esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## 3 Results  
### Gwt  
Daily _GWT_ averages from the KBDI and the in-situ measurements in _UF_, _DF_, and _BF_ were 23.5, 14.72, 55.6, and 12.18 cm, respectively. RMSEs between the KBDI-based and in-situ GWTs were 24.97, 39.32, and 21.03 cm, respectively. The largest discrepancy was shown in _DF_ among the three sites, since the _GWT_ was artificially decreased through drainage canals. When only _UF_ and _BF_ were considered, the RMSE of the KBDI-based and in-situ _GWT_s was 20.65 cm. Inundated seasons, which showed a _GWT_ greater than zero, usually appeared from November to April. As results of linear fitting between the KBDI-based and in-situ _GWT_s, the \(R^{2}\) values of the _UF_, _DF_, and _BF_ sites were 0.61, 0.64, and 0.66, respectively (Figure 5b-d). In terms of the slope of linear fittings, the _GWT_s at the _UF_ site were closer to a 1:1 relationship. In contrast, the in-situ _GWT_ at the _BF_ site showed less variations in data distributions than the variations of KBDI-based _GWT_ with a slope of the linear function of 0.77. The \(R^{2}\) of _BF_ was the highest; both the GWTs matched well under very dry (low _GWT_) conditions. Since _BF_ had undergone disturbance events (e.g., wildfires) and less vegetation cover than other sites, the characteristics of evapotranspiration could be simply explained.  
Figure 5: Satellite-based groundwater table (_GWT_) created by using the Keetch–Bryam Drought Index (KBDI) and in situ measurements of the _GWT_ at the Palangkaraya flux tower, Kalimantan, Indonesia. (**a**) Time series of the _GWT_s. Scatter plots of the GWTs between the KBDI-based one and the in situ one in (**b**) undrained forest (_UF_), (**c**) drained forest (_DF_), and (**d**) burned forest (_BF_) sites.
# Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China
## 6 Discussion
### Discussion About the Proposed Method  
The above-mentioned method represents a new method for evaluating the collapsibility of aeolian sand, which eliminates the need to take undisturbed samples and greatly reduces the workload. Through a standard penetration test and an indoor test with disturbed samples, four physical parameters, which included the moisture content, dry density, void ratio and saturation, can be obtained. Then, combined with a support vector machine, the collapsibility coefficient of aeolian sand can be predicted, thus improving the efficiency of site collapsibility evaluations. The standard penetration test in this method is widely used in engineering practices and is mainly applicable to sandy soil. Although the fitting degree between the hammer number and the dry density was 0.9, which is relatively high, considering its advantages, such as simple equipment, convenient operation, a high cost-effectiveness, and a wide applicability, it can better serve engineering practices. Moreover, with the high fitting degree of the support vector machine, the credibility of the entire method is improved. In general, this method can be used to achieve a collapsibility evaluation of aeolian sand in desert areas more economically and efficiently.
## 4 Experimental Results
### Statistical Analysis
FJS performed better in cases when \(|\Sigma|\geq 32\): (1) \(|\Sigma|=32\) and \(m\leq 10\); (2) \(|\Sigma|=64\) and \(m\leq 20\); (3) \(|\Sigma|=128\) and \(m\leq 40\); (4) \(|\Sigma|=256\) and \(m\leq 60\);  
When comparing FQS _vs._ HOR (Figure 13b), in a total of 209 cases, both had statically the same performance in 15 cases, and FQS outperformed HOR in the other 194 cases. Comparing FQS _vs._ QS (Figure 13c), FQS was statistically worse than QS in eight cases; both had similar performance in 34 cases, and FQS showed a statistically better performance in the remaining 167 cases.
## 3 Performance Analysis of the PSI Technique
### Computational Complexity Comparison
The peak extraction process needs \(n_{mul,Extracting}\) number of multiplications which is zero, as it does not require mathematical calculation; however, extracting each peak requires five bits of memory for each peak point and its surrounding points, two points at the left side and two points at the right side. To aid understanding, let us consider the following example, which shows an extracted peak and its surrounding points:  
\[ \begin{split} Extracted\ Peak\ Power\ set=&[P(t)_{L_{ index}}\ P(t)_{P_{index}}\ P(t)_{R_{index}-1}\ P(t)_{R_{index}}]\\ =&[P(t)_{81}\ P(t)_{82}\ P(t)_{83}\ P(t)_{84}\ P(t) _{85}]\\ =&[0.002\ 0.018\ 0.02\ 0.004\ 0],\end{split}  \tag{12}\]  
where \(P(t)\) indicates the sample point from the power of the signal \(S(t)\), which is the square of absolute the signal as:  
\[P(t)=|S(t)|^{2}. \tag{13}\]  
In order to implement the shrinking process itself, the five points are multiplied by a constant value of the shrinking ratio (\(SR\)); therefore, we see that \(n_{mul,Shrinking}=5\). For storing the constant value, one bit of memory is occupied. The last algorithm employed in PSI technique is the smoothing, or matching the border points, and this involves two \(if/then\) loops and it is equivalent to two subtractions and two multiplications [24]. It should be noted that in general the total number of bits required for each PSI loop can be represented as:  
\[n_{bit,borders}=2 bits\times RP \tag{14}\]  
Figure 11: The PSI algorithm iteration loop block diagram [24]. where number of PSI loops is shown by \(RP\), and by assuming that \(RP=10\), number of required bits for PSI loop can be calculated as:  
\[n_{mul,Boarders}=20 bits \tag{15}\]  
From (8) to (15), total computational complexity of PSI technique and CF technique can be calculated, and Table 4 presents a comparison in computational complexity among some the most popular CFR techniques. It should be noted that for techniques of PTS [18; 38; 39; 40], SLM based techniques [20; 36; 41], the set ups of the algorithms are considered in a way that the PAPR reduction performance is comparable with the PSI technique. This means that number of IDFT processed in both mentioned categories, is considered to be 16, and the length of the signal is considered to be 1024, which indicates the number of bts for storing the phases that are multiplied into the input signal [17; 41]. It can be seen in Table 4, that the newly developed technique of by two step peak clipping (TPC) [14], which shows 3.5 dB PAPR reduction with moderate EVM performance, has about double the complexity of the conventional CF, since there are two stages of clipping, and therefore, two stages of FIR filters. The outstanding computational complexity of the PSI technique, which is achieved by the idea of eliminating the filtration process, allows PSI to be considered a very good candidate for existing and future systems.
# Enhancing Tactile Internet Reliability: AI-Driven Resilience in NG-EPON Networks
## 4 Proposed Architecture
### Packet Drop Rate  
The packet drop rate shown in Figure 12 shows that the drop rates remain minimal at up to 70% traffic load across all scenarios, indicating healthy network functionality under moderate loads. However, as the load exceeds 80%, packet drop rates begin to rise, especially under conditions of three faults. The packet losses occur predominantly in AF and BE traffic categories, while EF and TI packets, given the highest priority in the network, experience no packet drop. This differentiation in packet treatment highlights the network's strategic prioritization, ensuring that critical real-time applications dependent on EF and TI traffic maintain uninterrupted service even as the system approaches or reaches full capacity.  
Figure 11: System throughput.
## 3 Simulation Results
Figure 2: Quadrature amplitude modulation (QAM) bit error rate (BER) performances of the UFMC-based underwater transceiver with an LDPC code, a perfect channel estimation (PCE) and channel estimation errors (CEEs) of \(5\%\) and \(10\%\). The power-saving ratio of the UFMC-based underwater transceiver is defined as follows:  
\[PS=(1-p_{k})\times 100\% \tag{6}\]  
where \(p_{k}\) is the transmission power weighting of the \(k\)th user. For 4-QAM, the power saving ratios for the \(No\) of 0.0392, 0.0353, 0.0313, 0.0274, 0.0235, and 0.0196, were 0%, 10%, 20%, 30%, 40%, and 50%, respectively, in the proposed system with a BER of \(4\times 10^{-4}\), for PCE. The power-saving ratios for the \(No\) of 0.0317, 0.0285, 0.0254, 0.0222, 0.0190, and 0.0158, were 0%, 10%, 20%, 30%, 40%, and 50%, respectively, in the proposed system with a BER of \(4\times 10^{-4}\), for CEE of 10%. The PCE outperformed the CES of 10%, under 4-QAM, with \(No\) gains of 0.0038, at a power-saving ratio of 50%. As the CEE increased, \(No\) decreased. For 16-QAM, the power saving ratios for the \(No\) of 0.0045, 0.0040, 0.0036, 0.0031, 0.0027, and 0.0022, were 0%, 10%, 20%, 30%, 40%, and 50%, respectively, at the proposed system with a BER of \(8.5\times 10^{-4}\), for CEE of 10%. The 4-QAM outperformed 16-QAM, under a CEEs of 10%, with \(No\) gains of 0.0136, at a power-saving ratio of 50%. The No of 16-QAM was lower than that of 4-QAM. The received voice signals used 16-QAM with a BER of \(8.5\times 10^{-4}\), for CEEs of 5%. The mean square error (MSE) between the original and received voice signals was 0.000015. The MSE of the original and received voice signals is expressed as follows:  
\[MSE=\frac{1}{T}\sum_{i=1}^{T}\bigl{(}O_{i}-\hat{O}_{i}\bigr{)}^{2} \tag{7}\]  
where \(O_{i}\) is the original voice signal, \(\hat{O}_{i}\) is the received voice signal, and \(T\) is the length of the original voice signal. The received voice signal was clear at the received transmission BER below \(10^{-3}\). The MSEs between the original and the received voice signals using 4-QAM with a BER of \(4\times 10^{-4}\) for PCE and CEEs of 5% and 10%, were all \(6.2\times 10^{-5}\). The SNRs were 14.06 dB, 14.52 dB, 14.98 dB for PCE and CEEs of 5% and 10%, respectively. As such, the received voice signal was clear. Further, the MSEs of the original and the received  
Figure 4: Power saving ratio of the UFMC-based underwater transceiver with an LDPC code and a BER of \(4\times 10^{-4}\) for PCE and CEEs of 5% and 10%. voice signals using 16-QAM with a BER of \(8.5\times 10^{-4}\), for PCE and CEEs of 5% and 10%, were \(1.5\times 10^{-4}\), \(1.4\times 10^{-4}\), \(1.4\times 10^{-4}\), respectively. The SNRs were 21.05 dB, 21.54 dB, and 23.46 dB, for PCE and CEEs of 5% and 10%, respectively. Thus, the simulation results showed that the proposed UFMC-based underwater acoustic transmission scheme would be suitable for voice communication.
## 3 Results
### EDOF CL Use Changed the Pupillary Dynamics and Some Biometric Parameters  
Pupillary size measured under scotopic (\(p=0.203\)) and mesopic conditions (\(p=0.227\)) did not experience a significant modification with the use of the CL. However, a reduction was observed in the pupil size measured under photopic conditions, obtaining a mean value of 4.18 \(\pm\) 0.08 mm before fitting and 3.99 \(\pm\) 0.07 mm after 30 days of CL use (\(p=0.024\)). At the end of the study, no significant variations were found in terms of mean keratometry (7.80 \(\pm\) 0.05 vs. 7.86 \(\pm\) 0.05 mm, \(p=0.329\)) or AXL (24.50 \(\pm\) 0.17 vs. 24.54 \(\pm\) 0.20 mm, \(p=0.285\)), indicating the absence of molding or modification of the corneal geometry after the use of CL and progression of myopia during the study period. However, a statistically significant reduction was found in the anterior chamber depth, changing from a mean value of 3.40 \(\pm\) 0.03 mm at the pre-fitting visit to 3.34 \(\pm\) 0.04 mm after 30 days of CL use (\(p=0.022\)). Regarding the central corneal thickness (CCT), the change was close to the limit of statistical significance (\(p=0.053\)). Specifically, CCT increased from a mean value of 536.80 \(\pm\) 5.88 \(\upmu\)m before CL fitting to a mean value of 543.54 \(\pm\) 6.52 \(\upmu\)m after 30 days of CL use.
## 3 Experimental Section
### Comprehensive Performance Analysis of UOCC System
#### 3.5.3 Performance Analysis under Different Water Environments  
In order to investigate the impact of different water environments, we measured the BER under different bubble environments in 25 \({}^{\circ}\)C tap water and 25 \({}^{\circ}\)C saline water at various data rates, as shown in Table 3. As can be seen, the BER performance of the optical signal in tap water and saline water was almost the same. This is mainly due to the fact that there were no changes in the index of refraction due to salinity variations within the water tank. The optical properties were not affected when the optical signal was propagating through such a homogeneous optical channel.  
Figure 12: Bit error rate (BER) performance between of the proposed methods with (**a**) dense bubbles (\(K\) = 10 cm); (**b**) sparse bubble degradation (\(K\) = 10 cm); (**c**) dense bubble degradation (\(K\) = 5 cm), and (**d**) sparse bubbles (\(K\) = 5 cm).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Bubble Type** & **Water Type at Normal** & \multicolumn{5}{c}{**Data Rate (Kbit/s)**} \\ (**Density, K**) & **Temperature (25 °C)** & \multicolumn{1}{c}{**3.6**} & \multicolumn{1}{c}{**4.8**} & \multicolumn{1}{c}{**6**} & \multicolumn{1}{c}{**7.2**} & \multicolumn{1}{c}{**8.4**} \\ (**BER**) & **(BER)** & **(BER)** & **(BER)** & **(BER)** & **(BER)** & **(BER)** \\ \hline (Dense, 10) & Clear up water & \(10^{-6}\) & \(10^{-6}\) & \(10^{-6}\) & \(5.83\times 10^{-4}\) & \(3.82\times 10^{-2}\) \\ (Dense, 10) & Saline water & \(10^{-6}\) & \(10^{-6}\) & \(10^{-6}\) & \(5.83\times 10^{-4}\) & \(3.81\times 10^{-2}\) \\ (Sparse, 10) & Clear up water & \(10^{-6}\) & \(10^{-6}\) & \(10^{-6}\) & \(10^{-6}\) & \(3.82\times 10^{-3}\) \\ (Sparse, 10) & Saline water & \(10^{-6}\) & \(10^{-6}\) & \(10^{-6}\) & \(10^{-6}\) & \(3.82\times 10^{-3}\) \\ (Dense, 5) & Clear up water & \(10^{-6}\) & \(10^{-6}\) & \(3.32\times 10^{-5}\) & \(1.2\times 10^{-3}\) & \(4.0\times 10^{-2}\) \\ (Dense, 5) & Saline water & \(10^{-6}\) & \(10^{-6}\) & \(3.33\times 10^{-5}\) & \(1.2\times 10^{-3}\) & \(4.02\times 10^{-2}\) \\ (Sparse, 5) & Clear up water & \(10^{-6}\) & \(10^{-6}\) & \(10^{-6}\) & \(10^{-6}\) & \(2.82\times 10^{-2}\) \\ (Sparse, 5) & Saline water & \(10^{-6}\) & \(10^{-6}\) & \(10^{-6}\) & \(10^{-6}\) & \(2.82\times 10^{-2}\) \\ (Sparse, 5) & Saline water & \(10^{-6}\) & \(10^{-6}\) & \(10^{-6}\) & \(10^{-6}\) & \(2.82\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: BER under different bubble environments in tap water and saline water.
## 3 System Model  
In this section, we introduce the system model by specifying its components. We start with assumptions on IAB system operation, then proceed to specify the topology and the packet transmission process and complement the model with a detailed TDM parametrization. Finally, the metrics of interest are introduced.
# Structure of Microbial Communities When Complementary
## 3 Results and Discussion
### Anaerobic Digestion of PE and OMW Mixture
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mixture**} & \multicolumn{2}{c}{**pH**} & \multicolumn{2}{c}{**Total Nitrogen**} & \multicolumn{2}{c}{**Ammonia Nitrogen**} & \multicolumn{2}{c}{**VFA**} \\ \cline{2-9}  & \(\text{pH}_{\text{in}}\) & \(\text{pH}_{\text{r}}\) & \(\text{TN}_{\text{in}}\) **(g/L)** & \(\text{TN}_{\text{r}}\) & \(\text{NH4+-N}_{\text{in}}\) & \(\text{NH4+-N}_{\text{r}}\) & **Total VFA\({}_{\text{in}}\)** & **Total VFA\({}_{\text{r}}\)** \\  & \(\text{pH}_{\text{in}}\) & \(\text{pH}_{\text{r}}\) & \(\text{TN}_{\text{in}}\) **(g/L)** & **(\%)** & **(g/L)** & **(\%)** & **(g/L AAc)** & **(\%)** \\ \hline PE & 7.3 & 8.1 & 4.9 \(\pm\) 0.3 & - & 3.2 \(\pm\) 0.0 & \(-\)21 \(\pm\) 4 & 5.7 & 85 \\
70P + 30O & 6.9 & 7.9 & 1.8 \(\pm\) 0.0 & 3.0 \(\pm\) 0.0 & 1.3 \(\pm\) 0.1 & \(-\)88 \(\pm\) 38 & 3.9 & 55 \\
50P + 50O & 6.7 & 7.3 & 1.3 \(\pm\) 0.0 & 2.2 \(\pm\) 1.5 & 0.8 \(\pm\) 0.3 & \(-\)14 \(\pm\) 6 & 3.7 & \(-\)81 \\
20P + 80O & 6.2 & 5.7 & 0.6 \(\pm\) 0.0 & 5.7 \(\pm\) 0.0 & 0.3 \(\pm\) 0.0 & 13 \(\pm\) 5 & 2.8 & 52 \\ \hline \hline \end{tabular}  
* TN—total nitrogen; VFA—volatile fatty acids; in—initial; f—final; r—removal; AAc—in terms of acetic acid.  
\end{table} 
Table 7: Anaerobic digestion of PE + OMW: Total and ammonium nitrogen, pH and VFA. of magnitude or even better than those reported in anaerobic digestion carried out with other substrates. This is the case of data obtained on the rice husk (4.36 KWh/kg SVin), coffee husk, or potato pulp with 1.78 and 3.99 KWh/kg SVin, respectively [47; 48]. The complementarity of pig effluent mixtures containing more than 30% OMW (50P + 50O and 20P + 80O) is of no industrial interest, producing less than 178 L CH4/kg SVin and 1.77 KWh/kg SVin. The recorded data suggests that the use of OMW to complement PE is feasible and is an advantageous way to provide treatment of both effluents, without additional expenses on substrate chemical correction or dilutions, and generate a digestate and biogas/methane. Despite this, it is only possible if a small proportion of OMW (about 30% \(v/v\)) is applied to produce methane in satisfactory amounts and avoid inhibitory effects on the digestion process. At higher volumes, OMW might hinder the microbial population, affecting the treatment.
## 5 User Interface and Set Up
### Configuring the Environment for the VoteChain System
#### 5.1.1 Installing the Necessary Software  
The administrator is responsible for installing and managing several essential software components:  
* **Ubuntu**: A reliable, open-source Linux distribution that serves as the foundation for the VoteChain system;
* **Node.js**: A JavaScript runtime essential for running backend services and ensuring the system's responsiveness;
* **Truffle**: A development framework for managing and deploying smart contracts, which is integral to executing the voting functions on the Ethereum blockchain;
* **MetaMask**: A cryptocurrency wallet that enables voters and administrators to interact with the blockchain and manage transactions securely.  
The administrator ensures these components are correctly installed and configured to provide a stable platform for VoteChain.
## 3 Results and Discussion
### Analysis of Dilatations in the Middle of the Beams Span and above the Middle Support  
The analysis of dilatation in concrete and tensioned reinforcement in Sections I and II in the middle of the beams span was performed for the tested beams depending on the percentage of reinforcement. The shape of the curves defining the load-strain dependence for continuous beams with different percentages of reinforcement does not differ significantly, whereby the nominal values are different, which can be seen in Figure 5.  
Dilatation in the tensile zone of the cross-section is the result of onset of cracks (about 98%) and dilatation of concrete (about 2%), and therefore dilatation of the concrete can be neglected. Hence, it can be adopted that recorded dilatation represents the sum of the crack's width along the length of the measurement base (100 mm). As only one crack usually appears at that length, it is possible to analyze its width as a function of load, very precisely and continuously. On the other hand, having in mind that the tensile stresses are assumed by the reinforcement at the point where the crack emerged, it can be considered that the measured dilatation correspond to that in the reinforcement.  
Based on the diagrams shown in Figure 5, three parts of the diagram are observed. The first part of the diagram is linear until the emergence of the first crack in the observed cross-section. With the formation of cracks, the stiffness of the beam decreases when the dilatations increase abruptly nonlinearly until the onset of yielding in the steel reinforcement, when the cracks stabilize. The third part of the diagram is from the moment of reinforcement yielding until the failure and it has an even more pronounced nonlinearity. This kind of a behavior is characteristic for concrete structures and can be find in literature [39]. With the emergence of cracks and the loss of adhesion between the concrete and the reinforcement, the stress is redistributed from the concrete part of the cross-section to the reinforcement.  
The cross-sectional behavior above the middle support (Figure 5c) is similar to the cross-sectional behavior in the beam span (Figure 5a,b). The diagram of strains in the compressed part of concrete Section III shown in Figure 5c, in qualitative terms is similar to the diagrams in the compressed parts of Sections I and II shown in Figure 5a,b. When strains in the tensioned reinforcement reach the limit of 2500 micro-strains, the intensity of load leading to flow in the tensioned reinforcement is defined.  
Figure 5: \(Cont.\)
# New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems
## 2 Methodology of Work  
### Dataset Overview  
Open-source datasets facilitate the development of new prediction techniques and provide a benchmark for making comparisons. The highly citable Georgia Tech. dataset is chosen for data collection, processing, and classification in this research. It is presented by [PERSON] et al. in 2021 [27]. The recorded activities are the main common activities required by elders and impaired patients in their Activities of Daily Living (ADLs), and they are used for identification of locomotion activities and gait cycle data for lower limb assistive devices [28]. Data were collected from 22 healthy subjects. There were 13 males and 9 females. Their age, height and weight are 21 \(\pm\) 3.4 years, 1.70 \(\pm\) 0.07 m, and 68.3 \(\pm\) 10.83 kg, respectively. The utilized sensors were 3 goniometers (Biometrics), four 6-axis inertial measurement units (Yost 3-space embedded IMUs), and 32 motion capture markers with a motion capture system (Vicon). The ground reaction forces were recorded by 2 force plates (Bertec). Additionally, 11 versions of sEMG (Biometric sEMG) were used for collecting muscle data. This dataset has been a benchmark for developing and comparing conventional and deep learning techniques, as can be seen in [29, 30].  
The data utilized in this research is mainly from IMUs and EMGs. The IMUs were placed on the thigh, shank, trunk, and foot. On the other hand, 8 versions of sEMG were placed on the muscles of both legs. The intended muscles are gastrocnemius medialis, soleus, tibialis anterior, vastus lateralis, vastus medialis, rectus femoris, biceps femoris, gracilis, gluteus medius, semitendinosus, and right external oblique. The sensors are located on the human body as shown in Figure 2. Using a low-pass filter with a cutoff frequency of 100 Hz, the IMU data were processed after being sampled at 200 Hz. The bandpass filter was used to process the 1000 Hz sampled EMG data, with a cutoff frequency ranging from 20 Hz to 400 Hz.