esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Design
### Datasets
Data Preprocessing  
The Kaggle and NLST data contain chest whole-slice CT scans with varied slice numbers and a slice thickness of less than 3 mm. The CT scans are a 3D volume, with a single intensity value expressed in Hounsfield scale standardized units for each voxel. An axial slice of the dataset is 512 \(\times\) 512, and the number of axial slices per CT scan varies between 150 and 225 in each volume. Chunks of 20 slices per patient were created by averaging adjacent slices, and each 512 \(\times\) 512 image was resized to 50 \(\times\) 50 due to the burden of computing resources such as RAM and CPU processing time. Thus, the dimensions of the input 3D volume for each patient is 50 \(\times\) 50 \(\times\) 20, with a label of 1 or 0 representing cancer and non-cancerous cases, respectively. Some benign and malignant nodules from an example CT scan are shown in Figures 2 and 3, in which the difference in appearance between a benign and a malignant nodule can be observed. The Kaggle dataset includes the data for 1375 patients with 356 cancer cases, accounting for 25.8% of the entire dataset, with the non-cancer cases representing 74% of the data. NLST data include 2538 cases, with 397 and 2141 cancer and non-cancer outcomes, respectively. The LIDC-IDRI dataset consists of 1018 CT scans, and each slice has a thickness of 512 \(\times\) 512 pixels. Radiologists outlined nodules larger than 3 mm in all sections where they appeared by marking the pixels that comprise the outline at the first pixel outside the nodule. These annotations are provided in the form of nodule regions of interest, along with their z-positions. These spatial co-ordinates where used to create a 3D box and a 3D mask centered on the annotations of the lung nodule. Thus, we cropped the nodules using a box size of 32 pixels \(\times\) 32 pixels \(\times\), with 16 slices centered on the annotated location of the CT scan. To assign a label for each nodule, the malignancy scores provided by board-certified radiologists were used. The malignancy scores range from 1 to 5 (1 indicates highly unlikely to be malignant, 2 is moderately unlikely, 3 is intermediate, 4 is moderately suspicious to be malignant, and 5 is highly suspicious to be malignant). The nodules of intermediate malignancy (malignancy score of 3) were not considered for binary classification in this work. In summary, a total of 4253 nodules with dimensions 32 \(\times\) 32 \(\times\) 16 were analyzed, each associated with a label of 1 (cancer) or 0 (non-cancer). In total, 1653 cancerous and 2600 non-cancerous nodules were included in the analysis.
## 4 Comprehensive Controller Considering Traffic Information
### Optimization of Instantaneous Power Allocation  
The future trend of the vehicle speed is obtained in Section 4.2 and the information of the road grade can be acquired by GIS. It should be noted that the grade of the front road of 50 m is regarded as the input for the controller. A fuzzy logic controller is designed to correct the output power allocation of the ultra-capacitor. In the controller, the demand power of the motor, \(P_{dem}\), the future trend of the vehicle speed, \(v_{trend}\), and the future road grade, \(G_{road}\), are chosen to be the input variables of the fuzzy logic controller, and the output variable is the correction coefficient of the output power of the ultra-capacitor, \(\alpha_{uc\_corr}\). The concrete definition of the controller is listed in Table 6.  
When \(P_{dem}\) is middle, if \(v_{trend}\) is rising and \(G_{road}\) becomes larger, \(\alpha_{uc\_corr}\) should be increased. \(\alpha_{uc\_corr}\) is decreased if \(v_{trend}\) is descending and \(G_{road}\) becomes smaller.  
When \(P_{dem}\) is small, \(\alpha_{uc\_corr}\) should be increased if \(v_{trend}\) is rising and \(G_{road}\) becomes larger. In the rest of the cases, \(\alpha_{uc\_corr}\) is reduced.  
When \(P_{dem}\) is large, if \(v_{trend}\) is greatly descending and \(G_{road}\) becomes very smaller, \(\alpha_{uc\_corr}\) is decreased. Under the remain circumstances, \(\alpha_{uc\_corr}\) is increased.  
The idea of the fuzzy logic controller is as follows, as described in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input and Output** & **Actual Domain** & **Fuzzy Domain** & **Membership Function** & **Fuzzy Subset Levels** \\ \hline \(P_{dem}\) & 0 \(\sim\) 70 & 0 \(\sim\) 1 & Gauss type/Bilateral Gauss & 3 \\ \(v_{trend}\) & \(-\)3 \(\sim\) 3 & \(-\)3 \(\sim\) 3 & Triangle & 7 \\ \(G_{road}\) & \(-\)10 \(\sim\) 10 & \(-\)1 \(\sim\) 1 & Triangle & 7 \\ \(\alpha_{uc\_corr}\) & \(-\)0.2 \(\sim\) 0.2 & \(-\)0.2 \(\sim\) 0.2 & Gauss type/Bilateral Gauss & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Input and output variables definition of fuzzy controller.  
Figure 7: State of vehicle speed under a WLTP driving cycle.  
Obviously, the traffic information cannot be obtained everywhere and every time. Thus, a comprehensive controller is established, as Figure 9 illustrates. If the traffic information cannot be acquired, the adaptive rule-based controller proposed in Section 2 is selected as the unique controller, and the final coefficient of the output power allocation of the ultra-capacitor \(\alpha_{uc\_final}\) is equal to \(\alpha_{uc\_adap}\). If the traffic information is received, the adaptive rule-based controller is regarded as the main controller, and the fuzzy logic controller is chosen as the auxiliary controller. The final coefficient of the output power allocation of the ultra-capacitor \(\alpha_{uc\_final}\) is equal to the sum of \(\alpha_{uc\_adap}\) and \(\alpha_{uc\_corr}\).
## 2 Preliminaries
### Problem Definition  
Given is a graph \(G(V,E)\) of \(n\) vertices with nonnegative edge lengths and nonnegative node weights, as well as an integer \(k\), and a real number \(\alpha\geq 0\). Let us denote the edge lengths by \(d:E\mapsto\mathbb{R}\), and the vertex weights by \(w:V\mapsto\mathbb{R}\). A partition of vertices of \(G\) into \(k\) parts \(\{V_{i}:i\in[k]\}\) is said to be \(\alpha\)-balanced if and only if  
\[(1-\alpha)\frac{w(V)}{k}\leq w(V_{i})\leq(1+\alpha)\frac{w(V)}{k}.\]  
In particular, a zero-balanced (or fully balanced) partition is one where all partitions have the same weight. The cut length of the partition is the total sum of all edges whose endpoints fall in different parts:  
\[\sum_{i}\sum_{j<i}\sum_{u\in V_{i}}\sum_{v\in V_{j}:(u,v)\in E(G)}w(u,v).\]  
Our goal is to find an \(\alpha\)-balanced partition whose cut size is (approximately) minimized.  
The problem is NP-hard as the case of \(\alpha=0,k=2\) is equivalent to the minimum bisection problem [3]. For arbitrary \(k\), we get the minimum balanced \(k\)-cut problem, that is known to be inapproximable within any finite factor [2]; the best known approximation factor for it is \(O(\log^{1.5}n)\) (if \(\alpha>0\) is constant).
## 2 Related Works
### Group Key Management Techniques
The approach distributes group keys with a single broadcast with minimal communication load. Still, the storage overhead is high as the devices store the tokens belonging to a device's cognate nodes. A novel protocol, GKMP, for key management in groups, is proposed in [43] to avoid collusion attacks during file sharing in the cloud. The scheme uses a group key generated by participants and not by a centralized cloud server, adding security in terms of file sharing. But the scheme uses RSA for key generation, which is expensive for IoT devices. Yet another communication-aware key management protocol for IoT networks is proposed in [44]. The scheme uses hyperelliptic curve cryptography for authentication along with bilateral generalization in a homogeneous short integer-based solution for effective key management in IoT groups. The scheme has lower computational time compared to its previous similar schemes; still, public key cryptography-based schemes increase the complexity and device overload in IoT networks. When evaluated in terms of computation, communication, storage load, scalability, and secrecy, the existing techniques in the literature attain efficiency in one parameter with a trade-off in other parameters. OFT reduces the communication cost but does not ensure secrecy. CRT reduces the overall computation, communication, and storage costs but lacks scalability. Public-key cryptography-based schemes increase the computation load on the device to a greater extent. To provide group key management as a service, a centralized group key management technique should offer the following properties:  
Secrecy, ensuring forward secrecy, backward secrecy, and collusion resistance. Reduced communication costs, leading to reduced network traffic. Limited usage of resources in IoT devices. The technique should be scalable even though the group is large and dynamic.
## 3 Experimental Results
### Experimental Setup  
The proposed model was implemented using an NVIDIA GeForce GTX 1080 Ti graphics card (NVIDIA, Santa Clara, CA, USA) and an Intel Core i7-8700 CPU (Intel, Santa Clara, CA, USA). Drone flight videos were collected using DJI Mavic Pro 2 and DJI Mavic Air 2. The dimensions of the DJI Mavic Pro 2 and DJI Mavic Air 2 were 183 \(\times\) 253 \(\times\) 77 mm (L \(\times\) W \(\times\) H) and 322 \(\times\) 242 \(\times\) 84 mm (L \(\times\) W \(\times\) H), respectively. Drone flights were conducted at altitudes ranging from 10 to 50 m in 10 m intervals. The distance between the vision sensor and the drone was set from 10 to 70 m. For each drone altitude and distance configuration, 200 images were randomly collected, resulting in 1400 images per altitude for evaluation. Drone distances beyond this range were excluded from the testing scope because visual drone identification is difficult at such distances.  
The original collected images had a resolution of 600 \(\times\) 600 pixels and were divided into nine equal parts, each with a resolution of 200 \(\times\) 200 pixels. When the ROI-extracted images were processed through SRCNN, they were upscaled to the original resolution of 600 \(\times\) 600 pixels. These SRCNN-enhanced images were used as inputs for the YOLO-v8 detection model, which required resizing to the input resolution of 640 \(\times\) 640 pixels of the detector, potentially causing pixel-level loss. To minimize such loss, the divided image size was set to 200 \(\times\) 200 pixels. The primary goal of NSSRD was to ensure that no small drones were missed, making recall the key performance evaluation metric. Recall, which is calculated as TP/(TP + FN), represents the proportion of correctly detected objects among all actual objects. Here, TP and FN denote true positives and false negatives, respectively [28].
## 3 Results and Discussion
### Depth-Wise PAH Concentration at Different Agricultural Soils  
Figure 2 illustrates the concentrations of \(\sum\)16 PAHs at different soil depths (0-5 cm, 5-10 cm, and 10-15 cm) across the various land-use categories. The results revealed that the highest concentration of PAHs was found from the surface soils and followed a sequence of 0-5 cm > 5-10 cm > 10-15 cm. The surface soil was characterized by the fact that the higher concentration of PAHs might be due to the combined effect of dry and wet deposition in the surface soil and the rhizosphere areas of PAHs. On the other hand, the sub-surface soil limits the atmospheric deposition sources. [PERSON] et al. [21] reported the higher concentration of PAHs in the rhizosphere zone over the bulk soil. PAH concentration was also increased in the surface agricultural soil due to wet deposition [38] and dry deposition [39]. The industrial area shows the highest PAH concentrations at all depths, peaking at 2420 \(\upmu\)g/kg in the top 0-5 cm layer, reflecting a significant industrial hub, excessive traffic, and human activities (cooking and smoking) along with the agricultural sources. The coastal area also has elevated PAH levels, particularly at the surface with 1777 \(\upmu\)g/kg, likely due to marine traffic, coastal activities, and agricultural sources. The local market area displays substantial PAH concentrations at the surface (762 \(\upmu\)g/kg) but a notable decline with depth. It might  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**PAHs**} & \multirow{2}{*}{**Poland**} & **Sierra** & **China** & **China** & **China** & \multirow{2}{*}{**China**} & \multirow{2}{*}{**China**} & \multirow{2}{*}{**Banglades**} & \\  & & **Leone** & **Changhun** & **Tianjin** & **Northwestern** & & & & **Gwangzhou** & **Barisal** & **Gazipur** & **Joypurhat** & **Khulina** & **Thakurgaon** \\ \cline{2-13}  & **Agricultural** & **Agricultural** & **Vegetable** & **Vegetable** & **Industrial** & **Flood** & **Dry** & **RESEARCH** & **Industrial** & **Local** & **Coastal** & **Rural** \\  & **Soil** & **Soil** & **Soil** & **Soil** & **Soil** & **Season** & **Season** & **AREA** & **Area** & **Market Area** & **Area** \\ \hline Nap & 19 & 189 & 33.73 & 264.6 & 230 & 39.6 & 33.0 & 165.7 & 218.2 & 155.2 & 547.0 & 234.3 \\ Acy & 1 & 12 & 86.65 & 7.9 & 23.03 & 5.54 & 4.17 & 4.  
be due to the human activities (smoking, cooking, vehicles, etc.). In contrast, the research and rural areas exhibit lower PAH levels, indicating less anthropogenic impact (restricted vehicular movement, human activities) and less vehicular movement, respectively. These findings highlight the influence of different land uses on PAH distribution in soils, with industrial and coastal areas showing higher contamination compared to research and rural areas.
## 4 Results and Discussion
[6; 7] demonstrated the possibility of stabilizing soils using locally available raw lime and raw natural pozzolan on three types of soils. Across the considered soil range, varying improvement effects were recorded in OMC, MDD, and UCS. In general, the soils were effectively stabilized to certainly induce physical and mechanical property changes on the main ingredients, making them fit for the production of wall building blocks and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Model Names** & \(MSE\) & \(R^{2}\) \\ \hline \multirow{8}{*}{Ensemble of regression trees} & OMC-EM & 13.23 & 0.76 \\  & MDD-EM & 21,201 & 0.59 \\  & UCS-EM\({}^{+}\) & 422,149 & 0.69 \\  & UCS-EM\({}^{-}\) & 694,752 & 0.49 \\  & OMC-ANN & 20.10 & 0.58 \\  & MDD-ANN & 31,011 & 0.50 \\ Artificial neural network & UCS-ANN\({}^{+}\) & 519,445 & 0.62 \\  & UCS-ANN\({}^{-}\) & 874,933 & 0.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical performance measurements for models based on ensemble methods and ANN on unseen data. jointing mortar. The trend shades promising limelight on the initiated make for a futuristic affordable, sustainable, and ecofriendly alternative earth-based built environment. With the availability of more and more such experiments (data), the same models with new training dataset could be utilized to predict the physical and mechanical properties of stabilized soils. Apart from that, incorporating additional features which describe the soil and the stabilizers such as soil grading, soil compaction state, or describing stabilizers explicitly (e.g., type of cement and lime) could enhance the prediction capability of the models. Both compaction and grading play a significant role in keeping the integrity of the stabilized soil. The type and proportion of the stabilizers content also significantly influence the development of the unconfined compressive strength of the stabilized soil. The inclusion of experimental data in the database in which the proportion of the utilized stabilizers in each range such as 0-10%, 10-20%, and 20-30% is sufficient enough. The models can be adopted to formulate the proportion of stabilizers that could meet the desired compaction properties (OMC and MDD) or strength of stabilized soils. It is important to note that the prediction accuracy of the current model is not high enough due to limited data. With sufficient data (both observations and number of predictors), the model performance could be enhanced, and it can substitute the cumbersome and expensive laboratory tests of stabilized soils. Moreover, it could assist the scientific community to obtain a better insight into how different complex soil types and stabilizers in relative proportions could be estimated to predict the possibly achievable compaction and strength properties of stabilized soils. Figure 3: Distribution of errors in models of: (**a**) OMC-ANN, (**b**) MDD-ANN, (**c**) UCS-ANN\({}^{+}\), and (**d**) UCS-ANN\({}^{-}\).
## 5 Discussion
The solution of our sparse model known from Equation (7) for the above example is \(\Delta S=(0\ 0\ 0\ 0\ \mathrm{\SIUnitSymbolO}\ \Delta\mathrm{s}_{1}\ \Delta\mathrm{s}_{2}\ \Delta\mathrm{s}_{3}\ \Delta\mathrm{s}_{4}\ 0\ 0\ 0\ 0\ \mathrm{ \SIUnitSymbolO}\ \Delta\mathrm{s}_{5}\ \Delta\mathrm{s}_{6}\ \Delta\mathrm{s}_{7}\ \Delta\mathrm{s}_{8})\). Obviously, this solution has a grouping effect. It is also called a block sparsity problem. In the LASSO method, when the number of variables obtained is much smaller than that of observations, the solution obtained by the LASSO method can ignore the grouping effect [41]. In our model, known from Equation (7), the number of observations is \(M^{2}\), and the number of variables is \(K\). Specifically, in our experiments, \(M^{2}\) was \(400\), and \(N^{2}\) was the number of grids. The greater \(N^{2}\) was, the stronger the grouping effect was. However, when \(N^{2}\) becomes larger, the solution of the LASSO method will be influenced by the grouping effect. Such a result can be found from our experiments as shown in Figures 6 and 9, where the more blurred result was seen when the grid density was increased from \(60\times 60\) to \(80\times 80\). In the same way, the depth estimations of the thinnest thickness also became worse. When the number of transducers was given, the maximum number of grids which can be taken in our experiments are shown in Table 4. To guarantee the performance of our method, \(N^{2}/M^{2}\) is recommended to be set smaller than \(10\) according our experiments.
## 6 Security Analysis and Results
### Additional Security Discussions
#### 6.5.4 Revocation  
As a final note, in this work, we focus on identifying only the duplicate nodes in a network. Revocation can be accomplished by using any of the schemes presented in earlier work [10,18], and we do not discuss this procedure any further in this work.
## 3 Routing Protocols for UWSN
### Protocols Featuring Channel Properties  
These routing protocols are designed to achieve better channel properties like better link quality, better Signal to Noise Ratio (SNR), less noise or less attenuation. These will greatly improve the reliability and delivery ratio of the network. This category includes DFR and LARP. As they maintain the desired link quality all the time, their preferred application is distant controlling of AUVs as like [21].
## 3 Experimental Tasks, Methods and Materials
### Task III: Comparison of Tests Results with Qatar's Construction Specifications (QCS 2014)
#### 3.3.1 TBM Muck as a Partial or Full Replacement for Concrete Coarse Aggregates  
Qatar Construction Specifications (QCS 2014, Section 5, Part 02) [18] and international standards specify the properties of the construction materials that are acceptable as coarse aggregates in normal concrete. The research team conducted the following tests on three samples of the TBM muck (size 10-20 mm) and similar tests on three samples of size 4-10 mm. The average values of the following results were considered and compared to the specification limits: particle density (BSEN 1097-6) [19], water absorption (BSEN 1097-6) [19], clay lumps and friable particle (ASTM C142) [20], shell content (BSEN 933-7) [21], flakiness index (BSEN 933-3) [22], aggregate drying shrinkage (BSEN 1367-4) [23], loss by magnesium sulphate soundness (BSEN 1367-2) [24], loss by Los Angeles abrasion (BSEN 1097-2) [25], acid-soluble chloride (BSEN 1744-5) [26], acid-soluble sulphate (BSEN 1744-1) [27], and lightweight particles(ASTM C123) [28].
# A Level-Set-Based Density Method for Buckling Optimization of Structure with Curved Grid Stiffeners
## 6 Conclusions  
In this study, we optimized the critical buckling load of curved grid stiffener structures based on a level-set-based density method. By transforming two fundamental level set functions, two clusters of stiffeners were obtained, and the real density of each element was obtained using density-based methods. Then, the overall structural element height distribution was obtained by adding the heights of the matrix and stiffeners. The Mindlin plate theory was used for finite element analysis of buckling, and the stiffness matrix and geometric stiffness matrix of each element were determined and assembled into the overall matrix. After that, the eigenvalue equation was solved to obtain the corresponding eigenvalues and eigenvectors. Combining this with the optimization objective, relevant sensitivity analysis calculations were performed. Simultaneously, volume and gradient constraints were added to improve material utilization and maintain uniform stiffeners width. Finally, the MMA algorithm was used to update the design variables. The effectiveness of the proposed method was validated through several numerical examples.  
In practical applications, this research method improves the stability of grid stiffener structures under specific load conditions in engineering applications. Optimizing the stiffener layout instead of increasing the stiffener thickness or base wall thickness helps the structure move towards lightweight development. In addition, introducing gradient constraints to control the minimum spacing between stiffeners enhances the manufacturing of the structure. In the future, the layout and shape of the obtained grid stiffener structure can be subjected to certain post-processing to meet further requirements, and specific experimental analysis can be added for verification and explanation. In addition, the method can be combined with topology optimization to change the shape of base structure while optimizing the layout of curved grid stiffeners.  
Conceptualization, Y.Z. and Q.X.; methodology, Y.Z.; validation, Y.Z.; formal analysis, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, Q.X. and Y.T.; supervision, Q.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 12272144.  
Not applicable.  
The data used to support the findings of this study are available within the article.  
The authors also gratefully thank [PERSON] for providing the MMA codes. The authors declare no conflicts of interest.