esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Feature Description
### Data Flow Diagrams of the Features  
In Figures 3 and 4, data flow diagrams for both architectures are presented.  
Despite them seeming to be very similar to each other, some crucial differences exist in the components and interactions, which are also highlighted in the data flow diagrams. In the [PERSON] et al. architecture [27], the sensors (i.e., accelerometers, gyroscope, speed, microphone, and temperature sensors) continuously collect data, which are then transmitted via Bluetooth. These data are gathered and analyzed by the ECU. When a predefined threshold is reached, the ECU prompts the TCU to send the crash event data to the cloud. At this point, the system makes a request to the ML model in order to assess the presence of a false alarm. The deep learning method then conducts a detailed analysis of the crash event data to determine whether the event corresponds to an accident or not. Particularly, if the data coming from the accelerometer and gyroscope are too random and there is no correlation that demonstrates the existence of a crash, then the request is aborted. Instead, if the data suggest a crash, a notification is sent to the user, asking for confirmation, and an emergency alert is simultaneously sent to the emergency services. If the event does not qualify as an accident, the system reverts back to its initial state of monitoring the sensors.  
Figure 2: Sequence diagram of [PERSON] et al. [28] architecture.  
Differently from the [PERSON] et al. [27] architecture, in the [PERSON] et al. [28], there is no usage of an ML model. After initialization, the accelerometer and gyroscope sensors periodically gather data and compare them to a predefined threshold. If the data surpass this threshold, a collision event is reported. Similarly, the sound, temperature, and pulse sensors are continuously monitored. If any of these sensors' data also exceed the threshold, a collision event is reported. Additionally, a picture from the internal camera and the vehicle's location data acquired with the GPS are included in the report.  
If the collected data do not exceed the threshold, a message is displayed to the driver along with an alarm. The driver can dismiss the message. If the driver is unable to dismiss it, the collision event report is sent to the edge gateway, which forwards it to the control room.
# An Enhanced Fusion Strategy for Reliable Attitude Measurement Utilizing Vision and Inertial Sensors
## 4 Experimental Test and Comparison  
A semi-physical simulation platform for attitude measurements is established, as shown in Figure 5, to evaluate the performance of the inertial and vision data fusion algorithm based on RBF and CKF. The camera is fixed onto the bracket, which is installed on the platform. Furthermore, the target and IMU are fixed on the turntable. The target is represented by four non-coplanar infrared LEDs [31; 32; 33; 34; 35; 36], as shown in Figure 5. An external PC collects the image data and the angular velocity from the camera and a built-in gyroscope of IMU separately. With only four feature points, the workload of image processing and feature matching is greatly reduced, and the computational efficiency of data fusion is largely improved. In this experiment, the specifications of the gyroscope in IMU are given in Table 1.  
In the experiment, to fuse the vision and inertial data, the time synchronization of the sensor fusion system is considered. To guarantee the time synchronization of the whole attitude measurement system, the key is to guarantee the time synchronization of both visual and inertial systems. The program calculagraph starts to obtain the system time at the start of the inertial measurement and visual measurement. The inertial system is comprised of a IMU sensor and the vision system is formed by a  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameters** & **Value** \\ \hline Gyro & In-run bias stability & 10 deg/h \\  & Bandwidth (\(-\)3 dB) & 450 Hz \\  & A/D resolution & 16 bits \\  & Noise density & 0.01 (deg/s)/\(\sqrt{\text{Hz}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gyroscope parameters.  
Figure 5: Experimental device.  
camera. When the inertial sensor captures inertial data, there is a time stamp attached to each data. When the vision sensor captures one frame of a picture, the time stamp is recorded with the picture. After this, the time stamps of the inertial system and vision system are matched in the calculation process. When the difference of inertial and visual time stamps is small enough, the mean inertial and vision data are synchronized. Thus, the time synchronization of a sensor fusion system is guaranteed.
## 3 Results  
### Hourly GPP at the Site Level  
Comparison between hourly modeled _GPP_ and FFNet DB/FLUXNET2015 estimation provides reference information about the modeling uncertainty of CO\({}_{2}\) uptake via photosynthesis in DBF, EBF, ENF, and GRS. As presented in Figure 2a (the evaluation metrics values also detailed in Table S2), the degree of similarity [131] between hourly _GPP\({}_{BEAMS}\)_ (Big-Leaf upscaling scheme) and _GPP\({}_{lower}\)_ was greater than 0.6 in both calibration and validation groups. Stated more specifically, the hourly _GPP\({}_{BEAMS}\)_ agreed closely with _GPP\({}_{lower}\)_ in ENF (\(R^{2}\geq 0.75\)), followed by DBF (\(R^{2}\geq 0.63\)), EBF (\(R^{2}\geq 0.53\)), and GRS (\(R^{2}\geq 0.49\)). The difference between flux tower-estimated and modeled standard deviation of hourly _GPP_ at  
Figure 1: Distribution of plant functional type (PFT) in the study area based on the MODIS land cover dataset (MCD12Q1, quality-control-screened) in 2018. Dashed cycles mark the Tokyo metropolis, Keihin Industrial Area, Keiyou Industrial Area, and Kashima Seaside Industrial Area, respectively.  
the site level was always within 1.22 \(\upmu\)molCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\), and therefore was acceptable. The RMSE of the hourly \(GPP_{\mathit{BEAMS}}\) was less than 6.1 \(\upmu\)molCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) or 0.26 gC m\({}^{-2}\) h\({}^{-1}\), where the largest was found for DBF and the smallest was found for GRS.  
The accuracy of the hourly modeled \(GPP_{\mathit{TLM}}\) (two-leaf upscaling scheme) was better than that of the hourly modeled \(GPP_{\mathit{BEAMS}}\) when compared to \(GPP_{\mathit{buver}}\), but with one exception in the validation group of ENF (QIA). As with \(GPP_{\mathit{BEAMS}}\), the similarity between hourly \(GPP_{\mathit{TLM}}\) and \(GPP_{\mathit{buver}}\) was greater than 0.6 in both the calibration and validation groups, according to Figure 2b and Table S2. Hourly \(GPP_{\mathit{TLM}}\) also showed very good agreement with \(GPP_{\mathit{buver}}\) in ENF (\(R^{2}\geq\) 0.74), DBF (\(R^{2}\geq\) 0.67), EBF (\(R^{2}\geq\) 0.61), and GRS (\(R^{2}\geq\) 0.50). The difference between the flux tower estimated and modeled standard deviation of hourly \(GPP\) in the two-leaf module was within 1.05 \(\upmu\)molCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\), smaller than that in the case of \(GPP_{\mathit{BEAMS}}\). The RMSE of hourly \(GPP_{\mathit{TLM}}\) was less tha 5.55 \(\upmu\)molCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\), or 0.24 gC m\({}^{-2}\) h\({}^{-1}\), which was again smaller than the value of hourly \(GPP_{\mathit{BEAMS}}\).
## 2 Experimental Section
### Enzyme Purification  
A sample (110 mL) of filtered culture supernatant (FCS) was obtained after _A. oryzae_ cultivation and was concentrated by ultrafiltration using an Amicon System with a 10 kDa cut-off point membrane (PM-10; Amicon Millipore Co., Billerica, MA, USA). The ultrafiltrate contained approximately 50% of the xylanolytic activity from the FCS and was subjected to lyophilization (Freeze Dryer Liobras, Brazil) for 48 h. The lyophilized material was resuspended in 50 mmol\(\cdot\)L\({}^{-1}\) sodium phosphate buffer, pH 7.0, containing 150 mmol\(\cdot\)L\({}^{-1}\) NaCl and 0.02% NaN\({}_{3}\). The solution was then centrifuged at 10,500 \(g\) at 4.0 \({}^{\circ}\)C for 10 min. This supernatant was used in the subsequent chromatographic steps. Aliquots of 4.5 mL were fractionated by gel filtration chromatography using a Sephacryl S-100 column (45 \(\times\) 2.6 cm) that had been pre-equilibrated with 50 mmol\(\cdot\)L\({}^{-1}\) sodium phosphate buffer, pH 7.0, containing 150 mmol\(\cdot\)L\({}^{-1}\) NaCl and 0.02% NaN\({}_{3}\). The enzyme sample was eluted at a flow rate of 20 mL\(\cdot\)h\({}^{-1}\), and 3.0 mL fractions were collected and analyzed by absorbance at a wavelength of 280 nm. Fractions with xylanolytic activity were pooled, and a combined volume of 6.5 mL was re-subjected to gel filtration chromatography on a Sephadex G-50 column (65 \(\times\) 2.6 cm) that was pre-equilibrated in the same buffer described above. Fractions were eluted with at a flow rate of 20 mL\(\cdot\)h\({}^{-1}\), and 3.0 mL aliquots containing xylanase activity were collected and pooled. Finally, a volume of 20 mL that was obtained in the earlier fractionation was pooled and dialyzed at 4.0 \({}^{\circ}\)C overnight against 20 mmol\(\cdot\)L\({}^{-1}\) sodium phosphate buffer, pH 7.0, with 0.02% NaN\({}_{3}\). The dialyzed volume was subjected to ion exchange chromatography on a Q-Sepharose column (2.6 \(\times\) 4.0 cm) and equilibrated with 20 mmol\(\cdot\)L\({}^{-1}\) sodium phosphate buffer, pH 7.0, containing 0.02% NaN\({}_{3}\). The column was washed with the same buffer and eluted with a linear gradient of NaCl (0-1.0 mol\(\cdot\)L\({}^{-1}\)). The fractions were eluted at a flow rate of 20 mL\(\cdot\)h\({}^{-1}\), and 3.0 mL aliquots containing xylanolytic activity were collected and pooled for further characterization. All chromatographic steps were performed at room temperature.
## 3 Analysis of MR Transmission Device Torque
### Quantitative Calculation of Transmission Performance  
It is assumed that the all the MRF in the working gap yields and performs shear flow under the action of the magnetic field, and the torque calculation formula of the disc MR drive is as follows [7]:  
\[\begin{array}{ll}M_{\rm M}&=\int_{\tau_{2}}^{\tau_{1}}2\pi\tau r^{2}dr\\ &=\frac{2\pi}{3}\big{(}r_{1}^{3}-r_{2}^{3}\big{)}\tau_{y}(B)+\frac{\pi}{2l} \big{(}r_{1}^{4}-r_{2}^{4}\big{)}(\omega_{1}-\omega_{2})\eta\end{array} \tag{20}\]  
where \(\eta\) refers to the zero-field viscosity, \(h\) refers to the thickness of the MRF gap, \(r_{1}\) and \(r_{2}\), respectively, refer to the large and small diameter of the working gap, and \(\omega_{1}\) and \(\omega_{2}\) refer to the angular velocity of the input and output shaft, respectively. \(\omega_{1}\) and \(\omega_{2}\) are 100 and 80 rad/s, respectively, which means the shear strain, \(\gamma\), is 0.2.  
According to the squeeze experiment of MRF [19], in the case of \(B_{0}\) = 50 mT, \(\Delta\) = 20 mT, and magnetic particles spaced far apart (\(R/d_{0}\) < 0.5), we set the squeeze deformation constants as X = 0.01 and Y = 0.4. The deformation relationship of magnetorheological fluid according to Equations (6) and (8) is shown in Figure 13.  
Figure 12: Relationship between recovery rate and temperature.  
Bringing the data of Figure 13 into Equation (20), the composite drive torque at the different currents of the SMA can be calculated, as shown below. Figure 14 depicts the effect of the electrothermal SMA current on the torque of the transmission for a coil current of 3 A. When the spring current exceeds 1 A, the torque of the transmission rapidly increases in an approximately linear manner, from 15.08 to 73.56 N\(\cdot\)m, a 4.88-fold increase. Eight SMA springs provided a total squeeze stress of 0.34 MPa.
## 2 Attenuation-Based Ultrasonic Scattering Measurement Method
### Theoretical Attenuation
#### 2.1.1 Attenuation Generated by Cement Paste and Aggregates  
The attenuation generated by the cement paste matrix \(\alpha_{a,f}\) can be directly measured with the ultrasonic scattering measurement on the separately prepared cement paste with the same water/cement ratio. The aggregate size distribution \(G(a_{i1})\) can be determined with the designed aggregate aggregation. Then, the corresponding amount of a certain size aggregate per unit volume \(n_{i}\) can be deduced as shown in Equation (2). Introducing Equation (2) into Equation (1), the theoretical attenuation generated by the aggregate can be determined as depicted in Equation (3).  
\[n_{i1}=\left[\left[G(a_{i1})-G(a_{i1-1})\right]\phi_{1}\right]\Bigg{/}\frac{4}{3} \pi a_{i1}^{3} \tag{2}\]  
\[\alpha_{f,aggregate}=\ \frac{1}{2}\sum_{i=1}^{m1}\left[\left[G(a_{i1})-G(a_{i1-1}) \right]\phi_{i1}\gamma_{i,f}^{Sca}\right]\Bigg{/}\frac{4}{3}\pi a_{i1}^{3} \tag{3}\]  
where \(\phi_{1}\) is the volume fraction of the total aggregate (%); \(a_{i1}\), \(\phi_{i1}\) and \(n_{i1}\) represent the size, volume fraction and count per volume of a certain type aggregate; \(\alpha_{f,aggregate}\) is the theoretical attenuation of the aggregate (Nepper/m).
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
## 2 Integrated Bearing Dynamic Performance Analysis
### Calculation Result Verification  
The maximum contact force between the integrated bearing cage and the rolling balls was calculated using the finite element analysis software (Ansys2016). As shown in Figure 17, the results of the finite element method are compared with the dynamics simulation software. The finite element model of the integrated bearing was established, with a total of 204,251 finite elements. The operational parameters (such as rotational speed and load) are consistent with those in the previous section.  
A comparison of the two calculation methods is presented in Figure 18. The results are in close agreement, with a maximum error of approximately 13%, thus validating the accuracy of the model developed in this paper.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Outer Diameter (mm) & 20 \\ Inner Diameter (mm) & 8 \\ Width (mm) & 6.3 \\ Number of Steel Balls & 7 \\ Diameter of Steel Balls (mm) & 2.38 \\ Pitch Circle Diameter (mm) & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Structural parameters of the deep groove ball bearing.  
Figure 17: Finite element model of integrated bearing.  
Figure 18: Comparison of finite element and dynamic calculations.
# Simulation of the Thermal Behavior of Cast Iron Brake Block during Braking Maneuvers
## 6 Conclusions  
The paper deals with the investigation of the thermal behavior of a cast iron brake block during braking maneuvers by means of both experimental tests and FE analyses performed using the Ansys Mechanical APDL commercial software package. The numerical model was validated against the experimental data collected by a monitoring system developed by the research group from Politecnico di Torino during a test campaign in the context of the Cluster ITS Italy 2020 project for two different braking maneuvers. The monitoring system can detect the brake block temperature by means of a thermistor installed close to the brake block holder. The results of the model validation showed a good agreement between the experimental temperature and the temperature computed by the FE code at the location where the thermistor is installed. Therefore, the model can produce reliable outputs and can be used to choose the best location for the installation of the thermistor for monitoring purposes. In fact, the FE model allows to estimate the temperature at the wheel-shoe contact interface starting from the measurements of a temperature sensor installed in a different location.  
In future upgrades, the numerical model can be improved to allow the investigation of the thermal behavior of not only the brake block but also of the wheel and axle-box, so that a better understanding of the relationship between the wheelset temperature and the wheel-shoe contact interface temperature can be obtained. Moreover, the code will be upgraded to consider different types of brake shoe material, including composite organic and sinter materials. Therefore, a new experimental campaign should be carried out to verify the model accuracy in the prediction of the brake shoe temperature for composite materials.  
Conceptualization, A.S. and N.Z.; methodology, N.Z.; software, M.A.; validation, A.S. and M.A.; writing--original draft preparation, M.A. and N.Z.; writing--review and editing, N.Z.; supervision, A.S. and N.Z.; project administration, A.S.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Cluster ITS Italy 2020 project.  
Figure 9: Braking maneuver B—comparison of measured and simulated temperature of the brake block at the position where the sensor is installed.
# Mesoporous Carbon as Galvanic-Corrosion Activator Accelerates Fe Degradation
## 3 Results and Discussion  
### Microstructure and Phase Composition  
The microstructure of Fe and Fe/MC composites were studied using SEM in secondary electron (SE) and back-scattered (BSE) mode, as presented in Figure 2. The SE model was usually adopted to study the topographic contrast, while the BSE model was favorable to observe the distribution of different phases. It could be seen that the as-built Fe part achieved an extremely high densification rate, without obvious cracks or pores. For Fe/0.5 MC and Fe/1.0 MC composites, BSE images showed that a small number of black particles were homogeneously located in the matrix. The EDS line-scan results confirmed that these particles belonged to MC. Meanwhile, the SE images also revealed that some pores appeared around the incorporated MC particles, indicating that the incorporation of MC reduced the densification rate of as-built parts. With MC further increasing to 1.5 wt.%, they aggregated into large-scale clusters, as marked by the arrows.  
The phase compositions of Fe and Fe/MC composites were investigated by XRD, with collected spectrum presented in Figure 3. The body-centered cubic \(\alpha\)-Fe phase (JCPDS No. 06-0696) with (110), (200) and (211) planes was determined in all parts. For Fe/MC composites, an extremely weak peak corresponding to Fe3C phase (JCPDS No. 761877) was determined, whereas no obvious peaks corresponding to C phase were collected. It was speculated that partial C dissolved in \(\alpha\)-Fe, thus forming the Fe3C phase [30]. Besides, a close observation revealed that the primary peak in \(\alpha\)-Fe in Fe/MC composites slightly shifted to a low location, which could be ascribed to the lattice distortion caused by the solid solution of the C atom [31; 32].
# TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4
## 4 System Model Assumptions and Proposed Mechanism  
### System Description  
To understand the behavior of resource collisions, a discrete-time ARS mechanism is considered. Vehicles having data to transmit must select RBs from a periodically reappearing pool of RBs. Subsequently, the selected RBs are used to transmit the data. According  
Figure 4: Resource allocation in SRA, Mode 3.  
Figure 5: Resource selection in ARS, Mode 4.  
to the standard [33], the model considers three processes, i.e., (1) RB selection, (2) counter selection, and (3) RB reselection. When a vehicle has data to transmit, the RB selection process executes, which refers to the initial selection of RBs from a pool of resources. Upon the selection of the RB, the counter selection process starts, which refers to the selection of an integer counter value. This counter defines the number of times the selected RB can be used for subsequent transmissions. Finally, upon expiry of the counter, the RB reselection process is called, which refers to the probability of reselecting the same RB for further transmissions. These three processes are called _Process 1_, _Process 2_, and _Process 3_, respectively.
## 8 Discussion
Considering the 0-th node as the sink node, the delay of a packet (\(Dp_{i}\)) generated at the \(i\)-th node will accumulate the latencies of the following relaying nodes as expresses Equation (10). This effect is clearly observed in Figure 11, in which higher PIR values induce higher collision probabilities and therefore larger delivery times. \[Dp_{i}=\sum_{j=1}^{i}D_{j}. \tag{10}\]  
Figure 13: Comparison between different simulation approaches  
Furthermore, the collision probability depends on the amount of traffic accumulated by each node, since larger buffer usage implies longer transmission times (and therefore channel occupation). The average transmission delay depends on the amount of retransmissions, which is inversely proportional to the collision probability. Moreover, medium access delay also depends on this probability and ultimately affects transmission delay, conforming a complex relationship that is, in addition, different for each node. Although the obtained results reflect the actual behavior of the STANAG 5066 stack assuming the EM-URFC model presented in Section 4, by the own definition of the real-time paradigm proposed in this work, simulations cannot be sped up as it occurs in DES. The unitary time scale of the _software-in-loop_ simulator may affect the design of routing or discovery protocols, due to the potentially-excessive time consumption. Nevertheless, the original motivation of this work was not aligned with network-level protocol design. The main objective of this work was to provide a robust and accurate platform to simulate production-ready protocol stacks (STANAG 5066 in this case) under real-time operation inside arbitrarily complex networks. The obtained results demonstrate that the proposed framework can be used to simulate STANAG 5066-based EM-URFC UWSN (and any other stack and channel with minimum adjustments).