esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Challenges in Existing Protected Areas to Mitigate Grassland Decline  
Assessing the conservation effectiveness of existing protected areas has significant implications for the planning and establishment of the Altai Mountains National Park. The framework used in this study, combined with Geodetector analysis, identifies the top five continuous variables influencing forest and grassland as key factors for evaluating the effectiveness of protected areas. Our findings indicate considerable variation in the effectiveness of protected areas in preventing forest and grassland loss. Specifically, protected areas were found to be far less effective in halting grassland decline compared to forest loss. The difference in forest loss between protected and matched unprotected areas remained relatively stable over time, whereas the difference in grassland loss showed greater fluctuations, potentially due to the policy-related neglect of grassland conservation, such as the insufficient inclusion of grassland protection measures in land-use planning and national reforestation programs [5].  
The observed discrepancies in conservation outcomes may be attributed to the prioritization of either forest or grassland conservation within each protected area. The results (Figure 10) also reveal important management implications. To improve the overall effectiveness of the Altai Mountains National Park Candidate Area, future planning should integrate tailored restoration and resilience strategies that address the concrete risks and challenges faced by different ecosystems. These strategies should be informed by lessons learned from successful conservation efforts in existing protected areas of the region. Specific strategies could include implementing rotational grazing to alleviate grassland pressure, incorporating grassland-specific restoration initiatives into conservation plans, and enhancing monitoring systems to track grassland health alongside forest conditions. Moreover, it is crucial to account for factors such as topography, climate change projections, land-use patterns, and ecological connectivity when designing restoration strategies.  
In this context, our findings provide valuable insights into the strengths and limitations of current conservation efforts, highlighting areas where strategies have been successful while identifying opportunities for improvement. These findings offer a useful framework for the future development of national parks, particularly in promoting balanced ecosystem conservation that addresses the needs of both forest and grassland preservation.
## 4 Simulations and Results  
Extensive simulations are performed to demonstrate the effectiveness of the proposed method under different scenarios. The simulation performance of the proposed method is compared with that of existing methods, referred to herein as Method 1 [26] and Method 2 [27]. The comparison of the overall system performances is carried out based on the following performance measures:A. Relative modeling error of the secondary path, defined as  
\[\triangle S(n)(dB)=10\log_{10}\frac{\parallel\mathbf{S(n)}-\hat{\mathbf{S}(n)} \parallel^{2}}{\parallel\mathbf{S(n)}\parallel^{2}} \tag{37}\]  
B. Mean square error (MSE) at the error microphone, \(E[e^{2}(n)]\).  
C. DFC estimation MSEs of primary noise \(p(n)\), which can be represented by  
\[E[\hat{e}_{\hat{a}_{i}}^{2}(n)]=E[(\hat{a}_{iopt}-\hat{a}_{i})^{2}],\quad E[ \hat{e}_{\hat{b}_{i}}^{2}(n)]=E[(\hat{b}_{i,opt}-\hat{b}_{i})^{2}] \tag{38}\]  
where constants \(\hat{a}_{i,opt}\) and \(\hat{b}_{i,opt}\) are the optimum values to eliminate all the sinusoid components in the primary noise, which can be computed using Equation (20) [26].  
The secondary path \(S(z)\) adopts a truncated impulse response of the acoustic path, which is an experimental datapoint provided in [29]. \(S(z)\) and its estimates \(\hat{S}(z)\) are FIR filters of the order \(M=\hat{M}=21\). The frequency responses of \(S(z)\) are shown in Figure 4. A zero-mean white Gaussian noise with variance 0.5 is used as a training signal \(v(n)\) in the SPM, and a scaled factor \(k\) is used to initialize the power of the \(v(n)\) for different methods to obtain better performances in the simulations. Moreover, a zero-mean white Gaussian noise of variance 0.01 is added to the primary noise as a measurement noise \(v_{p}(n)\). For each of the simulation results, a Monte Carlo average over 100 has been achieved. The main simulation results are given as follows.
## 5 Risk Assessment Method Based on Three-Parameter Interval Grey Number Decision-Making
### Three-Parameter Interval Grey Number Decision-Making  
The three-parameter interval grey number refers to the most likely value of the given grey number (center of gravity)--namely, the interval grey number [29] can be written as \(A(\otimes)=[\underline{a},\bar{a},\bar{a}]\).  
Assume that \(A(\otimes)=[\underline{a},\bar{a},\bar{a}]\) and \(B(\otimes)=[\underline{b},\widetilde{b},\overline{b}]\) are two three-parameters interval grey numbers, where \(\widetilde{a}\) and \(\widetilde{b}\) are the most likely values, \(\underline{a}\) and \(\underline{b}\) are the possible lower limit values, and \(\overline{a}\) and \(\overline{b}\) are the possible upper limit values. The distance between \(A(\otimes)\) and \(B(\otimes)\)[30] is  
\[d(A,B)=\sqrt{\frac{3}{5}\Big{(}\widetilde{a}-\widetilde{b}\Big{)}^{2}+\frac{ 2}{5}[\gamma(\underline{a}-\underline{b})^{2}+(1-\gamma)\Big{(}\overline{a}- \overline{b}\Big{)}^{2}]} \tag{24}\]  
where \(\gamma\) represents the risk preference of the decision-maker, \(\gamma\in[0,\ 1]\). When \(\gamma>0.5\), it indicates that the decision-maker is risk-averse (tends to use the left endpoint to measure the distance between gray numbers). When \(\gamma<0.5\), it means that the decision-maker is risk-seeking (tends to use the right endpoint to measure the distance between gray numbers). When \(\gamma=0.5\), it indicates that the decision-maker is risk-neutral (tends to comprehensively consider the endpoints on both sides to measure the distance between gray numbers). To fully consider the impact of the fluctuation of risk value on risk judgment caused by uncertainty, this paper takes \(\gamma=0.5\).  
Referring to the decision-making method of the three-parameter interval grey number distance measure, this paper uses expectation to represent the most likely value and represent the upper and lower limits of each point in the left and right fields of expectation, respectively. The steps are as follows: (1) Taking the interval corresponding to the three sampling points on the PDF obtained by Formula (23) as the three-parameter interval gray number \(A(\otimes)\), \(A(\otimes)\) = \(R\)= \([u_{x_{i}}-\sqrt{3}\sigma_{x_{i}}u_{x_{i}},u_{x_{i}}+\sqrt{3}\sigma_{x_{i}}]\); (2) according to the expression of \(A(\otimes)\),the normal cloud model corresponding to each risk grade range is converted into a three-parameter interval gray number \(B(\otimes)\), so \(B(\otimes)=[E_{x}-\sqrt{3}E_{n},\ E_{x},\ E_{x}+\sqrt{3}E_{n}]\); and (3) the risk grade of the equipment is determined by measuring the distance \(d(A,B)\) between \(A(\otimes)\) and \(B(\otimes)\).
# Occupational Inhalation Health Risk Assessment of TCE Exposure in the Korean Manufacturing Industry
## 3 Results
### Risk Characterization
#### 3.3.4 Risk Reduction Scenario  
Among the non-carcinogenic risks, 60% of the 95 th percentile HQ exceeded the acceptable risk standard of 1; among the carcinogenic risks, 85% of the 50 th percentile HQ exceeded 1. In the 95 th percentile HQ, all industries exceeded 1. Because TCE is a VOC, most reduction methods considered in this study were tested for VOCs. The reduction method was applied to manufacturing fabricated metal products with the highest non-carcinogenic and carcinogenic HQ values. As local exhaust ventilation is mandatory in Korea, it was not included in the reduction method. The proportion of workplaces with fewer than 50 employees among Korean manufacturing companies was 98.7% [55], and among these companies, the personal protective equipment wearing rate of 32.7% to 44.3% was applied [56], and the efficiency of respirators was also applied. Through a Monte Carlo simulation, the 50 th percentile HQ and 95 th percentile HQ for carcinogenicity and non-carcinogenicity were re-derived, and the reduction ratio of the rate at which the HQ exceeded 1 was derived.  
The VOC reduction efficiency using half-type masks with an activated carbon filter was 26%, and that using a half-type respirator mask with an activated carbon filter was 83% [57]. The reduction efficiency of a half-type respirator mask with cartridges replaced once a day was 83.6% [58], and that of a half-type respirator mask with cartridges replaced once a month was 46.6% [58].  
Consequently, Table 6 shows that the non-carcinogenic risk reduction effect decreased by 1.56 and 1.6 times in the ratio of HQ exceeding 1 when the mask efficiency was 83% and 83.6%, decreased by 1.22 times when the mask efficiency was 46.6%, and when the mask efficiency was 26%, it decreased by 1.12 times. However, the reduction effect of the ratio of HQ of 1 or more in cancer risk was reduced by 1.03 times when wearing masks with 83% and 83.6% efficiency, and there was no reduction effect by 1.00 times when wearing masks with 26% and 46.6% efficiency.  
Figure 3: Results of the carcinogenic hazard quotient (HQ) to trichloroethylene by industry type.
## 2 Service Restoration Problem Statements  
### Service Restoration Process  
To improve the reliability of distribution systems, SR is performed by distribution system operators to restore outage loads by finding proper backup feeders that can provide power supply for the out-of-service loads under a fault situation until the fault is eliminated [27]. For the SR, a series of switching operations is required to transfer the outage loads to the alternative feeders. Therefore, the SR results in a new network topology of the distribution system, and the number of possible topologies is exponentially increased according to the number of controllable switches in the system.  
Figure 1 illustrates the general SR process in distribution systems. When a fault occurs in a distribution system feeder, a proper protective device, such as a circuit breaker (CB), operates to cut off the power supply from a main source to the fault location. The DAS recognizes the fault through alarms that inform whether the CB operates or not. Then, fault information, such as fault indicators (FIs), received through communication from FRTUs is utilized to identify a fault location. Once the fault location is determined, the faulty section is isolated from the main source by opening the upstream and downstream automatic switches of the fault, and the power supply to the upstream loads is resumed by enclosing the CB. The SR is performed to restore the downstream outage loads by multiple switching operations causing a new system topology. If the fault is eliminated, then the system returns to the original topology before the fault.  
An example of the service restoration process is depicted in Figure 2. Suppose that the fault occurs in the section between S1 and S2 switches. The CB is tripped by a protection relay such as overcurrent relays (OCRs) and overcurrent ground relays (OCGRs), and FRTUs on CB and S1 experience fault currents exceeding a predetermined value for generating FIs. Due to the tripping of the CB, all the loads in the faulty feeder are out-of-service as shown in Figure 2a. The DAS determines the fault location using the FI information and performs the fault section isolation by opening S1 and S2. Then, CB is recolosed to restore loads in the section between CB and S1. To restore the downstream loads of S2, an SR is performed by closing a candidate switch such as S3, S4, and S5.
# Control of Ribosome Synthesis During the Cell Division Cycles of _E. coli_ and _Synechococcus_
## 2 Control of r-protein synthesis in _E. coli_
### Control of rRNA synthesis in rapidly changing environments
A light responsive DNA binding protein, Rbp1, stimulates rRNA synthesis in light but not in darkness. These observations led to the construction of a plausible model involving Rbp1 as a gene regulatory protein that coordinates r-protein production and rRNA synthesis. The location of the _rbp1_gene locus within a r-protein operon will be crucial in verifying the existence of the model. The value of a genetic scheme is in presenting a heuristic model of coordinate regulation of rRNA and r-protein syntheses where none exits at present. There are several important questions that remain. How is transcription of the entire set of r-protein operons initiated? What will trigger the cell to engage the feedback loop in terminating r-protein and rRNA syntheses? A similar conundrum exists in elucidating the coordinate regulation of cell cycle events in bacteria. In _E. coli_ and _Synechococcus_, the common characteristics of cell cycle events are the sequential occurrences of DNA replication, genome segregation and cell division septum formation. In _Synechococcus_, ribosome synthesis appears first in the cell cycle. The coordinate regulation of these major cell cycle events cannot be based solely on physical and physiological parameters or by cell age. The well-ordered sequence of cell cycle events must involve coordinated transcriptions by gene regulatory proteins. A model that describes the coordinate regulation of macromolecular synthesis by employing sequentially expressed regulatory molecules in _Synechococcus_ has been reported. It would be of great interest to determine the coordinate regulation of cell cycle in _E. coli_. As indicated by [PERSON] (2001) attempts must be made to clear the \"cloud of unknowing\" in the coordinate regulation of cell cycle events in _E. coli_. Recent advances in genetics and molecular biology techniques, and the availability of genomic sequences of increasing numbers of bacteria, may eventually reveal the gene regulatory proteins in coordinating cell cycle events.
# Production and Processing of a Spherical Polybutylene Terephthalate Powder for Laser Sintering
## 3 Results and Discussion
### Thermal Properties  
One of the key material features that ensure its successful processing by laser sintering is the presence of a wide sintering window, situated between the onset of crystallization, and the onset of melting. The DSC thermograms in Figure 8 show a sufficient window of 24.8 \({}^{\circ}\)C width for the starting granulate material. The window is however narrowed considerably when the material is transformed into a powder. The crystallization onset is shifted to higher temperatures by 10.7 \({}^{\circ}\)C, resulting in a sintering window that is merely 17.2 \({}^{\circ}\)C wide. This could lead to problems during laser sintering processing, because thermal gradients on the powder bed due to inadequate heating can be in the order of 10-15 \({}^{\circ}\)C, especially at the higher temperatures required for the processing of PBT.  
It is observed that the change in thermal properties already occurs after extrusion, and remains present as the powder is refined. [PERSON] et al. [13] observed similar behavior in  
Figure 8: Differential scanning calorimetry (DSC) thermograms of PBT granulate (starting material) and produced PBT powder.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Powder** & \(\alpha_{\mathbf{A}}\)[\({}^{\circ}\)] & \(\mathbf{f_{A}}\)[-] & **HR [-]** \\ \hline PBT & 42.05 \(\pm\) 3.52 & 1.51 \(\pm\) 0.16 & 1.097 \\ Duraform@PA & 43.76 \(\pm\) 4.25 & 1.48 \(\pm\) 0.20 & 1.221 \\ iCoPP & 45.01 \(\pm\) 6.24 & 1.56 \(\pm\) 0.26 & 1.236 \\ \hline \end{tabular}
\end{table}
Table 5: Powder flowability results for the PBT powder and commercially used reference powders.  
They attributed the shift in crystallization temperature to nucleation by microscopic metal particles introduced to the powder during the milling process. Since the powder in the current research was produced with a different method, it is unlikely that the same mechanism plays a role. Polymer degradation could be an alternative reason, especially considering that a grade without stabilizing additives was used. As it turns out however, even when a commercial, stabilized PBT grade is used, the same shift in crystallization temperature occurs. Results from [PERSON] et al. [34] on the other hand do support the notion of PBT hydrolysis being a major contributor to the raise in crystallization temperature. It is very well possible that despite stabilizing additives and preliminary drying of the PBT granulate, the water content in the hygroscopic PEG is so high, that degradation via hydrolysis during extrusion cannot be prevented. Additionally, the additives may be washed out during the extrusion process, if they have a higher affinity with the PEG phase.
# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 3 Materials and Methods
### Model Overview
#### 3.2.1 Pre-processing  
The preprocessing step is essential in cyberbullying detection. It consists of both cleaning of texts (e.g., removal of stop words and punctuation marks), as well as spam content removal [112]. In the proposed model, it has been applied to remove and clean unwanted noise in text detection. For example, stop words, special characters, and repeated words were removed. Then, the stemming for the remaining words to their original roots has been applied as a result of this preprocessing, and the dataset containing clean tweets is produced for the proposed model to be run and predicted.
# Parallel and Distributed Frugal Tracking of a Quantile +
## 1 Introduction
: _Given a multiset \(S\) with \(n\) elements drawn from a totally ordered universe set \(\mathcal{U}\) and a real number \(0\leq q\leq 1\), the inferior \(q\)-quantile (respectively, superior \(q\)-quantile) is the element \(x_{q}\) whose rank in \(S\) is such that_  
\[x_{q}\in S\ :\ R(x_{q})=\lfloor 1+q\cdot(n-1)\rfloor \tag{2}\]  
_(respectively, \(R(x_{q})=\lceil 1+q\cdot(n-1)\rceil\))._  
For instance, \(x_{0}\) and \(x_{1}\) represent, respectively, the _minimum_ and the _maximum_ element of the set \(S\), whilst \(x_{0.5}\) represents the _median_. Frugal[21] is an algorithm for tracking a quantile in a streaming setting; its name reflects the fact that it needs just a tiny amount of memory for this task. Two variants are available, namely Frugal-1U and Frugal-2U; the former uses one unit of memory whilst the latter uses two units of memory in order to track an arbitrary quantile in a streaming setting. EasyQuantile[22] is a recent frugal algorithm designed for the problem of tracking an arbitrary quantile in a streaming setting. This work extends [23] as follows: we shall present a comparative analysis of the speed of convergence of these algorithms; then, we shall design and analyze parallel, message-passing based versions that can be used for monitoring network latency quickly and accurately. Moreover, we shall also discuss the design of their distributed versions. Finally, we shall provide and discuss extensive comparative results. This paper is organized as follows. Section 2 introduces the sequential Frugal-1U and Frugal-2U algorithms. Section 3 presents the sequential EasyQuantile algorithm. Next, we analyze the speed of convergence of the algorithms in Section 4 and present the design of our parallel, message-passing based versions in Section 5, in which we also discuss corresponding distributed versions. The algorithms are analyzed in Section 6, in which we derive their worst case parallel complexity. Experimental results are presented and discussed in Section 7. Finally, we draw our conclusions in Section 8.
# Recent Advances in Hydrothermal Oxidation Technology for Sludge Treatment
## 2 Results and Discussion
### Hydrothermal Oxidation Technology
#### 2.3.2 Principle of HTO Technology
summarized the transformation of organic nitrogen in the SCWO process, finding that the primary products are formamide, NH3-N, CO2, and formic acid, with NH3-N primarily originating from the hydrolysis of formamide. Furthermore, NH3-N can be oxidized into N2O and N2 under suitable conditions. The yield of NH3-N increases with rising temperature. When dimethylamine and trimethylamine are used as reactants, NH3-N primarily originates from the intermediate dimethylamine, while the concentrations of NOx, N2O, and N2 remain relatively low. Similar conclusions apply to aromatic organic compounds containing amino groups. For instance, aniline, under the action of hydroxyl radicals (HO-), generates intermediates such as phenol and NH3-N. Under the influence of metal catalysts, phenol and NH3-N are further converted into final products like N2, N2O, and NO3-, as reaction temperature, oxygen availability, and retention time increase. Third is transformation of organic phosphorus [37]. Phosphorus in sludge is typically converted from organic phosphorus compounds (R-PO4) to inorganic phosphate (PO4-) during the oxidation process. Following this, dissolved phosphorus can be recovered by adding appropriate precipitating agents, usually magnesium (Mg2+) or calcium (Ca2+), along with available ammonium (NH4+). Magnesium phosphate precipitates primarily in the form of magnesium ammonium phosphate (struvite), while calcium phosphate (CaP) can form a broader range of compounds. The most common forms are hydroxyapatite (HAP) and dicalcium phosphate (HDP), but other forms, like amorphous calcium phos phate (ACP), brushite, or octa calcium phosphate, are also possible. The main reaction processes are represented by the following Equations (10)-(12):  
Figure 3.2  
Figure 3.3  
Figure 3.4