esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Nutritional Characteristics
### Minerals
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Composition** & **Head** & **Leaves** & **Bracts** & **Stem** \\ \hline Mn & (mg/100 g DW) & 0.97 \({}^{1}\) & 0.8 \({}^{2}\) & 1.46 \({}^{1}\) & 0.46 \({}^{3}\) \\ Cu & (mg/100 g DW) & 0.75 \({}^{1}\) & 3.6 \({}^{5}\) & 0.77 \({}^{1}\) & 0.53 \({}^{3}\) \\ \hline Dietary Fiber & & & & & \\ \hline Total dietary fiber & (g/100 g DW) & 58.8 \({}^{6}\) & 31.47 \({}^{7}\) & 60.35 \({}^{8}\) & 85.28 \({}^{9}\) \\ Insoluble dietary fiber & (g/100 g DW) & 44.5 \({}^{6}\) & - & 38.51 \({}^{8}\) & 67.95 \({}^{9}\) \\ Soluble dietary fiber & (g/100 g DW) & 14.3 \({}^{6}\) & - & 21.84 \({}^{8}\) & 17.33 \({}^{9}\) \\ Inulin & (g/100 g DW) & 13.9 \({}^{10}\) & 7.5 \({}^{11}\) & 20.2 \({}^{10}\) & 25 \({}^{10}\) \\ Cellulose & (g/100 g DW) & 18 \({}^{12}\) & 15.76 \({}^{12}\) & 24.15 \({}^{12}\) & 14.87 \({}^{4}\) \\ Hemicellulose & (g/100 g DW) & 8.27 \({}^{12}\) & 7.89 \({}^{12}\) & 10.86 \({}^{12}\) & 12.05 \({}^{4}\) \\ Lignin & (g/100 g DW) & 14.06 \({}^{12}\) & 10.78 \({}^{12}\) & 15.62 \({}^{12}\) & 6.43 \({}^{4}\) \\ \hline Vitamins & & & & & \\ \hline Vitamin A & (mg/100 g FW) & 0.024 \({}^{13}\) & 0.014 \({}^{13}\) & 0.016 \({}^{13}\) & - \\ Vitamin B\({}_{1}\) & (mg/100 g FW) & 0.071 \({}^{3}\) & 0.04 \({}^{13}\) & 0.05 \({}^{13}\) & - \\ Vitamin B\({}_{2}\) & (mg/100 g FW) & 0.058 \({}^{13}\) & 0.033 \({}^{13}\) & 0.04 \({}^{13}\) & - \\ Vitamin B\({}_{6}\) & (mg/100 g FW) & 0.11 \({}^{13}\) & 0.1 \({}^{13}\) & 0.1 \({}^{13}\) & - \\ Vitamin B\({}_{9}\) & (mg/100 g FW) & 68 \({}^{13}\) & 55 \({}^{13}\) & 59 \({}^{13}\) & - \\ Vitamin C & (mg/100 g FW) & 10 \({}^{14}\) & 5 \({}^{13}\) & 5.4 \({}^{13}\) & - \\ Vitamin E & (mg/100 g FW) & 0.25 \({}^{13}\) & 0.2 \({}^{13}\) & 0.2 \({}^{13}\) & - \\ Vitamin K & (\(\mu\)g/100 g FW) & 0.015 \({}^{13}\) & 0.012 \({}^{13}\) & 0.012 \({}^{13}\) & - \\ \hline \multicolumn{5}{l}{Phenolic compounds} \\ \multicolumn{5}{l}{Hydrooxycinnamic acids} \\ \multicolumn{5}{l}{1-O-Caffeoylquinic acid} \\ \multicolumn{5}{l}{3-O-Caffeoylquinic acid} \\ \multicolumn{5}{l}{5-O-Caffeoylquinic acid} \\ \multicolumn{5}{l}{5-O-Caffeoylquinic acid} \\ \multicolumn{5}{l}{4-O-Caffeoyl
## 5 Experimental Results and Analyses
### Testing and Comparing the Denoising Performance of Different Algorithms
\begin{table}
\begin{tabular}{c c c c c} \hline \hline _Level_ & **Image** & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**The Output Seismic Signal**} \\ \cline{4-5}  & & & **PSNR (dB)** & **SSIM** & **Time (s)** \\ \hline \multirow{8}{*}{Lena} & ATV & 23.44 & 0.7353 & 6 \\  & ITV & 23.56 & 0.7457 & **4.14** \\  & TGV & 25.05 & 0.7631 & 13.44 \\  & OGS-L1 & 25.08 & 0.7612 & 15.86 \\  & OGS-Lp & 25.74 & 0.8264 & 19.06 \\  & OGS-Lp-FAST & 25.72 & 0.8262 & 11.67 \\  & ATV & 26.48 & 0.8046 & 3.61 \\  & ITV & 27.09 & 0.8139 & **2.88** \\  & TGV & 27.88 & 0.8264 & 15.14 \\  & OGS-L1 & 27.85 & 0.8245 & 10.05 \\  & OGS-Lp & **31.24** & **0.8932** & 16.41 \\  & OGS-Lp-FAST & 31.15 & 0.8923 & 14.61 \\  & ATV & 30.99 & 0.9137 & 4.91 \\  & ITV & 31.07 & 0.9168 & 3.59 \\  & TGV & 31.93 & 0.8954 & 24.31 \\  & OGS-L1 & 30.99 & 0.9137 & 16.5 \\  & OGS-Lp & 31.94 & 0.9161 & 19.45 \\
50\% & OGS-Lp-FAST & **32.05** & **0.9277** & 17.72 \\  & ATV & 25.1 & 0.7761 & 5.58 \\  & ITV & 25.73 & 0.7977 & **3.3** \\  & TGV & 26.75 & 0.814 & 15.67 \\  & Girl & OGS-L1 & 26.45 & 0.811 & 14.16 \\  & OGS-Lp & 27.32 & 0.846 & 19.28 \\  & OGS-Lp-FAST & **27.35** & **0.8463** & 15.91 \\  & ATV & 26.19 & 0.8082 & 4.92 \\  & ITV & 26.51 & 0.8188 & **3.42** \\  & TGV & 27.01 & 0.8349 & 19.05 \\  & OGS-L1 & 27.61 & 0.8351 & 14.67 \\  & OGS-Lp & **28.65** & 0.868 & 17.8 \\  & OGS-Lp-FAST & 28.59 & **0.8682** & 14.11 \\  & ATV & 25.84 & 0.8049 & 5.11 \\  & ITV & 25.24 & 0.7939 & **3.67** \\  & TGV & 25.99 & 0.685 & 10.84 \\  & OGS-L1 & 26.29 & 0.8143 & 13.95 \\  & OGS-Lp & 26.56 & 0.8165 & 18.19 \\  & OGS-Lp-FAST & **26.61** & **0.8169** & 14.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Numerical comparison of our proposed method and other models (images are corrupted by impulse noise of 50%).
## 6 Sixth-Generation (6G) Testbeds and Platforms
### Experimental Platforms for Sixth-Generation (6G) Communication Channels
#### 6.1.1 Widespread Simulator for 6G Communication Channels  
Aiming to simulate real channel environments, this simulator considers the characteristics of channels covering all frequencies and situations in 6G. The pervasive 6G wireless channel modeling theory, proposed by [PERSON] et al. [235], adopts a unified channel impulse response (CIR) expression. The 6G pervasive channel model (6 GPCM) based on the Geometry-Based Stochastic Model (GBSM) framework serves as a benchmark for standardized 6G channel models, accommodating various spectra and scenarios.  
Guided by the 6G pervasive channel modeling theory, the 6 GPCM is designed to be suitable for mmWave, sub-6 GHz, IR, THz, and VLC spectra. It covers global-coverage scenarios like UAVs, maritime communication, low-earth-orbit (LEO) satellites, and full-application scenarios such as reconfigurable intelligent surface (RIS) channels and ultra-massive MIMO [235].
# Data Privacy and Ethical Considerations in Database Management
## 2 Literature Review
Recent developments in data governance and protection regulations, such as the EU's Data Act [21], Data Governance Act [22], Digital Services Act (DSA) [23], and the proposed EU AI Act [24], are also worth considering for future research. These regulations introduce new requirements and considerations for data management and use, particularly in the context of AI and digital services. Addressing the ethical implications of data management, a study by [25] explores the ethical challenges posed by big data. The authors discuss issues such as privacy, anonymity, transparency, and trust in the context of large-scale data collection and analysis. This work provides a broader perspective on the ethical considerations that should inform database management practices. In response to the growing concern over the right to be forgotten, ref. [26] discusses the technical and legal challenges of implementing this right in database systems. The paper explores the tensions between data retention, the right to privacy, and the practical implications for database design and management. Regarding the specific requirements derived from GDPR and HIPAA, ref. [27] provides a comprehensive analysis of the GDPR's impact on data management practices. Similarly, ref. [28] offers insights into the implications of HIPAA for healthcare data management. These works help in understanding the specific requirements that shape ethical and privacy considerations in database management. The work performed on these papers may differ from our paper but will serve as a guide to conducting our study.
# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 4 Results and Discussion
### Energy Consumption  
It indicates the energy consumption of network nodes. Figure 7 shows the node energy consumption relative to the network nodes. The energy consumption of RPL, E2 HRC-RPL, and CT-RPL is observed to be 0.95 mW, 0.75 mW, and 0.65 mW, respectively, for the network size of 100. It is noted that the amount of energy dissipation increases as the number node increases. In CT-RPL, the network nodes consume less energy as compared to RPL and E2 HRC-RPL. It is due to the efficient cluster head selection using a game theoretic approach. Additionally, the CT-RPL considers the metrics, namely _QU_, _ETX_, _RER_, and _CHNC_, to choose the optimum _CH_ nodes in the parent _CH_ list. It balances the network traffic among the _CH_ nodes. Thus, it prolongs the network lifetime, and it solves the hotspot problem to a certain extent.  
Figure 6: End-to-End delay vs. the number of hops for a hop count of 6; the delays of RPL, E2 HRC-RPL, and CT-RPL are 2700 ms, 2400 ms, and 1500 ms, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of Hops**} & \multicolumn{3}{c}{**End-to-End Delay (ms)**} \\ \cline{2-4}  & **RPL** & **E2 HRC-RPL** & **CT-RPL** \\ \hline
1 & 400 & 300 & 250 \\
2 & 500 & 450 & 350 \\
3 & 600 & 500 & 400 \\
4 & 1000 & 600 & 500 \\
5 & 1700 & 1300 & 850 \\
6 & 2700 & 2400 & 1500 \\ \hline \hline \end{tabular}
\end{table}
Table 5: End-to-End delay with respect to the number of hops.  
Table 6 indicates the node energy consumption among the network nodes. The energy consumption in CT-RPL is less as compared to other popular routing protocols, namely RPL and E2 HRC-RPL. It explicitly shows that cluster-based routing consumes less power than the multi-hop routing protocol.
## 2 Background
### Encoder  
The main issue when using large BCH codes, i.e., \(t\) greater than 30, is the fan-out issue created by implementing the Linear Feedback Shift Register (LFSR) method of the generator polynomial. [PERSON] has addressed this fan-out issue by breaking down the LFSR register into multiple cascaded LFSRs by realizing the circuit in the Z-domain [11]. [PERSON] addressed the same issue by using the Chinese Remainder Theorem (CRT) method [12], but this method requires more computation on the encoder and is applicable for encoders that have \(t\) higher than 100. Later, [PERSON] et al. proposed a hybrid approach for long BCH encoders that is area efficient [13]. The authors of this paper had proposed an area efficient method by sharing the hardware between the encoder and syndrome generator [14].
# Overcoming Individual Discrepancies, a Learning Model for Non-Invasive Blood Glucose Measurement
## 6 Experiment and Results
### Experiment Results
#### 6.2.2 Convergence Experiment  
We are particularly interested in finding how the size of training dataset affects the algorithm performance, because the size is related to how soon a good model can be built for a patient once he/she starts using our Earlier device. For this purpose, we construct a series of training datasets of different sizes, and use these training datasets to train the algorithm while recording the correlation coefficient in testing dataset. The relationship between the correlation coefficient and the number of samples in the training dataset is shown in Figure 6.  
From Figure 6, we find that the correlation coefficient reaches 0.5 rapidly where the number of samples is merely 10, and finally tend to a steady value of 0.80 where the number of samples is close to 60. This experiment result proves the convergence of the algorithm. In other words, the NGM  
Figure 5: Clarke Error Grid Analysis for our proposed NGM system (**(a)** is RF without ICA, and **(b)** is RF with ICA), in which the x-axis is the reference Blood Glucose Concentration (BGC) and the y-axis is the estimated BGC. In **(b)**, 64.7% of points fall in region A, while in **(a)** there is only 47.1%.  
Figure 6: Convergence Experiment results, in which the correlation coefficient varies by the number of samples in the training set.  
system in this paper can partially complete the parameters adjustment after 10 times of training by a standard blood glucose meter, and reaches to a high accuracy estimation state after 60 times of training. Similarly, as a comparison, we also did a convergence experiment on RF without ICA, and the experimental results are shown with dotted line in the Figure 6. As can be seen, without the help of ICA, the convergence speed of RF is significantly reduced.
# Water-Soluble Sugars of Pedigreed Sorghum Mutant Stalks and Their Recovery after Pretreatment
## 3 Results and Discussion
### Glucose Yield from Enzymatic Hydrolysis of Dilute Acid-Treated Solid Biomass  
The reaction temperature in dilute acid pretreatment ranged from 120 to 160 \({}^{\circ}\)C, while sulfuric acid levels were in the range of 0.5 to 2% (_w/w_) [12; 35]. The yield of reducing sugars through enzymatic hydrolysis was mainly influenced by acid concentration, reaction temperature, and reaction time. Acid hydrolysis releases oligosaccharides and monosaccharides but also results in the formation of degradation products such as aldehydes. In order to reduce the decomposition of the sugars, a hydrolysis time of 60 min was used in this study. Dilute acid-pretreated sorghum stalks demonstrated good enzymatic digestibility with more than 82.4% glucose yield for all the treated samples (Figure 2), which showed that the dilute acid pretreatment condition of reaction temperature 120 \({}^{\circ}\)C for 60 min with 2% (_w/w_) sulfuric acid was sufficient to disrupt the rigid cell-wall structure of sorghum biomass and significantly improve the enzymatic hydrolysis of treated sorghum biomass. Half of the selected sorghum mutants achieved greater than 90% glucose yields with a maximum glucose yield of 98.2% for sample E40B.
## 3 Validation

Table 1: Euclidian distance from the derived vine location to the reference measurement. Figure 12: Locations of the 23 vine height reference measurements. Linear regression analysis comparing manually measured vine heights to heights automatically derived from a nDSM was performed on a dataset comprising 23 data points (Figure 13). The coefficient of determination (R\({}^{2}\)) was found to be 0.83, indicating a strong positive correlation between the two sets of measurements. This suggests that the derived vine heights were highly predictive of the manually measured values. The regression equation provides a mathematical representation of this relationship. Specifically, for every unit increase in the automatically derived vine height, there was an associated 0.96 cm increase in the manually measured vine height, with an additional constant offset of 1.37 cm [30]. These findings affirm the validity and accuracy of the vine height estimation approach and underscore its potential utility in vineyard management and related applications. The validation of the leaf area index (LAI) in the vineyard will be deferred to a subsequent stage due to the unavailability of an LAI measurement device, such as a copetometer or LAI-2200 Plant Canopy Analyzer. Similarly, the validation of the leaf area will also be postponed. This is attributed to the labor-intensive nature of obtaining analog measures of one-sided leaf area, which will be addressed in subsequent phases of the study. It is important to note that these measures, while currently assumed, are based on established methodologies and demonstrate technical feasibility within the scope of this research.
# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
### Main Air Pollutants  
The AQI of Kampala is measured, following the three air pollutant concentrations in Kampala: nitrogen dioxide (NO\({}_{2}\)), sulphur dioxide (SO\({}_{2}\)) and particulate matter (PM\({}_{2.5}\)) based on the 24 h average of hourly readings.
## 2 Mathematical Modeling
### Optimal Super-Twisting Sliding Mode Controller Design
#### 2.2.2 Control Input Design  
The sliding mode controller based on the super-twisting algorithm could be designed as,  
\[u=c|s|^{\frac{1}{2}}\operatorname{sgn}(s)+b\int\operatorname{sgn}(s)dt, \tag{18}\]  
where \(c,\ b>0\). The control law in (18) drives the sliding variable (17) to zero in finite time. This control law has the following characteristics. First, it is a second-order sliding mode control, since it drives both the sliding variable and its derivative to zero in finite time. Secondly, it is a continuous and non-linear controller. After designing the controller, the compensated sliding surface dynamics are derived as follows:  
\[\begin{cases}\dot{s}=-c|\sqrt{s}|\operatorname{sgn}(s)-d-\psi(x,t),\\ \dot{d}=b\operatorname{sgn}(s),\end{cases} \tag{19}\]  
where the function \(\psi(x,t)\) is considered, the cumulative disturbance term of the system and is assumed to be upper-bounded by \(L\) and its derivative \(\dot{\psi}(x,t)\leq D\). Where \(s\) and \(d\) are the scalar state variables of the compensated dynamics (19). Here, instead of just guessing the value of the uncertain parameters, initially, the value of the parameters \(c\) and \(b\) have been computed as what is proposed in [34], which is \(c=1.5\sqrt{D};b=1.1D\). Geometrical methods have been proposed for the finite time convergence and robustness of (19). As a result, \(V_{w}=b|s|+\left(\frac{1}{2}\right)d^{2}\) is chosen as the weak Lyapunov function for the system (19) without bounded disturbance, since \(\dot{V}=-cb|s|^{1/2}\) is the only negative semidefinite [35]. However, based on [35], the weak Lyapunov function candidate is not practical, hence, further study is needed, so we are proposing a strong Lyapunov function candidate for exponential stability of the proposed system, which can be found in (33).  
**Remark 3**.: _The cumulative disturbance expressed in (19) is uncertain and bounded by the upper bound of \(L\) and since it appears in the main dynamics of the system, it is considered as a matched disturbance. The upper-bound value is not exactly known but it must be \(>0\)._
## 2 Experimental Setup
### Laser Writing Experiments  
Two fiber segments (one for 3D-printed and one for SMF28) were selected, each one having a length L-6-8 cm long. Their polymer coating was removed, and a high temperature adhesive was used to attach them onto a silica thin slide. A borosilicate glass  
Figure 1: Overview of 3D printed fibers fabrication: (**a**) The preform is produced using additive manufacture and annealed to remove the binding agent. It is then (**b**) sintered on an optical fiber draw tower and drawn into (**c**) optical fiber.  
Figure 2: GeO\({}_{2}\) concentration profile across the core for two fibers: 3D printed fiber (squares) and SMF28 fiber (dots). D is the distance relative to the fiber core center. Inset: scanning electron micrograph of the 3D printed fiber core cross-section.  
coverslip (170 \(\upmu\)m thin) was placed on top of the fiber segments, with some index-matching oil (GN Nettest, \(n_{\text{D}}=1.4580\) at T = 25 \({}^{\circ}\)C) introduced between the silica and the borosilicate slides, to facilitate the observation of the core and subsequent laser irradiation. Each sample was then placed on an XYZ motorized translation stage and irradiated using a femtosecond laser (\(\lambda=1030\) nm, t = 250 fs). The laser repetition rate was fixed to rr = 100 kHz and the scanning speed to v = 100 \(\upmu\)m/s. The laser beam was focused using a 0.6 NA aspheric lens. Geometry similar to a line-by-line FBG writing was performed to investigate the writing kinetics of Type II modifications. As represented in Figure 3a, several lines length L = 60 \(\upmu\)m are written, traversing both the core and the cladding. They are written perpendicular to the direction of the fiber longitudinal axis with a linear laser polarization oriented either parallel (labeled Yy configuration,//) or perpendicular (labeled Yx configuration, \(\perp\)) to the scanning direction. The spacing between each line is 5 \(\upmu\)m, and the pulse energy E\({}_{\text{p}}\) (in \(\upmu\)J/pulse) varies between each line in steps of \(\Delta\)E-0.1 \(\upmu\)J, ranging from Ep = 0.1 to 1.5 \(\upmu\)J. To measure the optical retardance R, an Olympus BX51 polarizing optical microscope equipped with a quarter waveplate was used. The retardance is defined as R = B \(\times\) L, where B is the birefringence and L is the length of the birefringent object being measured. In this work, the impact of pulse energy on the retardance response is investigated. This provides an indication of the kind of modifications (voids, nanogratings, densification) involved during the irradiation process. The impact of processing parameters such as repetition rate (rr), pulse duration (\(\Delta\)t) and writing speed (v) will be systematically studied in future work.
## 3 Results  
### Changes in Monthly Footprint Climatology  
The spatial distribution of the footprint climatology generally presented as similar spatial patterns to the prevailing wind direction in the study area. According to the analysis of the monthly footprint climatologies calculated from 2003 to 2010 (Figure 1), from February to May, the source area of the daytime footprint climatology presented a shuttle-shaped distribution extending from southeast to northwest. Then, the daytime source area was biased toward the southeast in June, July and August. From September to December and January, the daytime source area was biased toward the northwest. Meanwhile, the spatial distribution of nighttime sources was similar to that in the daytime, but the areas of the nighttime sources obtained a larger extent than that in the daytime due to the stable atmospheric conditions.  
The footprint fetch, which is the distance from the point of the flux tower to an 80% contour of the monthly footprint climatology, mainly ranged from 600 to 900 m in the daytime and from 1200 to 2200 m in the nighttime (10 th and 90 th percentiles, Figure 2a). The source area covered about 0.34 to 0.86 km\({}^{2}\) in the daytime and about 0.81 to 2.99 km\({}^{2}\) at nighttime (10 th and 90 th percentiles, Figure 2b). Due to the monsoon climate, the study site had an asymmetric footprint climatology with footprint symmetry indices ranging from 0.22 to 0.51 in the daytime and from 0.12 to 0.34 in the nighttime (10 th and 90 th percentiles, Figure 2c).  
For the overlap index between daytime and nighttime (Oday_night), all the months during the study period were greater than 0.75, and 80% of them were greater than 0.8, which indicates the footprint climatologies of daytime and nighttime overlapped largely. The seasonal overlap index of daytime (Oseason_day) was higher than that of nighttime (Oseason_night). During the study period, all eight years had Oseason_day greater than 0.5, but only three years had Oseason_night greater than 0.5 (Figure 2d), which indicates intense monthly variations in footprint climatology throughout the whole year.  
Figure 1: The 80% monthly footprint climatologies in daytime and nighttime were calculated from 2003 to 2010 over the study area.
## 3 Fault-Tolerant Control Design for the Attitude System of HSV
### Fault-Tolerant Control Design for Fast Loop of HSV  
In this part, for the challenges caused by unexpected centroid shift, as shown in (8)-(12), the fault-tolerant controller of the quick-loop is deduced from the three aspects: (1) uncertainties of inertial matrix (\(J\)); (2) system uncertainties and eccentric moments; (3) system input saturation.