esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study revealed the up-to-date regional spatial distribution pattern of cropland SOC in the plough layer and compacted layer of the Northeast China Plain, which could provide a reference for evaluating SOC spatial distribution patterns after long-term cultivation. The major conclusions were as follows:  
1. Boruta was a compelling feature selection method to eliminate redundant variables and develop the optimal QRF model.
2. SOC overall increased from the southern to the northern areas, with an average of 17.34 g kg\({}^{-1}\) in the plough layer and 13.92 g kg\({}^{-1}\) in the compacted layer. At the vertical scale, SOC decreased, with depths getting deeper. The average decreasing SOC is 3.41 g kg\({}^{-1}\), and the northern area decreased more than the southern area.  
3. Climate (i.e., average temperature, daytime and nighttime land surface temperature, and mean temperature of driest quarter) was the dominant controlling factor, followed by position (i.e., oblique geographic coordinate at 105\({}^{\circ}\)), and organism (i.e., the average and variance of net primary productivity in the non-crop period).
4. The average uncertainty values were 1.04 in the plough layer and 1.07 in the compacted layer. The high uncertainty appeared in the areas with relatively scattered fields, high altitudes, and complex landforms.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs15205033/s1](https://www.mdpi.com/article/10.3390/rs15205033/s1)]([https://www.mdpi.com/article/10.3390/rs15205033/s1](https://www.mdpi.com/article/10.3390/rs15205033/s1)), Figure S1: Scatter plot of SOC predictions (CV dataset) and SOC observations in (a) the plough layer and (b) the compacted layer; Figure S2. Spatial distribution pattern of 5% quantile predictions (a and e), average predictions (b and f), 95% quantile predictions (c and g) and uncertainty (d and h) in the plough layer and the compacted layer; Figure S3. Spatial distribution of the thickness of (a) the plough layer and (b) the compacted layer; Table S1: List of environmental covariates in the database.  
Conceptualization, X.Z. and Z.Z.; methodology, X.Z. and Z.S.; software, X.Z. and J.X.; validation, Z.Z., N.W. and T.X.; formal analysis, X.C.; investigation, Y.X.; resources, Y.H.; data curation, Z.Z.; writing--original draft preparation, X.Z.; writing--review and editing, J.X. and S.C.; visualization, T.X.; supervision, Z.S.; project administration, S.C.; funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (U1901601), the National Key Research and Development Program of China (2022 YFB3903503).  
Not applicable.  
The code is available upon reasonable request to the corresponding author.  
The authors declare no conflict of interest.
# Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication
## 2 Theory of Channel Capacity  
The input-output relationship of communication systems in a narrow-band Gaussian channel can be expressed based on the following expression:  
\[\mathbf{y}=\mathbf{H}\mathbf{\cdot}\mathbf{x}+\mathbf{n} \tag{1}\]  
where \(\mathbf{H}\) is the channel transmission matrix with the size of \(M\times N\). \(\mathbf{y}\) is a vector of \(M\) receiving antennas, \(\mathbf{x}\) is a vector of \(N\) transmitting antennas, \(\mathbf{n}\) is Gaussian noise.  
The channel matrix is normalized by Frobenius norm to eliminate the influence of large-scale fading. It can be defined as:  
\[\mathbf{H}_{nor}=\mathbf{H}.\sqrt{\frac{M\times N}{||\mathbf{H}||_{F}^{2}}} \tag{2}\]  
where \(||\mathbf{H}||_{F}\) is the Frobenius norm of \(\mathbf{H}\). The Frobenius norm is defined in the complex number domain, as follows:  
\[||\mathbf{H}||_{F}=\sqrt{\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{N} \abs{h_{ij}}^{2}} \tag{3}\]  
where \(h_{ij}\) is the \((i,j)\) th element in channel matrix \(\mathbf{H}\).  
In MIMO systems, if the transmitter has no channel state information and each antenna transmits the same power in the equivalent MIMO channel model [42], the total channel capacity can be defined as:  
\[C=log_{2}\text{det}\Big{(}\mathbf{I_{M}}+\frac{\rho}{N}\mathbf{H}_{nor} \mathbf{H}_{nor}^{*}\Big{)} \tag{4}\]  
where \(det\) is the determinant. \(\mathbf{H}_{nor}^{*}\) is the conjugate transpose of \(\mathbf{H}_{nor}\). \(\rho\) is SNR, which is defined as the ratio of the sum of the received power and the noise power.  
The determinant calculation approach can be used to explore the phase characteristics of the channel matrix \(\mathbf{H}\) at maximum channel capacity of the n-order MIMO system. When the number of receiving and transmitting antennas is the same, the \(n\times n\) MIMO channel matrix can be defined as:  
\[\mathbf{H}=\left(\begin{array}{cccc}A_{11}e^{i\varphi_{11}}&A_{12}e^{i\varphi_{1 2}}&\cdots&A_{1n}e^{i\varphi_{1n}}\\ A_{21}e^{i\varphi_{21}}&A_{22}e^{i\varphi_{22}}&\cdots&A_{2n}e^{i\varphi_{2n}} \\ \vdots&\vdots&\ddots&\vdots\\ A_{n1}e^{i\varphi_{n1}}&A_{n2}e^{i\varphi_{n2}}&\cdots&A_{nn}e^{i\varphi_{nn}} \end{array}\right)_{n\times n} \tag{5}\]  
where \(\varphi_{ij}\) is the phase of the channel matrix element and the size of \(\mathbf{H}\) is \(n\times n\). Generally, the distance of the transceiver antennas is quite larger than the spacing of the antenna array elements, resulting in identical amplitudes for the elements in the channel matrix. Therefore, the received power can be considered the same, which means \(A_{ij}=1(1\leq i\leq n,1\leq j\leq n)\), and the phase becomes the main factor that has critical influence on the channel capacity. Therefore, \(\text{det}\big{(}I_{n}+\frac{\rho}{n}\mathbf{H}\mathbf{H}^{*}\big{)}\) can be calculated as:  
\[\text{det}(I_{n}+\frac{\rho}{n}\mathbf{H}\mathbf{H}^{*})
## 3 The Case Study  
The following case study is an investigation of the packaging redesign challenge. The main aim is to show that a thorough analysis and an in-depth investigation of a product's packaging may lead to major changes and multiple benefits. Such benefits include savings in storage and transportation capacity, reduction in material usage through changes in the shape of the product, as well as additional advantages for the environment and the community.  
In this case study, two different bottle options are investigated in terms of shape and material efficiency. The first option is a conventional bottle with variable diameter, while the second is a uniform, fully cylindrical design. The two bottles have the same capacity, and the main aim is to improve material utilization and demonstrate the gains resulting from this process. The comparison between the bottles highlights the strengths and weaknesses of each option.  
To support the analytical formulation, the bottle is modeled as a simplified cylindrical structure, defined by its height and radius. The material is assumed to be homogeneous, with uniform thickness and mechanical properties across the entire surface. Geometric features such as base curvature and neck tapering are deliberately omitted at this stage to preserve generality and ensure parametric clarity. These assumptions provide a solid foundation for the model's initial development, with the possibility for refinement in later stages based on specific design requirements.  
Wolfram Mathematica v.12 was used to calculate the relationship between the size of the redesigned bottle and its height, ensuring that the volume of the initial bottle and the final bottle remains equal. A Mathematica script was developed to calculate the relationship between the parameters mentioned above.  
As a second step, Rhinoceros v.6 CAD software was used to design the bottles and ensure the accuracy of their dimensions.  
The analysis shows that packaging redesign based on a detailed examination can offer significant economic benefits for the company in terms of the volume of materials used during manufacturing. In addition, this substantial material reduction may also contribute to environmental benefits such as lower greenhouse gas emissions, reduced energy consumption in production, and decreased solid waste volumes.
# Reader-Tag Commands via Modulation Cutoff Intervals in RFID Systems +
###### Abstract
In the MCI process, as the tag can calculate different periods of CW \"silence\", the reader cuts off CW for specific periods known to the tags. The tags then interpret these periods as commands depending on the triggered flag by the DBMT circuit. This capability is utilized in anti-collision and counting protocols. The MCI process is applied to tag interrogation (or anti-collision) and tag-counting protocols, which are prominent communication protocols in inventory systems. MCI is evaluated under high and low tag population, and the time (and consequently the power) performance of such protocols was significantly enhanced in anti-collision and counting protocols, respectively. The rest of this paper is organized as follows. In Section 2, MSM is discussed with emphasis on its design limitations. In Section 3, the DBMT circuit design is proposed to target the first two improvement objectives. In Section 4, the MCI process is then proposed to target the third and final improvement objective. Finally, Section 5 evaluates the time efficiency of anti-collision and counting protocols, and the paper is concluded in Section 6.
## 4 Case Study Analysis
### Analysis of Simulation Results
This outcome further validates the advantages of grouped scheduling in enhancing user satisfaction, indicating that a well-structured regional division and resource allocation strategy can significantly improve user experience and optimize the balance of resource supplies in disaster emergency resource scheduling. The average user satisfaction was calculated over the entire simulation period for both scheduling strategies. The results show that the grouped scheduling strategy achieves 24.73% higher average user satisfaction compared to global scheduling. Figure 12: Comparative analysis of user satisfaction in Case 1 (four regions) and Case 2 (one region). Figure 13: Comparative analysis of average user satisfaction in the four regions of Case 1 with the global user satisfaction scenario in Case 2. The cost comparison of different cases is shown in Table 2. In the experiment, the penalty price for power load interruption is set at 6000 CNY/MWh. The interruption cost for freshwater load is set at 30 CNY/ton per hour, which is based on the transfer cost during the Daimon Island drought event [33]. Additionally, the total construction cost of charging stations and reservoirs for both Case 1 and Case 2 is assumed to be CNY 8000. All optimization models were solved using Gurobi. As shown in Table 2, the total cost in Case 1 is significantly lower than that in Case 2, with an optimization margin of approximately 31.4%. This substantial cost reduction is primarily attributed to the adoption of the fast Newman algorithm for regional division, which optimizes resource scheduling. By segmenting regions, resource scheduling becomes more efficient, thereby avoiding additional costs and transportation losses associated with cross-regional resource transfers. Through precise resource deployment and a more efficient scheduling strategy, Case 1 ensures energy and water supply while minimizing unnecessary procurement and transportation expenses. In contrast, Case 2 follows a global scheduling strategy without regional division. Although overall resource allocation is optimized, the lack of consideration of inter-regional resource balance leads to shortages in high-demand areas, subsequently increasing transportation costs and reducing resource utilization efficiency. This comparison demonstrates that the grouped scheduling strategy has significant economic advantages over the global scheduling strategy; it effectively enhances the timeliness and accuracy of resource scheduling, thereby significantly reducing overall costs.
# An Exploration into Damage Repair and Manufacturing Technology of Photomask Glass Substrates
###### Abstract  
There are strict requirements on the surface-shape accuracy, cracks, scratches, and subsurface damage of photomask glass substrates with the advancement of photo-etching technology. In this study, photomask quartz glass substrates were etched with the hydrofluoric (HF) acid etching method after chemical chemical polishing (CMP), so that the polishing hydrolysis layer could be completely removed. In addition, the surface scratches caused by CMP were observed with a surface quality detection device and a white light interferometer. Second, the high-efficiency array-type magnetorheological polishing technology was employed to eliminate the surface-shape error on the surface of quartz photomask glass substrates. Finally, the removal function rotation angle based magnetorheological polishing technology was utilized to evenly remove the mid-frequency ripples and scratches caused in the preceding technological process. The experimental results demonstrated that these technologies can be used to realize rapid damage removal and high-quality processing of photomask glass substrates.  
photomask glass substrate; array-type magnetorheological polishing; removal function rotation angle; damage repair  
Substrates. _Appl. Sci._**2022**, _12_, 10010. [[https://doi.org/10.3390/app12910010](https://doi.org/10.3390/app12910010)]([https://doi.org/10.3390/app12910010](https://doi.org/10.3390/app12910010))  
Academic Editor [PERSON] Received: 25 August 2022  
Accepted: 30 September 2022  
Published: 5 October 2022  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Copyright**: © 2022 by the authors. Liconsee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
**Item**: **Value**  
Surface shape errors Surface roughness (RMS) Less than 2 nm  
Cracks, ripples, pitting Not allowed for sizes larger than 1 \(\upmu\)m  
Scratches Not allowed for sizes less than 1 \(\upmu\)m  
0.031/cm\({}^{2}\) allowed for sizes less than 1 \(\upmu\)m
# A Review of Smart Grid Anomaly Detection Approaches
## 3 Results and Analysis
### Cyber-Attack Detection  
There are behaviors of devices or data on the network that, compared to expected situations, can be classified as unusual, so it is important to know whether such behavior is a cyber-attack. The summary is in Table 10.  
Being able to differentiate a real failure from a disturbance and a smart cyber-attack is critical. Unsupervised anomaly detection employs the Boltzmann machine to detect unobservable attacks based on free energy as the anomaly index and resorts to dynamic Bayesian networks as probabilistic graphical models that can represent the system state as a set of variables [37]. This solution was evaluated considering that occasionally an interaction between sub-systems may occur that is not necessarily an attack, that random attacks exist, or that individual or simultaneous FDI attacks may occur.  
Real-time monitoring and control in SGs are critical to improving reliability and operational efficiency; therefore, in real-time anomaly detection, it is important to take advantage of AMI technology and smart meter (SM) data. This information allows real-time anomaly detection by addressing three challenges: (1) large-scale multivariate count measurements; (2) missing points; and (3) variable selection. This is intended to diagnose, classify based on control limit policies, and evaluate consumer customer facilities [30].  
If an attacker uses reinforcement learning, multi-criteria analysis comes into play for all types of attacks, from network engineering to physical, software, and even social engineering. This is what not only SG managers have to worry about but, in general, all ICPS. The severity of smart attacks in industrial cyber physical systems (SSA-ICPS) framework is an example of what can be applied in this scenario considering four components: (1) the user interface to interact within the tool kernel; (2) the model builder to create and compose the model of the system and the attacker; (3) the verification engine to verify and enforce the system in case of successful attacks; and (4) the library containing templates, attack models, and countermeasures, all supported by a probabilistic symbolic model checker (PRISM), discrete-time Markov chains, continuous-time Markov chains, and Markov decision trees [8].
## 2 Thermal Evaluation of Overhead Electrical Lines
### Dynamic Line Rating  
DLR dynamically adjusts ampacity in real-time in response to changing environmental conditions, aiming to maximize current loading at any given moment. The thermal ampacity of overhead lines can be influenced by both heating and cooling, resulting in fluctuations in performance either upward or downward. The thermal ampacity of the line increases when the conductor is cooled by the wind or when the ambient temperature decreases, allowing for greater power transmission through the line [10].
# Compp-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network
## 1 Introduction
Based on the architecture, we formulated the cooperative optimization problem of computation offloading and resource allocation with the consideration of privacy preservation and task priority as a Markov decision process (MDP) based on three aspects: the end-to-end delay, the computational resource cost, and the ICV's privacy level to minimize the weighted cost in the VEN system. Furthermore, the state, action, and reward states are designed subsequently. 3. In order to effectively solve the above-mentioned problem with continuous variables and meet the requirement of convergence, cooperative optimization for privacy and priority based on DDPG (COPP-DDPG) is proposed. 4. The convergence of the proposed approach is verified by the simulation results. Furthermore, the sets of simulation results of the performance comparison demonstrate the proposed approach exhibits superior performance to the other four baseline algorithms.
## 4 Experimental Analysis and Verification
### Test 1
Due to JDA satisfying the equality of the joint probability density of the data distribution between the target domain and the source domain, it achieved a higher accuracy of 5%, 4.271%, 7.014%, 3.644%, 8.959%, and 16.63% compared to the DBN, S-Alexnet, IG-CWT-CNN, TF-MDA, ICPW-HPF-CNN, and ML respectively. However, JDA did not solve the problem of data distribution discrepancies in the subdomains, so its accuracy could only be maintained at approximately 90%. TCA-DBN globally aligned the data distribution between two domains but did not consider the discrepancies in the data distribution of the same fault type between different domains, resulting in an average accuracy of 95.382%. DSAN achieved subdomain adaptation using LMMD, with an average accuracy of 98.715%. The method proposed in this article aligns the data distribution discrepancies in the subdomains, resulting in an average accuracy of 99.445%. Although both the proposed method and DSAN use LMMD to achieve subdomain adaptation, the proposed method combines divergence factors and SRPLC-K-means, resulting in an accuracy of 0.73% higher than DSAN. Based on the fault diagnosis results of the different methods presented in Table 3 and Figure 6, it can be concluded that the proposed method outperforms the other different methods in diagnosing faults under cross-working conditions.
# A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis
###### Abstract  
Synthesising computed tomography (CT) images from magnetic resonance images (MRI) plays an important role in the field of medical image analysis, both for quantification and diagnostic purposes. Convolutional neural networks (CNNs) have achieved state-of-the-art results in image-to-image translation for brain applications. However, synthesising whole-body images remains largely uncharted territory, involving many challenges, including large image size and limited field of view, complex spatial context, and anatomical differences between images acquired at different times. We propose the use of an uncertainty-aware multi-channel multi-resolution 3D cascade network specifically aiming for whole-body MR to CT synthesis. The Mean Absolute Error on the synthetic CT generated with the MultiRes\({}_{unc}\) network (73.90 HU) is compared to multiple baseline CNNs like 3D U-Net (92.89 HU), HighRes3 DNet (89.05 HU) and deep boosted regression (77.58 HU) and shows superior synthesis performance. We ultimately exploit the extrapolation properties of the MultiRes networks on sub-regions of the body.  
MR to CT synthesis; multi-resolution CNN; uncertainty +
Footnote †: journal: Medical Sciences
## 3 Results and Discussion
### Bending Test at 16 mm
#### 3.3.2 Spring Back  
The spring back was then measured, with the values recorded in the box plot of Figure 13. Again, the dashed line represents the mean measure of the elastic recovery measured at the end of the bending test. The graph shows that lozenge and roller have a good recovery at the end of the third bending and recovery cycle, with a slight decrease in the value of the angle as the number of cycles increases. The data are variable, especially  
Figure 12: Lozenge (**a**) and roller (**b**) structure bending test at 16 mm.  
for the elastic recovery of the second cycle, while this variability is reduced for the first and third cycles.  
The observed spring back response confirms the typical viscoelastic and shape memory behavior of PLA. According to [9], shape memory polymers such as PLA exhibit gradual reduction in elastic recovery over successive cycles, especially when thermal recovery is incomplete. Ref. [56] also observed that simplified geometries tend to preserve their spring back capacity better, which supports the results seen in the roller structure.