esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Predictive Modeling Techniques  
### Bayesian Belief Network (BBN)  
Bayesian belief network (BBN) is a directed acyclic graph (DAG) comprising of a set of variables (nodes), arcs, and conditional probability tables, which indicate joint probability distributions among the nodes of variables. The nodes in BBN can be categorized into two kinds, parent node and child node, as shown in Figure 1.  
One of BBN's significances is that joint probability distributions can be easily identified. In BBN, if the probability of the variable \(X_{i}\)'s parent nodes is defined as \(Pa(X_{i})\), joint probability distributions \(P(X)\), \(X=(X_{1},\,X_{2},\,X_{3},\,...,\,X_{n})\) can be represented as:  
\[P(X)=(X_{1},X_{2},X_{3},\,...,\,X_{n})=\prod_{i=1}^{n}P\big{(}X_{i}\big{|}Pa(X _{i})\big{)} \tag{1}\]  
where \(X=(X_{1},\,X_{2},\,X_{3},\,...,\,X_{n})\) expressing various BBN variables, \(n\) is the number of BBN variables. Another significance is that the probability can be updated dynamically when there is new evidence. If new evidence (event \(Y\)) is provided to BBN, the posterior probability i.e., \(P(X|Y)\) of event \(X\) is found as:  
\[P(X|Y)=\frac{P(X)\,P(Y|X)}{P(Y)}=\frac{P(X)\,P(Y|X)}{\sum\limits_{i=1}^{n}P(Y |X_{i})} \tag{2}\]  
where marginal probability of event \(Y\) is \(P(Y)\) and prior probability of event \(X\) is \(P(X)\). The construction of a BBN primarily includes two steps: (1) Structure learning determine the factor variables (nodes) related to the study object, and then identify the dependent or independent relationship among the factor variables, so as to develop a DAG structure. (2) Parameter learning is based on the given BBN structure to study the conditional probability table at each variable node. Effective structure learning is essential in the construction of an optimal BBN network structure. The development of a BBN structure comprises of any one of the following listed three methods:  
(1) The variable nodes of a BBN are determined by prior or domain knowledge (DK) and expert experience. (2) A BBN structure is acquired through learning of sample data, automatically by using a machine learning (ML) algorithm. (3) A BBN structure is acquired by using data fusion methodology based on DK and ML of data. Since the third method integrates the strengths of both DK and ML, it eliminates the pitfalls that arise by utilizing only one method. Therefore, this paper uses this method to identify a BBN structure for assessment of seismic soil liquefaction potential. To perform structural learning from a dataset, frequently used ML algorithms, such as K2 and hill-climbing were used. In this study, the K2 algorithm [32] is applied to perform structure learning that carries out a search in accordance with the  
Figure 1: A simple Bayesian belief network. given order of nodes by means of restricted maximum number of parent nodes. A K2 machine learning algorithm employs posterior probabilities as the scoring function and adds arcs to BBN, which depend on the following rules [32]:  
(1) Calculate the Cooper-Herskovits (\(CH\)) score for \(X_{j}\) according to the nodes' order \(\rho\).
# Reinventing Mobility Paradigms: Flying Car Scenarios and Challenges for Urban Mobility
## 2 Main Features of Flying Cars and Air Urban Mobility Cases
### Flying Car Main Features
For CTOL vehicles, runways are necessary to run and gain enough speed to generate lift to go up. On the contrary, VTOL vehicles do not require long runways or do not require runways at all. VTOL features are particularly suitable for urban contexts, as they require reduced dedicated areas for taking off and landing. Flying and ground cars may be fully automated, partially automated or flight assisted, depending on the on-board automated driving systems. For automated vehicles, flying drivers can take control at any time also depending on their reactions to automation devices [35; 36]. \"Automated\" features differ from \"autonomous\" ones because these latter imply decision-making capabilities. An automated vehicle acts by following some automated mechanisms (e.g., forward-collision warnings and automatic  
Figure 1: Identifying the vertical position of a flying object. braking, also without driver intervention), but the driver may take control. Autonomous vehicles are fully automated, monitor what is happening around them, react on the basis of some kind of learning and are driverless. Safe interactions between automated/autonomous vehicles require suitable communications between the various entities involved, the main purpose of which is to prevent dangerous situations, using appropriate communication protocols and standards, designed to support vehicle transmissions in different application scenarios. In this context, communications are commonly classified in: (_i_) vehicle-to-vehicle (V2V), consisting in wireless communication between neighboring vehicles [37; 38] to realize 3D separations among vehicles and obstacles [39] also avoiding the effects of wake turbulence, which implies lift variations [40; 41]; (_ii_) vehicle-to-infrastructure (V2I), linking vehicles to infrastructures--including ground obstacles such as buildings and electric cables, traffic lights and roundabout [42; 43; 44]; (_iii_) vehicle-to-everything (V2X), which includes V2V and V2I and also communication between vehicle and pedestrian (V2P), roadside (V2R) and device (V2D) [45; 46] in order to create safe environments. Communication architecture, reliable routing protocols and potential disconnection outside the coverage range of the ground infrastructure are crucial key factors for connected vehicles. Particularly for flying vehicles, some recent studies explored V2V and V2I communication networks such as Vehicular Ad hoc Networks (VANETs) [47] and Flying Ad hoc Networks (FANETs) [48; 49], to improve traffic safety and prevent collision accidents. Some recent tests [50] also showed that conventional Air Traffic Control (ATC) approaches might be unfeasible for high urban traffic levels and complex urban environments.
## 2 UAV in Wireless Communication Networks
### Use Cases of UAV Wireless Networks
#### 2.2.1 UAV Base Station  
UAV BS assists the existing networks in extending service coverage and provides reliable and seamless connectivity to user equipments (UEs). UAV BS can set a backhaul link from satellite, nearby terrestrial macro base station (MBS) or adjacent UAVs. UAV BS can support the emergency area where the communication infrastructures are damaged or destroyed by natural disasters, hence, the temporary communications are needed, and the extremely crowd area such as sports stadium where the additional links are needed due to base station offloading. This is addressed in [58], as one of the key scenarios that 5G networks should guarantee. Therefore, UAV BS can provide not only the extended coverage [30], but also the improved throughput of the network [26] along with the fast deployment [31]. However, the interference between UAVs and the existing wireless networks should be precisely considered to guarantee the performance of the entire networks [59].
## 1 Introduction
[16] compared the effects of rail pads with constant stiffness and those with frequency-varying stiffness on rail vibration through theoretical calculations and actual data and found that the frequency-varying stiffness has a more significant impact on higher frequency vibrations. The mechanical interface is a typical type of nonlinear contact; in the study of some nonlinear problems, researchers have discovered the frequency-dependent characteristic of material stiffness in mechanical structures [17; 18]. Based on these studies of frequency-dependent characteristics, we propose that the contact stiffness on the mechanical interface is unevenly distributed across the entire joint surface, and the contact stiffness varies with the frequency of external excitation. This is because different excitation frequencies stimulate different modal shapes and corresponding contact surface positions and sizes, leading to changes in contact stiffness with frequency. For existing simulation analyses, there has been limited research on modeling contact stiffness in mechanical structures; alternatively, contact stiffness is simply set to a constant value. These approaches overlook the non-uniform distribution of contact stiffness and its frequency-dependent characteristics, thereby affecting the accuracy of analysis results. In this study, a method is proposed to model the mechanical interface using a combination of modal frequencies and mode shapes to identify non-uniform contact stiffness values in different contact regions and frequency bands. Firstly, a flat plate structure was designed to conduct modal tests on plates with and without contact, verifying that the normal contact stiffness significantly changes the plate's fundamental frequency and mode shapes. Then, BUSH elements were used to model the normal contact stiffness; by utilizing the feature of BUSH elements' stiffness values varying with frequency, the BUSH elements on the joint interface were arranged non-uniformly and set to vary with frequency based on the plate's modal shapes and the experimental frequency response curves. This approach achieved a match between simulation results and experimental outcomes. Then, taking an electronic device as the research object, frequency response tests under four different boundary conditions were conducted to verify the effect of normal contact stiffness on the dynamic characteristics of electronic devices. Subsequently, BUSH elements, whose stiffness values vary with frequency, were used to model the normal contact stiffness in the structure of electronic devices. A comparative analysis between the simulation results and experimental outcomes under the four boundary conditions was conducted. The comparison showed that simulation analysis considering the frequency-dependent normal contact stiffness could achieve a good restoration of the experimental results within the studied frequency range.
# Intelligent Video Surveillance Platform for Wireless Multimedia Sensor Networks
## 3 Description of WMSNs System
### Network Architecture  
Our wireless multimedia sensor network system consists of \(N\) number of WMSN nodes with a single sink node, as shown in Figure 3. The sink is capable of handling all the complex calculations. All sensor nodes are capable of creating and relaying video traffic. All sensor nodes are capable of retrieving multimedia contents and transmitting the captured video to the sink via multi-hops. The topology of our networks is a linear topology. In linear topology, each node (except leave and sink) has exactly two neighbors: one parent and one child node. Each node gets packets from its child node and forwards these packets to its parent node to reach the destination. The intermediate nodes forward all the incoming packets from their children to its single parent to reach the sink node. The video stream generated from each sensor node arrives at the sink through multi-hops.
###### Abstract  
The article is focused on the study of the effect of laser light with three wavelengths used in a laser profilometer for the measurement of selected roughness parameters of the shiny surface of stainless steel A304 material. The measured results were compared with the results we achieved with the reference contact roughness meter (SJ-400). The findings presented are relevant to the parameters of the experiment outlined within the article. In general, the obtained results make it possible to state that when measuring the roughness of shiny cut surfaces using non-contact laser profilometry, reflections of laser light occur. The relatively best results of measuring the parameters of the roughness of a shiny cut surface on the evaluated material (A304) were achieved by laser light with a wavelength of \(\lambda=445\) nm. In contrast, as the surface roughness of the cutting surface of the used material increased, the reflection of laser light decreased. Furthermore, we can state that the values of the roughness parameters Ra and Rz of the shiny surface measured by laser profilometry were several times higher than the values measured by the reference method. In contrast, the non-contact method of laser profilometry is not suitable for accurate measurements of the roughness parameters of shiny surfaces.  
profilometry; laser light; surface roughness; surface gloss; material cutting Article  
The Influence of the Wavelength of Laser Light on the Non-Contact Measurement of the Roughness of Shiny Cut Surfaces on Stainless Steel A304 Material
# EXPRESS: Exploiting Energy-Accuracy Tradeoffs in 3D NAND Flash Memory for Energy-Efficient Storage
## 2 Background
### Interfacing NAND Chip from the Host Controller  
COTS flash memory chips use a standardized low-level interface, which was developed by the Open NAND Flash Interface (ONFI) working group, a consortium of flash memory manufacturers [10]. The ONFI specifications define: the standard physical interfaces; the chip identification mechanisms; a standard command set for reading, writing, and erasing the NAND flash; the timing requirements; and the data integrity features.  
Depending on the chip package and the type of the interface, the number of bytes sent to, or received from, a device at a time can vary. In our case, both the commands and the data are carried through eight data lines (DQ0-DQ7). The control lines, CE# (Chip Enable, active low), CLE (Command Latch Enable), ALE (Address Latch Enable), RE# (Read Enable, active low), and WE# (Write Enable, active low), allow for the control of the functions and timing of the interface. As is shown in Figure 3, a command placed on the data lines by the host is written into the device's command register on the rising edge of WE# when CE# is low, ALE is low, CLE is high, and RE# is high. An address placed on the data lines by the host is written to the device's address register on the rising edge of WE# when CE# is low, ALE is high, CLE is low, and RE# is high. Data placed on the data lines by the host is written into the device's data register on the rising edge of WE# when CE# is low, ALE is low, ALE is low, and RE# is high. Data is output from the device if it is in a ready state. The data from the device's data register is output to the data lines on the falling edge of RE# when CE# is low, ALE is low, CLE# is low, and WE# is high.  
Figure 3 illustrates a sequence of commands that carry out a page program operation. The operation is initiated by the host that sends the command (0x80) to the device through the data lines. Next, the host writes five address cycles (A0-A4), while keeping the ALE signal high. Next, the host controller sends the data to be written to the device's data register, byte by byte. Finally, the host sends the PAGE PROGRAM command (0x10) that initiates the write operation to the specified page of the flash memory array. During the page program operation, the device's status control pin RB (Ready/Busy#) is low, indicating that the device is currently busy. Upon completion of the program operation, the RB signal is set high. Thus, the host can determine the page program time (\(t_{prog}\)) by monitoring the state of this pin after issuing the command sequence.
# Digestate as Sustainable Nutrient Source for Microalgae--Challenges and Prospects
## 5 Cultivation in Digestate  
As described above, digestate is characterized by high amounts of nitrogen (total nitrogen content could reach up to 12 g L\({}^{-1}\)) and phosphorus (total phosphorus could reach up to 5.8 g L\({}^{-1}\)) and by a slightly alkaline or neutral pH (see Table 2). Thus, digestate in general is suitable for the cultivation of many different microalgae strains. Various authors have already described the utilization of digestate for microalgae cultivation [44; 50; 60; 61; 62]. Nevertheless, prior to cultivation at a larger scale, it is mandatory to perform preliminary laboratory experiments in each specific case. This is necessary to find out whether the nutrient source is suitable for microalgal cultivation when obtained directly from the anaerobic process. Pre-treatment of the digestate, using some of the methods described above, might be necessary to provide microalgae with a suitable environment and proper nutrient content. As mentioned in Section 2, different microalgae species have different requirements regarding pH, nutrient composition, and salinity. Furthermore, anaerobic effluents differ in, e.g., solids concentration, nutrient composition, pH, and color, depending on the feedback and the variables used during anaerobic digestion, as described in Sections 3 and 4. Thus, the necessary steps to obtain a suitable cultivation medium strongly depend on the microalgae species to be cultivated as well as on the properties of the digestate. There is no universal solution that can be applied, but rather a pool of options from which the right combination of methods must be chosen for each specific application. The aim of this section is to give an overview of how digestate can be processed prior to cultivation and how this affects growth performance as shown in various studies.
# Multilevel Privacy Assurance Evaluation of Healthcare Metadata
## 2 Materials and Methods
### Privacy Assurance Evaluation at Inter- and Intra-Organization Level  
e3-value-modeling: In this model, the focus is drawn between the privacy assuring bilateral relationship between a diagnostic lab (biobank and biodepositary) as a healthcare provider with other key stakeholders in the Dutch healthcare backdrop. Laboratories (biobanks and biorepositories) play a pivotal role in facilitating healthcare data sharing. Samples and data collected from them pave the way for valuable clinical research. Efficient sharing and pooling of this data is an essential prerequisite for advancements in biomedical science [69]. In this respect, apprehension resides regarding \"where data goes\", \"by whom it is used\", and \"for what purpose\", which, so far, had not been dealt with aptly [70]. To simplify the understanding of this rather complex value network, the Padmock Chain Model provides a linear and easily comprehensible model to start with. This model represents the lab's bilateral relationship with other key-value actors in privacy-preserving healthcare data sharing. Process Mining and Resource Event Agent (REA) ontology: We discovered patients' un-anonymized PII share using Process Mining on Dutch healthcare event logs. Patients' un-anonymized information share occurs between the horizontally distributed caregivers (who are located within a hospital but belong to different suborganizations within the hospital). We located patients' sensitive information-share with their initiating and ending timestamps for each event. For example, one event belongs to a case (i.e., individual), an activity, and a timestamp. After the evaluations, we further verified our results with an IT expert affiliated with the Dutch hospital and diagnostic labs in Overiyssel province (Netherlands). The expert substantiated that our results show the actual data share status quo in the Dutch healthcare landscape. The results also apply to the metadata shared across vertically distributed caregivers (hospitals, labs, pharmacies, and GPs). Furthermore, we extended the Insurance Model (IM) of REA ontology [66] highlighting the key-value agents, their prime resource interactions, and mutual value gain/loss. Additionally, we explicated privacy as a Materialized Claim in the Dutch healthcare landscape using REA.
## 4 Results and Discussion  
### Overtaking Trajectory in the Vehicle Reference Frame  
In the vehicle reference frame, trajectories of the overtaking vehicle are categorized according to the initial lane position relative to the overtaken vehicle and the driving operation during the overtaking, as shown in Figure 7. Six different trajectory patterns were obtained for both before and after overtaking phases, and the basic characteristics of each type of trajectory are shown in Table 3, where the \"left lane\" and the \"right lane\" are both relative to the overtaken vehicle and the \"inner lane\" is the lane near the median strip.  
Figure 6: Lateral distance between parallel vehicles.  
Figure 7: Trajectories of overtaking vehicles in the vehicle reference frame. (**a**) Before overtaking; (**b**) After overtaking.  
Class I contains trajectories of keeping lane to overtake from the left and has the richest samples due to the higher running speed of the left lane. Trajectories in Class II are formed by the influence of a third vehicle, and if the third vehicle is taken as the reference frame, trajectories in this class are similar to that in Class III; Class III is common in many studies about overtaking, and their trajectories perform overtaking from the left lane (before overtaking) or merging to the right lane (after overtaking). The samples of trajectories in Classes IV-VI are relatively fewer, mainly because these trajectories do not meet the running speed difference between different lanes of the freeway or the overtaking habits of most drivers. Considering that the trajectories in Class II are greatly affected by a third vehicle, they will not be analyzed in depth. The trajectories in the last three classes will also not be analyzed because of their scarcity. Therefore, this paper focuses on the trajectories of lane-keeping overtaking represented by Class I and lane-changing overtaking represented by Class III.
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 4 The Design Procedure of Circuit Component Parameters
### Step 3: Calculating the Maximum and the Minimum Voltage Gains of Forward Mode  
Step 2 determines the transformer turn ratio to be approximately at 1.2. By applying (12) and (13), the maximum and the minimum gains of the forward charging mode are obtained.  
\[M_{vr\_max}=\frac{N\cdot V_{Bat\_max}}{V_{DC\ Grid\_nom}}=\frac{1.2\cdot 403}{400} \cong 1.21 \tag{12}\]  
\[M_{vr\_min}=\frac{N\cdot V_{Bat\_min}}{V_{DC\ Grid\_nom}}=\frac{1.2\cdot 280}{400}=0.84 \tag{13}\]  
Step 4: Selecting Parameters of \(Q_{1}\), \(k_{1}\), \(g_{1}\), and \(m_{1}\) to Meet the Forward Voltage Gain Requirement  
Initially, a set of proper parameter quantity was selected. Figure 10 shows the forward voltage gain with \(k_{1}\), \(g_{1}\), and \(m_{1}\) being fixed and the forward mode quality factor \(Q_{1}\) being adjusted. It clearly shows that the voltage gain curve is not a monotonic one and \(Q_{1}\) with 0.6-2 cannot realize the maximum and minimum gain requirement. Moreover, if \(Q_{1}=0.4\), the operating range of switching frequency is too wide and will result in longer response time. Therefore, a value of \(Q_{1}=0.2\) is suitable for the forward charging mode. With the same consideration, the selection of \(k_{1}\), \(g_{1}\), and \(m_{1}\) is to meet the maximum and minimum gain as the primary priority.  
Due to the bidirectional operation of the proposed circuit, the resonant parameters must meet the voltage regulation of both the forward and reverse charging modes. The parameter design can only be fulfilled by an iterative process. Finally, the design parameters are \(Q_{1}=0.2\), \(k_{1}=3.5\), \(g_{1}=1\), and \(m_{1}=1\), as shown in Figure 11.  
Figure 11: The voltage gain curves operating in the forward charging mode.  
Figure 10: Voltage gain curves with different Q1 and fixed \(k_{1}\), \(g_{1}\), and \(m_{1}\).
# Spatio-Temporal Heterogeneous Ensemble Learning Method for Predicting Land Subsidence
## 2 Methods
### Two-Stage Hybrid Spatio-Temporal Clustering Method
#### 2.1.1 Co-Clustering of Large-Scale Land Subsidence Data
This scale poses convergence and computational challenges for BBAC_I, limiting its use to the first stage for dividing the dataset into intermediate internally homogeneous clusters.