esa-sceva/satcom-chunk-collection huggingface dataset:
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 7 Evaluation of Web3602
### Performance Requirements Assessment  
#### 7.2.1 Required Network Performance  
Web360\({}^{2}\) implements a specification to load all audio data and up to 40 s of video data to view the content before playback begins. For both the concert and jazz session contents, the audio data size was approximately 10 MB and the 40 s of video data were approximately 30 MB. Hence, approximately 40 MB of media data had to be loaded before playback started. Therefore, if the allowable latency to start playback is less than 10 s, a communication speed of 32 Mbps is required. Because the number of audio objects and playback time were relatively small for the content used on Web360\({}^{2}\), we implemented a specification to load all audio data before playback began. If audio data are streamed in the same manner as video data, the application can reduce the amount of data to be loaded before playback starts to some extent. After playback starts, the application acquires the video data at any time in units of approximately 10 s. Consequently, it is necessary to retrieve 5-9 MB of video data approximately every 10 s, which requires a minimum communication speed of approximately 8 Mbps. However, a higher communication speed is required if audio data are streamed and played back. Although there is still room to optimize the buffering size and loading intervals of the audio and video data described above, which is conducted by Web360\({}^{2}\), we do not believe that they will change significantly. The communication speed requirement (32 Mbps) specified in the above evaluation is the allowable latency to start playback. An acceptable latency of 10 s is a rather loose setting  
Figure 23: Comparison of evaluation results with respect to headphone use.  
Figure 22: Comparison of evaluation results with respect to gyro sensor use.  
and should be set to 1-2 s so that users are unaware of the latency. The resolution of the 360\({}^{\circ}\) videos is 1920 \(\times\) 960, therefore, the image quality after rendering is quite rough. The audio data are also compressed at a low bit rate (64 kbps) from the recorded sound source, and the audio quality is not very good. Considering the resolution of a typical display, the resolution of the 360\({}^{\circ}\) videos should be at least 4K, and the audio quality should be at least 256 kbps. Furthermore, as the number of audio objects increased, the audio data will also increase proportionally.  
Considering the above, we can roughly estimate that a communication speed of approximately 0.5 Gbps or higher is required to provide a satisfactory viewing experience. This estimated value is feasible even under current conditions with a wired LAN or a high-speed WiFi network. However, it is still challenging to achieve this communication speed with tablets and smartphones in a mobile environment unless special conditions are met. There are high expectations regarding the future evolution of the communication environment.
## 2 Motion Model Selection
### Differential Flatness  
Here, differential flatness [63] provides algebraic functions (e.g., polynomials) which analytically map the trajectory and whose higher-order derivatives map to system states and inputs. Since the \(N\)th-order polynomial can be differentiated up to \(N-1\) times, the differential flatness property ensures the feasibility of the trajectory and generates appropriate control commands. More precisely, let the following:  
\[\dot{\mathbf{x}}=\mathbf{f}_{c}(\mathbf{x},\mathbf{u})\quad\mathbf{x}\in \mathbb{R}^{n_{x}},\mathbf{u}\in\mathbb{R}^{n_{u}}. \tag{5}\]  
be a nonlinear system. According to to [64], if the system is differentially flat, there always exists a flat output, namely \(\mathbf{z}\in\mathbb{R}^{n_{z}}\), where the dimension of the output is given by \(n_{z}\). In such a system, states and control inputs can also be formulated from the system flat outputs whose derivatives are mapped through functions, namely \(\varrho\) and \(\tau\). Let \(\mathbf{z}=\Im(\mathbf{x},\mathbf{u},\dot{\mathbf{u}},\ldots,\mathbf{u}^{(q)})\) be the flat output, holding \(\mathbf{x}=\varrho(\mathbf{z},\dot{\mathbf{z}},\ldots,\mathbf{z}^{(r)})\) and \(\mathbf{u}=\tau(\mathbf{z},\dot{\mathbf{z}},\ldots,\mathbf{z}^{(r)})\), where apices \({}^{(i)}\) stipulates the \(i\)th derivative. Along with that, the explicit trajectory generation process can benefit when it uses differentially flat systems, for example, \(\varrho\) and \(\tau\) can be a \(d\)th-order polynomial \(p(t)\). Then, \(\mathbf{x}^{\top}(t)=[p^{\top}(t)\ \dot{p}^{\top}(t)\ \dot{p}^{\top}(t)]\) are the state of the system at time \(t\) in which \(\dot{p}^{T}\) and \(\dot{p}^{T}\) indicate the velocity and acceleration of the system, respectively. Control inputs can be determined by jerk [65], namely \(\ \dot{p}^{\top}(t)\), where \(p(t)=\lambda_{d}t^{d}+\ldots+\lambda_{1}t+\lambda_{0},\ t\in[0,dt]\), where \(\lambda_{i},i=0,\ldots,d\) are the polynomial coefficients. There are various ways to construct these kinds of polynomials, including Minimum-snap, B-spline, etc.
# Multimodal Dynamic Journey-Planning +
## 2 Preliminaries  
The scheduled departure and arrival times of public vehicles in a (schedule-based) public transportation system is described by timetables. In this work, a _timetable_ is a set tuple \(\mathcal{T}=(\mathcal{H},\Phi,\mathcal{C})\), where \(\Phi\) is the set of _stations_ or _stops_ in which the passengers can get on/off a vehicle, \(\mathcal{H}\) is the set of _vehicles_ that perform the scheduled routes (e.g., train, bus, metro, or any other related means of public transport that can carry out scheduled routes), and \(\mathcal{C}\) is the set of _elementary connections_\(c=(h,S_{d},S_{a},t_{d},t_{a})\), where such an elementary connection represents the travel of a vehicle \(h\in\mathcal{H}\), leaving from stop \(S_{d}\in\Phi\) at time \(t_{d}\) and arriving at the immediate next stop \(S_{a}\in\Phi\) at time \(t_{a}\). Elementary connections of schedule-based transport are restricted with respect to the scheduled departure time of the vehicles.  
The _realistic time-expanded model_ (TE-real) [2] is one of the prime models for representing a timetable. The timetable \(\mathcal{T}\) in TE-real is encoded into a directed graph \(G=(V,E)\) with appropriate arc weights. Every time-event (arrival or departure time of a vehicle at a station) is represented by a graph node, while the graph arcs represent either elementary connections (travel of a vehicle between consecutive stations), or _transfer_ between different vehicles at the same station, or the waiting time between two time events (at the same station). The arc weight is the time difference between the time events associated with the endpoints of the arc.  
It is worth mentioning that _transfer times_\(\mathcal{F}\) introduce realistic transfer restrictions between vehicles. They represent the required minimum \(\textit{transfer}(S)\) time that a passenger needs to be transferred between different vehicles within the same stop, \(S\).  
A reduced version (TE-red) of TE-real was also presented in [2; 10]. TE-red eliminates nodes representing transfer events, without losing correctness, in order to reduce the graph size.
# Tunnel Geology Prediction Using a Neural Network Based on Instrumented Drilling Test
## 7 Conclusions  
Basic and deep neural network methods for geology prediction based on instrumented drilling test data were presented, along with an application to a case study. Nine drilling parameters were used for neural network training: drilling speed, torque, rotation speed, propulsion, blow energy, blow number, water supply rate, water supply pressure, and EV energy. Batch normalization of drilling parameters and GA optimization of hyperparameters were employed in the deep neural network model.  
A case study on Jiudingshan Tunnel showed that the neural network method predicted geological conditions well. Predictions matched the actual geological profile well, especially for frequently encountered rock types. On the other hand, the prediction was relatively poor for infrequently encountered rock types, as is typical for neural networks. Relatively low predictive accuracy also occurred at rock interfaces; such situations deserve further study. AUC and the confusion matrix evaluation showed that the deep neural network model displayed a higher accuracy than the basic neural network. Taking the number of epochs of training as a comparison, deep neural network was approximately 2.5 times more efficient than the basic neural network. If more data are available for training, the deep neural network will have more advantages in training efficiency and prediction accuracy. The feature importance of each drilling parameters was analyzed; the results indicate that a neural network model for geology prediction can perform well using fewer drilling parameters.  
In general, the neural network approach to geology prediction can be regarded as a useful complement to traditional geology prediction methods used during tunnel construction, providing reliable results for dynamic tunnel design.  
Methodology, Y.F. and Z.W.; software, Y.F.; validation, Q.S., H.T.; resources, D.L.; writing--original draft preparation, Y.F.; writing--review and editing, Z.W.; supervision, Q.S.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by Traffic Science and Technology Project of Yunnan Province, China ([2018]18).  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 3 Experiments
### Network Evaluation  
Further, to support the validity of the connectivity findings by the above hybrid feature selection method, we constructed a brain network based on these connections and explored its topological properties [50; 51]. More specifically, we first chose the clustering coefficient (\(C\)), characteristic path length (\(L\)), global network efficiency (\(Eg\)) and local network efficiency (\(Eloc\)) as the evaluation index for each network. Then, we constructed weight networks with a threshold of one for the original and selected FNC data. At last, these four parameters of both SZ and HCs were calculated and tested by a two-sample \(t\)-test. The \(p\)-values of these four parameters were \(1.70\times 10^{-1}\), \(5.02\times 10^{-3}\), \(2.99\times 10^{-2}\) and \(4.27\times 10^{-2}\) for the original FNC data and \(6.64\times 10^{-2}\), \(3.41\times 10^{-6}\), \(5.40\times 10^{-6}\) and \(1.90\times 10^{-2}\) after feature selection by our method. Obviously, from the results of the \(p\)-values of the four parameters, we can find that the \(p\)-values of all these parameters decreased significantly after feature selection, which means that the distinction of four parameters between the HCs and SZ became more apparent after feature selection, especially the characteristic path length and the global network efficiency. This shows that the HCs and SZ become obviously distinguishable by the hybrid feature selection method and shows the validity of our method.
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
## 1 Introduction
The aim of this paper is to investigate methods for the efficient handling of 3D building objects in mobile devices. To fulfil this aim, there are several issues that have to be addressed: (i) the usage of an efficient multiple representation data structure that supports progressive transfer; (ii) the development of generalization methods to populate this data structure with geodata; (iii) the storage techniques of the geodata that support the fast retrieval of data; and (iv) the implementation of a visualization framework that supports progressive transfer. In this paper, we address all of these issues. This paper is organized as follows. The related work is introduced in Section 2. Section 3 describes the methodology where we explain each of the four issues mentioned above. Section 4 presents the experimental results for both visual quality and the performance aspects. Finally, Sections 5 and 6 contain a discussion and the conclusions of the proposed methodology, respectively.
## 2 System Modelling
### Blocking Voltage and Maximum Current  
The selected MLI topology had considerably lower total standing voltage (TSV) compared to conventional and a number of newer topologies. Because the blocking voltages across the switches in MLI were not similar, knowing only the TSV was generally inadequate. Understanding these voltages can assist in the selection of suitable device models during the design stage. First, the relationships for the blocking voltages at the central packed H-Bridge stage are given as follows:  
\[\mathrm{VB_{s1}}=\mathrm{VB_{s2}}=2\mathrm{V_{DC}} \tag{1}\]  
\[\mathrm{VB_{s3}}=\mathrm{VB_{s4}}=6\mathrm{V_{DC}} \tag{2}\]  
\[\mathrm{VB_{s5}}=\mathrm{VB_{s6}}=8\mathrm{V_{DC}} \tag{3}\]  
where \(\mathrm{VB_{S1}}\) to \(\mathrm{VB_{S6}}\) are the blocking voltages across the switches \(\mathrm{S_{1}}\) to \(\mathrm{S_{6}}\). As for the basic units on the left and right sides of the bridge, the equations for blocking voltages can be represented as follows:  
\[\mathrm{VB_{s7}}=\mathrm{VB_{s8}}=\mathrm{V_{DC}} \tag{4}\]  
\[\mathrm{VB_{s9}}=\mathrm{VB_{s10}}=3\mathrm{V_{DC}} \tag{5}\]  
where \(\mathrm{VB_{S7}}\) to \(\mathrm{VB_{S8}}\) and \(\mathrm{VB_{S9}}\) to \(\mathrm{VB_{S10}}\) are the blocking voltages across the switches \(\mathrm{S_{7}}\) to \(\mathrm{S_{8}}\) and \(\mathrm{S_{9}}\) to \(\mathrm{S_{10}}\), respectively. From (1) to (5), switches \(\mathrm{S_{5}}\) and \(\mathrm{S_{6}}\) theoretically had the highest blocking voltages, while the lowest blocking voltages were across switches \(\mathrm{S_{7}}\) and \(\mathrm{S_{8}}\). With the presented equations, the TSV of the circuit was 40 \(\mathrm{V_{DC}}\). The maximum current flowing through every power switch, unlike the blocking voltage, was essentially the same as the maximum current produced at the load.
# Improving Vulnerability Management Through Process Mining
## 3 Process Mining
### Process Conformance  
\"Conformance checking refers to the analysis of the relation between the intended behaviour of a process as described in a process model and event logs that have been recorded during the execution of the process\" [32] (p. 3).  
The input for conformance checking is an event log and a process model. The outputs are diagnostics on how these align. Moreover, [PERSON] [33] divides these diagnostics into global conformance measures if they apply to the entire model and local conformance measures if they apply to single activities.  
Depending on the type of input model, there are two main use cases. If the input model is a mined as-is model, conformance checking can be used to evaluate the quality of the used process mining algorithm as well as the quality of the model. This use case is especially considered in process mining research, where it is utilized to create metrics that measure the fitness, precision, generalization, and simplicity of a process discovery algorithm. If the input model is a to-be model, conformance checking can be used to find desired and undesired deviations [32; 33].  
According to [PERSON] et al. [32], one of the main applications of finding undesired deviations is compliance management. Manual compliance checking can be costly and slow. With conformance checking, the required process model can be compared to the output data of the actual process. The output is the rate of violations as well as the specific violations, which can then be analyzed by the compliance managers. A commonly used approach is token replay [33]. The idea is to measure the fitness of a model by checking if traces in an event log can be replayed in a model. To achieve this, a Petri net is used to replay cases. Whenever the model cannot continue replaying a trace, the algorithm creates tokens so that the replay can continue. In the end, all remaining tokens get deleted. There are four counters, which count the tokens produced (\(p\)), consumed (\(c\)), missing tokens created (\(m\)), and remaining tokens deleted (\(r\)). The counters will be used in the following formula for one case:  
\[fitness=\frac{1}{2}(1-\frac{m}{c})+\frac{1}{2}(1-\frac{r}{p}).\]  
The result is a value between 1 and 0, with 1 being perfect conformance. The replaying will be repeated for the entire event log and the sums of the counters will be used in the formula, creating the conformance for the entire event log. Additionally, local conformance measures can be created by counting how often a missing token was created or a remaining token deleted for a single activity [32; 33].
## 4 Experimental Setup
### Dataset
#### 4.1.1 Data Curation  
The original dataset, Appendix A.2, Table A.2, is composed of a total of 93 variables and 1179 subjects, among which we find:  
* One subject identifier;
* Thirteen variables of general information about the subject such as weight, age, education, stress, etc.;
* Seven variables related to alcoholic drinks, distinguishing between distilled and fermented drinks;
* Seven variables on smoking habits, such as number of cigarettes, pipes, cigars. For ex-smokers: time since a smoker quit smoking;
* Fifteen variables related to pathologies, such as types of cancer, sleep apnea, and type 2 diabetes mellitus, among others;* Thirty-four variables on nutritional habits; we found information on the portions of different types of food and the points of adherence to the Mediterranean diet according to these portions;
* Sixteen variables related to physical exercise and its intensity.  
The dataset is balanced in terms of the predicted variable, overweight/obesity (BMI \(\geq\) 25), with 48% being obese/overweight individuals and 52% being non-obese/non-overweight individuals. Therefore, we considered unnecessary the use of classification techniques focused on imbalanced datasets.  
In order to avoid repeated information that may introduce noise in the system, a reduction in the number of variables was performed. An example of a reduction is the case of the variables referring to the food intake, which were replaced by a unique variable, namely adherence to the Mediterranean Diet (ADH). The original dataset, contains a set of variables related to food, 16 associated with the servings, and derived from these, another 16 measuring their adherence to the Mediterranean diet using points. If there are more than eight points in total, the subject is considered to have a high ADH.  
In addition, some redundant variables were eliminated. For instance, the dataset initially contains two variables referring to exercise that were computed with a set of features of the pool: Ca_IPAQ, which reports the calories burned as a function of physical exercise; and IPAQ, which reports the information on the exercise performed and its intensity. IPAQ takes into account the duration and intensity of exercise, pondering the value of sedentary, moderate, and vigorous exercise. Ca_IPAQ includes weight as a variable for its calculation and therefore cannot be used since weight is also present in the close form for computing BMI. Hence, we use only IPAQ as a training variable.  
There are also some features, such as the place or institution, where the sample was obtained (_center_), that were removed from the dataset since a high correlation with the BMI was observed due to the differences in the nature of the population of the places (policeman, sport teams, retired people, etc.).  
After processing the data, which in our case was supervised by the medical staff that participated in the project, the total number of variables was reduced, from 93 to 41, as shown in Appendix A.1, Table A.1. This table shows the different features of the study, providing its identifier, name, short description, and type.
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 5 Design and Implementation
### Latency Optimization Strategy
#### 5.2.2 Multiple Rx Queues  
Formula (9) is easily extended to multiple Rx queues scenarios. In the case of multiple Rx queues, the packets in each Rx queue may be sent to the same Tx queue. In Section 2.2, we mentioned that, under the spin-polling mechanism, the CPU is polling the Rx queues sequentially, so we can set multiple \(\theta_{Tx}\) on the Tx queue, corresponding to different Rx queues. This means that when the CPU switches the Rx queue, the \(\theta_{Tx}\) of the Tx queue need to be changed as well, as shown in Figure 9. When the CPU finishes processing the packets of the \(i\)-th Rx queue, \(P_{out,i}\) of them are put into the Tx queue. The \(\overline{P_{out,i}}\) is the average number of packets from the \(i\)-th Rx queue entering the Tx queue per round in \(T_{observed}\). Finally, our packet latency optimization strategy in a multiple Rx queues scenario can be represented by the following formula:  
\[\theta_{Tx,i}=\begin{cases}P_{out,i},\eta\leqslant H\\ f(\overline{P_{out,i}}),\eta>H\end{cases} \tag{10}\]
# Fuzzy Sliding Mode Control of Vehicle Magnetorheological Semi-Active Air Suspension
## 4 Design of Sliding Mode Controller for MR Semi-Active Air Suspension
### Design of Sliding Mode Controller
The smaller \(\varepsilon\) is, the smaller the approaching rate is, while the larger \(\varepsilon\) is, the greater the approaching rate is, and the more severe the system chattering is. In the sliding mode control, \(\varepsilon\) is 0.1 according to the experience and debugging results. The system stability was verified according to the Lyapunov stability criterion. If \(V(x)\) is positive definite and \(\dot{V}(x)\) is negative definite, the system is stable. Select the Lyapunov function as follows:  
\[V(x)=\frac{1}{2}s^{T}s \tag{40}\]  
Then it can be derived as follows:  
\[\dot{V}(x)=s^{T}\dot{s}=s^{T}(-\varepsilon\text{sgn}(s))\leq 0 \tag{41}\]  
According to the Lyapunov stability criterion, it can be inferred that the system is stable. Therefore, the final sliding mode control law of the system is as follows:  
\[\Phi(s)=\frac{Y(s)}{U(s)}=\frac{{\omega_{n}}^{2}}{s^{2}+2\xi\omega_{n}s+{ \omega_{n}}^{2}} \tag{33}\]0  
where \(m_{s}\) is the coefficient related to \(s\) and \(\dot{s}\). Finally, the variable damping force by the sliding mode controller is as follows:  
\[U_{d}=\begin{cases}U_{eq}+U_{sw}\ \ \ \ [U_{eq}+U_{sw}](\dot{x}_{b}-\dot{x}_{b1}) \geq 0\\ 0\
## 3 Our Implementation
### Environment Representation  
The robot consists of connected links with a known kinematic structure, where joint rotations define its state for trajectory planning. The environment is represented as an octomap [17], a 3D occupancy grid that efficiently models free and occupied space, with cells classified as unknown, occupied, free, and overlaid. Initially set to unknown, cells are updated based on visual data. To prevent the robot from erroneously perceiving its own body as an obstacle, standard self-filtering mechanisms are employed when integrating sensor data into the octomap. Additionally, to ensure valid initial path planning, octomap cells corresponding to the robot's volume at its initial pose are explicitly marked as free space.