esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion  
### Dynamic Response Characteristics of Fluid  
The dynamic response of fluid under four system work conditions (Table 2) is investigated in this subsection. The Figures 9 and 10 show the comparison of the flow velocity at the inlet of the pipe and fluid pressure at the outlet of the pipe by the DTTMM method and the experimental results.  
From Figure 9, it is observed that the flow velocity curves of the pipe inlet contain several sinusoidal components, show the periodic characteristics and the pulsating amplitude shows an increasing trend with increasing flow velocity. A key point here is that the waveform of flow velocity curves in Figure 9 is inconsistent with that in Figures 5a and 6a; this is due to only the first four-order harmonics of pump source pulsation being used for calculation.  
From Figure 10, it is observed that the fluid pressure curves of the pipe outlet contain several sinusoidal components, show the periodic characteristics and the pulsating amplitude increases with the increasing fluid pressure. Interestingly, the waveform of the fluid  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{3}{c}{**Translational Stiffness (N/m)**} & \multicolumn{3}{c}{**Rocking Stiffness (N-m/rad)**} \\ \hline \(k_{x}\) & \(k_{y}\) & \(k_{z}\) & \(t_{x}\) & \(t_{y}\) & \(t_{z}\) \\ \(8.74\times 10^{6}\) & \(5.84\times 10^{5}\) & \(7.29\times 10^{6}\) & \(1830\) & \(239\) & \(1890\) \\ \hline \end{tabular}
\end{table}
Table 7: Six-DOF equivalent stiffness of clamp.  
Figure 10: Fluid pressure of pipe outlet. (**a**) Work conditions 1 and 2; (**b**) Work conditions 3 and 4.  
Figure 9: Flow velocity of pipe inlet. (**a**) Work conditions 1 and 3; (**b**) Work conditions 2 and 4.  
pressure curves in Figure 10 agrees with the flow velocity curves in Figure 9; this happens because the differential pressure and flow velocity across the throttle valve are defined as a linear relation in Equation (49). Moreover, it takes about two milliseconds for the fluid pressure pulsation to appear in the DTTMM method, while the pulsation phenomenon exists in the experimental results from the initial time. This is because, the initial state of the fluid in the pipe is static in the numerical simulation, and the pressure wave propagates at the speed of sound along the pipe. This time difference may be just the time of pressure wave from the head end to the tail end of the pipe exactly, while the experimental results are obtained when the test system is stable.  
Figures 9 and 10 show that there is a good agreement between the DTTMM method and the experimental results. However, the waveform amplitude of the experimental results is generally lower than that of the DTTMM method, principally because some hydraulic components, such as the accumulator and filter, are installed in the experimental system, which will absorb part of the fluid pulsation; however, those components are not modeled and analyzed in the numerical calculation.
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 4 Methodology
### Data Collection Module  
The data collection module collects three main types of information: the historical signal control strategies, the vehicles' position information, and the IDs of CBCVs.  
The historical signal control strategies are the strategies from the previous optimization cycle. These strategies are incorporated into the new optimization round to avoid traffic disruptions caused by abrupt signal changes.  
The position information of vehicles is sourced through their vehicular network capabilities, which are utilized to identify which vehicles are within the control zone at the start of the optimization.  
The collection of IDs for CBCVs requires identifying which vehicles qualify as CBCVs. This identification is established prior to their entry into the control zone. Outside the control zone, CVs can choose whether to adhere to the speed guidance provided within the zone. If CVs accept the speed guidance, they are designated as CBCVs. To incentivize CVs to accept speed guidance, historical carbon benefits accrued by CBCVs are showcased during the decision-making phase. The process of identifying CBCVs is shown in Figure 3.  
Figure 2: Control structure.
# Streaming-Based Anomaly Detection in ITS Messages
## 4 Discussion
### Use Cases  
We selected streaming techniques because in real-world C-ITS setups, anomaly detection would be performed by roadside devices that gather a large number of signals from cars within range. This implies that it would have to process the messages on the fly for a variety of reasons (memory limitation, response time, etc.). The windowing concept makes it easier to discover abnormalities on the fly. This research can be applied to driver behaviour analysis, especially lane-changing behaviour analysis and obstacle detection. The  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **AUC-ROC** & **AUCPR** \\ \hline ELSCP & 0.8945 & 0.3841 \\ LSCP & 0.8581 & 0.2858 \\ IForest & 0.7825 & 0.2210 \\ MCD & 0.7587 & 0.1665 \\ RRCF & 0.6042 & 0.1146 \\ Exact-STORM & 0.5000 & 0.0885 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average AUC-ROC and AUCPR performance results for various anomaly detection models on C-ITS data.  
detection capabilities of ELSCP might be incorporated into road operators' decision-making processes in order to improve safety and traffic flow. The timely detection of anomalies is crucial, especially for emergency response teams, resulting in increased efficiency in rescue operations.
## 3 Experimental Tests
### Test Results
#### 3.2.2 Moment-Deflection Relationship  
The moment-deflection curves of the beams are displayed in Figure 9. All the FRP-reinforced beams yielded typical bilinear relationships before the peak load, namely, a steep linear stage that described the uncracked condition and a basically linear stage that represented the reduced postracking stiffness. Similar observations were reported for FRP-reinforced normal weight concrete (NWC) beams with and without fibers [30]. For beams SLC-G-5#13.77 and SLC-G-8#13.77, the slopes of the deflection curves slightly decreased just before the peak load. It could be explained by the fact that the presence of the steel fiber restrained the propagation of the transverse cracks in the compression zone.  
Furthermore, for FRP-reinforced specimens that were damaged by concrete crushing, a nonlinear stage that corresponded to the crushing process was observed after the peak load. In this stage, the specimens continued to sustain the load with increasing deformation. The inelastic deformations  
Figure 8: Typical failure of specimens: (**a**) Concrete crushing; (**b**) Balanced failure; and (**c**) Rupture of fiber-reinforced polymer (FRP) bars.  
Figure 9: Experimental moment–deflection relationships: (**a**) Series I; and (**b**) Series II and III.  
near failure lent a degree of ductility for this failure mode, which was in agreement with the results reported for GFRP-reinforced NWC beams with and without fibers [27].
# Self-Organized Proactive Routing Protocol for Non-Uniformly Deployed Underwater Networks
## 3 Overview of the SPRINT Protocol
Once the RR packet has gone through all the nodes in the network, the nodes at end of the network send a Route Request Response RR_RSP packet back to the gateway. Nodes assume they are end nodes when they do not receive any response packet after sending the RR packet. When the gateway receives the RR_RSP packet from all the first hop nodes it assumes that all the nodes have successfully formed the path to the gateway. The nodes keep measuring the RSS of the received packets during the data packet transmission to optimize the path. If a node finds a closer neighbor compared to the existing forwarding node, then the former selects the latter as the next hop to forward the packets.
## 4 Data Analysis  
We performed experiments in two facilities with different particles: heavy ions, in the Radiation Effects Facility (RADEF) at the University of Jyvaskyla, Finland, and protons, in the Proton Irradiation Facility (PIF) at the Paul Scherrer Institute (PSI), Switzerland. For RADEF, the beam has a 10% homogeneity for the selected irradiation area and heavy-ion cocktail of 16.3 MeV/n, capable of generating a flux up to approximately \(3\times 10^{5}\) ions/cm\({}^{2}\)/s. The ions selection included the following species and LET (at silicon surface and vacuum mode in MeV\(\cdot\)cm\({}^{2}\)/mg): \({}^{17}\)O\({}^{6+}\), 1.52; \({}^{40}\)Ar\({}^{14+}\), 7.2; \({}^{57}\)Fe\({}^{20+}\), 13.3; \({}^{83}\)Kr\({}^{29+}\), 24.5; and \({}^{126}\)Xe\({}^{44+}\), 48.5. In PSI, we selected the following proton energies (in MeV): 16, 29, 51, 70, 101, 151, and 200. Copper plates were used in the beam path to generate different energies and two primary proton energies were used to allow proper testing at the lower energy range (in MeV): 100 and 200. Proton fluxes of about \(3\times 10^{7}\) to \(8\times 10^{7}\) p/cm\({}^{2}\)/s were used, and all tests were performed at ambient room temperature and air pressure. The beam had a 10% homogeneity for the selected irradiation area and beam energies throughout the full experiment.  
The experimental setup was reused for the facilities with minor adaptations. For the heavy-ion experiment, eight SRAM devices were evaluated, covering two manufacturing lots, and they required delidding for proper particle penetration. Using microscopy in  
Figure 5: Instrumentation capabilities: precise synchronization between memory operations and current measurements. The current profile refers to a complete March C- (1) execution, in which operation stages are separated by vertical lines.  
the delidded chips allowed for verifying the die markings and some internal structures. Various irradiation angles and test modes were explored in the heavy-ion campaign. For the proton experiment, four SRAM devices were used, of which two were reused from the previous experiment for checking experimental data consistency and the remaining two (from the same manufacturing lot) were pristine to increase sample size but spot potential cumulative biases, if significant. Only the most relevant test modes were applied in this campaign, based on the outcomes from the heavy-ion experiments.  
As previously stated, the main objective of this work is not to present the characterization results or to extensively report cross sections, which is out of the scope of this article and subject to publication in [14]. Conversely, we present those outcomes that support the evaluation of the capabilities of the proposed instrumentation and their benefits in comparison to more traditional and simpler strategies. From the experimental results, we could observe many interesting behaviors that were decisive in corroborating or excluding formulated hypotheses and initial assumptions.
# Development and Testing of a Two-UAV Communication Relay System
## 3 Hardware Platform
### System Architecture  
The relay system can be conceptualized as the following major functional blocks, with the corresponding hardware architecture shown in Figure 3. The figure mainly describes the main components in UAV \"Mom\" while all other UAVs have the same hardware architecture.  
* **Aircraft**: This is the body of the UAV, usually consisting of structural components, power system, and battery. In the test system, the aircraft were constructed using commercially available frame kits. Both of the UAVs, i.e., \"Mom\" and \"Son\", were selected as quadrotors for easily launch and recovery ability during the development period. Communication relay systems, however, are not limited to quadrotors and can be integrated into any desired airborne platform such as fixed wing UAV for long endurance and distance relay service.
* **Flight Controller**: This includes main flight control boards as well as the necessary sensors, that together provide attitude and position control for the UAVs. In this test system, 3 DR Pixhawk kit [32] was selected as the main flight controller. The controller has an internal IMU, barometer, external GPS, and a compass for attitude and position measure. It also includes safety switch, buzzer, LEDs, and different kinds of interfaces combined to provide flight control.
* **Communication Module**: This functional block directly handles wireless radio communications, and consists of radio transceivers and antennas. The paired modules will connect automatically and transmit data instructed by the mission controller.
* **Mission Controller**. This functional block is a microcomputer running Linux operation system and mainly responsible for the following functions: (1) conducting communication flow control, message routing, and message error checking; (2) monitoring UAV flight conditions and reporting to GCS; and (3) translating mission commands from GCS to low-level instructions, which are directly supplied to the flight controller.
* **Ground Control Station**: This functional block refers to a Windows-based laptop running the GCS software to send commands to all the UAV nodes and monitor the status of all UAVs.
* **Emergency Remote Controller**: The remote controller is connected to the flight controller via 2.4 GHz wireless communication. In normal operation, all the commands and status can be  
Figure 2: Whole architecture of the communication relay system.  
set by the GSC point. This device has switch to directly takeover the control of UAVs in case of emergency situation in the debugging process.
# Multidisciplinary Approach to Characterizing the Fingerprint of Italian EVOO
## 1 Introduction
Thus, the composition and organoleptic characteristics of EVOO are strongly correlated with the geographical origin of production. From this perspective, great efforts have been made to catalogue and characterize the sensory and volatile profile of monovarietal EVOOs produced in a delimited geographical area. Moreover, the assessment of quality and geographical origin of EVOOs is becoming more and more important from the perspective of both the olive oil manufacturers and the customers. A differentiation based on the geographical or varietal origin and high-quality standards is one of the most powerful competitive strategies that olive oil producers are currently adopting to face the challenges of fraudulent activities and adulteration of EVOOs. The necessity of developing accurate analytical methods has prompted the present study to attempt the use of an array of metal oxide (MOX) gas sensors to discriminate the geographical origin of different PDO EVOOs. Gas sensors are a typology of chemical sensors that transduce a chemical interaction in an electrical signal. Interactions happen between volatile compounds and sensing material; this causes an exchange of electrons and a variation of conductivity of the sensor. MOX gas sensors are non-specific sensors; hence, they respond to different classes of compounds with different sensibility. For this reason, in order to understand which compounds have been detected by sensors, they are \"trained\" on a consistent dataset. Furthermore, this is the main reason that sensor arrays are used in most applications. In recent years, electronic nose systems with MOX semiconductor gas sensors have received much attention in the literature for the determination or classification of the geographical provenance of EVOOs [7; 8; 9; 10; 11; 12]. The link between an EVOO and the geographical area can characterize the product itself and determine its nutritional and organoleptic characteristics. Furthermore, the volatile composition of an EVOO is directly related to the production area, quality categories, and cultivars. All these factors interact with each other, resulting in a complex multivariate matrix [13]. In this study, the SPME-GC-MS, conducted in parallel with a versatile MOX nanowires gas sensor-based electronic nose (called S3), was applied to characterize the volatile chemical profile of EVOOs in relation to their geographical origins. The novelty of this approach compared to the cited papers lies in the use of sensors with nanotechnological properties. The aims of this work were twofold: firstly, it was important to assess the capability of the system to distinguish between olive oil, non-PDO EVOOs and PDO EVOOs; secondly, attention was focused on the discrimination of the four varieties of Garda PDO EVOOs, two from the eastern side of the lake, and the others from the western side.
# An Aircraft Object Detection Algorithm Based on Small Samples in Optical Remote Sensing Image
## 2 Theoretical Analysis and Methods
### Fusion Algorithm
#### 2.2.2 False Alarms Elimination  
There are not only airplanes in the original images, but also many buildings around the airport. Detecting aircraft only using circle frequency filter can produce many false alarms. Therefore, support vector machine (SVM) is used to eliminate false alarms. Firstly, the dataset is divided into a training set  
Figure 6: Clustering result graph. (**a**) Multiple small objects; (**b**) Weak object.  
and a test set. The training set is used to fit the SVM model, and the test set is used to finally evaluate the performance and classification ability of the model. The training set includes 66,300 negative samples and 13,260 positive samples. Then, the SVM model is trained on the training set. Finally, the classification ability of the model was evaluated on the testing set.
# Anisotropic Yield Criterion of Rolled AZ31 Magnesium Alloy via Nanoindentation
###### Abstract  
In this paper, the anisotropic mechanical properties of rolled AZ31 magnesium alloys are investigated using nanoindentation tests at room temperature. Nanoindentation was carried out at four angles, including the rolling direction (0\({}^{\circ}\)), diagonal direction (45\({}^{\circ}\)), transverse direction (90\({}^{\circ}\)), and vertical direction (ND). Experimental results show that hardness increases as the rolling angle increases from 0\({}^{\circ}\) to 90\({}^{\circ}\) and is lowest in the ND direction. The hardness independent of the effect of indentation depth is obtained by analyzing the indentation size effect and then converting hardness values into yield strengths. A new criterion is proposed on the basis of the Hill48 yield criterion. The data obtained through the above experiments are used to determine the parameters in the new criterion. Finally, a solution to the challenge of modeling a function that accurately describes the anisotropic yielding behavior of AZ31 magnesium alloys is proposed using the nanoindentation technique to solve the requirements of specimen size and experimental methods of the macro test.  
AZ31 magnesium alloy; nanoindentation; indentation size effect; anisotropic yielding criterion
# Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions
## 4 Case Study: Histological Image Analysis
### Federation  
The use case presented in this work is part of a concrete medical federation, CLARIFY ([[http://www.clarify-project.eu/](http://www.clarify-project.eu/)]([http://www.clarify-project.eu/](http://www.clarify-project.eu/)), accessed on 2 January 2023). In this multi-sectorial and multidisciplinary consortium, nine institutions across five cities in three different countries are brought together to collaborate on the automatic analysis of specific and challenging cancer types: triple negative breast cancer, high-risk non-muscle invasive bladder cancer, and spitzoid melanocytic lesions. In particular, CLARIFY aims to develop an automated diagnostic environment for digital pathology that leverages artificial intelligence methods together with cloud computing, to enable knowledge sharing among institutions and better-informed clinical decisions.  
Here, three of the nine collaborators are involved in the different steps of the proposed pipeline, both from the engineering and medical aspects. The cancer type under study in this work, spitzoid melanocytic lesions, would strongly benefit from a federation. Indeed, the few incidences of this lesion type in the population is a considerable limitation to gathering sufficient WSIs, both for DL models' training as well as for improving its clinical interpretation.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 5 Non-Orthogonal Multiple Access (NOMA)
### URLLC Rate under NOMA  
The URLLC per-UE rate for NOMA can be computed by leveraging results from finite blocklength information theory similarly to the OMA case, but accounting for the additional eMBB interference [41]. The URLLC per-UE rate under NOMA is thus well approximated by [32]  
\[R_{U}=\log_{2}(1+S_{U})-\sqrt{\frac{V}{n_{F}}}Q^{-1}(\epsilon_{U}^{D}),\]  
where  
\[S_{U}=\frac{\beta^{2}P_{U}}{1+(1+2\alpha^{2})P_{B}} \tag{33}\]  
is the Signal-to-Interference-plus-Noise Ratio (SINR) for the URLLC UE, and the channel dispersion \(V\) is given as  
\[V=\frac{S_{U}}{1+S_{U}}. \tag{34}\]Notice that in NOMA the incoming URLLC packet is always transmitted, and hence an URLLC error occurs only if the decoding of such packet fails, which happens with probability \(\epsilon_{U}^{D}\). This implies that under NOMA, the probability of URLLC error is given by  
\[\Pr[E_{U}]=\epsilon_{U}^{D}, \tag{35}\]  
hence imposing the condition \(\epsilon_{U}^{D}\leq\epsilon_{U}\) by the requirement (13).
## 2 Methodology
### Inputs: Base Cation Deposition  
All forms of atmospheric deposition were included, i.e., base cations dissolved in rainwater (wet deposition) or cloud water (occult deposition) and associated with fine particles or dust (collectively referred to as dry deposition). Wet deposition is easier to measure than cloud and dry deposition, with both of the latter interacting with and thus affected by the nature of the forest canopy, including canopy height and structure. Consequently, atmospheric deposition is spatially very variable, influenced by rainfall amount, aspect, altitude, distance from the sea, distance from pollution sources and the nature of the land cover.  
Wet deposition was determined using measured bulk precipitation, while dry and occult deposition were estimated from measured throughfall (the precipitation that has passed through the canopy). For each site, water samples from two bulk precipitation gauges (installed on open ground near to the forest plots) and ten throughfall gauges (installed under the forest canopy) were collected and volumes measured every two weeks from 1995 to 2007 and then monthly for some sites until 2019, spanning a 10 to 24 year period. Water flux measurements followed EU ICP Forest protocols described in detail in the ICP Forests manual [30].  
Water samples were filtered through a 0.45 \(\upmu\)m membrane and analysed for Ca, Mg and K by ICP-OES (Spectro flame, Spectro Ltd., Kleve, Germany). Quality assurance and quality control are as per the ICP Forest manual [30]. Measured base cation concentrations in bulk precipitation and throughfall were converted to monthly chemical fluxes using bi-monthly measured water volumes. The throughfall deposition base cation inputs were corrected for canopy leaching using the Ulrich canopy budget model [31]. Long term average annual canopy leaching among the different sites for Ca was between 9 and 35% of throughfall deposition, Mg was between 4 and 29% and K was between 55 and 86%. The time period of monitoring data varied between sites and is shown in Table 1. The mean annual deposition values for each monitoring period were used to estimate atmospheric deposition inputs for a complete 35-50-year forest rotation of Sitka spruce (depending on the yield class (YC)).