esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Novel Gear-like Disk Resonator Applied in Gyroscope
## 4 Resonator Characterization
### Frequency Response Test  
The frequency response test was performed to characterize the basic performances of the GDR and RDR. The drive electrodes of the resonators were connected to the output interface of the network analyzer (Keysight E5061B) which could produce a 5 Hz-10 MHz swept AC signal to drive the gyroscope. The capacitance signal of the resonator was converted into an AC signal by a charge amplifier. A band-pass filter was applied to reduce the noise, and then, the output signal was amplified. The output signal was collected by the input interface of the network analyzer (Keysight E5061B). Figure 8 shows the measurement results for the GDR and RDR. For the GDR, the resonant frequencies were 10.4163 kHz and 10.4254 kHz, and they were 16.6214 kHz and 16.6472 kHz for the RDR. The frequency split was 9.1 Hz for the GDR and 25.8 Hz for the RDR without tuning. The test results show that the symmetry of the GDR increased by about 64.7% compared with the RDR. This asymmetry came from a crystal orientation error and fabrication error, which could be reduced by structural trimming or electrostatic tuning. The improvement of the GDR over the RDR topology resulted in a lower effective stiffness and higher effective mass, and therefore a lower frequency. Additionally, the GDR had better immunity to crystal orientation error and fabrication error and therefore had a lower frequency split.  
Figure 7: Experimental setup for frequency response test and ring-down test.
# Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities
## 6 Application  
To show a possible usage of the characterization of V2V and V2I communication links in these types of scenarios, a simple application has been developed with the sole purpose of notifying the driver of traffic incidents in the simplest possible way, trying not to trouble him/her. For such a purpose we have built an Android-based mobile application following an MVC (Model-View-Controller) software  
Figure 19: Average jitter (ms) observed.  
pattern. The application, making use of the BATMAN-ADV protocol previously described, receives messages concerning traffic issues detected by other vehicles and by infrastructure. The application is just a simple traffic messaging tool that allows the exchange of messages in real time among the vehicles that take part of the vehicular network (VANET) at a given instant. In the same way, the application can warn other drivers of any incident detected in or near the vehicle by sending the corresponding message/s.  
Given that this article focuses exclusively on the communication of V2V and V2I, we have simulated the traffic issues/messages to validate the correct behavior of the system. We inject in the system synthetic messages announcing the arrival of emergency vehicles, the change of traffic lights, the existence of dense traffic, the presence of a damage vehicle, and even more. Some of those messages are sent by the infrastructure (e.g., change of traffic lights) and others by the vehicles (e.g., emergency vehicles) but in both cases, a message is exchanged with the other nodes of the network. We have defined a simple packet structure (see Figure 20) that includes the sender identification (car number plate), the level of priority to establish the ordering on the presentation of the messages received and their persistence on the visualization screen, and finally, the message to be displayed. The application is responsible for not displaying repeated messages that have a previously received message identifier. The application uses the sender's ID field, which contains a unique identifier of the sender and a counter of the messages send by this sender.  
Figure 21 shows the graphic user interface of the application. The App allows the selection of the text size, and allows the messages to be audible with the help of a voice synthesizer (TextToSpeech) within the device.  
Figure 21: GUI of the application.  
Figure 20: Packet structure.
## 2 Materials and Methods
Next, the trimmed model was digitized and a virtual implant image was precisely superimposed on the actual implant, matching congruent surface areas on the implant. The determined position of the implant was then used as the reference position for the analyzing the accuracy of each detection method. Figure 3: Implant detection methods. (**a**) Manual matching of a virtual implant image to the position of the radiographic image of the actual implant. (**b**) Manual matching of a virtual implant image to the position of the reconstructed mesh image of the actual implant. (**c**) Automatic creation of the virtual implant using scan abutment, digitization, and automatic image-matching. For discerning the accuracy of the implant detection methods, the resulting STL files from each group were matched to the reference image based on the images of the remaining teeth in the model. The geometric discrepancy between the estimated and actual positions was then analyzed in a 3D fashion (Figure 5). The discrepancy parameters of interest included linear deviation, vertical deviation, and angular deviation; among these, linear deviation was measured at the implant platform and bottom levels, vertical deviation was measured at the implant's center level, and angular deviation was measured using the centerlines of the implants in 3D. All measurements were carried out by a single investigator. All statistical analyses were carried out using a statistical software package (IBM SPSS Statistics v25.0 for Windows, IBM, Armonk, NY, USA). The error of each detection method is expressed as mean \(\pm\) standard deviation and visualized using a scatterplot. After determining the normal distribution (Shapiro-Wilk test) and equal variances (Levene test), one-way analysis of variance (ANOVA) and [PERSON]'s post-hoc test were used to compare the results of different groups. The statistical significance level was set at 0.05.
# Supply-Scalable High-Speed I/O Interfaces
## 1 Introduction
To resolve such efficiency loss due to the wide-range operation, introducing supply scalability, whose impact has been already well-demonstrated in microprocessors and Internet of Things (IoT) applications [36], to the I/O transceiver has been proposed. The basic concept is to lower the supply voltage when the I/O works at low speed where it does not require full voltage due to the relaxed bandwidth, in order to reduce the power consumption, which is similar to the concept of dynamic voltage and frequency scaling (DVFS) of microprocessors [24; 37; 38; 39; 40]. More details on the supply-scalable I/O interfaces will be discussed in the following section. Figure 2: CMOS inverter delay and rise/fall time as a function of fan-out.
# Heterogeneous Graph Purification Network: Purifying Noisy Heterogeneity without Metapaths
## 4 Proposed Method
### Heterogeneity Modeling  
The heterogeneity of graph structured data can be classified into two types: edge heterogeneity and node heterogeneity. In contrast to existing metapath-free models that aggregate on the original structure with relation-specific (R-GCN) or meta-relation-specific (HGT) parameters, we propose a decoupled solution that addresses both types of heterogeneity. Firstly, we decompose the original heterogeneous graph into subgraphs that contain a single edge type. Then, we introduce an inter-subgraph aggregation module to learn node representations from a simplified view. To handle node heterogeneity, we use node type-specific projection matrices in the inter-subgraph aggregation module. Finally, we employ an attention-based intra-subgraph aggregation module to aggregate information from subgraphs with different edge types.  
We list three advantages of our solution to model the heterogeneity as follows:  
1. The heterogeneity of edge types is naturally exploited without the need to introduce additional parameters for edges of different relations. With \(N\) types of nodes corresponding to \(N^{2}\) orders of magnitude of edge types, it is more sensible not to set  
Figure 1: Our HGPN framework: (1) Subgraph Decomposition method; (2) Inter-subgraph Aggregation (Inter-Agg); (3) Inter-subgraph Aggregation(Intra-Agg); (4) Type-specific Update(Update); (5) Cluster-Aware Loss. “Sources” (A1, A3, B1, B2, C1, C2) and “Target” (A2) respectively refer to neighbor source nodes and central target nodes in the process of graph message passing. “MHA” denotes “Multi Head Attention”. “TS-BN” is short for “Type-Specific Batch Normalization”, while “Dynamic-Res” means “neighborhood smoothness related Dynamic Residual connection”.  
different parameters for different edge types, while the increase in the number of parameters brought about by setting different parameters for different types of nodes is acceptable.
2. It prevents the aggregation of too much neighbor information for target nodes at one time, thus alleviating the well-known problem of over-squashing [26] in GNNs. This problem arises when the fixed embedding dimension cannot retain information from nodes that increase exponentially with neighbor hops, leading to issues of feature over-mixing and information loss.
3. The distribution of information from different relationships can vary significantly, and using the common graph attention module based on the \(Softmax\) function directly without subgraph decomposition can result in the loss of information with relatively small numerical scales due to the amplification effect of the exponential function.
## 3 Calculation Results and Influence Factors Analysis
### Ground Settlement of Special Points Under Different Excavation Steps
The land subsidence  
Figure 7: Comparison of the ground subsidence growth process of special points under different excavation conditions of a double-line tunnel: (**a**) simultaneous excavation; (**b**) excavation with the interval of 24 m; (**c**) excavation with the interval of 48 m; (**d**) excavation with the interval of 75 m.  
at the zero point of the left line continues to increase at a small growth rate; however, it begins to differentiate with different tunnel spacings. The smaller the tunnel spacing, the greater the disturbance of the right-line tunnel, the later the settlement of the left line reaches a stable value, and the greater the maximum settlement. For the right-line tunnel, as the spacing of the tunnels varies, the inclination angle of the ground settlement curve at the zero point of the axis during the excavation stage is analogous, and the settlement growth rate is greater than in the initial stage. Subsequent to the excavation of the left-line tunnel, the settlement rate of the right line slows down and gradually stabilizes at the 19 th excavation step. These outcomes demonstrate that the post-excavation tunnel and the initial excavation tunnel exert a reciprocal influence on the ground settlement of each other's axes during the excavation process, with the impact of the initial excavation tunnel appearing to be more substantial. In the static stage, as the excavation step size increases, the ground subsidence of the left and right tunnels remains constant. For the right-line tunnel at the zero point, the key step to achieve maximum settlement remains constant. Conversely, as the tunnel spacing decreases, the key step length of the left-line tunnel increases. As illustrated in Figure 7c,d, the ground subsidence of the left and right tunnels varies with different excavation steps. As illustrated in Figure 7d, these curves appear to exhibit three distinct stages. In the initial stage, the ground subsidence at the zero point of the left and right tunnels exhibited an increasing trend, followed by a slight decrease. This phenomenon can be attributed to the Mohr-Coulomb model employed in this simulation, which exclusively considers elastic deformation, thereby neglecting the influence of plastic yield. Consequently, the ground exhibits a minor uplift response. As illustrated in Figure 7a-d, the ground settlement of the left-line tunnel with 2.0D tunnel spacing attains its maximum value around the 15 th excavation step, when considering different excavation intervals. This indicates that when the tunnel spacing reaches 2.0D, the post-excavation of the right-line tunnel exerts minimal influence on the ground settlement of the left-line tunnel. The excavation influence range of the ground settlement of the single-line tunnel is approximately 45 m.
## 2 The Concept and Design: The Function Design of the CBTC System
### The start-up time control of the transport units  
The start-up time control of the transport units is able to improve the transport efficiency of the underground mines, and the function of the start-up time control is accomplished through the monitoring and control of the ATS system. As early as 2013, [PERSON] et al. [41] optimized the vehicle schedules in underground mines to control the start of the transport units. In 2016, [PERSON] [42] made the improvements to the CBTC system based on driver-less technology and Internet of Things technology, and he proposed using the ATS system of the CBTC system to realize the start and stop of the driver-less train.  
To solve practical problems in the underground mines, a process based on Internet of Things technology and AI Computing has been established. When the Stope in the intelligent underground mine does not need transport operations, each transport unit needs to be on standby at the depot. Once there is a Stope that needs transport operations, the process begins running: (1) Calculate the amount of transport units that might be required in the operations. (2) The ATS system controls the transport units to the Stope to participate in transport operations with parameters such as the ATP system and ATO system to assist in controlling the speed of the transport units. (3) The whole process is fed back to the monitoring screen of the ATS system.  
The whole process of the start-up time control is as shown in Figure 8.
# Multicriteria Route Planning for In-Operation Mass Transit under Urban Data
## 4 Multicriteria Route Planning for Mass Transit
### Problem Formulation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Description** & **Variable** & **Description** \\ \hline \(E\) & A set of edges (trajectories). & \(S\) & The set of stations. \\ \(E_{A}\) & The set of edges located in area \(A\). & \(S_{M}\) & The set of must-visit stations. \\ \(e_{ij}\) & The edge-connecting node \(i\) and node \(j\). & \(S_{Mi}\) & The must-visit station numbered \(i\). \\ \(V\) & A set of nodes (grids). & \(S_{R}\) & The set of recommended stations. \\ \(V_{A}\) & The set of nodes located in area \(A\). & \(S_{Ri}\) & The recommended station numbered \(i\). \\ \(v_{i}\) & The node \(i\). & \(R\) & The number of recommended stations. \\ \(V^{\prime}\) & A set of nodes (grids) as station candidates. & \(L\) & The maximum length for the route. \\ \(R\) & A route composed of stations and edges. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)\[S_{R_{i}}\subset\ V^{\prime} \forall\ S_{R} \tag{7}\] \[S_{R_{i}}\
ot\subset S_{M_{i}} \forall\ S_{R},S_{M}\] (8) \[S_{R_{i}}\subset V_{A} \forall\ S_{R}\] (9) \[S_{R_{i}}\
ot\subset\varnothing \forall\ S_{R}\] (10) \[|S_{R}|=r\] (11) \[V_{A}=V\cap A\] (12) \[E_{A}=E\cap A \tag{13}\]  
in which \(R\) refers to the final route and consists of successive stations along with trajectories. Furthermore, Equation (1) refers to the optimization objectives, including the profit under queried constraints (the expected passenger flow to be maximized, referring to the social welfare for the public) and cost for construction as well as maintenance (the cost of route to be minimized, referring to the sustainability for the government). Furthermore, Equation (2) indicates that the length of the route shall not exceed the requested maximum length. While Equation (3) ensures the legal formulation of the final route been included in the queried planning area. Accordingly, Equations (4) and (5) defines the relationship between the vertices \(V^{\prime}\) in the final route and deployed stations \(S\), as well as the deployed stations \(S\) and must-visit stations \(S_{M}\) with recommended stations \(S_{R}\). Next, Equation (6) requires that all the must-visit stations shall be connected in the final route \(R^{\prime}\) (the trajectories \(E^{\prime}\) and the stations \(V^{\prime}\) in \(R^{\prime}\)). Similarly, Equation (7) asks all the additional requested stations to be connected in the final route. Meanwhile, Equations (8)-(11) require the queried number of additional stations to be reached for further planning; furthermore, these additional stations shall be deployed in the delimited area without overlapping the vertices occupied by the must-visit stations. Accordingly, Equations (12) and (13) limit all the vertices and edges selected to be considered for route planning to be located in the requested area for planning.
## 2 Materials and Methods
### Data Acquisition
#### 2.2.2 Bathymetry Survey  
The bathymetry survey was conducted using the Aqua Ruler 200 echosounder from Sonar Tech Co., Ltd. (Busan, Republic of Korea). The detailed specifications of the equipment used are shown in Table 3. The bathymetry survey data were corrected for sound velocity and draft using the bar-check procedure. The tidal elevation was corrected using tidal data from the Gaduk Island tidal station of the Republic of Korea Hydrographic and Oceanographic Agency to convert the data to the Gadeok Island datum. The horizontal position of the offshore area was determined using Trimble's Differential Global Positioning System, and both shoreline survey results and offshore horizontal positions were determined using the Universal Transverse Mercator coordinate system. Figure 3 shows the shoreline survey and seabed topography investigation system.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 2 Grid Planning
### Load and Generation Forecasting in Planning
#### 2.1.2 Generation Forecasting: Enhancing Predictive Accuracy Generation  
Forecasting, particularly for renewable sources like solar and wind, employs advanced modeling techniques to predict energy output. Techniques such as physics-constrained long short-term memory (LSTM) models leverage domain knowledge and historical data to forecast photovoltaic power generation with greater precision [14]. Hybrid models that combine statistical approaches with machine learning, like combining wavelet transforms with support vector machines, further refine forecasting accuracy, especially when handling the variability and intermittency of renewable sources [51; 77; 78]. The integration of real-time data and continuous model refinement are critical for optimizing the performance of these forecasting systems [79].
# Factors of the Technology Acceptance Model for Construction IT
## 3 Research Model and Hypotheses  
### Research Model  
We derived external variables through a literature investigation and preceding study analysis for the extension application on the basis of [PERSON]'s TAM. In this study, we propose a research model for construction IT acceptance according to the traits of information accepters in the construction industry. We present six external variables (acceptance type, frequency of use, educational satisfaction, usage knowledge, usage enjoyment, and usage experience), based on brainstorming with three experts from academia and enterprises to verify the external variables already derived, as the hypotheses for verification.  
Moreover, the research model supposes that those six external variables have some effects on attitude, behavioral intention, and actual usage--which are components of TAM--as well as perceived usefulness and ease of use, as shown in Figure 2.
# Antimicrobial and Antioxidant Properties of Bovine Livers and Hearts Hydrolysates
## 2 Materials and Methods
### Determination of Antimicrobial Activity  
The agar well diffusion method was employed to assess the antibacterial activity of lyophilized hydrolyses derived from bovine meat by-products. The cultures of reference strains _Listeria monocytogenes_ ATCC 13932, _Bacillus cereus_ ATCC 11778, Staphylococcus aureus subsp. aureus ATCC 25923, _Salmonella enterica_ subsp. _ enterica_ serovar Typhimurium ATCC 14028 and _Escherichia coli_ ATCC 25922 were pre-cultivated on PCA (Plate Count Agar, Liofilichem, Roseto degli Abruzzi Terano, Italy) slants at 37 \({}^{\circ}\)C or 30 \({}^{\circ}\)C overnight. The mature bacterial cultures were rinsed off the agar using a sterile physiological saline solution composed of 0.85% sodium chloride in distilled water. The cell suspension of each culture was adjusted in accordance with the McFarland standard No. 0.5. Then, 1 mL of the prepared suspension was added into 100 mL of melted and cooled to 45 \({}^{\circ}\)C PCA medium. The prepared mixture of bacteria cell suspension and the medium was mixed thoroughly and poured into Petri dishes (90 mm), 12 mL each. After the medium had solidified, wells of 8 mm diameter were made in the plates, which were filled with 50 \(\mu\)L of the examined solution. The antimicrobial activity of lyophilized meat by-product hydrolyses against the pathogenic bacteria cultures was evaluated after 24 h of growth at 37 \({}^{\circ}\)C or 30 \({}^{\circ}\)C. The measurement of the diameter of inhibitory zones was conducted using calipers with a precision of 0.5 mm. Distilled water was employed as the control in the blank sample.
## 4 Experiments and Results  
Different components of the ATON framework and their interplay have been already investigated and assessed by previous work during the past few years. More specifically: (A) Framework integration with cloud architectures for publishing multi-resolution 3D landscapes within European infrastructures [50] and its exploitation for cross-device, online virtual museums [77]; (B) Presentation of virtual 3D collections and augmentation of past museum displays on mobile devices (Capitoline Museum in Rome) [59]; (C) Creation of applied CH games as web-applications [78; 79]; and (D) Visual/immersive analytics architectures for remote inspection of interactive WebXR sessions [80; 81].  
This section thus describes and reports a selection of experiments carried out on different case studies, to assess novel components of the ATON framework (light-probing, annotations, cross-device presentation and collaborative modules) with related results and discussion. Each section reports results for the given case study, while a final discussion Section 5 will overview obtained results, their interplay with the other framework components, and implications in a broadest context.
## 2 Motivating Applications and Use Cases  
Smart city and 5G network applications can be divided into two major categories: non-resource-intensive and resource-intensive applications. Both categories are further divided into two subcategories: non-real-time and real-time. Latency and data rate requirements of real-time and non-real-time smart city and 5G use cases are given in Table 1. Examples of motivating applications and use cases are briefly described as follows.