esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Surface Roughness
#### 3.1.2 Side Surface  
Regarding the side surface, the three chosen process parameters did not seem to have a significant influence, although a trend is recognizable in Figure 10 in all three plots. This  
Figure 8: Predicted effects of changes in process parameters on the roughness \(R_{a}\) on the top surface, where the green lines are the predictions of surface roughness on top surfaces and the dotted red lines present the 95% confidence bounds.  
Figure 9: Effects of the interaction of parameters on the measured \(R_{a}\) on the top surface.  
was verified by ANOVA, with laser power being the most influencing parameter with a \(p\)-value of 0.18, which was not considered as significant since it was greater than 0.05. The second- and third-most influencing aspects were the interaction of scan speed and hatch spacing (\(p\)-value = 0.24) and the interaction of scan speed and laser power (\(p\)-value = 0.28), which were also not regarded as significant. This result could be explained by the fact that the layer thickness was the same in all samples and therefore, the outer surface roughness on the side remained similar and unaffected by the different LED values.  
Figure 11 displays the interaction effects of the three parameters regarding the surface roughness on the side. By comparing these to the ANOVA results, it is visible that interactions with the parameter scan speed were more influencing since the gaps between the graphs and the tilt were greater. Low scan speeds, paired with either small hatch spacing or high laser power, caused higher roughness due to the over-melting of the powder.  
Figure 11: Effects of the interaction of parameters on the measured \(R_{a}\) on the side surface.  
Figure 10: Predicted effects of changes in process parameters on the roughness \(R_{a}\) on the side surface, where the green lines are the predictions of surface roughness on side surfaces and the dotted red lines present the 95% confidence bounds.
# Design and Implementation of Attitude Stabilization System for Marine Satellite Tracking Antenna
## 3 Modeling and Parameter Identification
### Parameters Identification
#### 3.3.2 \(\xi_{v}\) and \(w_{n}\)  
In this section, \(w_{n}\) is firstly calculated and then \(\xi_{v}\) and \(w_{n}\) are estimated using the experimental data. From Equations (9) and (13), we can get,  
\[w_{n}=\sqrt{\frac{K_{\theta}}{J}}=\sqrt{\frac{T_{\max}\cdot\pi}{2\theta_{step} \cdot J}}\]  
where \(T_{max}=1.85\) N\(\cdot\)m, \(\theta_{step}=\frac{1.8\cdot\pi}{180}\) rad, and \(J=0.08\) kg\(\cdot\)m\({}^{2}\). Thus, \(w_{n}\approx 30.4\) rad/s.  
\(t_{mc}=0.001\) s is obtained in the last part, which means the \(-3\) db bandwidth of \(\frac{1}{t_{mc}\cdot s+1}\) is about \(\frac{1}{t_{mc}}\approx 1000\) rad/s and is more than 10 times larger than \(w_{n}\). This means \(\frac{1}{t_{mc}\cdot s+1}\) can be removed from Equation (15), and a simpler version of \(G_{v}(s)\), \(G_{vs}(s)\), is expressed as,  
\[G_{vs}(s)=\frac{w_{n}^{2}}{s^{2}+2\cdot\xi_{v}\cdot w_{n}\cdot s+w_{n}^{2}}, \tag{18}\]  
which is the final model used for model identification.  
Many experiments were done to obtain the data for model identification. The command velocity is 35 deg/s, which, together with the measured velocity, is shown in Figure 7.  
From the experimental data, \(\xi_{v}\) and \(w_{n}\) can be obtained through one of those numerical optimization methods, such as gradient descent methods or Gauss-Newton methods. The identification results are \(\xi_{v}=0.022\) and \(w_{n}=27.3\) rad/s. The output of the model in Equation (18) is compared with the experimental data, as shown in Figure 7, which verifies the correctness of the model.
# Functionalization Strategies and Fabrication of Solvent-Cast PLLA for Bioresorbable Stents
While the NaOH treatment is a more aggressive non-selective hydrolysis that etches the surface [27; 28], oxygen plasma acts more superficially (some nanometers) and, depending on the treatment conditions, it can generate non-specific functionalization, hydrolysis and, eventually, etching [29; 30]. The cutinase enzyme allows a selective hydrolysis of the ester bonds of the outermost surface layers of the polymer (<100 nm), generating only hydroxyl and carboxyl groups [27; 31]. Furthermore, common stent fabrication processes do not allow personalizing the stent shape. Consequently, in complex arteries stent implantation is impossible. Three-dimensional printing could be a strategy to produce stents with a shape fully customized to the damaged artery. In particular, the solvent-cast direct writing technique has shown promising results to obtain complex 3D-printed shapes with PLLA inks [32]. The aim of this work was to design and characterize solvent-casted PLLA functionalized surfaces to enhance ECs adhesion and to assess the use of this process and biomaterial to fabricate the 3D-printed BRS. To assess biocompatibility, mechanical and degradation properties of the solvent-cast PLLA films, the cytotoxicity, crystallinity, molecular weight, Young's modulus, ultimate tensile strength (UTS), and degradation rates were evaluated. The effect of the three different functionalization processes on PLLA properties was compared by characterizing surface roughness, surface wettability, degradation of films, surface chemical properties, and ECs adhesion. Finally, to assess its feasibility, 3D printed PLLA bioresorbable stents were obtained by the solvent-cast direct writing technique.
###### Abstract  
Electrical properties provide essential information for cancer detection and specific absorption rate (SAR) estimation. Magnetic resonance electrical properties tomography (MREPT) is an approach to retrieve the distribution of electrical properties. The conventional method suffers from the locally homogeneous assumption and amplification of noise. In this study, a novel approach was introduced to improve the accuracy and the noise robustness of conductivity imaging. The proposed approach reformulated the central equation of the gradient-based method to avoid the calculation of the Laplacian operator. The equation was regularized using the second-order total generalized variation, which formulates an objective function. The optimization problem was solved by the alternating direction method of multipliers (ADMM) method. The proposed method was validated by the simulation data of the cylindrical phantom and Ella head model, and the performance was compared with existing methods. The results demonstrated that the proposed method reconstructed an accurate conductivity image and alleviated the noise effects. Furthermore, phantom and healthy volunteer experiments were implemented at a 3 Tesla (T) magnetic resonance imaging (MRI) scanner. The results suggested that the developed method can provide solutions for improved conductivity reconstruction and show potential for clinical application.  
electrical properties; electrical conductivity; magnetic resonance electrical properties tomography; total generalized variation (TGV); alternative direction method of multipliers
## 4 Experimental Results and Clinical Feedback
The NFC module together with the database indicates the arrival of patients for a specific purpose. In the database, the nurse can see who came, when and why they came, and how many patients are in the waiting room. Thanks to this, the nurse can better organize her working time and can also better send patients to the outpatient clinic. At the same time, it is of great benefit to be able to see in the database which patients are coming and why they are coming. As a big advantage, the nurses said that they can effectively manage patients in the outpatient clinic and thus reduce waiting times. The doctors boosted that they had noticed a calm in the operation of the ambulances. They liked that the nurse could answer questions about the number of patients waiting and about the patients' requirements. A big advantage that the nurses reported was that they did not have to go from patients in the clinic to patients knocking on doors. From the patient's point of view, the application had equally positive feedback. Patients most liked the reduction in waiting time for the control examination and acute blood sampling. The \"acute blood collection\" functionality also brought a great advantage, as it allowed patients to move more quickly to chemotherapy, which can take up to 6 h in an outpatient setting. The mobile application, together with the NFC module, contributed to speeding up the treatment process of the patient on individual days. In a month, such a saving of time translates into hundreds of saved minutes. Patients said that they would welcome the daily use of the mobile application and the NFC module because the unwanted effects of the treatment make the time spent in the waiting rooms of oncology clinics unpleasant. Patients like to spend time in the comfort of their own homes, and thanks to this mobile application during the test period, they evaluated that the treatment as being much more tolerable for them. At the same time, monitoring your health status through an open application and using self-management therapy, gives feedback not only to doctors but also to patients, Figure 10.  
Selected health status parameters can be quickly and clearly documented with a simple list of recorded events. This contributes to a more effective setting of the treatment itself and to another positive attribute of the patient's therapy. Figure 9: UML diagram showing practical value of the proposed solution: improvement in time-efficiency (red-line shortcuts).
# Pseudo-3D Receiver Deghosting of Seismic Streamer Data Based on \(l_{1}\) Norm Sparse Inversion
## 4 Discussion  
In this paper, we introduce the \(l_{1}\) norm sparse inversion method to improve the inversion accuracy. The curvelet and wavelet transforms are excellent tools for the sparse representation of seismic signals, which is fully data-driven. The core assumption of the sparse representation is that the noise energy is smaller than the signal energy in the transformed domain, otherwise signal damage may occur during the inversion. For complex geological conditions, the wavefield contains some weak signals generated by the small-scale structure, which more easily covered by noise. In this case, the parameter setting criteria of the proposed method is a trade-off between being signal-preserving and noise-free.  
In addition, although sparse representation improves the sparsity of seismic signals, the redundancy is also improved, which means an increase in data size. Therefore, the calculation efficiency of the proposed deghosting method is limited. To further improve the proposed method, we recommend two future research directions:  
1. Based on symmetry arguments, one would expect that they could also be applied for source-side deghosting in the common-receiver domain.
2. A combination of this method with a multiple suppression method to propose a set of pre-processing flow data suitable for offshore exploration, including degusting and demultiple.
## 4 Conclusions  
Focusing on the UV optical communication system with 4 FSK modulation, this study proposes a Poisson distribution-based code element level maximum likelihood decoding method and introduces LDPC codes to optimize the system performance. Key innovations include the following:  
1. Poisson modeling of photon statistical properties to optimize the decoding performance at low signal-to-noise ratio.
2. Expected photon number normalization based on frame header to improve channel adaptability.
3. Combination of code element level accumulation and maximum likelihood estimation to reduce BER.
4. Compensation of 4 FSK's BER defects under low-light channels through the error correction capability of LDPC codes.  
Based on theoretical analysis and computer simulation, this paper implements the decoding method in hardware and validates it through experiments. The experimental results demonstrate that the LOS communication distance of the 4 FSK system achieves 900 m at a transmission rate of 2.5 kbps. After incorporating LDPC code optimization, the 4 FSK-LDPC system maintains a stable BER between \(10^{-3}\) and \(10^{-4}\) over the 700-900 m range. Specifically, at 500 m LOS distance, the 4 FSK-LDPC system achieves a BER as low as \(10^{-5}\). In NLOS communication testing, with a straight-line distance of 60 m, receiver elevation angle of \(45^{\circ}\), and transmitter elevation angle of \(10^{\circ}\), the 4 FSK system exhibits a BER of \(2.45\times 10^{-4}\), while the 4 FSK-LDPC system reduces this to \(6.12\times 10^{-5}\). Across transmitter elevation angles ranging from \(20^{\circ}\) to \(45^{\circ}\), the 4 FSK-LDPC system maintains BER values below \(10^{-3}\).  
Conceptualization, L.K. and Y.Z.; methodology, L.K. and Y.Z.; software, L.K. and Y.Z.; validation, L.K., Y.Z., K.L. and X.W.; writing--original draft preparation, L.K., Y.Z., K.L. and X.W.; writing--review and editing, K.L., X.W., L.L. and H.Z.; supervision, H.Z. and W.Z.; project administration, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC), grant number 62231005.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Dwell Time Estimation of Import Containers as an Ordinal Regression Problem
## 3 Materials and Methods  
There is a lack of public databases for evaluating the performance of algorithms for estimating the dwell time [29; 30]. Therefore, we have set up two datasets with data collected from a yard between 2014 and 2017. The first dataset has 1816 records captured during the years 2014 and 2015. The second dataset has 2974 records captured during the years 2016 and 2017. Besides the dates, another difference between both datasets is that only the first one includes an attribute called \"Product\", which describes the content of the containers. This difference allows us insight into the relevance of the \"Product\" stored in a container to predict its dwell time, debated in the literature [14; 17; 20]. We have split each dataset into two, one for training and validating and the other for testing the algorithms (see Table 1).  
The diagram in Figure 1 describes the method followed in our experiments. We present six steps and three data sources. In step 1, we propose, evaluate, and select a set of 35 attributes using two train and validation datasets. Such a set of attributes is one of our contributions. In step 2, we train, validate, and select the algorithm for ordinal regression with the lowest \(MAE\mu\) and the classifier with the highest F1 measure [31], performing cross-validation tests with ten folds. In step 3, we train the selected algorithms with the entire training data. Following, we test such algorithms in step 4, computing their \(MAE\mu\) using two testing datasets. In step 5, we estimate the dwell time for containers in the testing datasets. Next, we determine the storage space for each container using the estimated dwell time. Following, we compute the reshuffles by replacing the estimated dwell time with their actual dwell time. Finally, we compare the ordinal regression approach against the classification approach regarding their \(MAE\mu\) and reshuffles in step 6.
## 2 Materials and Methods
### Numerical Study
#### 2.2.2 Combustion Analysis in Ansys Forte  
In order to perform combustion analysis in diesel engines, the combustion chamber of the piston is firstly modeled. For creating the piston models, coordinates of the model profiles were saved in.csv files. Then, these.csv files were imported into Ansys Forte software (Ansys 2021 R1). To create a 1/8 model of the piston, the sector angle was defined as 45\({}^{\circ}\), the piston bore as 92 mm, and the stroke as 75 mm. Topology 1 in the Ansys Forte library was used to create the mesh structure of the piston. Five control points were defined on the profile, and the network structure of the piston was formed according to these points. The largest mesh size of the created mesh structure was 1.6 mm, and the mesh structure consisted of 25,952 cells. When meshing was finished, the computational grid of the combustion chamber was as shown in Figure 5. As in the studies in the literature, Reynolds-averaged Navier-Stokes (RANS) equations were used while modeling the in-cylinder flow process in this study [22]. In the analyses, the initial temperature was taken as 362 K, and the initial pressure as 2.215 bar. In the studies in the literature, the porosity rate in thermal barrier coating applications was between 10% and 30% [23]. In this study, the porosity rate of the TBC material applied to the piston surface was accepted as 10%. Mass fuel consumption values of 0.062 mg, 0.06 mg, 0.059 mg, and 0.057 mg obtained as a result of the experiments were used for NC, PC-5, PC-10, and FC at the 1500 rpm engine speed. For the piston model, the piston surface temperatures and heat transfer rates obtained in the transient heat transfer simulations were used in the combustion simulations. The temperature of the piston head was defined as 470 K, and the piston wall temperature was defined as 420 K.  
Injection start and injection duration were defined as 10 CA and 7.75 CA in the simulations. Indicated mean effective pressure (IMEP) was 0.396 MPa, and piston surface temperatures obtained as a result of transient thermal analysis were used. The starting and ending crank angles for the simulations were defined as \(-\)165 CA and 120 CA. The  
\begin{table}
\begin{tabular}{c c c} \hline
**Thermal Boundary Conditions** & **Temperature (\({}^{\circ}\)C)** & **Heat Transfer Coefficient (W/m\({}^{2}\)C)** \\ \hline A & Parametric boundary condition & Parametric boundary condition \\ \hline B & 300 & 230 \\ \hline C & 290 & 230 \\ \hline D & 280 & 230 \\ \hline E & 85 & 60 \\ \hline F & 180 & 200 \\ \hline G–H & 160 & 200 \\ \hline \end{tabular}
\end{table}
Table 3: Thermal boundary conditions applied to the piston.  
boundary conditions used in the simulation are given in Table 4. In this study, the [PERSON] and Reitz model was adopted for the heat transfer between the substrate and the coating surface [23; 24].
# Instance Segmentation of Shrimp Based on Contrastive Learning
## 4 Experimental Results
### Ablation Studies
#### 4.4.2 Data Scale Comparisons  
The use of a network trained with contrastive learning as the backbone of an instance segmentation network offers several advantages, including the ability to achieve a relatively high accuracy with a small labeled dataset. This is because the backbone trained with contrastive learning can efficiently extract features containing semantic information from images, allowing the subsequent modules in the instance segmentation network to obtain more precise features, which in turn ensures the accuracy of the pixel-wise classification and bounding box regression. Table 3 presents the instance segmentation results under different data scales, where the number of images in the training dataset is 800 and the number of images in the test dataset is 100 for all data scales. Figure 5 shows the visualized curves of the table. The results indicate that fine-tuning using contrastive learning models performs better than the common supervised learning method in terms of the bounding box regression task under all training data scales. MoCov2 achieves the best performance among all other methods when the training dataset is larger than 20%. When the training dataset is less than 60%, the supervised learning method can outperform the contrastive learning models in the mask segmentation task, except for MoCov2, and the difference is relatively small. When the amount of training data increases, the contrastive learning models perform better than the direct fine-tuning method, and the difference becomes more significant. These results effectively demonstrate the effectiveness of our idea that fine-tuning using contrastive learning models can outperform the commonly used fine-tuning method.  
Figure 5: Average precision curves of the bounding box (**left**) and mask (**right**) under different data scales. _bb_ and _mk_ indicate the bounding box and mask, respectively.
## 5 Energy Harvesting  
As previously outlined, harvesting energy from the sensed environments for powering computing devices is mandatory when monitoring surface flow in remote locations. Furthermore, even when an energy source is available nearby, it would be undoubtedly desirable to rely on renewable energy for sustainability reasons to accomplish the measurement task with a self-powered system.  
Regarding energy harvesting, several solutions are feasible, such as solar, eolic, and hydro. We decided to investigate the deployment of sunlight energy generation since solar panels are cheap, effective, and readily available on the market. Among the several solutions currently available, the Raspberry Pi ecosystem provides excellent solutions for managing self-powered systems through solar energy harvesting. Specifically, they enable to couple a small rechargeable battery, referred to as Pijuice Hat, perfectly fitting the multiple Raspberry Pi designs with small solar panels named Pijuice Solar. Figure 4 shows a Raspberry Pi self-powered by these two devices. Despite our choice, it is worth noticing that there are even cheaper alternatives for solar panels and rechargeable batteries. For instance, a general-purpose 6 W solar panel costs around 60 $ ([[https://www.sparkfun.com/products/13783](https://www.sparkfun.com/products/13783)]([https://www.sparkfun.com/products/13783](https://www.sparkfun.com/products/13783))) while the equivalent Raspberry Pi device is priced around 120 $ ([[https://uk.pi-supply.com/products/pijuice-solar](https://uk.pi-supply.com/products/pijuice-solar)]([https://uk.pi-supply.com/products/pijuice-solar](https://uk.pi-supply.com/products/pijuice-solar))). However, devices within the Raspberry Pi ecosystem can be seamlessly deployed without any hardware modification, simplifying the overall setup even when faced by not-specialized operators. For this reason, we stick to these devices, although, with minor efforts, the overall cost of the setup proposed could be significantly reduced.  
The Pijuice Hat is a small rechargeable battery fitting on the top of every Raspberry Pi device, including models 3B/3B+ and 4 considered in this paper. It is available with a different battery capacity of 1820 mAh, 2300 mAh, 5000 mAh, and 12,000 mAh, and includes a real-time clock. More details are available at this link: [[https://uk.pi-supply.com/products/piuice-standard](https://uk.pi-supply.com/products/piuice-standard)]([https://uk.pi-supply.com/products/piuice-standard](https://uk.pi-supply.com/products/piuice-standard)) (accessed on 28 July 2021).  
The Pijuice Solar is a cheap, lightweight, and water-resistant solar panel compatible with the Pijuice Hat. It is available in multiple variants providing 6, 12, 22, and 20 Watt when exposed to direct sunlight. More details are available at this link: [[https://uk.pi-supply.com/products/piuice-standard](https://uk.pi-supply.com/products/piuice-standard)]([https://uk.pi-supply.com/products/piuice-standard](https://uk.pi-supply.com/products/piuice-standard)) (accessed on 28 July 2021).