esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Control System Design
### Controller Analysis  
The above research contents give the structure and approximation algorithm of fuzzy neural network. In order to verify its control effect, this section analyzes the performance of the controller. Replace the dynamic inverse error \(\Delta(x,\dot{x},u,\dot{u})\) with \(\chi\).  
\[\hat{\chi}=\hat{W}^{T}L_{3}, \tag{36}\]  
where \(\hat{W}^{T}\) is the estimated value of \(W^{T}\). (24) can be rewritten as:  
\[\dot{\mathbf{e}}=\Lambda\mathbf{e}+B[\hat{\chi}-\chi]. \tag{37}\]  
Take the optimal weight matrix as:  
\[W^{*}=\arg\min_{W\in\Omega}[\sup]\hat{\chi}-\chi], \tag{38}\]  
where \(\Omega\) is the set of \(W\).  
The minimum approximation error is defined as:  
\[\varepsilon=\hat{\chi}(x|W^{*})-\chi, \tag{39}\]  
Rewrite (37) as:  
\[\dot{\mathbf{e}}=\Lambda\mathbf{e}+B[\hat{\chi}-\chi+\hat{\chi}(x\big{|}W^{*})-\hat{ \chi}(x\big{|}W^{*})], \tag{40}\]Substitute (36) and (39) into (40) to obtain:  
\[\dot{\mathbf{e}}=\Lambda\mathbf{e}+B[(\hat{W}-W^{*})^{T}L_{3}+\varepsilon]. \tag{41}\]  
Define the Lyapunov function as:  
\[V=\frac{1}{2}\mathbf{e}^{T}\mathbf{p}\mathbf{e}+\frac{1}{2\beta}(\hat{W}-W^{*})^{T}(\hat{W} -W^{*}), \tag{42}\]  
where \(\beta\) is a positive constant and \(P\) is a positive definite matrix satisfying the Lyapunov equation:  
\[\mathbf{\Lambda}^{T}\mathbf{P}+\mathbf{P}\mathbf{\Lambda}+\mathbf{Q}=0, \tag{43}\]  
Derivation with respect to time gives:  
\[\begin{array}{l}\dot{V}=\frac{1}{2}\mathbf{e}^{T}\mathbf{P}\mathbf{e}+\frac{1}{2}\mathbf{e}^{ T}\mathbf{P}\mathbf{e}+\frac{1}{\beta}(\hat{W}-W^{*})^{T}\hat{W}\\ =\frac{1}{2}\mathbf{e}^{T}\Lambda^{T}\mathbf{P}\mathbf{e}+B^{T}[(\hat{W}-W^{*})^{T}L_{3}+ \varepsilon]^{T}\mathbf{P}\mathbf{e}\\ +\frac{1}{2}\mathbf{e}^{T}\mathbf{P}\Lambda\mathbf{e}+\frac{1}{2}\mathbf{e}^{T}\mathbf{P}B^{T}[( \hat{W}-W^{*})^{T}L_{3}+\varepsilon]^{T}+\frac{1}{\beta}(\hat{W}-W^{*})^{T} \hat{W}\\ =-\frac{1}{2}\mathbf{e}^{T}\mathbf{Q}\mathbf{e}+\mathbf{e}^{T}\mathbf{P}B^{T}[(\hat{W}-W^{*})^{T} L_{3}+\varepsilon]^{T}+\frac{1}{\beta}(\hat{W}-W^{*})^{T}\hat{W}\\ =-\frac{1}{2}\mathbf{e}^{T}\mathbf{Q}\mathbf{e}+\mathbf{e}^{T}\mathbf{P}B\mathbf{e}+(\hat{W}-W^{*})^{T} \mathbf{e}^{T}\mathbf{P}BL_{3}+\frac{1}{\beta}(\hat{W}-W^{*})^{T}\hat{W}\\ =-\frac{1}{2}\mathbf{e}^{T}\mathbf{Q}\mathbf{e}+\mathbf{e}^{T}\mathbf{P}B\mathbf{e}+\frac{1}{\beta}( \hat{W}-W^{*})^{T}[\hat{W}+\beta\mathbf{e}^{T}\mathbf{P}BL_{3}]\end{array} \tag{44}\]  
Take the adaptive law as:  
\[\dot{\hat{W}}=-\beta\mathbf{e}^{T}\mathbf{P}BL_{3}, \tag{45}\]  
Substitute the adaptive law into (44)  
\[\dot{V}=-\frac{1}{2}\mathbf{e}^{T}\mathbf{Q}\mathbf{e}+\mathbf{e}^{T}\mathbf{P}B\mathbf{e}. \tag{46}\]  
Since the minimum approximation error \(\varepsilon\) can be taken as a sufficiently small value, and \(-\frac{1}{2}\mathbf{e}^{T}\mathbf{Q}\mathbf{e}\leq 0\), it is concluded that \(\dot{V}\leq 0\).
## 4 Development and Validation of the Platform  
This section provides a comprehensive description of the experimental development and validation process of the proposed IoT-to-Edge-to-Cloud platform architecture. This section is structured into several parts to provide a comprehensive overview of the platform's architecture, experimental setup, and evaluation metrics. Initially, it delves into the detailed architecture of the platform, covering its multi-layered design, integration with Edge nodes, private 5G network, and advanced orchestration mechanisms to support real-time, latency-sensitive applications. This is followed by a thorough explanation of the experimental setup used to validate the architecture, focusing on the performance of critical components such as the 5G core and RAN. Finally, we demonstrate the platform's capabilities by evaluating its performance under real-world conditions using a demanding application for immersive remote driving. The results provide insight into the platform's ability to handle complex, latency-critical scenarios, ensuring scalability, resilience, and efficient resource utilization across IoT, Edge, and Cloud environments.
## 2 Circular Array Synthesis Approach
For the evaluation of the total radiated field distribution, one must also consider the element factor of the cell, where the single Huygens cell exhibits a directive radiation pattern, as given by the following Equation (2) [26]:  
\[g_{n}(\theta,\varphi)= \begin{cases}\cos(\phi-\phi_{n})&-90^{\circ}<\phi<90^{ \circ}\\ 0&otherwise\end{cases} . \tag{2}\]  
In Equation (2), \(\phi\) denotes the azimuth angle measured from the positive \(x\)-axis, while \(\phi_{n}\) specifies the angular position of the n-th element within the xy-plane. The elevation angle \(\theta\) is defined with respect to the positive \(z\)-axis, as illustrated schematically in Figure 1b. This directive behavior affects the overall radiation characteristics of the metasurface, and accounting for it is essential to accurately model the performance of the entire structure. By integrating the element pattern into the synthesis, we ensure that the individual contributions of each Huygens cell are properly accounted for, leading to a more precise and effective beamforming design. After deriving the expressions for the cell element pattern and the array factor, the complete array pattern is given by  
\[AP(\theta,\varphi)=AF(\theta,\varphi)\ g_{n}(\theta,\varphi). \tag{3}\]  
Although the beamforming capabilities and the underlying physical principles of this design approach were demonstrated in our previous works, not all the design possibilities offered have been investigated. To further enhance the capabilities of the overall system, in this paper, we employ an evolutionary optimization method of the metasurface parameters, specifically a GA. This approach allows for improved control over the radiation pattern, addressing the requirements of both the main beam and radiation null directions.
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 4 Results
### Band Ratios Indices  
For the analysis of the results, we considered the pixels of interest, which correspond to classes between 12 and 16, with class 16 having maximum relevance and class 12 having medium relevance (Figure 4). The data were calculated based on the proportion of pixels of interest rather than the total image, but this information can also be consulted in Figure A1 (Appendix A). When analyzing the most significant satellites for each class, we observed the following order of pixel identification by proportion:  
* Class 12: Sentinel-2 (72.23%) > PS (67.86%) > Landsat 9 (64.34%) > ASTER (40.40%).
* Class 13: ASTER (43.86%) > Landsat 9 (32.55%) > Sentinel-2 (18.48%) > PS (18.12%).
* Class 14: ASTER (13.29%) > PS (5.95%) > Sentinel-2 (5.64%) > Landsat 9 (2.91%).
* Class 15: PS (3.71%) > Sentinel-2 (2.34%) > ASTER (2.09%) > Landsat 9 (0.18%).
* Class 16: PS (4.37%) > Sentinel-2 (1.29%) > ASTER (0.33%) > Landsat 9 (0.00%).
# A Case Study Comparing Methods for Coal Thickness Identification in Complex Geological Conditions
## 2 Geological Test
### Static Correction  
Given the complex surface topography of the study area, effective field static correction is a critical step in the subsequent processing and interpretation of seismic data for coal thickness. Static correction eliminates time shifts in reflection waves caused by changes in excitation and reception conditions, primarily due to variations in topography and near-surface velocity structures. Static correction involves two main tasks: calculating the static correction values and applying the correction [23].  
We adopted the Green Mountain first-break refraction static correction method and used a specialized module to separate long- and short-wavelength correction values. The Fathom method decomposes refraction information into the delay times at shot and geophone points and the velocity of the refracting layer. In the velocity analysis algorithm, the reciprocal velocity analysis (RVA) method was chosen and the near-surface model was gradually analyzed and interpreted [24]. The delay time analysis algorithm performs three iterations on the shot and geophone points, first using median estimates and then refining them with mean estimates, as shown in Figure 4.  
Figure 4: Comparison of static correction before and after: (**a**) before static correction; and (**b**) after static correction.  
Figure 3: Coverage in the exploration area. The color scale represents the number of stacking iterations.  
In the subsequent work after the static correction, dip-moveout (DMO) stacking was performed in conjunction with multiple automatic residual static corrections (short-wavelength). Considering the characteristics of the Fathom method, the following key calculation methods and parameters were used for field static correction: the reference elevation for static correction was set at 1550 m; and the replacement velocity was set at 3500 m/s to account for variations in geophone elevation and source well depth.
# Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach
## 4 Experiments and Results
### Performance Evaluation Metrics  
In this section, the adopted performance evaluation metrics such as MAE, MSE, RMSE and MAPE were used to measure the forecasting accuracy of the new model. These error metrics show the difference between the forecasted values and original (real DO concentration) values and the smaller the differences; better is the performance of the proposed novel hybrid EEMD-based LSTM forecasting model. The error metrics formulas are given as:  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}\left|V_{i}-F_{i}\right| \tag{16}\]  
\[\text{MSE}=\frac{1}{N}\sum_{i=1}^{N}\left(V_{i}-F_{i}\right)^{2} \tag{17}\]  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(V_{i}-F_{i}\right)^{2}} \tag{18}\]  
\[\text{MAPE}=\frac{1}{N}\sum_{i=1}^{N}\left|\frac{V_{i}-F_{i}}{V_{i}}\right| \tag{19}\]  
where \(N\) denotes the number of data points, \(V_{i}\) and \(F_{i}\) represent the real and forecasted values, respectively.
## 3 Control Design
### Proposed Control
#### 3.2.1 Adaptive Approximation Based on RBFNN
\begin{array}{c}\widetilde{\mu}_{i1}\\ \widetilde{\mu}_{i2}\\ \vdots\\ \widetilde{\mu}_{i[3n]}\end{array} \right]=\mu_{mi}\widetilde{m}_{ei}+\mu_{ei} \widetilde{\
u}+v_{i} \tag{30}\]  
where \(\widetilde{m}_{ei}={m_{ei}}^{*}-\hat{m}_{ei}\); \(\widetilde{\sigma}_{i}={\sigma_{i}}^{*}-\hat{\sigma}_{i}\); \(v\in R^{3n}\) is a vector of higher order terms; \(\mu_{mi}=\left[ \begin{array}{cccccccc}\frac{\partial\mu_{i1}}{\partial m_{ei}}& \frac{\partial\mu_{i2}}{\partial m_{ei}}&\cdots&\frac{\partial\mu_{iin}}{ \partial m_{ei}}\end{array} \right]^{T}\right|_{m=\hat{m}}\in R^{3n\times 3 nm}\); and \(\mu_{ei}=\left[ \begin{array}{cccccccc}\frac{\partial\mu_{i1}}{\partial \sigma_{i}}&\frac{\partial\mu_{i2}}{\partial\sigma_{i}}&\cdots&\frac{\partial \mu_{i[3n]}}{\partial\sigma_{i}}\end{array} \right]^{T}\right|_{\sigma=\hat{ \sigma}_{i}}\in R^{3n\times 3 nm}\). The equation can be rewritten as follows:  
\[{\mu_{i}}^{*}=\hat{\mu}_{i}+\mu_{mi}\widetilde{m}_{i}+\mu_{ei}\widetilde{ \sigma}_{i}+v_{i} \tag{31}\]  
Replacing (31) into (29), it is presented that  
\[\widetilde{D}_{i}=\widetilde{W}_{i}^{T}\hat{\mu}_{i}+\hat{W}_{i}^{T}\mu_{mi} \widetilde{m}_{i}+\hat{W}_{i}^{T}\mu_{ei}\widetilde{\sigma}_{i}+\phi_{i} \tag{32}\]  
where \(\phi_{i}=\widetilde{W}_{i}^{T}\mu_{mi}\widetilde{m}_{i}+\widetilde{W}_{i}^{T} \mu_{ei}\widetilde{\sigma}_{i}+{W_{i}}^{*}{}^{T}v_{i}+\varepsilon_{i}\)
# Comparative Study of Blockchain Hashing Algorithms with a Proposal for HashLEA
###### Abstract  
Blockchain has several unique features: data integrity, security, privacy, and immutability. For this reason, it is considered one of the most promising new technologies for a wide range of applications. Initially prominent in cryptocurrencies such as Bitcoin, its applications have expanded into areas such as the Internet of Things. However, integrating blockchain into IoT systems is challenging due to the limited computing and storage capabilities of IoT devices. Efficient blockchain mining requires lightweight hash functions that balance computational complexity with resource constraints. In this study, we employed a structured methodology to evaluate hash functions for blockchain-IoT systems. Initially, a survey is conducted to identify the most commonly used hash functions in such environments. Also, this study identifies and evaluates a lightweight hash function, designated as HashLEA, for integration within blockchain-based IoT systems. Subsequently, these functions are implemented and evaluated using software coded in C and Node.js, thereby ensuring compatibility and practical applicability. Performance metrics, including software efficiency, hardware implementation, energy consumption, and security assessments, were conducted and analyzed. Ultimately, the most suitable hash functions, including HashLEA for blockchain-IoT applications, are discussed, striking a balance between computational efficiency and robust cryptographic properties. Also, the HashLEA hash function is implemented on a Raspberry Pi 4 with an ARM processor to assess its performance in a real-world blockchain-IoT environment. HashLEA successfully passes security tests, achieving a near-ideal avalanche effect, uniform hash distribution, and low standard deviation. It has been shown to demonstrate superior execution time performance, processing 100 KB messages in 0.157 ms and 10 MB messages in 15.48 ms, which represents a significant improvement in execution time over other alternatives such as Scrypt, X11, and Skein.  
blockchain; internet of things; hash function; security tests; performance analysis Article
# Precoding for RIS-Assisted Multi-User MIMO-DQSM Transmission Systems
## 4 Interference Cancellation
### Strategy II. Joint-BD Precoding
Note that, differently from Strategy I, the precoding matrix \(\mathbf{Z}_{k}^{Eq}\) can be used for arbitrary values of \(N_{s}\). From here on, the systems based on Strategy I and Strategy II shall be referred to as RIS-MU-DQSM-I and RIS-MU-DQSM-II, respectively.
## 3 Silicon Germanium Waveguides and Nonlinear Applications
### Linear and Nonlinear Optical Characterization
#### 3.1.2 Kerr Nonlinearity  
The same group of waveguides was characterized in terms of nonlinearity using the CW-FWM method [42]. The results are summarized in Figure 7. As expected, the nonlinearity (Re\(\{\gamma\}\)) increases with Ge concentrations (because of the increased optical mode confinement, i.e., reduced \(A_{\text{eff}}\)). Furthermore, waveguides with smaller width exhibit larger values of Re\(\{\gamma\}\) up to a width (typically between 0.6 \(\upmu\)m and 0.8 \(\upmu\)m) where the mode can no longer be confined in the waveguide and the trend is reversed. The figure also shows a good agreement between the experimental measurements of Re\(\{\gamma\}\) and those predicted by simulations [55].
## 2 Feasible Design Space for ET Operation  
### Nonlinear Capacitance and Parasitic Model  
The variation of \(C_{ds}\) with the drain voltage (\(v_{ds}\)) is the main reason why conventional PA cannot be fully optimized over the full range of DC drain supplies (e.g., 3.5-30 V) under ET operation. It is therefore necessary to obtain a nonlinear capacitance model and embedding transfer network. The nonlinear \(C_{ds}\) profile given by Equation (1) and the parasitic model extracted in [17] are used in this work, as shown in Figure 1. This network offers an approximated large-signal model for the Cree commercial GaN HEMT device CGH40010F (Cree Inc., Durham, NC, USA).  
\[C_{ds}(v_{\rm ds})=0.95+1192.4\times[1+\tanh(-0.0594714\times v_{\rm ds}-2.946 96)]\;[\rm pF]. \tag{1}\]  
Figure 1: Nonlinear embedding model and continuous class-B/J power amplifier (PA) loads.  
For simplicity, it is feasible to use average drain voltage \(V_{ds}\) to calculate \(C_{ds}\), and then S-parameters (\(\mathbf{S}^{(\mathrm{P})}\)) of the embedding transfer network at different supply voltage can be computed conveniently utilizing the ABCD parameter matrix, as shown in Equation (2). Subsequently, the optimal impedances \(Z_{Igen}\) at the current generation plane can transform to \(Z_{pkg}\) at the package plane.  
\[\mathbf{S}^{(\mathrm{P})}=\left[\begin{array}{cc}S_{11}^{(\mathrm{P})}&S_{12 }^{(\mathrm{P})}\\ S_{21}^{(\mathrm{P})}&S_{22}^{(\mathrm{P})}\end{array}\right]=\left[\begin{array} []{cc}\frac{A+B/Z_{0}-CZ_{0}-D}{A+B/Z_{0}+CZ_{0}+D}&\frac{2(A-D\mathcal{D})}{ A+B/Z_{0}+CZ_{0}+D}\\ \frac{A+B/Z_{0}+CZ_{0}+D}{A+B/Z_{0}+CZ_{0}+D}&\frac{-A+B/Z_{0}-CZ_{0}+D}{A+B/Z_{ 0}+CZ_{0}+D}\end{array}\right] \tag{2}\]  
Under different drain supply conditions, the multiple fundamental and harmonic impedance solutions of continuous class-B/J PA at both the current generation plane and package plane are calculated and shown in Figure 1. Evidently, the trajectory of the required impedances varies with drain supply voltage, which means that it would be inappropriate to select a fixed \(Z_{Igen}\) at different supply voltages. Another observation is that there is a possibility of achieving a fixed \(Z_{pkg}\) when \(Z_{Igen}\) at different supply voltages are appropriately selected, which is beneficial to design matching circuits for ET's RF PA.
## 4 Experimental Results and Discussion
### Cobotic Preliminary Experiments
#### 4.3.1 Spraying Test
The spraying workflow described at the beginning of Section 4.3.1 is relatively straightforward. Once the vision system recognizes a bunch to be treated, the corresponding bounding box is sent to the robotic orchestrator, which, in turn, determines the target pose of the arm based on the _centroid_ of the bounding box itself. Based on this premise, it seems legitimate to assume that there is a direct correlation between the performances of the vision system and the performance of the actual administration:  
* The _accuracy_ of the spraying action, intended as how reliable is the administration process, is directly proportional to the percentage of true positives in the grape recognition process, thus, it is very likely interconnected to the concept of _precision_ in grape cluster recognition;
* The _efficacy_ of the spraying action, deemed as a _completeness_ measure, is proportional to the percentage of false negatives in the grape recognition process (i.e., the orchestrator misses a chemical treatment pose because no bounding box has been given), thus, it is strictly related to the _recall_ performance metric in grape detection;
* The _efficiency_ is measured in terms of the chemical waste during the spraying action. Three main factors play a crucial role in this sense: (i) the percentage of false positives in the grape recognition process (i.e., the vision system feeds the orchestrator with a wrong place to treat chemically); (ii) the precision (in terms of the IoU threshold) of  
Figure 11: Robotic preliminary experiment: spraying test in vineyard. the detected bounding boxes (i.e., how much the detected bounding box matches the optimal area to treat); (iii) the diffusion strategy that optimizes the extracted bounding box area, given the bounding box width and height, and the technical specifications of the chemicals diffuser (e.g., diffusion shape, diffusion distance, dispersion rate and so on).
## 4 Simulation and Analysis
### Simulation and Analysis of GM-CPHD and Pd-GM-CPHD  
In this part, in order to verify the performance of proposed Pd-GM-CPHD algorithm, we use the same adaptive gating technical to GM-CPHD algorithm, and then compare two algorithms in different clutter environments. Simulation results of the estimation of target number are shown in Figures 9-11. Also, the average time OSPA distance is used to evaluate the performance of the algorithm which is shown in Table 3.  
From Figures 9-11, we can see that estimation of target number is credible after 80 s. The estimated value obviously deviates from the true value when using GM-CPHD with constant detection probability. As the detection probability decreases with distance in practice, the higher the detection probability, the greater the estimation error of the target potential. On the contrary, a low detection probability performs better than a high detection probability, in which the reliability of the measurement decreases, and this leads the missing detection part to increase to remedy the update value, so the target number is more close to the true value. For the Pd-GM-CPHD algorithm, the detection probability changed with distance. However, when the target distance is more than 2 km, the detection probability dropped to less 0.4. In this case, even the estimation of the target number deviates from the true value, but it still has a certain tracking ability. In general, the Pd-GM-CPHD algorithm is more accurate in estimating the target number.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Time Average OSPA-Distance (m)**} & \multicolumn{3}{c}{**GM-CPHD**} & \multirow{2}{*}{**Pd-GM-CPHD**} \\ \cline{2-3} \cline{5-5}  & \(P_{d}=0.7\) & \(P_{d}=0.8\) & \(P_{d}=0.9\) & \\ \hline \(\kappa_{k}=5\) & 34.04 & 43.34 & 52.04 & 25.22 \\ \(\kappa_{k}=10\) & 39.29 & 46.61 & 55.63 & 26.54 \\ \(\kappa_{k}=20\) & 40.29 & 47.63 & 56.18 & 30.05 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of time average OSPA-distance (m) in different clutter environments. GM-CPHD: Gaussian Mixture Cardinalized Probability Hypothesis Density.  
Figure 11: Comparison of target number estimation with different \(P_{d}\) in clutter intensity \(\kappa_{k}=20\).  
Figure 10: Comparison of target number estimation with different \(P_{d}\) in clutter intensity \(\kappa_{k}=10\).  
Table 3 shows the average OSPA distance between GM-CPHD with constant detection probability and Pd-GM-CPHD under different clutter environment. Due to the detection probability decreasing with distance, the higher detection probability leads to the unreliable measurement being used to estimate the target state. So the tracking performance is reduced. The average OPSA distance is bigger. The Pd-GM-CPHD algorithm uses the adaptive detection probability, which not only can make the estimation of the target number more reliable but also can improve the performance of tracking accuracy. So we can get the similar results with Figures 9-11 that the Pd-GM-CPHD have a good performance.
# Blockchain for the Healthcare Supply Chain: A Systematic Literature Review
## 3 Results
### Study Type, Domain, and Technology  
Of the 28 articles included in this systematic review, 15 (54%) were classified as \"simulated\" studies and 12 (43%) were classified as \"theoretical\". Only one was classified as a \"real case study\".  
Among the different domains of application, the application of blockchain technology for drug SCs was the most frequently investigated (n = 17, 61%). Three (11%) studies investigated the use of blockchain technology in medical devices' SCs, three (11%) were in vaccines, and two (7%) were in blood management. Three (11%) studies simultaneously investigated the use of general applications for drugs, vaccines, and tools.  
The types of blockchains implemented by the included studies were classified as \"private blockchain\" (n = 12, 43%), \"consortium blockchain\" (n = 6, 21%), \"hybrid\" (n = 5, 18%), and \"public\" (n = 5, 18%).  
The most frequently adopted blockchain platforms were Ethereum (n = 8, 32%) and Hyper-ledger Fabric (n = 6, 21%). In two studies, ETH was used alongside FileCoin. IBM Hyperledger Fabric was adopted in two studies, and Open-Source Blockchain was implemented in two cases. Only one study developed an \"in-house\" blockchain technology. In the remaining articles (n = 9, 32%), it was not possible to identify the adopted technology.  
Most of the articles included the adoption of smart contracts (n = 23, 82%).  
Among the 28 included studies, only seven investigated the system's scalability--six of which declared that the investigated solution was scalable, while the remaining one declared that their system was not. The majority of the articles (n = 21, 75%) did not explore the scalability of the proposed system.