esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 1 Introduction
Obviously, nodes closer to the gateway will forward a larger number of packets, using up energy faster than nodes located farther, which cannot be avoided  
Our proposal uses a cross-layer approach by merging some steps of the route creation process with the medium access control layer, in order to minimize the odds of packet collision. The algorithm self-adapts to different network sizes and number of nodes, and, for example, would allow one ship to drop the nodes into the sea and they would find their neighbors and automatically create the paths. It minimizes the cost, in time and computational effort, of forwarding a packet and requires minimal periodic maintenance of the routes. The rest of the paper is organized as follows. Section 2 discusses existing routing protocols. The overview of our proposed protocol is described in Section 3. Details of the protocol are given  
Figure 1: Example of node positions in a 3D spherical Wireless Sensor Network. in Section 4. In Section 5, the packet formats are described. Section 6 mathematically analyzes the performance of our proposal. Section 7 contains the simulation results for the validation of the protocol's algorithm.
## 2 Material and Methods  
### Experimental Design  
Three-year-old _Quercus__i_ex_ saplings of approximately 60 cm height were obtained in May 2015 (Forestal Catalana, Barcelona, Spain) and were re-potted in 3.5 litre pots, with a substrate consisting of 45% sand, 45% autoclaved peat, and 10% natural soil inoculum. The soil was collected from a natural holm oak forest on a south-facing slope (25% slope) in the Prades Mountains in Northeastern Spain (41\({}^{\circ}\)13\({}^{\prime}\) N, 0\({}^{\circ}\)55\({}^{\prime}\) E). There were 162 saplings in total, divided into six blocks, and plants were grown in the greenhouse of the Autonomous University of Barcelona (Barcelona, Spain) with a six-week period of adequate watering, to allow them to acclimate to greenhouse conditions. During the experiment, in the greenhouse the mean air temperature was 26.7 \({}^{\circ}\)C (measured with EL-USB-2 data logger, Lascar Electronics, Wiltshire, UK), and the soil temperature monitored at a fine scale in five pots across the different soil types was 27.0 \({}^{\circ}\)C on average (measured with Decagon Em50 data logger with 5 TM soil probes, Decagon Devices, Pullman, WA, USA). The saplings were then exposed to water stress by withholding water for nine different drought treatments with 18 plants each. The length of time without water ranged from 0 to 21 days, which means the first drought level, with 0-day withholding water, was effectively the control treatment. At the end of each treatment, the foliar photochemical efficiency of photosystem II (\(\rm\Phi\)PSII) and the reflectance were synchronously measured on half of the pots (nine per drought level) inside the greenhouse under clear skies within one hour of solar noon (12:00 to 14:00). The remaining pots then had six weeks of recovery, which involved re-watering at optimal levels, and then identical leaf measurements were collected for these plants.  
The water content of the substrate was determined in each pot at the start of the study and at the end of its drought period, and recovery period, if relevant (using ML3 Theta Probe connected to a HH2 Moisture Meter from Delta-T Devices, Cambridge, UK). Mean soil moisture at the start of the experiment was 22.6% and it decreased exponentially to 0.3% at the end of the most extreme drought treatment. Soil moisture recovered quickly to ca. 20% within one week of re-watering and was 24.7% on average during the recovery phase. The differential \(\rm SWC\) (\(\rm\Delta\)SWCrecovery) was calculated by subtracting the nine different drought treatments values from corresponding recovery treatments.
## 1 Introduction
Six different fuel compositions are selected: First, the authors focus on natural gases eventually enriched with hydrogen that are in order: Pure methane, methane with 10% of hydrogen, methane with 20% of hydrogen, successively three different biogas are considered: A biogas made of methane and carbon dioxide, two innovative biogas containing hydrogen, and thus made of methane, hydrogen, and carbon dioxide with a ratio H\({}_{2}\)/CH\({}_{4}\) equal to 1/9 and 2/8, respectively. The detailed chemical kinetics is taken into account with ANSYS Chemkin 17.0 that allows modelling a single cylinder CAI engine to predict engine performance in terms of Indicated Mean Effective Pressure (imep), engine efficiency, and pollutant emissions. Initial temperature and equivalence ratio are optimized for each fuel, and by comparing the results it is possible to understand the effect of hydrogen addition on the combustion process in CAI engines. A parametric study, performed varying the temperature at the start of compression and the equivalence ratio, allowed to evaluate the temperature requirements for all fuels and the effect of hydrogen addition on the auto-ignition process. The results show that, at constant initial temperature, hydrogen promotes the ignition, which then occurs earlier, as a consequence of higher chemical reactivity. At a fixed indicated mean effective pressure, hydrogen presence shifts the operating range towards lower initial gas temperature and lower equivalence ratio and reduces the NOx emissions. Such reduction, somewhat counter-intuitive if compared with similar studies on spark-ignition engines, is the result of operating the engine at lower initial gas temperatures thanks to the presence of hydrogen.
## 4 Results and Discussions
### Evaluation of the Monitoring Messaging Methods
#### 4.1.3 End-to-End Testing of the Messaging Methods  
A custom GNU radio block was created to perform end-to-end testing of monitoring messaging methods. This block generates messages of varying sizes and rates using the messaging methods described in Section 3.3. To achieve this, a small GNU radio process flow was built, which contains S message generators and a single probe (see Figure 17). The GNU radio application was executed in the networking environment presented in Section 3.1 in different scenarios, and the CPU usage was measured. The obtained results are shown in Figure 18 and indicate that PROTO-based messaging outperforms pure PMT, especially on the broker's side. This is because the broker collects data from all probes.  
Figure 16: The time necessary to generate and process the monitoring messages in specific conditions using the implemented messaging methods.  
Figure 15: Monitoring message size obtained in specific conditions using the implemented messaging methods.  
The gain in CPU usage at the GNU radio application is not as significant as the computing time gains presented before because the network operations are only slightly improved by the smaller size of the PROTO message. The results in Figure 18 present an average value based on more than 100 messaging-method-evaluation application runs.  
To verify the CPU usage results presented earlier, we conducted a Wilcoxon signed-rank test [58], which considered the slight difference between the average values shown in Figure 18, illustrating the flow CPU usage. The test was performed under the alternative hypothesis that the CPU usage values obtained for PROTO-based messaging methods are lower than those obtained for PMT messaging. The p-values obtained for all the scenarios were very close to 0, indicating a high degree of confidence that the PROTO-based messaging methods always perform better than the method that uses only GNU radio PMT messages.  
Figure 17: GNU radio process flow for evaluation of the messaging methods.  
Figure 18: The CPU usage of the GNU radio process flow and of the broker in specific conditions using the implemented messaging methods.
## 4 Visual-Inertial Odometry
### Visual Odometry  
The task of the VIO front-end is to execute visual odometry. Firstly, roughly estimating the camera pose by minimizing the photometric error. Assuming that there is a point \(p\) in the reference frame \(i\), \(p\in\Omega_{i}\), which is also observable under another frame \(j\), the photometric error of the two adjacent frames images is  
\[E_{pj}=\sum_{p\in N_{p}}w_{p}\bigg{\|}(I_{j}\big{[}p^{\prime}\big{]}-b_{j})- \frac{t_{j}e^{a_{j}}}{t_{i}e^{a_{i}}}(I_{i}[p]-b_{i})\bigg{\|}_{\gamma} \tag{17}\]  
where \(N_{p}\) is the pixel around point \(p\). \(I_{i}\), \(I_{j}\) is the image of frame \(i\) and frame \(j\). \(t_{i}\), \(t_{j}\) is the exposure time of the image. \(a_{i}\), \(a_{j}\), \(b_{i}\), \(b_{j}\) is the coefficient for correcting affine illumination variation. \(\gamma\) is the Huber parametric number. \(\omega_{p}\) is the weight related to the gradient. \(p^{\prime}\) is the projection on \(I_{i}\). Then the total photometric error is  
\[E_{picture}=\sum_{i\in\mathcal{F}}\sum_{p\in\mathcal{P}_{i}}\sum_{j\in obs(p)}E _{pj} \tag{18}\]  
where \(\mathcal{F}\) is the set of keyframes to be optimized. \(\mathcal{P}_{i}\) is the sparse set of points in the keyframes. \(obs(p)\) is the set of keyframes where the point \(p\) is observed.
## 6 Conclusions  
In this work, we presented a new channel estimation technique based on LSTM, aiming to improve the outage probability, BER, and user sum rate of the conventional NOMA system. Furthermore, we proposed a new power coefficient allocation algorithm based on binomial distribution and Pascal's triangle. This new power coefficient allocation algorithm is used to divide power among \(N\) users according to each user channel condition. Initially, LSTM channel estimation was performed and the system was evaluated by means of the outage probability, sum rate, and BER. The new NOMA system with the LSTM CE added had a 10% average reduction in the outage probability, a 37% average increase in the sum rate, and a 50% maximum reduction in the BER in the range of \(-2\) to 12 dB in comparison with the conventional NOMA system. To further improve the BER, adaptive BCH codes, constellation rotation, and cyclic Q-delay were added to the new NOMA system. The BCH code rates used were dynamically adapted according to each user channel condition and SNR value. The resulting data were modulated by either QPSK or QAM, and the resulting constellation was rotated. The cyclic Q-delay was applied to the rotated constellation. This added modifications to the new NOMA system, resulting in an additional 23% decrease in the BER in the range of \(-2\) to 12 dB. It is evident from the results obtained that LSTM improves the performance of the NOMA system in terms of outage probability, capacity, BER, and user power allocation accuracy. Furthermore, the BER performance of the system was further improved by adding the proposed adaptive modulation and coding schemes.  
Methodology, M.A.; Resources, S.M.G., M.S.E.-M. and A.S.; Software, M.A., M.W.F. and A.S.; Supervision, S.M.G., M.S.E.-M. and M.W.F.; Writing--original draft, M.A.; Writing--review & editing, S.M.G. and M.S.E.-M.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Yield and Chemical Composition of _C. japonica_ EO  
Plant-derived EOs are generally complex mixtures of low-molecular-weight metabolites, particularly monoterpenes (C10) and sesquiterpene (C15) hydrocarbons, and their oxygenated derivatives, usually extracted from non-woody parts of the plant, such as foliage. However, to a lesser extent, it could also be extracted from other plant tissues (e.g., wood, seeds and bark). Hydrodistillation and steam distillation are the traditional extraction methods used to obtain EO from plants. Another less-used technique is the classic extraction with organic solvents [23], which is a non-eco-friendly process. In order to improve conventional extraction methods, other innovative and green techniques have been developed, such as supercritical fluid extraction [24; 25; 26]. Leaves of _C. japonica_ are by far the plant organ most used to obtain EO. _C. japonica_ leaf EO, typically obtained by hydrodistillation and chemotyped by gas chromatography and mass spectrometry analysis, is pale yellow in color and has a fresh aromatic odor [27; 28; 29]. Tepenes and terpenids are the major components found in _C. japonica_ leaf EO, as illustrated in Figure 1.  
Table 1 shows the yield and major components of _C. japonica_ leaf EO according to geographic region. The results revealed that _C. japonica_ leaf EO is often characterized by two or more major compounds, which may be present in similar amounts, and that  
Figure 1: Some of the major components of _Cryptomeria japonica_ leaf essential oil. in most countries, namely in East Asia, ent-kaurene is the major _C. japonica_ leaf EO constituent, followed by elemol. However, based on other geographical regions and using PCA (Figure 2), we can categorized the _C. japonica_ leaf EO into three distinct chemotypes, regarding major constituents: (1) ent-kaurene chemotype (very frequent in Taiwan [10] and Korea [30]) (2) elemol plus ent-kaurene chemotype (more frequent in Japan [31], China [27] and Nepal [11]) and (3) \(\alpha\)-pinene chemotype (found in Azores islands [32], Corsica island [33] and Japan [18]). The corresponding PCs data are found in Table 2.
# 2D Triangulation of Signals Source by Pole-Polar Geometric Models
## 1 Introduction
### Considerations about Signal Data
Intrinsic errors are related to: (1) clock measurements accuracy; (2) printed circuit board trace (circuit impedance), that means that the electronic signal travels some distance on a printed circuit board trace and this internal path of the signal causes delay on signal propagation; (3) difference in the phases between emitter and receiver; and (4) relative velocity on signal treatment between emitter and receiver, which promotes frequency errors and, hence clock errors. Extrinsic errors comprise: (1) signal reflection; (2) diffraction; (3) scattering; (4) absorption/attenuation; (5) environmentally-dependent the speed signal propagation; and (6) physical model and computational errors (truncation and rounding). Reference [6] outlined sources of error and considerations about some methods of localization using radio frequency. TOF-based methods suggest inaccuracy does not increase when distance increase. In general, for methods based on RSSI and angle AOA the error increases when distance increase. To apply the TDOA approach, the clocks of the receivers system or of the emitters system need to be synchronized (e.g., for GPS, only the clocks of the satellites constellation need synchronized--this means that a mobile device GPS unit is not an expensive device). However, each satellite in this constellation needs to send in its signal the coordinates of its spatial position and the precise time registered by its clock. According [7], to estimate a position of a point using a satellite navigation system, it is thus necessary to have two things ready: the position of the satellites and the distance of the point from these satellites. Range observation equations are generated from this information and the position is estimated in 3D space by solving these equations. Therefore, two aspects are important at this point: to obtain this required range data and to solve the equations
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 3 Materials and Methods
### Methodology
#### 3.2.2 Uploading and Sharing Data via IPFS  
Once the threat intelligence is securely packaged into an encrypted JSON file, the first participant (User A) uploads the file to the **IPFS**. After the upload, **User A** receives a **Content Identifier (CID)**, a unique hash that acts as a pointer to the encrypted data on the IPFS.  
**User A** then sends the CID to a trusted partner (User B) via a transaction submitted to **Hyperledger Fabric**. This step utilizes Hyperledger's chaincode and endorsement system to ensure that the transaction is validated and immutable. The CID is sent along with the sender's verified identity.
## 3 The Dynamic-Static Coupling Compaction Method and Control Parameters
### The Impact of Dynamic Compaction Layer Thickness  
As previously mentioned, in roadbed construction, layer thickness is a crucial factor in roadbed construction, impacting both construction progress and compaction quality. Excessive thickness can hinder the effective transmission of compaction effects, while insufficient thickness can lead to effective compaction but may extend the construction period and escalate costs. Therefore, in the embankment construction experiment, maintaining the use of the static compaction method, layer thicknesses of 30 cm, 40 cm, and 50 cm were employed. The variations in deflection, compaction density, and porosity were then examined, as detailed in Table 2.  
Referring to Figure 5, it becomes apparent that an increase in layer thickness leads to higher deflection and porosity values. Specifically, deflection increases from 1.18 mm to 1.32 mm, and porosity rises from 2.71 to 6.08. However, compaction density decreases from 97.71% to 91.09%. This suggests that when the layer thickness is relatively small, applying the same external load to a thinner roadbed results in smaller inter-particle gaps, thereby achieving more efficient compaction.  
Taking into account these three indicators and assuming the availability of high-energy impact compaction, it is recommended to choose a layer thickness of 30 cm to achieve optimal compaction. Specifically for soil-rock mixed-fill embankments, especially those incorporating red bed soft rock, employing a construction method involving high-energy impact compaction in passes of approximately 4 m in height is advisable. This approach allows for adjusting the layer thickness to 50 cm, effectively controlling post-construction settlement, improving construction efficiency and benefits, and ensuring compaction quality.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Deflection/0.01 mm** &
\begin{tabular}{c} **Compaction** \\ **Density/\%** \\ \end{tabular} & **Porosity/\%** \\ \hline
30 & 118.34 & 97.71 & 2.71 \\
40 & 124.34 & 93.86 & 4.06 \\
50 & 131.66 & 91.09 & 6.08 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of data for different dynamic compaction thicknesses.
## 5 Results: The Geometry Assurance Process Today
### The Planning Phase  
In the planning, or verification, phase, the interviews focused on system verification activities from a geometry assurance perspective and on inspection preparation.  
The system verification (4), Figure 6) is often done through physical tests and try-outs. This is to complement and extend learnings from simulations. Of course, it would be desirable to reduce, or even exclude, the physical tests and rely only on simulations. This is a matter of \"virtual trust\" (i.e., trust in simulated results), a topic discussed in the following sections. Changes are done to increase producibility and quality. The changes can include minor changes of the geometries and changes of tolerances, fixtures/locating schemes, joining sequences, etc. After those changes, it is of course very important to update the CAD models and other information in the digital model and the digital thread.  
One of the companies reported ongoing work to replace physical trimming, where a part is positioned in a checking fixture to control the shape of the flanges, with a virtual method based on the variation simulation model [33]. This is something that most companies want to do, but most of them are not there yet. Physical tests are expensive and should be kept to a minimum. At one of the bigger companies, it was reported that over 180 persons work with handling the most acute geometry-related problems exposed during system verification.  
The inspection preparation (5), Figure 6) is an activity where the practices at the companies differ a lot. Some companies set inspection points according to the final requirements defined earlier and complement this with process points to monitor the process. At other companies, the chain from the final requirements to inspection points is not that clear. The inspection point preparation is done by the design engineer. Other aspects mentioned were that it is bad practice to do an inspection point preparation leading to points that cannot be evaluated/measured in a good way, that it is difficult to measure very flexible parts (due to effects from gravity), and that the number of inspection points often is too large and lead to difficulties in the evaluation and storage of data. Inspections are also associated with a cost, and it is desirable to keep the number of inspection points to a minimum. At the more advanced companies, 3D scanning is mixed with a CMM (Coordinate Measuring Machine) inspection. At other companies, gauges and other manual inspection tools are used.  
For the digital thread, it is important to update the 3D models with inspection point coordinates. This is not always done. Quite often, an inspection point report is generated in PDF or similar, but the information is not fed into the CAD system.
## 1 Introduction
The distribution of workstations and requirements within the factory are considered in order to account for the distribution of the wireless transceivers, as well as the number and type of sensors/actuators to be included in the prototype nodes to be developed. Radio planning analysis is performed based on in-house deterministic wireless channel estimation and validated through site measurements in order to assess the selection of the wireless communication protocol. ZigBee technology has been chosen for the wireless connectivity of the sensor nodes due to its intrinsic characteristics, which adapt perfectly to the application within this environment: the very high number of nodes that can be connected to the network (up to 64,000), making it easily scalable; the mesh topology and the dynamic autoconfiguration of the network, which provide robust routing for the sent packets and protection against interference and node failures; and the higher duty cycle compared with other low-power wireless technologies (such as LoRaWAN), which provides the possibility of sending a higher number of packets if required. It is worth noting that the presented methodology could be applied to any wireless communication technology, such as LoRa,NB-IoT, Bluetooth, or ESP-Mesh [18; 19; 20; 21], for monitoring tasks, as long as the requirements of the application are satisfied, and considering that the radio planning study will need to be adapted to each technology's characteristics. The paper is organized as follows: Section 2 describes the workshop of Galeo Enrollables Company, the scenario where the proposed system was implemented, as well as its radio characterization prior to wireless communication system deployment. Section 3 presents the proposed wireless sensor network deployment. Results obtained under real conditions are presented and discussed in Section 4. Finally, conclusions are presented in Section 5.