio and network latency. The authors in [38] proposed an energy-efficient and QoS-aware routing algorithm for industrial WSNs that aims to deliver the data in a timely and reliablemanner. First, the proposed algorithm offers a link reliability estimation method and increases the delivery performance in data routing. Second, this solution guarantees that the data of different types are transmitted using various routing strategies.