esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results
Figure 5 illustrates the EC of the first and the third users in term of perfect and imperfect SIC to investigate an impact of quality of SIC process on the performance of users. We can recognize that the EC of the third user in the case of perfect SIC outperforms its capacity of imperfect SIC, whereas the EC of the first user is fixed for both cases. The reason is the same as explained above, the first user does not apply the SIC operation to cancel the interference, it considers the signal of other users as the interference signal. Moreover, the EC of the third user in perfect SIC is much higher than the EC of the first user, however it is rapidly decreased when the \(\xi\) increases, and it trends to be lower than that of the first user when the \(\xi\) becomes higher. It can be said that, the impact of imperfect SIC on the user close to the BS is high, the EC of the nearer user decreases more rapidly in the case of imperfect SIC. Because the quality of SIC significantly affects the performance of users, designing high quality of SIC operation at the receiver is important for NOMA systems. Figure 4: OP of the first user with different data rates. Figure 3: OP in term of perfect and imperfect SIC with residual of coefficient \(\xi=0.05\). The EC of the first and the second users in terms of perfect and imperfect SIC is demonstrated in Figure 6. The EC of the first user is higher than that of the second user in both perfect and imperfect SIC. Especially in the case of imperfect SIC, the EC of the second user decreases significantly whereas the EC of the first user is unchanged. This result also demonstrates the saturation of EC in the high SNR region. The reason is, in the high SNR region, the power level of designed signal is high, however the power level of remained interference signal after imperfect SIC operation is also higher. Therefore, the SINR as well as the EC is saturated. Our proposed method is compared with OMA method in Figure 7 for the both cases perfect and imperfect SIC with \(\xi=0.01\) and BPSK modulation. Similar to our proposed  
Figure 5: Comparing the EC of the first and the third users for perfect and imperfect SIC with \(\xi=0.01\). Figure 6: Comparing the EC of the first and the second users in two cases perfect and imperfect SIC with \(\xi=0.01\). method, the conventional OMA method is also combined with the beamforming method to cancel the infer-beam interference. As shown in Figure 7, in the case of perfect SIC, the proposed method outperforms the conventional OMA method because of higher spectrum efficiency of NOMA method [24]. However, the proposed method is significantly affected by the remained interference signal due to the imperfect SIC, the total EC of proposed method therefore becomes lower than that of conventional OMA method when the SNR is over 20 dB, it is the trade-off of our proposed method.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 3 Methodology
### System Architecture and Dataset Design for IoT-Supported Blockchain Integration
#### 3.1.1 RFID Registration  
At the farm level, RFID technology can be used to assign a unique identity to each coffee bean batch immediately after harvest. Impinj Speedway RFID Readers [38] and UHF RFID tags [39] can be used to log critical information such as the farm ID, harvest date, and batch number. This batch ID establishes the foundation of traceability, serving as a consistent reference point for all future blockchain transactions for identity establishment and provenance tracking. Smart contracts can utilize this identity to securely link every ownership transfer, quality verification, and sale event back to the original farm source.
# Frequency-Domain Filtered-x LMS Algorithms for Active Noise Control: A Review and New Insights
## 2 FxLMS Algorithm  
The diagram of the FxLMS algorithm is presented in Figure 1, where \(P(z)\) is the primary path and \(S(z)\) is the secondary path. We define the weight vector of the control filter at the time index \(n\) as \(\mathbf{w}(n)=[w_{0}(n),w_{1}(n),\cdots,w_{N_{\mathrm{w}}-1}(n)]^{T}\) with a length of \(N_{\mathrm{w}}\). The reference signal \(x(n)\) is picked up by a reference microphone and then filtered by the adaptive filter to generate the cancelling signal \(y(n)\) driven by a secondary loudspeaker  
\[y(n)=\mathbf{x}^{T}(n)\mathbf{w}(n) \tag{1}\]  
where \(\mathbf{x}(n)=[x(n),x(n-1),\cdots,x(n-N_{\mathrm{w}}+1)]^{T}\) is the reference signal vector. The cancelling signal \(y(n)\) is then filtered by the secondary path \(S(z)\) to obtain the control signal \(z(n)\) at the location of the error microphone  
\[z(n)=\mathbf{\hat{y}}^{T}(n)\mathbf{s} \tag{2}\]  
where \(\mathbf{\hat{y}}(n)=[y(n),y(n-1),\cdots,y(n-N_{\mathrm{s}}+1)]^{T}\), and \(\mathbf{s}=[s_{0},s_{1},\cdots,s_{N_{\mathrm{s}}-1}]^{T}\) is the weight vector of the secondary path with a length of \(N_{\mathrm{s}}\). The residual error signal picked up by the error microphone is  
\[e(n)=d(n)+z(n), \tag{3}\]  
where \(d(n)\) is the disturbance signal at the error microphone.  
The FxLMS algorithm is commonly used to update the weight vector [3; 4]  
\[\mathbf{w}(n+1)=\mathbf{w}(n)-\mu\mathbf{v}(n)e(n) \tag{4}\]  
where \(\mu\) is the step size, and \(\mathbf{v}(n)=[v(n),v(n-1),\cdots,v(n-N_{\mathrm{w}}+1)]^{T}\) the filtered reference signal vector, where the filtered reference signal \(v(n)\) is given by  
\[v(n)=\mathbf{x}_{s}^{T}(n)\mathbf{\hat{s}} \tag{5}\]  
where \(\mathbf{x}_{s}(n)=[x(n),x(n-1),\cdots,x(n-N_{\mathrm{s}}+1)]^{T}\), and \(\mathbf{\hat{s}}=[s_{0},s_{1},\cdots,s_{N_{\mathrm{s}}-1}]^{T}\) is an estimate of the actual weight vector of the secondary path. An accurate estimate of the secondary path is required for the FxLMS to work properly, which can be obtained by an offline- or online-modeling method [4; 6].  
The FxLMS algorithm requires \(2N_{\mathrm{w}}+N_{\mathrm{s}}\) multiplications. In certain scenarios, the length of the weight vector may reach several thousand taps. Thus, the computational complexity is a major concern for real-time systems.
# Prediction Method of Underwater Acoustic Transmission Loss Based on Deep Belief Net Neural Network
###### Abstract  
The prediction of underwater acoustic transmission loss in the sea plays a key role in generating situational awareness in complex naval battles and assisting underwater operations. However, the traditional classical underwater acoustic transmission loss models do not consider the regional hydrological elements, and the performance of underwater acoustic transmission loss prediction under complex environmental conditions in a wide range of sea areas is limited. In order to solve this problem, we propose a deep learning-based underwater acoustic transmission loss prediction method. First, we studied the application domains of typical underwater acoustic transmission loss models (ray model, normal model, fast field program model, parabolic equation model), analyzed the constraint rules of its characteristic parameters, and constructed a dataset according to the rules. Then, according to the characteristics of the dataset, we built a DBN (deep belief net) neural network model and used DBN to train and learn the dataset. Through the DBN method, the adaptation and calculation of the underwater acoustic transmission loss model under different regional hydrological elements were carried out in a simulation environment. Finally, the new method was verified with the measured transmission loss data of acoustic sea trials in a certain sea area. The results show that the RMSE error between the underwater acoustic transmission loss calculated by the new method and the measured data was less than 6.5 dB, the accuracy was higher than that of the traditional method, and the prediction speed was faster, the result was more accurate, and had a wide range of adaptability in complex seas.  
deep learning; DBN; underwater acoustic transmission loss; hydroacoustic survey +
Footnote †: journal: Medical Imaging
# Simulative and Experimental Investigation of Vibration Transfer Path at Gearboxes
## 4 Measurement Setup  
The test rig, introduced in Section 2, is used for the experimental tests. The four acceleration sensors from Hettinger Briel&Kjaer, Virum, Denmark, of Type BK-4518 and BK-4519 are sampled continuously, and parallel with a sampling frequency of 100 kHz. All sensors have similar specifications to compare the sensor signals. The signals of the rotating sensors are transferred with shielded cables to the mercury slip ring Magtrol SA, Rossens; Switzerland, 4 MTA/T [34]. Between the slip ring and the conditioning amplifier, the signals are also transferred in the shielded cables. A from Hettinger Bruel&Kjaer, Virum, Denmark, BK-2693-A [35] conditioning amplifier with a high-pass filter at 1 Hz and a low-pass filter at 30 kHz is used to check the signal quality. The data of the four acceleration channels are then fed into a National Instruments, Austin, USA, BNC-2090A [36] connector and then processed by National Instruments, Austin, USA, PXI-8110 [37]. The position of the accelerometers has been displayed in Figure 7 below. The positions are chosen as close to the gear as possible to achieve high signal amplitudes. Multi-body simulations were conducted for the validation. Channel 1 and Channel 2 are the rotating accelerometers of BK-4518 [38] for Channel 1 and BK-4519 [39] for Channel 2. Both sensors measure in the same absolute direction. Related to the gear, Channel 1 is in the tangential direction, and Channel 2 is located in the radial direction. This measurement system is based on an earlier work by [PERSON] [40]. For the validation, an inductive speed sensor has been equipped at the test rig, which is Channel 3 and not considered here. The sensors on the housing of type BK-4518 [38] (Channels 4 and 5) are located at the housing close to the bearing to measure as high of amplitudes as possible. The measurements were conducted over 45 s, so 45,000,000 data points per sensor were measured.  
Figure 7: _Cont._
# Microwave Pyrolysis of Biomass: The Influence of Surface Area and Structure of a Layer
## 4 Results and Discussion
### Influence of Biomass Particle Size
Experiments showed (Figure 18) that the release of CO started faster during the pyrolysis of biomass with a particle size of 250 \(\upmu\)m. The release of CO\({}_{2}\) was the first to start during the pyrolysis of biomass with a particle size of 140 \(\upmu\)m and 250 \(\upmu\)m. The CO\({}_{2}\) concentration started to grow after 2 min. During the pyrolysis of sawdust with an average size of 2000 \(\upmu\)m, the growth of CO\({}_{2}\) concentration occurred after 5 min. The shortest gas  
Figure 18: Trends of the concentrations of gas mixture components when varying the average particle size: CO\({}_{2}\) (**a**); CO (**b**); H\({}_{2}\) (**c**); a comparison of individual concentration trends (**d**). emission time was recorded for sawdust with a size of 140 um; the longest gas emission time was recorded during the pyrolysis of sawdust with an average particle size of 2000 um. The release of H\({}_{2}\) started immediately at the beginning of the experiment. In [64], microwave pyrolysis of wheat straw was studied. For a particle size of 0-0.09 mm and a microwave power of 800 W, the release of CO\({}_{2}\) began at the 2 nd min of the experiment. The emission of CO and CH\({}_{4}\) began after 3 and 4 min of the experiment, respectively. Based on the experimental data, the efficiency coefficient K\({}_{\text{eff. }}\) was calculated using the formula: K\({}_{\text{eff. }}=\text{N}_{\text{max}}/\text{N}_{\text{min}}\), where N is the average gas concentration. The results of the calculations are shown in Table 8. The analysis showed that the efficiency of microwave pyrolysis can vary in a fairly wide range when the process conditions change.
## 2 Materials and Methods  
### Microalgal Cultivation in Swine Wastewater (SW)  
#### 2.1.1 Characteristics of SW  
SW was collected from a livestock farm in Dalseong County, Daegu, Republic of Korea. While SW serves as an appropriate substrate for microalgae cultivation due to its rich nutrient content, its high concentrations of pollutants, such as heavy metals, can adversely affect microalgal growth rate and biomass production [2]. To enhance light penetration and ensure an adequate nutrient supply, the SW was filtered through a 0.22 um bottle-top vacuum filter (Sartolab RF, Sartorius Stedim Biotech, Gottingen, Germany) and subsequently diluted with distilled water at a ratio of 1:20. The nutrient concentrations in the DSW were quantified, including total nitrogen (TN), ammonia nitrogen (NH\({}_{3}\)-N), nitrate (NO\({}_{3}\)-N), and total phosphorus (TP), using a water analysis kit (Humas, Daejeon, Republic of Korea). Additionally, concentrations of heavy metal ions were measured using an inductively coupled plasma spectrometer (Optima 7300 DV, PerkinElmer, Waltham, MA, USA).
## 2 Materials and Methods
### Catalyst Performance Test
#### 2.4.2. Calculations  
A standard curve was generated by varying the AA amount (0.02-0.27 g, 0.333-0.4.496 mmol) while maintaining the amount of the internal standard (nDD) fixed (0.04 g, 0.235 mmol), as well as an excess amount of nHTL (1.8 g, 15.400 mmol). The tests were performed in triplicate by injection of 1 \(\mu\)L of the mixture to be analyzed by GC and, from the chromatogram obtained, the areas of the peaks due to AA (_S_) and nDD (_S_o) were calculated, with the data averaged. Thus, the AA conversion percentage can be calculated via Equations (1)-(4).  
\[K=\frac{\frac{M}{\text{{M}}_{o}}}{\frac{S}{S_{o}}} \tag{1}\]  
\[X_{AA}=\frac{KM_{o}S}{S_{o}} \tag{2}\]  
\[Y_{AA}=M-X_{AA} \tag{3}\]  
\[C\;(\%)=\frac{Y_{AA}}{M}\;\times\;100 \tag{4}\]  
where _M_/_M_o and _S_/_S_o are the AA/nDD feedstock (by mole) and the peak area ratios, respectively, \(K\) is the conversion factor, \(X_{AA}\) and \(Y_{AA}\) are the number of moles of AA unreacted and converted, respectively, and \(C\%\) is the conversion percentage of AA under the experimental conditions.
## 3 Methods
### Supervised Classification
#### 3.4.2 Random Forest  
Ensemble classifiers, such as RF, increasingly gain popularity in EO applications using high spatial resolution imagery, as they can effectively manage large volumes of data [7]. RF starts with forming an  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**\# Evaluation** & **Box Constraint** & **10-Fold Loss (\%)** & **Iteration Time (s)** \\ \hline
1 & 1.048 & 5.97 & 35.29 \\
2 & 736.8 & 8.54 & 286.29 \\
3 & 46.36 & 7.29 & 177.32 \\
4 & 220.22 & 8.32 & 689.06 \\
5 & 998.40 & 8.34 & 450.62 \\
6 & 0.0052 & 22.92 & 18.38 \\
7 & 0.17 & 8.82 & 17.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Box constraint (C) parameter optimization using 10-fold cross-validation loss minimization.  
ensemble of standard decision trees, also known as weak learners. In a simple decision tree, the input entity is entered at the top and as it moves towards the bottom it gets grouped into progressively smaller subsets. The labels are then assigned to parcels based on the maximum number of votes among the ensemble of weak learners [19; 32]. The RF model was built using the MATLAB function _fitensemble_, under the \"Bag\" method. Bagging refers to the repeated selection of random samples from the entirety of the training set, on which the weak learners train separately. Even though the estimates of a single decision tree would be noisy, the mean estimates over multiple decision trees are both unbiased and resilient to over-fitting [19]. In the same manner as for the SVM classification, the minimization of 10-fold cross-validation loss is used for the optimal hypermeter tuning. Once again, thirty different objective evaluations were performed, of which an indicative subset is presented in Table 3.  
The hyperparameters \"number of weak learners\" and \"maximum number of splits\" were varied and evaluated against the minimization of the objective function. The third evaluation of 30 decision trees was selected as the optimal set of parameters. Nonetheless, it is observed that the first evaluation that uses 479 weak learners gives a lower 10-fold cross-validation loss, but this marginal increase in accuracy cannot excuse the additional processing effort required.
## 5 Energy Dissipation Analysis
### Kinetic Response  
The kinetic energy response of the gully A-C sections in the model during erosion is shown in Figure 17. The kinetic energy curves of Sections A and C show a stepped course. Integrating the previous analyses, the stepped course of the curves records the erosion damage mode of the No. 3 gully. We can reasonably divide the entire process of erosion damage into three stages, corresponding to the periods of 0-20 min, 20-37 min, and 37-60 min, respectively (Figure 17). The kinetic energy changes at Section A during the first and second stages are small, where each has an energy increase of about 0.5 MJ (Figure 17a). During the third stage, the kinetic energy shows a very large variation, with values ranging from 0.5 MJ to 8.8 MJ. The sharp increase in kinetic energy at this stage was caused by the destruction of soil particles at the bottom and top of the slope, along with the accumulation and collision of many loose particles. Meanwhile, profile C increased to a maximum of 8.9 MJ in the first stage (Figure 17c), related to the initial scour failure in the slope, indicating that Section C is more susceptible to scour damage. The kinetic energy increased continuously, from 5 MJ to 16.2 MJ, in the third stage of erosion damage in Section C, then subsequently decreased to about 7.4 MJ and remained stable. These data indicate that the kinetic energy response at Section C was faster than at Section A, and the energy was higher, with a difference of about 7.4 MJ. Section B was the most important section for gully failure. The curve clearly documents that the first stage was mainly the failure stage in the center of the slope, where its instantaneous kinetic energy attained a maximum of 25 MJ (Figure 17b). Towards the second and during the third stage, the instantaneous kinetic energy decreased, as the fluid mainly affected Section B, gradually reducing the bonding strength between the particles. Therefore, the instantaneous kinetic energy decreased during the break-up of the particles.  
In summary, the maximum kinetic energy of the No. 3 gully was concentrated in the erosion damage during the first stage slope, where the maximum kinetic energy reached 25 MJ in Section B. Section C presented a maximum kinetic energy of 16.2 MJ in the later failure of the top and bottom of the slope.
## 2 Study Area and Data Sources
### Data Sources and Preprocessing  
The LUC data of Guilin were obtained from the global land cover data of the National Basic Geographic Information Center ([[https://www.ngcc.cn/](https://www.ngcc.cn/)]([https://www.ngcc.cn/](https://www.ngcc.cn/)) (accessed on 16 June 2023)) in 2000, 2010, and 2020. The LUC types were mainly divided into the following six categories: arable land (AL), woodland (WL), grassland (GL), waters (WT), construction land (CL), and wetland (WTL), and the spatial resolution of the data was 30 m (Figure 1). The traffic data came from the National Basic Geographic Information Database, and, given the availability of the data, 1:100,000 road data were used for 2017. The digital elevation model (DEM)  
Figure 1: The spatial distribution of land use in Guilin.  
was obtained from the geospatial data platform ([[https://www.gscloud.cn/](https://www.gscloud.cn/)]([https://www.gscloud.cn/](https://www.gscloud.cn/)) (accessed on 28 June 2023)) with a spatial resolution of 30 m. Slope and aspect data were derived from it using ArcGIS software (v 10.6). Annual rainfall, average annual temperature, and GDP data were obtained from the Resources and Environmental Sciences and Data Center of the Chinese Academy of Sciences ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)) (accessed on 3 July 2023)), with a spatial resolution of 1 km.
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 4 Results
### Training Process  
The training process for lung and infection segmentation on CT-scans was conducted using UNet, Linknet, Attention UNet, UNet 3+, and TransUNet architectures. The hyperparameters and dataset were standardized to ensure a fair and appropriate comparison. Figure 7 depicts the loss and the IoU score graphs of UNet, Linknet, Attention UNet, UNet 3+, and TransUNet during the training process.  
From Figure 7, it is evident that all five algorithms exhibit the expected decreasing pattern. Notably, both UNet and Attention UNet stand out for their smoother convergence trajectories, distinct from the other algorithms that exhibit sporadic anomalies with intermittent high loss values across various epochs. Taking a closer look at the convergence behaviors, LinkNet, while exhibiting more abrupt fluctuations with occasional spikes in loss values, demonstrates a relatively rapid pace in reaching the learning limit. Specifically, LinkNet achieves learning convergence in just 542 epochs, making it the quickest to approach the learning limit within the experiment. On the other hand, among the other models, UNet 3+ requires 665 epochs, standard UNet requires 948 epochs, Attention UNet needs 953 epochs, and TransUNet exhibits the longest convergence trajectory, taking 1789 epochs to reach the learning limit. Similarly, from the IoU score graph in Figure 7, all five algorithms exhibit the expected increasing pattern, with UNet and Attention UNet standing out for their smoother convergence trajectories.  
Figure 6: The modeling schemes.  
Figure 7: Comparison of the loss and the IoU score graphs of the UNet, Linknet, Attention UNet, UNet 3+, and TransUNet algorithms during the training process.
## 3 Results
### Spatial Resolution with the FLUKA Simulation
#### 3.5.1 Edge Spread Function  
Figure 15 shows the ESF for 40 kV X-rays. The ESF was determined by projecting the edge profile in the direction perpendicular to the edge. The spatial resolution was then estimated by fitting the measured ESF by the logistic function of Equation (1). The data points were shifted to center the distribution at the center of the image sensor; the bin size is 0.25 \(\upmu\)m (twice the pixel size). When limiting the fit to the full active area of the sensor (6 cm along the x-axis), the estimated value of the FWHM is 4.53 \(\pm\) 0.01 mm (statistical errors only).  
Figure 14: The simulated LaGEMPix with the 2.5 cm thick lead block in front, at 4 cm distance from the Mylar window (**a**) and a 1 mm-thick copper plate placed at 3.3 cm from the Mylar window (**b**). The figures were produced using FLAIR [56] version 3.1-8, the FLUKA graphical user-interface.  
Figure 15: Simulated ESF for the edge projection fitted by a logistic function. The results were obtained for a detector-source distance of 230 cm, a beam aperture of 1 cm and 40 kV X-rays. The error bars are smaller than the data points.