esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodological and Operational Framework for CDTs
### Enablers for CDTs in Agile Supply Chains
#### 2.3.7 Trustworthiness  
This is a combination of all technologies, models and applicable regulations (e.g., GDPR) to ensure that data are generated, transmitted, and received among CDTs under a holistic trustworthy framework [31]. This includes data security, traceability, transparency, confidentiality and integrity.  
The above aspects are very critical and apart from the (cyber-)security technological aspects to be deployed we need to enforce the right rules and policies for data sharing, use and authorization. Let us consider a scenario of materials flow monitoring in the supply chain: information such as product quality, delivery times and potential events (machine breakdown, delays or predictive maintenance activities) have to be shared in a transparent way in order to allow for re-scheduling from other stakeholders in the chain. In such scenario where a factory CDT has to inform about an event, we need to ensure the following:  
* The right information is generated with respect to factory CDT's sharing policies. This means that the factory CDT will send only the necessary information in the supply network without disclosing anything that is marked as \"confidential\".
* Such information is sent and can be used only by the authorized CDTs. This is done by enforcing access control policies in the network and applying the rules of collaboration and governance aspects (which are detailed in the next section).
* Such data is actively monitored to ensure its integrity and detect potential data poisoning, that could disrupt machine learning models training, and their outcomes.
* Any action from the CDTs on the information shared can be traced, thus providing transparency of use in the whole supply chain and ensuring trust among the collaborators.
* The information is accurate and values the rest stakeholders by updating their production schedules and other internal operations.
## 3 Numerical Studies
### Two-Degree-of-Freedom System
#### 3.1.2 Number of Hidden Layers and Neurons  
This section studies the predictive performance of the PINN under different numbers of hidden layers and neurons. The training set is generated using the Sobol sequence, and 500 training samples are generated. Figure 7 shows the convergence process of the loss function, where \"Epochs\" represents the number of training epochs. The specific configuration of the neural network is shown in Table 2, while the other parameters remain the same as in Section 3.1.1. When the number of hidden layers or neurons is too low, the constructed PINN tends to underfit, resulting in significant prediction errors. The number of hidden layers and neurons significantly affects the prediction results, and appropriately increasing the width and depth of the network can help improve the predictive performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Active Function** & **Hidden Layers** & **Neurons** & **Loss Value** \\ \hline
1 & Tanh & 2 & 10 & 5259.4248 \\
2 & Tanh & 4 & 10 & 72.4586 \\
3 & Tanh & 6 & 10 & 11.2092 \\
4 & Tanh & 2 & 20 & 138.7688 \\
5 & Tanh & 4 & 10 & 0.89231 \\
6 & Tanh & 6 & 20 & 0.24468 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Case settings and loss values.  
Figure 6: Error variation over time for \(m_{1}\) and \(m_{2}\).  
Figure 7: Loss function iteration results.
## 3 Results and Discussion
Consequently, we can observe that the responsivity and specific detectivity of the PbS/Bi\({}_{2}\)O\({}_{2}\)Se hybrid photodetector have improved by an order of magnitude compared to the standalone Bi\({}_{2}\)O\({}_{2}\)Se photodetector. The PbS QDs and two-dimensional Bi\({}_{2}\)O\({}_{2}\)Se form a type-II heterojunction, resulting in the creation of an internal electric field within the device. This field can promote the separation of carriers, improving the device's photoresponse speed. However, experimental results reveal that the response time of the hybrid heterojunction device is slower compared to devices constructed with Bi\({}_{2}\)O\({}_{2}\)Se and other two-dimensional materials in type-II heterojunctions. This slower response time is attributed to Forster resonance in the 0D/2D heterojunction. Forster resonance energy transfer (FRET) is a highly distance-dependent non-radiative energy transfer mechanism [48; 49]; the energy transfer diagram is shown in Figure 5b. In the PbS QDs/Bi\({}_{2}\)O\({}_{2}\)Se hybrid photodetector, when PbS quantum dots, acting as energy donors, are photoexcited to an excited state, they can transfer their energy non-radiatively to Bi\({}_{2}\)O\({}_{2}\)Se through dipole-dipole interactions. This causes electrons in the valence band of Bi\({}_{2}\)O\({}_{2}\)Se to transition to the conduction band. In the PbS QDs/Bi\({}_{2}\)O\({}_{2}\)Sehybrid photodetector, the primary factor enhancing photoresponse time is the capture of photogenerated carriers by defect states in Bi\({}_{2}\)O\({}_{2}\)Se rather than the separation of photogenerated carriers at the interface. Therefore, while the photoresponse speed of the hybrid heterojunction photodetector is improved compared to a standalone Bi\({}_{2}\)O\({}_{2}\)Se photodetector, it does not match the improvement observed in other type-II heterojunctions.
## 3 Methodology  
### Measurement Development  
The survey questionnaire was developed to collect research data. In order to have better validity and reliability, all indicators should be designed by specific references. English learning motivation can be divided into intrinsic and extrinsic motivation [41]. The present study adopted English learning motivation as intrinsic and extrinsic motivation by referencing [PERSON]'s design. There were six items of intrinsic motivation measurement, such as \"English will be very useful in any major I choose in the future\", \"I realize the important role of English in the world today\", and \"I would learn more about the customs and lifestyles of different countries through English\". The extrinsic motivation contained eight different indicators, such as, \"I want to travel to other countries (Japan, Germany, Spain, etc.)\", \"I want to communicate with people who are not native speakers of English\", and \"I want to go to English-speaking countries (UK, USA, Australia, etc.)\".  
Moreover, about the English learning attitude, the measurement of [PERSON] [42] was adopted and modified. There were three first-order constructs of learning attitude, including toward teachers' teaching attitude, toward curriculum and toward language itself. Attitude toward teachers' teaching techniques had five items, including \"When I have doubts and ask a school English teacher, the teacher will teach me patiently\", \"I think English teachers are very serious in teaching\", and \"I feel that the English teacher is very careful about the homework\". There were nine items in attitude toward curriculum, such as \"I think English words are approachable and easy to learn\", \"I think the English grammar rules are approachable and easy to learn\", and \"I think I can concentrate on listening in English classes\". Lastly, regarding attitude toward language itself, there were two items, including \"I think learning English is helpful to me\" and \"I like the accent and pronunciation of English\". The Cronbach \(\alpha\) values of the instrument were 0.865.  
English learning well-being referenced Zhu [43]. Still, the first order constructs were English learning self-awareness and classroom activity and teaching methods. English learning self-awareness had seven indicators, such as \"I feel very happy to have English class every day\", \"I am confident in studying English\", and \"I have no pressure in studying English\". Then, classroom activity and teaching methods contained five items, including \"In English class, students are very happy to cooperate with me\", \"In English classes, I often have the opportunity to complete learning tasks with  
Figure 1: The research model.  
my classmates\", and \"English teachers often help me solve my learning problems\". The Cronbach \(\alpha\) values of the instrument were 0.85. These adopted instruments had high reliabilities. Additionally, they were designed for EFL learners. The present study used as a Likert-type, 5-point scale to develop the measurement from 1 to 5, representing strongly disagree, disagree, undecided, agree, and strongly agree.
## 4 Non-Thermal Plasma
### Plasma Generation and Treatment  
The two most commonly used NTP (DBD and Jet) configurations are shown in Figures 4 and 5, respectively. However, the three different approaches that can be used to treat samples with NTP are explained below:  
Direct plasma treatment--It uses the target area as a counter electrode where there is a homogenous generation of plasma with high concentrations of plasma-generated species [95], e.g., DBD plasma.  
Indirect plasma treatment--In this treatment technique, the plasma produced between two electrodes are later transported to the target area either by a carrier gas or diffusion [95], e.g., plasma needles, jets and torches.  
Hybrid plasma treatment--In this approach, the plasma is produced in multiple micros and nano-discharges in a grounded wire mesh electrode, e.g., surface micro discharge (SMD).  
Figure 3: Schematics of DBD plasma operation. (**a**,**b**) arc production between two electrodes without insulation. (**c**,**d**) NTP generation after placement of dielectric barrier layer in one of the electrode [41].
# Blockchain-Based Licensed Spectrum Fair Distribution Method towards 6G-Envisioned Communications
## 1 Introduction
### Novelty and Contributions  
Utilizing the unique characteristics of blockchain and combining the 6G application scenarios, we propose a blockchain-based spectrum primary-level distribution method (BEAST), which can realize fair and secure primary-level spectrum distribution. To the best of our knowledge, our achievement is one of the first works aiming at 6G licensed primary-level spectrum fair distribution towards multiple MNO scenarios. The main contributions of the paper are listed as follows.  
1. We propose a blockchain-based spectrum resource distribution method, that is, BEAST, to apply it to a 6G LSA problem. By constructing a proof of trust consensus module, the method can be used to protect the honest participants' interest and penalize the dishonest participants, realizing fair spectrum distribution from MNO to PUs and PBS.
2. By constructing a PoT-based LSA regulation compliance framework, the behaviors of spectrum users are assessed. The proposed framework can encourage the PUs and PBS to behave as honest users. Furthermore, for the MNO service degradation risk, a more efficient incentive mechanism combining economic incentive and credit incentive is proposed. The proposed incentive mechanism can surveil and audit an MNO service level.
3. To evaluate the effectiveness and performance of BEAST, we deploy it on Ethereum test blockchain; both simulation results and a theoretical analysis show that the proposed method has good performance on fairness and security.  
The rest of the paper is organized as follows: Section 2 introduces the related work to this paper. Section 3 describes the system composition and working process of BEAST. In Section 4, the trust value construction process is given, then the PoT procedure and incentive mechanism are described. We construct a proof-of-trust-based regulation compliance framework to guarantee the fairness in spectrum distribution. We present a theoretical analysis and numerical results for the proposed algorithms in Section 5. We summarize the whole paper in Section 6.
## 3 Experiment
### Ablation Experiment
#### 3.4.2 Effect of SPAC Encoder  
In order to verify the effectiveness of the SPAC Encoder module designed in this paper, SFA Encoder and SFA Encoder + SPAC Encoder are respectively acting as the backbone of GLFFNet, and other modules are consistent for ablation experiments. The experimental results are shown in Table 2. From these three metrics, the SPAC encoder has made a certain contribution to the performance improvement of the model. Specific to the five classes of foreground objects in the Potsdam dataset, the experimental results are shown in Table 3 (MIoU (%) as the metrics). The segmentation effect of GLFFNet with the SPAC Encoder module is slightly reduced for building, but improved for other classes. In particular, the recognition ability of low-vegetation objects has been greatly improved. To sum up, the comprehensive performance of GLFFNet with the SPAC Encoder module has been improved.
## 3 Results
### Synthesis of Results  
#### 3.4.1 Effect of AI-Assisted Physiotherapy on Pain Intensity  
Pain intensity outcomes were reported in all eight included studies. Among these, three studies [29; 31; 32] demonstrated significant efficacy of AI-assisted physiotherapy for pain reduction, while five studies [21; 30; 33; 34; 35] reported no significant differences compared to usual physiotherapy. Pain intensity was consistently measured before and after intervention in all six studies included in the meta-analysis [29; 31; 33; 34; 35]. Five studies employed the Numerical Pain Rating Scale (NPRS) or Numerical Rating Scale (NRS) [21; 33; 34; 35], and one study used the Visual Analog Scale (VAS) [30].  
A pooled effect size was calculated using a random-effects model. The results indicated that AI-assisted physiotherapy did not demonstrate a statistically significant effect in pain management compared to usual physiotherapy, suggesting no conclusive advantage of AI-assisted physiotherapy in this aspect (SMD = \(-\)0.2711, 95% CI: \(-\)0.5109 to \(-\)0.0313,  
Figure 2: Risk of bias assessment and summary for each study. Note: Study 1 = [35], Study 2 = [31], Study 3 = [32], Study 4 = [29], Study 5 = [21], Study 6 = [33], Study 7 = [30], Study 8 = [34].  
\(p=0.267\)) (Figure 3a). Heterogeneity analysis revealed a moderate level of heterogeneity among the included studies (I\({}^{2}\) = 48.10%, \(\tau^{2}\) = 0.0384, \(p=0.1200\)).  
Publication bias was assessed using the Egger regression test, which found no significant evidence of bias (intercept = \(-\)0.2083, 95% CI: \(-\)0.7837 to 0.3671, t = \(-\)1.005, \(p\) = 0.3717). This finding was further corroborated by visual inspection of the funnel plot (Figure 3b), where data points were distributed symmetrically around the center axis (x = 0).  
To explore the source of heterogeneity, a sensitivity analysis was conducted using a random-effects model (See Figure 3a). Each study was excluded individually to assess its impact on the overall meta-analysis results. The sensitivity analysis revealed that excluding any individual study did not significantly alter the overall effect size, demonstrating the robustness of the meta-analysis findings. Additionally, the analysis consistently showed a negative SMD, suggesting that AI-assisted physiotherapy has a potential advantage over usual physiotherapy in pain reduction, despite the lack of statistical significance (See Figure 3b).  
These findings suggest that while AI-assisted physiotherapy may offer slight benefits in reducing pain intensity, further high-quality studies are needed to confirm its clinical significance and better understand its role in pain management.
# Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective
## 3 Microbial H\({}_{2}\)-Bio-H\({}_{2}\) Generation in DORs
### Reservoir Engineering Aspects
#### 3.2.2 Economics  
Because of potentially high adverse effects (like plugging due to biomass excess [42]), the carbohydrate concentration to be injected is limited to ca. 10 mg/L or approximately 0.05 moles/L. Independently, the max. H\({}_{2}\) yield of DF is 4 mole H\({}_{2}\)/mole glucose. This means 180 g of glucose generates a maximum of 8 g H\({}_{2}\). Assuming the price of glucose as 0.7 USD\({}_{2023}\)/kg at the international market (which is currently cheaper compared to historical levels), 1 kg of H\({}_{2}\) generated would cost 15.75 USD\({}_{2023}\), based on the carbohydrate cost. To obtain the theoretical yield, additives like yeast, which costs 6 USD\({}_{2023}\)/kg, are often included to the reactant in the experiments. This range of costs for the substrate, which has to be injected in a reservoir, is comparable with the H\({}_{2}\) production costs in surface DF processes if similar high-grade feedstocks like glucose are used [21; 33]. Assuming the theoretical yield is attainable under reservoir conditions, and also considering other operational and relatively minor CAPEX (capital expenditure) costs, with actual technology, the unit cost from microbial in situ bio-H\({}_{2}\) generation is likely to be higher than 25 USD\({}_{2023}\)/kg. Injectant solutions may contain higher (e.g., sucrose) or lower (e.g., molasse) potent carbohydrates, which are associated with high yields/high costs vs. low yields/low costs per unit substrate, respectively. However, the ratios of yields to substrate costs are approximately equal for both cases, which leads to similar H\({}_{2}\) production prices irrespective of the carbohydrate quality.
## 2 Background Study
### Rainfall Rate Conversion  
The rainfall rate is the main parameter to determine the rain attenuation. The rainfall rate is measured in mm per time unit. Depending on the sensor device used to record the rainfall rate, the integration time unit may be 10 s, 20 s, to 1 min, 5 min, or even 1 h. The rain attenuation model may need to convert the rainfall rate over different integration times. The International Telecommunication Union-Radiocommunication sector (ITU-R) P.618 [45] recommends applying a 1 min integration time. If local rain data are available \begin{table}
\begin{tabular}{l l l} \hline \hline
**Ref.** & **Methematical Procedure** \\ \hline
[35] & \(L_{\mathit{eff}}=13.367R^{-0.21}\), where \(R\) is the rain rate. \\ \hline
[42] & \(L_{\mathit{eff}}=\frac{1}{1+\frac{15\mathrm{mm^{2}}}{\mathrm{cos}\theta}}\cdot L _{S}\), where \(\theta\) is the elevation angle, \(H_{S}\) is the station height above mean sea level of the Earth, and \(L_{G}\) is the horizontal projection length in the precipitation. \\ \hline
[44] & \(L_{\mathit{eff}}=\frac{L_{\mathit{th}}N_{\mathit{eff}}}{\mathrm{cos}\theta}\) (km) where \(L_{H}\) is the projected path-length, which is defined by \(R_{0}\), a boundary rain rate. If \(R<R_{0}\) then \(L_{H}=\frac{H_{\mathit{th}}-H_{\mathit{t}}}{\mathrm{tan}\theta}\) (km), and if \(R\geq R_{0}\) then \(L_{H}=\mathit{a}(R)\) (km) (Figure 1), and \(\mathit{N}_{\mathit{eff}}\)’ is the effective number of rain cells given by \(N_{\mathit{eff}}(\theta,R)=n_{1}(\theta)+n_{2}(\theta)\cdot R\), where \(n_{1}(\theta)=2.38\theta^{-1.45}+0.82\), and \(n_{2}(\theta)=-0.01\theta^{0.52}+0.09\) (\(\theta\) is elevation angle). \\ \hline
[44] & The estimation of the rain path can be determined by calibrating the reflected signal \(Z_{\mathit{th}}\) in clear-sky conditions and in rainy conditions \(Z_{\mathit{rain}}\). Once rainfall events are identified the first step of the procedure involves the determination of the volume of the rain cell with \(Z_{\mathit{rain}}>Z_{\mathit{th}}\). \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effective path length estimation techniques.
## 2 Materials and Methods
### Material
#### 2.1.2 Parameter of Experiments  
The parameters of experiments in this study are both wavelet basis function (WBF) and decomposition level (DL). Ten WBFs from the Daubechies wavelet family, DB\({}_{1}\) to DB\({}_{10}\), together with DL\({}_{1}\) to DL\({}_{10}\), have been selected. Daubechies are wavelets with a number of vanishing moments (N), and the minimum filter size is 2N [47; 48]. The simplest and the oldest function in Daubechies is the Haar wavelet (DB\({}_{1}\)). It has a value of either 1 for \(0\leq\mathrm{x}\leq 0.5\) or \(-1\) for \(0.5\leq\mathrm{x}\leq 1\); otherwise, it will be 0. This wavelet has a disjointed pattern, resembling a square shape [49], as shown in Figure 2.  
Figure 2: Haar wavelet (DB\({}_{1}\)), which has a square shape; it is the simplest wavelet in the Daubechies family.  
\(\text{DB}_{\text{N}}\) wavelets, except for Haar wavelets, with a length of \(2\text{N}-1\) (orders 2 to 10) are presented in Figure 3[49].  
The wavelet decomposition level is a level or order of decomposition of a signal in the wavelet domain. In discrete wavelet transform (DWT), a signal form can be approximated (to be decomposed) by N orthogonal wavelet signals consisting of detailed and approximation signals. N is the order of decomposition or decomposition level (DL) [50]. The DL is  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**NO** & **AF Data Record Number (AFDB)** & **Normal Data Record Number (NSRDB)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: Experimental data for detecting AF.  
Figure 3: Daubechies wavelet order 2 until 10, which have an asymmetric shape and regularly increase with order.  
very important in determining the accuracy of the wavelet function approach to a signal form. DWT decomposition can be defined in Equations (1) and (2) as follows:  
\[s_{(i)}(l)=x(k)*\varphi_{i,l}(k), \tag{1}\]  
\[d_{(i)}(l)=x(k)*\psi_{i,l}(k), \tag{2}\]  
where \(s_{(i)}(l)\) and \(d_{(i)}(l)\) are the approximation coefficient and the detail coefficient at resolution \(i\) of scaling function \(\varphi_{i,l}(k)\) and orthogonal wavelet function \(\psi_{i,l}(k)\). Wavelet scale basis functions \(\varphi_{i,l}(k)\) and wavelet functions \(\psi_{i,l}(k)\) are defined by Equations (3) and (4) as follows:  
\[\varphi_{i,l}(k)=2^{\frac{i}{2}}h_{l}\left(k-2^{i}l\right), \tag{3}\]  
\[\psi_{i,l}(k)=2^{\frac{i}{2}}g_{i}\left(k-2^{i}l\right), \tag{4}\]  
where factor \(2^{\frac{i}{2}}\) is the normalized inner product, \(i\) and \(l\) are scale parameters and translational parameters, and \(k\) is a discrete time sample [51].
# Improving the Solving of Optimization Problems:
## 4 Quantum Annealer
### Hardware Implementations  
A leading company in the field of QAs is D-Wave Systems [48], which, since 2011, has been developing QAs with a growing number of qubits (more than 5000), enabling the solution of increasingly complex optimization problems. These devices are implemented in superconductive technology, which requires very strict operating conditions, e.g., 15 mK, and presents limited connectivity. Therefore, to map a generic fully connected problem, minor embedding [49] is needed. This technique consists of splitting a logical bit into multiple physical bits of the target architecture, each one belonging to a different subgraph. The physical bits associated with the same logical bit are strongly ferromagnetically coupled, so that discordant states are penalized. Therefore, the physical bits are expected to collapse to the same value at the end of the annealing process. It is necessary to note that using a minor embedding configuration rapidly increases the number of involved physical bits, which adds a time overhead in the overall execution.  
D-Wave has proposed three minor embedding topologies: Chimera at the beginning, and Pegasus and Zephyr, recently. The Chimera structure is a connected network of bits with groups of densely connected nodes sparsely connected to other groups of densely connected nodes. In this topology, variables have degree 6, which means that each bit is connected through couplers to six others. The instructions for obtaining a Chimera mapping for some important problems are reported in [50]. It is the topology of D-Wave 2000Q. Pegasus [51] is able to provide a more efficient embedding compared to Chimera and requires a shorter embedding time. This topology has a degree of 15, so a lower number of bits is required to obtain an equivalent configuration of the Chimera one. The additional connectivity of the Pegasus graph can be also used to construct a simple error correction scheme for quantum annealing. It is the topology of Advantage devices. Zephyr represents a further advantage, with a qubit degree of 20.  
It is possible to use the real quantum annealer by creating a free account on Dwave Leap, a cloud service providing one minute of free computing access per month.