esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Comparison with Terrestrial Landform Element Mapping  
These three methods have different critical values for subaqueous bedforms in river systems and terrestrial landforms. Using [PERSON]'s criteria, the DEM (Digital Elevation Model) size employed in a geomorphologically diverse region of Central Mexico was \(90\times 90\) m, and the suitable tolerances of slope and curvature were set as \(6^{\circ}\) and \(0.0001\), respectively [40]. In addition, a \(90\times 90\) m DEM was used for morphologic element mapping and \(1^{\circ}\) and \(0.0005\) were employed as the suitable tolerances of slope and curvature respectively, at the border of Poland, Slovakia, and Ukraine [20]. Furthermore, a DEM of \(50\times 50\) m was utilized for landform delineation in South Tyrol with slope and curvature set as \(14^{\circ}\) and \(0.002\), respectively [41]. In this article, these critical values were set as \(5^{\circ}\) and \(0.01\) when data in the resolution of \(1\times 1\) m were obtained via measurements in two areas of the Yangtze River. Although the slope tolerance is similar to that of other studies, the curvature tolerance is considerably larger than that used for terrestrial landforms. For geomorphons, the reported flatness in the literature varies with the area of focus; however, it essentially ranges from \(0^{\circ}\) to \(3^{\circ}\)[42; 43; 44; 21; 45], which is close to the \(2^{\circ}\) used in this study.  
For the SOM technique, the optimal classification was obtained when a suitable number and combination of map units were set. The optimal number of map units changed with the four input morphometric parameters, which was also mentioned in a previous study [36]. All studies have analyzed different terrain, which means that a variety of vectors have been input to the SOM. In a previous study, a suitable number of 10 map units was determined at the boundary between Poland, Slovakia, and the Ukraine [20], and 8 was suggested for use in a loess area [36]. In this research, 8 and 10 map units were used for the two samples, respectively. It thus appears that the common number of map units used is 8 or 10, although the optimal number of map units changes.  
A digital terrain model obtained via remote sensing basically contains no missing data for entire regions. However, bathymetric data always contain blank areas in a general situation. The SOM technique and [PERSON]'s criteria do not operate effectively in the presence of blank data, because their use is based on the calculation of slope, cross-sectional curvature, maximum curvature, and minimum curvature.
###### Abstract  
The primary objective of this study is to investigate the carbon footprint, resilience levels, and optimal landscape area ratios of various parks. Additionally, it explores the relationships between landscape element proportions (LEP), the normalized difference vegetation index (NDVI), resilience indicators (RI), and the carbon reduction benefits associated with carbon neutrality (CN). Six parks were assessed for resilience, NDVI, LEP, and CN values, with Pearson correlation analysis conducted. The results revealed that parks with or without waterbodies exhibited ideal LEP area ratios of 6.5:2:1.5 (Softscape:Waterbody:Hardscape) and 8.3:1.7 (Softscape:Hardscape), respectively. Enhanced Softscape and reduced Hardscape proportions in parks correlated with increased NDVI and CN. NDVI exhibited a positive correlation with Softscape percentage and a negative correlation with Hardscape percentage. Conversely, CN demonstrated a negative correlation with Hardscape percentage and a positive correlation with Softscape percentage. Suggesting Softscape should constitute over 65%, and Hardscape should be under 15% in parks with water bodies. Waterless parks are advised to maintain a Softscape ratio exceeding 83% and a Hardscape ratio below 17%. Finally, the study extended to assess the LEP of 22 additional parks, validating the suitability of the ideal LEP area ratio.  
carbon-neutral park; life cycle assessment; landscape element; carbon footprint; normalized difference vegetation index; resilience index +
Footnote †: journal: Applied Sciences  
1
# Design Procedure for Motion Profiles with Sinusoidal Jerk for Vibration Reduction
###### Abstract
Motivated by these early works, the main purpose of this paper is to propose a novel motion profile design method characterized by a continuous sinusoidal jerk model for the vibration mitigation of high-speed industrial machines. As a consequence, a smooth motion is ensured to reduce the positioning time and error, leading to improved productivity and quality. The contributions of this work are as follows: (1) First, a closed-form solution for the minimum-time sinusoidal jerk profile was derived, enabling the full exploitation of the actuator limits based on profile type evaluation. The critical kinematic values were identified to classify the proposed model into four specific types according to the input displacement and actuator limits. (2) Furthermore, a quantitative investigation of the relationship between the profile parameters and the response characteristics was performed to identify the vibration suppression conditions. The analysis revealed that the vibration level is primarily influenced by the proportion between specific time intervals and the natural period, rather than lower jerk values. (3) Finally, an optimization procedure was suggested to obtain the time-frequency-optimal sinusoidal jerk profile that minimizes the motion duration while considering the desired vibration cancellation and robustness constraints without violating the physical limits. Numerical and experimental tests were carried out to demonstrate the effectiveness of the proposed method. The rest of this paper is organized as follows: Section 2 begins with a brief review of the S-curve profile, followed by the definition and analytical expressions of the sinusoidal jerk profile. The minimum-time solution subject to the actuator limits is described in Section 3. After that, the characteristics of motion-induced residual vibrations are analyzed, and the optimization strategy of the profile parameters for vibration suppression is also developed. Then, Section 4 presents the kinematic and dynamic simulation results, assessing the vibrations induced by different motion profiles. Section 5 reports the experimental results to evaluate the performance of the optimized profile in comparison with other baseline methods on a motion stage. Finally, the conclusions and future work are drawn up in Section 6.
## 2 System Modeling
### Power Consumption Modeling of Data Acquisition  
The power grid status in the smart grid can be estimated and observed by monitoring the collected data. Monitoring applications of power systems can be categorized into two main parts:  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**NB-IoT** & **3 GPP Model**[5] & **Actual Devices**[19] \\ \hline Standby & 0.015 mW & 0.013–0.035 mW \\ \hline Sleep & 3 mW & 21–23 mW \\ \hline Transmit & 480 mW & 716–840 mW \\ \hline Receive & 75 mW & 213–240 mW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Communication Power consumption for NB-IoT model and devices (\(E_{\text{Communication}}\)).  
Figure 3: Protocol flow for the uplink of NB-IoT showing an example of possible energy consumption.  
1. Monitoring regularly the power system which can happen periodically with a fixed time interval in between;
2. Monitoring power systems in an event-driven way, observing exceptional cases that happen randomly or due to alarms.
## 4 Network-Reconfiguration Optimization Model
### Objective Function  
Considering rapidity, reliability, and economy, network-reconfiguration optimization aims at minimizing the number of switching operations. Concrete objective function can be expressed as follows:  
\[min(F(s,t)=\sum_{a=1}^{k_{1}}abs(s_{a}-s_{a\_ini})+\ \sum_{b=1}^{k_{2}}abs(t_{b}-t_{b\_ini}) \tag{5}\]  
where \(k_{1}\) and \(k_{2}\) are the amount of section switches and tie-switches of the island, respectively; \(s_{a}\) and \(s_{a\_ini}\) are the optimized and initial states of the \(a\)th section switch, respectively; and \(t_{b}\) and \(t_{b\_ini}\) are the optimized and initial states of the \(b\)th tie-switch.
###### Abstract  
This study developed decision-making factors to classify electromagnetic pulse (EMP) protection levels and determine various protection measures. We proposed three EMP protection levels of 80, 60, and 40 dB by considering the characteristics of military equipment and factors that determine EMP protection level, based on a Delphi study. We modeled EMP protection facilities for brigade-level troops to evaluate the derived decision-making factors and applicability of differential protection levels. The natural attenuation effect of soil was confirmed for structures installed underground. The shielding effect of wet soil was up to 30 dB. Considering the 20 dB EMP resistance of military equipment and the 30 dB of attenuation of wet soil, new materials with 30 dB of shielding efficiency could be used to meet an EMP protection level of 80 dB. Therefore, we confirmed that EMP protection measures could be established to build mobile, lightweight shelters. If lightweight shelters are constructed by applying the differential protection level scheme, they can be applied as a more effective EMP protection measure. Furthermore, differential protection measures can be adopted as a sustainable defense facility policy approach, wherein lightweight protection facilities replace conventional heavyweight facilities.  
Electromagnetic pulse (EMP); shielding effectiveness (SE); EMP protection level; EMP protection facility; METT + TC +
Footnote †: 2021: 11, 5227. [[https://doi.org/10.3390/app1115227](https://doi.org/10.3390/app1115227)]([https://doi.org/10.3390/app1115227](https://doi.org/10.3390/app1115227))
## 3 Results
### Effect of MSS and DF Supplementation on the Composition of Rumen Prokaryotic Community  
The dominant phyla across all samples were Bacteroidetes (average relative abundance 34.14-58.96%), Firmicutes (6.81-26.89%), Tenericutes (4.46-40.75%), Euryarchaeota (2.22-10.12%) and Spirochaetes (1.30-11.43%) (Figure 3). Different substrates resulted in variations in the overall prokaryotic community composition. The relative abundances of Euryarchaeota and Tenericutes were significantly influenced by substrate (\(p<0.05\)). The relative abundance of Euryarchaeota with F as substrate increased significantly by 2.91% compared to V, but Tenericutes was reduced by 28.93% (\(p<0.05\)). Supplementation with MSS and DF, alone and in combination, caused a substantial increase in the relative abundance of Bacteroidetes, Fusobacteria, and Proteobacteria, and decrease in the relative abundance of Euryarchaeota and Firmicutes.  
Figure 1: Effect of monensin sodium salt (MSS) and/or disodium fumarate (DF) supplementation on in vitro methanogenesis under different substrates (forage or vinasse). Inoculated bottles with a no-supplemented control (T1) and non-substrate control bottles (TB).  
At the genus level, the seven most abundant genera (average relative abundance >1%) included _Prevolella_ (26.34%), _Anaroplasma_ (20.37%), _Methanobrevibacter_ (4.31%), _Trepenema_ (3.30%), _Butyriviibrio_ (1.41%) and _Succinlasticum_ (1.39%) (Table S5). _Methanobrevibacter_ was the dominant methanogen genus across all samples, which mainly use H\({}_{2}\)/CO\({}_{2}\) for growth and methanogenesis. The addition of MSS and DF, alone and in combination, increased the relative abundance of _Prevolella_, _Fibrobacter_, _Selenomonas_, _Succinlasticum_ and _Succiniibrio_, but decreased the relative abundance of _Methanobrevibacter_.
# Emerging Industrial Internet of Things Open-Source Platforms and Applications in Diverse Sectors
## 3 IIoT Requirement Analysis
### Application Sector Specific Requirements
#### 3.2.3 Healthcare  
In the healthcare industry, several important needs have been highlighted. These include the necessity of a scalable architecture capable of handling a large number of patients while considering constraints such as computer resources, memory, and energy consumption [40; 48]. A modular design of solutions is also crucial to allow for the reuse of platforms in various healthcare contexts [40]. Moreover, data freshness is a key consideration, ensuring that no outdated messages are transmitted within healthcare systems [48]. These requirements play a vital role in addressing the unique challenges and demands of the healthcare sector.
## 4 Experimental Section
### Experimental Results
#### 4.3.3 Resource Consumption  
As noted in Section 4.1.3, resource consumption is represented by the rate of messages exchanged between sensor nodes: _i.e._, as message rate increases, processor load and battery consumption increases. For this measure, all of the experimental factors described in Section 4 are significant. The main effect plot for this measure is shown in Figure 10.  
Figure 10: Main effect plot for resource consumption. \(n=160\), ANOVA, \(1\)_df_, \(p<0.05\).  
Figure 9: Interval plot for effectiveness. \(n=160\), ANOVA, \(1\)_df_, \(p<0.05\).  
The results for the first two factors are a direct consequence of increasing the number of participants in the bidding process. More specifically, as the number of nodes (Sink Nodes and Anchor Nodes) increase, more CNP and Vickrey auction messages are sent. The third factor, signal blockage, impacts the number of messages received: _i.e._, as blockage increases, signal paths are limited, and consequently, message exchange decreases.  
Figure 11 provides a comparison of the clustering techniques for this measure. As can be seen in this figure, the _ad hoc_ approach results in the highest message rate, and as a result, the greatest consumption of network resources. This heavy resource consumption is exacerbated by the load distribution and effectiveness results described previously. More specifically, in addition to resulting in a higher number of messages per second to support its protocol, the _ad hoc_ technique involves fewer Anchor Nodes in these communications. Given the direct link between communications and battery life, the _ad hoc_ technique will result in a much shorter mean time between failure of sensor nodes, than the two pre-defined clustering techniques.
## 3 Analysis of Optical OFDM for Underwater Communications
### U-Gfdm  
The unipolar OFDM works as a novel optical OFDM technique, which was proposed in [21]. It has advantages over DCO- and ACO-OFDM. Unlike ACO-OFDM, all the subcarriers can be used for modulating. Being superior to DCO-OFDM, there is no zero clipping for U-OFDM.  
Figure 7: Simulation and analytical results of the pdf of the ACO-OFDM signal.  
U-OFDM successively transmits the positive and negative information using two time slots to avoid the zero clipping distortions. A block diagram of U-OFDM is shown in Figure 8. When transmitting the positive values of signals, zeros are set at the negative values' positions. Then, the absolute value of the negative information is sent. To recover the data easily, the two time slots should have the same length. For example, if the bipolar information is \([1,-2,-4,2,-5,3,0,-1]\), the U-OFDM transmits \([1,0,0,2,0,3,0,0]\) using the first slot. Then, \([0,2,4,0,5,0,0,1]\) are transmitted in the following slot. Thus, only the peak power constraint can affect the signal. At the receiver, the signal received from the first and second slots can be used to reconstruct the original bipolar signal. Obviously, U-OFDM requires a twice larger bandwidth than DCO-OFDM when transmitting the same bit rate using the equivalent modulation scheme.  
Similar to DCO- and ACO-OFDM, we can calculate the clipping coefficient, clipping noise, and SNR for U-OFDM. After computing, the clipping coefficient for U-OFDM can be represented as:  
\[\alpha_{\text{U}}=1-\text{erfc}\left(\frac{P_{\text{m}}}{\sqrt{2\sigma_{x}^{2} }}\right). \tag{22}\]  
Since U-OFDM signal is unipolar, the pdf can be represented as:  
\[\begin{split} f_{\text{U}}(x)=&\frac{1}{\sqrt{2 \pi\sigma_{x}^{2}}}\exp\left(\frac{-x^{2}}{2\sigma_{x}^{2}}\right)\Pi(x,P_{ \text{m}})\\ &+\frac{1}{2}\delta(x)+\frac{1}{2}\text{erfc}\left(\frac{P_{ \text{m}}}{\sqrt{2\sigma_{x}^{2}}}\right)\delta(x-P_{\text{m}}).\end{split} \tag{23}\]  
Thus, the variance of clipping noise can be calculated and obtained as:  
\[\begin{split}\sigma_{\text{U}}^{2}&=\int_{P_{ \text{m}}}^{\infty}(x-P_{\text{m}})^{2}\cdot f_{\text{U}}(x)dx\\ &=\left(P_{\text{m}}^{2}+\sigma_{x}^{2}\right)\text{erfc}\left( \frac{P_{\text{m}}^{2}}{2\sigma_{x}^{2}}\right)-\sigma_{x}P_{\text{m}}\exp \left(\frac{-P_{\text{m}}^{2}}{2\sigma_{x}^{2}}\right).\end{split} \tag{24}\]  
The received SNR of U-OFDM can be represented as:  
\[\gamma_{\text{U}}(d,\beta)=\frac{(\tilde{h}h_{\text{o}}^{2}\beta\alpha_{\text {U}})^{2}\sigma_{x}^{2}}{2N((\tilde{h}^{2}h_{\text{o}}^{2}+\sigma_{h}^{2}) \sigma_{\text{U}}^{2}+\sigma_{n}^{2})}, \tag{25}\]  
and the average SNR for U-OFDM is calculated by:  
\[\hat{\gamma}_{\text{U}}(d,\beta)=\int_{0}^{\infty}\gamma_{\text{U}}(d,\beta) f_{\tilde{h}}(\tilde{h})d\tilde{h}. \tag{26}\]  
Since U-OFDM transmits positive and negative signals successfully, after reconstructing the signal at the receiver, the additive Gaussian noise variance is twice that of ACO- or DCO-OFDM.
## 5 Results and Discussion
### Statistical Analysis Results
#### 5.4.2 Principal Component Analysis (PCA)  
Using PCA on pyrolysis variables revealed three factors influencing source rock evaluation (Table 3). TOC, S\({}_{1}\), and S\({}_{2}\) were variables in Factor 1 that determined the organic richness and hydrocarbon potentiality of source rocks. S\({}_{3}\) and PI, which represent the source of organic matter, were included in Factor 2. Factor 3 reflected the kerogen type and maturity of the source rock and had the parameters Tmax and HI.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **TOC** & **S\({}_{1}\)** & **S\({}_{2}\)** & **S\({}_{3}\)** & **Tmax** & **HI** & **OI** & **PI** & **GP** \\ \hline TOC & 1.000 & & & & & & & & \\ S\({}_{1}\) & 0.407 ** & 1.000 & & & & & & & \\ S\({}_{2}\) & 0.625 ** & 0.864 ** & 1.000 & & & & & & \\ S\({}_{3}\) & \(-\)0.061 & 0.269 * & 0.148 & 1.000 & & & & & \\ Tmax & 0.271 * & \(-\)0.296 * & \(-\)0.113 & \(-\)0.327 * & 1.000 & & & & \\ HI & 0.215 & 0.781 ** & 0.849 ** & 0.196 & \(-\)0.273 * & 1.000 & & & \\ OI & \(-\)0.694 ** & \(-\)0.103 & \(-\)0.311 * & 0.731 ** & \(-\)0.464 ** & 0.020 & 1.000 & & \\ PI & \(-\)0.200 & 0.478 ** & 0.019 & 0.293 * & \(-\)0.214 & 0.066 & 0.284 * & 1.000 & & \\ GP & 0.489 ** & 0.986 ** & 0.917 ** & 0.281 * & \(-\)0.256 & 0.805 ** & \(-\)0.140 & 0.370 ** & 1.000 \\ \hline \end{tabular}
\end{table}
Table 2: [PERSON]’s correlation coefficient (\(\rho\)) between pyrolysis parameters for the studied Montney Formation samples.  
Figure 5: Thermal maturation of Montney Formation source rock, BC area, Canada. (**A**) Tmax vs. HI cross plot showing the maturation level and kerogen type. (**B**) Tmax vs. PI showing the hydrocarbon yield.
# Recent Advances in Hydrothermal Oxidation Technology for Sludge Treatment
## 2 Results and Discussion
### Resource Recycling  
From the literature reviewed, it is evident that the sludge treated by hydrothermal oxidation contains rich nutritional components such as proteins, nitrogen (N), and phosphorus (P). Below, we summarize several potential and common applications of the products obtained from treated sludge, categorized into three main aspects: solid products, liquid products, and gaseous products, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Original Sludge Type** & **Treatment Process** & **Before Treatment** & **After Treatment** & **Reference** \\ \hline \multirow{3}{*}{Pharmaceutical Sludge} & \multirow{3}{*}{CWO} & COOD: 15,000–16,000, VSS: & \multirow{3}{*}{COD and VSS removal rates: 72.6\%,} & \multirow{3}{*}{[22]} \\  & & 13,500–13,800, VSS/SS ratio: & & 87.3\% \\ \cline{3-5}  & & 39–40\%, pH: 7.5–8.0 & & \\ \hline \multirow{3}{*}{Pharmaceutical Sludge} & \multirow{3}{*}{CWO} & TCOD: 16,500–18,000, TSS: & \multirow{3}{*}{TCOD and VSS removal rates: 76.5\%,} & \multirow{3}{*}{[46]} \\  & & 16,300–17,800, VSS: & & 93.6\% \\ \cline{3-5}  & & 13,200–14,100, pH: 7.3–7.8 & & \\ \hline \multirow{3}{*}{Pharmaceutical Sludge} & \multirow{3}{*}{CWO} & TCOD: 19,000–20,000 g/L; & \multirow{3}{*}{COD and VSS removal rates: 57.3\%,} & \multirow{3}{*}{[47]} \\  & & VSS: 15,500–15,800 g/L, pH: & & 95.2\%; acetic, formic, and oxalic acid concentrations: 8000, 2000, 2250 \\ \hline \multirow{3}{*}{Olifield Sludge} & \multirow{3}{*}{SCWO} & Volatile: 7.3 wt\%, fixed & \multirow{3}{*}{TOC removal rate: 95.2\%, TOC} & \multirow{3}{*}{[382,5]} \\  & & carbon: 73.84 wt\%, C: 48.18 & & \\ \cline{3-5}  & & wt\%, N: 0.36 wt\%, S: 0.28 & & \\ \cline{3-5}  & & wt\% & & \\ \hline \multirow{3}{*}{Pharmaceutical Sludge} & \multirow{3}{*}{CWO} & VSS: 160.6 g/L; TSS: 192.3 & \multirow{3}{*}{TSS: 90.1\%, VSS: 98.4\%, TCOD:} & \multirow{3}{*}{[16]} \\  & & g/L; VSS/TSS: 83.5\%; pH: & & 78.7\%, COD: 81.6\%, volatile fatty & \\ \cline{3-5}  & & 8.17; TCOD: 206.3 g/L & & acids: 4819 \\ \hline \multirow{3}{*}{Urban Sludge} & \multirow{3}{*}{HTO} & TS: 1.7 + 0.1\%, VSS/TSS: & \multirow{3}{*}{58.5} & \multirow{3}{*}{Ammonia nitrogen: 3609, amino} \\  & & \(\pm\) 0.5\%; pH: 7.8 + 0.1; & & \\ \cline{3-5}  & & TCOD: 12.0 \(\pm\) 0.3 g/L; & & \\ \cline{3-5}  & & SCOD: 7.1 \(\pm\) 0.3 g/L; TN: & & \\ \cline{3-5}  & & 3.3 \(\pm\) 0.2 g/L & & \\ \hline \hline \end{tabular} Note: The above ununited data use a unit of uniform concentration—mg/L.  
\end{table}
Table 8: _Cont._
## 2 Modelling Degradation
### Filter  
Even though time has been removed from the process, spatial localisation makes it possible to consider situations where different parts of the body are either exposed to their environment at different times or intensities. Butterworth filters are well suited to this task [16].  
Suppose that \(\Gamma\) is a positively oriented boundary of some domain \(G\subset\mathbb{C}\). By the Cauchy integral formula  
\[r(\xi)=\frac{1}{2\pi i}\oint_{\Gamma}(z-\xi)^{-1}dz=\begin{cases}1,\xi\in G,\\ 0,\xi\in G^{c}.\end{cases} \tag{2}\]  
The idea is to approximate \(r(\xi)\) with \(r_{N}(\xi)=\sum_{k=0}^{N-1}w_{k}(z_{k}-\xi)^{-1}\), for some \(w_{k},z_{k}\in\mathbb{C}\).  
**Definition 1** (Butterworth filter).: _Selecting the points and weights for \(k=0,1,\ldots,N-1\),_  
\[z_{k}=\gamma e^{i(\theta_{k}+\phi)}+y,\quad w_{k}=\gamma e^{i(\theta_{k}+\phi)}/N, \tag{3}\]  
_with \(\theta_{k}=2\pi k/N\) and \(\phi=\pm\pi/N\). These weights and points are obtained using the N-point uniform trapezoid rule approximation of the contour integral in (2) when \(\Gamma\) is set to the circle \(\Gamma=\{\gamma e^{i(\theta+\phi)}+y:\theta\in[0,2\pi)\}\) enclosing the interval of interest._  
In Figure 2, the effect of filtering on the degradation process is illustrated. Two filters are applied with variation in \(N\) and \(\gamma\).