esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP
## 7 Conclusions  
In this work, an improved approximation for the bundle forwarding time with the LTP as the convergence layer (i.e., the link service time) was proposed for optimization of the bundle route selection in a space DTN. While it is reasonable to expect that the use of underestimated values for the service time will impact the routing performance, the quantification of such performance degradation is less evident. This study addressed this gap in the literature by conducting a stochastic modeling of a DTN with known statistical properties for the contact and duration times. The results have shown that solely using the propagation delay or the aggregation of the nominal service time (i.e., the expected one-hop delivery time over a lossless channel) and the link propagation delay, as performed in CGR, underestimates the bundle service time, which in turn leads to significant performance drops compared with the optimal levels. Moreover, the results have also shown that a geometric approximation of the extended bundle service time achieved by the reliable LTP is generally not sufficient to achieve the optimal routing performance. The tests with standard space DTN protocols demonstrated that the proposed service time expression for the bundle forwarding time can help CGR discover better paths for bundles. The channel BER is currently not used for routing nor included in the contact plan used by CGR. This study suggests that its inclusion and use of the proposed service time expression can lead to significant performance gains in a space DTN.  
Figure 11: Bundle loss rate and mean response time results for test case 2 with the traffic flowing from g1 to g0 (reverse direction). Charts (**a**,**c**) depict the results with a light sending rate \(\lambda=1\). Results for a high sending rate \(\lambda=20\) are shown in charts (**b**,**d**).  
**Funding:** This work was supported by grant no. 80 NSSC22K0259 from the National Aeronautics and Space Administration (NASA).  
**Conflicts of Interest:** The author declares no conflict of interest.
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 7 Future Directions
### Advanced Technology Integration
#### 7.1.1 IoT and AI Convergence
These frameworks typically employ Bi-GRU models and self-attention mechanisms for improved accuracy in data processing and decision-making, achieving up to 99.73% accuracy in intrusion detection scenarios [28]. On the IoT communication side, standard and new protocols are being developed to ensure seamless data exchange between physical assets and their digital counterparts. The integration of the IoT with cloud and edge computing infrastructures has become particularly significant, with edge computing gaining traction for its ability to process data closer to the source, thereby reducing the latency in DT implementations [35].
## Materials and Methods  
AFPMSMs can be designed using analytical or numerical methods (FEM) [19].  
FEM is used for solving complex problems, where the method usually uses magnetic potentials and divides two-dimensional (2D) or three-dimensional (3D) models of the machine to a limited number of elements.  
The initial model of the machine can be determined, with a known magnetic circle and current in the windings (dimensions of PMs, rotor disks, air gap and stator), using a set of general equations, also known as \"Sizing equations\" [9; 24; 25; 26; 27].  
This model is then used as a starting point for more accurate calculations of individual parameters using FEM [28; 29].  
Using FEM for designing AFPMSMs is oftentimes consuming both in the construction of the model itself and in the computation time [3; 30; 31; 32; 33]. So far, published scientific papers deal with the solution of this problem through the development of analytical methods [18; 27; 29; 30; 31; 32; 33; 34; 35; 36], whose derivations are often mathematically demanding and require certain simplifications and assumptions [37; 38; 39], which on one side influences the accuracy of the analytical model and on the other side enables a faster calculation by means of derived equations for the magnetic flux density in an explicit form [40].  
The presented methodology uses simplified FEM calculations for the initial dataset. A simplified 3D model was constructed in Ansys Maxwell software, where instead of the stator only air was included. This simplification was chosen due to the similarity of the permeabilities of air and copper.  
Simplified calculations with FEM were performed at various stator thicknesses with two air gaps, with particular interest in the values of the axial component of the magnetic flux density in the middle of the stator, since at this point the magnetic flux density is the lowest, which gives us reliable starting points for further and more-accurate calculations.  
In the first step of the design, the types of PMs and their shapes and dimensions are usually chosen, as they have the greatest influence on the magnetic flux density in the air gap. The density of the magnetic flow in the air gap is mainly influenced by the thickness of the stator, the width of the two air slots, and thus the distance between the TM between the two disks and the thickness of the rotor discs.
## 2 Biosynthesis of Monoterepenes in Macroalgae
### Biosynthesis of Cyclic Monoterpenes in Macraalgae  
Although GPP is considered as the universal precursor for monoterpene biosynthesis, it has limited flexibility for the formation of mono- and bicyclic carbon skeletons. Namely, GPP _trans_-2,3-double bond prevents direct cyclization. Consequently, the enzyme-catalyzed cyclization of GPP and its conversion into tertiary allylic isomer, linalyl pyrophosphate (LPP), occurs in formation of cyclic monoterenes. The most important step of this mechanism in the algae is the initiation of divalent cation-assisted ionization of the pyrophosphate group which will allow rotation around new single bond and new  
Figure 1: Mevalonate (MVA) and methylerythtritol phosphate (MEP) pathway.  
highly reactive carbocation intermediate is formed, cyclic \(\alpha\)-terpinyl cation (Figure 2). \(\alpha\)-Terpinyl cation intermediate will undergo several mechanisms such as oxidation, reduction, isomerization, or conjugation, and it will generate a variety of monoterpene cyclic and bicyclic carbon skeletons [41; 42]. Figure 2 shows the mechanism of formation of various monoterpene structures from left-handed helical conformer of GPP, but a similar pathway would occur from GPP right-handed helix which would result in the stereochemical antipodes of formed structures.  
[PERSON] et al. [41] tested neryl pyrophosphate (NPP), _cis_-isomer of GPP, and LPP as alternate substrates for monoterpene synthesis. The results showed that myrcene synthase exhibited the ability to form cyclic structures from NPP and LPP resulting in different product profiles. Limonene was produced  
Figure 2: The pathway of monoterpene cyclization through divalent cation-assisted ionization of pyrophosphate group and formation of \(\alpha\)-terpinyl cation intermediate (**I—**limonene; **II—**\(\delta\)-terpinene; **III—**\(\gamma\)-terpinene; **IV—**\(\alpha\)-terpinene; **V—**\(\alpha\)-pinene; **VI—**\(\beta\)-pinene; **VII—**car-3-ene; **VIII—**sabinene; **IX—**boreol; **X—**-camphene; **XI—**1,8-cineol).  
from NPP, while the mix of products was utilized from LPP, such as myrcene, _cis_-ocimene, terpinene, and limonene. Although the enzyme is capable for cyclization of NPP and LPP, it does not catalyze the isomerization of GPP to an intermediate that is able to cyclize. Moreover, they demonstrated for the first time in vitro activity of monoterpene synthase from marine source, which provided the basis for understanding and further exploration of biosynthesis of halogenated monoterpenes.
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 3 Methodology
### Multimodal Data Feature Extraction and Fusion  
The proposed multimodal data fusion framework is shown in Figure 7, including image feature extraction, POI semantic feature extraction, and feature fusion. Image features are extracted from remote sensing images using a semi-transfer convolutional neural network (ST-CNN), semantic features are extracted from the POI data using the LDA topic model, and two parts of features are fused together.
# System of Systems Lifecycle Management--A New Concept Based on Process Engineering Methodologies
## 8 Discussion on Evaluation
### Benefits and Limitations of SoSLM within Arrowhead  
Several challenges arose during the development of the Arrowhead framework, which initiated the generalized design of the presented SoSLM model. The conceptualization and development of the Arrowhead framework include the appearance of new systems, the transformation of existing systems, and the removal of obsolete systems. Although initially only a few systems were present, now, there are many more systems within the framework, and this number is dynamically growing.  
Therefore, it became a significant challenge to manage the ever-changing Arrowhead ecosystem [83; 87]. Stakeholders were taking into account new requirements in various subcycles related to various systems. Examples in the _Research_ and _Development_ subcycles include the refinement of secure inter-cloud servicing [98; 107], the Event Handler [108], or improving the handling of the Quality of Service [109]. Examples in the _Initiation_ and _Development_ subcycles include integration and interoperability issues of new systems, such as the Workflow Choreographer [82; 87; 105], among others. There are integrative examples for the _System and Application Management_ subcycles, such as the communication of the operational local cloud, the Arrowhead Management Tool, and system modeling tools [110]. The model also guides developers and operators of the given system's dependencies, either on the used development tools or systems within the Arrowhead framework (addressed in the fourth, i.e., \"tools\" ring of SoSLM).  
However, there are also factors for which the model does not provide an answer. One such area is source code management [111], in which case the model includes information about the used tools that can help in the reverse and forward engineering processes. However, it does not address issues such as code structuring and documentation or methodology used during code development, which can definitely influence the system's operation. It is also recognized [112] that the model-driven development could support the safety run-time, of which the approach is also implemented by SoSLM. Furthermore, [113] have identified the critical role--and based on the advantages--of effective maintenance. Here, the SoSLM model helps in the aspect of necessary development that must be implemented if there is any change in the system's serving infrastructure. In addition to these, there are more general possibilities for using the model (outside Arrowhead), of which benefits and limitations are present by the next subsection.
## 5 Simulation Results
### Attitude Angle Experiment under Turbulent Disturbance  
To fit the flight conditions in the actual environment, the atmospheric turbulence sequence established in the second section of this paper is used to simulate the interference suffered by UAV in the external flight, and then the experiment is carried out. Figure 11 shows the results of the influence of atmospheric turbulence on roll angle tracking. Figure 12 shows the results of the influence of atmospheric turbulence on pitch angle tracking. Figure 13 shows the results of the influence of atmospheric turbulence on yaw angle tracking. Obviously, the pitch angle is most affected by the atmospheric turbulence. PD-DIC seeks to make the controlled object be intensively adjusted in the shortest time, while there will be adjustment static error. It cannot perform adaptive control perfectly when there is external disturbance, and the anti-interference ability to the outside world is weak, so the tracking error always exists in the attitude channel. When encountering strong external interference, the control effect of PD-DIC drops sharply because of the limitation of its own algorithm. Especially in the control of the pitch angle, it shows obvious tracking error and becomes difficult to keep stable. Compared with the almost ideal tracking effect in a calm atmosphere, Fuzzy-A also has a relatively obvious jitter phenomenon when dealing with the disturbance of quadrotor UAV by atmospheric turbulence. FNN introduces the learning ability of the neural network into the fuzzy system, and uses the distributed neural network to represent the fuzzy processing, fuzzy reasoning and fuzzy calculation of the fuzzy system. The experimental results show that FNN can still track the desired input value of the attitude angle in the case of strong external disturbance, Table 5.
# Fabrication and Analysis of a Ti6 Al4V Implant for Cranial Restoration
###### Abstract  
A custom made implant is critical in cranioplasty to cushion and restore intracranial anatomy, as well as to recover the appearance and attain cognitive stability in the patient. The utilization of customized titanium alloy implants using three-dimensional (3D) reconstruction technique and fabricated using Electron Beam Melting (EBM) has gained significant recognition in recent years, owing to their convenience and effectiveness. Besides, the conventional technique or the extant practice of transforming the standard plates is unreliable, arduous and tedious. As a result, this work aims to produce a customized cranial implant using 3D reconstruction that is reliable in terms of fitting accuracy, appearance, mechanical strength, and consistent material composition. A well-defined methodology initiating from EBM fabrication to final validation has been outlined in this work. The custom design of the implant was carried out by mirror reconstruction of the skull's defective region, acquired through computer tomography. The design of the customized implant was then analyzed for mechanical stresses by applying finite element analysis. Consequently, the 3D model of the implant was fabricated from Ti6 Al4V ELI powder with a thickness of \(\simeq\)1.76-2 mm. Different tests were employed to evaluate the bio-mechanical stability and strength of the fabricated customized implant design. A 3D comparison study was performed to ensure there was anatomical accuracy, as well as to maintain gratifying aesthetics. The bio-mechanical analysis results revealed that the maximum Von Mises stress (2.5 MPa), strain distribution (\(1.49\times 10^{-4}\)) and deformation (\(3.26\times 10^{-6}\) mm) were significantly low in magnitude, thus proving the implant load resistance ability. The average yield and tensile strengths for the fabricated Ti6 Al4V ELI EBM specimen were found to be 825 MPa and 880 MPa, respectively, which were well over the prescribed strength for Ti6 Al4V ELI implant material. The hardness study also resulted in an acceptable outcome within the acceptable range of 30-35 HRC. Certainly, the chemical composition of the fabricated EBM specimen was intact as established in EDX analysis. The weight of the cranial implant (128 grams) was also in agreement with substituted defected bone portion, ruling out any stress shielding effect. With the proposed approach, the anatomy of the cranium deformities can be retrieved effectively and efficiently. The implementation of 3D reconstruction techniques can conveniently reduce tedious alterations in the implant design and subsequent errors. It can be a valuable and reliable approach to enhance implant fitting, stability, and strength.  
cranial restoration; electron beam melting; custom-made implant; 3D comparison; tensile strength; mirror reconstruction; rockwell hardness
## 3 Simulation Results and Analyses
### Simulation of an Eight-Channel TIADC  
To demonstrate the extension of the proposed calibration technique for any channel TIADC, the simulations of an eight-channel TIADC are introduced in this subsection. The time skew errors are set as [0.001, 0.002, \(-\)0.0015, 0.0013, 0.003, \(-\)0.004, \(-\)0.003, 0.002]\(\cdot T_{s}\).  
Figure 14 illustrates the output spectra of an eight-channel TIADC prior to and subsequent to calibration with a single-tone signal input at \(0.432\cdot f_{s}\). The proposed calibration technique results in a significant improvement in both the SNDR and the SFDR. Specifically, the SNDR and SFDR are improved from 43.64 dB and 49.36 dB to 76.39 dB and 90.39 dB, respectively.  
Figure 15a,b show the performance of SNDR and SFDR versus different input sinusoidal frequencies. Our proposed calibration technique can also maintain a stable efficiency as the input frequency increases, even for an eight-channel TIADC.  
Table 1 illustrates the comparison of the proposed fully-digital calibration technique with the other state-of-the-arts. Compared to Ref. [18], our proposed calibration technique demonstrates no limitation on the channel number of the TIADC and performs effectively under any number of channels. Owing to the limit of Hadamard transform, Ref. [18] only adapts to the TIADC with specific channel numbers. On the contrary, our proposed algorithm is based on the correlation between channels, making it applicable to TIadc with any number of channels. Compared to Ref. [19; 20; 21], our proposed calibration technique utilizes the conjugate property between spurious signals to reduce the number of estimation parameters, suggesting that this technique requires fewer hardware resources. We constructed the underdetermined equation and solved it using the matrix equation, which makes our calibration technique more compact and efficient. Furthermore, the proposed technique offers substantial advantages in terms of convergence time due to its optimal feedforward architecture.  
Figure 14: Spectra of an eight-channel TIADC output for a single tone input at \(0.432\cdot f_{s}\). (**a**) Before and (**b**) after the proposed digital timing mismatch calibration.
# Privacy and Security Issues in Online Social Networks
## 3 Privacy and Security Threats in OSNs
### Modern Threats
#### 3.2.5 Inference Attacks  
Inference attacks on social networks are applied to predict the sensitive and personal information of a user that they may not want to disclose, for example, age, gender, religious, and political affiliations. The attributes or information that are revealed inside the network are supposed to be private, but it is possible to use data-mining techniques on the released OSN data to predict a user's private information. Machine-learning algorithms can be applied for inference attacks by combining publicly available social-network data, for example, network topology and contents from users' peers. A mutual-friend-based attack can be used to find the common neighbor of any two users [36]. An inference attack was presented in Reference [37] to predict the attributes of a user based on their other public attributes that were available online. The technique was tested on Facebook to infer different users' attributes, such as educational background, preferences, and location information.
# Joint Deployment of Sensors and Chargers in Wireless
## 3 Model and Problem Statement
### Charging Model  
As the wireless charging power from the charger to the sensor decreases with the increase in distance, the sensor cannot obtain power when the charging distance exceeds a certain threshold \(d_{th}\). Specifically, when \(d(s_{j},c_{x})\leq d_{th}\), the sensor \(s_{j}\) is covered by the charger \(c_{x}\). The radio-frequency radiation power model in [35] is used:  
\[P_{h}^{s_{j},c_{x}}=\begin{cases}\frac{\tau P_{s}}{\left(d(s_{j},c_{x})+\epsilon \right)^{2}}&d(s_{j},c_{x})\leq d_{th}\\ 0&d(s_{j},c_{x})>d_{th}\end{cases} \tag{2}\]  
where \(\tau\) is the wireless charging efficiency constant in the range of (0, 1), \(P_{s}\) is the transmit power of the charger, \(d(s_{j},c_{x})\) is the Euclidean distance between the locations of \(s_{j}\) and \(c_{x}\), with \(s_{j}\in\Phi,c_{x}\in\Phi\), \(s_{j}\) is the location of a sensor, \(c_{x}\) is the location of a charger, \(\epsilon\) is a fixed small parameter, and \(d_{th}\) is the maximum cover distance of a charger.  
A number of studies have shown that the charging power of sensors can be aggregated from multiple chargers [36; 37]. Therefore, the total charging power of \(s_{j}\) from all chargers is:  
\[P_{h}^{s_{j},C}=\sum_{c_{x}\in C}P_{h}^{s_{j},c_{x}} \tag{3}\]  
Limited by the factors such as charging safety and hardware performance, the charging power of sensor has an upper limit \(P_{max}\). We assume that the average power consumption of the sensor is \(P_{c}\). Then, the maximum operating time slots \(\tau_{max}\) of the sensor in one life cycle can be calculated as follows.  
\[\tau_{max}=\begin{cases}J-1&P_{c}\leq P_{max}/(J-1)\\ \left\lfloor P_{max}/P_{c}\right\rfloor&P_{max}(J-1)\leq P_{c}<P_{max}/(J-1)\\ No\ solution&P_{c}>P_{max}(J-1)\end{cases} \tag{4}\]  
When \(P_{c}>P_{max}(J-1)\), the harvested power of sensors in up to \(J-1\) time slots is less than the power consumption in one time slot, and thus the deployment of the charger is meaningless.  
Then, to maintain the continuous operation of the sensor, the minimum charging power \(P_{min}^{s_{j}}\) of sensor \(s_{j}\) is:  
\[P_{min}^{s_{j}}=P_{c}*\frac{\left|\left|s_{jt}\right|\right|_{1}}{J-\left| \left|s_{jt}\right|\right|_{1}} \tag{5}\]  
where \(s_{jt}\) is the schedule of \(s_{j}\) in one cycle. \(\left|\left|s_{jt}\right|\right|_{1}\) is the 1-norm of \(s_{jt}\).  
Specifically, when \(P_{h}^{s_{j},C}\geq p_{min}^{s_{j}}\), the sensor \(s_{j}\) is fully charged by the charger set \(C\).  
Figure 2: An illustration of sensor scheduling.