esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Remote Sensing Indices and Analytical Tools for Shoreline Monitoring
#### 3.2.2 Digital Shoreline Analysis System (DSAS) Overview
Further analysis showed that WLR provided a mean shoreline change of 0.55 m/year, incorporating uncertainty measurements that enhance the accuracy of coastline change estimates, but all of this was achieved by adding more complex calculations and data for the accurate weight of results. [PERSON] et al., 2022 [69] conducted research along the coast of Keta (Ghana) using EPR, resulting in a mean erosion rate between 1991 and 2018 of 1.64 m/yr, while the investigation of the mean erosion rate using LRR between 1991 and 2018 resulted in a value of 2.22 m/yr. [PERSON] et al., 2023 [50] investigated the Southwestern Coast (South Africa), indicating that 95% of the study area had received negative NSM over the past 83 years, with an average movement of \(-\)38 m. The greatest NSM recorded was \(-\)99.29 m along Sixteen Mile Beach, while the least affected area was Yzerfontein Main Beach, with an average of \(-\)8.58 m. The SCE showed variability in shoreline movement, with Sixteen Mile Beach experiencing the most significant average change of 52.13 m, while the EPR results indicated that all Pearl Bay Beach transects had negative EPRs, averaging \(-\)0.36 myr, while Yzerfontein Main Beach had the smallest average EPR of \(-\)0.10 myr. LRR revealed a dominant trend of landward movement along the coastline, with 86% of the study site showing negative rates and an average rate of \(-\)0.28 myr. Finally, the WLR provided a more reliable rate of change, showing that Sixteen Mile Beach had a maximum erosion rate of \(-\)1.19-0.47 myr. The author acknowledged that the data extracted from Landsat imagery have large uncertainty values (>15 m), which can affect the reliability of the results. This uncertainty necessitates the use of methods like the WLR to provide more reliable rates of change, s only 34% of the study site transects have significantly negative rates of change and none of the positive LRR values are considered statistically significant for any of the beaches, which may limit the robustness of the findings. Conclusively, EPR is best for short-term analyses where simplicity is key, but its lack of intermediate data consideration limits its use in dynamic contexts. LRR and WLR excel in long-term, comprehensive analyses, with WLR offering enhanced accuracy through uncertainty weighting. NSM and SCE are straightforward for spatial trend visualization but lack temporal and rate-based insights.
## Appendix B The Carbon Flux Module
### Tree Photosynthesis  
The potential \(GPP_{tree}\) (in Mg C ha\({}^{-1}\) a\({}^{-1}\)) for one tree is calculated on the leaf level using a light-response function and is then integrated over the entire canopy [40]. The light availability for each tree is based on the individual light climate (i.e., shading) and the photosynthetically active period defined by day length and vegetation period. Climatic input variables are used to calculate reduction factors of potential \(GPP\) due to a reduced soil water content or temperature stress.  
Without any reduction (under optimal climate conditions) the potential \(GPP_{tree}\) of a tree is:  
\[GPP_{tree}(I_{ind}(PPFD(t)))=\frac{p_{max}}{k}ln\Bigg{\{}\frac{\alpha kI_{ind} (PPFD(t))+p_{max[1-m]}}{\alpha kI_{ind}(PPFD(t))e^{-kLAI}+p_{max[1-m]}}\Bigg{\}} A_{c}\psi\]  
where \(p_{max}\) is the species-specific maximum photosynthetic rate (\(\mu\)mol(CO\({}_{2}\)) m\({}^{-2}\) s\({}^{-1}\)) of the tree species, \(\alpha\) is the species-specific initial slope of the light-response curve (\(\mu\)mol(CO\({}_{2}\)) \(\mu\)mol(photons)\({}^{-1}\)), \(k\) is the light extinction factor and m is the transmission coefficient of the leaves. \(I_{ind}\) is the fraction of the PPFD at daily time step \(t\) that reaches the top of the individual tree. \(A_{c}\) (m\({}^{2}\)) is the crown area, and \(\psi\) the photosynthetically active period of the time scale.  
Under climate stress conditions, \(GPP_{tree}\) can be reduced due to a limited available soil water content and due to temperature dependency [41]. The reduction of \(GPP\) is expressed through a reduction factor \(\varphi_{SW}(t)\) (0, 1) for temperature and \(\varphi_{SW}\) (0, 1) for water stress:  
\[GPP_{tree}=GPP_{tree}(t)\cdot\varphi_{SW}(t)\cdot\varphi_{T}(t)\]  
The temperature reduction factor is calculated by the following normal distribution function based on \(T_{opt}\), an optimal temperature without reduction, and \(T_{sig}\), which defines the range of temperatures distributed around \(T_{opt}\) with nearly no reduction [41]:  
\[\varphi_{T}*(t)=e^{-(\frac{-T(t)-T_{opt}}{T_{sig}})^{n}}\]  
Water deficit influences the potential photosynthesis negatively when the soil water content is below the available soil water content for the trees (\(SW_{mns}\) which can be calculated through the \(SW_{pwp}\) and the \(SW_{fc}\) of the soil [71].  
\[SW_{msw}=SW_{pwp}+0.4\Big{(}SW_{fc}-SW_{pwp}\Big{)}\]  
\(SW_{msw}\) defines the minimum soil water content with no reduction, therefore the reduction factor \(\varphi_{SW}\) is calculated as followed [41]:  
\[\varphi_{SW}(t)=\left\{\begin{array}{rl}0:SW(t)<SW_{pwp}\\ \frac{SW(t)-SW_{pwp}}{SW_{mns}-SW_{pwp}}:SW_{pwp}<SW(t)<SW_{mns}\\ 1:SW(t)>SW_{mns}\end{array}\right.\]  
The soil water content (SWC) is calculated using the daily sum of incoming precipitation, interception by leaves, transpiration of trees, and above- as well as below-ground water runoff.
## 4 Discussion
### Comparison of Existing Studies
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Ecological** & **Items** & **This** & **[36]** & **[37]** & **[38]** & \multicolumn{3}{c}{[42]} & \multicolumn{3}{c}{[44]} & \multicolumn{3}{c}{[56]} & \multicolumn{3}{c}{[62]} & \multicolumn{3}{c}{[70]} & \multicolumn{3}{c}{[71]} & \multicolumn{3}{c}{[72]} & \multicolumn{3}{c}{[73]} \\
**Service Types** & **Study** & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** & **J** & **K** & **L** \\ \hline Provisioning & Primary products supply & 5293 & 8933 & 33,685 & 9778 & 13,877 & 12,115 & 22,100 & 11,913 & 3643 & 15,500 & 18,622 & 16,697 & 17,590 \\ \hline \multirow{6}{*}{Regulating service} & Carbon fixation and oxygen release & \multirow{2}{*}{10,015} & \multirow{2}{*}{87,34} & \multirow{2}{*}{19,152} & \multirow{2}{*}{/} & \multirow{2}{*}{11,141} & \multirow{2}{*}{9726} & \multirow{2}{*}{2300} & \multirow{2}{*}{3295} & \multirow{2}{*}{10,643} & \multirow{2}{*}{16,500} & \multirow{2}{*}{24,282} & \multirow{2}{*}{5071} & \multirow{2}{*}{3430} \\ \cline{2-2} \cline{6-13}  & Climate regulation & 40,125 & / & 16,174 & 11,406 & 16,175 & 15,294 & 1700 & 32,942 & 813 & 1000 & / & / & 26,330 \\ \cline{2-2} \cline{6-13}  & Groundwater recharge & 1546 & 45,931 & / & 1891 & 11,518 & 12,009 & 15,000 & 19,200 & 9000 & 600 & / & / & / \\ \cline{2-2} \cline{6-13}  & Flood regulation & 4530 & / & / & / & / & / & / & 3473 & 1463 & 1400 & / & / & 3000 \\ \cline{2-2} \cline{6-13}  & Material purification & air purification & 1944 & / & 47,313 & 941 & / & / & / & 2610 & / & / & / & 204 & 500 \\ \cline{2-2} \cline{6-13}  & water purification & 2027 & / & 12,530 & / & / & / & 800 & 2027 & 810 & / & & / & \\ \hline Cultural service & Landscape aesthetic & 9 & / & / & / & / & / & 10 & 9 & / & / & / & / & / \\ \hline \multirow{6}{*}{Negative service} & Subtotal & 65,488 & 63,598 & 128,854 & 24,016 & 52,711 & 49,144 & 41,910 & 75,470 & 26,381 & 35,000 & 42,904 & 21,972 & 50,850 \\ \cline{2-2} \cline{6-13}  & GHG emission & 9126 & / & / & / & 2385 & 2385 & 18,300 & 8244 & 7524 & 600 & / & / & 10,140 \\ \cline{2-2} \cline{6-13}  & Water consumption & 612 & / & / & / & / & / & / & 4071 & 300 & / & / & / \\ \cline{2-2} \cline{6-13}  & Agricultural chemical pollution & 1034 & / & / & / & / & / & / & 1018 & 691 & 100 & / & 1961 & 1240 \\ \hline \multirow{6}{*}{Net total ecosystem service value} & \multirow{2}{*}{54,716} & \multirow{2}{*}{63,598} & \multirow{2}{*}{128,8
# Evolutionary Game for Content Cache in a mm-Wave-Based Vehicular Fog
## 2 Related Works  
### Vehicular Fog and Coded Cache  
[PERSON] et al. [5] proposed a vehicular fog that consists of embedded computers of vehicles and roadside units, in which vehicles share computing resources such as processors, storages, various sensors, etc and exchange safety data using multi-hop V2X communications. Several researches [8; 9; 10] about efficient resource allocation and energy-saving followed. As mentioned in [11], the vehicular fog prefers information-centric networking (ICN) to conventional host-centric networking [12; 13; 14; 15] for data delivery.  
The ICN consists of two key functionalities; one is to discover a publisher location of a Named Data Object (NDO) (or content) and the other is to deliver the NDO from a publisher to a subscriber. Those two procedures happen together or separately. In the coupled approach, the NDO query results in the NDO acquisition without any further procedure, while the decoupled approach requires additional procedures asking the NDO with the publisher's location. Additionally, existing ICN solutions can be classified into two categories, proactive and reactive approaches, according to the discovery mechanism, e.g., NetInf, PURSUIT and COMET [12; 13; 14; 15]. Content-centric networking (CCN) as the reactive approach can know NDO location by flooding Interest packets without any registration procedure. Content Routers (CRs) maintain a Pending Interest Table (PIT) to prevent duplicated Interest flooding and trace a reverse route to a subscriber using the \"breadcrumb\" in the PIT. Such CCN can be applied to the vehicular network as a vehicular content-centric network (VCCN) [16].  
[PERSON] et al. [14] showed that caching technique can improve ICN performance by increasing content availability. Especially, it is useful for the VCCN that suffers from a dynamic network topology due to mobility. Considering limitation in local storage compared to countless contents, efficient caching policy is needed, for example, the popularity of contents following the zipfs law [17; 18]. However, the simple distribution of content replicas still wastes limited vehicle storage.  
Instead, coded cache that has only partial blocks of a coded content can be used for the VCCN, which removes the duplication and complexity of the download scheduling.  
Coded cache for distributed storage has been explored in many previous researches. First, [PERSON] et al. [19] proposed to apply network coding (NC) and its architectural case for the ICN. Such CCN-NC was exploited more by [20; 21], where they designed protocols and cache management framework in detail. [PERSON] et al. [22] showed an optimal decentralized scheme using multicast of coded contents without any coordination in a content delivery network (CDN) and which is feasible only when nodes have homogeneous storage capacity [23]. Recently, [PERSON] et al. [24] proposed a coded caching algorithm for a distributed multi-server system to reduce the peak data rate and [PERSON] et al. [25] also proposed a practical coded storage and delivery scheme using maximum distance separable (MDS) codes.
## 5 Simulation Experiment and Analysis  
We analyze from three aspects: multi-machine collaborative path planning, algorithm performance, and knowledge correlation. The effectiveness of the KD-SAC algorithm proposed in this paper is verified by comparing it with RI-MAC [21], PSO [22], and SAC [23] under the same environment, where SAC is a DRL algorithm with no policy model, which is similar to the strategy in this paper. Consider the following three experimental scenarios: (1) Multi-UAV collaborative search trajectories planning based on invoking knowledge base in a barrier-free environment; (2) Incorporating two UAVs to execute missions in complex mission area environments; (3) Incorporating four UAVs to execute missions in complex mission area environments. To verify and compare the learning performance of KD-SAC and SAC, we provide the values of similar simulation hyper-parameters in paper [23], as shown in Table 2. In addition, to accelerate the learning rate of KD-SAC, we increase the values of \(\lambda_{Q},\lambda_{\pi}\), and \(\lambda\).
## 3 Problem Formulation  
For the considered VEC network, we aim to minimize the average AoI under given resource constraints. This is achieved by jointly optimizing the variables associated with the offloading ratio \(\xi\), offloading object \(\mathbf{B}\), and subcarrier allocation \(\mathbf{W}\). Mathematically, the task offloading and subcarrier allocation problem for the considered VEC network is expressed by  
\[\begin{array}{ll}\mathbf{P_{1}}&:\min_{\mathbf{B},\mathbf{B},\mathbf{W}}\lim_{T\to\infty} \frac{\sum_{i=1}^{T}\sum_{j=1}^{V}AoI_{i}(t)}{\mathcal{V}T}\\ \text{s.t.}&\text{C1}:0\leq\xi_{i}\leq 1\\ &\text{C2}:\beta_{ij}\in\{0,1\},\quad i\in\mathcal{TV},\ j\in\mathcal{N}\\ &\text{C3}:\sum_{j\in\mathcal{N}}\beta_{ij}=1\\ &\text{C4}:\mathcal{N}\in\{0,1,2,\dots,S\}\\ &\text{C5}:w_{s,i,j}\in\{0,1\}\\ &\text{C6}:\sum_{i\in\mathcal{TV},j\in\mathcal{N}}w_{s,i,j}=1,\quad s\in \mathcal{K}\end{array} \tag{15}\]  
where C1 is the range of values for the offloading ratio; C2 and C3 denote that only one offloading object can be selected, i.e., either to the RSU or to the service vehicles; C4 denotes the set of nodes that can be served; and C5 and C6 denote that each subcarrier is assigned to at most one task vehicle.  
It is noted that problem \(\mathbf{P_{1}}\) is an NP-hard mixed-integer nonlinear programming problem (MINLP) because of its non-convex objective function and constraints, high coupling, and the involvement of both continuous and discrete variables. Therefore, the formulated optimization problem (\(\mathbf{P_{1}}\)) is highly complex and challenging to solve using conventional mathematical approaches [39; 40]. Due to the limitations of traditional methods, DRL has gained increasing attention for its ability to adapt to dynamic environments. To address this challenge, Section 4 introduces an effective DRL-based method designed to tackle this problem efficiently.
# Error Analysis of Integrated Absorbance for TDLAS in a Nonuniform Flow Field
## 5 Conclusions  
This work studied the IA error of TDLAS technology in the case of a nonuniform flow field. The IA error was quantified using line-shape parameters (i.e., the line damping parameter \(y_{i}\), normalization coefficient \(x^{\prime}_{i}\), and IA \(A_{i}\)). See Supplementary Materials for the IA error distribution table. A general method of estimating the IA error for the nonuniform flow field was given. The three main findings of the present study are summarized as follows.  
First, the error is affected by the coupling of the flow field nonuniformity, and the absorption line features. For any isolated absorption line, the error is basically less than 0.01 when the physical environment of the flow field does not change drastically. The error may exceed 0.1 when the temperature or pressure gradient is large. An absorption line overlap affects the magnitude of the IA error for poorly isolated absorption lines. However, the absorption line overlap hardly affects the overall error distribution.  
Second, the IA error is basically less than 0.05 for a flow field in which the temperature and the measured partial pressure have a specific linear relationship. TDLAS technology has good applicability in such nonuniform flow fields.  
Third, the fitting function has an important effect on the IA error. The IA error obtained using the numerical approximation algorithm is closer to that of the Voigt theoretical model than that of the linear approximation method. When all line damping parameters of the nonuniform flow field are greater than 1, the effect of using the Lorentz function for fitting is basically the same as that of using the numerical approximation of the Voigt function for fitting.  
This work illustrated the applicability of TDLAS technology in nonuniform flow fields and provided input parameters for the evaluation of TDLAT measurement error.  
The following are available online at [[https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)]([https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)) 3390/app112210936/s1, Integrated Absorbance Error Distribution Table for the IA error distribution table.  
Conceptualization, F.L.; software, R.L.; validation, X.L.; data curation, R.L.; writing--original draft preparation, R.L.; writing--review and editing, X.L.; project administration, X.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 12072355, 11802315, 11927803 and Youth Innovation Promotion Association of CAS, grant number 2018023.  
Not applicable.  
Data Availability Statement:Data supporting the results of this work can be obtained from the corresponding author upon reasonable request.  
**Acknowledgments: This work was funded by National Natural Science Foundation of China, grant number 12072355, 11802315, 11927803 and Youth Innovation Promotion Association of CAS, grant number 2018023.**
# Relay Selection Schemes for FSO Communications over Turbulent Channels
###### Abstract  
Free Space Optics (FSO) Communication has attracted the attention of the researchers in the last decade due to its high data rate, security, and low cost. Relay-assisted techniques are used to divide the distance to shorter hops in order to mitigate the effects of turbulence, weather attenuation, pointing error, and geometric loss. Choosing an active relay per time slot has been proven to enhance the performance of the system and decrease the loading effect on the system when compared to all active relays. This paper investigates the best relay that can be selected according to the source to relay (S-R) channel coefficient, relay to destination (R-D) channel coefficient, and source to destination (S-D) channel coefficient. A comprehensive comparison is applied to the three following cases: (a) Broadcast phase from source to relay to select the best (Proactive-Relay); (b) Broadcast phase from relay to destination after broadcasting to all relays then select (Reactive-relays); and, (c) Direct link from source-to-best relay-to-destination to conclude which method is better for different scenarios, such as turbulence regime, number of relays, different pointing error effect, and severity of S-R as compared to R-D and vice versa. The selection methods regard to four aspects: (1) Number of relays (two or three relays); (2) Distance between Source-Relay and Relay-Destination (D = 400-600 m, 500-500 m, and 600-400 m); (3) The different turbulence of Log-normal channel and Gamma-Gamma channel (with a refractive index coefficient(\(C_{n}^{2}\) = 0.5 \(\times\) 10\({}^{-14}\), 2 \(\times\) 10\({}^{-14}\) and 5 \(\times\) 10\({}^{-14}\))); and finally, (4) Beam waist \(\omega_{z}\)(pointing error).  
Free Space Optics (FSO); atmospheric turbulence; relay selection; bit error rate (BER); Decode-and-Forward (DF); On-off keying (OOK) modulation
# Defect Tailoring in HfO2/Si Films upon Post-Deposition Annealing and Ultraviolet Irradiation
## 2 Materials and Methods  
HfO\({}_{2}\) thin films were deposited on (100) p-type silicon substrates (p-Si) by the dip-coating technique. First, substrates were cleaned by the HF solution, deionized (DI), water, and ethanol, and then by the RCA1 (5 DI H\({}_{2}\)O: 1 NH\({}_{4}\)OH: 1H\({}_{2}\)O\({}_{2}\) at 70 \({}^{\circ}\)C) and RCA2 (6 DI H\({}_{2}\)O: 1 HCl: 1 H\({}_{2}\)O\({}_{2}\) at 70 \({}^{\circ}\)C) processes. All reactants are electronic grade and were purchased from Sigma-Aldrich (St. Louis, MA, USA). Thereafter, 5.16 g of hafnium chloride (HfCl\({}_{4}\), 99.99% Sigma-Aldrich, St. Louis, MA, USA) was dissolved into 40 mL of ethanol. Four samples were prepared by dipping the substrates into the prepared solution at room temperature for 20 s using a 10 cm/min immersion speed. After being removed from the bath, all the samples were baked in the air for 5 min at 200 \({}^{\circ}\)C (sample S1). Afterwards, another sample was air-annealed at 350 \({}^{\circ}\)C by 1 h (sample S2). Another sample was irradiated with a UV LED source centered at 380 nm and 36 W for 1 h (sample S3), with a sample-lamp separation of 5 cm. The later sample (sample 4) was simultaneously air-annealed at 350 \({}^{\circ}\)C during UV irradiation for 1 h. Figure 1 shows a flow diagram of the process and the samples prepared.  
Films were characterized by grazing incidence X-ray diffraction (GIXRD) in a Siemens D5000 instrument (Siemens, Munich, Germany), using Cu K\(\alpha\) radiation (0.15408 nm), at an incidence angle of 1\({}^{\circ}\), a step size of 0.02\({}^{\circ}\), and a step time of 3 s. Raman spectroscopy was performed in a Raman i-Plus spectrometer (BW Tek, Plainsboro, NJ, USA), with a 532 nm laser with a nominal power of 36 mW, focused through a 20\(\times\) microscope objective. UV-visible spectroscopy in the diffuse reflectance mode was performed in a Varian Cary 5000 Instrument (Varian Inc., Palo Alto, CA, USA) using an integrating sphere. Optical fluorescence spectroscopy was performed using an excitation wavelength of 270 nm in a Horiba-Jobin Yvon instrument. Atomic force microscopy was conducted in a TT AFM Workshop instrument (Hilton Head Island, SC, USA) in the intermittent contact mode, using a DLC-coated tip with a 190 \(\upmu\)m cantilever with a resonance frequency of 190 kHz and a constant force of 48 N/m. To obtain the film's resistivity, 4-point measurements were carried out in a Signatone Pro SP 4 instrument (Signatone, Sunnyvale, CA, USA) with a tip separation of 0.1 cm, and a bias was applied from \(-\)5 to + 5 V.