esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Thus, this **research aims** to develop a neural network method for variable-structure controllers with semi-permanent integration parametric optimization, taking into account nonlinearities, delays and high uncertainty, to improve the accuracy and stability of the ACS. The **research object** is an ACS with variable-structure controllers and semi-permanent integration. The **research subject** is parametric optimization methods for controllers based on calculating second-order sensitivity functions and using neural network approaches to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Advantages** & **Disadvantages** & **Studies** \\ \hline Gradient optimization methods & High accuracy in calculating the gradient quality functional in ACSs using first-order sensitivity. & Limited applications in systems with pronounced nonlinearity, high computational complexity in multidimensional systems. & [14; 15] \\ \hline Methods of sensitivity theory & Takes into account parameter interactions and their influence on changes in the system by calculating second-order sensitivity functions. & High computational load, implementation complexity, need for specialized software. & [16; 17; 18] \\ \hline Optimal control methods & Effective for stable control objects; they take into account control errors and the control signal impact. & Scalability issues, difficulties in integration with real-time adaptive controllers. & [10; 11; 12; 13] \\ \hline Neural networks with fixed switching functions & Ability to process large amounts of data and change parameters in real time. & Limited ability to take into account nonlinearities and changes in the dynamics of the control object. & [19; 20; 21; 22; 23; 24] \\ \hline Controllers with variable structure and semi-permanent integration & Flexible adaptation of system parameters depending on the current state; improved tuning accuracy. & Complexity in setting up switching functions, high computational complexity in analytical calculation of second-order sensitivity functions. & [3; 4] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The developed algorithm for the proposed neural network training. take into account the control object's nonlinear and dynamic characteristics. This **research's main contribution** is the development of a neural network method for variable-structure controllers with the semi-permanent integration of parametric optimization, which takes into account nonlinearities, delays and high uncertainty. This allows for improving the ACS's accuracy and stability, increasing its adaptability in the face of complex transient processes and objects' dynamically changing characteristics.
## 5 Attributes and Characteristics of FL
### Privacy-Preserving Data Aggregation  
Differential privacy adds \"noise\" during data aggregation to protect individual data points [76; 77]. k-Anonymity hides records among others to ensure privacy, while data masking alters data values to obscure true information. Secure aggregation protocols use encryption and integrity checks to maintain the privacy and accuracy of aggregated data.
## 3 Methodology
### MDDA Augmented GNSS/INS Integration
#### 3.6.6 TC Integration Model
The following step is that for the TC algorithm, which comes into play after the selection of the optimal satellite set. In this phase, pseudorange computation takes place. The KF is then employed to gauge the discrepancies between GPS pseudo-range and pseudo-range rate measurements and their respective INS-predicted values. These disparities are utilized to estimate the errors inherent to the INS. Subsequently, the output produced by the INS is fine-tuned to accommodate these errors, ultimately yielding an enhanced integrated navigation solution.
## 1 Introduction
However, full coverage is sometimes not necessary. For some application scenarios, only a part of the nodes to achieve partial coverage can better meet the needs. [PERSON] et al. [10] considered the problem of partial coverage and emphasized that proper partial coverage saves more energy than full coverage. [PERSON] et al. [11] focused on partial coverage, and present an efficient algorithm based on learning automata that aims at minimizing the number of sensors to activate. [PERSON] et al. [12] proposed a greedy heuristic algorithm to deal with the coverage problem of heterogeneous WSNs in the case when full coverage of the network is not needed. In this paper, construction method of special wireless sensor network for railway status monitoring is proposed. Based on the deployment characteristics of railway monitoring and wireless communication system, a double-layer communication network based on wireless sensor network is constructed; it is composed of monitoring sub-network layer and transmission backbone network layer. Then an optimal energy resources allocation method of monitoring sub-network for intelligent railway systems is proposed. From routing optimization and data fusion rate optimization, the method is studied to maximize the lifetime of the monitoring sub-network and enhance the ability to perceive and transmit the service status information of railway systems. Because of the status monitoring of railway systems, only partial coverage can meet the demand; at the same time, it can save energy and prolong network life. Therefore, a new partial coverage model of wireless sensor network is proposed. The rest of the paper is organized as follows: Section 2 presents the methodology. In Section 3 we give experiments and results analysis. The last section is a summary with conclusions.
# A Comparative Study on Violent Sloshing with Complex Baffles Using the ISPH Method
## 1 Introduction
Since it is not so easy to change the geometric shape of the tank by a large margin, the installation of baffles in the tank becomes the primary way to constrain the liquid sloshing. Quite a few studies on liquid sloshing in the tank with different baffles have been documented. For example, [PERSON] et al. [18] studied the effect of horizontal baffles on liquid sloshing. [PERSON] et al. [19] studied liquid sloshing in a rectangular tank with ring baffles and monitored the pressure variation on the tank walls and the surface elevation. [PERSON] and [PERSON] [20] simulated a sloshing tank with both horizontal and vertical baffles by using a virtual boundary force (VBF) method and explored the reduction of violent sloshing. [PERSON] and [PERSON] [21] made various experiments to study the influence of horizontal and vertical baffles. Besides, [PERSON] and [PERSON] [22] also studied liquid sloshing in a rectangular tank with vertical baffles under seismic excitation. In this paper, liquid sloshing in a rectangular tank with complex baffles will be studied by using the incompressible SPH (ISPH) method. Through a comparison of different combinations of horizontal and vertical baffles, the effect of these internal structures on reducing the impact pressure is studied. The aim of this study is to identify the most effective baffle configuration to protect the container structure so as to mitigate sloshing problems in practical engineering.
# Adaptive Cruise Control with Look-Ahead Anticipation for Driving on Freeways
## 3 Evaluation of the Proposed La-ACC
### Evaluation of La-ACC on Multiple Vehicles  
A vehicle usually influences the behavior of its followers and sometimes may trigger shockwaves in the following traffic that may eventually turn into a breakdown. Therefore, it is crucial to investigate the relative performances of La-ACC and examine the stability of the vehicles in dense traffic with varying speeds. We have conducted such a study and for a fair evaluation compared the performance with the recent ACC described above. Figure 4a,b illustrate the driving characteristics of a platoon of ten vehicles using ACC and La-ACC, respectively. In each case, the lead (first) vehicle slowly increases its speed from 80 km/h to 100 km/h in 10 s and later slows down to 70 km/h in 30 s. Both acceleration and deceleration are smooth with very low magnitudes (within \(\pm\)0.4 m/s\({}^{2}\)). The relative position (the distance from the first vehicle), speed, and acceleration of these vehicles are compared. As in Figure 4a, the ACC vehicles follow the respective preceding vehicles by causing speed oscillations, which propagate from the second to tenth vehicles. Despite the moderate acceleration of the first vehicle, the eighth to tenth vehicles have to brake aggressively by deactivating ACC temporarily as marked by the red color. These results accord with the results of [22], where they tested a platoon of five vehicles using ACC and found similar propagation of speed oscillations.  
In contrast, the La-ACC vehicles have cautiously followed their respective preceding vehicles and could keep their acceleration limited, as shown in Figure 4b. For this low magnitude of braking by the first vehicle, the La-ACC vehicles are found fully string table as the magnitudes of their acceleration are never amplified in the following vehicles. Consequently, La-ACC remains activated in the entire driving period, i.e., La-ACC has enhanced the automated driving operating range compared with ACC for the same limits of acceleration and braking.  
Figure 3: Evaluation of La-ACC and ACC in a simple car-following task in two scenarios. (**a**) Speed (speed of the preceding vehicle is shown by the dotted curve), (**b**) acceleration, (**c**) performance index, and (**d**) fuel consumption.  
A similar observation with different levels of acceleration and braking by the preceding vehicle is given in Appendix A. From these observations, it is clear that our approach ensures better stability of the vehicles, i.e., without a need for deactivation when the leading vehicle changes its speed moderately, resulting in smoother flows of the vehicles with less fuel consumption than the ACC vehicles. However, when the leading vehicle brakes extensively, the La-ACC has to be deactivated similarly to ACC. Therefore, it can be concluded that our proposed approach yields almost the same or better performances compared to the recent ACC in terms of average speed, fuel consumption, and stable operating range.
## 3 Proposed Approach
### Ambiguity Resolution  
To perform disambiguation, the distance between the context embedding and each analysis embedding is calculated. Since context embedding is based on subwords, for a word \(w_{i}\), we only compute the distance between the first subword \(s_{i1}\) of that word with its corresponding analysis embeddings. For the subsequent subwords \(s_{i2},s_{i3},\dots,s_{im}\) the distance calculation uses the padding analysis embedding \(E_{a}(\text{pad})\) as follows:  
\[a_{i}^{*}=\arg\min_{a_{ik}\in\mathbf{A}_{i}}\text{d}(E_{\text{PLM}}(s_{i}),E_ {a}(a_{ik})) \tag{8}\]  
\[E_{a}(a_{ik})=\begin{cases}E_{a}(a_{ik})&\text{if }j=1,\\ E_{a}(\text{pad})&\text{if }j>1,\end{cases} \tag{9}\]  
The following distance metrics can be calculated for measuring the distance between the context and analysis embeddings:  
(i) Euclidean distance: this measures the straight-line distance between two points in the embedding space.  
\[\text{d}(E_{\text{PLM}}(s_{i}),E_{a}(a_{ik}))=\left\|E_{\text{PLM}}(s_{i})-E_ {a}(a_{ik})\right\| \tag{10}\](ii) Cosine similarity: this measures the cosine of the angle between two vectors, reflecting how similar their directions are.  
\[\mathrm{d}(E_{\mathrm{PLM}}(s_{i}),E_{a}(a_{ik}))=\frac{E_{\mathrm{PLM}}(s_{i}) \cdot E_{a}(a_{ik})}{\|E_{\mathrm{PLM}}(s_{i})\|\|E_{a}(a_{ik})\|} \tag{11}\]  
(iii) Dot product: this measures the magnitude of projection of one vector onto another, reflecting their similarity.  
\[\mathrm{d}(E_{\mathrm{PLM}}(s_{i}),E_{a}(a_{ik}))=E_{\mathrm{PLM}}(s_{i})\cdot E _{a}(a_{ik}) \tag{12}\]  
(iv) Linear layer with a single output: this combines the embeddings through a weighted sum and a bias term to produce a scalar output.  
\[\mathrm{d}(E_{\mathrm{PLM}}(s_{i}),E_{a}(a_{ik}))=\mathbf{W}_{t}\cdot\begin{bmatrix} E_{\mathrm{PLM}}(s_{i})\\ E_{a}(a_{ik})\end{bmatrix}+b \tag{13}\]
## 2 Numerical Model and Validation  
### Governing Equations and TWW  
The governing equations of two-dimensional incompressible flow in a Cartesian coordinate system can be written as follows.  
\[\frac{\partial u_{i}}{\partial x_{i}}\ =\ 0 \tag{1}\]  
\[\frac{\partial u_{i}}{\partial t}+u_{j}\frac{\partial u_{i}}{\partial x_{j}}\ = \ -\frac{1}{\rho}\frac{\partial p}{\partial x_{i}}+\frac{\mu}{\rho}\frac{ \partial^{2}u_{i}}{\partial x_{j}^{2}} \tag{2}\]  
where \(u_{i}\) is the velocity components, i.e., \(u_{1}\) is the velocity component at the inline flow direction and \(u_{2}\) is the velocity component at the cross-flow direction, \(\mu\) denotes the kinematic viscosity coefficient, \(\rho\) is the fluid density, and \(p\) denotes the pressure in flow field.  
As shown in Figure 1, The transverse traveling wave at the rear section of cylinder was established, i.e., the transverse traveling wave can propagate from both point B and C to point A at the same time.  
The origin \(O\) is positioned at the center of the plane polar coordinate system (\(r\), \(\theta\)), and the wave equations of the TWW can be written as,  
\[\left\{\begin{array}{c}x\ =\ r\cos\theta\\ y\ =\ r\sin\theta\\ r\ =\ r_{0}+A(l)\cos[k(l-ct)]\end{array}\right.,-\pi/2\leq\theta\leq\pi/2 \tag{3}\]  
\[A(l)\ =\ \left\{\begin{array}{c}\hat{A}\frac{l}{\lambda},0\leq l\leq\lambda\\ \hat{A},\ \lambda\leq l\leq(N-1)\lambda\\ \hat{A}\frac{(N-1)}{\lambda},(N-1)\lambda\leq l\leq N\lambda\end{array}\right. \tag{4}\]  
where \(r_{o}\ =\ D/2\) is the radius of the standard circular cylinder without any traveling wave, \(D\) is the diameter of the cylinder, \(A(l)\) is the vibration amplitude of each point on the TWW, \(k\ =\ 2\pi/\lambda\) is the wave number, \(l\) is the arc length from point A on the cylinder rear edge to any point P on the TWW, \(c\) is the wave velocity, \(t\) is the time, \(\lambda\) is the wave length, \(\hat{A}\) is the maximum wave amplitude, \(N\) is the wave number in 1/4 circle. In the first wave, the amplitude increases linearly from 0; In the end wave, the amplitude decreases linearly to 0, while other intermediate waves have the same amplitude as the complete waveform, as shown in Equation (4). The TWW connect smoothly with the non-moving cylinder surface in this configuration.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 5 Modification Processes of Kaolin: Enhancing Industrial Applicability
### Intercalation/Explation Treatment  
Intercalation involves the effective insertion of a neutral polar organic substance into the interlamellar spaces of an inorganic host lattice, resulting in nanosized composite materials with a regularly spaced stack. Wide interlamellar spaces are crucial as they prevent interactions between randomly dispersed layers within a consistent polymer matrix, thereby facilitating the exfoliation of the synthesized material. The unique, notably non-centrosymmetric structure of kaolinite renders it an ideal host for intercalation [72], although the efficiency of intercalation is influenced by factors such as the crystallinity and the particle size of the host material. Higher crystallinity typically results in an increased rate of intercalation, while smaller particle sizes generally lead to a reduced intercalation rate [73]. This is due to the fact that a decrease in particle size is associated with a reduction in structural order, which affects the intercalation process. The primary challenge impeding the use of kaolinite as a host material is its rigid interlamellar spacing and absence of charge. Consequently, only a limited range of interlamellar compounds--such as methanol, acetamide, octadecyl amine, dimethylformamide, potassium acetate, N-dimethylformamide, deuterated dimethylsulfoxide, and dimethylsulfoxide (DMSO)--are capable of intercalating directly into the kaolinite layers. Nonetheless, after introducing initial guest compounds into kaolinite, it is possible for further intercalation to occur with additional organic guests that cannot penetrate the kaolinite layers directly [74]. This can be achieved through the covalent grafting of small polar molecules, such as DMSO or methanol, which form Al-O-C bonds with the aluminol basal plane of the kaolinite, thereby expanding the interlamellar spacing [75]. This expansion creates an environment in which subsequent guest compounds can replace the initially intercalated ones. Another approach to facilitating intercalation involves mechanochemical methods, in which the organic guest is co-ground with the inorganic host to achieve the desired insertion [76].
# Experimental Methods to Evaluate the Carbonation Degree in Concrete--State of the Art Review
## 2 Qualitative Methods
### Phase Analysis
#### 2.3.3 Fourier Transform Infrared Spectroscopy (FTIR)  
Another method, FTIR, has been harnessed to identify the main phases of carbonation products due to the relative intensity of the absorption bandwidths associated with the creation of chemical compositions during the carbonation process. FTIR spectra could be utilized as a useful tool to acquire information concerning the carbonation reaction and hydrated cement products, including C-S-H and calcite, based on absorption peaks at different wavelengths caused by mineralogical components [76]. For example, the effects of types of binder, mineral addition, quartz and limestone on the carbonation process of concrete have been studied by [PERSON] et al. [77]. They indicated that the FTIR spectra fluctuated significantly after carbonation curing. The absorption peak of CH at 3642 cm\({}^{-1}\) totally vanished due to the complete carbonation of CH to form calcium carbonate, as shown in Figure 7.  
In addition, the absorption peaks of CSH which normally appeared at 3413 cm\({}^{-1}\) and 1655 cm\({}^{-1}\) became weaker than the peaks belonging to the non-carbonated samples due to the fact that the C-S-H was dehydrated. The FTIR results are matched with the outcome of TGA and XRD methods. These methods could help to detect the main phases concerning carbonation of concrete such as AFt, CH, CC, dolomite and CSH [54], which are the typical carbonation reaction products.
# A Review of Systems and Technologies for Smart Homes and Smart Grids
## 6 Discussion
### Barriers and Futures Trends  
Future research should be continued to develop algorithms and to study human habits to improve the efficiency and flexibility of the energy management strategy [119]. The potential benefit of communicating households energy usage in a systemic context, _i.e._, considering the dwelling as a part of a smart grid, has also been explored and has been recognized as a significant potential driver for the deployment of energy management solutions in households [45].  
The effect of feedback on end-users has been proven to be more effective the more direct it is. Furthermore, the representation of data should ideally be personalized to fit end-users' needs, habits, and education. Supplying users with the possibility of comparing their home energy usage with benchmarks as well as with their own historical data has also been shown to be an advantage. However, what is a relevant comparison will also vary among the users. For instance, a few user may be interested in comparing their own usage with that of others, hence adding a reference element, while others may not be interested at all in competing in this context. Moreover, users may by grouped according to different rules, _e.g._, per homes of comparable size, buildings with analogous occupation time, or buildings with similar average energy usages [45]. The most challenging aspect of users' motivation is that whichever type of feedback is used, the technology needs to be continually developed to meet the challenge of domestication. The process implies that end-users adapt the technology according to their own needs and expectations. However, this process has, from a visualization point of view, an adverse side effect [120]: any positive effect of any visualization methods used for end-users' benefit eventually stop as part of the domestication process. Consequently, any management system, independent of how effective it might prove to be, will only remain effective if the presentation of data is flexible and can be adjusted to vary over time [118]. The challenge lies in offering the end-users not only relevant data but also continual involvement in this adaptation and customization process.  
Recently, the potential of offering gamification, _i.e._, the use of videogame elements and concepts from non-gaming contexts to improve user experience and engagement with an interface has started to be explored [121]. Having the option to compare own energy usage with that of others may include a potential gamification element.  
Finally, another significant barrier, represented by users' knowledge about smart technologies has to be taken into consideration to realize a proper home automation system able to interact with users. Not only do smart appliances have to be correctly installed and properly configured, but the users' knowledge of smart devices needs to be consistently increased as well as their awareness of energy management.
# Scenario-Based Configuration Refinement for High-Load Cellular Networks: An Operator View
## 3 High Population Density Scenario
### Time Slots for Uplink and Downlink Traffic  
As the red line shows in Figure 5, the traffic load of downlink is much larger than that of uplink in different areas of university campuses, such as dormitories, teaching buildings, libraries, and dining halls. Moreover, the PRB utilization ratio of downlinks is much lower than that of uplinks. The reason is that different vendors offer similar proactive mechanisms to adjust the PRB ratio of uplink, which leads to imbalances between the PRB utilization ratio of uplink and the PRB utilization ratio of downlink (i.e., as shown in Figure 5, the PRB utilization ratio of uplink is significantly larger than that of the downlink in low traffic scenarios).  
Telecom operators always adjust the time slot ratio of uplink to downlink to achieve PRB utilization ratio balance between uplink and downlink. In Jiangsu province, the time slot ratio of uplink to downlink is generally configured as 1:3 for foundation networks provided by operators. Using this configuration, we take the dormitories of the university as an example. As shown in Table 6, we observe that the PRB utilization ratio between uplink and downlink approaches a balance within different vendors' devices when the PRB utilization ratio of downlink is more than 20%. Similar trends can be found in different areas of university campus. Therefore, we can configure the time slot ratio of uplink to downlink as 1:3 for university campuses, which is consistent with foundation networks.  
Figure 4: Main flow of network configuration refinement for high traffic load cells.