esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sensitive Wavelengths Selection Method
#### 2.2.1 The Soil Radiative Transfer Model  
Our previous studies have constructed an inversion model of soil moisture content, which can be referred to [20]. The soil radiative transfer model is the reverse form of the inversion model, which describes the change of spectral reflectance with soil moisture content.  
According to [20], the relationship between transformed reflectance \(r\) and soil moisture content \(\theta_{SM}\) can be expressed as:  
\[r(\theta_{SM})=\frac{r_{1}(1-\theta_{SM})+a(\theta_{SM}-\theta_{SM})}{1-\theta_ {SM}}. \tag{1}\]  
where, \(r_{1}=\frac{(1-R_{1})^{2}}{2R_{1}}\), \(R_{1}\) is the reflectance of the soil when soil moisture content is \(\theta_{SM}\), \(a\) is the unknown parameter.  
Figure 1: Experimental device including ASD FieldSpec.3 Portable Spectrometer, light source, optical fiber probe, experimental sample and stage.  
According to the KM theory, the relationship between infinite reflectance \(R_{\infty}\) and soil moisture content \(\theta_{SM}\) is derived as:  
\[R_{\infty}(\theta_{SM})=1+r(\theta_{SM})-\sqrt{r^{2}(\theta_{SM})+2r(\theta_{SM})} \tag{2}\]  
For wet soil, reflectance, which is related to soil moisture, mainly depends on diffuse scattering. The soil radiative transfer model can be expressed as:  
\[\begin{array}{ll}R(\theta_{SM})&=\frac{(1-R_{i})^{2}.R_{\infty}(\theta_{SM}) }{1-R_{i}:R_{\infty}(\theta_{SM})}\\ &=\frac{(1-R_{i})^{2}\left[1+r(\theta_{SM})-\sqrt{r^{2}(\theta_{SM})+2r( \theta_{SM})}\right]}{1-R_{i}\left[1+r(\theta_{SM})-\sqrt{r^{2}(\theta_{SM})+2 r(\theta_{SM})}\right]}\end{array} \tag{3}\]  
With  
\[\left\{\begin{array}{l}r(\theta_{SM})=\frac{r_{1}(1-\theta_{SM})+a(\theta_{ SM}-\theta_{SM})}{1-\theta_{SM}}\\ r_{1}=\frac{(1-R_{1})^{2}}{2R_{1}}\end{array}\right.\]  
where, \(R_{i}\) is Fresnel reflectance [21]:  
\[R_{i}=\left(\frac{n_{water}-n_{air}}{n_{water}+n_{air}}\right)^{2} \tag{4}\]  
where \(n_{water}\) is refractive indices of water (\(\approx\)1.33) and \(n_{air}\) is refractive indices of air (\(\approx\)1).
## Materials and Methods
### Mapping Various Rice Disease Infested Areas
#### 2.4.4 Diseases Occurrence Mapping Using Partial Least Squares Discriminant Analysis (PLS-DA)  
In the PLS-DA frame, the response variable (i.e., disease species) is binary and expresses class membership. The PLS procedure create several eigenvectors of spectral matrices which will produce scores that explain both the variance of the spectral features, as well as the correlation with the response variables [37]. It is essential to address the multi-collinearity between variables to reduce the risk of overfitting due to correlated variables in the PLS procedure [32]. Therefore, it is necessary to test and select the optimal PLS components (i.e., VIs in this study). A reliable way of testing the significance of each PLS component is cross validation. By using the candidate VIs, the parameters of the PLS-DA model are optimized based on ten-fold cross-validation, conditioned on the training dataset.  
An essential requirement for the PLS-DA model is to calculate the variable importance in the projection (VIP) score of each variable (i.e., VIs in this study). The VIP score serves as a measure of the contributions of input variables to the classification results [41]. The VIP score is defined as follows:  
\[VIP_{k}=\sqrt{K\sum_{a=1}^{A}\left[(q_{a}^{2}t_{a}^{T}t_{a})\left(w_{ak}/\|w_{ k}\|^{2}\right)/\sum_{a=1}^{A}(q_{a}^{2}t_{a}^{T}t_{a})\right]} \tag{2}\]  
where \(VIP_{k}\) is the importance of the \(k\)th VIs, \(w_{ak}\) is the corresponding loading weight of the \(k\)th VIs in the \(a\)th PLS-DA component, \(t_{a}\), \(w_{a}\) and \(q_{a}\) are the \(a\)th column vectors, and \(K\) is the total number of VIs (\(K=6\) in this study, as given in Table 2).  
Subsequently, a new PLS-DA model was computed from the selected VIs, and then used to execute classification of rice diseases on satellite imagery. In this section, PLS-DA model development, VIP score calculations and model optimization were processed using the PLS Toolbox 8.1.1 (Eigenvector Research Incorporated) for Matlab R2017a (Mathworks, Natick, MA, USA).
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## Appendix A  
**Proof of Lemma 1.** Let the Fourier transform of \(C_{i}(t)\) in Equation (5) be \(C_{i}(\omega)\) and the autocorrelation function be \(R_{c}(\tau)\). According to the Fourier transform of the autocorrelation function and the power spectral density \(S_{X}(\omega)\), we can obtain  
\[R_{c}(\tau)=\frac{1}{2\pi}\int S_{X}(\omega)e^{i\omega\tau}\mathrm{d}\omega \tag{10}\]  
The signal power spectral density \(S_{X}(\omega)\) is  
\[S_{X}(\omega)=\lim_{T\to\infty}\frac{1}{T}|C_{i}(\omega)|^{2} \tag{11}\]  
where \(T\) is the length of the signal in the time domain and, according to IMF Definition 1, the IMF is similar to a smooth narrowband Gaussian process, so, \(C_{i}(t)\) is a bandlimited signal. Substituting Equation (11) into Equation (10), we obtain  
\[R_{c}(\tau)=\frac{1}{2\pi}\int\lim_{x\to\infty}\frac{1}{T}|C_{i}(\omega)|^{2}e ^{i\omega\tau}\mathrm{d}\omega=\frac{1}{2\pi T}\int|C_{i}(\omega)|^{2}e^{i \omega\tau}\mathrm{d}\omega \tag{12}\]  
Since \(R_{c}(\tau)\) is a real function, Equation (12) can be simplified as  
\[R_{c}(\tau)=\frac{1}{2\pi T}\int|C_{i}(\omega)|^{2}\cos(\omega\tau)\mathrm{d}\omega \tag{13}\]  
Let the maximum and minimum values of \(|C_{i}(\omega)|^{2}\) in the frequency band \(\Omega\) be \(M\) and \(m\), respectively, then  
\[\frac{m}{2\pi T}\int_{\Omega}\cos(\omega\tau)d\omega\leq R_{c}(\tau)\leq\frac {M}{2\pi T}\int_{\Omega}\cos(\omega\tau)d\omega \tag{14}\]  
i.e.,  
\[R_{c}(\tau)\sim\int_{\Omega}\cos(\omega\tau)d\omega=\frac{1}{\tau}\sin(\omega \tau)|_{\Omega} \tag{15}\]  
Therefore, \(R_{c}(\tau)\) is integrable. Thus, it is proven that the individual IMF components obtained from self-similar network traffic after EMD decomposition have SRD characteristics. \(\square\)
# Easy Fabrication of Ultrafiltration Membrane via Polyethersulfone-Fumed Silica
## 3 Results and Discussion
### Pure Water Flux Test Experiments  
Figure 3 illustrates the pure water flux of PES/FS blend membranes in a dead-end filtration cell under a constant pressure of 2 bar. The pure water flux for the FS-0.0 membrane at this condition was 18.6 LMH/Bar. The value increased with FS concentration and reached the highest flux of 30.6 LMH/Bar when the membrane contained 0.3 wt.% tuned silica. This enhancement corresponded to a 64% improvement in flux. At concentrations beyond this point, the pure water flux declined to a level similar to that of the FS-0.2 membrane. This observation aligns with the research conducted by [PERSON] et al., which investigated the effect of adding fumed silica to polyvinylidene fluoride (PVDF) membranes. They found that adding FS improved the membrane's permeability, showing a declining trend after reaching its optimum FS concentration [17]. Their analysis using ImageJ software further revealed that the increase in FS loading correlated with an enhancement in the membrane's surface porosity.  
Figure 2: The morphologies of the blend membranes. Low and high magnification on the cross-section images is shown in the first and second rows. Top surface micrographs are shown in the third row: (**a**) FS-0.0, (**b**) FS-0.1, (**c**) FS-0.2, (**d**) FS-0.3, (**e**) FS-0.4.  
The water flux values improved substantially with a higher fraction of fumed silica. This enhancement can be attributed to improved interconnectivity within the membrane sublayer due to the presence of fumed silica. This observation aligns with the findings of [PERSON] et al., who developed a composite membrane using spray-dried silica granules and polysulfone. Their study revealed that incorporating silica granules led to a twofold increase in flux compared to the native Psf membrane, which was due to additional pore channels generated by the spray-dried particles [34].
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AWGN & Additive White Gaussian Noise \\ ECEF & Earth-Centered Earth-Fixed coordinate system \\ GEO & Geosynchronous Orbit \\ GEOSAR & Geosynchronous Orbit Synthetic Aperture Radar \\ LEO & Low Earth Orbit \\ LNB & Low-Noise Block \\ MUOS & Mobile Users Objective System \\ NORAD & North American Aerospace Defense Command \\ NZI & Near-Zero Inclination \\ SAR & Synthetic Aperture Radar \\ SNR & Signal-to-Noise Ratio \\ SoOp & Signal of Opportunity \\ SRP & Solar Radiation Pressure \\ TLE & Two-Line Element \\ \end{tabular}
## 3 Sustainability of Additive Manufacturing
### Sustainable Benefits of AM  
AM introduces numerous significant changes in product design, materials processing, manufacturing processes, and supply chain management. Compared to traditional production (such as machining, forging, finishing, casting, etc.), AM provides a great opportunity in sustainable production [119]. The advantages of sustainable manufacturing provided by AM processes are as following [120, 121, 122]:  
1. The less raw material which is required in the supply chain process;
2. Higher resource efficiency in manufacturing processes;
3. Reduced consumption, waste material, and pollution in the manufacturing process;
4. Higher efficiency and flexibility in product design;
5. The lower number of transportation processes and reduced carbon footprint;
6. Decentralized and close-to-consumer manufacturing;
7. Shorter supply chains, more localized production using innovative distribution methods, and collaborations;  
Figure 7: Application of AM features in different industries.  
(8)  
Extended product life by novel technical methods such as remanufacturing, reusing, repairing, refurbishing, and sustainable socio-economic production.  
Value chains of AM are shorter, smaller, more localized, and they offer considerable sustainability advantages. Therefore, there needs to be a better understanding of value chain reconfigurations in the sort of interactions between stakeholders and relationships while the product life cycles are considered. As shown in Figure 8, four main fields exist in the life cycle of materials, in which the adoption of AM is conducted to improved resource efficiency. These main stages are (1) design of product and process, (2) the material processing, (3) product order and manufacturing processes, and (4) close loop with end-of-life production [13; 123].
# Dynamic Virtual Network Slicing and Orchestration for Selective MEC Services over Wide-Area SDN
## 5 A Selective MEC Service Model Based on a Multitier Architecture
### End-to-End Latency Model  
The end-to-end latency between UE and an MEC node is a crucial requirement for comfortably supporting MEC services. The latency can be modeled by the summation of the wireless transmission latency between a UE and an access point (AP), the network latency between an AP and a MEC node, and the computing latency at the MEC node for processing a service request. Consequently, the end-to-end latency is expressed as  
\[L_{E2E}=L_{WL}+L_{N}^{n}+L_{C}^{n,\ i}. \tag{1}\]  
There are several studies about wireless transmission latency models considering the radio types and properties [28; 29]. We adopted the previous research model for \(L_{WL}\). The \(L_{N}^{n}\) is newly defined according to the hierarchical MEC service architecture. We also define \(L_{C}^{n,\ i}\) by considering the average processing latency in MEC nodes according to the type of service request. \(L_{N}\) consists of processing, queuing, transmission, and propagation delays. Many factors affect the network latency [30]. The main factor is the distance between an AP and a MEC node because the propagation delay occurs for data (generally, in the form of packets) to traverse through transmission media. The second factor is network congestion. However, it is difficult to estimate the fluctuation of network congestion. Therefore, we reflect the factor of network congestion by considering network utilization between an AP and a MEC node in the procedure of MEC selection instead of \(L_{N}\) modeling. Finally, the transmission delay is determined by the packet length and link bandwidth. The packet (data) length for a specific MEC service does not change depending on the selected MEC server (regardless of the access/edge/core sites). Furthermore, the conditions of the link bandwidth on the  
Figure 7: Large file transfer experiments based on virtual network slice and Globus Online (Korean characters in this figure indicates the name of the experimenter). MEC infrastructure are mostly invariable. Therefore, the proposed end-to-end latency model does not consider the transmission delay. In the proposed hierarchical MEC service architecture, multitier MEC service zones are considered: AP site (zone\(-\)1), network edge site (zone\(-\)2), network core site (zone\(-\)3), and regional network core sites (zone\(-\)4). Almost all the MEC services should be handled in a single regional MEC site (from zone\(-\)1 to zone\(-\)3) because of the latency requirement and resource utilization for MEC services. Therefore, MEC nodes in zone\(-\)4 are not considered as service candidates at the beginning. Only if the service is not provided by one of the zone\(-\)1 to zone\(-\)3 MEC sites can the service request be handled
## 2 Materials and Methods
### Material Characterization
#### 2.4.2 Infrared Spectroscopy  
Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy chemical analysis was performed on the film surfaces. Spectra were recorded with a Vector 22 from Bruker S.A. (Madrid, Spain) coupling a PIKE MIRacle(tm) ATR accessory from PIKE Technologies (Madison, WI, USA). Ten scans were averaged from 4000 to 400 cm\({}^{-1}\) at a resolution of 4 cm\({}^{-1}\).
# Digital Twin-Enabled Decision Support Services in Industrial Ecosystems
## 4 Results
### Multi-Agent Simulation Supporting a Process Twin Construction for SMEs
#### 4.5.1 Case Description for Simulation  
On the basis of the findings from Section 4.1 we designed a process twin for a hypothetical Swiss manufacturing SME, a company that is in the process of servitization. The SME already has a basic understanding of its installed base in terms of behavior, maintenance history, and location. This understanding is based on collected machine data from 100 machines located in Switzerland since 2016. Based on these data, it is already able to use machine learning to detect some component failures 24 h before they occur. The information about the installed base and the prediction of failures in components are helpful for the operation of the service offering that the company sells to the customers. However, there are still many unanswered questions regarding service operations, especially in dealing with predicted failures in components and the appropriate response to those eminent failures. This is where the digital process twin comes into action. In this case, the digital process twin has two tasks that involve different actors from the service ecosystem of the company. On the one hand, the digital process twin must map all relevant service operations actors that have been established in Switzerland. This includes the customers where the machines are located, and the condition of the machines with their history of maintenance and errors. On the other hand, the process twin must also be able to map the service technicians, their location, and routes to and from the customers. Furthermore, the digital service twin must be able to simulate repair and maintenance processes based on the service technicians' tasks, to estimate the working times on site. Based on these fundamentals, the following business questions need to be answered for the SME with the simulation of the digital process twin:  
1. Based on the current situation, what is the best response to a predicted breakdown?
2. With how many service technicians can the service level promised to the customer be met?
3. How many spare parts are needed in stock to meet the service level promised to the customer?  
The list of business questions is kept concise for the purpose of comprehensibility. It can be extended at will.
## 5 Implementation
### Implementation Design
#### 5.2.1 Creation of the Smart Contract  
First, we will explain the implementation of the creation of firmware update smart contract of Figure 3. As shown in Figure 6, the manufacturer has a new firmware update in the software update repository. To distribute the new firmware, the manufacturer stores the metadata to the smart contract. By the end of this process, the IoT gateways that participate in the blockchain network will be able to obtain the new firmware update information and save it to its local database. The detail explanation is described as follows:  
1. The manufacturer creates a firmware update smart contract.
2. The manufacturer stores the metadata (\(U\)) to the smart contract. The metadata consist of \(U_{id},f_{v},M_{id},dtype,purl\) which are signed with the manufacturer's secret key (\(SK_{m}\)).
3. \"NewFirmwareStored\" event in the blockchain network is triggered. Since the IoT gateways is a blockchain node, it will receive the event notification. The \(U_{id}\) is sent through this event.
4. The IoT gateways call the \"getMetadata\" function in the smart contract to request the full metadata.
5. The IoT gateways receive the metadata (\(U\)).
6. The IoT gateways verify the signature. We use the \"recover\" function from EthCrypto's library to recover the signer's public address in the blockchain network with their signature as input [28]. If verified, it will save the metadata to the local database; otherwise, the data are rejected.  
Figure 6: Implementation of creation of firmware update smart contract.
## 5 Results and Analysis
### Cross-Domain Generalization Analysis  
To evaluate the model's ability to transfer knowledge learned from FOMC data to ECB data (or different economic cycles), we focus on analyzing the effect of the domain-adversarial training (DANN) component. We compare the performance of the full model versus a model with the DANN component removed on the ECB test set (both trained on FOMC data and incorporating Delta-LoRA and MTL).  
The results in Table 7 show that incorporating DANN improves the macro F1 score on the target ECB domain by 1.8 pp. This strongly supports the hypothesis that domain  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Prediction Label Definition Window** & **FOMC Macro F1** \\ \hline Within 3 days post-release & 0.855 \\
**Within 7 days post-release** & **0.862** \\ Within 14 days post-release & 0.848 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Impact of prediction window length (defining the label) on FOMC macro F1.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model Configuration** & **FOMC Macro F1** & **\(\Delta\) vs. LoRA** \\ \hline Ours (Hyena + LoRA, r = 16) & 0.833 & - \\
**Ours (Hyena + \(\Delta\)-LoRA, r = 16)** & **0.850** & **+1.7 pp** \\ Ours (Hyena + \(\Delta\)-LoRA, r = 8) & 0.845 & - \\ Ours (Hyena + \(\Delta\)-LoRA, r = 32) & 0.851 & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of Delta-LoRA and its rank (r) on FOMC macro F1.  
adaptation via DANN significantly enhances the model's generalization performance on cross-central bank corpora.  
Figure 9 illustrates the typical loss dynamics during training. The domain classifier's loss tends to decrease as it learns to distinguish domains, potentially oscillating due to the adversarial nature. Concurrently, the feature extractor, influenced by the gradient reversal layer, aims to maximize this domain loss, pushing it towards producing domain-agnostic features. The main task loss consistently decreases, indicating simultaneous learning of the prediction task and domain adaptation. This adversarial dynamic confirms the successful operation of the DANN mechanism, compelling the feature extractor to generate more domain-invariant representations and thereby enhancing cross-domain generalization.
# Networking of Multi-Robot Systems: Architectures and Requirements
## 4 Communication Issues and Requirements for MRS Systems
### MANETs and MRS
Hierarchical routing protocols include Low-Energy Adaptive Clustering (LEACH), Power-Efficient GAthering in Sensor Information Systems (PEGASIS), and AdaPtive Threshold sensitive Energy Efficient sensor Network protocol (APTEEN) [37, 38, 39].
## 5 The CDS case study for the OFDM-Based Long-Haul Standard Single-Mode Fiber system
### Simulation Results in Presence of Disturbance  
If the fiber optic system is disturbed, then the CDS can sense it and adapt to the new environment. We simulate the CDS for the disturbed system by assuming that the tenth inline amplifier of the fiber optic link is partially damaged. The noise figure (NF) of this amplifier is increased from 4.77 to 23 dB, while the rest of the inline amplifiers have the normal NF of 4.77 dB. As a result, the received signal becomes noisier. The simulation results using the CDS in the presence of disturbance are shown in Figure 19.  
Figure 19(a) shows that in the PAC numbers 1 to 4, the BER is under the FEC-threshold (see Figures 15-18), and the CDS achieves a data rate of 54 Gb/s at fourth PAC. Hence, the steady state data rate is 54 Gb/s, and the CDS operates in s steady state mode. At the fifth PAC, the system is disturbed because of the partial damage to the tenth amplifier. The current statistical model for the system operating at 54 Gb/s cannot bring the BER under FEC-threshold. As a result, the CDS turns the steady state mode off and activates the pre-adaptive actions (see Figure 7). Therefore, the CDS lowers the data rate with a step of 2 Gb/s until the BER is under the FEC-threshold. At \(k=6\), \(j\) is reset to 1, data rate is 38 Gb/s, and the BER is under the FEC-threshold (see Figure 19(a)). Since \(R_{1}^{6}=38\) Gb/s, \(R_{ref}\) is updated to \(R_{1}^{6}-d=36\) Gb/s. The procedure for PAC numbers 6 to 9 is similar to that for PAC numbers 1 to 4 (see Algorithm 1). Additionally, the rewards are calculated using Equation (4), and Figure 19(b) shows the internal reward \(rw_{j}^{k}\) as a function of the PAC number \(k\) calculated. Before disturbance (i.e., \(k<5\)) we see that the internal reward decreases as \(k\) increases. This is because the weight of the data-rate is more than the weight of the BER (see Equation (4)). The executive would increase the  
Figure 19: The simulation results for the cognitive dynamic system (CDS) in presence of disturbance (**a**) bit error rate (BER) vs perception action cycle (PAC) numbers, (**b**) CDS learning curve.  
data rate if the internal reward is decreasing (provided that the BER is less than the FEC-threshold). At \(k=5\), there is a disturbance leading to an increase in the BER and hence, internal reward also increases. The internal reward reaches its peak at \(k=6\) since the difference between \(R_{1}^{6}\) and \(R_{ref}\) is the lowest (see Equation (4)). Thereafter, the internal reward decreases at \(k=7\), the CDS applies the same procedure used at \(k=2\). At ninth PAC number (\(k=9\)), the new steady-state data rate in the presence of disturbance is the 44 Gb/s (similar to fourth PAC number). If the amplifier is repaired in future, the CDS follows the same procedure as in PAC numbers 1 to 4 to increase the data rate again.