esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Particulate Removal of PES Electrospun Nanofibrous Membranes in Liquid Filtration
#### 3.2.3 Pure Water Flux and PS Retention Test  
The pure water flux test was carried out to investigate the relationship between the fiber diameter and the water permeability of the PES ENMs. The initial pure water fluxes of ENM 30, ENM 30-0.01, and ENM 30-0.10 were obtained as 15,900, 11,400, and 10,800, respectively (Figure 5a). This decline tendency is similar to the results of other research groups [30; 31]. We found that three PES ENMs exhibited higher pure water fluxes compared with the commercial GSWP 0.22 \(\upmu\)m membrane and had a pure water flux of 9155 L/(m\({}^{2}\) h bar) at 0.17 bar [32].  
On the basis of [PERSON]'s law [33; 34], the pure water flux through a MF membrane can be estimated as follows:  
\[J=\frac{\varepsilon r^{2}}{8\mu\tau}\frac{\Delta P}{\Delta x} \tag{9}\]where J is the water flux (m\({}^{3}\)/s), \(\varepsilon\) is the porosity (-), \(r\) is the pore radius (m), \(\Delta P\) is the pressure difference across the membrane (Pa), \(\mu\) is the dynamic viscosity (Pa s), \(\tau\) is the tortuosity (-) and \(\Delta x\) is the membrane thickness (m).  
Equation (9) implies that the pore size is a very effective factor in determining the initial pure water flux because the porosities and the thicknesses of the PES ENMs have little difference in our study. It should be noted from Figure 3 that the number of nanofibers per unit area is arranged in the order of ENM 30-0.10 > ENM 30-0.01 > ENM 30. Thus, smaller pore size and more nanofibers can make the transfer route of water molecules complicated in the membrane. This results in the reduction of pure water flux.  
The retention test was carried out to investigate the relationship between the fiber diameter and the liquid filtration properties of the PES ENMs. On the basis of the pore size characteristics of the PES ENMs, 0.6 \(\upmu\)m polystyrene (PS) suspensions were selected as target particles. The separation factors in ENM 30, ENM 30-0.01, and ENM 30-0.10 were obtained as 10%, 53%, and 97%, respectively (Figure 5b). The membranes composed of thinner nanofibers had smaller pore sizes and a narrower pore size  
Figure 5: Water permeation (**a**) and 0.6 \(\upmu\)m PS suspension filtration (**b**) performance of the PES ENMs.  
distribution. They can also have a high specific surface area for particle adsorption and a large interconnection among nanofibers [13]. This results in the improvement of liquid filtration performance.
## 4 Role of sRNA in Antibiotic Resistance
### Biofilm-Mediated Increased Drug Resistance in Bacterial Pathogens  
One of the adaptive strategies employed by bacteria to evade the host immune response and antibiotic action is biofilm formation [213; 214; 215]. Biofilms are structured communities of bacteria, encased in an extracellular polymeric substance (EPS) composed of polysaccharides, proteins, lipids, and extracellular DNA [216]. This EPS matrix acts as a physical barrier, limiting antibiotic penetration and creating chemical gradients that reduce the effectiveness of antibiotics [217]. The formation of biofilms is tightly regulated by transcriptional factors. In _P. aeruginosa_, key regulators include AlgR, which promotes the production of alginate (a major biofilm polysaccharide), and the two-component systems GacS/GacA and BifSR, which regulate biofilm maturation [218]. The quorum sensing regulators LasR and RhIR also play crucial roles in coordinating biofilm development by inducing EPS production and virulence factor expression [219].  
sRNAs are now recognized for their crucial role in biofilm formation, a prevalent multicellular bacterial lifestyle across various bacterial species [41; 220]. In _E. coli_, CsrB and CsrC sequester CsrA, resulting in the increased expression of the genes involved in extracellular polysaccharide synthesis. Similarly, in _S. enterica_, small RNAs ArcZ and SdsR regulate the transcriptional switch CsgD, which governs biofilm matrix production and motility repression. Additional sRNAs, McaS, RprA, and OmrA/OmrB, integrate environmental signals to modulate _csgD_ expression, thereby influencing lifestyle transitions. The second messenger c-di-GMP further reinforces biofilm formation by promoting extracellular polysaccharide production [221]. Moreover, in _V. cholerae_, the sRNA RybB represses _ompA_, modulating membrane permeability to favor biofilm formation and environmental adaptation [108]. Finally, in _Bacillus subtilis_, BsrG and SR1 contribute to biofilm formation, with SR1 modulating metabolic pathways for extracellular polysaccharide synthesis and BsrG enhancing stress adaptation. Collectively, these regulatory sRNAs enable bacteria to fine-tune biofilm formation in response to environmental cues.  
Although sRNAs play a crucial role in regulating biofilm formation across multiple bacterial pathogens, this review focuses on three specific sRNAs involved in biofilm formation in _Pseudomonas_ sp. The recent rise in multi-drug-resistant (MDR) _P. aeruginosa_ has become a significant global health concern, largely driven by the overuse and misuse of antibiotics in clinical and environmental settings. Biofilm formation is a key contributor to this multi-drug-resistant phenotype, enhancing bacterial survival and persistence. A deeper understanding of the molecular mechanisms underlying biofilm formation should provide new avenues for the development of targeted therapeutic strategies.
## 3 Results
### Trace Element Concentrations in Water
The highest concentrations of Cr and Cd were observed in the upstream and downstream regions. These trends of spatial variation exhibit two sources \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Trace Elements**} & \multicolumn{6}{c}{**Semiviarogram Models and Parameters**} \\ \cline{2-7}  & **Models** & **Nugget (Co)** & **Sill (Co+C)** & **Co/(Co+C)** = SD (*) & **Range (km)** & **ME * & **RMSE * \\ \hline Cd & Gaussian & 33.43 & 66.78 & 50.06 & 0.14 & 0.004 & 1.05 \\ \hline Pb & Stable & 248.62 & 529.66 & 46.94 & 0.24 & 0.001 & 1.17 \\ \hline Cr & Exponential & 99.25 & 4475.17 & 2.22 & 0.22 & \(-\)0.018 & 0.64 \\ \hline Ni & Stable & 447.82 & 1454.77 & 30.78 & 0.12 & 0.017 & 1.07 \\ \hline Cu & Exponential & 0.21 & 78.50 & 0.27 & 0.17 & \(-\)0.02 & 1.23 \\ \hline As & Stable & 0.17 & 0.21 & 81.90 & 0.02 & \(-\)0.001 & 1.06 \\ \hline Zn & Spherical & 28.46 & 39.54 & 71.97\% & 0.07 & \(-\)0.002 & 5.78 \\ \hline Mn & Exponential & 0.28 & 99.63 & 0.28 & 0.02 & 0.004 & 0.83 \\ \hline \hline \end{tabular}
* ME: mean errors; RMSE: root mean square error; SD: spatial dependence.  
\end{table} 
Table 4: Semi-variogram models and their parameters of trace elements. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Statistical Parameters**} & \multicolumn{6}{c}{**Trace Elements (\#g/L)**} \\ \cline{2-9}  & **Cd** & **Pb** & **Cr** & **Ni** & **Cu** & **As** & **Zn** & **Mn** \\ \hline Minimum & 20.26 & 99.62 & 44.78 & 28.01 & 72.70 & 1.43 & 11.71 & 10.95 \\ \hline Maximum & 51.93 & 198.12 & 219.94 & 180.30 & 102.20 & 3.92 & 36.00 & 50.86 \\ \hline Average & 31.96 & 141.63 & 133.02 & 83.28 & 85.12 & 2.46 & 19.63 & 28.75 \\ \hline Median & 30.15 & 141.31 & 132.51 & 79.11 & 86.67 & 2.23 & 17.09 & 28.63 \\ \hline SD * & 8.37 & 21.15 & 56.75 & 33.41 & 6.72 & 0.58 & 6.53 & 8.87 \\ \hline CV * & 26.19 & 14.93 & 42.66 & 40.11 & 7.90 & 23.77 & 33.26 & 30.84 \\ \hline Skewness & 0.64 & 0.66 & 0.16 & 0.79 & 0.11 & 0.88 & 0.83 & 0.58 \\ \hline Kurtosis & 0.04 & 0.88 & \(-\)1.44 & 0.96 & 0.00 & 0.46 & \(-\)0.17 & 1.11 \\ \hline
1 st quartile & 26.15 & 126.56 & 77.15 & 55.51 & 79.19 & 2.08 & 14.26 & 23.39 \\ \hline
3 rd quartile & 37.07 & 154.24 & 189.59 & 109.57 & 89.70 & 2.89 & 24.07 & 31.73 \\ \hline Guidelines WHO * & 3 & 10 & 50 & 70 & 2000 & 10 & 3000 & 400 \\ \hline \hline \end{tabular}
* CV: coefficient of variation; SD: standard deviation; WHO: World Health Organization.  
\end{table} 
Table 3: Trace element concentration of waters from the hydrosystem. of the hydrosystem contamination by trace elements, such as the input of marine waters containing phosphoric effluents during high tides [37; 55] and that of Zio and Haho Rivers after leaching different kinds of soils (agricultural, mining, and urban soils). Indeed, the extracted phosphorites contain a high concentration of trace elements [35; 36].
## 2 Materials and Methods
### Validation with NWP Data Reanalysis  
First, we have to stress that no reference-independent ZPD measurements exist that provide data at the same time and location of our moving instruments. To overcome this problem, the vertical profile extracted from the weather models can be used. A dataset was selected from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) [41], as these weather model reanalyses represent one of the most complete and reliable reconstructions of the atmospheric state in any place and at any past time in the reanalyses domain.
# A New Resource Allocation Protocol for the Backhaul of Underwater Cellular Wireless Networks
## 3 Description of UBSI Resource Allocation Protocol
### Queue Model and Backhaul Capacity of UBSs
#### 3.1.2 Backhaul Capacity of UBSs  
The backhaul capacity of a UBS is determined by the corresponding UBSC. The UBSC calculates the backhaul capacity of the UBS by considering the traffic information sent from the UBS. The traffic information includes the queue length and the number of packet drops of the UBS. Each traffic information is a mean value averaged during a period \(\omega\) (\(\omega>0\)) which is a positive integer and its value can be changed as a network parameter. The mean value of the queue length is represented as  
\[\overline{Q_{i}}=\frac{1}{\omega}\sum_{n=n+1-\omega}^{n}Q_{i}(a). \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline \(i\) & The index of a UBS \\ \(N\) & The number of UBSs \\ \(n\) & The index of discrete time \\ \(K\) & The maximum queue length \\ \(\mu_{UBSC}\) & The UBSC capacity \\ \(\mu_{UBSmax}\) & The maximum backhaul capacity of a UBS \\ \(\mu_{i}(n)\) & The backhaul capacity of UBS \(i\) at time \(n\) \\ \(\lambda_{i}(n)\) & The input traffic rate of UBS \(i\) at time \(n\) \\ \(\mu_{i}(n)\) & The reserved capacity of a UBS at time \(n\) \\ \(Q_{i}(n)\) & The queue length of UBS \(i\) at time \(n\) \\ \(P_{i}(n)\) & The number of packet drops of UBS \(i\) at time \(n\) \\ \(\rho_{i}(n)\) & The resource utilization of UBS \(i\) at time \(n\) \\ \(O_{i}(n)\) & The message overhead of UBS \(i\) at time \(n\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in the underwater base station initiating (UBSI) resource allocation protocol. UBS: underwater base station; UBSC: underwater base station controller.  
The average number of packet drops of a UBS, denoted by \(\overline{P_{i}}\), can be obtained similar to the Equation (3).  
Using the sum of averaged queue length and packet drops (i.e., \(\overline{Q_{i}}\) and \(\overline{P_{i}}\)), the backhaul capacity of a UBS is proportionally determined on the basis of a linear-proportional algorithm as in [17; 18; 19]. And the backhaul capacity, denoted by \(\mu_{i}\), is also limited no more than the predetermined maximum value \(\mu_{max}\), and thus expressed as  
\[\mu_{i}=min\left[\mu_{max},\mu_{UBSC}\times\frac{\overline{Q_{i}}+\overline{P _{i}}}{\sum_{j=1}^{N}\left(\overline{Q_{j}}+\overline{P_{j}}\right)}\right]. \tag{4}\]  
The backhaul capacity of a UBS will actually be determined by considering the multiple access protocol used in the real UCWN. For example, in case of TDMA, the backhaul resource of a UBS is given by the number of slots per TDMA frame, being proportional to its calculated backhaul capacity. In case of FDMA, the backhaul resource of a UBS is the channel bandwidth, or the number of narrow band channels.  
Using Equation (4), the reserved capacity of a UBSC at the time \(n\), which can be utilized to readjust the capacity of all UBSs on occasion, is also expressed as  
\[\mu_{e}(n)=\mu_{UBSC}-\sum_{i=1}^{N}\mu_{i}(n). \tag{5}\]
## 5 Analysis of Model Performance
### Comparison of Strategies  
Under four different scheduling strategies, the proposed IABC algorithm was used to solve the multi-objective optimization model, and the scheduling results of the CCHP system are shown in Table 5. As can be seen from Table 5, under the two scheduling strategies of FEL and FHL, the capacity allocation of photovoltaic power generation (PV) is less, while the capacity allocation of photovoltaic power generation (PV) under the two scheduling strategies of IFEL and IFHLis greater. Therefore, the _ATC_ of these two strategies is larger, while the _TEC_ and _CDE_ are smaller. Under the two scheduling strategies of FEL and IFEL, the capacity configuration of ECis less than that of the latter two strategies, while the capacity configuration of AC is more than that of the latter two strategies due to firstly satisfying the demand of the system electrical load. Under the two scheduling strategies of IFEL and IFHL, a battery is usedto maintain the stable electric load, and a heat storage tank is used to maintain the stable thermal load, alleviating the instability of photovoltaic power generation. In general, the value of the target function \(F(X)\) using FHLis the smallest, indicating the best performance.
## 3 Methodologies
### Long Short-Term Memory  
LSTM (long short-term memory) is a deep-learning model commonly used to process sequence data and is particularly good at capturing long-term dependencies. It controls the flow of information through gating mechanisms, including input gates, forgetting gates, and output gates, so as to efficiently process sequence data and avoid the problem of vanishing or exploding gradients [36]. LSTM has achieved a wide range of applications in natural language processing, time series prediction and other fields [37]. In LSTM, the cell state acts like a conveyor belt that passes information through the chain structure with minimal changes so that the information flows through almost unchanged [38]. Gates control the inflow and outflow of information, and they are composed of sigmoid neural network layers and a dot-multiply operation that is able to decide which information is important enough should be retained or forgotten. Its structure is shown schematically in Figure 1, as well as the formulas for each variable as follows:  
\[\left\{\begin{array}{c}f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f})\\ i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i})\\ C_{t}^{\prime}=\tan\mathrm{h}(W_{C}\cdot[h_{t-1},x_{t}]+b_{C})\\ C_{t}=f_{t}*C_{t-1}+i_{t}*C_{t}^{\prime}\\ o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o})\\ h_{t}=o_{t}*\tan\mathrm{h}(C_{t})\end{array}\right. \tag{7}\]  
Forget gate  
Figure 1: LSTM schematic diagram.
## 4 Static Network Performance of a TFBN
### Fault Tolerance  
Fault tolerance is a common and important parameter that must be analyzed during the design phase of interconnection networks. The fault tolerance is determined by the connectivity of the graph, where fault tolerance is the highest possible number of vertices that can be disconnected from the graph before the node becomes fully disconnected from the network i.e., the node becomes completely unreachable. High fault tolerance for any network is always desirable; however, it results in a high node degree of the network, which eventually increases the power usage for the node-node packet routing. The \(4\times 4\) BM of TFBN network can support up to five links, which means a \(4\times 4\) BM of TFBN has a connectivity of six. On the other hand, a TFBN(2, L, 0) with \(m=2\) can still be connected to up to six links.
## 4 Experimental Result
### Result and Comparison
#### 4.3.2 Train and Test with Random Spatial datasets
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**[PERSON] Model**} & \multicolumn{2}{c}{**Li Model**} & \multicolumn{2}{c}{**[PERSON] Model**} \\ \hline
**\#** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** \\ \hline
1 & 0.000 & 0.00\% & 0.319 & 26.83\% & 0.527 & 58.54\% \\ \hline
2 & 0.000 & 0.00\% & 0.477 & 48.79\% & 0.575 & 55.49\% \\ \hline
3 & 0.000 & 0.00\% & 0.325 & 28.11\% & 0.401 & 38.02\% \\ \hline
4 & 0.000 & 0.00\% & 0.330 & 32.86\% & 0.241 & 22.54\% \\ \hline
5 & 0.000 & 0.00\% & 0.448 & 39.31\% & 0.616 & 52.64\% \\ \hline
6 & 0.000 & 0.00\% & 0.746 & 74.28\% & 0.856 & 86.15\% \\ \hline
7 & 0.000 & 0.00\% & 0.105 & 8.00\% & 0.474 & 36.00\% \\ \hline
8 & 0.000 & 0.00\% & 0.808 & 75.35\% & 0.829 & 77.21\% \\ \hline
9 & 0.000 & 0.00\% & 0.000 & 0.00\% & 0.435 & 27.78\% \\ \hline
10 & 0.000 & 0.00\% & 0.403 & 39.20\% & 0.505 & 50.17\% \\ \hline
11 & 0.000 & 100.00\% & 0.600 & 61.90\% & 0.678 & 71.45\% \\ \hline
12 & 0.397 & 0.00\% & 0.300 & 26.40\% & 0.438 & 37.08\% \\ \hline
13 & 0.000 & 0.00\% & 0.706 & 64.32\% & 0.866 & 89.19\% \\ \hline
14 & 0.000 & 0.00\% & 0.785 & 80.68\% & 0.834 & 83.76\% \\ \hline
15 & 0.000 & 0.00\% & 0.546 & 51.30\% & 0.554 & 53.31\% \\ \hline
16 & 0.000 & 0.00\% & 0.809 & 67.86\% & 0.863 & 78.57\% \\ \hline OA(\%) & 0.000 & & 54.504\% & & 62.829\% \\ \hline AA(\%) & 6.25\% & & 45.33\% & & 57.37\% \\ \hline Kappa & 0.010 & & 0.481 & & 0.575 \\ \hline  & \multicolumn{2}{c}{**[PERSON] Model**} & \multicolumn{2}{c}{**Hu Model**} & \multicolumn{2}{c}{**Proposed Model**} \\ \hline
**\#** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** \\ \hline
1 & 0.000 & 0.00\% & 0 & 0.00\% & 0.791 & 82.93\% \\ \hline
2 & 0.424 & 0.00\% & 0.424 & 34.63\% & 0.810 & 82.57\% \\ \hline
3 & 0.126 & 0.00\% & 0.126 & 11.51\% & 0.788 & 76.97\% \\ \hline
4 & 0.000 & 0.00\% & 0 & 0.00\% & 0.673 & 68.08\% \\ \hline
5 & 0.000 & 0.00\% & 0 & 0.00\% & 0.886 & 87.59\% \\ \hline
6 & 0.663 & 0.00\% & 0.663 & 97.87\% & 0.935 & 96.65\% \\ \hline
7 & 0.000 & 0.00\% & 0 & 0.00\% & 0.683 & 56.00\% \\ \hline
8 & 0.873 & 0.00\% & 0.873 & 98.60\% & 0.965 & 98.37\% \\ \hline
9 & 0.000 & 0.00\% & 0 & 0.00\% & 0.606 & 55.56\% \\ \hline
10 & 0.071 & 0.00\% & 0.071 & 4.80\% & 0.805 & 76.69\% \\ \hline
11 & 0.583 & 64.89\% & 0.583 & 82.94\% & 0.854 & 85.20\% \\ \hline
12 & 0.000 & 0.00\% & 0 & 0.00\% & 0.803 & 85.77\% \\ \hline
13 & 0.000 & 0.00\% & 0 & 0.00\% & 0.933 & 94.05\% \\ \hline
14 & 0.836 & 0.00\% & 0.836 & 98.60\% & 0.938 & 95.96\% \\ \hline
15 & 0.000 & 0.00\% & 0 & 0.00\% & 0.602 & 51.59\% \\ \hline
16 & 0.000 & 0.00\% & 0 & 0.00\% & 0.958 & 94.05\% \\ \hline OA(\%) & 49.821\% & & 49.821\% & & 84.715\% \\ \hline AA(\%) & 26.81\% & & 26.81\% & & 80.50\% \\ \hline Kappa & 0.009 & & 0.402 & & 0.826 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification results of Indian Pines dataset to which random spatial information is applied.
## 5 Discussion
### Practical Implications  
To our knowledge, no other study has compared the effectiveness between physiological and speech measures through their respective features in explaining user emotions provoked by voice user interface technologies. This novel study not only contributes to the literature regarding voice user interface technology but may also have managerial implications. Indeed, results from this study are particularly relevant within today's context, as the field of voice recognition continues to gain ground. The global voice recognition market size is expected to reach 27.16 billion U.S. dollars by 2026, an increase of 16.8% from 2020 [104]. Consequently, various companies have adopted voice user interface technologies as a competitive advantage. For example, certain high-volume call centers have adopted voice recognition technology to better serve their customers, allowing them to navigate the menu's options in an autonomous, intuitive, and time-saving manner through speech command [105]. To benefit from the success of this user-centric technology, early evaluation of such a product is key. Results from this study not only assist companies seeking to evaluate voice user interface products more efficiently, but also contribute to the underdeveloped guidelines of voice user interface evaluation. Put into context, limited resources may force a UX professional to select a single measure within their vocal product evaluation. Thus, understanding which measure is more informative of user emotions is a valuable insight, strategically and economically.
# A Discrete Hidden Markov Model for SMS Spam Detection
## 5 Conclusions and Future Work  
This paper proposed a discrete hidden Markov model for SMS spam detection, and it is the first research taking advantage of word order information to detect spam SMS. Compared to other traditional and even novel machine learning models, the proposed HMM method scored excellent results among them. In addition, HMM is a relatively simple machine learning model. It can be implemented into the spam filtering industry to meet the huge throughput requirement. In addition, this paper resolved the issue in which the traditional feature extraction algorithm, like TF.IDF, does not work well for SMS spam detection, due to the extremely low term frequency. The proposed HMM is not language-sensitive, which was also validated on Chinese SMS spam detection. The overall performance of the proposed HMM is better on the Chinese dataset than the English dataset.  
The proposed HMM still has limitations. The accuracy depends highly on the size of the training set. The bigger the training set, the more likely SMS words will reoccur in the testing dataset and the better the achieved accuracy. In our future research, we will provide an improved HMM model to make it suitable to a small training set scenario. In addition, as there is no training dataset that has a label for each word of the SMS, each word was labeled based on the property of the SMS in this research. This compromised word labeling method also affected the spam classification accuracy. We will tackle this issue by applying artificial neural networks. Furthermore, the other HMM variants will be explored for SMS spam detection.  
[PERSON] and [PERSON] conceived the idea and developed the algorithm; [PERSON] developed the experimental software and performed the data analysis. [PERSON] prepared the manuscript; [PERSON] and [PERSON] wrote and made revisions to the paper. Both authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Soft Engineering of Key Subjects Construction in Shanghai Polytechnic University, grant number xxkzd1604 and US National Science Foundation, grants number CNS-1801811.  
The authors declare no conflict of interest.