esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Methods
### Experimental Apparatus  
The IR and AE test system for coal failure is shown in Figure 1, which mainly includes three parts: loading control, IR monitoring and AE monitoring. The IR monitoring area and the layout of the AE sensors is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **_E_/**GPa** & **_Rc_/**MPa** & **_DT/ms_** & **\(\mathbf{K_{\text{E}}}\)** & **\(\mathbf{W_{\text{ST}}}\)** \\ \hline No-impact coal & 1.44 & 6.49 & 2594.8 & 1.42 & 0.57 \\ Weak-impact coal & 2.30 & 13.69 & 414.85 & 3.40 & 8.35 \\ Strong-impact coal & 4.69 & 34.94 & 41.72 & 31.95 & 766.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Impact index parameters of different coal impact types.  
Figure 1: Experimental schematic diagram.  
1. The loading system adopts a micro-computer-controlled pressure machine. The displacement is 0.3 \(\upmu\)m, the maximum test force is 600 kN, the sampling frequency is 1 kHz. It can collect and record mechanical and deformation parameters such as load and displacement during coal failure in real time.
2. The IR monitoring instrument model is Optris PI 450 (Optris, Berlin, Germany), with an optical resolution of 382 \(\times\) 288. It can capture up to 80 Hz thermal images per second and accurately measure three temperature ranges: \(-\)20-100 \({}^{\circ}\)C, 0-250 \({}^{\circ}\)C, and 120-800 \({}^{\circ}\)C, with a temperature sensitivity of 0.04 K, and can accurately analyze the IRT change during coal body failure.
3. The AE monitoring adopts an 8-channel full waveform soft DS5 series acquisition instrument(Ruandao, Beijing, China), with a maximum sampling frequency of 10 MHz and an input signal voltage range of \(\pm\)10 V. The response frequency of the AE sensor is 50-400 kHz, and the amplification factors of the preamplifier are adjustable at 20, 40, and 60 dB.
# Improvement of the Sensor Capability of the NAO Robot by the Integration of a Laser Rangefinder
## 4 Discussion
The measurement was performed at 50 and 100 cm, respectively, and repeated 100 times. The obtained values (Table 1) are within the measurement error given by the manufacturer, and the values obtained by the US sensor present about the same uncertainty as the laser sensor (see Figure 11). Nevertheless, the critical issue is that for the sonar sensor, unlike the laser sensor, both the measurements obtained do not fall within the error range. Furthermore, using the laser rangefinder allowed for a broader richness of information about the surrounding environment, whereas the US sensor can only provide measures of one-point distances. In contrast, the laser rangefinder can analyze the whole plane around the robot. In particular, this is visible in the polar diagrams shown in Figure 8a,b, as the system has been able to detect, at a precise distance, the room's three walls within an angle of 240\({}^{\circ}\). Furthermore, it could locate the recess of the window and the presence of the radiator at the left of the robot (partially visible in the photo in Figure 8a). In test #2, the robot correctly performed the assigned task, moving without any stability problems in the three scenarios. Figures 12-14 depict the results of these tests. As shown in the polar diagrams, the system can detect the presence and relative distance from the robot of the standing and moving objects in the scene. For example, in Figure 12 (scenario #1), it can be observed that the sensor was able to detect at a precise distance, in addition to the side of the box located at 0\({}^{\circ}\), the three walls visible in the detection range of the sensor, the wall recess and the radiator (located between 330\({}^{\circ}\) and 360\({}^{\circ}\) at a distance of 200 cm). In the test depicted in Figure 13 (test #2--scenario #2), it is possible to note that the system can detect (see the top polar diagram) the opened door and some other obstacles in the scene. In the polar diagrams below, one can observe the position (circled in red) of the Pepper robot that is advancing towards it. In particular, in the lowest row of the figure, one can note the rangefinder sensor can detect the exact position of the Pepper robot (1 m, 300\({}^{\circ}\)), which is located out of the on-board camera's field of view. Finally, Figure 14 describes the test where both robots move toward each other. In this case, it is possible to note that the distance from the wall at the left of the robot reduces as the NAO's moves and the Pepper robot (whose position is circled in red in the figure) approaches. As in the previous scenario, the rangefinder sensor can detect the Pepper robot's position (50 cm, 60\({}^{\circ}\)) outside of the on-board camera's field of view.
# SURROGATES: Virtual OBUs to Foster 5G Vehicular Services
###### Abstract  
Virtualization technologies are key enablers of softwarized 5G networks, and their usage in the vehicular domain can provide flexibility and reliability in real deployments, where mobility and processing needs may be an issue. Next-generation vehicular services, such as the ones in the area of urban mobility and, in general, those interconnecting on-board sensors, require continuous data gathering and processing, but current architectures are stratified in two-tier solutions in which data is collected by on-board units (OBU) and sent to cloud servers. In this line, intermediate cache and processing layers are needed in order to cover quasi-ubiquitous data-gathering needs of vehicles in scenarios of smart cities/roads considering vehicles as moving sensors. The SURROGATES solution presented in this paper proposes to virtualize vehicle OBUs and create a novel Multi-Access Edge Computing (MEC) layer with the aim of offloading processing from the vehicle and serving data-access requests. This deals with potential disconnection periods of vehicles, saves radio resources when accessing the physical OBU and improves data processing performance. A proof of concept has been implemented using OpenStack and Open Source MANO to virtualize resources and gather data from in-vehicle sensors, and a final traffic monitoring service has been implemented to validate the proposal. Performance results reveal a speedup of more than 50% in the data request resolution, with consequently great savings of network resources in the wireless segment. Thus, this work opens a novel path regarding the virtualization of end-devices in the Intelligent Transportation Systems (ITS) ecosystem.  
NFV; OBU; Data collection; MEC; 5G; ITS +
Footnote †: journal: Electronics Electronics
## 2 Theoretical Investigations for Antenna Design and Proposed Algorithm
### Bayesian Neural Network Model for Optimized Antenna Design  
To meet the requirements for efficient antenna design, such as minimum training cost and prediction uncertainty for design, as well as provide high-quality predicted value, the Bayesian Neural Network (BNN) is a promising solution. The BNN procedure has valuable contribution towards optimized antenna design. Taking the variable \(u\) and \(v\) for antenna design while using the ANN approach, the model parameters are written as  
\[\theta=w_{1}\ldots\ldots w_{n},\ g_{1}\ldots\ldots\cdot g_{m} \tag{6}\]  
where \(g\) denotes the basis and \(w\) is used as the weight. The nonlinear activation function is used in the multilayer ANN model, aiming to transform the linear domain of each layer. The optimized \(\theta\) is then used for prediction. Comparing with the ANN approach, the BNN-based network structure remains constant, however, the \(\theta\) becomes a stochastic variable, including \(p(\theta)\) probability distribution. The basic description of the BNN model is declared in Figure 2. Applying the Bayes theorem on input and output training is written as  
\[p\bigg{(}\frac{\theta}{T_{d}}\bigg{)}=\frac{p\bigg{(}\frac{T_{du}}{T_{du}}, \theta\bigg{)}p(\theta)}{\int p(\frac{T_{du}}{T_{du}},\theta)p(\theta)d\theta} \tag{7}\]  
where \(p(\theta)\) and \(p\Big{(}\frac{\theta}{T_{d}}\Big{)}\) are the prior and posterior, respectively, and \(p\Big{(}\frac{T_{du}}{T_{du}},\theta\Big{)}\) is the likelihood. The prediction uncertainty and predicted value are achieved by the posterior, which are the objectives of this work.
## 3 Materials and Methods
### Monitoring Equipment
NICO 8108 LON: This equipment has a total of 8 measurement channels, of which only 2 of them are occupied for measuring the electrical energy use of the HVAC units and the total electrical energy use of the corresponding test cell. * PLC--Distech ECL 400: it is used as an analog input module for the heat flux meters and the temperature probes. This PLC equipment has the advantage of being programmable for the most suitable monitoring strategy to the study intended. * Amplifier (mV-V), which measures the signal from the heat flux meters and adapts it to allow the PLC equipment to read it. * Power Source (24 Vcc) required for power supply to the PLC and the multi-sensor (indoor temperature, relative humidity, and CO\({}_{2}\) concentration) device. The following sensors were integrated into the monitoring network:  
* Surface temperature probes: Distech PDCSY-TT-351-A-5M. They are NTC 10K thermistors with an operating range between \(-10\)\({}^{\circ}\)C and \(100\)\({}^{\circ}\)C.
* Multisensor probes: WRF04 CO\({}_{2}\) RH LON. They are composed of an internal temperature sensor, a relative humidity (RH) sensor, and a CO\({}_{2}\) sensor. The ranges of each sensor are as follows: Measuring range: Temperature: 0-50 \({}^{\circ}\)C; RH: 0-100% RH non-condensing; CO\({}_{2}\): 0-2000 ppm. Figure 5: Example views of the main components of the monitoring infrastructure placed inside the test cells: (**a**) electrical cabinet with datalogger, PLC and auxiliary monitoring devices, (**b**) surface temperature probes, (**c**) heat flux meter on an internal wall. Heat flux meters: HUKEFLUX HFP01. They were connected to the analog inputs of the PLC, which must be previously programmed to be able to adjust the precision, the measurement tolerance, as well as the voltage reading range. In order for the PLC to interpret the voltage signal introduced by the heat flux meter, the connection of an amplifier was required thus as to increase the voltage of the signal. * Power meter: a 60 A clamp was connected to a NICO 8108L power meter in order to measure the electricity consumption of the HVAC units. The meter accuracy reported by the manufacturer is: \(\pm\)1% fs (50/60 Hz) for voltage and current measurements; \(\pm\)1.5% fs for active power. CO\({}_{2}\) and relative humidity values were monitored by multi-sensors placed inside both test cells. However, the corresponding data collected by these sensors (as well as heat flux measurements) were not used for the thermal and energy analyses within the scope of this work. All the sensors used during the monitoring campaign were calibrated according to the calibration curves provided by the manufacturers. Regarding the temperature probes, preliminary analyses were performed comparing those temperature values measured by the different sensors inside the target operating range. Maximum temperature differences reached 0.2 \({}^{\circ}\)C, in line with the accuracy value referred in the technical datasheet.
# Long-Range Wireless Radio Technologies: A Survey
## 2 Long-Range Wireless Technologies
### Mobile Band-Based Technologies
#### 2.1.1 Enhanced Machine-Type Communication (eMTC, a.k.a. LTE-M)  
Long Term Evolution (LTE), a.k.a. 4G, is a standard from the 3 GPP. LTE Cat M1, which is known as either LTE-M or eMTC, is derived from the LTE standard and specified in 3 GPP release 13 [11]. It is designed for Machine to Machine (M2M) communications (e.g., IoT). eMTC is a simplified version of LTE that aims to draw less battery power and to extend its range. In contrast to classic LTE, eMTC reduces the data rate to a tenth of LTE (up to 1 Mbps) and strips down the bandwidth from 20 MHz to 1.4 MHz. eMTC supports full duplex communications, as well as optional half duplex operations to reduce consumed power. FotA updates are possible using eMTC. It may be worth noting that Voice over LTE (VoLTE) is also usable on LTE-M communications. Two new features are enabled in eMTC, namely extended Discontinuous Reception (eDRX), and Power Saving Mode (PSM). The former allows longer paging cycles, while the latter allows nodes to be inactive for an indefinite period of time. Both aim at reducing the power consumption. eMTC also supports handover, which makes it usable when considering mobile IoT applications.  
eMTC is already deployed in numerous countries worldwide, as we can see in Figure 3. It is rather easy for Mobile Network Operators (MNO) to deploy eMTC, as the infrastructure only needs a software upgrade, without any physical hardware modifications. As an example, Orange, one of the main MNO in France, recently launched its own eMTC network. Its current coverage is available in [12].
# Parallel Image Completion with Edge and Color Map
###### Abstract  
Over the last few years, image completion has made significant progress due to the generative adversarial networks (GANs) that are able to synthesize photorealistic contents. However, one of the main obstacles faced by many existing methods is that they often create blurry textures or distorted structures that are inconsistent with surrounding regions. The main reason is the ineffectiveness of disentangling style latent space implicitly from images. To address this problem, we develop a novel image completion framework called PIC-EC: parallel image completion networks with edge and color maps, which explicitly provides image edge and color information as the prior knowledge for image completion. The PIC-EC framework consists of the parallel edge and color generators followed by an image completion network. Specifically, the parallel paths generate edge and color maps for the missing region at the same time, and then the image completion network fills the missing region with fine details using the generated edge and color information as the priors. The proposed method was evaluated over CelebA-HQ and Paris StreetView datasets. Experimental results demonstrate that PIC-EC achieves superior performance on challenging cases with complex compositions and outperforms existing methods on evaluations of realism and accuracy, both quantitatively and qualitatively.  
image completion; edge; color; generative adversarial network (GAN) +
Footnote †: journal: Applied Sciences
# Blockchain-Empowered Fair Computational Resource Sharing System in the D2D Network +
## 3 System Overview
### Cooperative Task Scheduling and Roles of System Users  
As shown in Figure 2, our D2D network consists of users with smart devices and a supernode at the base station (BS). Communications between user devices are through direct D2D links like Bluetooth or WiFi Direct, and communications between user devices and the supernode (e.g., reporting mobility and task information) are through a cellular link like 4G or LTE. D2D task cooperation is coordinated by the supernode bearing the mobility and task information among users in mind. In this paper, we assume that some necessary information related to a properly sliced task piece (including some overhead and necessary execution files, which are assumed to be of limited size not comparable to large multimedia content) will be sent from a requester to a helper, and the calculation result (which is even smaller) will be sent back to the requester once the helper has finished. The information exchanged between a user node and the supernode will be of a much more limited size, whose transmission time can also be negligible compared to the cooperative task execution time. More importantly, the D2D task cooperation is coordinated and assigned by the supernode at the base station, meaning that each helper is assigned specific time slots (to be elaborated in Section 5) and a corresponding amount of work to help each requester. Thus, we do not emphasize the difficulty of assigning dedicated in-band channels for the D2D communications in our system. Instead, we emphasize the difficulty of a user executing her/his own task in a timely manner, and since our system is not proposed for content sharing that is bandwidth significant, we assume that D2D communications between user nodes use dedicated in-band channels assigned by the supernode. Hence, mutual interference is not emphasized in our work.  
At any moment, we may further divide system users into computational resource users and miners. Miners will write transactions into the main blockchain and grant credits for keeping our blockchain-based credit system safe. Computational resource users consist of requesters and helpers: requesters in an task period are devices in need of computational assistance, and helpers are devices that may offer help requesters. Each successful computational assistance will be recorded as a transaction and will be written into the blockchain. Therefore, a requester will need to pay the corresponding amount of credit to a helper after receiving the computational assistance from that helper.
# A Research of Pulse Source for EFT Test of Electrical Vehicle Piles
## 1 Introduction  
Generally, the charging station for electric vehicles (EV) has two types of electromagnetic interference (EMI). First, the pile is easily subjected to the surge shock from inductive or direct lightning strikes because it is installed outside. In addition, ground faults or cut-off inductive loads would cause EMI to the system. Both types of interference signals are characterized by high amplitude, short period, and wide spectrum; they occur in the form of pulse group [1, 2, 3]. The signal, which could induce transient high voltage and large current on the wire of the charging station, has destructive effects on the devices, such as equipment misoperation and logic malfunction [4, 5, 6, 7]. In particular, the surge shock signal of inductive or direct lightning causes permanent damage to the equipment; thus, it becomes unusable. The consequences become increasingly serious when an EV is charging. Therefore, conducting an electromagnetic compatibility (EMC) test on the piles is extremely indispensable. Among the numerous test approaches of EMC for the charging station, EFT test is one of the most effective methods; it simulates EMI signals in the form of rapid high-voltage transient pulse signals generated by high-voltage pulse source and tests the ability of anti-interference for the charging station [8, 9].  
In the EFT test, the interference signal waveform is analogous to the lightning current waveform with rapidly increasing edge based on double-exponential fitting in the time domain [10; 11; 12]. Lightning has the characteristics of large current amplitude, high steepness, and tough impact overvoltage. In addition, the lightning discharging does not occur only once in space; it is an intermittent pulse development process called \"stepped leader,\" and the intermittent duration is approximately tens of microseconds. Thus, the lightning discharging process could be regarded as a group of several sharp pulses in an instant [13; 14; 15]. Hydrogen thyratron was selected as the switch of the developed sources to realize pulse outputs to simulate pulse group with the time interval of several hundred microseconds because of the characteristics of lightning. The repetition rate of the thyratron cannot satisfy the requirements under the current technological condition. Thus, a trigger circuit with special topology, where the same four modules were connected to the load, should be adopted. Each module was triggered in the time sequence to obtain four continuous electromagnetic pulses with 100 us step adjustable time interval and up to 35 kV amplitude.
## 5 Sensitivity Analysis
### Air Humidity  
The effect of the air humidity is here analyzed varying the critical electric field according to Reference [21]. A minimum value of 2 g/m\({}^{3}\) and a maximum value of 25 g/m\({}^{3}\) are here considered. Considering \(m=0.8\), a line diameter of 10 mm and the line details previously described, Figure 7 shows that the influence of the air humidity is substantially negligible. This is ascribed to the low variation of the critical electric field according to Reference [21].
# A Knowledge Graph Framework for Dementia Research Data
## 4 Results
### Use Case: Graph-Enabled Phenotype, Flow, and Protein Exploration from AT(N) Biomarker Profiles
#### 4.1.1 Experimental Setup  
The central DDI data platform is the XNAT archiving system [98], which is complemented by tailored customizations and data export functionalities, including automatic biomarker-based AT(N) classification, and population-adjusted norming for pertinent screening tests such as CERAD [99; 100], VOSP [77], and TMT [78; 101]. We implemented the transformation descriptor for the DDI data, involving direct mappings from clinical codes and rules to translate assay and experiment results into specific phenotype and disease entities. We then fed the descriptor along with the aggregated Comma-separated values (CSV) dump from XNAT to the transformation module to obtain the graph representation.  
Figure 4: Overview of the DemKG framework components.  
The DDI cohort graph comprises 96,939 nodes and 362,824 edges, whereas an average subject subgraph with four visits has 3469 nodes and 8284 edges. This transformed graph was merged into the final DDI-KG, which we ingested using the KGX module into a Neo4j Community instance deployed in a Podman container configured with eight cores and 16 GB of RAM, running on the secured servers of the TSD (Tjeneste for Sensitive Data) facilities managed by the University of Oslo. We opted for Neo4j due to its widespread adoption, the capabilities of its Cypher query language, and its reliable performance. Furthermore, KGX automatically creates node indices and constraints to improve loading and query performance for this platform.  
Taking advantage of these features, the setup proves efficient with the resultant graph model, particularly for queries with clearly defined traversals and designated node labels. Figure 5 offers a preliminary analysis for estimating query performance, tracing the time consumed in navigating paths that extend from one to ten hops from subject nodes to various relevant node types in the graph. As anticipated, the number of target nodes considerably affects query performance, primarily driven by the increased number of edges to evaluate and traverse, coupled with the augmented data volume to handle. This scenario is especially pronounced in the most populated and interconnected node types, namely, proteins, genes, and diseases. Therefore, queries involving numerous or unrestricted quantities of such nodes require thoughtful design.
## 4 Evaluation Criteria  
### The MAV-RCFL  
The MAV-RCFL is easily obtained from Equation (3). At a steady state of non-zero angular speed and neglecting the phase resistance, Equation (3) can be represented as follows, where \(V_{dc}\) is the phase DC-link voltage:  
\[\frac{d\psi_{k}}{d\theta}=\frac{V_{dc}}{\omega} \tag{9}\]  
It is evident that the MAV-RCFL depends on the phase DC-link voltage \(V_{dc}\) and the rotor angular speed \(\omega\) with respect to the position \(\theta\). That means that, for a given \(V_{dc}\), the flux linkage derivatives \(d\Psi_{k}/d\theta\) can estimate the rotor speed range, at which the torque-speed performance theoretically allows a torque-ripple-free operation. It can be seen from Equation (9) that the maximum absolute value of \(d\Psi_{k}/d\theta\) limits the maximum speed range \(\omega_{max}\). The MAV-RCFL denoted as \(M_{R}\) is defined as:  
\[M_{R}=\max_{\theta_{on}\leq\theta\leq\theta_{off}}\{|d\Psi_{k}/d\theta|\} \tag{10}\]  
According to Equations (9) and (10), the maximum speed range \(\omega_{max}\) for operating torque ripple performance freely must satisfy the following:  
\[\omega_{max}\leq\frac{V_{dc}}{M_{R}} \tag{11}\]  
To be specific, the smaller the maximum \(M_{R}\), the larger the maximum rotor speed \(\omega_{max}\) is. In other words, the MAV-RCFL is expected to be as small as possible.