esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Optimization of Extraction Process of Total Phenolic Content from the Selected Plants  
Different solvents are used to extract phenolic compounds from the selected herbs and spices. The results are given in Table 1.  
Five different solvent systems were used: water, 80% acetone, 80% chloroform, 80% methanol, and 80% ethanol. The highest extraction of the total phenolic content of mint leaves in terms of 80% methanol was found to be 125.42 \(\pm\) 10.89 mg GAE/g. As opposed to fennel seeds, which had a minimum extraction of 9.90 \(\pm\) 0.27 mg GAE/g for total phenolic content. All the treatments and various solvent extracts showed significant differences in the total phenolic contents. The study's findings also revealed important interactions between the solvents and spices. The levels of TPC obtained from fennel seeds in both solvents--ethanolic and aqueous were in the range of 1020.3 \(\pm\) 11.40 to 1257.8 \(\pm\) 19.0 \(\upmu\)g GAE/g and 1114.0 \(\pm\) 6.11 to 1208.3 \(\pm\) 21.36 \(\upmu\)g GAE/g, respectively. Similar to this, the maximum TPC for peppermit was discovered in the aqueous extract of treatment P1 (2094.7 \(\pm\) 55.36 \(\upmu\)g GAE/g), whereas the least TPC was discovered in the ethanolic extract of treatment P1 (1824.2 \(\pm\) 30.40 \(\upmu\)g GAE/g) [16]. According to [PERSON] et al. [17], spices and  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Variables** & **Basil Leaves** & **Fennel Seeds** & **Coriander Seeds** & **Mint Leaves** & **Fenugreek Leaves** \\ \hline
80\% Methanol & 26.99 \(\pm\) 1.28 \({}^{\text{a}}\) & 9.90 \(\pm\) 0.27 \({}^{\text{a}}\) & 23.02 \(\pm\) 1.53 \({}^{\text{a}}\) & 125.42 \(\pm\) 10.89 \({}^{\text{a}}\) & 17.21 \(\pm\) 1.08 \({}^{\text{a}}\) \\
80\% Ethanol & 23.02 \(\pm\) 1.53 \({}^{\text{b}}\) & 9.61 \(\pm\) 0.76 \({}^{\text{a}}\) & 21.39 \(\pm\) 1.91 \({}^{\text{b}}\) & 116.84 \(\pm\) 8.47 \({}^{\text{b}}\) & 15.43 \(\pm\) 1.21 \({}^{\text{b}}\) \\
80\% Chloroform & 17.02 \(\pm\) 1.01 \({}^{\text{c}}\) & 6.33 \(\pm\) 0.22 \({}^{\text{b}}\) & 19.32 \(\pm\) 1.14 \({}^{\text{c}}\) & 88.98 \(\pm\) 7.32 \({}^{\text{c}}\) & 7.86 \(\pm\) 0.19 \({}^{\text{c}}\) \\
80\% Acetone & 25.02 \(\pm\) 2.61 \({}^{\text{a}}\) & 9.32 \(\pm\) 0.52 \({}^{\text{a}}\) & 22.15 \(\pm\) 1.28 \({}^{\text{ab}}\) & 123.47 \(\pm\) 11.04 \({}^{\text{a}}\) & 17.01 \(\pm\) 0.32 \({}^{\text{a}}\) \\ Water & 3.15 \(\pm\) 0.03 \({}^{\text{d}}\) & 1.01 \(\pm\) 0.01 \({}^{\text{c}}\) & 3.02 \(\pm\) 0.04 \({}^{\text{d}}\) & 15.53 \(\pm\) 1.37 \({}^{\text{d}}\) & 1.52 \(\pm\) 0.02 \({}^{\text{d}}\) \\ \hline \end{tabular} According to this table given values mean \(\pm\) standard deviation (SD) per dried weight where number of replicates per sample was 3 (n = 3). Values in each column having different superscript letters (\({}^{\text{a}-\text{d}}\)) are profoundly distinct from one another at \(p<0.05\).  
\end{table}
Table 1: TPC (mg GAE/g) of different extracts of the selected herbs and spices.  
herbs have significant phenolic contents. Fennel water extracts have a higher phenolic content than ethanolic extracts, according to [PERSON] et al. (2018).
## 7 Simulations and Results
### Scenario 3: HEMS with ESS and PV
#### 7.3.3 AWT Using RTP and CPP  
In present scenario, the AWT for the GA, GWO, and HGWGA was found to be 1.55 h, 1.44 h, and 1.61 h, respectively. It can be clearly observed that HGWGA had the maximum waiting time compared with other implemented optimization techniques, which means that the consumers were bound to sacrifice their comfort to achieve the cost-minimization objective. The AWT for the GA, GWO, and HGWGA is shown in Figure 21.  
Figure 19: Total cost with an ESS and PV using the CPP.  
Figure 20: PAR with an ESS and PV.  
The percentage reduction in the consumption cost and the PAR for both the RTP and CPP tariffs displays the effectiveness of the proposed hybrid technique (HGWGA) as it gave better results compared to the unscheduled and scheduled load techniques using the GA and GWO.
## 4 The Proposed Authentication Mechanism
### Key Session Phase  
As soon as the authentication phase is successfully passed, a mutual symmetric key \(K_{j}\) is generated to give the channel of communication its security. \(K_{j}\) can be computed as:  
\[K_{j}=h\ (\alpha_{j}\ \parallel\beta_{j}\ \parallel IDsb_{j})\]  
The key \(K_{j}\) is used in data privacy of the existing session. After the ending of this session, the value of the secret parameters \(\alpha_{j}\) and \(\beta_{j}\) are deleted from the system. The key session from the SB to the CC is shown in Figure 6.
# A Novel Resource Allocation Scheme in NOMA-Based Cellular Network with D2D Communications
## 4 Numerical Results and Discussions  
In this section, we present the performance of the proposed JSPLA through simulations. To evaluate the performances, a Monte Carlo based system-level simulator has been built. Each point of the simulation result is averaged over 1000 times. We first study the convergence performance of the proposed algorithm. Then we investigate the data rates of three search algorithms including the fractional power allocation algorithm (FRPA), the fixed power allocation algorithm (FIPA) and the TSA. Moreover, the performance comparison of the conventional ESA and OMA based D2D communications demonstrates the potential benefits of the proposed NOMA enhanced D2D scheme. The specific parameter value settings are summarized in Table 1.
# Advances in the Noninvasive Diagnosis of Dry Eye Disease
## 4 Aberrometer  
Aberrometry uses wavefront sensing to analyze deviations in the wavefront exiting the eye from a reference wavefront. This technique is useful for measuring the complete refractive status, including irregular astigmatism, or any other optical irregularity [20].  
[PERSON] et al. demonstrated that BUT was associated with increased higher-order aberrations (HOAs) both in photopic and scotopic conditions [21]. Sequential measurement of HOAs demonstrated that normal eyes can be classified into three sub-groups as having stable, small-fluctuation, and sawtooth patterns, respectively. In the latter group, significant changes were found in the sequential post-blink changes in the coma-like and spherical-like aberrations and total HOAs [22]. In eyes with DED or irregularities in their refractive surfaces, dynamic wavefront analysis post-blink can detect an increase of the HOAs, and this is correlated with the OSDI score and BUT [23].  
In patients with DED, superficial punctate keratopathy may aggravate both baseline HOAs and sequential post-blink changes in HOAs [24]. In eyes with a short BUT, a prolonged blink interval leads to increased HOAs, suggesting that suppressed blinking (e.g., for workers who operate video display terminals), can result in reduced optical quality [25].  
[PERSON] et al. demonstrated the impact of tear film-related aberration changes on activities of daily living in DED, specifically during a driving simulator test [26].  
An interventional study investigated the effects of topical drops (sodium hyaluron) on dynamic aberrometry in patients with DED and discovered no significant changes [27]. On the other hand, another study examining the effect of rebamipide 2% (a mucin secretagogue) demonstrated decreased HOAs after 4 weeks of treatment in DED patients with short BUT [28].
### Problems in Application  
Generally speaking, LiDAR-SLAM methods build occupy grid map, which is ready for path-planning and navigation control. However, closed-loop detection and correction are needed for larger map building, and that is not easy for a grid map. Because the scan data acquired are two-dimensional point cloud data, which have no obvious features and are very similar to each other, the closed-loop detection based on scan data directly is often ineffective. And this flaw also extends to fast relocation function when robot running with a given map. In the navigation package provided by the Robot Operating System (ROS), the robot needs a manually given initial pose before automotive navigation and moving. On the other hand, Most Visual-SLAM approaches generate feature maps, which are good at localization, but not good for path-planning. RGB-D or 3D-LiDAR are capable of building a complete 3D scene map, but it is limited in use because of high calculating or founding cost.  
For consumer robots, the cost of sensors and processing hardware is sensitive. Low-cost LiDAR sensors become popular. However, to realize a robust low-cost navigation system is not easy work. Because low-cost LiDAR sensors have much poorer performance in frequency, resolution and precision than normal ones. In one of our previous work [28], we have introduced methods to improve the performance of scan-matching for such low-cost LiDAR, however, that only works for adjacent poses. The accumulating errors may usually grow too fast and cause failure to larger map building. To find an effective and robust SLAM and relocation solution with low computation cost and the low founding cost is still a challenging work for commercially-used service robots.
## 3 Machine Learning Modeling  
### Fundamental Theory of PCA, LSTM, and GA  
#### 3.1.1 Pca  
Principal component analysis (PCA), as a kind of unsupervised feature reduction method [47], normalizes the original data first, and then extracts important components with non-repetitive correlations from the processed data, to effectively decrease the influences of interfering data, and thus to achieve the purpose of raising the accuracy of recognition (regression or clustering) and lessening the feature dimensions of data [48; 49]. The specific procedure is introduced bellow:  
1. The original data are normalized for the purpose of eliminating the impact of dimensions on the calculation results. On this basis, the original matrix \(X^{*}\) is established.
2. The covariance matrix \(P\) can be determined based on Equation (1), and then can eigenvalues \(\lambda_{i}\) and eigenvectors \(\alpha_{i}\) be obtained. \[P=\frac{1}{S-1}(X^{*})^{\mathsf{T}}X^{*}\] (1) where \(S\) is the number of samples.
3. The number of principal components can be calculated by Equation (2): \[a=\min\{k|\sum_{i=1}^{k}\lambda_{i}/\sum_{i=1}^{n}\lambda_{i}\geq\delta\}\] (2)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Mean Value** & **Standard Deviation** & **Min Value** & **Max Value** \\ \hline \(\tau\) (min) & 97 & & 5 & 205 \\ \hline Q\({}_{w}\) (mL/min) & 125 & & 50 & 200 \\ \hline \(T_{w}\) (\({}^{\circ}\)C) & 60 & & 40 & 80 \\ \hline \(T_{a}\) (\({}^{\circ}\)C) & 37.8 & 1.4 & 34.3 & 42.2 \\ \hline \(T_{f_{m}}\) (\({}^{\circ}\)C) & 23.0 & 0.8 & 21.0 & 24.2 \\ \hline \(T_{b}\) (\({}  
where \(\delta\) is the threshold value of the cumulative contribution rate, normally set at 85%; and \(\sum\limits_{i=1}^{k}\lambda_{i}/\sum\limits_{i=1}^{n}\lambda_{i}\) is the cumulative contribution rate of principal components of the first \(k\) eigenvalues (\(k<n\)).
4. The original matrix can be dimension reduced by combining the covariance matrix \(P\) and the number of principal components \(a\).
# Electrical Monitoring under Transient Conditions:
## 4 Results
### Results in Field Motors
#### 4.2.3. Wound Rotor Induction Motor in a Cement Plant (1.5 MW, 6.6 kV)  
Figure 10 shows the time-frequency analyses (two graphs with different frequency ranges are shown in the y-axis for a better fault harmonic identification) corresponding to a H.V. wound rotor induction motor that was driving a ball mill in a cement production factory [42]. The motor was suspected to have a certain level of rotor winding asymmetry. Therefore, the starting current analysis was carried out in order to confirm this diagnostic. The maps shown in Figure 10 reveal a significant level of rotor winding asymmetry: the evolution of the lower sideband given by \(f\)-\((1-2\cdot s)\) is clearly evident. Moreover, other higher-order harmonics such as the upper sideband (\(f\)-\((1+2\cdot s)\)) are also present. All these evidences led to diagnosing a severe level of rotor winding asymmetry. As a consequence, the rotor winding was inspected, and a deficient contact was found between the slip rings/brushes system of one phase [42], which was causing the asymmetry. This problem, which could have had very negative repercussions for the company, was properly detected and corrected thanks to the application of the starting current analysis.  
Figure 9: Time-frequency maps of the starting current for a Low Voltage (L.V.) cage motor of the sewage treatment plant.
# Potential of a SAR Small-Satellite Constellation for Rapid Monitoring of Flood Extent
## 2 Methods
### Virtual Experiment of Flood Monitoring
#### 2.3.3 Case Studies of Flood Events in Japan  
One flood event included in the case study was heavy rainfall in the Kyushu region in July 2012. The torrential rainfall associated with the rain front caused floods and landslides in the northern Kyushu region, mainly in Kumamoto, Oita, Fukuoka, and Saga prefectures from 11-14 July 2012. There were 5 points where the total amount of rainfall during 4 days was more than 500 mm, such as 816.5 mm at the Aso city of Kumamoto, 616.5 mm at the Hita city of Ohita, 649.0 mm at the Yame city of Fukuoka. The rainfall event caused 32 fatalities and damaged 13,263 houses (769 collapsed and 12,494 were inundated). In this case study, the Chikugo River basin (33\({}^{\circ}\) N-34\({}^{\circ}\) N/130\({}^{\circ}\) E-131\({}^{\circ}\) E) was selected for analysis.  
The other flood event included was Typhoon No. 19 in October 2019, which caused heavy rain, stormy winds, high waves, and storm surges across an extensive area from 10-13 October 2019. There were 17 points where the total amount of rainfall was more than 500 mm, such as 1001.5 mm at the Hakone town of Kanagawa, 760.0 mm at the Izu city of Shizuoka, 687.0 mm at the Chichibu city of Saitama. River flooding and landslides resulted in 99 fatalities, and 4008 houses completely or partially collapsed while 70,341 houses were inundated. In this case study, we focused on the Chikuma River basin (36\({}^{\circ}\) N-37\({}^{\circ}\) N/138\({}^{\circ}\) E-139\({}^{\circ}\) E), where 5086 houses were inundated.
# RD-SLAM: Real-Time Dense SLAM Using Gaussian Splatting
## 4 Results
### Quantitative Evaluation  
4.2.1. Tracking Performance  
Table 1 demonstrates the comparison of tracking accuracy between RD-SLAM and other SLAM systems on the TUM-RGBD dataset. Our approach achieves SOTA performance in both NeRF-based SLAM and 3 DGS-based SLAM systems, with an average ATE RSME of 2.11 cm at real-time operating speeds, which is on average 0.93 cm higher than the second best approach, Point-SLAM. However, due to many voids in the depth image and the extreme blurring of the RGB image, the well-designed feature-based conventional SLAM method still outperforms the neural SLAM method.  
In Table 3, we compare the tracking accuracy of RD-SLAM with other SOTA methods on the Replica dataset. Our approach achieves the best performance in all eight scenes, with a 59% reduction in trajectory error compared to SplATAM, which is also a 3 DGS-based SLAM system. Compared with other methods, our system exhibits better tracking performance on real-world and virtual synthetic datasets. This is because we actively use the 3D point cloud information for accurate pose estimation directly through G-ICP.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Methods** & **fr1/desk** & **fr2/xyz** & **fr3/Office** & **Avg.** \\ \hline Kintinuous [37] & 3.70 & 2.90 & 3.00 & 3.20 \\ ElasticFusion [38] & 2.53 & 1.17 & 2.52 & 1.50 \\ ORB-SLAM [5] & **1.60** & **0.40** & **1.00** & **1.00** \\ \hline NICE-SLAM [29] & 4.26 & 31.73 & 3.87 & 15.87 \\ Point-SLAM [32] & 4.34 & 1.31 & 3.48 & 3.04 \\ GS-SLAM [20] & 3.30 & **1.30** & 6.60 & 3.73 \\ SplaTAM [19] & 3.34 & 1.34 & 5.18 & 3.29 \\
**Ours** & **2.29** & 1.80 & **2.24** & **2.11** \\ \hline \end{tabular}
\end{table}
Table 1: Camera tracking results on TUM-RGBD dataset (ATE RMSE \(\downarrow\) [cm]).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Methods** & **0000** & **0059** & **0106** & **0169** & **0181** & **0207** & **Avg.** \\ \hline NICE-SLAM [29] & 12.00 & 14.00 & **7.90** & **10.90** & 13.40 & **6.20** & **10.70** \\ Point-SLAM [32] & **10.24** & **7.81** & 8.65 & 22.16 & 14.77 & 9.54 & 12.19 \\ SplaTAM [19] & 11.53 & 9.99 & 17.86 & 12.49 & **9.61** & 7.81 & 11.55 \\
**Ours** & 10.73 & 12.56 & 9.34 & 11.62 & 10.67 & 11.69 & 11.10 \\ \hline \end{tabular}
\end{table}
Table 2: Camera tracking results on ScanNet dataset (ATE RMSE \(\downarrow\) [cm]).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Methods** & **Room0** & **Room1** & **Room2** & **Office0** & **Office1** & **Office2** & **Office3** & **Office4** & **Avg.** \\ \hline NICE-SLAM [29] & 0.97 & 1.31 & 1.07 & 0.88 & 1.00 & 1.06 & 1.10 & 1.13 & 1.06 \\ Point-SLAM [32] & 0.61 & 0.41 & 0.37 & 0.38 & 0.48 & 0.54 & 0.69 & 0.72 & 0.52 \\ GS-SLAM [20] & 0.48 & 0.53 & 0.33 & 0.52 & 0.41 & 0.59 & 0.46 & 0.70 & 0.50 \\ SplaTAM [19] & 0.27 & 0.41 & 0.32 & 0.55 & 0.23 & 0.36 & 0.32 & 0.53 & 0.37 \\
**Ours** & **0.15** & **0.16** & **0.10** & **0.18** & **0.12** & **0.16** & **0.16** & **0.20** & **0.15** \\ \hline \end{tabular}
\end{table}
Table 3: Camera tracking results on Replica dataset (ATE RMSE \(\downarrow\) [cm]).
# Varroa Control by Means of a Hyperthermic Device
## 2 Materials and Methods
### Apiary Trials  
All experiments were carried out in 2018, 2020 and 2021 at the DISAFA (University of Turin) apiary located in Grugliasco (Turin District) (45\({}^{\circ}\)03\({}^{\circ}\)59\({}^{\prime\prime}\) N 7\({}^{\circ}\)35\({}^{\prime\prime}\)33\({}^{\prime\prime}\) E). Dadant-Blatt hives with ten frames, eight Bee Ethic frames and two standard frames placed on the sides of the hive were used to carry out the heat treatments; honey supers were added when necessary to store surplus honey. The results obtained were compared with those obtained in identical Dadant-Blatt hives, equipped with standard combs, used as controls; these bee-hives were subjected to standard varroa control practices using trickled oxalic acid and amitraz strips.  
All hives were standardized during spring in terms of colony strength and level of varroa infestation by intermixing brood frames and workers between hives so that they were as comparable as possible at the start of hyperthermia treatments. The first treatments were applied on 23 and 18 May in 2018 and 2021, respectively; in 2020 it was delayed to 25 June due to lockdown measures introduced in Italy to counteract the spread of COVID-19. Heat treatments were repeated every three weeks until mid-October.  
Varroa infestation trends were assessed in 21 hives equipped with the Bee Ethic device (6 hives in 2018, 10 in 2020 and 5 in 2021) and the same number of control hives.  
For all hives, both those heat-treated with the Bee Ethic system and the untreated control hives, colony strength was assessed periodically (at one- or two-week intervals in 2018, every three weeks in 2020, and weekly in 2021) using a modified Liebefeld method to evaluate worker and brood cell numbers [15]. Moreover, the varroa mites that fell on the bottom boards were counted weekly all three years [16; 17].  
To assess the effectiveness of hyperthermia on the reproductive phase of varroa, two brood combs from each of the heat-treated colonies were brought to the laboratory 48 h after every treatment. Here, 300 cells per hive (150 per comb) were uncapped along the diagonals of both sides of the two combs, using the sampling method developed by [PERSON] [18] (Figure 2). The number of live and dead individuals of _V. destructor_ present in the cells was counted, recording the number of adult females, males and juveniles separately.
## 4 Results and Discussion
### Rolling Bearings Test Results  
#### 4.2.1 Power Loss Tests  
Figure 9 shows the value of the rolling bearing friction torque (Mt), which was the average of five replicates for each of the biogenic greases selected. For the rolling bearing tests, model grease samples 1 and 2 were selected since they produced lower friction coefficient values and the smallest wear marks from the ball on the disc tribometer.  
As expected, the friction torque at very low speeds was high and decreased rapidly as the speed increased and the lubricant film built up. In this low-speed region, the sliding friction torque was dominant due to more frequent interactions of the surface asperities [42; 43]. The friction torque then reached a minimum value around 400 rpm for both greases; above that speed, the friction torque started to increase again, albeit only slightly. This increase was due to greater drag losses and increased rolling friction torque [37; 38; 39; 40; 42].  
Figure 8: Graph of the basic energy equation of wear for all the model greases.  
Moreover, grease 1 showed higher friction torque than grease 2, both at low and high speeds. From our understanding, the differences found at low speeds should be related to the thickener type and oil-bleeding rate, while at high speeds, the differences should mainly be related to the higher base-oil viscosity of grease 1. This approach was supported by data regarding base-oil viscosity and oil-bleeding tests. The base-oil viscosity of each grease was determined at 40 \({}^{\circ}\)C and 100 \({}^{\circ}\)C according to the ASTM (American Society for Testing and Materials) D341 test (Standard Practice for Viscosity-Temperature Charts for Liquid Petroleum Products). The results showed that although model grease samples 1 and 2 contained the same type of base oil, i.e., a combination of castor oil and HOSO, grease sample 1 had a higher value of base-oil viscosity at 40 \({}^{\circ}\)C and 100 \({}^{\circ}\)C than model grease 2. In addition to this analysis, the bleed-oil of each grease was extracted using a non-standard dynamic bleeding process, similar to the ASTM D4425 standard method. The bleed-oil obtained through this non-standard dynamic method showed the same viscosity as the bleed-oil obtained using the static ASTM D6184 method. This dynamic method was used to calculate the oil-bleeding rate of each grease sample. The results showed that the amount was very similar between the two greases, although slightly higher for model grease 2 due to having a lower base-oil viscosity. Finally, grease 2 showed a better wear performance from a power loss perspective.
# Coexistence of 5G/6G and Digital Terrestrial Television Networks
## 1 Introduction
### Possible Solutions for IMT/DTT Coexistence  
Now it is necessary to obtain the results of technical analyses that allow the assessment of various approaches to this issue, particularly ensuring the continued functioning of DVB-T/T2 terrestrial television, which is widely watched and received in many countries. This paper attempts to resolve the conflict of access to the UHF spectrum 470-694 MHz on a \"win-win\" basis: to address the growing spectrum demand from 5G/6G systems and the desire for IMT access to the 470-694 MHz band, while also meeting the needs of maintaining the broadcast of DVB-T/T2 digital terrestrial television in the 470-694 MHz band. The paper is based on detailed analyses, using Poland as a case study.  
As a result of these detailed analyses, two solutions were proposed to potentially increase the availability and efficiency of radio spectrum use in the bands below 1 GHz for IMT systems, while ensuring appropriate DVB-T/T2 emissions:  
1. Dividing the 470-694 MHz frequency range into two parts: 602-694 MHz for IMT and 470-602 MHz for DVB-T/T2.
2. Sharing the entire 470-694 MHz spectrum range using an interleaving spectrum concept, with IMT as a secondary service and DVB-T/T2 as the primary service, based on compatible cooperation without modifying existing TV network plans.  
The rest of the paper is organized as follows: network planning basis and current spectrum usage, requirements and DTT network planning conditions, allotment allocation modification results after spectrum splitting (method 1), detailed results of possible spectrum sharing and interleaving (method 2), and conclusions and practical applications.