esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancement of Yaw Moment Control for Drivers with Excessive Steering in Emergency Lane Changes
## 6 Conclusions  
An active safety control system is proposed in this study to respond to emergency situations where the driver steering angle exceeds the safety range and the vehicle speed undergoes noticeable changes during control. A reference state and a reference steering angle obtained from a linear vehicle model with a tire/road friction saturation limitation are used by the control system. In case of any discrepancy between the intended reference values and that of the driver steering angle and vehicle state, the system intervenes by implementing the saturation angle scheme and DYM method. To accommodate varying speeds, polytopic LPV modeling is employed, and uncertainties are addressed through Lyapunov redesign, categorizing them into structured matching and non-matching uncertainties. The design of DYM control utilizes numerical solutions to LMIs, incorporating constraints on DYM values based on the friction circle's limit condition for combined wheel slip. Before completing the design, we apply an input saturation technique using a high-gain approach to improve the utilization of the control and enhance the system response in emergency situations. The resulting enhanced yaw moment is distributed to the wheels to generate wheel control torque. The proposed control system integrates the enhanced DYM with a steering saturation scheme, which prevents the driver's overreacting steering from destabilizing the vehicle. As a result, the control system maintains tire/road friction within the feasible linear operating range, enabling the DYM to allocate more resources to other stabilization tasks. Simulation examples demonstrate the effectiveness of the enhanced integration control.  
Conceptualization, S.-T.P. and C.-K.C.; methodology, S.-T.P.; software, Y.-R.S.; formal analysis, S.-T.P. and Y.-C.C.; investigation, Y.-R.S.; resources, S.-T.P. and C.-K.C.; writing--original draft preparation, S.-T.P. and Y.-C.C.; writing--review and editing, S.-T.P. and C.-K.C.; supervision, S.-T.P. and C.-K.C.; funding acquisition, S.-T.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Council of Taiwan, ROC. Grant number NSTC 112-2221-E-218-015.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 2 Supervisory and Control System Based on Real-Time Power Management
### Energy Cost Optimization
#### 2.3.2 Stationary Storage  
The stationary storage, represented by lithium-ion batteries, must be protected from overcharging and over-discharging; thus the maximum storage power \(P_{S\_max}\) and the maximum and minimum state of charge of the storage \(SOC_{S\_max}\) and \(SOC_{S\_min}\) must be respected to extend the storage lifetime [45,48] as given by (7) and (8). The simplified state of the charge of the storage \(soc_{S}\) evolution [43] is given by (9) for simplicity, where self-discharge and temperature are not considered:  
\[-P_{S\_max}\leq p_{S}(t_{i})\leq P_{S\_max}, \tag{7}\]  
\[SOC_{S\_min}\leq soc_{S}(t_{i})\leq SOC_{S\_max}, \tag{8}\]  
\[soc_{S}(t_{i})=SOC_{S_{0}}+\frac{1}{3600\cdot E_{Bat}}\int_{t_{0}}^{t}\ p_{S}( t_{i})dt, \tag{9}\]  
where \(SOC_{S_{0}}\) is the initial \(soc_{S}\), and \(E_{Bat}\) is the storage energy capacity (kWh) and the storage power \(p_{S}(t_{i})=p_{S\_C}(t_{i})-p_{S\_D}(t_{i})\). The PV power should not be limited if \(SOC_{S\_max}\) is not reached; this constraint is given by (10):  
\[ppv_{\_S}(t_{i})=0\ if SOC_{S}(t_{i})<SOC_{S\_max}. \tag{10}\]
## 5 Conclusions  
This study applies bootstrap DEA to analyze the source of management efficiency of the 4G LTE-oriented companies in wireless industry. The three-year data for the representative wireless carriers in USA--Verizon Wireless, AT & T, Sprint Nextel, and T-mobile--are analyzed through the research model designed to analyze the management, service quality, network quality, and market efficiencies of each company.  
This study is unique its application of [PERSON] [1] classification of service quality to analyze how network quality has affected management efficiency by defining functional quality as service quality efficiency and technical quality as network quality efficiency. By looking into service quality and network quality separately, this study made it possible to evaluate the management efficiency of U.S. wireless carriers through a matrix that plots each wireless carrier based on service and network quality, which in turn, provided a strategic guideline for the wireless carriers on how to improve their management efficiency.  
Using four DEA models applying the bootstrap method and verified by Mann-Whitney U test and Wilcoxon W test, this study also tested three hypotheses to examine the relationship among management efficiency, network quality efficiency, service efficiency, and market efficiency. This investigation revealed that the distribution of wireless carriers with high management efficiency and that of wireless carriers with high network quality efficiency were identical. As network quality was analyzed using upload/download speed as input factor, this result is significant in that it provides empirical analysis to support the FCC's report [27] on the U.S. wireless industry which highlights upload/download speed as the most important factor for data network quality.  
Another implication revealed through the hypotheses test was that the distribution of market efficiency, which was deduced using network quality and service quality as input factors, was identical to that of management efficiency. This result is supported by AT & T and Verizon Wireless, which were DMUs showing highest management efficiency for 2013 and, at the same time, showed market shares of 32% and 35% respectively in the fourth quarter of 2013. Thus, this study confirms and corroborates with previous reports that U.S. wireless market customers select their wireless carriers based on network quality such as upload/download speed, and presents a guideline for wireless carriers on where to focus in order to increase their market share.
## 3 Method for Evaluating Stability of Manholes during Liquefaction
### Safety Factor for Manhole Uplifting (\(F_{S}\))
#### 3.2.5 Uplifting Pressure Due to Hydrostatic Pressure Loading on Bottom of Manhole (\(U_{s}\))  
The uplifting pressure on the bottom of the manhole (\(U_{S}\)), due to the hydrostatic pressure acting on it, is estimated using Equation (6).  
\[U_{S}=\gamma_{w}(Z_{B}-Z_{W})\times A \tag{6}\]  
where \(\gamma_{w}\) is the unit volume weight of water (kN/m\({}^{3}\)), \(Z_{B}\) is the depth to the bottom of the manhole (m), \(Z_{W}\) is the depth to the water table (m), and \(A\) is the area relative to the outer diameter of the manhole (m\({}^{2}\)).  
.2.6 Uplifting Pressure Due to Excess Pore Water Pressure Loading on Bottom of Manhole (\(U_{D}\))  
The uplifting pressure on the bottom of the manhole (\(U_{D}\)) due to excess pore water pressure loading is estimated using Equation (7).  
\[U_{D}=\Delta u\times A=L_{u}\times\sigma^{\prime}_{V}\times A \tag{7}\]  
where \(\Delta u\) is the excess ore water pressure (kN/m\({}^{3}\)), \(A\) is the area relative to the outer diameter of the manhole (m\({}^{2}\)), \(\sigma^{\prime}_{V}\) is the effective loading pressure (kN/m\({}^{2}\)) in the soil at the same depth as the bottom surface of the manhole under hydrostatic pressure, and \(L_{u}\) isthe excess pore water pressure ratio. Excess pore water pressure ratio \(L_{u}\) is estimated with Equation (8) using the resistance value (\(F_{L}\)) against liquefaction [4; 5].  
\[\left.\begin{array}{ll}L_{u}={F_{L}}^{-7}&(F_{L}\geq 1)\\ L_{u}=1&(F_{L}<1)\end{array}\right\} \tag{8}\]  
In this study, \(U_{D}\) is estimated based on the assumption that the ground is liquefied with a resistance value of \(F_{L}<1\) against liquefaction.  
The weighting method is a construction method in which the \(F_{S}\) is estimated using the balance of forces in the vertical direction at the time of an earthquake, as described above, and the vertical load is increased by the self-weight of the manhole, such that the \(F_{S}\) is equal to or greater than the predetermined value. Therefore, the physical significance is relatively clear.  
This study adopted a value of 1.1 as the standard for evaluating the \(F_{S}\), because \(F_{S}\geq 1.1\) is one of the design criteria for manholes set by the Japan Sewage Works Association (JSWA). The change in \(F_{S}\) and the effect of the weighting method on \(F_{S}\) are considered.
## 4 Conclusions  
DOX was successfully encapsulated into three MSN-based systems, namely MSN, MSN-PLH and MSN-PLH-TAM, using a post grafting loading method. The release kinetics in all three systems demonstrated a pH-sensitive pattern, with more DOX released at acidic pH. Following the attachment of TAM molecules into the MSN-PLH, the pH-effect was still retained. Despite the lower amount of DOX released from our functionalized system as compared with native MSNs, our developed functionalized MSNs were able to demonstrate an effect of pH on the release kinetics. Even though obtained data demonstrated expected pH- sensitive patterns of the functionalized systems, more in vitro pharmacodynamics investigations need to be performed, to confirm if this effect is sufficient to off-set the dose and drug-associated toxicities of DOX on healthy cells, while still maintaining its efficacy against tumours. Our future work will improve the release of DOX, as well as investigate the targeting capacity of our systems against breast cancer with cellular uptake studies.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11136121/s1](https://www.mdpi.com/article/10.3390/app11136121/s1)]([https://www.mdpi.com/article/10.3390/app11136121/s1](https://www.mdpi.com/article/10.3390/app11136121/s1)), Figure S1: FT-IR spectrum of DOX@MSNs vs unloaded native MSNs, Figure S2: FT-IR spectrum of DOX@MSN-PLH vs unloaded MSN-PLH.  
Conceptualization, C.M.D., S.E.P. and S.G.; methodology, C.M.D., M.J.S., S.E.P., Y.S. and S.G.; validation, C.M.D.; formal analysis, C.M.D.; investigation, C.M.D. and M.J.S.; resources, S.E.P., S.G. and Y.S.; data curation, C.M.D.; writing--original draft preparation, C.M.D.; writing--review and editing;; S.G., S.E.P., M.J.S. and Y.S.; visualization, C.M.D.; supervision, S.E.P., Y.S. and S.G.; project ad-ministration, Y.S., S.E.P. and S.G.; funding acquisition, S.G. and S.E.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** The authors would like to acknowledge all the members of the _Pharmaceutical Innovation and Development Group_ (PIDG--UniSA) and _Bioinorganic Synthesis and Imaging Group_ (BSIG--UniSA) for their support throughout this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Efficient Melody Extraction Based on Extreme Learning Machine
## 3 Extreme Learning Machine-Based Melody Extraction
### Coarse Melody Pitch Estimation  
In this work, the coarse melody pitches are estimated by ELM, with one semitone interval. ELM parameters need to be trained based on training set. The training audios are first pre-processed, as described in Section 3.1, and the input vectors are generated by incorporating some CQT amplitude spectra of adjacent frames.  
Pitch estimation and voicing detection are two sub-problems in the melody extraction task. Either performance of these two sub-problems affects the other. To avoid their mutual inhibitions, pitch estimation and voicing detection are conducted simultaneously. That is, the unvoiced situation is also considered as one pattern, the same as pitches. More specifically, the labels are one-hot vectors with the first element equal to one for the unvoiced frames. The other elements correspond to the individual pitches.  
During the training stage, given the training set \(\{\mathbf{X}_{l},\mathbf{Y}_{l}\}=\left\{\left(\mathbf{x}_{i},\mathbf{y}_{j} \right)\right\}_{j=1}^{l}\), where \(\mathbf{x}_{j}\) is an input feature vector, \(\mathbf{y}_{j}\) is the corresponding label, and \(l\) is the number of training samples, the input weights \(\mathbf{w}_{i}\) and hidden layer biases \(b_{i}\) are first generated following uniform distribution on [-1,1]. Then, the hidden layer output matrix \(\mathbf{H}\) is determined according to Equation (5), where the activation function is the sigmoid function, i.e.,  
\[g(\mathbf{w}_{i}\mathbf{\bullet x}_{j}+b_{i})=\frac{1}{1+e^{-(\mathbf{w}_{i} \mathbf{\bullet x}_{j}+b_{i})}} \tag{15}\]  
Afterwards, the output weights \(\mathbf{\hat{\beta}}\) can be calculated according the Equation (9). All parameters of ELM are now known, including the input weights \(\mathbf{w}_{i}\), hidden layer biases \(b_{i}\), and the output weights \(\mathbf{\hat{\beta}}\).  
During the test stage, the coarse melody pitches can be obtained based on the ELM parameters. Specifically, given the input vectors of a recording, the input feature vectors can be obtained, as described in Section 3.1.  
Then, the output of sample \(\mathbf{x}_{j}\) is  
\[f(\mathbf{x}_{j})=h(\mathbf{x}_{j})\mathbf{\beta} \tag{16}\]  
where \(h(\mathbf{x}_{j})=[g(\mathbf{w}_{1}\mathbf{\bullet x}_{j}+b_{1}),g(\mathbf{w} _{2}\mathbf{\bullet x}_{j}+b_{2}),\ldots,g(\mathbf{w}_{N}^{\top}\mathbf{ \bullet x}_{j}+b_{N})]\).  
Next, the function \(\operatorname{argmax}(\cdot)\) is utilized to locate the maximum value. If the maximum value is the first element of vector \(f(\mathbf{x}_{j})\), the corresponding frame is considered as unvoiced. In other cases, the pitches, with respect to these locations, are the coarse melody pitches.
# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 4 Experimental Validation  
In order to further verify the damage identification performance and robustness of the proposed system, considering the noise and measurement errors are inevitable in actual measurement process, a seven-story steel frame structure in laboratory was selected for damage identification. Here, two damage magnitudes and three damage locations were set up for examining the capability of proposed system.
## 2 Results and Discussion  
### Microencapsulation Efficiency  
Spectrophotometric methods were used to determine the encapsulation efficiency of the microcapsules, allowing us to measure how well the core material (aromatic compounds, phenolics) was encapsulated. The amounts of phenolic or other bioactive compounds released from the microcapsules were measured before and after encapsulation. High encapsulation efficiency ensures that the bioactive compounds remain protected during processing and storage, maintaining the quality and aroma of the tea. Figure 1 presents the encapsulation efficiency of four different wall materials, evaluated based on total polyphenol content. The extract obtained contained 192.26 mg/g of polyphenol (calculated based on catechins) per gram of dry extract. The polyphenol contents in the encapsulated powders are shown in Figure 1. Gelatin encapsulation demonstrated higher efficiency, ranging from 74% to 79%, while maltodextrin had the lowest, ranging from 37% to 41%. This difference can be attributed to the structure of different wall materials. This finding is in accordance with other studies [29; 30]. Gelatin, as a protein with various functional groups, such as amino, carboxyl, and hydroxyl groups, can form hydrogen bonds and hydrophobic interactions with the hydroxyl and other groups in polyphenol molecules,enabling more effective encapsulation and stabilization of polyphenols. Additionally, the high molecular weight and multifunctional groups of gelatin enable it to provide greater efficiency and stability during the encapsulation process [30].
## 6 Case Study
### Scoring and Ranking of the Candidate Consolidation Centers for a Single Mode  
Firstly, according to the complex network theory, the raw data of the 44 node cities of the railway transportation network was normalized using Equation (10) in Section 5 in this paper, and then the CRITIC was employed to obtain the weights of six indicators of the railway transportation network according to Step 3 in Section 5. The concrete values of each indicator were 0.1939, 0.1363, 0.2029, 0.1994, 0.1335, and 0.1339. Similar to the process described above, the weights of the six indicators for the four transportation modes were calculated and are shown in Table 2.  
Next, the weighted normalized matrix was obtained by multiplying the weights with the normalized matrix. Finally, the values of the six indicators for the 44 city nodes of the railway transportation network, including closeness centrality, eigenvector centrality, betweenness centrality, degree centrality, constraint coefficient, and clustering coefficient, were determined through the application of the combination of GRA and the TOPSIS. The concrete rankings are also shown in Table 6.  
Similarly, the scores of the six indicators for the candidate consolidation centers of the highway, water, and air networks are also shown in Appendix B Tables 7-9. Tables 8 show the indicator values, scores, and rankings of the six indicators for each candidate consolidation center in the railway transportation network, highway transportation network, air transportation network, and water transportation network, respectively.
# Physical-Chemical and Thermal Properties of Clays from Porto Santo Island, Portugal
## 3 Materials and Methods
### Methods
#### 3.2.2 Chemical Analysis  
The major elements (%) SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), Fe\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\), CaO, MgO, K\({}_{2}\)O, Na\({}_{2}\)O and P\({}_{2}\)O\({}_{5}\) were determined by X-ray fluorescence spectrometry (XRF), using the AXIOS PW4400/40 XRF-Panalytical equipment (manufactured by Marvel Panalytical, in Almelo, Netherlands), operating on a Rb tube under argon/methane. The loss on ignition (LOI) was also determined by heating the samples at 1000 \({}^{\circ}\)C for 2 h. Additionally, trace and minor elements were obtained from an aqua regia digestion of the samples and were determined by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) using Agilent Technologies 7700 Series equipment (manufactured by Agilent Technologies, in Santa Clara, CA, USA), following the method of [PERSON] and Rocha [4]. The detection limits in mg/kg were as follows: 0.01 for Cd, Ce, Hg, Mo and Y; 0.02 for In, Nd, Sb and Tl; 0.03 for Cs; 0.05 for Sn; 0.1 for As, Be, Co, Dy, Er, Eu, Gd, Ho, Li, Lu, Nb, Ni, Pb, Pr, Rb, Sc, Se, Tb, Th, Um, U, W, Yb, Zn and Zr; 0.2 for Cu; 0.5 for Ba, La and Sr; 1.0 for B, Cr, Hg, Mn and V.
## 2 Post NG-PON2 Research: Emerging Trends and Open Challenges
### NG-PONs and Converged Fiber-Wireless (FiWi) Access Networks: State of the Art and Recent Progress
A scheduling and DBA scheme that allows the co-existence of high-speed 10 Gb/s EPONs and WDM PONs on the same optical infrastructure was studied in [21]. A cost-effective and fully centralized RoF network architecture based on integrated long-reach WDM PON, with an optical fiber range of 100 km, and LTE/WiMAX networks, where the optical and wireless transmission impairment compensation is done only at the central office, was proposed and experimentally investigated for so-called quintuple-play services (high-speed data, voice, television, management, and home security) [22]. In [23], a novel MAC protocol for FiWi access networks to efficiently carry the ever increasing amount of video traffic was proposed and investigated. Recently, the _integration of wireless and fiber optic sensors_ into FiWi access networks has begun to receive increasing attention. In [24], a hybrid approach combining genetic algorithm and tabu search techniques was proposed to determine the optimal placement of optical network units (ONUs)--the CPE of a PON--in hybrid PON-wireless sensor network (WSN) based networks, whereby the ONUs' high power and processing capabilities were used to serve as cluster heads with the objective to minimize the wireless sensors' energy consumption. In [25], a DBA algorithm was developed for a hybrid PON-WSN network, where ONUs are connected to FTTH terminals or to WSN cluster heads for remote monitoring systems in a ubiquitous city, e.g., medical sensors in a hospital monitoring system. The integration of solar-powered wireless sensors and low-power fiber optic sensors into a PON was experimentally demonstrated in [26]. Finally, the research and development vision of an ideal access system architecture was outlined in [27], identifying the following three key design goals of future broadband access network architectures: (i) \"adaptable;\" (ii) \"dependable;\" and (iii) \"eco-conscious\". Toward this end, the authors concluded that passive (optical) network infrastructures should be used as much as possible and passive equipment may be shared in order to enable cost reduction, promote open and fair service competition, create novel value-added services, and introduce original business ideas for the realization of future-proof broadband access networks based on both wireless and fiber media.
## 4 Results  
### Molybden Re-Os Age  
The abundances and isotopic compositions of Re-Os for four molybden samples of the Xiawolong gold deposit are shown in Table 1 and illustrated in Figure 5. The Re and \({}^{187}\)Os abundances in these samples are 33.85-192.6 ppm and 41.3-234.72 ppb, respectively, which are much higher than the Re and \({}^{187}\)O blanks of 4 pg and 0.09 pg, respectively, in this test, and the \({}^{187}\)Re concentrations are 21.28-121.1 ppm. However, the common Os is also detectable in these molybden samples; three samples of which have low common Os contents of 0.0043-0.7217, with \({}^{187}\)Os/common Os ratios of 288.61-23,239.53 much higher than 20, suggesting that common Os have little effect on calculating the model Re-Os age [61]. Although the sample of XML-g4 contains a relatively higher common Os of 8.1377 ppbwith a \({}^{187}\)Os/common Os ratio of 16.38, a little lower than 20, the calculated model age of 118.57 \(\pm\) 1.62 Ma is consistent with those of the other three samples, which range between 116.26 \(\pm\) 1.84 Ma and 119.59 \(\pm\) 1.62 Ma, within uncertainty. All the four model Re-Os ages give a weighted average age of 118 \(\pm\) 1.7 Ma (MSWD = 0.83, 2\(\sigma\)) (Figure 5a). Furthermore, the regression for the \({}^{187}\)Re and \({}^{187}\)Os data of all the four molybdenum samples yields a well-constrained isochron age of 118.4 \(\pm\) 2.5 Ma (MSWD = 2.2, 2\(\sigma\)), with a bigger error initial \({}^{187}\)Os abundance of 0.22 \(\pm\) 1.94 ppb (Figure 5b), which is consistent with the weighted average Re-Os model age within errors. Based on the identical single Re-Os model age, the weighted mean model age and the isochron age within uncertainties, it is suggested that the molybden Re-Os analytical results are accurate.