esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study: NCS Performance over WirelessHART
### Control System Model  
Although, often, fast dynamic systems are used for NCS performance evaluations, such as inverted pendulums [19], state-of-the-art wireless mesh networks are unable to to provide an adequately low latency for such applications. We use a classical example from the network control system literature, a two-input, two-output open-loop unstable batch reactor model [20], which is still highly delay-sensitive, but is feasible for control using sensor data obtained from wirelessly connected sensors. The plant is characterized by the following linearized state-space model:  
\[\dot{\mathbf{x}}=\left[\begin{array}{cccc}1.38&0.2077&6.715&5.676\\ 0.5814&4.29&0&0.675\\ 1.067&4.273&6.654&5.893\\ 0.048&4.273&1.343&-2.104\end{array}\right]\mathbf{x}+\left[\begin{array}{cccc}0& 0\\ 5.679&0\\ 1.136&-3.146\\ 1.136&0\end{array}\right]\mathbf{u} \tag{5}\]  
and:  
\[\mathbf{y}=\left[\begin{array}{cccc}1&0&1&-1\\ 0&1&0&0\end{array}\right]\mathbf{x} \tag{6}\]  
where \(\mathbf{x}\), \(\mathbf{y}\) and \(\mathbf{u}\) denote the state, input and output vectors, respectively.  
Figure 6: Frame error rate for 802.15.4 and 802.15.4 g Option 2 MCS3.  
Although traditional standard benchmarks, such as the one in [20], assume that the plant is controlled by a fixed proportional-integral (PI) controller, in this paper, we use a model predictive controller (MPC). This kind of controller, with its variable time horizon, is generally more robust and provides better control performance with increased tolerance against the delays and outages of the wireless transmission; therefore, it is more relevant for today's practical deployments than the static PI controller often used for this particular benchmark. As in [20], we assume that only sensor measurements are transmitted over the wireless networks, whereas the controller output is connected to the actuators using a wired connection having negligible delay compared to the delays occurring in the wireless network. The step response of the controlled system with negligible delays in both directions is illustrated in Figure 7. The controller is designed with the following parameters: the control interval is 100 ms; the prediction horizon is 500 ms; and the actuation horizon is 200 ms.
## 6 Case Studies
### Operation Environment and Flight Corridors  
The airspace of Seoul City was used as an operation environment, as shown in Figure 5, and UAMs flew within specific flight corridors from one vertiport location to the next. The corridor and fixed points specified in [2] were applied in this environment, which was developed as initial vertiport locations using ConOps 1.0 [2]. The flight corridors specified in the figure include takeoff and landing locations depending on approach distance, cruise, landing, climb and takeoff segments. The names of the vertiports were set at each corresponding location with their latitude and longitude coordinates and are listed in Table 3.  
The flight corridors from one vertiport location to the next are specified, and the distance between pairs of locations can also be specified depending on the way points of the corridor. These corridors include the altitude, longitude and latitude locations. The cruise altitude when travelling from east to west is set to 1000 ft, and that for west to east is defined as 2000 ft to ensure the safety of the UAMs [2,27,28]. The matrix of the distances between the vertiports includes the altitude and coordinates; the distances are not symmetric and depend on the corridor definitions. The matrix of distances from one vertiport to another is shown in Table 4.  
Figure 5: Initial UAM routes in Seoul City. The operation area considered in this research includes five vertiports: Gimpo Airport, Yongsan Station, Seoul Express Bus Terminal, Jamsil Station and Incheon Airport. The locations of the vertiports and corridors are specified in [2].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Longitude (Deg)** & **Latitude (Deg)** \\ \hline Gimpo (GMP) & 37.5608 & 126.8031 \\ Yongsan (YGS) & 37.5318 & 126.9680 \\ Bus Express Terminal (SEBT) & 37.5052 & 127.0056 \\ Jamsil (JSL) & 37.5141 & 127.0689 \\ Incheon (ICN) & 37.44556 & 126.45313 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Names of vertiports and their corresponding locations with longitude and latitude.
## 5 Methods for Identification and Quantification of Coumarins
### Analysis
#### 5.2.2 High-Performance Liquid Chromatography  
High-performance liquid chromatography (HPLC) is a sophisticated chromatographic method used to separate, identify, and quantify components of mixtures. HPLC is extremely adaptable and can be used to analyse a wide range of substances, from small molecules to polymers. HPLC has been the most widely used method for identification and quantifying coumarins and their derivatives, particularly in spirit beverages (Table 4) [163, 168]. An advantage of this technique is the possibility of coupling it to different spectroscopic or spectrometric detectors; for coumarin analysis, the most suitable/accurate one is the fluorescence detector (FLD) [168], taking advantage of the properties of these phenolic compounds (reported in Section 3). It should be noted that the separation of simple coumarins may not be accurate due to the similarity of chemical structure and polarity [168].  
Ultra-performance liquid chromatography (UPLC) was launched in 2004 [170] and represents a significant breakthrough in liquid chromatography, expanding on the concepts of HPLC and introducing improvements that allowed for even faster and more efficient separations. The UPLC technique is a viable alternative for the determination of coumarins, since it allows the development and application of a method with a shorter running  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spirit Beverage** & \multicolumn{3}{c}{**Analytical Methods**} \\  & **Separation** & **Detection** & **References** \\ \hline \multirow{2}{*}{Wine Spirit} & \multirow{2}{*}{HPLC} & FLD & [36, 38, 39, 40, 152, 164, 165] \\  & & HMRS with ESI/HESII II & [22] \\  & & FLD & [38, 123] \\ \multirow{2}{*}{Brandy} & \multirow{2}{*}{HPLC} & HMRS with ESI/HESII II & [22] \\  & & Fluorescence Spectrometry & [48] \\  & & TLC & Fluorometer & [35] \\ \multirow{2}{*}{Whiskey} & \multirow{2}{*}{HPLC} & FLD & [38] \\  & & HMRS with ESI/HESII II & [22] \\  & & TLC & Fluorometer & [35] \\ \multirow{2}{*}{Sugar cane spirit} & HPLC & FLD & [43] \\  & & HPLC & DAD & [166] \\ \multirow{2}{*}{} & \multirow{2}{*}{UV-Vis spectrophotometry} & \multirow{2}{*}{[167]} \\  & & & UV-Vis spectrophotometry \\ \multirow{2}{*}{} & \multirow{2}{*}{and spectrofluorimetry} & \multirow{2}{*}{[167]} \\  & & and spectrofluorimetry & \\ \multirow{2}{*}{HPLC} & HMRS with ESI/HESII II & [22] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analytical methods used for coumarin determination in spirit beverages.  
time, lower solvent consumption and good separation/resolution of the chromatographic peaks [168].
## 2 Transmission Mode Analysis and System Modeling
### Analysis of Simulation Results Obtained Using the Range Method

Table 6: The collocation effect of switching time. Figure 19: Simulation data before and after optimization. allows the hydraulic system to absorb some of the vibration and shock caused by the mode switching process, resulting in a reduction in shock.
## 5 Conclusions  
The current article combined TFWO and TLBO algorithms to introduce a novel optimization algorithm named TLTFWO. The OPF problem was then formulated as a nonlinear optimization problem with some constraints and limits. To improve voltage profile and reduce the fuel cost as much as possible, various objective functions are expressed while considering the impact of the valve point and the presence of PV and WT generating units. The simulations are implemented on the IEEE 30-bus network. According to the findings, the TLTFWO algorithm shows promising performance by successfully solving the multi-objective OPF problem. Simulations prove the robustness of TLTFWO in reaching the optimum global point with optimal adjustments of control variables. The suggested approach can be adopted as the desired tool to address complex power systems and experience more updates and improvements in the upcoming years.  
M.A.: Conceptualization, methodology, software, writing--original draft; A.A.: Conceptualization, methodology, software, writing--original draft; A.Y.A. and PS.: Supervision, validation, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors are grateful to the Prince Faisal bin Khalid bin Sultan Research Chair in Renewable Energy Studies and Applications (PFCRE) at Northern Border University for its support and assistance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Min** & **Mean** & **Max** & **Std.** & **Time (s)** \\ \hline \multicolumn{6}{c}{Case 7} \\ \hline PSO & 782.7100 & 783.2148 & 783.7969 & 0.89 & 33 \\ GA & 783.2565 & 784.6206 & 786.7536 & 1.37 & 41 \\ TFWO & 782.3068 & 782.6754 & 783.2645 & 0.63 & 35 \\ TLBO & 782.4558 & 782.9740 & 783.8231 & 0.94 & 38 \\ TLTFWO & 781.9791 & 782.2216 & 782.4136 & 0.20 & 35 \\ \hline \multicolumn{6}{c}{Case 8} \\ \hline PSO & 811.4062 & 812.5325 & 813.5510 & 0.57 & 33 \\ GA & 812.6163 & 813.7541 & 815.4792 & 2.25 & 40 \\ TFWO & 811.3949 & 812.3127 & 813.1720 & 1.16 & 35 \\ TLBO & 811.5219 & 812.3812 & 813.2546 & 1.43 & 39 \\ TLTFWO & 810.8444 & 810.9632 & 811.2148 & 0.18 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 13: _Cont._
# Six-Gear Roadmap towards the Smart Factory
## 5 The Six-Gear Roadmap
### Gear 2: Connectivity
#### 5.2.2 Industrial Internet of Things (IIoT) Connectivity
Key communication protocols for data representation that are widely used in industrial environment are OPC-UA, DPWS, UPnP, MT-Connect, DDS and other key messaging transfer protocols includes MQTT, JSON, XML, HTML, HTTP, CoAP, REST [62]. The 'Application and Management' layer consists of software components to communicate and manage the 'Thing' connected as well as creating the environment for the IIoT applications. The 'Processing and Analytics' layer is associated with the processing and analysis of data and subsequently converting it into meaningful information. Further insights to the data can be achieved by applying data science techniques for deeper insights or even correlating and contextualizing it with the data obtained from other IT systems connected via the communication layer. The 'Visualization' layer is where the meaningful information and insights are presented in visual format in the form of metrics dashboards for ease of interpretation by non-specialists. The 'Cybersecurity' layer encapsulates all the different layers of the stack as secure by design approach of the IIoT architecture. The stack layers 'Sensor', 'Edge' and 'Communication' can sometimes be combined in one device often referred to as an 'IIoT Gateway' combing both the hardware and software with internet-enabled components enabling communication and data transfer to a digital platform or infrastructure. In the aerospace industry, regular audits, inspections and quality checks are performed during manufacturing and assembly as it is less dependent on the use of robotics and automation because of the tight regulations regarding aircraft construction, therefore, IIoT enabled gateway devices and tools are key to enable workers to increase productivity [48]. The 'Storage' layer is where the IIoT platform will be hosted in the chosen IT infrastructure to operate, store, organize and manage the data flow. IIoT platforms are excellent examples of software applications that can facilitate, organize and manage communication, data flow, storage, device management and the functionality of applications addressing the layers of the IIoT stack (Figure 7). This further enables integration with Information Technology (IT) and Operation Technology (OT) that will be discussed in detail in the next stage.
# Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints
## 3 PL-MMSE Kalman Filter with Linear State Constraints
### Mean Square Method  
The idea of the mean square method is to obtain the state estimation \(\hat{x}\) of the moving target with linear constraints by minimizing the conditional mean square error. Let  
\[\hat{x}_{k}=\min_{\hat{x}_{k}}E(\|x_{k}-\hat{x}_{k}\|^{2}|Z_{k})\quad s.t.\quad G \hat{x}_{k}=g \tag{25}\]  
where  
\[E(\|x-\tilde{x}\|^{2}|Z) =\int(x-\tilde{x})^{T}(x-\tilde{x})P(x|Z)dx\] \[=\int x^{T}xP(x|Z)dx-2\tilde{x}^{T}\int xP(x|Z)dx+\tilde{x}^{T} \tilde{x}. \tag{26}\]  
A Lagrangian function is constructed to solve the constrained problem as  
\[J =E(\|x-\tilde{x}\|^{2}|Z)+2\lambda^{T}(G\tilde{x}-g)\] \[=\int x^{T}xP(x|Z)dx-2\tilde{x}^{T}\int xP(x|Z)dx\] \[\quad+\tilde{x}^{T}\tilde{x}+2\lambda^{T}(G\tilde{x}-g). \tag{27}\]  
The conditional mean of \(x\) is  
\[\hat{x}=\int xP(x|Z)dx. \tag{28}\]  
After substituting (28) into (27), taking the partial derivatives of \(\tilde{x}\) and \(\lambda\), respectively, leads to  
\[\frac{\partial J}{\partial\tilde{x}} =-2\hat{x}+2\tilde{x}+2G^{T}\lambda\] \[=0, \tag{29}\] \[\frac{\partial J}{\partial\lambda} =G\tilde{x}-g\] \[=0. \tag{30}\]  
Solving (29) and (30) gives  
\[\tilde{x} =\hat{x}-G^{T}(GG^{T})^{-1}(G\hat{x}-g), \tag{31}\] \[\lambda =(GG^{T})^{-1}(G\hat{x}-g). \tag{32}\]  
From (31), the constrained estimate of motion state is the unconstrained estimate minus the correction term.
## 3 Application of Multi-Sensor Covert Underwater Communication
The high frequency component is removed through the filter by multiplying the signal \(r(n)\) by \(\cos(w_{c}n)\) for demodulation and \(p_{sensor\_}k(T)\) that is the sensor identification code, and the sensor identification code component disappears so that the \(k\)-th sensor's data can be extracted. Equation (5) presents an equation that expresses the demodulation process to recover data of the \(k\)-th sensor from the receive signal \(r(n)\). \[ \begin{array}{l}y_{k}(n)=r(n)p_{sensor_{k}}(T)cos(w_{c}n)\\ =\sum\limits_{l=1}^{L}c_{K,l}s(n)p_{sensor_{k}}(T)cos(w_{c}n)+\omega(n)p_{sensor _{k}}(T)cos(w_{c}n)\\ =\sum\limits_{l=1}^{L}\sum\limits_{k=1}^{K}c_{k,l}(n)b_{k}(n)p(T_{c})p_{sensor _{k}}^{2}(n)cos^{2}(w_{c}n)+\overline{\omega}(n)\\ =\sum\limits_{l=1}^{L}c_{k,l}(n)b_{k}(n)p(T_{c})+\overline{\omega}(n)\end{array}  \tag{5}\]  
\(y_{k}(n)\) refers to the data of the signal extracted regarding the \(k\)-th sensor from the receive signal, and \(\overline{\omega}(n)\) refers to noise. After sensor identification is complete, data that require recovery pass through the RAKE process, which is a receive structure that shows excellent performance in an underwater channel environment such as multi-path. After completing the RAKE process, the receive symbols are input into the turbo equalizer. The turbo equalizer consists of DFE and BCJR encoder. The equalizer and encoder are connected via the interleaver and de-interleaver to update the other's information iteratively, thereby improving the performance.
## 3 Results
### Patients' Perceptions of Dental Soundscapes  
Patients' perceptions of dental soundscapes are shaped by more than just volume; they are deeply influenced by associations with pain, anxiety, and prior traumatic experiences [2; 5]. High-frequency sounds from dental drills and suction devices are consistently reported as among the most distressing elements in the clinical environment [5]. These sounds have been directly linked to anticipatory fear, particularly among those with previous negative dental experiences or high auditory sensitivity [52]. Importantly, perceptual responses to sound are not uniform. Certain populations, including children, elderly patients, and individuals with neurodevelopmental conditions such as autism spectrum disorder, exhibit heightened reactivity to unpredictable or high-pitched stimuli [27; 40]. This can result in increased distress, avoidance behaviors, or non-compliance during treatment. Such findings support the need for inclusive acoustic design, tailored to accommodate diverse sensory thresholds [39; 51].  
The psychophysiological impact of dental noise is also measurable. Exposure to sharp or sudden clinical sounds can activate the sympathetic nervous system, leading to elevated heart rate, blood pressure, and cortisol levels, as seen in both patient and staff populations [5; 37; 44]. In this context, non-invasive interventions such as music therapy have gained clinical attention. [PERSON] et al. (2022) demonstrated that customized music sessions reduced not only subjective anxiety but also lowered objective stress markers in patients undergoing dental treatment [44]. Moreover, carefully curated background music has been shown to mask aversive clinical sounds, creating a more calming and supportive environment [2; 4]. This strategy aligns with broader healing environment design principles, which emphasize multisensory comfort as a pillar of care quality [31; 63]. Together, these findings affirm that dental soundscapes are not merely a technical concern but a central element of the patient's experience. Perception of sound operates as a complex interplay of physical stimuli, emotional interpretation, and environmental context, one that can and should be actively shaped through architectural, technological, and psychological design strategies [5; 19; 27].
# Key Technologies for 6G-Enabled Smart Sustainable City
## 4 Key Candidate Technologies for Future SSC
### Prime Technologies for SSC  
In this section, we put forth prime technologies that could be key enablers to realize 6G-enabled SSC. We highlight five prime technologies, namely, AI-based wireless communication, advanced mobile edge computing, non-terrestrial networks, vision-aided wireless communication, and integrated sensing and communication, as represented in Table 5. It is expected that the market sizes for five prime technologies will reach USD 1345.2 billion [30], USD 111.3 billion [31], USD 23.5 billion [32], USD 21.3 billion [33], and USD 4.2 billion [34], respectively, by around the year 2030. The relative importance of the five prime technologies can be assessed using the analytic hierarchy process (AHP) method [35; 36; 37]. This involved considering multiple criteria including market size, applicability, potential for growth, security, and user convenience for the comparison. Through the AHP method, we obtain the relative importance of core technologies in smart cities: (1) AI, (2) Advanced MEC, (3) NTN, (4) VAWC, and (5) ISAC, aligning with their market sizes. In the rest of this section, we discuss the key technical challenges, potential benefits, and urban practice cases for each prime technology. To this end, we have investigated much of the literature since 2017.
## 2 Materials and Methods
### Texture and Colour Analysis  
Cheese texture was measured using a Texture Profile Analysis with an Instron Universal Testing machine equipped with Bluehill Texture Analyser software (3344 Model, Norwood, MA, USA) according to [PERSON] et al. [22]. Briefly, 50 g of cheese blocks (\(n\) = 24) were left at room temperature for 1 h prior to analysis. The samples were cut into cubes of 2 cm \(\times\) 2 cm \(\times\) 2 cm, and five cheese cubes were tested for each sample. The samples were subjected to a double compression cycle, and up to 50% of the original sample height was compressed with an aluminium cylinder probe (diameter 3.6 cm). A crosshead speed of 1 mm/s was applied with a load cell of 5000 N. Cheese hardness (N) was recorded as the maximum force used during the first compression cycle. Springiness (%) was defined as the extent to which the sample returned to its original height between the first and second compressions [23]. Cohesiveness was calculated as a ratio; that is, the energy (work done) of cheese during the second compression in relation to the first compression. Chewiness (J) was calculated as a product of hardness\"cohesiveness\"springiness [24].  
Colour measurements were performed directly on the cheese blocks surfaces. Measurements were taken 3 times at different locations per sample. The lightness (L*), redness (_a*_) and yellowness (_b*_) parameters were recorded using the CIELAB colour meter: Colour guide 45\({}^{\circ}\)/0\({}^{\circ}\) colorimeter (BYK-Gardner GmbH, Geestried, Germany) with an 11-mm diameter aperture using an illuminant/observer of D65/10\({}^{\circ}\) observer settings. The hue angle (H\({}^{\circ}\)) and chroma (C) levels were calculated as:  
\[\text{H}^{\circ}=\tan^{-1}\left(\frac{b*}{a*}\right)\times 57.29\ (\text{expressed in degrees});\ \text{C}=\sqrt{\left(a*^{2}+b*^{2}\right)}.\]
# Automatic Definition of Traffic Analysis Zones Based on Big Data
## 2 TAZ Definition
### Number of TAZs  
The classic method said 50 TAZs for an area with 100,000 inhabitants, 100 TAZs for 1 million inhabitants. This approach has already been overwritten by modern computers as we have enough resources for the calculations and data collection. Today the number of TAZs is much higher, in a city with 100,000 inhabitants the average number of TAZ is around 100, or even higher. In the Unified Transport Model of Budapest, there are almost 1500 TAZs. The number of TAZs are in good correlation with the desired level of detail of the model; regarding the level of detail, there is no direct rule. Here, we follow two guidelines:  
* Cost/benefit;
* Level of error.  
Based on the first, cost and benefit, we can draw the following connection; see Figure 1.  
On the other hand, there is the risk of error. In a modeling task, we could have two types of errors: model error and data error. Model error means that the simplification  
Figure 1: Cost to benefit ratio as a function of the level of detail.  
of the model imparts some error on the modeled process, while data error means the collected data are maybe not precise enough. These two also lead to an optimum (Figure 2).  
Based on both approaches, we can state that the desired number of TAZs is somehow a golden middle.