esa-sceva/satcom-chunk-collection huggingface dataset:
# Industrial IoT Monitoring: Technologies and Architecture Proposal
## 4 Proposed Architecture
### Sensor Node Monitoring Agent Overview  
Sensor node metrics (hardware and firmware) are collected by the sensor node monitoring agent, as opposed to network metrics, which are collect by the gateway monitoring agent. Independence between the acquisition of network and sensor node metrics is one of the main features of the proposed architecture. This makes it possible to monitor the network independently from the acquisition of sensor node metrics. This separation also allows having closed modules in IWSN gateways (provided by IWSN vendors) and a more open monitoring logger module (that can be extended by developers if needed).  
This section presents and discusses the main requirements for hardware and firmware metrics acquisition in the sensor node monitoring agent, and provides guidelines as to the technologies that can be used in this scope. Additionally, the approaches used in the transport of the logged data over the IWSN will be also presented.
# A Finite Element Method for Nonlinear and Fully Coupled Transient Hydrothermal Transfer
###### Abstract  
A fully coupling nonlinear finite element algorithm for moisture and heat transfer is developed and implemented. The finite element model starts from a suitable formulation of the constitutive and equilibrium equations. The nonlinearity arises due to the strong moisture and temperature dependence of the material properties. Four examples are presented to check the validity and precision of the finite element model. Two of the examples compare the numerical results with simplified, one-dimensional analytical solutions. Perfect agreement is obtained between the analytical and numerical solutions. The results show that the dependency of the material coefficients on the moisture and temperature is important, even for one-dimensional cases. The other two examples, without direct analytical solution due to the material nonlinearity, show that the fully coupling between the moisture and heat conduction is essential. The formulation can be used for the transient analysis of moisture and heat conduction with arbitrary geometrical dimensions and time-dependent loading conditions.  
moisture transfer; heat transfer; hygrothermal effect; finite element method; nonlinear Article
## 3 Results and Discussion
### Lubricant Viscosity  
The viscosity is one of the most important parameters in lubricated contacts, as it can determine the tribological properties of the lubricants [28]. Viscosity is a variable parameter in this study since different temperatures were applied for the tests. Figure 2 shows the variations of the viscosity of PAO 8 oil with the temperature changes. Comparing the viscosity of this oil with the commercial engine oil (SAE 10W-40) used in the previous work [17], it was observed that PAO 8 oil has a much lower viscosity in all temperatures. This proves that PAO 8 oil is more suitable for this study to ensure contact in the boundary lubrication regime at higher temperatures.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline rpm & 4.6 & 5.4 & 7.7 & 12 & 17 & 30 & 45 & 70 & 100 & 130 \\ \hline m/s & 0.028 & 0.033 & 0.047 & 0.073 & 0.103 & 0.182 & 0.273 & 0.425 & 0.607 & 0.7790 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sliding speeds that were applied for the tests in the angular (rpm) and linear (m/s) speeds.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline Element & C & Si & Mn & S & P & Others & Fe \\ \hline Typical & 2.95–3.45 & 2.1–2.9 & 0.55–0.75 & 0.04–0.07 & 0.1–0.2 & Residual & Balance \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chemical elements table of grey cast iron EN GJL-300.  
As the tests were performed at four distinct temperatures, it is essential to calculate the viscosity at these temperatures. The equation that was obtained from the viscosity-temperature curve in Figure 2 can be expressed as Equation (1):  
\[\eta=\exp\left[(\Gamma-136.05)/-27.58\right] \tag{1}\]  
where T is the temperature, and \(\eta\) is the dynamic viscosity of the lubricant at the specified temperature.  
Table 4 shows the calculated values, using Equation (1) for the viscosity at the temperatures that the tests were performed.
# Reasoning about Quantum Information: An Overview of Quantum Dynamic Logic
## 4 Application to Quantum Communication Protocols
We start from a given physical system that has four components, i.e., \(N=\{1,2,3,4\}\), it has a single qubit 1 and three more entangled qubits prepared in a GHZ triple state \(\beta_{00}^{2,3,4}\). We distribute the GSZ state over the three agents [PERSON], [PERSON], and [PERSON] so that [PERSON] qubit 2, [PERSON] has qubit 3, and [PERSON] has qubit 4. In addition, [PERSON] also has the qubit 1 in the unknown state \(q_{1}\). [PERSON] wants to split the information \(q_{1}\) into three shares, so she measures her two qubits 1 and 2 in the Bell basis, obtaining two classical bits \(x,y\) (corresponding to which of the four Bell states \(\beta_{12}^{xy}\) she obtained). After that, [PERSON] measures his qubit 3 in the dual basis \(\{|+\rangle,|-\rangle\}\), obtaining another bit \(|-\rangle^{z}\) as result, with \(z=\{0,1\}\). Finally, [PERSON] is given qubit 4, which is now in one of 8 possible states (depending on the results \(x,y,z\) obtained by [PERSON] and [PERSON]). To recover the original secret \(q\) from his qubit 4, [PERSON] can now apply a local unitary transformation \(Z_{4}^{z};X_{4}^{y};Z_{4}^{x}\) for which he needs to know the values \(x,y,z\). Hence, the three agents have to share their information in order to recover the secret \(q\). Expressed in our language, the protocol expresses the following quantum program  
\[\pi=\bigcup_{x,y\in\{0,1\}}\beta_{xy}^{12};|-\rangle_{3}^{z};Z_{4}^{z};X_{4}^{ y};Z_{4}^{x}\]  
The statement of the correctness of the protocol is the following expression in our logic \(DQL\):  
\[\vdash q_{1}\wedge\beta_{000}^{234}\Rightarrow[\pi]q_{4}\]  
The proof of correctness was given in [3], and a generalization to the case of an \(m\)-share split among \(m\) agents has been provided in [34]. **Probabilistic Protocols. ** As our logic has the expressive power to handle probabilistic statements, it can be applied to a wider range of protocols that contain essential probabilistic elements. Examples include [PERSON]'s quantum search algorithm [35] (see also [36]) and the quantum leader election protocol [37], which have been verified using this logic in [7]. The verification of the main BB84 quantum key distribution protocol has been provided in a follow-up paper in [38].
## 3 Current State of Object Detection Algorithms
### Yolo
#### 3.2.1 YOLOv3  
YOLOv3 [39] has a total of 106-layer architecture, including 53 convolutional layers. This model has nearly tripled the number of CNN layers compared to YOLOv2. This allows for the detection of objects on three different scales, which is a major improvement over the previous version. One of the key features of YOLOv3 is its ability to resample or down sample, the incoming image, which determines the different scales at which objects can be detected. This resampling leads to the detection of 10-times more frames than was possible with YOLOv2, sacrificing some detection speed for improved accuracy. Additionally, YOLOv3 includes skipping connections and up sampling, which further improves its ability to detect small objects. Another notable difference between YOLOv3 and its predecessor is the method used for class prediction. While earlier versions used a Softmax function, YOLOv3 employs independent logistic classifiers. This allows a single object to belong to multiple classes, making the system more flexible and accurate.
## 3 Results and Discussion
### Plugging Performance
#### 3.2.3 Marble Plugging Performance  
The marble mixture with no shearing was able to plug the simulated fracture maximum width of 1.5 mm, while the sheared mixture with size degradation was able to plug a smaller fracture width with a maximum size of 1.0 mm, as shown in Figure 7. The plugged fracture width size with shearing was 33% less than without shearing (1.5 mm and 1.0 mm, respectively). The D\({}_{50}\) of the marble before and after degradation was 535 and 322 \(\upmu\)m, respectively. The newly developed particle size selection criterion for marble is shown in Equation (4). It states that the median particle size of marble over the fracture width size must be equal to or greater than 0.5 to plug the fracture.  
\[\frac{\text{D}_{50}}{\text{Fracture width size}}\geq 0.5 \tag{4}\]  
Figure 6: Graphite maximum plugged fracture diameter before and after shearing force.  
Figure 7: Marble maximum plugged fracture diameter before and after shearing force.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 7 Conclusions  
The high accuracy and low cost of UWB radios for indoor positioning makes it the perfect technology for tracking drones in warehouses for autonomous inventory-taking. However, most UWB solutions require communication infrastructure and are energy consuming, thereby limiting placement options and increasing deployment costs. To remedy this, we designed a multi-technology UWB MAC protocol for localization that allows battery- or energy harvesting operated anchor nodes and does not require existing infrastructure such as Wi-Fi or Ethernet. To wake-up anchor nodes, the tag uses low-power sub-GHz messages to activate a list of anchors. Anchor nodes consumes less energy since they only have to listen to synchronization messages, and use TDMA to activate their UWB radio during the specified slot. The overall system potentially has a high update rate, theoretically up to 2892 Hz (without considering hardware dependent properties) and a localization accuracy of 5 cm. Anchor nodes consume only 3.4 mA in stand-by mode. The overall system has been validated using two large-scale set-ups: one for drone tracking in a warehouse mock-up, and one for localizing runners in a challenging infrastructure-less indoor running track.  
conceptualization, N.M., J.R., B.V.H. and E.D.P.; software, N.M., J.B. and B.J.; validation, N.M. and B.V.H.; formal analysis, N.M. and B.V.H.; writing--original draft preparation, N.M.; writing--review and editing, N.M., E.D.P.; supervision, E.D.P., J.H.; project administration, E.D.P., J.H.; funding acquisition, E.D.P., J.H. This work has partially been funded by the following projects: H2020 interreg Nano4 Sport and the VLAIO prefutifuproject SmartConnectivity.  
The authors declare no conflict of interest.
# Rapid Prototyping of Multi-Functional Battery Energy Storage System Applications
## 4 Application Development with the EMSOnto Approach
### General Overview of the Approach  
The whole picture of the EMSOnto framework and the support it provides to control engineers during EMS-development are shown in Figure 2. Across the different phases in the design and development process, requirements R1-R4 are properly addressed.  
As an initial step, the control engineer defines the specification of an EMS by selecting one of the approaches from the list presented in Section 3.2. This knowledge may be used during the design stage to pre-fill the assertional component of the EMS-ontology (\(ABox\)). Additionally, at the design phase, further information about structure and data exchanged in the scope of the EMS application is included within the EMS-\(ABox\). Current smart grid data models ease the realization of the EMS-\(ABox\) by providing knowledge of IEDs and corresponding control functions (like the Frequency-Watt or Volt-VAR functions in IEC 61850 [12]). Such device-specific models are usually stored within a repository and are being made available to the control engineer during the whole engineering process.  
The other component needed to fully define the EMS-ontology is the terminological box (\(TBox\)). A set of axioms conforms the EMS-\(TBox\) which serve as the basis for the inference of knowledge. A main advantage of ontologies is consistency checking of the \(ABox\) regarding the pre-defined \(TBox\). This and the inference of knowledge are achieved by the setting up of a reasoner engine [39]. Additionally, a set of premises that lead to conclusions (i.e., rules) extends the quantity of knowledge that can be inferred by just the EMS-\(TBox\) setup. On top of that, a list of queries leads to the extraction of selected data. As a result, to achieve data inference the EMS-\(TBox\), pre-defined rules and also queries are implemented by the EMSOnto. This setup is transparent to the control engineer.  
Once the \(ABox\) is fully defined and compliant to the \(TBox\), corresponding reports can be generated showing the critical and severe issues regarding the \(ABox\). At this stage, the control engineer gets helpful information about potential controller conflicts (i.e., at \(ABox\) level) which can be solved before starting the implementation and proof-of-concept of EMS applications. Additionally, a list of potential conflict-solving solutions per triggered inconsistency is also generated. At this stage, control engineers require adapting the EMS-\(ABox\) to reach an error-free EMS database. The final step in the overall process is to use model-driven engineering techniques in order to generate software artifacts and platform-specific code as outlined in Figure 2.
## 5 Discussion
The theory is that the wells are completed across the oil column during drilling operations to avoid producing gas from the cap [66]. The pressure in the reservoir drops quickly when the gas that forms from the gas cap is produced [66], leading to underpressure in the GOC (Figure 10). Contrarily, the gas-water and oil-water contacts are associated with overpressure due to the buoyancy of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Well** & \multicolumn{2}{c}{**Well Log GWC Depth (m)**} & \multicolumn{2}{c}{**RFT GWC Depth (m)**} \\ \hline  & Resistivity & Neutron/Density & RFT & Observation \\ EBK-1 & 2967.5 & 2967.5 & – & - \\ \hline  & **Well Log OWC Depth (m)** & & **RFT OWC Depth (m)** \\ \hline  & Resistivity & Neutron/Density & RFT & Observation \\ E-AJ1 & 2723 & 2723 & 2723 & Agreement \\ E-CB1 & 2523 & 2523 & 2528.5 & Disagreement \\ \hline  & **Well Log GOC Depth (m)** & & **RFT GOC Depth (m)** \\ \hline  & Resistivity & Neutron/Density & RFT & Observation \\ E-AJ1 & 2699 & 2699 & 2699 & Agreement \\ \hline \hline \end{tabular}
\begin{tabular}{c} RFT = repeat formation test; GWC = gas-water contact; GWC = oil–water contact; GOC = gas–oil contact; (m) = meters. \\ \end{tabular}
\end{table} 
Table 4: Log and RFT fluid contacts comparison results summary. the hydrocarbons and the fluid density differences. Therefore, an oil and gas column above the water contact leads to high pore pressure (overpressure) in the GWC and OWC contact compared to water alone [66]. Figures 10 and 11 demonstrate the high pore pressure in the GWC and OWC contact. These observations are consistent with those of [15], which demonstrated that hydrocarbon buoyancy may cause an increase in pressure due to the differences in pressure gradients between oil, gas, and water.
## 2 Microchip Laser
### Design of Movable Cavities  
The first setup is based on previous works [22; 23; 24]. It allowed comparisons between different configurations of laser cavities. For this, four optical components were used. The active medium (AM) was an erbium and ytterbium co-doped phosphate glass with either 1 mm or 1.5 mm of thickness. Both glasses had the same doping concentrations, \(E^{+3}\) with a concentration of \(1\times 10^{20}\) cm\({}^{-3}\) and \(Yb^{+3}\) with \(2\times 10^{21}\) cm\({}^{-3}\), and highly reflective (HR) at 1550 nm (HR@1550 nm) coatings as input couplers. The output coupler (OC) was a silica glass with a HR@1550 nm coating with 98% reflectivity. The Q-switch was a saturable absorber (SA) of Co:Spinel glass with an initial transmission of 98%.  
To allow the alignment of the glass components in the assembly, aluminum holders were designed and manufactured to fit the equipment. This allowed small changes in the XYZ axes and tip-tilt movements. Setup for the assembly of the microchip laser during the first implementation is shown in Figure 1.  
The active medium crystal was mounted on an aluminum holder, as shown in Figure 2a,b. The output coupler and the Q-switch were placed in another aluminum holder designed and manufactured to hold both. They were separated by a Viton O-ring, as shown in Figure 2c,d. The computer-aided design (CAD) of the aluminum holder allowed the alignment of both glasses with the assembly axis as well as the alignment between the two.  
Figure 1: Setup for the assembly of the microchip laser for the first implementation.  
Figure 2: CAD design of the aluminum holder for (**a,b**) the active medium and (**c,d**) the aluminum holder for the output coupler and Q-switch.  
Each optical glass was assembled and aligned individually. The alignment used a visible-spectrum (600 nm) laser and the specular back reflections of the individual items. After the first alignment of the output coupler and Q-switch using the alignment laser, an active alignment process was performed. The active alignment was carried out with the pump laser on and by adjusting the angles (tips/tilts) of the active medium and output coupler. The first detection of emission was made with the support of an infrared detector card in front of the laser cavity. With the microchip laser cavity aligned, the emission was characterized by a measure of the output optical power for each pumping level, the pulse shape, and the emission spectrum.
## 2 Fundamental Theory  
### Propagation of Ultrasonic Guided Waves in SMA490 BW Steel Plate  
Figure 1 illustrates the dispersion curves of ultrasonic guided waves in an SMA490 BW steel plate, which is calculated using DISERSER(r). Generally, the phase velocity and group velocity of the propagating guided waves are respectively described by \(\mathrm{C_{p}}\) and \(\mathrm{C_{g}}\). The phase velocity is the speed of a particle moving at a specific frequency. The group velocity is the disseminated speed of all  
wave packets [27]. The phase velocity \(C_{p}\) has the relationship with the wavelength \(\lambda_{guided~{}wave}\) and frequency f, as shown in Equation (1).  
Since the selected excitation frequency of guided waves is 270 kHz, the uniform thickness of the SMA490 BW steel plate is 3 mm, and the product of frequency-thickness is 0.81 MHz-mm, as marked in Figure 1. The S0 mode propagates at the fastest velocity among all available modes, thus, it can be completely separated from the other modes, boundary reflection, attenuation, and mode conversion signals. Therefore, the acquired signals of the S0 mode are adopted for the corresponding signal processing and analysis in this paper.
# Microgravity, Bone Homeostasis, and Insulin-Like Growth Factor-1
## 6 The Somatotrophic Axis
### Insulin-Like Growth Factor-1 (IGF-1)  
IGF-1 is structurally related to insulin. It consists of 70 amino acids in a single chain with three intramolecular bridges and has a molecular weight of 7649 Daltons [41, 42]. This growth factor is produced by GH-stimulated hepatocytes (the main source of circulating IGF-1) and by all non-hepatic tissues where it acts in a paracrine/autocrine fashion [7].  
In serum, ~75% of IGF-1 exists in a 150-kDa complex consisting of IGF, IGFBP-3 or -5 and the ALS [7, 43]. About 20% of IGF-1 is bound in binary complexes with IGFBPs, and ~5% is found in the free, biologically active state, which has a half-life of ~10 min. IGFBP binding prolongs the half-life of IGF-1 and assists in its delivery to specific tissues [7].  
When in close proximity to its receptor, IGF-1 is liberated from its BP by the actions of one of the serine proteases, pregnancy-associated plasma protein-A or prostate-specific antigen. In tissue, kallikreins, cathepsins, and matrix metalloproteinases have been shown to assist in tissue remodeling and repair by cleaving the IGFBPs and releasing IGF-1 [7]  
Figure 3: The somatotropic axis. The hypothalamus produces growth hormone releasing hormone stimulating the pituitary to release growth hormone which acts on hepatocytes to produce insulin-like growth factor-1 and some of its binding proteins. Circulating IGF-1 reaches the bone matrix where it and some of its binding proteins are produced primarily by osteoblasts in an autocrine/paracrine manner. Released from its binding proteins by proteases, IGF-1 binds to its receptor IGF1R initiating its radial growth effects on osteoblasts and, with the aid of growth hormone, its linear growth effects on chondrocytes.
## 2 Method
### Calculation of Point Cloud Height Difference  
In this paper, a filtering reference surface is defined, which is obtained by means of irregular triangulation interpolation [48]. The filtering reference surface plays the role of simulating the ground, and can better distinguish between ground points and non-ground points. The height difference of the point cloud is defined as the partial height difference of the point cloud and the height difference between the point cloud and filtering reference surface. Selecting two height differences as variable factors at the same time can reduce the error caused with some special terrains. Figure 2 shows the difference between the two height differences.  
Step 1: Calculation of local height difference of point cloud. First, data preprocessing on point clouds. Read the data, establish a KD tree [49] according to the position information of the point cloud, use a KNN [50] search to perform hierarchical processing on the point cloud from bottom to top, and extract the point cloud with the smallest elevation in each layer as the partial lowest point. The \"layer\" here means that after KNN searches for some point clouds, the elevation of these point clouds is restricted, so that the point clouds are divided into layers. Define the partial height difference as the difference between the point cloud height of the layer and the partial lowest point of the layer, and then calculate this difference layer by layer to obtain the partial height difference of all point clouds.  
Step 2: Calculation of the height difference of the point cloud filtering reference surface: First, set a height threshold and compare it with the local height difference obtained in step 1 to obtain preliminary ground points. The function of the height threshold here is to roughly distinguish between ground points and non-ground points. It does not need to be very accurate and can be changed according to the specific terrain. Generally speaking, 1 m is suitable for most terrains. It is assumed that if it is within the range of the height threshold, the point is regarded as a ground point, and if it is greater than the height threshold, it is regarded as a non-ground point. Subsequently, the ground points within  
Figure 1: Flow of the method.  
Figure 2: Illustration of two height differences: (**a**) Partial height difference of the point cloud; (**b**) Height difference of the point cloud filtering reference surface.  
the height threshold range are interpolated in the form of an irregular triangulation [48] to obtain the filtering reference surface. Then, extract the distance between each point cloud and the filtering reference surface, allowing the height difference between the point cloud and the filtering reference surface to be obtained.
# Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices
## 3 Results
### Predicted Pathloss Using P.E Simulation Tool  
Simulation of average path loss prediction in ED at 5.8 GHz, shows a constant increase with respect to the distance as shown in Figure 6. The path loss increases up to 170 dB for a 100 km distance. Average path loss in ED at 10 GHz is much better than 5.8 GHz as in [22], but to utilize already installed Wi-Fi equipment around the seaport, 5.8 GHz frequency is chosen. This frequency has a much better response as compared to 2.4 GHz. As 5.8 GHz based equipment is already installed around the seaport, an extension in communication range is possible using propagation in ED with very low antenna heights.