esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Acquisition and Processing
### Processing and Interpretation
This method substitutes the derivative of the Euler equation into the analytical signal equation, allowing for the simultaneous estimation of magnetic source parameters, depth, and \(SI\), with the calculation equations as follows:  
\[Depth=\frac{AS2*AS1}{AS3*AS1-AS2*AS2} \tag{3}\]  
\[SI=\frac{2 AS2*AS2-AS3*AS1}{AS3*AS1-AS2*AS2} \tag{4}\]  
where  
\[AS1(x,y)=\sqrt{\left(\partial f/\partial x\partial z\right)^{2}+\left(\partial f /\partial y\right)^{2}+\left(\partial f/\partial z\right)^{2}} \tag{5}\]  
\[AS2(x,y)=\sqrt{\left(\partial f/\partial x\partial z\partial z\right)^{2}+ \left(\partial f/\partial y\partial z\right)^{2}+\left(\partial f/\partial z \partial z\right)^{2}} \tag{6}\]  
\[AS3(x,y)=\sqrt{\left(\partial f/\partial x\partial z\partial z\right)^{2}+ \left(\partial f/\partial y\partial z\partial z\right)^{2}+\left(\partial f/ \partial z\partial z\partial z\right)^{2}} \tag{7}\]  
In Equations (3) and (4), depth and \(SI\) represent the depth and structural index at the \((x,y)\) position, and in Equations (5)-(7), \(f\) denotes the magnetic field, while \(x\), \(y\), and \(z\) represent the directions of the derivative. We used this method to obtain the average \(SI\) value of the study area for located Euler deconvolution. Compared to the standard Euler deconvolution, the number of false solutions is significantly reduced, and the solution set is more concentrated. Equation (5) was used to calculate the analytical signal grid from the reduced-to-pole grid, and then the picking peaks method was used to obtain the peak positions. In a regular grid, a cell G is surrounded by eight neighbors, forming four groups in two directions (rows, columns, and two diagonals), with G at the center. If both neighbors in a direction have values less than G, G is a peak value in that direction. After comparing the four direction groups, the number of G being a peak in different directions can be from 0 (none) to 4 (all). Therefore, an integer from 1 to 4 can be set as a sensitivity coefficient to determine if G is a peak point: 1 means that G is a peak value in at least one direction, while 4 requires it in all directions. Obviously, using 1 can obtain more peak positions. Then, based on these peak positions, Equations (3) and (4) were used to calculate the estimated depth of the magnetic source and \(SI\) in the study area. Then, the average values of these \(SI\) were used as input parameters for located Euler devolution. The width of the main anomaly in the study area is approximately 3 km. The reduced-to-pole data were re-gridded with a spacing of 150 m, so that a window of 20 grid cells could just cover these anomalies, and an upward continuation of 100 m was applied to reduce the influence of high-frequency data. During the calculation, the maximum depth tolerance was set to 15%, and the maximum depth was not limited. After removing the points with negative depth values and positions that deviate significantly from the main range, a color symbol map of the located Euler deconvolution results was plotted.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 4 Technical Discussion  
### 4.1. Note Specifications and Applications  
#### 4.1.1 Processor and Memory Capabilities  
As can be observed from Table 1, the most widely used microcontrollers in the wireless sensor nodes considered in this paper from 2016 to 2022 are the Atmega series, which are targeted for low-power operation.  
Generally, regarding the RAM, Flash and EEPROM capacities, most of these sensor nodes and also commercial motes reviewed in this paper have comparable memory that is limited. Hence, they are most suitable for applications that are not data intensive. It is also noted that some of the motes, such as the Waspmote Plug & Sense! V15 utilize external SD cards with large storage capacities. However, writing to the SD cards is power-demanding and therefore limits the lifetime of the already power-constrained nodes. As can be seen in Table 1, from 2019 to 2022 the ARM Cortex series of MCUs have been dominating in most of the recent wireless sensor nodes reviewed in this paper. For example, the ICARUS [35] and that by [36] exploit the ARM Cortex-M4 32-bit RISC core MCU. The ARM Cortex series have the advantage of high-speed clock operation and improved memory capacity. Therefore, they are suitable for applications that are data intensive and require high-speed operation. Adjustable CPU clock rates permit a trade-off between power consumption and processing speed as guided by the WSN application requirements.
# Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data
## 3 Results
### Regional-Scale CLQ Estimation Based on HJ-1A Data
Compared with previous studies (R\({}^{2}\) from 0.59 to 0.69) based on vegetation indicators [7; 10; 13], the accuracy of CLQ estimation was improved with R\({}^{2}\) of 0.71, indicating that the proposed method has potential for estimating CLQ. In addition, the R\({}^{2}\) of the regional-scale modeling was lower (R\({}^{2}\) = 0.69) than that of the model using the dry soil spectral variables obtained from the measured spectral data (R\({}^{2}\) = 0.71). The reason is the limitation in the wavelength range (459 to 956 nm) of the optimal spectral variables for estimating CLQ at the regional scale.
## 6 Conclusions  
In the WMSN, multimedia data should be transmitted in real time. It is essential to support high throughput for multimedia data from sensors. When all of data are transmitted to a central aggregator, a centralization problem can occur. Therefore, D2D communication should be considered in WMSN. In the D2D WMSN, using an underlaying system is more effective because sensors in an underlaying system can share a channel. Sensors can use channels as two links: the cellular link and D2D link. An inter-cell interference management scheme is required to use the same channel versus D2D and cellular links. Therefore, the required interference management scheme should be able to control the two links effectively. The proposed scheme, FFR-3, divides an ordinary cell into six zones and allocates an orthogonal frequency section for each zone. In addition, the frequency sections allocated cellular links are reused by the D2D links of the adjacent zones to increase throughput. Through this scheme, inter-cell interference is decreased and the FRF is increased to maximize throughput. It is expected that the throughput will be almost two times better than the semi-static scheme by controlling the transmission power of each link.  
Conceptualization, J.K.; Investigation, T.K.; Supervision, S.C.; Validation, J.K.; Writing--original draft, J.K.; Writing--review & editing, J.N.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning and the Ministry of Trade, Industry, and Energy South Korea under Grant 20161520302230 and Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(No. NRF-2015R1D1A1A01059473).  
The authors declare no conflict of interest.  
Figure 18: D2D link throughput versus D2D transmit power (cellular Tx power = 40 dBm).
## 4 Discussion and Limitations
In contrast, Table 6, which reports the estimated stiffness parameters for patients with PD before -- OFF state -- and after therapy -- ON state -- ( the estimates of the parameters obtained for the ON estimation procedure are used as initial conditions for the OFF estimation procedure to verify how the OFF status varies starting from the ON status) and the corresponding MDS-UPDRS item 3.3 scores related to stiffness (one of the pivotal symptoms for PD), illustrates the following: (i) the estimated stiffness coefficients approximately tend to decrease (increase in absolute value) after taking therapy, which is in accordance with MDS-UPDRS scores [coefficients closer to zero corresponds to lower stiffness]; (ii) subjects characterized by an MDS-UPDRS score different from zero have estimated stiffness coefficients higher with respect to the HS group, while those with a score equal to zero are characterized by estimated coefficients more in line with healthy subjects; (iii) even though there is no direct correspondence between MDS-UPDRS scores and estimated stiffness values, there is a correspondence in their variations; indeed, subjects transitioning from a score of 1 (rigidity only detected with activation manoeuvre) to 0 (no rigidity) exhibit a smaller change in estimated parameters (especially at the level of the elbow). It is worth noticing that in contrast to our previous work [16], whose aim was just to quantify muscle rigidity at the elbow level in patients with PD, a more comprehensive analysis of human gait is presented in this paper with the role of whole arm movementwithin it being highlighted: sustained arm oscillations involve a combined action of an active term, muscle activity, and a passive term, with the proposed active component shape, at comfortable walking speeds, reproducing the behavior of the muscles that primarily serve to initiate the movement rather than to maintain it continuously. Indeed, the modifications of the estimated stiffness parameters in response to pharmacological therapy might shed more light on the relationship between motor and musculoskeletal biomechanical control in generating the movement of body segments. Finally, the closed-loop estimation method allows us to validate the model by comparing simulated joint profiles with measured ones without restrictions to averaged populations. Some limitations can be drawn as well. Further research is in fact needed to enhance this study. For example, expanding the patient cohort and including individuals at various stages of PD, encompassing diverse phenotypes, disease durations, and clinical characteristics, would allow for a more comprehensive assessment of the robustness of the proposed approach across different stages and etiologies. Validating the model in distinct PD cohorts, such as those with early-onset Parkinson's disease (EOPD) [39] or genetically driven forms of PD [40], could further reinforce its reliability and generalizability.
## 3 LEO Satellite Network Model  
The proposed LEO satellite network architecture for MRFCO ZTS comprises four primary components: (1) a centralized Network Control Center (NCC) managing satellite operations and user access, (2) an authorized user segment with terminals such as VSATs and GPS receivers, (3) a constellation of LEO satellites (including both inter-satellite and ground feeder links), and (4) communication links, encompassing uplink/downlink paths between users and satellites as well as inter-satellite and feeder links, which have been shown in Figure 1.  
The NCC handles tasks such as user registration, key distribution, and satellite configuration. The user segment, consisting of diverse end devices, interfaces through satellite links for services like broadband and navigation. LEO satellites maintain essential on-board capabilities (AD&C, TT&C, payloads) and enable secure channel setup via narrow-band inter-satellite links (ISLs). These ground-to-satellite links, while pivotal for global connectivity, require robust trust configurations to mitigate vulnerabilities. This architecture draws on the model introduced by [PERSON], [PERSON] et al. [1] with adaptations for the MRFCO-ZTS framework.
## 3 Rotational Feature Enhancement FCOS
### Objective Function  
As depicted in Figure 2, the network structure comprises three distinct output branches: the classification branch, the centerness branch, and the regression branch. In the regression branch, we incorporate the proposed DARIoU loss function to optimize the bounding box regression task. The classification and centerness losses are computed using the focal loss and binary cross-entropy loss, respectively, as defined in the baseline model. The final objective function is defined as follows:  
Figure 6: The concise summary of the proposed loss function. The goal is to minimize the loss in both horizontal and rotational dimensions. As can be imagined, when the two bounding boxes align horizontally, achieving an alignment in the rotational aspect becomes simpler, as only the angle requires adjustment.  
\[\text{Loss} =\frac{1}{N_{\text{pos}}}\sum_{x,y}L_{\text{cls}}(p_{x,y},p_{x,y}^{*}) \tag{19}\] \[\quad+\frac{1}{N_{\text{pos}}}\sum_{x,y}\text{obj}\cdot L_{\text{ DARIoU}}(t_{x,y},t_{x,y}^{*})\] \[\quad+\frac{1}{N_{\text{pos}}}\sum_{x,y}\text{obj}\cdot L_{\text{ ctrmess}}(s_{x,y},s_{x,y}^{*}),\]  
where \(N_{\text{pos}}\) represents the number of positive samples, \(p_{x,y}\) represents the predicted category score, with \(p_{x,y}^{*}\) indicating the corresponding true category label. _obj_ signifies the classification of the target as either foreground or background, denoted by the values 1 and 0, respectively. Additionally, \(t_{x,y}\) refers to the predicted bounding box information, and \(t_{x,y}^{*}\) corresponds to the true bounding box information; \(s_{x,y}\) indicates the predicted centrality score, with \(s_{x,y}^{*}\) representing the true centrality score.
## 3 CPT-Based Simplified Liquefaction Procedure and Consolidation Settlement Model
### Assessment of Liquefaction-Induced Site Settlement  
Soil liquefaction-induced settlement can cause significant damage to buildings on the ground surface, and therefore characterizing soil spatial variability in engineering practice plays an important role in seismic hazard assessment and mitigation. Assuming that the site is in level ground conditions, the liquefaction-induced surface reconsolidation settlement is estimated at the depth of interest (\(z\)) based on a one-dimensional model that estimates liquefaction-induced surface settlement (\(S_{v}\)) by integrating the volume strain (\(\varepsilon_{v}\)) [12; 30].  
\[S_{v}=\int_{0}^{z_{\max}}\varepsilon_{v}dz \tag{7}\]  
where \(z_{max}\) represents the maximum of interest z. The \(\varepsilon_{v}\) formed during soil reconsolidation is related to the maximum cyclic shear strain (\(\gamma_{\max}\)) and the relative density of soil (\(D_{r}\)) [31]. Therefore, \(\varepsilon_{v}\) can be expressed as a function of \(\left(q_{\text{cIN}}\right)_{\text{cs}}\) and \(\gamma_{\max}\).  
\[\varepsilon_{v}=1.5\times\exp\left[2.551-1.147(q_{\text{cIN}})_{\text{cs}}^{0.2 64}\right]\times\min(0.08,\gamma_{\max}) \tag{8}\]  
[PERSON] and [PERSON] [32] studied soil liquefaction during earthquakes and proposed a relationship between \(\gamma_{\max}\) and \(FS\) and limiting shear strain constraints (\(\gamma_{\lim}\)):  
\[\gamma_{\max}=\left\{\begin{array}{ll}0,&FS\geq 2\\ \min\Bigl{(}\gamma_{\lim},\ 0.035(2-FS)\Bigl{(}\frac{1-F_{\text{a}}}{FS-F_{\text{ a}}}\Bigr{)}\Bigr{)},&F_{\text{a}}<FS<2\\ \gamma_{\max}=\gamma_{\lim},&FS\leq F_{\text{a}}\end{array}\right. \tag{9}\]  
where the boundary parameters (\(F_{\text{a}}\)) and \(\gamma_{\lim}\) can be expressed using \(\left(q_{\text{cIN}}\right)_{\text{cs}}\) as:  
\[F_{\text{a}}=-11.74+8.34(q_{\text{cIN}})_{\text{cs}}^{0.264}-1.371(q_{\text{cIN }})_{\text{cs}}^{0.528} \tag{10}\]  
\[\gamma_{lim}=1.859\Bigl{(}2.163-0.478(q_{\text{cIN}})_{\text{cs}}^{0.264} \Bigr{)}^{3}\geq 0 \tag{11}\]  
The spatial variation of surface reconsolidation was estimated by Equations (1)-(11). Although there are many uncertainties and approximations in the model for estimating \(S_{v}\), the results estimated using the proposed method provide valuable insights for evaluating the spatial distribution of surface settlement [18]. This study extends the 1D settlement model combined with 2D BCS to 2D space. It is worth noting that the settlement model used only estimates the settlement associated with soil liquefaction followed by reconsolidation, without considering the effects of soil-structure interactions and settlement due to ejecta [12].
# A New Method to Lightweight Magnesium Using Syntactic Composite Core
## 4 Conclusions  
As-cast pure Mg and Mg/Mg-20 GMB hybrid composite were synthesized by using a combination of powder metallurgy (blending, compaction, and hot extrusion) and disintegrated melt deposition (DMD) techniques. The Mg/Mg-20 GMB hybrid composite exhibited lower density and moderate metallurgical bonding between the Mg-shell and Mg-20 GMB core regions. EDX results confirmed the presence of Mg\({}_{2}\)Si as the secondary phase in the Mg-20 GMB core material. The Mg-20 GMB core showed higher hardness than the shell zone (pure Mg) due to the presence of a secondary phase (Mg\({}_{2}\)Si) and the Mg/GMB interfacial regions. The increment of yield strength of the as-cast Mg/Mg-20 GMB hybrid composite was by +71.6%, and a drop in the UCS by -23.3% was observed when compared to as-cast pure Mg. The shell-core structure provides a new way to improve the mechanical properties of the hybrid composites.  
M.G., B.W.C. and S.C.K.W.: conceptualization, methodology; J.G.Y.S. and P.R.M.: data curation, writing--original draft preparation; G.P. and V.M.: investigation; M.G.: supervision; P.R.M., G.P. and V.M.: writing--reviewing and editing. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Agency for Science, Technology, and Research (A*STAR), Singapore, under its program \"Manufacturing of lightweight free-form shell, sandwich, and hybrid cellular structures for aerospace component manufacturing and repair\" (WBS # R 265 000 638 305).  
The authors declare that there is no conflict of interest.  
Figure 8: Fractography analysis of as-cast (**a**) pure Mg, and (**b**) Mg/Mg-20 GMB hybrid composite.
# Evaluation of a Reputation Management Technique for Autonomous Vehicles
## 2 Background
### Vehicle-to-Vehicle Communications  
The planned deployment of vehicle-to-vehicle (V2V) technology relies on an ad-hoc mesh network using a decentralized connection system [20]. The vehicles in this system can either be fully or partially connected. In a fully connected system, each vehicle is connected for direct communication, whereas a partially connected system is comprised of only a few vehicles that are connected to all of the nodes, with the remaining being connected to high traffic nodes. Both of these technologies are possible to implement because of wireless communication and wireless personal area networks (WPANs) [20]. The ability to efficiently communicate allows vehicles to gain a better understanding of their surrounding environment, enabling them to proactively adapt to crash threats and other irregularities [21].
# AHC: Advanced Network-Hiding Access Control Framework
## 4 Methods
### Cryptographic Protocols  
Compared to commonly used digital signature schemes, the advanced framework proposed in this paper utilizes a lightweight Noise Protocol for mutual identity verification. This key negotiation protocol combines random key pairs with the ECDH algorithm. The Noise Protocol implicitly authenticates the identities of interacting parties during the generation of the final key. It is well suited for single-session data encryption and decryption applications due to the introduction of random key pairs at each interaction. Additionally, the algorithm uses a much shorter key length than the RSA algorithm to provide the same level of security. For example, RSA2048 encryption requires a 256-byte public key, whereas ouralgorithm requires only 32 bytes for equivalent security. Therefore, the ECDH algorithm is more secure than RSA signatures, with smaller key sizes, shorter ciphertext lengths, and lower computational overhead.  
The Noise Protocol is a modular framework that can decompose the entire protocol into multiple interchangeable modules, allowing for combination and replacement. The Noise Protocol supports various types of encryption algorithms and hash functions, enabling flexible configuration based on specific requirements. In the AHAC framework, the device key can be randomly generated by software or obtained from a U-key or digital certificate. AHAC utilizes the Noise Protocol for mutual authentication and data encryption between parties, and defines an extended 64-byte (512-bit) public key in the packet header for steganographic access, accommodating future cryptographic algorithm updates. The AHAC supports the use of Chinese National Cryptographic Algorithms (SM2, SM3, SM4, etc.) for two-way authentication and encrypted communication.
# Bayesian Survival Analysis Model for Girth Weld Failure Prediction
## 4 Conclusions  
This study first proposes the Staged Bayesian survival analysis model for girth-weld failure prediction using the tree-type cause theory and survival analysis method. The results show that the Staged Bayesian model is consistent with the Kaplan-Meier model. Meanwhile, the results of the model fit the trend of the \"Bathtub Curve\" of a pipeline and the intrinsic characteristics of a girth weld. Further analysis based on the survival model shows the tendency of the survival probability of the girth weld under the influence of a single factor and of several combinations of different factors, according to data of previous 14 accidents that happened within 10 years of pipe life after hydro-pressure. The model and analysis method can also be used to improve the risk assessment and provide guidance for risk-mitigation measures for the company operating a pipeline.  
Figure 5: Survival function of the girth weld under the influence of several different factors.  
**Author Contributions:** [PERSON] and [PERSON] conceived and performed the investigation, and [PERSON] analyzed the data. [PERSON] and [PERSON] wrote the article.  
**Funding:** This research was funded by the China National Key Research and Development Program of China (No. 2016 YFC0802100) grant number [No. 2016 YFC0802100] and PetroChina Pipeline Company research projects grant number [20140201].  
**Acknowledgments:** We would like to thank [PERSON], University of Tulsa, for his help in research.  
**Conflicts of Interest:** The authors declare there is no other conflict of interest regarding the publication of this paper.
## 4 Discussion
In this regard, the identified osmotic fragility is linked to decreased cell deformability, membrane fluidity, and structural damage to RBC membranes [35]. To corroborate this, a change in the morphology of erythrocytes can be visualised in images acquired by light microscopy. Indeed, these show more aggregates in the experimental blood smears than in the control ones in both bovine and horse blood samples at all concentrations tested. In agreement with the current study, [46] report an increase in the osmotic fragility of rat erythrocytes after exposure to certain insecticides. According to [47], on the other hand, the use of certain detergents induced osmotic fragility in erythrocytes caused by the formation of pores in the lipid region of the membrane, resulting in the release of red blood cell vesicles. Moreover, it has been shown that osmotic fragility in erythrocytes can be induced by the elevation of erythrocyte calcium caused by impairment of the ATP-Ca2+ pump [21]. The results obtained in the present study clearly showed that also WBC and PLT from blood samples of horses and cows suffered the exposition to both xenobiotic compounds. As a matter of fact, these cell populations showed significantly decreased values following Quaternium and Thiacloprid exposure. Concerning PLT, it has been previously shown that the production of free radicals in the bone marrow as a consequence of xenobiotic exposition causes a decrease in PLT, altering thrombopoiesis [37]. The decreased values of PLT as well as of the WBC found in the blood samples from horses and cows exposed to Quaternium and Thiacloprid, compared to the blood samples not exposed to these xenobiotics, may be related to the toxic effects of these compounds on the cells in the blood circulation. Indeed, being this study an in vitro investigation on peripheral blood samples, it is not possible to know the outcomes of Quaternium and Thiacloprid at the hematopoiesis level.