esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Petrophysical Properties and Pore Types  
The porosity and permeability results of 753 sandstone core samples from the E\({}_{84}\) sandstone reservoirs indicate that the petrophysical properties are generally unfavorable with porosity mainly ranging from 3.0% to 24% (average 12.3%) and permeability ranging from 0.005 to 191.68 mD (averaging 10.75 mD) (Figure 3). The majority of the samples belong to the category of low-permeability sandstones (Figure 3). When excluded samples with micro-fractures, the porosity and permeability show a good correlation (R\({}^{2}\) = 0.70) because the presence of micro-fractures can significantly improve the reservoir permeability but has little influence on the porosity. The samples with micro-fractures are generally characterized by high permeability values but highly variable porosities (5-25%) (Figure 3B).  
As can be seen from SEM images and casting thin sections, the pore systems are mainly constituted by residual intergranular pores, dissolution pores, micropores, and a minor amount of micro-fractures (Figure 4). The residual intergranular pores are irregular in shapes (Figure 4A,B,H,IJ), which are often associated with carbonate cement (Figure 4A,B). The primary intergranular pores visible under petrographic microscope are generally characterized by larger pore diameter as compared to secondary dissolution pores. The dissolution pores are predominantly in feldspars, lithic fragments, and carbonate cements  
Figure 3: Reservoir porosity histogram (**A**) and porosity–permeability cross-plot (**B**) of the E\({}_{84}\) sandstone reservoirs in the Dongying Depression.  
(Figure 4A,B,H-J). Some of the intergranular pores and dissolution pores are filled by clay minerals, forming intercrystalline pores (Figure 4E,F). There are a large number of intercrystalline pores within kaolinite, chlorite, and mixed layer Illite/smectite, and the pore diameter is less than 1 \(\upmu\)m (Figure 4E,F). The micropores formed by authigenic clay minerals and dissolution clastic particles contribute to high porosity but have low permeability [53,54].
## 3 Method  
### Crop Growth Model  
#### 3.1.1 Description of the Crop Growth Model  
The CERES-Wheat model was developed and integrated into the Decision Support System for Agro-technology Transfer (DSSAT) software in the International Benchmark Sites for Agro-technology Transfer project (IBSNAT) sponsored by the United States Department of Agriculture (USDA) [58]. The model is a mechanistic process-based model and uses a generic process description that is suitable for large-scale and regional simulations by the strategy of data assimilation. The model has been widely applied to assess potential crop productivity, determine the influences of climate change on grain yields, farmland water and fertilizer management [66; 67]. Thus, this model was selected to estimate the yield of winter wheat in this study.  
Driven by input data on soil, meteorological data, genetic information and field management, the CERES-Wheat model can simulate daily phenological development, vegetative and reproductive plant developmental stages, biomass accumulation and ultimate yield [22; 33]. In this model, the yield is simulated as a function of the grain weight. Grain growth is developmentally dependent on the wheat daily growth, which is a function of the mean daily temperature and daily cumulative solar radiation, a temperature control factor, vegetation biomass and the model parameters [68]. Given the way in which the model develops the vegetation and grain components of the wheat plant, the LAI will determine yield based on the vegetation biomass [33].
# Deep Learning-Based Sound Source Localization: A Review
## 3 General Principles of Deep Learning-Based Sound Source Localization
### Model Architectures
#### 3.2.5 Gan  
Reference [26] proposes an unsupervised adversarial domain adaptation (UDA) algorithm that addresses the distribution shift between simulated and measured data through a modified generative adversarial network (GAN) framework. Unlike conventional GANs focused on data synthesis, this methodology eliminates domain discrepancy by aligning latent space representations across domains. The architecture employs a feature generator (G) and domain discriminator (D) in an adversarial learning paradigm (see Figure 9).  
The generator, implemented as a deep neural network, maps both simulated and measured acoustic pressure signals into a shared latent space through multi-band normalized covariance matrix inputs. To enhance frequency-specific feature extraction, the generator incorporates a modified ResNet architecture with grouped convolutional layers that independently process distinct frequency bands of acoustic field data. Simultaneously, the discriminator learns to classify feature origins (source vs. target domain) using binary cross-entropy loss. A gradient reversal layer inverts discriminator gradients during backpropagation, enforcing adversarial optimization where the generator minimizes domain discriminability while the discriminator maximizes classification accuracy. The optimization objective combines source domain classification error minimization with domain discrepancy reduction, measured as follows:  
\[\varepsilon_{\mathcal{T}}(h)\leq\varepsilon_{\mathcal{S}}(h)+\frac{1}{2}d_{ \mathcal{H}\Delta\mathcal{H}}(\mathcal{S},\mathcal{T}) \tag{14}\]  
where \(\varepsilon_{\mathcal{T}}(h)\) and \(\varepsilon_{\mathcal{S}}(h)\) denote target/source domain errors, respectively. The adversarial training dynamically estimates and optimizes the distributional distance \(d_{\mathcal{H}\Delta\mathcal{H}}\). For practical implementation, continuous source distance estimation is discretized into 93 equidistant intervals, transforming the regression task into a multi-class classification problem. The final classifier produces a 93-dimensional probability vector, with the maximum probability interval selected as the predicted distance. This domain-invariant feature learning enables robust cross-domain generalization, allowing the classifier trained on labeled simulated data to achieve accurate source localization in unlabeled measured environments without explicit target domain annotations.
## Appendix D Proof of Theorem 1 in (38) and (39)  
From Equation (32), the outage probability of IoT relay node is:  
\[\begin{array}{l}P_{Out_{LoT_{R}}}=Pr\big{(}min(\gamma_{s_{mc}}^{x_{loT_{R} \to s_{x}}},\gamma_{1 oT_{mc}})<\psi\big{)}\\ P_{Out_{LoT_{R}}}=1-Pr\big{(}\frac{\phi_{0}bXY}{\phi_{1}bXY+1}\geq\psi,\frac{ \phi_{0}bXZ}{\phi_{1}bXZ+1}\geq\psi\big{)}\end{array}\]  
where  
\[\begin{array}{l}P_{IoT_{R}}=\eta\varepsilon P_{s}|h_{IoT_{R}}|^{2}\triangleq \eta\varepsilon\delta X,b=\eta\delta\varepsilon\\ X=|h_{IoT_{R}}|^{2},Y=|h_{s_{mc}}|^{2},Z=|h_{IoT_{mc}}|^{2},\sigma_{IoT_{mc}}^{ 2}=1,\sigma_{s_{mc}}^{2}=1\\ =1-Pr\big{(}Y\geq\frac{\psi}{(\phi_{2}-\phi_{1}\psi)bX},Z\geq\frac{\psi}{(\phi _{2}-\phi_{1}\psi)bX}\big{)}\end{array}\]  
Conditioning on \(X\), we have,  
\[=1-\int_{0}^{\infty}Pr\big{(}Y\geq\frac{\psi}{(\phi_{2}-\phi_{1}\psi)bx}\big{)} \times Pr\big{(}Z\geq\frac{\psi}{(\phi_{2}-\phi_{1}\psi)bx}\big{)}f_{X}(x)dx\]  
putting,  
\[\begin{array}{l}\frac{\psi}{(\phi_{2}-\phi_{1}\psi)bx}=U\\ =1-\int_{0}^{\infty}Pr(Y\geq U)Pr(Z\geq U)f_{X}(x)dx\\ =1-\int_{0}^{\infty}\big{(}\int_{U}^{\infty}\lambda_{g}e^{-\lambda_{g}y}dy \big{)}\big{(}\int_{U}^{\infty}\lambda_{z}e^{-\lambda_{z}z}dz\big{)}\lambda_{ h}e^{-\lambda_{h}x}dx\\ =1-\int_{0}^{\infty}e^{-\lambda_{g}x}U\varepsilon^{-\lambda_{z}U}\lambda_{h}e^{- \lambda_{h}x}dx\end{array}\]  
substituting the value of \(U\) above  
\[=1-\int_{0}^{\infty}e^{-\lambda_{g}\frac{\phi}{(\phi_{2}-\phi_{1}\psi)bx}}e^{ -\lambda_{z}\frac{\phi}{(\phi_{2}-\phi_{1}\psi)bx}}\lambda_{h}e^{-\lambda_{h}x }dx\]  
let  
\[\begin{array}{l}c=\frac{\psi}{(\phi_{2}-\phi_{1}\psi)b}\\ =1-\int_{0}^{\infty}e^{-\lambda_{g}\frac{\phi}{c}}e^{-\lambda_{z}\frac{\phi}{c} }\lambda_{h}e^{-\lambda_{h}x}dx\\ =1-\lambda_{h}\int_{0}^{\infty}e^{-\lambda_{g}+\lambda_{z}\frac{\phi}{c}}\lambda _{h}e^{-\lambda_{h}x}dx\end{array}\]Now, using the formula,  
\[\int_{0}^{\infty}e^{-\frac{\beta}{4x}-\gamma x}dx=\sqrt{\frac{ \beta}{\gamma}}K_{1}\big{(}\sqrt{\beta\gamma}\big{)}\] \[=1-\lambda_{h}\sqrt{\frac{4(\lambda_{g}+\lambda_{z})c}{\lambda_{h}} K_{1}}\bigg{(}\sqrt{4(\lambda_{g}+\lambda_{z})c\lambda_{h}}\bigg{)}\] \[P_{Out_{lat}T_{R}}=1-2\sqrt{c\lambda_{h}(\lambda_{g}+\lambda_{z})} K_{1}\bigg{(}2\sqrt{c\lambda_{h}(\lambda_{g}+\lambda_{z})}\bigg{)}\]  
Putting the value of \(P_{Out_{lat}T_{R}}\) in Equation (34), we get,  
\[Thr_{10T_{R}}=\frac{R}{2}\bigg{(}2\sqrt{c\lambda_{h}(\lambda_{g}+\lambda_{z})} K_{1}\bigg{(}2\sqrt{c\lambda_{h}(\lambda_{g}+\lambda_{z})}\bigg{)}\bigg{)}\]  
This ends the proof of Theorem 4.
## 5 Experiments and Discussion
### Methane and Carbon Dioxide Detection Accuracy Test  
Methane-air mixtures with concentrations of 1%, 2%, and 4% were selected for testing. The test results are shown in Table 4, where the absolute error is the difference between the measured value and the actual methane concentration. From the test results, it can be seen that the relative error of detection was less than 3%, and the measurement accuracy met the commercial requirements.  
Since the detection of carbon dioxide was used to correct the measured value of methane, the measurement results directly affected the measurement accuracy for the methane concentration, and the detection accuracy for carbon dioxide needed to be tested. Table 5 shows the test results for the carbon dioxide concentration when using air as the background gas with concentrations of 0.2%, 0.5%, and 1%. As can be seen in Table 5, the relative error for carbon dioxide did not exceed 3%, and the measurement results met the requirements for high-precision gas concentration detection.
# Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements
## 2 Problem Formulation
### JAH Algorithm
This type of problem is known as GTRS [24], and its _exact_ solution is given by  
\[\hat{\mathbf{\theta}}(\lambda)=\left(\mathbf{H}^{T}\mathbf{\Omega}^{T}\mathbf{\Omega}\mathbf{H}+ \lambda\mathbf{D}\right)^{-1}\left(\mathbf{H}^{T}\mathbf{\Omega}^{T}\mathbf{\Omega}\mathbf{g}- \lambda\mathbf{f}\right),\]  
where \(\lambda\) is a unique solution to \(\varrho(\lambda)=0\), for \(\lambda\in I\), with \(\varrho(\lambda)=\hat{\mathbf{\theta}}(\lambda)^{T}\mathbf{D}\hat{\mathbf{\theta}}(\lambda )+2\mathbf{f}^{T}\hat{\mathbf{\theta}}(\lambda)\) and the interval \(I=\left(-\frac{1}{\lambda_{1}(\mathbf{D},\mathbf{H}^{T}\mathbf{\Omega}^{T}\mathbf{\Omega}\mathbf{ H})},\infty\right)\), where \(\lambda_{1}\) is the maximum eigenvalue of a matrix. It is known that \(\varrho(\lambda)\) is strictly decreasing over \(I\)[24]; thus, the optimal \(\lambda\) can be readily obtained by a bisection procedure.
## 3 Vitamin A, Its Vitaminoids and Carotenoids' General Profile and Health Benefits
### Vitamin A-Related Bioactive Compounds: Provitamins (Carotenoids)
#### 4.1.2 Topical Absorption of Carotenoids, Vitamin A, and Its Vitaminoids  
In contrast to oral vitamin A, its vitaminoids, and carotenoid absorption, topically applied vitamin A and its derivatives are absorbed through the skin, targeting the epidermis and dermis. There are several pathways of penetration, involving the transcellular (via keratinized cells of the _stratum corneum_), intercellular (between cells via lipid bilayers), and appendageal (via hair follicles and sweat glands, which bypass the _stratum corneum_ barrier) routes [102,103]. Retinoids like retinol and tretinoin are often stabilized in formulations with liposomes or nanocarriers for enhanced penetration, due to the fact that skin absorption is boosted by occlusion, hydration, and the presence of penetration enhancers such as ethanol, in order to influence gene transcription, promote collagen synthesis, and improve skin elasticity. The conversion, storage-distribution, and metabolism of vitamin A and its derivatives also differ depending on the absorption route because of the distinct cascades via which orally ingested and topically applied vitamin A compounds enter the body and their subsequent metabolic fates. Skin metabolism is localized, prioritizing direct biochemical action [15,102,103].  
Retinol skin-absorbed via any of the above routes is enzymatically converted to retinal and then ATRA in local skin cells, according to the oral route. ATRA directly acts at the site of absorption, binding both to RARs and retinoid X receptors (RXRs) by CRABPs, which are strong modulators of apoptosis, proliferation, differentiation, and cell cycle, influence gene transcription and keratinocyte proliferation, promote collagen synthesis, and improve skin elasticity and barrier function. If ATRA binds to CRABPI, it is transferred to CYP26 to be degraded, while if bound to CRABPI, it is relocated in the nucleus so as to interact with  
Figure 7: \(\beta\)-carotene’s conversion and metabolic pathways (the central cleavage is depicted with the black arrows and the eccentric cleavage with the purple arrows).  
RARs and RXRs, along with several co-activators, and promote transcriptional activity of ATRA target genes [104]. This conversion mainly occurs in the epidermis and dermis, with minimal systemic involvement, and this conversion takes place locally, primarily yielding ATRA for immediate use, in contrast to oral absorption, where this conversion takes place in the intestine and liver [11,100,104]. Meanwhile, carotenoids, including \(\beta\)-carotene, are less commonly utilized topically and are not enzymatically cleaved to vitamin A in the skin [7,19,105]. Retinol, retinoids, and carotenoids are retained locally in the skin cells (and peripheral tissues) and metabolized in situ, while minimum systemic distribution--unless high retinoid concentrations are applied, causing overflow into circulation--occurs (Figure 8) [7,15,19,102,103,106].
Sensors_**2014**, _14_, 21117-21139; doi:10.3390/s141121117  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**Remote Sensing of Ecosystem Health: Opportunities, Challenges, and Future Perspectives**  
**[PERSON], [PERSON] and [PERSON] ***  
Department of Geography and Planning, University of Saskatchewan, 117 Science Place, Saskatoon, SK S7N 5C8, Canada; E-Mails: [EMAIL_ADDRESS] (Z.L.); [EMAIL_ADDRESS] (D.X.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-306-966-5663; Fax: +1-306-966-5680.  
External Editor: [PERSON]  
_Received: 14 August 2014; in revised form: 21 October 2014 / Accepted: 30 October 2014 / Published: 7 November 2014_  
**Abstract:** Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.  
**Keywords:** ecosystem health assessment; optical remote sensing; radar; LiDAR; ecosystem vigor; ecosystem organization; ecosystem resilience
# Combined Optical Fiber Transmission System Based on QNSC and BER-LM
## 5 Conclusions  
In previous studies, encryption and key distribution were carried out in independent channels. This paper proposes a scheme that combines the two technologies of encryption and key distribution in a single fiber channel. The simulation results show that by reasonably setting the negotiated bit position of key distribution, better security of key distribution can be achieved while ensuring the transmission performance. When the negotiated bit position was seven, the Q-factor of the 20 Gbps coherent transmission system was nine, meeting the 7% FEC limit. This scheme does not require a secure key provided by a third party. This scheme can provide a self-sufficient secure key for data encryption. On the basis of the encrypted transmission system, this scheme only needs to improve the DSP algorithm without adding additional expensive optical equipment, which reduces equipment costs and is conducive to large-scale popularization.  
Conceptualization, X.Y. and X.W.; methodology, X.Y.; software, X.Y., D.W., and X.W.; validation, X.W., L.Z., and H.Z.; investigation, B.W.; resources, X.Y.; data curation, Z.Y.; writing--original draft preparation, X.Y.; writing--review and editing, D.W.; visualization, D.W.; supervision, L.Z.; project administration, X.Y.; funding acquisition, B.W. All authors have read and agreed to the published version of the manuscript. Funding: Funding: This research work was supported by the Special Fund of Advantageous and Characteristic disciplines (Group) of Hubei Province. This research work was supported in part by BIGC Project (Ec202201); The Initial Funding for the Doctoral Program of BIGC (27/10122006). This research work was supported in part by Scientific Research Project of Fuyang Normal University (2022 KYQD0004) and Key scientific research project of Universities in 2021 (KJ2021A0897), Anhui Education Department. This research work was supported in part by Guiding project of Scientific Research Plan of Education Department of Hubei Province (B2022338), and by University Natural Science Research Project of Anhui Province (Grant No.: 2022 AH051338). This research work was supported in part by University-level Natural Youth Funding Project of West Anhui University (Project No.: WXZR201907).  
Figure 9: (**a**) Q-factor vs. bit position. (**b**) BER measured by [PERSON] and [PERSON] at different bit positions.  
**Data Availability Statement:** This study does not report any data.  
**Acknowledgments:** We acknowledge the support given by [PERSON] and [PERSON] during this project.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Trusted Energy-Aware Hierarchical Routing (TEAHR) for Wireless Sensor Networks
## 2 Related Studies
### Trust Models and Their Classification
Such a model could use the sum of all incoming edge weights as a measure of the trustworthiness of the Node:  
\[H=-\sum_{i=1}^{n}p_{i}logx_{2}p_{i} \tag{2}\]4Here, T(v) is the trust value for mode v, E is the set of edges, and w(u,v) refers to the weight of the edge from node u to node v. In undirected graph-based models, the edges are not directed and represent the mutual trust relationship between the nodes. The level of Trust, in this case, can be calculated based on the average weight of the edges that it connects as follows:  
\[H=-\sum_{i=1}^{n}p_{i}logx_{2}p_{i} \tag{2}\]5  
where deg(v) is the degree of node v, and w(v,u) is the weight of the edge connecting nodes v and u. This graph can also support extra features, such as history data, node behavior, and social trust metrics. For instance, a dynamic weighted graph model may change the edge weights with time, according to node actions, following mathematical expressions, to gradually increase or decrease the level of Trust from positive or negative interaction. Also, some of the most recent studies have been using sophisticated techniques, such as machine learning, for predictions and updating trust levels in conjunction with graph-based models. For example, the study conducted by [PERSON] et al. [26] presented a dynamic model of trust management based on the directed graph, where temporal and spatial parameters are taken into account to change the trust levels depending on network conditions and behaviors. In his study, [PERSON] et al. [27] have established the Energy Trust Model (ETM), which includes the remaining energy of nodes and trust level to obtain secure communication paths that will consequently guarantee the confidentiality of transmitting data within a WSN.
### Our Contributions  
To resolve the aforementioned problems, SP-MAACS, a secure and privacy-preserving multi-authority access control system for cloud-based data sharing is proposed. Our SP-MAACS is a secure MA-CP-ABE scheme with a partial policy hiding feature. Two components make up an attribute: the name and the value. Concrete attribute values are used in the access policy. They are encoded in the ciphertext components. The access policy in plaintext is also saved with the encrypted data. It includes attribute names, not values. In our scheme, we used the access policy in DNF form, also called \"alternative routes to authorization\" [47]. A set of satisfiable sub-policies can be derived from the main policy. For example, a policy for the data D represented as an arbitrary logical formula such as [a1 \(\land\) (a2 \(\lor\) a3)] can be written as [(a1 \(\land\) a2) \(\lor\) (a1 \(\land\) a3)]. Here the set of sub-policies is [(a1 \(\land\) a2), (a1 \(\land\) a3)]. Generally, when a data owner decides on an access policy, he starts framing it using a combination of alternatives. This standard method of specifying access policy corresponds to the DNF structure employed by our approach. The following is a concise summary of the primary features of our system:  
* The proposed scheme incorporates the important aspect of privacy preservation in a multi-authority setting. Along with this added-on feature of privacy preservation, our multi-authority access control scheme also achieves better decryption efficiency;
* The scheme is designed to support open- and closed-domain users and allows for employing fine-grained access control. The access policy formulated using DNF makes the policy specification more flexible and expressive. As our system is scalable, it allows users from varied domains and makes it better suited for real-world applications;
* The scheme is adaptively secure. It achieves resistance to collusion attacks, as the users cannot integrate their attributes to access shared data. The scheme is demonstrated as secure in the standard model.