esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Principles and Enzymes Used in EAE
### Types and Activity of Hydrolytic Enzymes
#### 2.2.2 Pectinases  
Pectinases are the most widespread enzymes found in fungi, bacteria, and plants [37]. They are responsible for the degradation of pectin substances. Their potential use in, e.g., the food industry, production of juices and wines, and many others has been indicated as well [15; 37]. Interestingly, the use of pectinases strengthens the taste and the colour of wine [37]. Moreover, these enzymes are used to reinforce and accelerate tea fermentation and to remove the mucilaginous layer of coffee beans [30]. Similarly to cellulases, pectinases are not a single molecule but a mixture. Pectinases comprise protopectinases, pectin methyl esterases, pectin acetyl esterases, polymethylgalacturonases, polygalacturonases, pectate lyases, pectin lyases, rhamn hydrolases, rhamnocarbonan galacturonohydrolases rhamnoblasturonan hydrolases, rhamnogalacturonan lyases, rhamnocalacturonan acetylesterases, and xylogalacturonan hydrolase [37]. Pectinases can be produced by a number of microorganisms, such as Aspergillus sp., _Streptomyces_ sp., _Penicillium chrysogenum_, and _Bacillus_ sp. [37; 38; 39]. For instance, acid pectinase commonly used in extraction and clarification is produced by the fungi Aspergillus niger_[37]. As mentioned previously, enzymes from different organisms exhibit different activity in various conditions. For example, fungal polygalacturonases have high activity at pH 3.5-5.5 and an optimal temperature between 30 and 55 \({}^{\circ}\)C, whereas polygalacturonases from _Bacillus licheniformis_ and _Fusarium oxysporum_ have an optimum pH of 11. The optimum pH for pectin lyases is estimated at 4.0-5.0. Pectin lysates from _Aspergillus_ sp. are efficient at pH 5.5 and a temperature range of 40-50 \({}^{\circ}\)C [30; 40; 41]. In turn, pectate lyases need higher pH values (between 7.5 and 10) for their optimal activity. Similarly, enzymes from _Ewinia_ sp. achieve their activity at pH 6, whereas this enzyme from _B. licheniformis_ is active at pH 11 and requires an optimal temperature in the range of 40-50 \({}^{\circ}\)C [30]. It is worth noting that the enzyme from _B. licheniformis_ needs calcium ions for its activity.  
Pectinases can be purchased in liquid or powder form and their efficiency is product-dependent. Pectinase from Aspergillus aculeatus is in a liquid form with declared activity of approx. \(\geq\)3.800 units/mL. However, the enzyme from Aspergillus niger shows specific activity of >1 U/mg and is commercially available as a powder. Pectinase from _Rhizopus_ sp. can also be purchased as a powder with specific activity estimated at 400-800 units/g solid (www.sigmaaldrich.com; accessed on 18 January 2022).
## 4 In-Loop Testing of the Control Model
### Results and Analysis of Multi-Layer Decoupling Control Strategy Based on Wavelet Rules  
The wavelet rule-based multi-layer decoupled control strategy proposed in this article is used. Model-in-the-loop simulation experiments are conducted for the tractor load demand power.  
First, the load demand power is input into the Haar wavelet-based two-channel orthogonal filter set. The first decoupling layer is performed for the high-frequency and low-frequency power. The results are shown in Figure 15. It can be seen that the high-frequency and low-frequency signals in the load demand power are effectively decoupled after the first filtering layer of the Haar wavelet transform, in which the mean value of high-frequency power is 0.003 kW, and the variance is 0.054 kW\({}^{2}\), which approximately conforms to the standard normal distribution. The Haar wavelet-based two-channel filter has both time and frequency resolution to meet the control requirements.  
Figure 14: The actual working point of the hydrogen fuel cell.  
Figure 13: The power output of each energy part.  
Figure 15: First\(-\)layer decoupling results based on the Haar wavelet.  
Second, logic rules are used to decouple the low-frequency power signals, which are decoupled by wavelet transform for the second time, and the results are shown in Figure 16. It can be seen that the sub-high frequency signal and steady-state signal in the low-frequency power are effectively decoupled after the second layer decoupling of the logic rule, in which the mean value of the sub-high frequency power is 2.44 kW. The mean value of the steady-state power is 16.57 kW, and the variation of the steady-state power signal is smoother compared to the sub-high frequency signal.  
The decoupled power signals are distributed according to the multi-layer EMS designed in Figure 8. Some of the results are shown in Figure 17.  
Figure 16: Second\(-\)layer decoupling results based on the logic rules.  
Figure 17: _Cont._  
As can be seen from Figure 17, the load demand power is reasonably distributed to each energy source after the multi-layer energy management strategy. The output power of hydrogen fuel cell, power cell, and ultracapacitor shows stable, sub-frequency, and high-frequency characteristics. The output power of the hydrogen fuel cell has a slight fluctuation and a slight span.  
As shown in Figure 18, since it has the function of optimizing the working points of the PEMFC system, the working points of the hydrogen fuel cell are more concentrated during the operation, and most of them are concentrated in its high-efficiency zone.
## 3 Results
### BDS Antenna Calibration Results  
In this section, we show the antenna calibration results for BDS. The elevation mask angle was set to 0\({}^{\circ}\). The most significant features of the BDS data were a longer data collection duration and denser satellite distribution (as shown in Figure 6). Therefore, a better calibration result than GPS could be expected.  
Figure 11 depicts the BDS antenna calibration results, which were more comparable to the reference phase pattern, compared with the GPS calibration results. Figure 12 shows the deviation distributions of each calibration result. For better comparison, we use a uniform color map with Figure 10. It can be seen that only some small areas below the elevation angle of 10\({}^{\circ}\) suffered from deviations larger than 1 cm, and the maximum deviation was lower than 2 cm. Table 3 summarizes the statistics of the BDS calibration deviations. The mean value of eight RMSEs was 3.378 mm, which was about half that of the GPS calibration results. This is mainly due to more available BDS observations, which improves the calibration results at low elevation angles.  
Figure 11: BDS antenna calibration results (**a**–**h**) different examples.  
Figure 10: GPS antenna calibration deviations (**a**–**h**) different examples.
# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 4 Experimental Results and Analysis
### Evaluation Metrics  
To evaluate the model's performance across diverse datasets, we employ dataset-specific metrics. For the ISIC 2018 dataset, segmentation is assessed using five metrics: mean Intersection over Union (mIoU) [16], Dice Similarity Coefficient (DSC) [39], Accuracy (Acc) [2], Sensitivity (Sen), and Specificity (Spe) [40]. For the Synapse, the DSC is used to measure organ-wise segmentation accuracy, while the Hausdorff Distance (HD) [41] quantifies the shape similarity between predicted and ground truth contours, with a focus on boundary precision. In the ACDC dataset, the DSC is similarly used to evaluate the segmentation quality of cardiac structures.  
\[IoU=\frac{TP}{TP+FP+FN} \tag{6}\]  
\[mIoU=\frac{1}{n}\sum_{i=1}^{n}IoU_{i} \tag{7}\]  
\[DSC=\frac{2\times TP}{2\times TP+FP+FN} \tag{8}\]  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN} \tag{9}\]  
\[Sen=\frac{TP}{TP+FN} \tag{10}\]  
\[Spe=\frac{TN}{TN+FP} \tag{11}\]  
\[HD(Y,\hat{Y})=\max\{\max_{y\in Y}\min_{y\in\hat{Y}}d(y,\hat{y}),\max_{y\in\hat {Y}}\min_{y\in Y}d(y,\hat{y})\} \tag{12}\]  
where the false negative (FN) refers to a pixel belonging to the ground truth target region but mistakenly classified as background, while the true negative (TN) denotes a background pixel correctly identified as such. The false positive (FP) is a background pixel incorrectly predicted as part of the target region, and the true positive (TP) is a pixel correctly identified within the target region. In medical image segmentation, positive samples are pixels within the lesion or organ of interest, while negative samples are background pixels. \(Y\) denotes the ground truth mask and \(\hat{Y}\) the corresponding prediction. The term \(d(y,\hat{y})\) refers to the Euclidean distance between a ground truth point \(y\) and the nearest predicted point \(\hat{y}\).
## 3 A Trust Anchor for a Home Network's CA
### Home TPM CA  
When using a TPM to secure the Home CA's private signing key (Home Key, HK), the key hierarchy inside the home network needs to be enhanced by the keys and their certificates that enable and prove the HK's protection by a TPM. As explained above, an AIK needs to be created as an alias for the EK first. Due to constraints of the TPM's specification, the AIK may not be used as HK, but it can be used to sign another non-migratable TPM-protected key, the Home Key. This leads to the following key and certificate hierarchy within the home network: \(\text{EK}\rightarrow\text{AIK}\rightarrow\text{HK}\). The HK can then be used to sign the certificate of an entity (see Figure 5).  
Malfunction of a TPM chip can lead to the loss of the HK. This means that the Home CA is not able to issue certificates to new entities anymore. The home network needs to create a new HK and Home Certificate and finally re-establish trust relationships to other home networks. A higher level of usability and a backup in case the TPM is malfunctional can be provided by making the HK migratable. In order to sign a migratable HK, another non-migratable key, referred to as _Intermediate Key (IMK)_, has to be added to the key hierarchy. Finally, the following key chain is built: \(\text{EK}\rightarrow\text{AIK}\rightarrow\text{IMK}\rightarrow\text{HK}\). Again, the HK is used to sign the entity certificate.  
Figure 5: Changed key and certificate hierarchy inside the home network. Certificates depicted with grey boxes are TPM-related and used to attest that the HK is stored within a TPM. For compatibility with non-TPM systems, a self-signed Home Certificate is still created. The certificate for an entity’s public key is signed by the HK.  
Both key hierarchies can be used to prove to others that the HK is stored within a standard compliant TPM. There is practically no difference in the security of the key's protection. But Home Networks that trust a Home Certificate that belongs to a non-migratable HK should assign a higher level of confidence to this certificate than into a Home Certificate of a migratable HK. The reason is that the backup of a migratable HK could be stored insecurely.  
The HK is protected against unauthorized use by applications or malware by using a PIN. The owner of the home network needs to provide this PIN when access to the HK inside the TPM is needed to sign a new certificate of an entity.
## 3 Results
### Corrugated Steel Plate (CSP) Capacity  
Allowable stress capacity based on the quality of the corrugated steel plate can be calculated using the following equation:  
\[\sigma_{all}=0.6\times F_{y} \tag{11}\]  
\[\sigma_{all}=0.6\times 240\;\mathrm{MPa}=144\;\mathrm{MPa} \tag{12}\]  
So, the allowable stress capacity of corrugated steel plate is 144 MPa or 144,000 \(\mathrm{kN/m^{2}}\).  
The stress occurring on the corrugated steel plate based on the bending moment can be calculated using the following equation:  
\[\sigma_{N}=\frac{M_{max}\mathrm{y}}{\mathrm{I}} \tag{13}\]  
With a moment of inertia (\(I\)) equal to 0.000142 m\({}^{4}\) and \(\mathrm{y}=0.1185\;\mathrm{m}\), the stress value that occurs can be calculated and the results are displayed in Table 8. Figure 13 shows the output of the bending moment that occurs during the construction of Phase 14.  
According to the result in Table 8, it is obvious that the corrugated steel pipe's (CSP) maximum stress remains below the allowable stress as a result of the loading during the filling. Therefore, it can be determined that the CSP is still capable of resisting the loading resulting from the embankment.  
Table 9 displays the horizontal displacement on the corrugated steel plate. The results indicate that the embankment process has an impact on the direction of horizontal displacement. Figure 14 shows the output of the maximum lateral deformation in the corrugated steel plate. The results show that the construction stages lead to a change in the direction of lateral deformation, which follows the direction of embankment filling. Negative displacement values indicate a horizontal displacement toward the left. The largest displacement occurs in the right direction, exceeding 41.37 mm.  
Figure 12: Settlement result on construction phase 14 with reinforcement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phase** & **Mmax (kNm)** & **\(\sigma_{\text{N}}\) (kN/m2)** & **\(\sigma_{\text{all}}\) (kN/m2)** \\ \hline
1 & 2.062 & 1725.61 & 144,000 \\
2 & 4.446 & 3720.70 & 144,000 \\
3 & 5.545 & 4640.41 & 144,000 \\
4 & 8.883 & 7433.87 & 144,000 \\
5 & 16.62 & 13,908.69 & 144,000 \\
6 & 21.84 & 18,277.12 & 144,000 \\
7 & 23.23 & 19,440.36 & 144,000 \\
8 & 21.43 & 17,934.00 & 144,000 \\
9 & 34.32 & 28,721.19 & 144,000 \\
10 & 38.06 & 31,851.06 & 144,000 \\
11 & 41.91 & 35,072.99 & 144,000 \\
12 & 44.19 & 36,981.04 & 144,000 \\
13 & 42.42 & 35,499.79 & 144,000 \\
14 & 2.062 & 1725.61 & 144,000 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Corrugated steel plate capacity.  
Figure 13: Bending moment of CSP on construction phase 14.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Phase** & **Horizontal Displacement Result (mm)** \\ \hline
1 & - \\
2 & \(-\)10.29 \\
3 & \(-\)9.23 \\
4 & \(-\)11.77 \\
5 & \(-\)13.70 \\
6 & \(-\)14.62 \\
7 & 17.07 \\
8 & 18.63 \\
9 & 19.87 \\
10 & 29.74 \\
11 & 34.06 \\
12 & 38.49 \\
13 & 41.37 \\
14 & 37.01 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Horizontal displacement result.
# A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation
## 2 Fault Detection and Isolation Scheme
### Online Fault Detection Phase
#### 2.2.1 Fault Detection  
The health indicator is calculated as  
\[h_{M}=\sqrt{(E_{M}-\hat{\mu})MCD^{-1}(E_{M}-\hat{\mu})^{\mathsf{T}}} \tag{9}\]  
\[E_{M}=Y-\overline{Y} \tag{10}\]  
where \(\hat{\mu}\) and \(MCD^{-1}\) are adopted directly from the training phase as calculated in Equation (5). \(E_{M}\) is the residual between the actual measurement values \(Y\) and the reconstructed output \(\overline{Y}\) obtained using the trained SAE fault detection model.  
A fault is detected when the real-time MD value \(h_{M}\) exceeds the predefined threshold \(d\).
## 4 Simulation Case Study
### Assumptions  
Assumptions were defined to simplify the model with a focus on the simulation goal:  
* Time restrictions: 10 working years in periods of weeks assuming 50 working weeks per year, 500 periods in order to evaluate implications in the manufacturing organization in the short, medium, and long terms.
* Fixed lead times for production processes, material transport, and employee movements.
* Real demand that is requested by the manufacturing organization depends on the service level. As a result, the company cannot cover the whole potential market demand if the service level is not appropriate.
* Each order had a production unit.
* Same demand using replication for all the models.
* Same initial situation, with initial inventory in the different warehouses for all the models.
* Same number of employees with the same initial distribution for all the models.
* Same supply chain distribution network (production facility, warehouses, etc.).
* The warehouses had no stock capacity limitations.
* There were no transport limitations between the different production stages.
* One product unit was assumed to be in a mature stage with stable demand and provided a sales value of USD 10,000/unit. The second product was in the process of being launched and provided USD 20,000/unit. These values were used to calculate sales. It was assumed that the new model would have a loss in volume due to unknown future demand.
* The simulation model considered sales loss starting from a customer order lead time greater than 60 days.
* A product was considered a finished product after it left the production facility.
* If no human factor malfunctions, it is assumed that the employees have adequate qualification as well as alignment with organizational, department, and position goals they are in charge of.
## 3 Snake Robot in SAR
### Popular Snake Robots and Effectiveness Metrics  
Over time, robotic snakes have been developed consistently for years. A few of these examples include the Active Cord Mechanism series (ACM III, ACM R2, ACM R3, ACM R4) robots developed by Prof. [PERSON] at the Tokyo Institute of Technology, the Kulko, Aiko, Anna Konda & Wheeko robots developed at NTNU's Snake robot lab, the Unclesam robot developed at CMU, and the Mamba underwater robot developed at NTNU, etc. [82, 83, 84]. Some of these robots have consequently been employed in Search and Rescue efforts [5, 85, 86].  
The major limitation of most of the available snake robots is their practical application and interaction with humans. The robots which use traditional rigid actuation design in most snake robots have the drawback of increasing the risk of harmful contact with people. Although rigid actuation systems can be powerful and precise, they also have the propensity to hurt individuals when they come into contact. Further, it lacks environmental compliance and is in rough interaction with the surroundings. This is especially troublesome when snake robots are employed for SAR missions where it interacts with the environment and people. Hence, it suggests using elastic or soft actuation methods in snake robots to prevent these risks. These systems can conform to their surroundings and provide a more gentle movement during the interactions. A few examples of such design approaches can see in [87, 88, 89].  
In fact, it is crucial to employ a variety of criteria to determine the snake robot's effectiveness for SAR. Depending on the particular application and its requirements, different metrics are used to assess the effectiveness of snake robots. The main metrics that can be used to compare effectiveness include the robot's locomotion speed, manoeuvrability, controllability, robustness, sensing and perception, autonomy, energy efficiency, and human-robot interaction. The effectiveness of various gait patterns was examined by [PERSON] et al. in their study by using locomotion speed and energy efficiency [90]. Moreover, several other studies have employed forward velocity, path-following or trajectory tracking ability [91, 92], and power required for snake movement to determine the systems effectiveness [93].  
In addition to current advancements in snake robotics, the robot in any competing SAR scenario must deal with several ethical and design issues relating to human-robot interaction (HRI), communication, environmental circumstances, etc., which are challenging for any robot or operator to judge. Therefore, it is crucial to consider potential ethical and design considerations before developing or deploying robotic technology for SAR. In this work, a snake robot is used as a case study to better understand ethical concerns and design specifications for use in the SAR response phase.
# Energy Efficient IoT Data Collection in Smart Cities Exploiting D2D Communications
## 5 Performance Evaluation
### Impact of Devices Density  
Interesting is to understand how the distribution of the devices within the cell has an impact on the performance improvements obtained by the D2D-EE solution. In particular, the density of the devices influences the D2D communication possibilities and we will investigate up to which value of density the D2D-EE solution is still the most convenient solution. In particular, in Figure 6 the Transport Block utilization and the energy efficiency are shown for a varying value of the node density in the cell, we report the results for a [0.6-40] devices/km\({}^{2}\) range as this is the range where the convergence of the D2D-EE solution to the LTE-EE is visible. As we can observe from the plots, the Transport Block utilization for the D2D-EE and the LTE-EE always outperform the LTE-A solution. Moreover, the D2D-EE solution shows better performances w.r.t. the LTE-EE for values below 2.5 devices/km\({}^{2}\). For higher density values, that is when the devices in the cell are very densely distributed, the D2D-EE converges to the LTE-EE, as it can be seen in Figure 6a.  
Also the energy efficiency for the D2D-EE converges to the LTE-EE solution for even more dense distribution of devices in the cell, _i.e._, 10 devices/km\({}^{2}\), see Figure 6b. In all other cases the D2D-EE solution outperforms the LTE-EE solution from the energy efficient point of view (and the LTE-A solution as well). These results, witness to the fact that the possibility to set up D2D links among the devices is the key feature for the implementation of the D2D-EE. Nevertheless, in modern IoT scenarios the density of devices is typically high which suggests that the proposed D2D-EE solution can successfully be implemented.  
Figure 5: Energy efficiency. (**a**) Varying the packet size (number of devices is 50); (**b**) Varying the number of devices (data size is 10 bytes).  
What we can observe from the resulting plots reported in Figure 8, is that indeed a performance reduction is observed, but still better performance figures are obtained when choosing as cluster head the nodes until the third/fourth best CQI level. On the other hand, there are multiple devices with the same CQI level. This means that taking turns in acting as the aggregator over all the devices with the 3-4 highest CQI level towards the eNodeB, may be a good solution to share the burden and thus avoiding the cluster head to run out of its battery.
## 2 Materials and Methods
### Multivariate Data Analysis  
#### 2.6.1 Partial Least Squares Regression (PLSR)  
In order to create quantitative models between the preprocessed spectral data and the measured reference values of TWC, PLSR method was applied for multivariate data analysis. PLSR is a commonly used and reliable analytical tool for spectra data processing and predictive model development [46]. The multivariate data analysis was performed with the _plsregress_ function of MATLAB. The PLSR model was calculated as follows:  
\[\mathrm{X}=\mathrm{TP}^{\mathrm{T}}+\mathrm{E}\, \tag{13}\]  
\[\mathrm{Y}=\mathrm{UQ}^{\mathrm{T}}+\mathrm{F}\, \tag{14}\]  
where the spectral data matrix \(\mathrm{X}\) is decomposed into the score matrix \(\mathrm{T}\), loading matrix \(\mathrm{P}\), and error matrix \(\mathrm{E}\). The reference values matrix \(\mathrm{Y}\) is decomposed into the score matrix \(\mathrm{U}\), loading matrix \(\mathrm{Q}\), and error matrix \(\mathrm{F}\)[46].  
PLSR estimates latent variables (LV) that describe the maximum covariance between the spectral data and the measured TWC (response variables). The optimal number of LVs was determined using the minimum predicted by the root mean square error (RMSE) method during cross-validation.