esa-sceva/satcom-chunk-collection huggingface dataset:
# Engineering Design of the European DEMO HCPB Breeding Blanket Breader Zone Mockup
## 3 Measurement of the Mockup  
To obtain the unknown quantities in Section 2, many temperature measures are needed. The locations of the thermocouples in the test section are displayed in Figure 5. The whole mockup is installed into a robust metallic tank, which is exposed to environment air through small, opened windows. By doing this, the staff would not be affected in case of a breakage of the mockup. In order to minimize the heat loss from the mockup to the environment, all the mockup parts are wrapped in 4-layer thick isolation layers (totally about 115 mm thick, not scaled in Figure 5). At the test section, two axial locations are chosen to measure the temperature in order to obtain information both at the axial-half length and at the proximity of the full length of the test section. To be redundant on the measure, pairs of thermocouples are used at the same axial and radial location, e.g., TC-1 pairing with TC-3. The number of thermocouples is also limited by the maximum number that the facility can accommodate. The thermocouples TC-1 to TC-8 are on the Tube-4 for measuring the temperature at the middle and on the end of Tube-4 to determine the heat flow from the mockup to the isolation layer. The thermocouples TC-22 to TC-25 are attached on the outer-surface of the isolation layers; together with TC-1 to TC-4, these TCs are used to obtain the heat loss from the mockup to the environment. The thermocouples TC-9 to TC-16 are used to determine the heat flux from the solid to the coolant. The thermocouples TC-17 to TC-19 are used for monitoring the temperatures of three electric heaters.  
Upstream of the test section, there are two small pipes (called Pipe-1a and Pipe-1b) for accommodating the thermocouple (TC-20) and pressure tap (DP-1). At the downstream region of the test section, there are also thermocouple (TC-21) and pressure tap (DP-2). The locations of these thermocouples and pressure taps are depicted in Figure 6. Since we are more interested in the temperature measure in this work, the TC-20 and TC-21 are placed closer than DP-1 and DP-2 to the test section. A differential pressure sensor is connected to the mock-up inlet (Pipe-1b) and outlet (Pipe-2b) via two pressure taps DP-1 and DP-2.  
For the safety of the mockup and the HELOKA facility, several safety measurements needed to be implemented to protect the electric heaters from overheating and damaging the mockup and the HELOKA facility. The thermocouples TC-17 to TC-19 are attached to the safety circuit, with a temperature limit of 500 \({}^{\circ}\)C. The thermocouple TC-6 located on the inner surface of Tube-6 and the thermocouple TC-21 for measuring the coolant temperature at the outlet are used for triggering safety signals, with 400 \({}^{\circ}\)C as the corresponding temperature limit. Once any of the temperatures on these thermocouples exceeds the above-defined temperature limits (500 \({}^{\circ}\)C for TC-17 to TC-19 and 400 \({}^{\circ}\)C for TC-6 and TC-21), the electric heaters will be switched off automatically.  
Figure 5: TCs on test section.
# Location-Aware Node Management Solution for Multi-Radio Dual Connectivity Scenarios
## 2 Scenario  
Several services provided over cellular networks are increasingly demanding more bandwidth. To address this issue, in addition to the use of new features introduced in current non-standalone networks such as MR-DC or millimeter-waves, network operators are deploying network topologies with higher spectrum reuse [23]. Hence, the node intersite distance and transmit power are decreased to set up denser scenarios, which allows to increase the reuse of the same frequencies. This topology is defined by 3 GPP as dense urban scenarios, which proposes 200 m as average inter-site distance [24].  
These scenarios can encompass many types of areas with different characteristics, such as user density, predominant services, obstacles distribution, eventuality of social events, etc. For example, residential areas will have the most indoor users, while rural areas will have a low density of users. Moreover, video services will be more frequently used in a residential zone than in a park. There will also be spots with high frequency of social or sporting events, etc. Therefore, in addition to network performance indicators, the characteristics of the environment surrounding the nodes and users must be considered to properly manage network resources.  
Furthermore, the service of the UE must be taken into account to maximize his perceived QoE, since each service can have different requirements. This perceived QoE is usually measured using the mean opinion score (_MOS_), which ranges from 1 (poor experience) to 5 (excellent experience) [25; 26]. Since the availability of QoE data reported by actual users is often scarce, the MOS is computed using utility functions mapping high-level service performance indicators, e.g., initial playback time for video streaming services [27]. Therefore, different QoS parameters, such as latency or reliability, are intrinsically involved in estimating users' level of satisfaction depending on the type of service [28; 29]. Hence, the UE location and a different QoE model for each service are used to maximize the MOS provided by the methodology introduced in the next Section 3.
## 3 Results  
### Correlation between SIFc and GPP  
SIFc (both CSIF and GOSIF) showed significant positive correlations with GPP (GPP_D, GPP_N, and GPP_M) worldwide across all the 12 biome types and available years (from 2001 to 2014) (Figure 2). Among biomes, the highest GPP-SIFc correlation was manifested in DBF, and the lowest was in EBF (Figure 2A). The correlations between GOSIF and GPP (i.e., GPP_D, GPP_N, and GPP_M) were higher than those from CSIF in general. However, the r values for GOSIF-GPP were lower than those for CSIF for OSH and SAV biomes (Figure 2A). Strong positive correlations were also observed between SIFc and GPP across all 14 years (r > 0.71, _p < 0.001_; Figure 2B). Among all the SIFc-GPP correlation coefficients, those of SIFc-GPP_M were the highest: concentrated at 0.76 \(\pm\) 0.01 and 0.77 \(\pm\) 0.00 for CSIF and GOSIF, respectively, across 14 years (Figure 2B).
## 3 Simulation Results
### Control Design for the Nominal System Model with Precisely Known Parameters  
To perform the oscillation attenuation, a differentiating control law is implemented in terms of a feedback of an approximation of the velocity \(x_{2}\) by means of \(u=-K_{\mathrm{D}}\xi_{2}\) with a suitably chosen, stabilizing gain value \(K_{\mathrm{D}}\in\mathbb{R}\).  
Setting  
\[\gamma=2.5\quad\text{and}\quad\delta=50 \tag{56}\]  
for the range of admissible eigenvalues in Theorem 2, the gain  
\[K_{\mathrm{D}}\approx-0.386 \tag{57}\]  
with  
\[\mathbf{k}_{\mathrm{f}}^{T}\approx\begin{bmatrix}578.4&356.2&53.7\end{bmatrix} \tag{58}\]  
is obtained with \(\xi=3\) if the algorithm summarized in Figure 4 is applied. Corresponding simulation results for the controlled position \(x_{1}\) and the system input \(u\) are shown in Figure 4(a),b. These graphs further contain a comparison with the simulation results for the control and filter optimization when the polytopic system model described in the following subsection is considered.
###### Abstract
Hydrogen can also turn into a liquid at low temperatures, but it does not turn into a liquid at temperatures as low as helium. Hydrogen's response to temperature changes is similar to that of helium, and its relationship with pressure also follows the ideal gas law. This difference is one of the important factors in choosing helium and hydrogen for experiments and applications. Helium is commonly used in low-temperature experiments, but hydrogen is also used in some cases in low-temperature experiments and is used in a variety of other industrial and scientific fields [18]. The choice must be made by taking the properties of both gases into account with respect to pressure and temperature changes. Therefore, this study aims to analyze the temperature and pressure characteristics of helium gas as an alternative to hydrogen gas in a vessel during gas filling and discharge. To minimize the risk of explosion, the important thing is to demonstrate the thermal circumstances of helium gas instead of hydrogen gas in this experiment. Another important objective of this study is to investigate the temperature and pressure increase resulting from the variable valve of a mass flow controller during the charging and discharging of helium gas, which is being used as an alternative to hydrogen gas in a vessel. The hypothesis of this study aims to experimentally verify whether the loss of pressure and temperature energy can be promptly observed through monitoring the helium gas mass flow rate upon filling the vessel. In simpler terms, the authors sought to confirm that the loss of pressure and temperature energy exhibits more significant changes under fast-flow supply conditions than under slow-flow conditions. This verification offers engineers a more dependable measurement solution for addressing this loss. The structure of this research work is explained in the subsequent sections, which include the experimental method, results and discussion, and conclusion. It can be observed that Section 2 presents the research contents, which comprise the experimental method, system design, control, chemical properties of helium, and experimental conditions. Section 3 presents the experimental results and discussions of temperature and pressure increases in relation to the mass flow controller valve rates during the charging and discharging of helium gas. The summary and conclusion are presented in Section 4, followed by the references.
# A Review of Tunable Orbital Angular Momentum Modes in Fiber: Principle and Generation
## 4 Methods for Generation of Tunable OAM
### The Fiber-Based Generation of Tunable OAM
For the third type, a method is reported to generate the beam with tunable OAM in the fiber by continuously changing the angle of linear polarization state of the input light [67], as described in Figure 12. The setup is composed of three parts, including a mode converter, a few-mode fiber that is mounted as coils in a paddle of a fiber polarization controller (PMC) and a polarizer. Considering about four linear polarization (LP) mode bases in the fiber, we deduce the transmission matrix of the first-order modes in PMC. Then, one polarization is filtered out through a polarizer. It is well known that the FMF is wound around the circumference of the PMC's paddle, and stress will induce the refractive RI difference between four orthogonal LP mode bases. If the relative phases between LP\({}_{\rm 1 lax}\) and LP\({}_{\rm 11 bx}\), LP\({}_{\rm 11 ay}\) and LP\({}_{\rm 11 by}\) are \(\pi\)/2 and \(-\pi\)/2, the average OAM value of mode will smoothly vary from \(-1\) to \(1\) with the input light polarization angle changing from \(0\) to \(\pi\). The polarization angle (\(\alpha\)) can be adjusted by electrical polarization controller. \(\delta\) in the Figure 10 represents the relative phase between LP\({}_{\rm 11 bx}\) and LP\({}_{\rm 11 by}\), which decides the orientation of longitude on the orbital PS. At the end of the paper, we draw conclusions about the fiber-based generation methods, as shown in Table 2. The table lists the combination modes, adjusting variations, adjusting methods and the types of tunable OAM mode. Figure 11: The flow chart of tunable OAM generation by adjusting the polarization direction. Figure 10: The flow chart of tunable OAM generation by adjusting the relative phase between two fiber modes.
# Numerical Modeling of Temperature Effect on Tensile Strength of Granitic Rock
## 1 Introduction
Unlike in previous studies, the granite rock material properties are taken as homogeneous at room temperature. However, the tensile strength is different for each rock-forming mineral, i.e., quartz, feldspar and biotite. Moreover, only the thermal expansion coefficient of the quartz mineral is assumed to depend on temperature. This simplifying choice reflects the above-mentioned fact that the thermal heterogeneity of granite increases with increasing temperature. The numerical simulations of a uniaxial tension test on the numerical granite demonstrate the validity of this simplified approach of reducing all heterogeneity of granite into the deviant behavior of quartz.
## 3 Contextual Received Signal Strength Approach
### Flowchart and Algorithm  
To summarize the previous discussion, the flowchart in Figure 2, and the neighbor discovery phase, as well as Algorithm 1, to jointly estimate the PLE value, the inter-node distances are presented below.  
Figure 2: CRSSA flowchart.  
```
Input :\(N\): Unknown nodes, \(\lambda\): Network density, \(A\): Deployment area Output :\(\hat{R}\): Estimated mean range, \(\hat{\gamma}\): Estimated mean path loss exponent, \(\hat{d}_{ik}\): Estimated inter-node distance, node position (\(\hat{x}_{i},\hat{y}_{i}\)) Data:\(P_{r}(d)\): Received power at distance \(d\)
1 fori=1 to Ndo
2 forj=1 to Ndo
3 Evaluate \(C_{ij}\leftarrow\) using (4)
4 end for
5
6 end for
7 fori=1 to Ndo
8 Calculate the number of neighbors of node \(i\), \(n_{i}\leftarrow\) using (15)
9 end for
10 Derive the average number of adjacent neighbors \(\bar{n}\leftarrow\) using (16)
11 Estimate the mean range \(\hat{R}\) using (17) fori = 1 to Ndo
12 Estimate the inter-node distance between neighbor nodes \(i\) and \(k\), \(\hat{d}_{ik}\leftarrow\) using (19) ; /* this distance is estimated iff \(C_{ij}=1\). */
13 end for
14 Estimate the positions of nodes (\(\hat{x}_{i},\hat{y}_{i}\)) using gradient-based optimization method with weighted residuals.
```  
**Algorithm 1**Contextual Received Signal Strength Approach  
Neighbor Discovery Phase  
To build the neighbor connectivity context matrix, each node \(i(i=1,\cdots,N)\) has to discover its neighbors by sending through the network a HELLO message containing its identity, a single-bit variable _receivedhello_ equal to 1 if a full HELLO has been received from the neighbor, and a neighbor Set (_NeighS_), which is updated when a HELLO packet is received from the neighbor. The neighbor connectivity context matrix \(C_{ij}\) assigns a value of 0 or 1 for each pair of nodes depending on the neighborhood information. We assume a non-symmetric connectivity context matrix. The value of the matrix is set as follows for each pair of sensors \(i\) and \(j\), where \((i=1,\cdots,N)\), \(j=(1,\cdots,N)\) and \(j\
eq i\).  
1. If _receivedhello_ is 1 for both sensors \(i\) and \(j\), \(C_{ij}=C_{ji}=1\).
2. If _receivedhello_ is 1 for sensor \(i\) and 0 for sensor \(j\), \(C_{ij}=1\) and \(C_{ji}=0\).
3. If _receivedhello_ is 0 for both sensors \(i\) and \(j\), \(C_{ij}=C_{ji}=0\).  
Once the packet is successfully received by a node, the _NeighS_ is updated by adding the identity of the node transmitting the HELLO packet. At this step, each node is aware of its neighbors and their numbers. The information concerning each node connectivity context matrix is sent to the sink node in charge of computational operations. After a successful exchange of HELLO packets, nodes can automatically establish adjacency and can start to send and route data between them. After the neighbor discovery phase, the information on global connectivity is used by the sink node to estimate the channel parameters and the inter-node distances.
## 4 Analysis and Performance Comparison
### CoAP Packet Size Effects on Lower Levels
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline  & \multicolumn{2}{|c|}{**Total number of 802.15.4-frames**} & \multicolumn{2}{|c|}{**Total number of bytes**} \\ \cline{2-5}  & _Min (n = 1)_ & _Max (n = 2)_ & _Min (n = 1)_ & _Max (n = 2)_ \\ \hline
**GET** & 116 & 179 & 13,946 B & 21,740 B \\
**POST** & 51 & 71 & 4,887 B & 6,935 B \\
**PUT** & 51 & 71 & 4,890 B & 6,938 B \\
**DELETE** & 21 & 34 & 1,859 B & 3,041 B \\ \hline \end{tabular}
\end{table} 
Table 4: Analysis of CoAP on a transaction basis; _Blockwise transfer_ mode activated. \begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline  & \multicolumn{2}{|c|}{**Total number of 802.15.4-frames**} & \multicolumn{2}{|c|}{**Total number of bytes**} \\ \cline{2-5}  & _Min (n = 1)_ & _Max (n = 2)_ & _Min (n = 1)_ & _Max (n = 2)_ \\ \hline
**GET** & 24 & 31 & 2,984 B & 3,850 B \\
**POST** & 30 & 44 & 3,775 B & 5,507 B \\
**PUT** & 30 & 44 & 3,778 B & 5,510 B \\
**DELETE** & 12 & 18 & 1,379 B & 2,199 B \\ \hline \end{tabular}
\end{table} 
Table 3: Analysis of CoAP on a transaction basis. Figure 7: Added overhead _(BW_ stands _for Block-Wise)_. In conclusion, the use of the _Blockwise transfer_ mode solves the _packet rejection_ problem and avoids creating a conversation state on the server by distributing the load on a larger transmission and by serving more requests simultaneously. On the other hand, _it does not solve the fragmentation issue but just migrates it to upper layers_. Moreover, the overall load for the same transaction (GET, POST, PUT, or DELETE) increases, as a comparison between values in Tables 3 and 4 shows. Finally, the total added overhead (difference between bytes sent by 6 LoWPAN and CoAP packet sizes) is definitely higher in the _Blockwise_ (BW) case, as shown in Figure 7.
## 5 Link layer Optimization  
In order to increase the energy efficiency of the communication protocol several improvements to the BMAC [21] have been introduced. A receiver oriented slotted MAC have been implemented. Under this model time is divided into frames of a fixed duration. Each node chooses a slot within a frame and listens only on its designated slot for a short duration (in our implementation 6 ms-15 ms depending on the payload length). Other nodes willing to send to this node have to synchronize and send during its corresponding slot.  
Likewise the Low Power Listening (LPL) when a node aims to send for the first time to any other node, it sends for the duration of the overall frame in order to synchronize with a receiver. Any of the possible receivers has a slot scheduled within this period, therefore, upon reception of the packet, the receptor will reply an acknowledgement message (ACK). The transmitter receives the ACK and thus knows the moment when that particular node has its slot scheduled. Further transmission will wait until the receiver's slot to transmit and update the receptor schedule to compensate for the clock drift.  
During a transmission, since transmitter could have been desynchronized, the transmitter sends continuously for the duration of a window. If a send fails, the transmitter keeps a history of NACKs for each neighbour. The duration of the send window is proportional to the number of NACK for that particular neighbour. Broadcast packets contrarily are sent for the duration of the whole frame.  
The wake schedule is implemented by a periodic timer. A node powers up the radio at each period and listens for a period of time determined by their listen period that should not be shorter than the double of the round trip time.  
\[T=2\times(T_{time}+T_{ack})\]  
Neither a scheduled listening slot nor a sending request will ever interrupt other ongoing operations. If a send is taking place and a scheduled listening slot fires, the listening slot is lost. If this happens too many times (50 in our implementation), a new slot that does not collision with neighbours' slots is chosen.  
Any node gathers information about all neighbours that the node has sent a message at some time. The neighbour table's size is our implementation is 20. It is used to save information about the last successful transmission, the number of NACKs so far since the last successful transmission and the number of packets sent to that neighbour. If a new neighbour is discovered and the table is full, a bad neighbour (any of those with NACK\(\geq\)5) is substituted by the new node.
## 8 Discussion and Future Scope  
This overview of this paper covers several important topics of wearable antennas, all essential for comprehending their problems, applications, and design. Flexible and conformal, wearable antennas can be integrated into various technological products, including  
Figure 16: Applications for wearable antenna.  
fitness trackers, smart watches, and medical equipment. The flexibility and conformability of an antenna should be adaptable enough to stretch, twist, and bend in response to the wearer's movements. Wearable antennas are often made from flexible substrates such as fabrics, polymers, or other non-conductive materials, as described in this work. Copper, metallic inks, and conductive textiles are among the complex materials used to make antennas [56; 72]. When assessing antenna performance, variables including radiation efficiency, gain, and bandwidth are essential. Antennas should adhere to safety regulations concerning the body's absorption of electromagnetic radiation. Wearable antennas must resist deterioration from washing, perspiration, and other environmental conditions. The development of compact antenna designs prioritizes minimizing size while maintaining optimal functionality, ensuring their seamless integration into wearable applications. These antennas are engineered to be lightweight and unobtrusive, enabling incorporation into clothing or other wearable devices without causing discomfort or impeding the user's activities. It should be ensured that antennas may be worn on clothes or other wearables without being uncomfortable or in the way. Metamaterials can be used to attain compactness and enhance antenna performance. Processes such as screen printing and inkjet printing are utilized to create antennas on flexible substrates. Antennas' variable frequency or other characteristics accommodate various uses.  
Future trends in wearable antennas will concentrate on flexible, wearable, robust, and biocompatible materials. Scalable and precise techniques of fabrication, such as 3D printing and screen printing, will also be explored. The size, the specific absorption rate, and the durability against bending and environmental effects will be minimized. There will be an integration of 5G/6G technology with the IoT and AI-driven systems to further improve performance for applications in healthcare, smart textiles, and augmented reality.  
Advanced antennas featuring energy-harvesting functionalities will drive innovation forward, while sustainability objectives will emphasize biodegradable materials and a minimum environmental impact. Interdisciplinary research and international standardization will ensure the safe, efficient operation of wearable antenna technologies.