esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Case Study
### Importance of Considering Retransmission  
Either with or without retransmission, the optimal distance between the neighboring sensors can be obtained from the associated optimization model. The results of optimal lifetime are shown in Tables 14 and 15 for uniform and non-uniform deployments, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(\alpha\) & 4 & \(E_{0}\) & \(54\) J & \(P\) & 1 packet/s \\ \(\beta_{1}\) & \(5.0\times 10^{-8}\) J/bit & \(f\) & \(8.68\times 10^{8}\) Hz & \(P_{t}\) & \(-6\) dB \\ \(\beta_{2}\) & \(1.3\times 10^{-15}\) J/bit & \(l_{ack}\) & 120 bits & \(R_{s}\) & 50 m \\ \(\beta_{3}\) & \(5.0\times 10^{-8}\) J/bit & \(l_{data}\) & 160 bits & \(R_{t}\) & 80 m \\ \(\beta_{4}\) & \(4.0\times 10^{-8}\) J/bit & \(m_{ack}\) & 120 bits & \(S_{r}\) & \(-98\) dB \\ \(R_{a}\) & 500 m & \(m_{data}\) & 448 bits & \(t\) & 300 s \\ \(S^{*}\) & 0.6 & \(a\) & \(1\) & \(\tau^{*}\) & 365 days \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the homogeneous WSN. The details of the symbols can be seen in the Appendix.  
From Tables 11 and 12, one can see that the optimal sensor deployment scheme of WSN with retransmission (WSNWR) is quite different from that of WSN without retransmission (WSNWoR). Basically, the distance between sensors for WSNWR is longer than that of WSNWoR, which results in a smaller number of layers. From the two tables above, one can see that the success transmission rate is much higher with retransmission, as retransmission can improve the success transmission rate between hops, which enhances the whole WSN. The lifetime of WSNWoR is overestimated since the energy consumption of retransmission is not included. Moreover, to satisfy a given \(\tau^{*}=365\) days, the total number of sensors required for WSNWR is less than that of WSNWoR. This is because the WSN will be more reliable when retransmission is considered. Therefore, it is meaningful to consider retransmission in the sensor-deployment optimization for a WSN. In the following sections, we only focus on the WSN under retransmission.
## 7 Concluding Remarks  
The results obtained in this paper highlight the importance of highway alignment in vehicle dynamic load evaluation. In fact, the vehicle-highway alignment interaction influences vehicle dynamic loads and should be considered in pavement design and maintenance. Based on this study, the following comments are offered:  
* A novel approach was presented to evaluate the effect of the vehicle-highway alignment interaction on vehicle dynamic loads using a WFT. The interaction was verified using field investigation of pavement damages, alignment characteristics, and a dynamic load test.
* The wheel six-component force measurement system proposed in this paper and the use of data mining techniques were critical to understanding the relationship between the vehicle-highway alignment and vehicle dynamic loads.
* The proposed dynamic load prediction models implemented two data mining techniques (ANN and SVM). The ANN-based models with different parameters (five, four, and three) were established to evaluate the relative importance of geometric variables. The results showed that model prediction performance decreased as the number of parameters decreased. The ANN-based model shows a better performance in predicting vehicle dynamic loads than the SVM-based model.
* This research presented in this paper is an initial study aimed at establishing a comprehensive, rapid, and reliable approach to identify pavement distress under vehicle-alignment interactions. Future research will focus on: (a) _verifying model robustness_, where solid work will be conducted under large-scale scenarios (road sections and vehicle types); and (b) _developing an integrated software platform_ that will assist engineers in making more appropriate highway alignment design and maintenance decisions based on the dynamic loads.  
**Author Contributions:** Conceptualization, M.L.; Data curation, M.L.; Formal analysis, M.L.; Investigation, M.L.; Methodology, M.L.; Project administration, C.H.; Resources, C.H.; Software, M.L.; Supervision, M.L. and C.H.; Validation, S.M.E. and Z.J.; Visualization, S.M.E. and Z.J.; Writing--original draft, M.L.; Writing--review & editing, S.M.E. and Z.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research work was supported by the National Natural Science Foundation of China (Grant Number: 51978172).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing is not applicable to this article.  
**Acknowledgments:** The authors also thank the teammates who contributed to this project in various ways.  
**Conflicts of Interest:** The authors declare no potential conflict of interest.
# Digital Twin-Based Clamping Sequence Analysis and Optimization for Improved Geometric Quality
## 1 Introduction
### Geometry Assurance Digital Twin  
Digital twin-based manufacturing and process optimization has been introduced recently, controlling the manufacturing processes in real time, utilizing a digital counterpart of the process [30; 31; 32; 33]. For controlling the geometric quality of the assembly process, the geometry assurance digital twin concept has been introduced, steering the sheet metal assembly process [34]. The digital twin for the geometry assurance of sheet metal assemblies is a digital replica of the assembly system, including the physics of the process, to steer and control the assembly of the sheet metals. This twin relies on the 3D-scanned geometries of the incoming fabricated components. Based on this information, the parts are being matched for the optimal geometric outcome. Part matching has been considered as a Selective Assembly process for the sheet metal parts [35]. Later, the shimming process for the fixture locators is optimized based on the incoming geometric error of the parts in the fixture [36]. While the parts are being held in the fixture, the welding sequence is being optimized. In the twin, the critical welding points are being identified, and the sequence is deterministically decided for the existing error after fixturing [37]. A rapid sequence optimization approach has been specifically developed for this purpose [38]. In the geometry assurance twin, the clamping step has been considered to take place simultaneously with the locating step. In this paper, we introduce a computationally efficient clamping sequence analysis and optimization for this twin, taking into consideration the individual part deviation based on the 3D-scanned geometries.
# Agricultural Waste-Based Biochar for Agronomic Applications
## 3 Biochar as a Soil Amendment
### Impacts of Biochar on Plant Growth  
Biochar addition stimulates soil fertility and then improves seed germination and crop productivity. [PERSON] et al. [142] recorded an increase in maize grain yield by about 98% at biochar application rates of 15 and 20 t/ha. Similarly, an increase of 12 to 14% was observed in rice yield in a paddy rice soil in China after the addition of biochar at application rates of 10 and 40 t/ha without fertilizer addition [91]. [PERSON] et al. [143] reported a three-fold increase in biomass production of rapeseed crop in a heavy metal-contaminated soil after 10% biochar addition. [PERSON] et al. [144] reported an increase in cherry tomato yield by about 64% during a pot experiment, with simultaneous enhancement in P and K uptake and soil chemical properties. An increase in pH, N, P, K, Ca and Mg concentrations in a low-organic carbon and -fertility soil in the southeastern United States was observed by [PERSON] et al. [145] after addition of peanut hull biochar. In the same study, authors recorded a simultaneous significant response of corn yield following biochar application during the experiment lasting for two-year. The depth of roots and the existence of biochar in the root zone play a primordial role in defining the influence of biochar addition on plant growth. [PERSON] et al. [146] reported that a deep rooting of more than 1 m is responsible for the insignificant productivity of maize as compared to pasture grass crop characterized by shallow rooting zones (<30 cm).  
Although biochar has been shown to have a positive effect on soil, this cannot always be confirmed for plant growth and yield [98]. [PERSON] et al. [147] reported an increase in radish dry matter after the addition of both biochar and nitrogen fertilizer, but no increase in yield was observed when biochar was used alone, even at the highest application rate of 100 t/ha. [PERSON] et al. [148] observed neither a positive nor a negative effect on maize yield after biochar application, and also [PERSON] et al. [149] observed lower biomass yield of Suaeda salsa in saline sodic soil. According to some results in the literature, biochar can act positively on same plant species, while negative or no significant effect can be recorded for other species. [PERSON] et al. [98] observed a reduction of 18% in banana yield, while no remarkable effect was reported for papaya yield. [PERSON] et al. [150] also reported that the application of biochar did not induce any effect on sunflower biomass yield. Similarly, [PERSON] et al. [151] showed that biochar addition can increase the flower and fruit yield of pepper plants, but no simultaneous increase in tomato yield was observed.
# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
###### Abstract  
Salinization in arid or semiarid regions with water logging limits cropland yield, threatening food security. The highest level of farmland salinization, that is, abandoned salinized farmland, is a tradeoff between inadequate drainage facilities and sustainable farming. The evolution of abandoned salinized farmlands is closely related to the development of cropping systems. However, detecting abandoned salinized farmland using time-series remote sensing data has not been investigated well by previous studies. In this study, a novel approach was proposed to detect the dynamics of abandoned salinized farmland using time-series multispectral and thermal imagery. Thirty-two years of temporal Landsat imagery (from 1988 to 2019) was used to assess the evolution of salinization in Hetao, a two-thousand-year-old irrigation district in northern China. As intermediate variables of the proposed method, the crop-specific planting area was retrieved via its unique temporal vegetation index (VI) pattern, in which the shape-model-fitting technology and the \(K\)-means cluster algorithm were used. The desert area was stripped from the clustered non-vegetative area using its distinct features in the thermal band. Subsequently, the abandoned salinized farmland was distinguished from the urban area by the threshold-based saline index (SI). In addition, a regression model between electrical conductance (EC) and SI was established, and the spatial saline degree was evaluated by the SI map in uncropped and unfrozen seasons. The results show that the cropland has constantly been expanding in recent decades (from \(4.7\times 10^{5}\) ha to \(7.1\times 10^{5}\) ha), while the planting area of maize and sunflower has grown and the area of wheat has decreased. Significant desalinization progress was observed in Hetao, where both the area of salt-affected land (salt-free area increased approximately \(4\times 10^{5}\) ha) and the abandoned salinized farmland decreased (reduced from \(0.45\times 10^{5}\) ha to \(0.19\times 10^{5}\) ha). This could be mainly attributed to three reasons: the popularization of water-saving irrigation technology, the construction of artificial drainage facilities, and a shift in cropping patterns. The decrease in irrigation and the increase in drainage have deepened the groundwater table in Hetao, which weakens the salt collection capacity of the abandoned salinized farmland. The results demonstrate the promising possibility of reutilizing abandoned salinized farmland via a leaching campaign where the groundwater table is sufficiently deep to stop salinization.  
s 2021 13 4057 10.3390/rs13204057 Issue Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, Wuhan 430072, China; [EMAIL_ADDRESS] (L.Z.); [PERSON]IL_ADDRESS] (Q.Y.); [EMAIL_ADDRESS] (Q.Z.)  
salinization; abandoned salinized farmland; remote sensing; Hetao Irrigation District
# Integrating a Blockchain-Based Governance Framework for Responsible AI
## 2 Programming Smart Contracts for Responsible AI  
Smart contracts can be used to enforce specific ethical or compliance-related norms around the development and deployment of AI systems in the context of responsible AI [49]. A smart contract, for example, might be used to verify that an AI system is only utilised for its intended purpose and that the system's decision-making processes are visible and auditable [50]. The following are some of the uses of adopting smart contracts for responsible AI:  
* **Algorithmic Transparency:** Smart contracts can be used to ensure that the decision-making processes of an AI system are transparent and auditable [51]. For example, a smart contract can be used to automatically log all of the inputs and outputs of an AI system, as well as the specific decision-making rules that it uses [52].
* **Bias Mitigation:** Smart contracts can be used to ensure that an AI system is not biased against certain groups of people. For example, a smart contract could be used to automatically check that an AI system's training data are diverse and representative of different groups of people, and that the system's decision-making processes do not disproportionately harm certain groups [53].  
Figure 3: Taxonomy of blockchain for responsible AI.  
* **Explainability:** Smart contracts can be used to ensure that the reasoning behind an AI system's decisions are explainable to human stakeholders [54]. For example, smart contracts can be used to automatically generate a human-readable explanation of the decision-making process used by an AI system [55; 56].
* **Data Privacy:** Smart contracts can be used to ensure that an AI system's access to and use of sensitive data is controlled and compliant with regulations such as the GDPR [57]. For example, a smart contract could be used to automatically encrypt and decrypt data and to ensure that data are only used for specific pre-approved purposes [58].  
It is worth noting that this is an area that is currently under active research and development [59]. There are many challenges to be addressed, such as the complexity of AI systems and the lack of clear regulations and standards for responsible AI [60]. Thus, although smart contracts can be a useful tool for enforcing certain aspects of responsible AI, it is important to keep in mind that they are not a panacea and there is a great deal of work to be done in order to ensure that AI systems are truly responsible and ethical.
# Review of Advanced Implementation of Doppler Backscattering Method in Globus-M
## 1 Introduction
An in-depth look into the gradual change and improvement of the DBS systems on Globus-M from a one-frequency homodyne detection system to several multi-frequency systems can also be found in this review.
## 2 Materials and Methods
### Theoretical Principle Used for Energy and Exergy Analyses  
The below hypotheses were completed for developing the solution scheme for the energy and exergy analyses equations of the corn drying system:  
* The initial total weight of the corn in the dryer was regarded as 50 t;
* All components of the dryer were assumed to be operating in a stable state;
* The moisture of corn diffuses to the drying medium in the form of gas;
* The temperature potential inside a single corn kernel was ignored;
* The loss of heat stemming from inertia, convection and radiation in the drying chamber was ignored;
* The moisture (M\({}_{\text{ar}}\)), oxygen (O\({}_{\text{ar}}\)), ash (A\({}_{\text{ar}}\)) content and low heat value (\(LHV\)) of coal used in the present project were assumed to be 8.0%, 3.19%, 19.02% and 28.073 MJ/kg, respectively [34].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Instruments** & **Model** & **Measurement Scope** & **Accuracy** \\ \hline Temperature resistance & PT100 & \(-\)200–450 \({}^{\circ}\)C & \(\pm\)0.1 \({}^{\circ}\)C \\ Pyrometer couple & WRN-130/230 & 0–1300 \({}^{\circ}\)C & \(\pm\)0.1 \({}^{\circ}\)C \\ Anemograph & DT-8893 & 0.001–45 m/s & 0.01 m/s \\ Temperature and humidity sensor & AM2301 & 0–100\%/\(-\)40–80 \({}^{\circ}\)C & \(\pm\)3\%/\(\pm\)0.5 \({}^{\circ}\)C \\ Data-acquisition system & Self-developed & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptions of the related experimental instruments.  
Figure 3: Thorough schematic view of the dryer and instrumentations assisted by energy and mass flow.
## 2 Materials and Methods
### EO Composition Analysis
#### 2.4.1 Gas Chromatography-Flame Ionization Detection (GC-FID)  
A Perkin-Elmer Clarus 400 gas chromatograph (PerkinElmer, Waltham, MA, USA) with two flame ionization detectors, a data handling system, and a vaporizing injector port was used in GC-FID analyses. The chromatograph was fitted with two columns of varying polarities: a DB-1 fused-silica column (100% dimethylpolysiloxane, 30 m \(\times\) 0.25 mm i.d., film thickness 0.25 \(\upmu\)m) and a DB-17 HT fused-silica column ((50% phenyl)-methylpolysiloxane, 30 m \(\times\) 0.25 mm i.d., film thickness 0.15 \(\upmu\)m), both from J & W Scientific Inc. (RanchoCordova, CA, USA). The oven temperature was set at 3 \({}^{\circ}\)C/min from 45 \({}^{\circ}\)C to 175 \({}^{\circ}\)C and at 15 \({}^{\circ}\)C/min until reaching 300 \({}^{\circ}\)C, where it was maintained isothermally for the last 10 min. The injector and detector were set to 280 \({}^{\circ}\)C and 300 \({}^{\circ}\)C, respectively. The carrier gas used was hydrogen at 30 cm/s. The samples were injected using the split sampling technique at a ratio of 1:50. The volume of injection was 0.1 \(\upmu\)L of an \(n\)-pentane-EO solution (1:1). The percentage composition of the volatiles was computed by the normalization method from the GC peak areas and calculated as mean values of two injections, from each sample, without using the response factors, in accordance with ISO 7609 [18].
# A Simple Designed Lens for Human Centric Lighting Using LEDs
## Ray-Tracing Simulation of Lighting Using Freeform Lenses  
### Free-Form Lens Design  
The free-form lens does not have any predefined shape, but in order to keep it simple and easy to produce, we concentrate on changing the inner and outer curvatures of a conventional lens to find the best luminous intensity distribution. Figure 1 shows different shapes of lenses for simulation. It is noticed in these models that they are asymmetrical and the thickness of each corner is at variance. This is done on purpose, since the thickness of the lens and angle of incidence affect the bending of light. The farfield of one LED shown in Figure 2 presents the relation between luminous intensity and the beam angle; at angle 0, the intensity is 100 percent then it drops down to 60 percent at 50\({}^{\circ}\) then 20 percent at 70\({}^{\circ}\), which implies that one light source alone provides high luminosity onto a certain narrow area and the surroundings are weakly illuminated. Intensive narrow illuminated area scales down the effective reflectance surface, hence, scale down the productivity of uplighting. This leads to the idea of using light accumulated from many LED sources for a uniform distribution. The lenses take part in widening this intensive illuminated area by bending the light from the direct angle (0-30 degrees) to the non-direct (30 degrees and more). To cover a 3 mm \(\times\) 3 mm single LED, we design a lens with a width of about 1.5 cm to cover the whole LED. All the lens models have identical width but different curvatures and smooth surfaces are required. These lenses can be easily manufactured with high production rate using moulding methods since their forms do not require extreme accuracy for experimental tools [19; 25].  
Figure 1: Designed lenses by modifying inner and outer curvatures and thickness.
## 1 Introduction
The agent employs a Temporal Differences (TD) learning technique [29] to learn from the neighboring clusterheads, evaluates its local decision accuracy, distance to the clusterheads which translates into energy consumption and selects an optimal action policy that optimizes its performance in determining an optimal clusterhead. Although RL is a well-known machine learning technique and has been extensively applied to many fields such as cooperative sensing [3, 30], channel sensing sequence [26, 22], energy efficient communication [2, 31], its application in clustering algorithm is still at the infancy stage. Therefore, our contributions in this paper can be summarized as follows:  
1. We propose a novel energy-efficient clustering algorithm that is aware of the dynamic radio environment and allows member nodes to learn an optimal policy for choosing optimal clusters based on local decision accuracy and energy consumption for cooperative sensing and data communication. 2. The proposed algorithm implements pairwise constraints in spectrum-aware clustering such that only SUs with at least one common vacant channel with a clusterhead and within the clusterhead's one hop radio range can form a cluster. 3. We model network energy consumption, cooperative channel sensing, inter-cluster and intra-cluster data communication energy consumptions and determine an optimal number of network clusters that minimizes network energy consumption. 4. We show the performance improvements of the proposed clustering algorithm over Groupwise constraint-based algorithms [17, 18] in terms of energy efficiency, channel sensing performance and computational complexity, which make it more attractive for resource constrained devices such as CR-WSNs. In addition, the algorithm eliminates network instability due to re-clustering when the SUs detect PUs' arrival.
# Effect of Substrates on the Dynamic Properties of Inkjet-Printed Ag Thin Films
## 2 Vibration Transfer of Ag Inkjet-Printed on Flexural Substrates
### Bending Stiffness of the Bilayer Beams for Analyzing Vibrations on the Inkjet-Printed Ag Thin Films  
To analyze the vibration transfer of ink on the substrates, the combined bending stiffness of the bilayer beam was used. The bilayer beam consists of the substrate and the printed ink. To obtain the Young's modulus of the film on the substrate, the bending stiffness of the substrate should be identified. When the strain distribution is linear across the thickness of the bilayer beam, the stress equilibrium for the neutral axis is expressed as [28]  
\[\int_{-h_{n}}^{h_{s}-h_{n}}E_{s}zdz+\int_{h_{s}-h_{n}}^{h_{s}-h_{n}+h_{f}}E_{ f}zdz=0 \tag{7}\]  
where \(h_{s}\) and \(h_{f}\) are the thicknesses of the substrate and the film, respectively, \(h_{n}\) is the distance of neutral axis from the surface of the beam, \(E_{s}\) and \(E_{f}\) are the Young's moduli of the substrate and the film, respectively, and \(z\) is the thickness-wise coordinate from the neutral axis. The distance, \(h_{n}\), is determined from Equation (7) as  
\[h_{n}=\frac{1}{2}h_{s}+\frac{E_{f}h_{f}\Big{(}h_{f}+h_{s}\Big{)}}{2\Big{(}E_{ f}h_{f}+E_{s}h_{s}\Big{)}} \tag{8}\]  
The moment equilibrium about the neutral axis is given as [29]  
\[D_{c}=\int_{-h_{n}}^{h_{s}-h_{n}}E_{s}z^{2}dz+\int_{h_{s}-h_{n}}^{h_{s}-h_{