esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Estimation of Logistic Models with Banking Data Using Particle Swarm Optimization
## 7 Tuning PSO Parameters  
A critical aspect of employing an algorithm for optimization problems is its robustness, typically described as the algorithm's capacity to function well amidst variations in its parameters. One approach to address the robustness issue is the Taguchi method [32; 33]. It is a signal-to-noise (SN) ratio which can be defined as follows [53]:  
\[SN=-10\log_{10}(\text{MAPE})^{2}. \tag{22}\]  
A higher value of the SN ratio means the parameter settings of the algorithm are more robust. Several studies have employed the Taguchi method in their optimization problems, especially when using PSO [26; 27; 28; 53; 54].  
In this section, we apply a robustness check of the PSO's parameter settings using the Taguchi method. To this end, we consider five levels of robustness check for setting the parameters listed in Table 6. We execute the PSO algorithm with all parameter settings in the table to estimate the Isoularis model's parameters for the commercial bank case and the Verhulst model's parameters for the rural bank case. At the end, we calculate each SN ratio based on the MAPE obtained from the parameter estimation. The result is illustrated in Figure 6. We must observe which parameter's value gives the highest SN ratio. It is found that the optimal setting of the PSO's parameters is identified as \(m=50\), \(c_{1}=0.5\), \(c_{2}=1.5\), \(w=3\), and \(k_{max}=50\).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**PSO’s Parameter** & **Level 1** & **Level 2** & **Level 3** & **Level 4** & **Level 5** \\ \hline Number of particles (\(m\)) & 10 & 50 & 100 & 200 & 300 \\ Weighted coefficient (\(c_{1}\)) & 0.5 & 1 & 1.5 & 2 & 2.5 \\ Weighted coefficient (\(c_{2}\)) & 0.5 & 1 & 1.5 & 2 & 2.5 \\ Inertia weight (\(w\)) & 1 & 2 & 3 & 4 & 5 \\ Maximum iteration (\(k_{max}\)) & 25 & 50 & 75 & 100 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The levels of the PSO’s parameters setting.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{8}{c}{**Tsoularis Model for Commercial Banks Data**} & \multicolumn{8}{c}{**Verhulst Model for Rural Banks Data**} \\ \cline{2-11}  & \(r\) & \(\alpha\) & \(\beta\) & \(\gamma\) & \(K\) & **MAPE t.** & **MAPE v.** & \(r\) & \(K\) & **MAPE t.** & **MAPE v.** \\ \hline SpO & 0.8027 & 2.1869 & 0.8894 & 3.4229 & 20,757,461 & 3.04\% & 2.98\% & 0.2043 & 264,499 & 2.17\% & 3.24\% \\ PSO & 0.5401 & 2.9998 & 0.9158 & 3.0812 & 13,585,176 & 2.77\% & 0.52\% & 0.2052 & 259,816 & 2.16\% & 2.82\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Result of SpO compared to PSO.  
Figure 6: The SN ratio for PSO’s parameters.
## 3 Results
### Antibacterial Activity against MDR Bacterial Strain
#### 3.3.2 Antibacterial Activity against MDR _E. coli_ Strains  
Three out of four strains were extended spectrum beta-lactamase (ESBL) producing _E. coli_ (EC2, EC5, and EC8). Like MRSA, bacteria am caused by ESBL producing _E. coli_ are associated with higher length of hospital stay and mortality, and are therefore, classified as critical by the WHO. In addition, to beta-lactam antibiotics, these clinical isolates resist to various other commercial drugs; for instance, EC2 and EC5 resist to 15 and 16 antibiotics out of 28 tested, respectively.  
As against wild-type strains, EtOAc extracts were found to be more interesting than methanolic extracts when tested against MDR strains (Table 4). Only the methanolic extract from _Fomitopsis pinicola_ was still active against _E. coli_ clinical isolates: one inhibition band was visible at Rf 0.65 on the bioautogram towards EC5, whereas two less polar molecules inhibited the growth of EC1, one of the most resistant strains in this study; EC6 and EC8 were found to be a little bit more sensitive as four inhibition bands could be observed at Rf between 0.50 and 0.80. Although no clear inhibition zone was detected against the wild-type strain with the methanolic extracts from _Scleroderma citrinum_, a thin but resolved band was visible just above the deposit line for _S. citrinum_ #1 and #4 against EC2 and EC8.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Mushroom Species** & **Extract** & **EC2** & **EC5** & **EC6** & **EC8** \\ \hline _Clitocyte nebularis_ & EtOAc & 0.78 & 0.78 & 0.78 & 0.78 \\  & MeOH & / & / & / & / \\ \hline _Hyphaloma fasciculare_ & EtOAc & / & 0.78 & 0.78 & 0.78 \\  & MeOH & / & / & / & / \\ \hline _Pleurotus ostreatus_ & EtOAc & 0.78 & 0.78 & 0.40, 0.62 & 0.78 \\  & MeOH & / & / & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 4: Rf values of inhibition bands induced by mushrooms extracts on bioautograms against the four MDR _E. coli_ strains EC2, EC5, EC6, and EC8.  
Figure 4: TLC bioautograms obtained with the EtOAc extracts from the four _Scleroderma citrinum_ samples against the MDR strain SA2. Compounds **a–d** are discussed more in details in the text regarding their occurrence and selectivity.  
Regarding the EtOAc extracts, inhibition bands were mainly observed in the upper part of bioautograms, indicative of bioactive compounds of relatively low polarity (Figure 5).  
An intense inhibition band at Rf 0.75 was present in several bioautograms obtained with extracts from phylogenetically unrelated mushroom, such as _Pleutorus ostreatus_, _Lactifluus piperatus_, _Fomitopsis pinicola_, or _Scleroderma citrium_. This inhibition band was also observed in several mushrooms' extracts tested against _S. aureus_ MDR strains (Table 3), suggesting that a broad-range antibacterial compound may be produced by most, if not all, fungal lineages. We thus sought to identify the chemical nature of this promising molecule, using the EtOAc extract of _Lactifluus piperatus_ as a source, as this compound was predominant in this species.
## 4 Results
### Accuracy Evaluation of Time Series InSAR Results  
To assess the internal precision of the subsidence rate field extracted from Sentinel-1A SAR data processed by PSInSAR technique in Shanghai area, the standard deviation of the obtained subsidence rate is statistically analyzed in this section. According to the statistical results, 637,213 PS points were detected in the study area, 98% of PS points standard deviation are less than 1 mm/year, and the maximum standard deviation reaches 2.28 mm/year. In conclusion, PSInSAR technique exhibits high reliability and stability in monitoring surface subsidence in Shanghai area.
## 4 Fingerprinting-Based Positioning with Spatial Side Information  
In this section, we discuss fingerprinting-based positioning with spatial side information. First, we elaborate on the 2D sensor array and then, in association with this, explain the discretization process in the area fractions with tiles of the area in which the positioning is intended. Next, we describe the positioning process. Finally, we introduce the four pattern-matching methods selected for our study.
## 4 SOC Estimation Based on EKF and UKF  
According to Equations (3)-(5) and (9), the state-space equation is as follows:  
\[\left[\begin{array}{c}U_{1,k}\\ h_{k}\\ SOC_{k}\end{array}\right]\] \[=\left[\begin{array}{ccc}\exp\Bigl{(}-\frac{T_{s}}{R_{1}C_{1}} \Bigr{)}&0&0\\ 0&\exp\Bigl{(}-\Big{|}\frac{yI_{k}\gamma T_{s}}{C_{N}}\Bigr{|}\Bigr{)}&0\\ 0&0&1\end{array}\right]\left[\begin{array}{c}U_{1,k-1}\\ h_{k-1}\\ SOC_{k-1}\end{array}\right] \tag{13}\] \[+\left[\begin{array}{ccc}1-\exp\Bigl{(}-\frac{T_{s}}{R_{1}C_{1}} \Bigr{)}\Bigr{]}R_{1}&0&0\\ 0&\Bigl{(}1-\exp\Bigl{(}-\Big{|}\frac{yI(t)\gamma T_{s}}{C_{N}}\Bigr{|}\Bigr{)} \Bigr{)}&0\\ 0&0&\frac{\eta T_{s}}{C_{N}}\end{array}\right]\left[\begin{array}{c}I_{k-1} \\ M(S,\hat{S})\\ I_{k-1}\end{array}\right]+\omega_{k-1}\]  
\[U_{L,\;k}=U_{\infty,k}+R_{0}I_{k}+U_{1,\;k}+h_{k}+v_{k} \tag{14}\]  
where \(\omega_{k-1}\) is the system process noise and \(v_{k}\) is the measurement noise. Based on the above state-space equations, the proposed model can be implemented on EKF and UKF, respectively
## 5 Discussion
### Applicability Analysis of High Mining Height in Ultrathick Coal Seams  
In recent years, the mining height in the Shendong mining area has increased from working face heights of 4.5 m to 5.5 m and 6.3 m, and the mining technology used to achieve high mining heights in this mining area is state-of-the-art within China and even worldwide.  
Large-mining-height technology can adapt to the mining of thick coal seams in the Shendong mining area  
There have been many successful production practices in the Shendong mining area using high-mining-height technology, with rich technical experience. Moreover, the heights of the large working faces in the Shangwan and Bulianta mines reached 6.3 m. With the study of practical rock control and coal mining technology at a height of 6.3 m and scientific research and key technology development for a height of 7 m, it is possible to carry out high mining of the 7 m thick coal seam in the Shendong mining area while ensuring safety, high production, and efficiency.  
The Shendong mining area has an extremely thick coal seam, and high-mining-height technology is economically optimal  
The main factors affecting the economic viability of the Shendong mining area's ultrathick coal seam, if high-mining-height technology is used, are as follows:  
1. The amount of excavation work for high-mining-height mining tunnels is small  
Compared to layered mining and fully mechanized top coal caving mining, mining extra-thick coal seams with working face heights greater than 7 m requires less excavation and support at high mining heights. The tunnel system is simple, and excavation replacement is easier to coordinate, which is conducive to the high yield and efficient mining of extra-thick coal seams.  
2. Low frequency of movement during high-mining-height mining  
For a coal seam with a thickness of 7 m, an area of coal seam is moved only once at full height, with the lowest cost.  
3. High coal seam recovery rate during high-mining-height mining  
The Shendong mining area uses a 7 m thick coal seam and hydraulic supports with appropriate support heights. High-mining-height mining technology can extract most of the resources from thick coal seams, and the recovery rate of the working face can reach over 95%.  
Based on the above analysis, high-mining-height mining can achieve a high yield and efficient mining of 7 m thick coal seams both technically and economically.
## 4 Yaw Stability Control Method for the Liquid Tank Semi-Trailer and Its Implementation
### Verification of the Robustness of the Yaw Stability Control Method  
The effectiveness of the proposed control method in controlling the vehicle yaw stability is verified in the previous two sections. As another important criterion to evaluate the quality of the control system, the robustness of the proposed control method is analyzed and verified.  
The robustness of control system means that the system has the ability to maintain a certain performance under the disturbance of uncertainty [19]. The proposed multi-objective PID control method is established based on the fuzzy PID control method, which has good stability and robustness [20]. To verify the robustness of the proposed control method, the tractor yaw rates under the step-steering-angle input working condition are compared when the control parameters \(K_{P}\), \(K_{I}\), and \(K_{D}\) of the multi-object PID control method are increased by 30%. The other simulation parameters are the same with those in Section 5.2, and the comparison results are shown in Figure 20.  
It can be seen from Figure 20 that compared with the control effect of the optimal parameters, the differences between the tractor yaw rate curves are not significant when these three parameters \(K_{P}\), \(K_{I}\), and \(K_{D}\) are increased by 30%, which proves the robustness of the proposed control method.  
Figure 20: Comparison of the tractor yaw rates under the step-steering-angle input working condition when the control parameters \(K_{P}\), \(K_{I}\), and \(K_{D}\) of the multi-object PID control method are increased by 30%.
## 2 Measurements and Quality Control Processes
### Parameterizing Daytime Longwave Downward Radiation
#### 3.7.3 Parameterizing Longwave Downward Radiation for the All-Day Time Interval  
The previous section described the estimation of \(L_{dtn}\) during the daylight hours. The longwave radiation during daytime is necessary for the energy balance applications, while that during the nighttime is more favourable for the building passive cooling systems [4]. [PERSON] and [PERSON] [7] have proposed an empirical equation to calculate (\(L_{dtn}\)) for the all-day, nighttime and daytime intervals. The proposed equation has the following form:  
\[L_{dtn}=\sigma*\left(T_{a}\right)^{4}*\left(a*\ln(e/T_{a})+b*RH+c\right) \tag{18}\]  
where \(T_{a}\) is the air temperature in Kelvin, \(e\) is the actual vapor pressure in hPa and \(RH\) is the relative humidity in percentage; \(a\), \(b\), and \(c\) are empirical coefficients. The proposed equation was selected because it is based on routine measurements of a meteorological station. For the all-day time interval, the original coefficients proposed by the authors have the following values: \(a\) = 0.08, \(b\) = 0.0011, and \(c\) = 1.029. The coefficients of daytime and nighttime intervals showed almost similar values with those for the all-day time interval. Based on a training data set which covers the period of 2013-2015, the locally calibrated coefficients have been estimated giving the following values: \(a\) = 0.039, \(b\) = 0.0007 and \(c\) = 0.884 [9]. The performance of the model showed a coefficient of determination of R\({}^{2}\) = 0.74 and a RMSE = 22.95 W/m\({}^{2}\). Figure 17 shows the comparison of the estimated \(L_{dtn}\) for the all-day time interval and the measured values of \(L_{dtn}\) for the data set of the period 2016-2017. The R\({}^{2}\) is about 0.68 and the standard deviation of the residuals (\(S\)) is 21.869 (Figure 17). Almost all the data points are within the 95% prediction intervals.
# AG1478 Elicits a Novel Anti-Influenza Function via an EGFR-Independent, GBF1-Dependent Pathway
## 2 Results
### AG1478 Had a Broad-Spectrum Antiviral Activity across Different Cell Models Independent of IFN
Cells were infected with A/WSN/33 viruses and anti-influenza activities of AG1478 were tested in (**A**) 293T cells, (**B**) human primary epithelial cells, (**C**) mice primary epithelial cells, and (**D**) Raw 264.7 cells, \(n=4\). (**E**) C57 BL/6 mice were treated with 20 mg/kg AG1478 (\(n=5\)) or 20 mg/kg Oseltamivir (\(n=5\)) or vehicle control (\(n=8\)). Then, lung viral titers (PFU/g) were determined. Beas2B cells were infected with (**F**) A/California/07/09 H1N1, (**G**) A/Udom/72 H3N2, and (**H**) B/Brisbane/60/2008 Type B under the treatment of 2\(\mu\)M AG1478. \(n=4\). Data shown are PFU/mL, mean \(\pm\) SEM. #: \(p<0.05\).
# Modeling the 2013 Zika Outbreak in French Polynesia: Intervention Strategies
## Methodology
### Mosquito Ecology  
The mosquito life cycle can be modeled using differential equations that define the rates of change in each stage with respect to time. This continuous system governs the transition from eggs to larvae, larvae to pupae, and pupae to adults and their respective mortality rates ([PERSON], 2018; [PERSON] and [PERSON], 2018; [PERSON] and [PERSON], 2018). In this study, the differential equations are discretized and the number of mosquitoes in each compartment, Eggs (\(E\)), Larvae (\(L\)), Pupae (\(P\)) and Adults (\(A\)) are computed on a daily basis. Because only female mosquitoes bite, and contribute to the transmission of Zika, the adult compartment represents just the female population; the gender ratio for the mosquitoes is assumed to be 1:1 ([PERSON] et al., 2018). The adult compartment is further divided into three stages, the female mosquitoes searching for hosts for a blood meal (\(A_{h}\)), female mosquitoes in the resting stage waiting for eggs to develop (\(A_{r}\)), and the females in the oviposition stage (\(A_{o}\)) who lay eggs and return to the \(A_{h}\) stage. This model was introduced and is described in detail in [PERSON] and [PERSON] (2018). The maturation of mosquitoes in stage \(j\) into the next stage is governed by the maturation rate \(\rho_{j}\) (mosquitoes per day) and each stage \(j\) has its own mortality rate \(\mu_{j}\) (mosquitoes per day) where \(j=\{E,L,P,A_{h},A_{r},A_{o}\}\). \(\rho_{A_{o}}\) is the oviposition rate instead of the maturation rate for mosquitoes in the oviposition stage. Furthermore, the larval stage has a density independent mortality rate (\(\mu_{L_{1}}\)) and a density dependent mortality rate (\(\mu_{L_{2}}\)) because competition for food and other resources is a considerable factor in this stage [PERSON] and [PERSON] (2018).
# A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
## 1 Introduction  
Sentiment analysis, often referred to as opinion mining [1, 2], is a key area within natural language processing (NLP) that focuses on recognizing and categorizing emotions or sentiments expressed in text. With the digital era generating an enormous amount of user-generated content, sentiment analysis has become an essential tool across many fields [3]. Businesses use it to track customer feedback, healthcare providers use it to assess mental health, politicians to monitor public sentiment, and social scientists to explore societal trends [4]. It is vital to understand how people feel about a product, service, or issue, which in turn can influence everything from brand management to market predictions based on consumer input and online conversations [5].  
This research focuses on addressing one of the most pressing challenges in sentiment analysis within such interconnected ecosystems: mitigating bias in sentiment predictions. Bias can manifest in many ways, such as favoring one demographic over another, misclassifying sentiments in underrepresented groups, or amplifying stereotypes [4]. Such biasescan have far-reaching implications, from reinforcing societal inequalities to eroding trust in AI systems [6]. By developing a bias-aware sentiment analysis framework, this study aims to enhance the reliability and fairness of sentiment predictions, particularly in applications where fairness is paramount [7].
# Deadlocks Detection in Multithreaded Applications Based on Source Code Analysis
## 2 Thread Synchronisation Mechanisms
### Locks  
The locks are used to implement critical sections, where the critical section should be understood as a fragment of the application code located between calling the lock function and calling the unlock function. The critical section is most often used to perform operations on a shared resource. Locking is performed by three functions, i.e., pthread_mutex_lock, pthread_mutex_trylock and pthread_mutex_timedlock. These functions differ in behaviour when they cannot set a lock when it is already in use. The first of them waits for the lock to be released by the thread that currently occupies it. The second function returns the EBUSY value when the lock fails and allows the thread that called it to continue working. Calling the last function results in waiting for the lock to be occupied until the time specified as the second parameter. If the operation fails, the ETIMEDOUT value is returned, and the thread that was waiting for the lock resumes. Any thread that has created a lock must release it by using the pthread_mutex_unlock function regardless of which function was used to create the lock.  
Locks established using the above functions can have one of four types:  
* PTHREAD_MUTEX_NORMAL (PMN)
* PTHREAD_MUTEX_ERRORCHECK (PME)
* PTHREAD_MUTEX_RECURSIVE (PMR)
* PTHREAD_MUTEX_DEFAULT (PMD)  
The lock type has no effect on cross-thread collaboration. However, it has significance within one thread in situations when there is an attempt to re-lock or attempt to release a lock that is not created.
# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 3 Results
### Validation of Estimated GPP and NPP  
#### 3.2.1 Validation Against Ground-Based GPP  
Spatial distribution of high resolution (30 m) GPP (Figure 10) and NPP (Figure 11) were obtained according to the method proposed in Section 2.3. We found the GPP and NPP reached their maximum in July and August. Areas covered with crops occupied the highest values of GPP and NPP, followed by the mountainous areas. Regions covered with deserts and Gobi demonstrated the lowest GPP and NPP values. Validation against the ground measurements demonstrated that both the GPP base on downscaled GLASS data (D_GPP_GLASS) and GPP based on downscaled MODIS data (D_GPP_MODIS), had high accuracy. In general, a good linear relationship existed between the downscaled GPP and ground-based GPP. R\({}^{2}\) between the ground-based GPP and D_GPP_GLASS could be as high as 0.78, and the RMSE was 2.69 g C\(\cdot\)m\({}^{-2}\).d\({}^{-1}\) (Figure 12a). The R\({}^{2}\) between the ground-based GPP and D_GPP_MODIS could be as high as 0.84, and the RMSE is 2.20 g C\(\cdot\)m\({}^{-2}\).d\({}^{-1}\) (Figure 12b). Mean relative error (MRE) of D_GPP_GLASS was 5.07%, and MRE of D_GPP_MODIS was 3.21%.  
We compared the precision of the high resolution (30 m) D_GPP_GLASS, D_GPP_MODIS, with the low resolution (1 km) MODIS GPP (MOD17 C55) and the estimated GLASS GPP (Figure 12c,d). MOD17 products were an application of the described radiation conversion efficiency concept to predictions of daily GPP using a LUE model based on the MOD12 land cover product, Data Assimilation Office (DAO) meteorological datasets, and the MOD15 LAI/FPAR products [55]. The estimated GLASS GPP and NPP products with a spatial resolution of 1 km and temporal resolution of the 8-day were generated by the GLASS FPAR and LAI data from the MusyQ GPP/NPP algorithm [5]. We could find that estimated GLASS GPP and MODIS GPP underestimate ground-based GPP in most vegetation types. RMSE between estimated GLASS GPP and ground-based GPP could be as high as 6.42 g C\(\cdot\)m\({}^{-2}\).d\({}^{-1}\), while the RMSE between estimated MODIS GPP and ground-based GPP could reach 7.09 g C\(\cdot\)m\({}^{-2}\).d\({}^{-1}\). A higher R\({}^{2}\) and lower RMSE demonstrated that the GPP from the downscaled data achieved a better precision than the low resolution (1 km) estimated GLASS GPP and MODIS GPP,  
Figure 9: Comparison of MODIS LAI, GLASS LAI, D_LAI_GLASS and D_LAI_MODIS: (**a**) Cropland; (**b**) orchard; (**c**) vegetable field; (**d**) wetland. Note that the LAI are the mean values of pixels from all sites in cropland.