esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Thermal Performance of the Insulating Envelope and Energy Simulation
#### 2.4.2 Energy Simulation  
DesignBuilder version 5.5.2.007 is a comprehensive building simulation software that utilizes the open-source EnergyPlus version 8.6 engine to simulate indoor microclimates and energy consumption. The software allows users to create detailed building models with ease, offering a user-friendly interface. It enables users to define every aspect of the building's systems, including schedules, construction materials, lighting, HVAC systems, and window-to-wall ratios, providing a thorough and customizable modeling experience [45].  
Scenario Setup  
The simulation sequence is as follows: A modular housing unit, which features an area of 14.75 m\({}^{2}\) (Figure 5) and is located in Temuco, Chile within thermal zone F according to the Chilean building standards [46] and has a Koppen climate classification of Cfb, is built using Skylark 250. The modular housing unit will be modeled to evaluate energy consumption, indoor thermal comfort, and CO\({}_{2}\) emissions under three different scenarios. The first scenario uses rice husk fiber thermal insulation, the second employs a recycled PUR insulation material, and the third considers the absence of any insulation. The modular unit will have a standardized design to ensure comparability between scenarios. The parameters for energy simulation in DesignBuilder are presented in Table 1. Simulations will be carried out for the city of Temuco, using weather data derived from the EnergyPlus climate file database provided by the U.S. Department of Energy. Parameters such as outdoor Dry-Bulb Temperature, wind speed, and relative humidity will be enforced via DesignBuilder's pre-installed weather file. Additionally, the simulations will allow for the assessment of the thermal envelope's efficiency under each scenario, providing a comparative analysis of energy performance and carbon footprint.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{3}{c}{**Parameters for Energy Simulation in DesignBuilder**} \\ \hline
**Parameters** & **Tipe** & **Characteristics** \\ \hline Activity & Residential & 0.02 (people/m\({}^{2}\))—residential, residential occ schedule \\ Construction & Project template & Walls, roofs, and floors of Skylar 250 system have \\  & & model infiltration of 0.7 (ac/h) \\ Opening & Project template & 30\% wall to window ratio—double glazing, clear, 6 mm/6 mm \\ Lighting energy & Project template & 5.00 W/m\({}^{2}\)—100 lux \\ HVAC & & ON, heating COP 0.5 and cooling COP 4.5, fuel \\  & & electricity from grid \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for energy simulation in DesignBuilder.  
Figure 5: Modular housing unit.
## 2 Theoretical Background  
### Bearing Faults and Lubrication  
#### 2.1.1 Bearing Faults  
The study of bearing faults has been an important research area for decades due to the significance of these elements in most mechanisms in all industries. Many researchers have analyzed the causes and mechanisms that originate the different types of faults. The leading causes of bearing failure can be summarized as improper selection or design, improper material selection and quality, common defects in the bearings, and incorrect assembly, use, and maintenance [2; 3; 4; 5; 6; 7], with the last being responsible for up to 49% of bearing failures [2]. For the mechanisms of occurrence, the classification considers deformation, fracture, corrosion, and wear caused by fatigue, erosion, cavitation, and stress [2; 3; 4; 5; 6; 7].  
Bearings can present defects in different locations, and numerous researchers have categorized these faults into OR, IR, rolling element, and cage defects, with all being important due to the vibrations created with each kind of fault, which affect the kinematic chain and impact the state of other elements being capable of producing more significant damage. The most common way to diagnose these defects is by searching for frequency components in vibration signals from the electric motors that have been well reported in the literature, where data from the bearing and the speed of the motor are used to identify characteristic frequencies related to OR, IR, cage, and rolling element faults. Similarly, for the analysis of current signals in the frequency domain, the literature focuses on the search for frequencies that are related to the characteristic frequencies in the vibration spectrum in relation to the supply frequency to identify the presence of faults according to the amplitude of these components [1; 3; 7]. These frequency components are approximated using Equations (1)-(4) in Table 1 and considering \(N\) as the speed of the shaft in revolutions per minute (RPM), \(n\) as the number of balls in the bearing, \(\alpha\) as the contact angle of the bearing due to load from the radial plane, \(d\) as the diameter of the ball, and \(D\) as the mean diameter of bearing [1; 3; 7].  
Another approach to the diagnosis of faulty bearings is using statistical techniques, using large datasets of time domain information, finding trends and relations in the data when calculating different statistical indicators, such as RMS or Kurtosis [3; 4], and finding thresholds related to the severity of the defects that allow the diagnosis. However, today's industry requires more advanced techniques as the availability of information from multiple sensors requires novel methodologies to deal with larger amounts of data, making the fusion of the information a promising approach for better diagnostic technology [15; 16].
# Key Issues on Integrating 5G into Industrial Systems
## 5 Integration of 5G with Industrial Systems
### Key Technologies for Replacing Field Buses with 5G
#### 5.3.1 Slicing Techniques  
For different real-time requirements of different hierarchical networks, 5G slicing technology can be used to adapt to diverse and complex application scenarios. Network slicing technology divides physical networks into multiple different types of virtual networks according to different levels of service requirements (in terms of latency, bandwidth, reliability, etc.), so as to cope with complex and ever-changing application scenarios. For example, real-time 5G slicing can be used at the field bus layer to meet real-time requirements and the remaining 5G bandwidth and resources can be placed at other levels [53].  
Ensuring collaborative work between different shard networks is one of the core challenges in implementing the \"one network to the bottom\" architecture in 5G. A good slicing scheme can make each shard act like an independent network, and their collaboration is similar to that of any independent network. If dynamic sharing technology is adopted, network resources can be borrowed between different shards, thus creating a different pattern from the situation of independent networks. In addition, as network slicing shares physical resources, it is also necessary to consider the negative impact on each other [54].
# A Network Analysis-Based Approach for As-Built BIM Generation and Inspection
## 4 Experiment
### Network Analysis
#### 4.3.2 Node Matching  
Node matching is a very important process for existing element-by-element approaches because the high matching accuracy is the foundation for comparison. The pipeline of matching in the current study is illustrated in Figure 13. The first step is the generation of the accurate matching table, which is achieved by manually matching the building elements in the as-built and as-designed models. BIM authoring tools like Autodesk Revit and XDimxplore are utilised as the visualisation platform, and the accurate matching table is created in Excel. For the initial attempts, nine types of centralities were selected as input parameters to ensure a comprehensive analysis of the network from multiple perspectives. These centralities capture various aspects of node importance and influence. The definitions are presented in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Centrality Type** & **Definition** \\ \hline Degree Centrality & Measures the number of direct connections a node has. \\ Betweenness Centrality & Assesses how often a node lies on the shortest paths between other nodes. \\ Closeness Centrality & Calculates the average shortest path from a node to all other nodes. \\ Eigenvector Centrality & Evaluates a node’s influence based on its neighbours’ influence. \\ PageRank Centrality & Ranks nodes based on the structure of incoming links. \\ Katz Centrality & Measures node influence considering all paths, with longer paths weighted less. \\ Harmonic Centrality & Averages the reciprocal of shortest path distances to all other nodes. \\ Subgraph Centrality & Quantifies a node’s importance based on its participation in subgraphs. \\ Current Flow Centrality & Evaluates a node’s centrality based on the idea of electrical current flow. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definition of selected centralities.  
Figure 11: Statics by the community (As-built).  
Figure 12: Statics by the community (As-designed).  
In the matching process, both the K Nearest Neighbours (KNN) and neural network methods are attempted, but ultimately, the KNN approach was selected for its higher accuracy. Type matching and ID matching were both conducted during the initial experiment. Currently, only wall, slab, beam, and column components are considered. Figure 14 presented the centralities distribution of as-built and as-designed models.
## 2 Deployment and Proposed Transmission/Reception Timing Schemes
### Proposed Schemes
In the paper, Scheme 2 in Figure 4b is proposed as a scheme that is the most time efficient and power saving. Scheme 3 in Figure 4c is a scheme for a comparison with a lower time efficiency but more SIR gains. This is made by deploying the diagonal patterned designed, as shown in Figure 4b, and placing some empty areas. Because the idle areas account for 1/3 of all areas, the time efficiency decreases from 1/2 to 1/3 but the SIR gain becomes approximately 9.03 dB as the dominant interference occurs on the left upper region at a distance of four boxes.
## 4 Lagrangian Relaxation-Based Solution Procedures
### Procedure of Step 6: Obtaining the Primal Feasible Solutions
#### 4.4.4 Shortest-Path-Based Significance Index (\(\mathit{SP}\))  
We can find three independent dimension problems with time slot \(\tau\), task \(i\), and server \(s\) from the observation of objective function in the primal problem. The aggregation of the assigned tasks does not exceed the capacity of the servers. Furthermore, a tree data structure can be represented as a set of linked nodes for task \(i\) in which the root is the starting time of the task assignment. The branches are the assigned servers after a global analysis of the objective function. The destination is the deadline related to the servers. Suppose there is any path available from the root to the destination. In that case, the task assignment series can be determined, as shown in Figure 6.  
The Border Gateway Multicast Protocol (BGMP) and a scalable multicast routing protocol use heuristics to select the global root of the delivery tree [33]. We develop source-specific shortest-path distribution branches to supplant the shared tree. The arc weights are the coefficients of SUB1. The arc weights of migration are also considered. Moreover, the problem is transformed into a path length-restricted shortest path problem. We identify the paths with weights from the task arrival to departure. Furthermore, the shortest path problem can be effectively solved using the Bellman-Ford algorithm [34]. Link constraints with server CPU and memory capacities are the restrictions set on using links to form a routing tree [34]. The path constraints are the restrictions to initialize the time slot and select the branches related to the servers switched on or off. The detailed flowchart is shown in Figure 7, where the decision variables are fine-tuned to feasible from the dual problem to the \(\mathit{SP}\) model.  
Figure 6: Tree structure of the \(\mathit{SP}\).  
Figure 7: Flowchart of the \(\mathit{SP}\).
## 3 Results and Discussion
(**Left**): open sky (special training area); (**right**): different driving environments (open sky in motorways and rural roads, hilly and forested, urban and suburban). Due to some issues with the wireless connection in other tests, we were also able to investigate the position accuracy during periods without correction data. For that matter, the receiver was configured to use base station data with ages up to 50 s. In Figure 8, on the left we see the 2D position error against the mean correction's age showing only RTK-fix solutions, while on the right side, one can see the 2D position errors for RTK-float solutions. In both figures, within the first seconds, one can see the errors that are typically encountered in urban and suburban situations. Afterwards, one can see the influence of older correction data on the RTK solution (mostly for highway and rural road conditions). As long as an integer fix is possible, one can be quite sure that the 2D position error stays below 20 cm up to 50 s, which was not expected. For float solutions, it can stay as good as 50 cm if the initial float solution is stable, and otherwise the errors can be as large as 22 m (maximum observed 2D error of float solution). Figure 6: CDF functions of the 2D localization error depending on the number of satellites. (**Top left**): 4 to 6 satellites; (**top right**): 7 to 8 satellites; (**middle left**): 9 to 10 satellites; (**middle right**): 11 to 12 satellites; (**bottom left**): 13 to 14 satellites; (**bottom right**): \(>\)=15 satellites. Figure 7: Probabilities of 20 cm 2D position error depending on the number of satellites.
# Application of Deep Neural Networks in Recognition of Selected Types of Objects in Digital Images
## 2 Results of Research
### Description of Selected Convolutional Network Structures
#### 2.1.2 AlexNet
Softmax auxiliary layer structure:
* An averaging layer with a filter size of 5 \(\times\) 5 and stride 3, giving an output of size 4 \(\times\) 4 \(\times\) 512 for the first auxiliary layer softmax0 and 4 \(\times\) 4 \(\times\) 528 for the second auxiliary layer softmax1;
* A 1 \(\times\) 1 convolutional layer with 128 filters, with a ReLU function after each of them;
* A fully connected layer with 1024 neurons and a ReLU activation function;
* A dropout layer with a value of 70%;
* A linear layer with 1000 classes and a softmax function. The inception module is a set of parallel convolutional and pooling operations that allow for feature extraction at different abstraction levels simultaneously. Within one module, convolutional operations with filters of sizes 1 \(\times\) 1, 3 \(\times\) 3, and 5 \(\times\) 5 and max-pooling operation are used. The results of these operations are then combined along the channel dimension, which allows for reducing the number of parameters and the depth of the network without losing important information. GoogLeNet is the most complex of the selected architectures, which will allow us to compare how increasing the complexity of the architecture improves the results. It offers excellent results in detecting more complex objects in images, making it an ideal choice for research on the detection of image classes depending on the animal species. This architecture requires images of 224 \(\times\) 224 pixels.
## 3 Control Layer Architecture
### Control Metadata
#### 3.1.2 Control Directive  
A _control directive_ is an action to be performed by the contacted node to modify a forwarding algorithm parameter. A directive is represented as the tuple: \(\delta_{s}=(\mathit{H}_{s},\theta_{s})\)  
Figure 1: Node’s GAFA functionality: (**a**) gathering measurements, (**b**) aggregating and forwarding them, (**c**) applying the directive created by the controller.  
where \(H_{s}\) identifies a forwarding algorithm setting from the list of settings managed by the controller, and \(\theta_{s}\) is the value for this setting. The controller generates the directive based on context information (Figure 1c) aiming to improve the forwarding performance. The controller disseminates this directive to the nodes. When a node receives the directive, it applies it by modifying the node's forwarding setting \(H_{s}\) with the new value \(\theta_{s}\). As examples of forwarding settings, we could consider the message TTL, weights, and thresholds intrinsic to the forwarding algorithm, among others.
## 4 Simulation Example and Analysis
The boresight changes by 0.31\({}^{\circ}\) and 0.23\({}^{\circ}\), beamwidth by 0.34\({}^{\circ}\) and 0.46\({}^{\circ}\), and sidelobe level by 0.62\({}^{\circ}\) and 1.65\({}^{\circ}\) in \(\phi\) = 0\({}^{\circ}\) and \(\phi\) = 90\({}^{\circ}\) planes, respectively. The total error in both planes is about 0.02 dB, which is almost equal to the values computed from the proposed model. Furthermore, we verified the computational time and resources of the proposed method against the full-wave simulation. Matlab installed on a personal computer (PC); Intel CPU, Core i7-8565U, 8 th Gen., RAM 16 GB, 1 Terabyte was used to simulate the model against the HFSS-based model. The proposed method exhibits faster computational time (about 11 s) than the full-wave simulation (about 86 s). Hence, the proposed method requires less memory than the traditional full-wave simulation. Finally, the proposed coupledstructure-EM statistical model is a good tool to easily and efficiently evaluate the antennas' pattern sensitivity to structural error. Figure 6: Designed antenna array with reflection coefficient. Figure 7: Antenna array performance with different random errors and systematic distortion (**a**) \(\phi=0^{\circ}\) and (**b**) \(\phi=90^{\circ}\). When developing technological advancements, there is high demand for correct and accurate design mechanisms and array optimization. The beamwidth, gain, sidelobe, and boresight-pointing accuracy of the antenna are the determinants of the performances of array antennas. Also, random errors emanated from fabrication/manufacturing, assembling procedure, and structural/mechanical deformation are caused by vibration, impact force, and high thermal power, which results in the displacement of the elements and consequently affects the performance of the antenna, such as higher sidelobe level, gain loss, inaccurate boresight pointing, and beamwidth broadening. Hence, the findings of this paper spur out insights into the design and optimization of emerging and future antennas. The method proposed may be implemented in metasurface hardware optimization, such as programmable surface plasmonic as in [35], digital-coding metasurface array as in [36], and reconfigurable intelligent surface-assisted antenna systems. Consequently, we must address the problem of mechanical deformations and environmental conditions, which may lead to deviations in beamforming characteristics and radiation patterns. Therefore, the proposed method finds applications in aerospace, spacecraft, radar, satellite systems, and wireless communication engineering.
# Toward the Generation of an Isolated TW-Attosecond X-ray Pulse in XFEL
## 3 Methods with Current Modulation
### Irregularly-Spaced Multi-Current Spikes  
It would be nicer to have an isolated pulse without the satellite pulses shown in Figure 4b. If the current spikes can be produced with irregular spacing, the satellite radiation spikes in Tanaka [37] can, in principle, be removed. This idea was first proposed in [PERSON] et al. [40] as shown in Figure 5a. Three different ideas have been proposed to generate the irregularly spaced current spikes (see [PERSON] et al. [40] for details). The most important and realistic idea among these three is a chirped-laser method. If a chirped laser was used in the energy modulation process in the wiggler in Figure 3, the current spikes would be generated with irregular spacing followed by the wavelength change in a chirped laser pulse [40; 41].  
Simulation results with five different cases are shown in Figure 5b. The highest XFEL power with the chirped laser method reached a power of 1.8 \(\pm\) 0.45 TW with a pulse duration of 62 \(\pm\) 3 as.  
Figure 4: **(a)** Amplification process of the TRP (“Target Pulse” in the figure) by using regularly-spaced current spikes. The electron bunch has multiple current spikes with the same spacing (top). (i) Multiple radiation pulses are generated from the first undulator unit. (ii) The radiation pulses are delayed to match the target pulse to the tail peak. (iii) In the second undulator unit, only the target pulse is amplified. (iv) We now delay one more time the electron bunch to match the amplified target pulse to the second -last current spike (just before the tail peak). This step is repeated. (b) A result of this process: a strong main radiation pulse with side peaks. Reproduced from Tanaka [37] with permission.  
The method proposed in [PERSON] et al. [40] is a brilliant idea for solving the satellite radiation pulses that appeared in Tanaka [37]. In this proposal, there is no optical delay unit (X-ray mirrors), which increases the feasibility of this method compared to Tanaka [37]. There is still the stability issue related to the electron beam delay. The output of a power supply to control the field of the magnets in an electron delay unit may fluctuate with the voltage level; hence, there would be an error in the field amplitude, which would lead to a mismatch between the radiation pulse and the electron current spikes.
## 2 Main Metabolomics Methodologies
### Separation Techniques  
Several separation techniques are routinely applied according to the characteristics of the putative compounds to be identified. Among them, Gas Chromatography (GC) is an excellent separation technique that was considerably improved with the introduction in 1979 of fused-silica capillary columns that resulted in higher resolution, higher efficiency, better reproducibility and smaller sample size [40]. In addition, the combination of gas chromatography with a mass spectrometer may be a highly sensitive approach. However, GC is limited to small compounds, which are thermally stable, volatile or can be rendered chemically volatile, for instance, by trimethylation [41]. Detection in GC analysis may be limited to certain compounds unless Mass Spectrometry (MS) is the method of choice, however, even when MS is used, some of the compounds may not be ionized sufficiently to be detected at low levels unless they are derivatized with an ionisable moiety [42]. HPLC and UHPLC are powerful tools for metabolomic studies that enable the separation and characterization of metabolite similarities. These techniques represent efficient separation technologies, which can be used to determine different groups of compounds, hydrophilic as well as hydrophobic, salts, acids, bases, etc. [6]. HPLC, unlike GC, is not limited to the separation of thermally stable volatile compounds or large molecules. The separation of each group within the HPLC is a function of the solute properties that determine the column type (stationary phase) and mobile phase to be used for successful separation [43]. These modes include RP (reverse phase), normal phase, ion exchange, chiral, size exclusion, hydrophilic interaction chromatography (HILIC) and mixed modes [42; 44].