esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Inverted Pendulum on a Cart Application
In this paper we are interested on the proposed controller part; that is, on the adaptive fuzzy controller. The values of the optimized parameters obtained by every algorithm and those of the previous heuristic tuning (not optimized parameters) are contained in Table 1.  
On the other hand, Figure 9 outlines the membership functions for each one of the inputs, which have been drawn according to the values of the optimized and not optimized parameters obtained by the three different algorithms. In Figure 10 all membership functions are plotted together in such a way that the effect of each tuning algorithm can be appreciated. Figure 8: Operation outline of the swing-up controller. Figure 7: Experimental inverted pendulum on a cart. The values of the objective function obtained by the optimization process in the first and second stages are presented in Table 3. It can be seen that the PSO algorithm gets the smallest value in both optimization stages. Also, in order to quantify the control performance, we have computed three indexes: the root-square average of the regulation error of the pendulum, the root-square average of the tracking error of the cart and the root-square average of the applied torque (all of them based upon the \(\mathcal{L}_{2}\) norm) which are given in general form by  
\[\mathcal{L}_{2}[(\cdot)]=\sqrt{\frac{1}{T-t_{0}}\int_{t_{0}}^{T}\|(\cdot)\|^ {2}\ dt} \tag{46}\]  
where \(T\) is the total control time and \(t_{0}\) is the initial time of interest. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Stage 1** & **Stage 2** \\ \hline NOT OPT. & 175.939 & 235.608 \\ FFA & 96.835 & 115.871 \\ DE & 96.544 & 115.741 \\ PSO & 96.520 & 114.916 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Obtained values of the objective function in every optimization stage. Figure 9: Optimized and not optimized membership functions for each one of the inputs.
## 4 Example Analysis
### The Time-Varying Performance Function of the In-Service Transmission Tower
#### 4.3.3 The Structural Reliability Analysis  
Finally, the reliability index of \(t_{r}=\)30 is calculated based on Equations (18)-(21), and the calculation results are shown in Tables 4 and 5. In order to verify the accuracy of the proposed method, the reliability index calculated by the traditional MEM is given, and the MCS with \(3\times 10^{6}\) sample points is used as the standard solution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Raw Moments** & **MCS** & **The Proposed Method** & **Relative Error \(\varepsilon\) (\%)** \\ \hline \(M_{Z,1}\) & \(8.9690\times 10^{7}\) & \(8.9231\times 10^{7}\) & \(0.51\) \\ \(M_{Z,2}\) & \(8.7464\times 10^{15}\) & \(8.6250\times 10^{15}\) & \(1.39\) \\ \(M_{Z,3}\) & \(9.1014\times 10^{23}\) & \(8.8797\times 10^{23}\) & \(2.44\) \\ \(M_{Z,4}\) & \(9.9986\times 10^{31}\) & \(9.6338\times 10^{31}\) & \(3.65\) \\ \(M_{Z,5}\) & \(1.1512\times 10^{40}\) & \(1.0931\times 10^{40}\) & \(5.05\) \\ \(M_{Z,6}\) & \(1.3815\times 10^{48}\) & \(1.2897\times 10^{48}\) & \(6.64\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of the raw moments.  
Figure 3: Sample point.  
Table 5 shows that the reliability index of the proposed method is 3.802, which is almost identical to the reliability index of MCS, which is 3.798. This indicates that the proposed method can provide highly accurate reliability results, with a relative error of only 0.11%. In contrast, the traditional MEM based on the first four moments [24] could not yield accurate results, with a relative error of the reliability index as large as 10.72%. These results demonstrate that for global reliability analysis of in-service transmission towers, the proposed method significantly improves the accuracy of the traditional MEM.
# Tag-to-Tag Interference Suppression Technique Based on Time Division for RFID
## 2 RFID Collision Problem
### Related Works on Collision Problem
Tree-based anti-collision algorithms use a virtual tree to read all tag data within the read range of the reader by iteratively querying a subset of tags at different levels, and tag IDs are distributed based on their prefixes. The root is the set of tags to be identified, intermediate nodes represent the group of colliding tags, and the leaves represent individual tag responses [24]. They are further classified as tree splitting (TS), query tree (QT), binary search (BS), and bitwise arbitration (BTA) [17]. In the TS protocol, multiple responding tags are grouped using a random number generator. On the other hand, the QT protocol stores the tree information at the reader. The reader broadcasts a query with a bit-string prefix, which is then matched with the tag ID's prefix, after which the tag with the matched prefix responds to the query with its ID to the reader. This process continues until only one tag responds [25]. This overcomes the problem of generating a random number using TS, and decreases the cost and computational complexity. In BS, the communication starts by the reader transmitting the serial number of tags. The serial number is then compared against their ID. In this protocol, communication is possible if tags with an ID equal to or lower than the serial number respond to the reader. Unlike the above protocols, BTA operates by requesting tags to respond bit-by-bit from the most significant bits to the least significant bit of their ID [17]. The ALOHA protocol is a probabilistic approach, where a signal responds within a random time slot in a frame to identify tags. Of the different ALOHA-based protocols, the frame-slotted ALOHA (FSA) and dynamic frame-slotted ALOHA (DFSA) are most commonly used in RFID systems because they can reduce the collision probability between tags. In FSA, the reader determines the frame size and the actual duration of the slot, and tags use random slots from among the entire set of slots (the total number of slots and the frame size are fixed). Therefore, in a reader interrogation zone, for a small number of tags, there is the problem of wastage because of the empty slots, and for a large number of tags, there is a collision problem. This drawback of FSA is partially solved by the DFSA. In DFSA, the reader will add the number of slots in one frame when there a large number of slots have collided. Similarly, the reader will decrease the number of slots in one frame size when there are many empty slots [26]. In DFSA, the size of the next frame is adjusted after estimating the number of remaining tags at the end of each frame without performing any operation for the ideal slots and collided time slots within the current frame. Therefore, the request time by the reader during these time slots are wasted. Consequently, if we try to reduce the request time, the probability of tag collisions may be increased [27].
## 3 Results and Discussion
### CH/CNC and CH/CD Edible Films
#### 3.1.3 Optical Properties  
Optical properties are crucial features that affect the suitability, the appearance and the marketability of edible films and coatings for various applications. They include color and transparency and can be easily detected by human vision. They characterize the surface and affect certain aspects of foods' quality [43; 55]. Therefore, it is desirable to produce films with lighter color and increased transparency. For this reason, the difference of color with a white plate (\(\Delta\)E), the white index (WI), the chrome (C*), the yellow index (YI) and the transparency was measured in all film samples.  
The effect of CNC or CD on the optical properties of the final formed edible films is shown in Table 3.  
The \(\Delta\)E values of the films indicate differences that can be perceived with a naked eye, as \(\Delta\)E > 1 [56]. In particular, the final formed edible films with only CH presented the highest \(\Delta\)E value, which was expected as these films had a light-yellow color. For the same reason, these films presented the lowest WI value. The incorporation of CNC and CD caused significant changes (\(p\) < 0.05) in most of the optical properties, leading to the improvement of the final formed films (Table 3). Both CNC and CD reduced the color difference between the final formed edible film and the white plate (\(\Delta\)E). This is indicated by the increased WI and the decreased YI values, as well as the increased transparency, which is desired for edible films and coatings. In general, the transparency of the films was increased with the addition of both CNC and CD. The C* value, which is indicative of the color intensity, also remained low in all cases [38].  
The above experiments show that both materials (CNC and CD) contributed to the improvement of the optical properties with comparable results. Nevertheless, the CH/CD samples showed slightly lower C* values giving a small advantage in choosing CD over CNC.
###### Abstract  
**Abstract:** The advances in the digital era have necessitated the adoption of communication as the main channel for modern business. In the past, business negotiations, profiling, seminars, shopping, and agreements were in-person but today everything is almost digitalized. **Objectives:** The study aims to examine how the Internet of things (IoTs) connects text-object as part of NLP and AI responding to human needs. Also, how precipitated changes in the business environment and modern applications such as NLP and AI embedded with IoTs services have changed business settings. **Problem statement:** As communication takes lead in the business environment, companies have developed sophisticated applications of NLP that take human desires and fulfill them instantly with the help of text, phone calls, smart records, and chatbots. The ease of communication and interaction has shown a greater influence on customer choice, desires, and needs. Modern service providers now use email, text, phone calls, smart records, and virtual assistants as first contact points for almost all of their dealings, customer inquiries, and most preferred trading channels. **Method:** The study uses text content as part of NLP and AI to demonstrate how companies capture customers' insight and how they use IoTs to influence customers' reactions, responses, and engagement with enterprise management in Industry 4.0. The _\"Behavior-oriented drive and influential function of IoTs on Customers in Industry 4.0\"_ concept was used in this study to determine the influence of Industry 4.0 on customers. **Results:** The result indicates the least score of 12 out of 15 grades for all the measurements on a behavior-oriented drive and influential function of IoTs on customers. **Conclusion:** The study concluded that NLP and AI are the preferred system for enterprise management in the era of Industry 4.0 to understand customers' demands and achieve customer satisfaction. Therefore, NLP and AI techniques are a necessity to attain business goals.  
Keywords:natural language processing; artificial intelligence; Internet of Things; enterprise management; Industry 4.0 +
Footnote †: journal: Applied
# Application of Microgel as a Sorbent for Bisphenol Analysis in Liquid Food Samples
## 3 Results
### Effect of the Amount of Microgel  
In order to achieve the maximum quantitative recovery in the extraction of bisphenol's, different amounts of microgel were tested. Therefore, 1, 2.5 and 5 mg of microgel were applied for the sorption of different concentrations of BPA. Figure 2 illustrates that the more microgel sorbent was applied, the higher concentration of BPA was adsorbed. Although our poly(MEO\({}_{2}\)MA) microgel had a large adsorption capacity (Q\({}_{\text{max}}\) in the range of 55 to 159 mg:g\({}^{-1}\) for all tested bisphenol's [28]), 1 mg of microgel was not sufficient for the total sorption of even the smallest concentration of BPA. The amount of 5 mg of the microgel  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Packaging Type**} & \multirow{2}{*}{**pH of the Sample**} & **BPF** & **BPE** & **BPA** & **BPB** \\ \cline{3-6}  & & & & **Recovery [\%] \(\pm\) RSD** & \\ \hline Tap water & - & 6.9 & 75.2 \(\pm\) 1.76 & 84.6 \(\pm\) 0.66 & 106 \(\pm\) 0.91 & 109 \(\pm\) 2.06 \\ \hline Mineral water & Polycarbonate bottle & 5.2 & 81.0 \(\pm\) 2.34 & 86.8 \(\pm\) 4.34 & 85.8 \(\pm\) 3.21 & 90.9 \(\pm\) 4.54 \\ \hline Sour cabbage & Package no 7 & 3.4 & 77.1 \(\pm\) 5.01 & 88.5 \(\pm\) 0.83 & 90.0 \(\pm\) 1.11 & 102 \(\pm\) 0.21 \\ \hline Energy drink & Can & 3.5 & 80.9 \(\pm\) 1.23 & 75.2 \(\pm\) 4.36 & 73.2 \(\pm\) 3.74 & 95.4 \(\pm\) 4.22 \\ \hline Pineapple & Can & 3.6 & 75.4 \(\pm\) 3.09 & 84.9 \(\pm\) 3.41 & 109 \(\pm\) 0.76 & 104 \(\pm\) 1.17 \\ \hline Figs & Can & 3.7 & 70.5 \(\pm\) 0.54 & 77.5 \(\pm\) 0.95 & 101 \(\pm\) 1.48 & 83.8 \(\pm\) 1.66 \\ \hline Fruits in syrup & Can & 3.8 & 71.2 \(\pm\) 1.71 & 72.4 \(\pm\) 1.22 & 101 \(\pm\) 1.91 & 108 \(\pm\) 1.54 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recoveries of BPF, BPE, BPA and BPB in different liquid food samples spiked with 50 mg:L\({}^{-1}\) of a standard mixture of four bisphenol’s using microgel as a sorbent for preconcentration. Data are means with RSD (\(n=3\)).  
provided the best sorption of BPA, which remained above 95% in the range from 0.025 to 1 mg\(\cdot\)L\({}^{-1}\), while for 2.5 mg the sorption for the concentration higher than 0.025 mg\(\cdot\)L\({}^{-1}\) gradually decreased to 90%. As our goal was to use the microgel for the sorption of several bisphenols, 5 mg of microgel sorbent was selected for further experiments.
# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## 3 Research Analysis
### Traffic Management Methodologies Consisiting of Primary Goals
#### 3.2.4 Efficiency and Ecology  
Specific papers suggested several techniques to manage AV traffic at conflicting zones while considering efficiency and ecology. One study [46] suggested a method they called \"cooperation between a traffic signal and vehicles (CTV)\", which determines the best signal timing, driving order, and arrival time for each vehicle. AVs' trajectory, engine power profile, and acceleration/deceleration behavior are optimized when utilizing optimal control. When compared to the actuated signal control approach, the recommended strategy reduces average travel delays and average fuel economy by 19.8% and 23.8%, respectively. Initial vehicle clustering, intra-cluster sequence optimization, and cluster formation management are all components of the cluster-wise cooperative eco-approach and departure application (coop-EAD), developed by [70] to reduce energy use, emissions, and traffic throughput. The suggested coop-EAD approach outperforms the current ego EAD method in terms of energy usage and traffic flow by 11.0% and 50%, respectively. Moreover, 2.29-19.91% less pollution is emitted as a result.  
Recently, considering mixed traffic flows, ref. [48] focused on the issue of minimizing traffic's harmful environmental effects, such as congestion, energy and gasoline utilization, and flue gas pollution. They used electric CAVs as speed-limit operators in the control loop of a variable speed limit (VSL) based on Q-Learning. The collected findings demonstrate that Q-Learning-based VSL can train the control strategy for various electric CAV penetration rates, improve the macro traffic parameters and total energy consumption, and minimize exhaust gas emissions.  
Table 3 displays several strategies; altogether, the suggested solutions exceed the virtual instances by 2.30-60% when various performance measures are considered. Additionally, to validate the proposed methodologies.
# Linear Approximation Signal Detection Scheme in MIMO-OFDM Systems
## 4 Proposed Detection Scheme
### Partial ZF Stage  
The partial ZF stage nulls not all signals unlike the conventional ZF. The purpose of the partial ZF is to simply divide 4 \(\times\) 4 channel matrix into two 2 \(\times\) 2 channel matrices by using cancelling process. That is, one matrix contains only \(X_{1}\) and \(X_{2}\), another matrix contains only \(X_{3}\) and \(X_{4}\). The cancelling process is as follows,  
\[\left[\begin{array}{c}\widetilde{Y}_{3}\\ \widetilde{Y}_{4}\end{array}\right]=\left[\begin{array}{c}Y_{1}\\ Y_{2}\end{array}\right]-\textbf{W}^{T}\left[\begin{array}{c}Y_{3}\\ Y_{4}\end{array}\right], \tag{16}\]  
where \(\textbf{W}=\left[\begin{array}{cc}w_{11}&w_{12}\\ w_{21}&w_{22}\end{array}\right]\) is a weight matrix as follows,  
\[\left[\begin{array}{cc}H_{31}&H_{32}\\ H_{41}&H_{42}\end{array}\right]^{T}\!\!\left[\begin{array}{cc}w_{11}&w_{12} \\ w_{21}&w_{22}\end{array}\right]=\left[\begin{array}{cc}H_{11}&H_{12}\\ H_{21}&H_{22}\end{array}\right]^{T}\!\!. \tag{17}\]  
Figure 1: The tree structure of the conventional QR decomposition-M algorithm (QRD-M) (\(M\) = 2) in 4 \(\times\) 4 multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system using quadrature phase shift keying (QPSK) modulation.  
The modified received vector \(\left[\begin{array}{cc}\widetilde{Y}_{3}&\widetilde{Y}_{4}\end{array}\right]^{T}\) in Equation (16) is as follows,  
\[\left[\begin{array}{c}\widetilde{Y}_{3}\\ \widetilde{Y}_{4}\end{array}\right]=\left[\begin{array}{cc}\widetilde{H}_{31 }&\widetilde{H}_{32}\\ \widetilde{H}_{41}&\widetilde{H}_{42}\end{array}\right]\left[\begin{array}{c} X_{3}\\ X_{4}\end{array}\right], \tag{18}\]  
where \(\widetilde{H}_{ij}\) is modified channel coefficient.  
From above results, the received vector has only \(X_{3}\) and \(X_{4}\). So, the dimension of the modified channel matrix in Equation (18) is half of the dimension of existing channel matrix in Equation (15). The modified channel matrix is as follows,  
\[\left[\begin{array}{cc}\widetilde{H}_{31}&\widetilde{H}_{32}\\ \widetilde{H}_{41}&\widetilde{H}_{42}\end{array}\right]=\left[\begin{array}[] {cc}H_{13}&H_{14}\\ H_{23}&H_{24}\end{array}\right]-\textbf{W}^{T}\left[\begin{array}{cc}H_{33} &H_{34}\\ H_{43}&H_{44}\end{array}\right]. \tag{19}\]  
Likewise, another modified received vector \(\left[\begin{array}{cc}\widetilde{Y}_{1}&\widetilde{Y}_{2}\end{array}\right]^ {T}\) which has only \(X_{1}\) and \(X_{2}\) is obtained in a similar method. Another modified received vector is as follows,  
\[\left[\begin{array}{c}\widetilde{Y}_{1}\\ \widetilde{Y}_{2}\end{array}\right]=\left[\begin{array}{cc}\widetilde{H}_{1 1}&\widetilde{H}_{12}\\ \widetilde{H}_{21}&\widetilde{H}_{22}\end{array}\right]\left[\begin{array}{c }X_{1}\\ X_{2}\end{array}\right]+\left[\begin{array}{c}\widetilde{Z}_{1}\\ \widetilde{Z}_{2}\end{array}\right] \tag{20}\]  
Therefore, the original \(4\times 4\) MIMO-OFDM system is divided into two \(2\times 2\) MIMO-OFDM systems.
## 4 Research Results: The Resettlement Communities as an Opportunity for Renewal
### Statistical Analysis of Six Communities and Nanhuan Community  
The 6 communities analyzed in detail have a floor area ratio that ranges from 0.95 to 1.75, on average 1.27, while the average FAR of all 176 cases is 1.73, the minimum is 0.7, and the maximum is 3.87 (Table 5).  
For example, the Nanhuan New Village in Suzhou is a noteworthy case of densification. It is a demolition and reconstruction project in Suzhou, which was completed in 2013. Unlike the original community, the new community uses high-rise building types and different green space layouts, increasing the FAR value from 1.1 to 3.87 and tripling the total number of units. This attempt has shown that renewal is also a viable option when reassessing and transforming demolished and rebuilt communities, which can promote sustainable urban development while providing more housing.  
The highest FAR is in Nanhuan New Village, Gusu District, which is the only case in Suzhou of the renewal with densification of a resettlement community, Nanhuan Community, initially built in 1996 on agricultural land. In 2013, the former community was partially demolished and rebuilt with a different housing type, high-rise buildings, a different layout of open space and green areas, and a FAR more than 3 times the former one, which was 1.1.  
Figure 4: The GIS analysis. (**a**) The location and the FAR of the 176 resettlement communities identified in Suzhou. (**b**) The resettlement communities and the areas with different housing conditions. (**c**) The resettlement communities and the areas with different accessibility (bus stops and subway stations): the red areas represent the best accessibility in Suzhou. Note: accessibility is calculated using sDNA from the urban road axis model. (**d**) The combination of three indicators: FAR, housing conditions, and accessibility.
# Denitrification in Intrinsic and Specific Groundwater Vulnerability Assessment: A Review
## 4 Results and Discussion
### Hybrid Methodologies  
Some authors tried to enhance the results of the assessment of vulnerability to NO3\({}^{-}\), combining classical rating methods along with process-based and statistical elaborations. Sometimes, these new hybridizations introduced new parameters related to DNT that are usually not considered in the standard methodologies. [PERSON] et al. [68] hybridized N budget with a basin-scale process-based model to simulate the fate of Nr in soils and groundwater, along with N2O production at the European scale. The model offers the output calculation of travel time, which integrates in its computation the DNT rate for the main European countries. [PERSON] et al. [154, 155] developed a simplistic N-budget approach described by indices named LOS, which account for DNT, NH3 volatilization, mineralization, and nitrification. The new indices involve detailed information on landscape management and climate and are calibrated using the results of a GLEAMS model thought regression analysis. [PERSON] and [PERSON] [156], following the example of [PERSON] et al. [157], who subjectivized the DRASTIC framework, replaced some parameters in the DRASTIC-PAN methodology. This new method, integrated within the LOS indices, indicates the soil Nr losses based on climatic, soil, and topographic data. Similarly, [PERSON] et al. [11; 158] hybridized the SINTACS method, called SINTACS-SVN and SVAP, introducing the LOS indices inside the evaluation process to assess intrinsic and specific groundwater vulnerability to NO3\({}^{-}\) and SO4\({}^{2-}\) pollution. In both methodologies, the weights and scores were calibrated using observed NO3\({}^{-}\) concentrations and pollution factors following the routine of canonical process-based methodologies, making the evaluation less subjective than standard rating methods. These indices proved to produce reliable results in different hydrogeological settings [159]. [PERSON] et al. [160] developed the DRANTHVP method, optimizing weights and rates of the DRASTIC method by using the statistical analysis of projection pursuit dynamic clustering (PPDC). Moreover, during the choice of parameters, the authors introduced the NO3\({}^{-}\) attenuation factor to replace the soil media factor. The proposed classification is based on the difference of soil moisture content, porosity, and organic matter content, factors that are directly linked to DNT rate [161; 162]. [PERSON] et al. [163] integrated subsurface redox conditions inside the DRASTIC classification. This parameter is considered indicative of DNT as the NO3\({}^{-}\) removal by microbial oxidation depends on the availability of reductants species, such as pyrite and organic matter [164].