esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainable Liver Segmentation and Volume Assessment Using Parallel Cropping
## 6 Experimentation and Discussion
### Liver Volume Assessment
The cropped volume of liver (with voxels 309775) is shown in Figure 17. The proposed parallel cropping algorithm crops liver volume of 512 \(\times\) 512 \(\times\) 816 to 285 \(\times\) 229 \(\times\) 190 in 44.6 ms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Size** & **ECS** & **SCS** & **n** & **SCT (s)** & **SCTPS (ms)** & **\% of MUD** & **\% of UDR** \\ \hline
512 \(\times\) 512 \(\times\) 816 & 285 \(\times\) 229 \(\times\) 190 & 305 \(\times\) 260 \(\times\) 230 & 816 & 7.21 & 8.84 & 94.203 & 91.473 \\ \hline
512 \(\times\) 512 \(\times\) 629 & 264 \(\times\) 242 \(\times\) 227 & 297 \(\times\) 250 \(\times\) 240 & 629 & 8.78 & 13.96 & 91.205 & 89.193 \\ \hline
512 \(\times\) 512 \(\times\) 671 & 326 \(\times\) 290 \(\times\) 233 & 337 \(\times\) 313 \(\times\) 253 & 671 & 6.89 & 10.27 & 87.477 & 84.828 \\ \hline
512 \(\times\) 512 \(\times\) 722 & 305 \(\times\) 257 \(\times\) 300 & 317 \(\times\) 281 \(\times\) 321 & 722 & 7.98 & 11.05 & 87.576 & 84.892 \\ \hline
512 \(\times\) 512 \(\times\) 841 & 344 \(\times\) 203 \(\times\) 264 & 381 \(\times\) 221 \(\times\) 324 & 841 & 9.81 & 11.66 & 91.638 & 87.626 \\ \hline
512 \(\times\) 512 \(\times\) 771 & 290 \(\times\) 228 \(\times\) 249 & 323 \(\times\) 242 \(\times\) 260 & 771 & 7.54 & 9.78 & 91.854 & 89.945 \\ \hline
512 \(\times\) 512 \(\times\) 836 & 308 \(\times\) 271 \(\times\) 180 & 320 \(\times\) 279 \(\times\) 197 & 836 & 11.34 & 13.56 & 93.144 & 91.974 \\ \hline
512 \(\times\) 512 \(\times\) 751 & 253 \(\times\) 262 \(\times\) 190 & 271 \(\times\) 269 \(\times\) 208 & 751 & 10.87 & 14.47 & 93.603 & 92.298 \\ \hline
512 \(\times\) 512 \(\times\) 756 & 284 \(\times\) 220 \(\times\) 195 & 297 \(\times\) 241 \(\times\) 203 & 756 & 9.24 & 12.22 & 93.852 & 92.668 \\ \hline
512 \(\times\) 512 \(\times\) 856 & 186 \(\times\) 252 \(\times\) 203 & 191 \(\times\) 271 \(\times\) 209 & 856 & 8.75 & 10.22 & 95.760 & 95.179 \\ \hline Average & & & & 8.84 & 11.60 & 92.031 & 90.007 \\ \hline Standard Deviation & & & 1.505 & 1.913 & 2.731 & 3.406 \\ \hline Size = original size of liver volume; ECS = exact cropped size; SCS = semiautomatic cropped size; n = number of original slices; SCT = semiautomatic cropping time; SCTPS= semiautomatic cropping time per slice; \% of MUD (\% of maximum unnecessary data) = (1 \(-\) (ECS/size)) \(\times\) 100; \% of UDR (\% of unnecessary data reduced) = (1 \(-\) (SCS/size)) \(\times\) 100. & \% of UDR (\% of unnecessary data reduced) = (1 \(-\) (SCS/size)) \(\times\) 100. The time required for cropping is reduced, as four kernel calls are involved (three for finding the cropping points in each dimension and one for copying cropped data) in the state of the art. The average time taken by the non-PT and slice-based parallel cropping is 103.529 \(\pm\) 8.31 ms, whereas the proposed voxel-based parallel cropping takes 45.227 \(\pm\) 4.9 ms, providing an average speedup of 2.289 times compared to the state of the art. The average time for the volume assessment of the liver is 0.781 \(\pm\) 0.271 s.
## 4 Discussion
### Suggestions  
* Environmental impact:  
One of the key insights from this study is the impact of seasonal environmental fluctuations--particularly in the temperature and rainfall--on WWTP performance. The results of our study shed light on how these fluctuations may affect treatment efficiency, emphasizing the need for WWTPs to adapt to weather variability. Understanding how environmental conditions influence treatment efficiency can help plant managers better prepare for changing climate conditions, improving the resilience of treatment systems.  
* Operational efficiency:  
Our findings suggest that plant managers and operators should account for seasonal weather conditions to optimize plant operations. A better understanding of temperature and rainfall patterns allows for the improved planning of operational strategies, including adjustments to chemical dosages, energy usage, and process control during peak seasonal fluctuations.
# A Road Defect Detection System Using Smartphones
## 3 A Proposed Road Defect Detection System
### An Automatic Data Collection Mechanism for Road Defect Classification
#### 3.1.3 Data Generation  
The final step is to combine the 3-s sliced acceleration data with labels derived from the data preprocessing step. This composite dataset forms the foundational input for constructing deep learning models aimed at road defect detection. Within the scope of this study, we have focused on generating labeled data for three specific road defects: speed bumps, manholes, and potholes. However, the versatility and efficiency of our automated data collection system facilitate the easy and effective expansion of this dataset to include a wider array of road defect types. This capability underscores the system's potential to significantly contribute to the development of more comprehensive and accurate road defect detection models, leveraging deep learning to improve the safety and maintenance of transportation infrastructure.
## 3 Results
### Residual Analysis  
Processing residuals are important indicators that reflect the consistency between the observations and the function model [8]. In this study, four models introduced in Section 2 are employed for satellite clock estimation, and the difference between them is the processing strategy of code biases.  
The time series of the root mean square (RMS) of the code residuals for different models on DOY 031 are shown in Figure 2. Obviously, the RMS values of code residuals of the four models are in a reasonable range and very close, but there are still slight differences. In order to compare the residuals of each model, the mean RMS values are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Code Residuals (m)** \\ \hline Undifferenced & 1.033 \\ Undifferenced-SCB & 1.014 \\ Undifferenced-RCB & 0.997 \\ Undifferenced-SRCB & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The mean RMS values for code residuals.  
Figure 1: Overview of the experimental stations. Red circles denote 70 stations for satellite clock estimation, and blue triangles denote 57 stations for PPP experiment. The observation data are obtained from the IGS MGEX networks.  
Figure 2: Time series of RMS of the code residuals for different models on DOY 031.  
As shown in Table 2, if code biases are extracted in satellite clock estimation, the mean RMS values for code residuals will be reduced, which indicates that the time-varying part of the code biases are driven into the code residuals of the Undifferenced model. However, it does not mean that the code residuals contain all the time-varying code biases as in Equation (1), because some time-varying code biases may be absorbed by other parameters, such as clocks.  
In addition, it can be seen from Table 2 that the mean RMS value decreases from 1.033 to 0.997 m after extracting RCBs, while the mean RMS value only decreases to 1.014 m after extracting SCBs, which means that the time-varying characteristics of RCBs in the Undifferenced model code residuals are stronger than that of SCBs.  
Theoretically, the Undifferenced-SRCB model should have the smallest mean RMS value, but it is not the case, as shown in Table 2, which may be caused by too many constraints on the model.  
The time series of the RMS of the phase residuals for different models on DOY 031 are shown in Figure 3. The phase residuals of the four models are approximately equal, which indicates that the code bias extraction does not affect the residuals of phase equation. It should be noted that this does not mean that the code biases will not affect other parameters of the phase equation [25].
## 3 Guidance Law and Analysis  
### Sideslip Angle Observer  
In this subsection, the high-gain extended state observer was designed to exactly estimate the time-varying sideslip angles. Before starting, the cross-track error dynamics (15) is rewritten as:  
\[\dot{y}_{e}=u\sin\psi+g(u,\,\psi,\,\beta), \tag{16}\]  
where  
\[g(u,\,\psi,\,\beta)=v\cos\psi=u\cos\psi\cdot\alpha, \tag{17}\]where \(\alpha=\tan\beta\).  
**Proposition 1.**_The nonlinearity \(g(\cdot)\) is \(C^{1}\) differentiable and satisfies \(\left|\dot{g}\right|\leq G<\infty\) (\(G\) is a positive constant)._  
**Proof.** The derivative of \(g\) is  
\[\begin{array}{ll}\dot{g}&=\dot{v}\cos\psi-v\sin\psi\cdot r\\ &=f_{v}(v,\,r,\,y,\,\delta)\cos\psi-v\sin\psi\cdot r,\end{array} \tag{18}\]  
where \(f_{v}(\cdot)\) is continuous, and is determined by the dynamics of the vessel. According to extreme value theorem, \(f_{v}(\cdot)\) is bounded for \(v\), \(r\), \(y\) and \(\delta\) are bounded for a vessel sailing along the bank. Therefore, there exists a positive constant \(G\) satisfying \(\left|\dot{g}\right|\leq G<\infty\). \(\square\)  
Design the high-gain extended state observer as:  
\[\left\{\begin{array}{l}\dot{\tilde{y}}_{e}=u\sin\psi+\dot{g}+\frac{\alpha_{1 }}{\varepsilon}(y_{e}-\tilde{y}_{e}),\\ \dot{\tilde{g}}=\frac{\alpha_{2}}{\varepsilon^{2}}(y_{e}-\tilde{y}_{e}).\end{array}\right. \tag{19}\]  
where \(\tilde{y}_{e}\) and \(\dot{g}\) are estimation value of \(y_{e}\) and \(g\) respectively. Define the estimation error as \(\tilde{y}_{e}=\tilde{y}_{e}-y_{e}\), \(\widetilde{g}=\dot{g}-g\), whose derivatives combining with Equations (16) and (19) can be expressed as  
\[\left\{\begin{array}{l}\dot{\tilde{y}}_{e}=-\frac{\alpha_{1}}{\varepsilon} \tilde{y}_{e}+\widetilde{g},\\ \dot{\widetilde{g}}=-\frac{\alpha_{2}}{\varepsilon^{2}}\tilde{y}_{e}-\dot{g}.\end{array}\right. \tag{20}\]  
**Lemma 5.**_The system (20), with the states being \((\tilde{y}_{e},\,\widehat{g})\) and the input being \(\dot{g}\), is ISS._  
**Proof.** Rewrite Equation (20) into a compact form  
\[\dot{x}_{o}=A_{o}x_{o}+B_{o}\dot{g}, \tag{21}\]  
where  
\[x_{o}=(\tilde{y}_{e},\,\widehat{g})^{T},\,\,A_{o}=\left[\begin{array}{cc}- \frac{\alpha_{1}}{\varepsilon}&1\\ -\frac{\alpha_{2}}{\varepsilon^{2}}&0\end{array}\right],\,\,B_{o}=\left[ \begin  
Choosing \(\alpha_{01}(s)=\alpha_{02}(s)=\frac{1}{2}s^{2}\), \(\rho_{0}(s)=\frac{\|B_{\rho}\|s}{\lambda_{\max}(A_{\theta})_{\theta_{1}}}\), by Definition 2 and Lemma 2, it follows from Equation (25) that  
\[\|x_{o}(t)\|\leq\zeta_{0}(\|x_{o}(0)\|,\,t)+\gamma_{0}(\|\breve{g}\|), \tag{26}\]  
where \(\zeta_{0}(\cdot)\) is a class KL function, \(\gamma_{0}(\cdot)\) is a class K function. This concludes the proof.
# The Use of Machine Learning Methods in Road Safety Research in Poland
## 2 Materials and Methods
### Random Forest (RF) Classification with Cross-Validation Algorithm  
Random Forest (RF) is an ensemble-learning-based machine learning method used for both classification and regression problems [60]. In the case studied, a classification model was used. It is a random-forest-based algorithm that constructs a \"forest\" of multiple decision trees, making it more resistant to overfitting compared to single decision trees. RF generates independent decision trees based on random subsets of training data, and the final classification result is determined by plurality vote (the most frequently chosen class) [61]. In this study, the Random Forest method was combined with cross-validation to obtain a more accurate model evaluation. Cross-validation divides the data into \(K\) folds. The model is trained on \(K-1\) folds and tested on the remaining one. This process is repeated for each fold, and the final result is the average of the performance metrics obtained. Cross-validation provides a more reliable model evaluation, avoiding the randomness associated with a single train-test data split [62; 63].
## 2 Methodology
### Experimental Design  
To enhance the effect of insulin niosome for transdermal delivery, it was decided to develop a niosome gel as well as a combination approach of niosome emulgel system. Based on preliminary studies of batches containing insulin gel, insulin niosome, insulin niosome gel, insulin emulgel and insulin niosome emulgel, it was observed that niosome emulgel formulation shows better percutaneous permeation than other formulations. Hence, for further optimization, in experimental design study, niosome emulgel formulations were taken into consideration. By looking at the composition of niosome emulgel and data of preliminary batches, it was found that the concentration of paraffin oil, Tween 80 and sodium carboxymethyl cellulose have significant effect on formulation viscosity, drug release and permeation. This information helped us to select a 2\({}^{3}\) full-factorial design--an ideal method for optimizing various factors with fewer but enough runs when the number of variables are two to four. The full-factorial design creates experimental points using all the possible combinations of the levels of the factors in each complete trial or replication of the experiments. It can vary all factors simultaneously at a limited number of factor levels. Three factors were considered at two levels for further optimization as shown in Table 2. The niosomes were prepared as per method mentioned earlier and utilized for experimental design study. Design expert software (DX 11 Software, StatEase Design Expert, Statistical software, Minneapolis, MN, USA) was used for optimization of insulin loaded niosome emulgel, the effect of 3 and 2 leveled numeric variable (2\({}^{3}\) full-factorial design) was selected for evaluation using contour plot and 3D response surface methodology. For this optimization process, concentration of paraffin oil, Tween 80 and sodium carboxymethyl cellulose were taken as independent variables and in vitro release, viscosity and in vitro permeation were chosen as dependent variables. According to factorial design, 08 runs were designed to perform the best close-fitting model.  
\[\text{Y}=\beta_{0}+\beta_{1}\text{x}_{1}+\beta_{1}\text{x}_{2}+\beta_{1}\text{ x}_{3}+\beta_{12}\text{x}_{1}\text{x}_{2}+\beta_{23}\text{x}_{2}\text{x}_{3}+\beta_{13} \text{x}_{1}\text{x}_{3}+\beta_{123}\text{x}_{1}\text{x}_{2}\text{x}_{3}+ \text{C} \tag{1}\]  
where, Y is response, \(\beta_{0}\) is intercept, \(\beta_{1,2,3}\) are the main effect of the variables x\({}_{1}\), x\({}_{2}\) and x\({}_{3}\), \(\beta_{123}\) is the interaction effect of all three variables and C is standard error of design.
## 3 Experimental Setup
### Strain Measurement Setup  
Several experiments with different lengths/volumes were performed. Brick shaped metal molds were chosen for the study. All were made of stainless steel and had a height of 10 cm. The mold was made of stainless steel and the walls were not specially prepared to avoid attachment of the epoxy. However, the epoxy did not bond to the walls since in none of the experiments signs of attachment to the walls were found. The epoxy filling height was kept constant at 6 cm for all experiments to rule out a possible influence of this parameter. In Table 1, the details regarding the different molds are stated. In the text, samples are referenced according to the length of the mold. The general setup is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimensions (l\(\cdot\)w\(\cdot\)h, cm\({}^{3}\))** & **Mass of Epoxy** & **No. \& Position of Temperature Probes** & **Comments** \\ \hline \(10\cdot 10\cdot 6\) & 0.6 kg & 5, random position & \\  & & & Different width to keep the amount \\ \hline \(20\cdot 20\cdot 6\) & 2.6 kg & 5, random position & of epoxy the same as for the 40 cm \\  & & & mold at constant filling height. \\ \hline \(40\cdot 10\cdot 6\) & 2.6 kg & 16, aligned above fiber & distance between probes: 2–3 cm \\ \hline \(60\cdot 10\cdot 6\) & 3.9 kg & 24, aligned above fiber & distance between probes: 1–3 cm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the setup for the different experiments. Epoxy Filling height was always 6 cm.  
In the lower image in Figure 1, a black thread visualizes the position of the optical fiber. By leading the fiber through two small holes at each end of the mold, the fibers were held in a horizontal position (usually) in the middle of the epoxy and about 3 cm high. To prevent the epoxy from seeping through the holes, the holes were sealed with rubber from the inside. The rubber did not encumber the movement of the fiber along the vessels axis. Due to the sealing from the inside, it was made sure that the fiber was at no time in contact with the steel, not even indirectly due to hardened epoxy inside the hole.  
The measurement fiber (a single-mode fiber from OFS Fitel, LLC with an operating wavelength of 1550 nm; core, clad and coating diameters of, respectively, 6500 nm, 0.125 mm and 0.155 mm; Pyrocoat(r) coating; and an operating temperature between \(-65\) to \(+300\) degrees Celsius) was inside the mold. The S 178A Fitel Fusion Splicer from Furukawa Electric was used to splice a more durable and thicker fiber to one end of the measurement fiber. Finally, the connection to the \"OBR 4600\" measurement instrument from Luna Instruments [25] was made with durable fiber. In regular time intervals (15 min or 3 min), measurements were taken with the OBR. Due to steep strain gradients, pinching or microbending, even the running reference method may fail to yield meaningful results for this particular type of epoxy. Thus, as a backup, at least one other fiber, parallel to the first one and separated in plane by 1 cm, was present in each experiment.
# Client-Aware Negotiation for Secure and Efficient Data Transmission
## 2 System Description
### Message Exchange  
The SSL protocol library contains a variety of protocols. Among them, the handshake protocol is necessary to ensure the security of data transmission, being used to negotiate secure transmission parameters before secure data transmission. There are three common handshakes: (1) a full handshake, named one-way authentication, where only the server is authenticated; (2) a full handshake, named two-way authentication, where both the client and the server are authenticated; and (3) a short handshake, named session resume, which is used to restore the previous session with a small cost.  
Figure 1 shows the message exchange of a full handshake; it can be used to describe both one- and two-way authentication. They both require server authentication. The solid line indicates the process of one-way authentication; the dotted line describes the message passing that is unique to two-way authentication, which needs to authenticate the client. The messages with * represent messages that are used only during two-way authentication. Both the client and server are authenticated by means of a certificate; the failure of authentication directly leads to the failure of the handshake, which will then terminate the connection. The specific meanings and the latest details of the protocol can be found in [12].  
Figure 1 shows the processing phase of the CAN-SEAT strategy. After the first handshake, according to the identity of the client, the corresponding secure transmission scheme is deployed. The strategy aims at the data transmission encryption algorithm, mainly at the asymmetric encryption and symmetric encryption algorithm. In addition to the method shown here, this article provides an alternative method based on redirection to support different clients. Finally, as shown in Figure 1, after the completion of the deployment of the CAN-SEAT strategy, the secure data transmission starts.
## 4 Experiments and Discussion
### Comparison of Different Methods in Terms of Segmentation Results  
In this section, we compare our proposed method with some current mainstream segmentation methods using the ISPRS Vaihingen and Potsdam datasets to show the effectiveness of our method.  
**Comparison using ISPRS Vaihingen dataset.** We tested all the compared methods with the test set of the Vaihingen dataset. The quantitative results are listed in Table 4.  
For the impervious surfaces category, our proposed MSCSA-Net (\(d=1\)) had the best F1-score of 85.7. For the buildings category, our proposed MSCSA-Net (\(d=1\)) had the best  
Figure 11: ISPRS Potsdam dataset: training and validation curve maps.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Impervious** & \multirow{2}{*}{**Buildings (F1)**} & **Low** & \multirow{2}{*}{**Trees (F1)**} & **Cars (F1)** & **m-F1** & **mIoU** \\  & & & & & & \\ \hline MSCSA-Net (\(d=1\)) & 86.9 & **90.0** & **74.3** & 74.2 & 80.6 & **83.7** & **71.9** \\ MSCSA-Net (\(d=2\)) & 86.6 & 88.6 & 73.6 & **75.2** & **80.9** & 83.0 & 71.0 \\ MSCSA-Net (\(d=4\)) & **87.0** & 89.1 & 73.3 & **75.2** & 80.8 & 83.2 & 71.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study of MSA channel reduction rate with the Potsdam dataset. The F1-score was used as the evaluation metric for each category. The overall results were evaluated using the m-F1-score and mIoU. The bold number means the best performance among all methods.  
F1-score of 90.5. For the low vegetation category, SCAttNet had the best F1-score of 80.0. For the trees category, our proposed MSCSA-Net (\(d=1\)) had the best F1-score of 82.7. For the cars category, our proposed MSCSA-Net (\(d=4\)) had the best F1-score of 62.5.  
Although MSCSA-Net (\(d=1\)) did not have the best F1-scores for all five categories, it achieved the best overall performance. Our MSCSA-Net (\(d=1\)) achieved m-F1 = 83.0 and mIoU = 70.9.  
**Comparison using the Vaihingen dataset.** The F1-score was used as the evaluation metric for each category. The overall results were evaluated using the m-F1-score and mIoU.  
**Comparison using the ISPRS Potsdam dataset.** Our proposed method was compared with other state-of-the-art methods using the test images of the Potsdam dataset. The F1-scores, m-F1-scores, and mIoU are shown in Table 5.  
For the impervious surfaces category, our proposed MSCSA-Net (\(d=4\)) had the best F1-score of 87.0. For the buildings category, our proposed MSCSA-Net (\(d=1\)) had the best F1-score of 90.0. For the low vegetation category, our proposed MSCSA-Net (\(d=1\)) had the best F1-score of 74.3. For the trees category, our proposed MSCSA-Net (\(d=4\)) had the best F1-score of 75.2. For the cars category, our proposed MSCSA-Net (\(d=4\)) had the best F1-score of 80.8.  
MSCSA-Net (\(d=1\)) did not have the best F1-scores for three categories: impervious surfaces, trees, and cars. However, it still attained the best m-F1-score (83.7) and mIoU (71.9) among all the methods compared.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 2 UAV Tracking System Overview
### Steering System
#### The electrical steering system  
Instead of utilizing the mechanical type of beam-steering mechanism, electrical beam steering can be utilized. In electrical beam steering, phase shifters add propagation delay into the system to induce a phase shift [28]. Phase shifters can be analog or digital, active or passive. RF micro-electrical mechanical systems (MEMs) phase shifters are available for passive high-performance antenna arrays for Ka-band (26-40 GHz) applications that exhibit superior performance to solid-state alternatives. Analog phase shifters are controlled by a voltage compared to a digital phase shift which is specified with a bit precision related to a phase shift at a frequency or over a frequency band. The electrical steering system has more considerable tracking speed, acceleration, and mean time between failure (MTBF) over its mechanical counterpart. Buoyed by their compact structure, they are more suitable to be installed in mini structures and have lower production losses [29]. However, they are less efficient and have higher losses than their mechanical counterpart. Some other techniques to achieve electronic/electrical beam steering include active phased arrays, passive phased arrays, and switched multiple-beam antennas [30].