esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling Methods
### Process Assumption  
For the modeling of the biomass gasification process, the following assumptions were considered:  
* The process is modeled in steady-state and isothermal conditions.
* The reactive gases are H2, CO, CO2, CH4, and H2O.
* N2, NH3, H2S, and SO2 are considered chemically inert components in gasification reactions.
* The char is modeled with only components of carbon black and ash.
* All gases are uniformly distributed within the emulsion phase.
* Particles are spherical and of uniform size. Their average diameter remains unchanged during the gasification.
* Char gasification starts in the dense zone and completes in the freeboard.
* Ash and sand are chemically inert under process conditions.  
For the hydrodynamic calculation, the following assumptions were made:  
* There are two regions in the fluidized bed reaction: bed and freeboard.
* The bubbling regime is maintained in the bed region.
* The volumetric flow rate of gas increases along with the reactor height, corresponding to the gas products generated.
* The mixing of solid particles in the reactor is perfect.
* The reactor is divided into many control volumes with constant hydrodynamic parameters.
* The fluidized bed is one-dimensional.
## 3 Results  
### Design and Preparation of MIPs  
The design and preparation of the MIPs is shown in Scheme 1. MIPs were prepared via a Pickering emulation polymerization, which was composed of a water phase and an oil phase. During imprinting, an NAC monomer containing enough free amino groups was first prepared by reacting acrylol chloride with the amino groups in chitosan. Meanwhile, the CdTe QDs were functionalized with the carboxyl group from the TGA. NAC-QDs were obtained through the amide bond between the amino groups from the NAC and the carboxyl group from the CdTe QDs. The positively charged NAC-QD complex was easily bound with the negatively charged template _L. monocytogenes_ via electrostatic interactions in the water phase [35]. The oil phase consisted of TRIM and DVB as co-cross-linkers and BPO as an initiator. After mixing the two phases, a stable emulation was obtained after vigorous hand shaking (see Figure 1), indicating the high efficiency of the self-assembled bacteria-NAC-QD network to construct a stable Pickering emulsion as a surfactant.  
When polymerization was induced, the NAC-QD complex at the oil-water interface and the co-cross-linkers in the oil phase (TRIM and DVB) polymerized to form solid polymer beads; the template bacteria were located on the surface of the polymer beads. After removing the template bacteria, the imprinted sites were generated and were completely fitted with the template. Furthermore, the fluorescence intensity of MIPs decreased when the template was bound to the MIP beads; the intensity could recover after removing the template.
# Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models
## 1 Introduction
The paper measures the metrics by the deployment of the popular YOLOv8 object-detection model on both cloud platforms and uses the Python SDK for each platform to query the model and measure the metrics. On the other hand, qualitative metrics are compared based on the general availability and information provided by each platform. This paper then uses these metrics to compare the performance of the Azure and AWS platforms for object-detection models and justify the better platform. This paper ends with a comparison of each platform based on both empirical and theoretical metrics based on different business requirements to allow readers to choose a suitable platform based on their needs. This paper is organized into three main sections. First, Section 2 contains a literature review to identify and justify the gap in the literature in terms of a comparison of cloud providers with empirical testing and the lack of a requirement-wise suggestion for the better cloud platform. Next, in Section 3, we explore the methodology to be followed with more details on the metrics to be measured and how the analysis for both quantitative and qualitative results will be conducted. Finally, in Section 4, we examine the results and compare the platforms, providing a final comparison in general and based on business requirements. The literature review conducted in Section 2 highlights the scarcity of studies that bridge the gap between empirical experimentation and theoretical considerations. While some studies focus solely on quantitative benchmarks, few provide a holistic evaluation that considers the real-world implications for practitioners. Our methodology fills this void by adopting a dual-pronged approach, systematically exploring both the quantitative and qualitative facets of Azure and AWS. By undertaking a comprehensive examination of these cloud platforms, this paper contributes to the evolving discussion on cloud-provider selection, offering practitioners a well-rounded understanding of the strengths and limitations of Azure and AWS for machine learning model deployment. The emphasis on requirement-wise suggestions further distinguishes this study, providing actionable insights tailored to the specific needs of developers and data scientists. Overall, by considering both technical and nontechnical aspects, this paper aims to provide a comprehensive and holistic comparison of Azure and AWS for machine learning applications, specifically focusing on object-detection algorithms. The findings of this study can assist developers, data scientists, and cloud architects in making informed decisions when selecting a cloud platform for their specific use case, taking into account both performance metrics and other factors that can impact the overall success and efficiency of their machine learning projects.
## 2 Materials and Methods
### Statistical Analysis  
Multivariate analysis techniques, specifically principal component analysis (PCA), partial least squares analysis (PLS-DA), and orthogonal partial least squares analysis (OPLS-DA), were applied to the entire dataset using the SIMCA Software. These techniques are commonly used to analyze complex datasets and identify patterns, relationships, and differences between groups. To validate the models created during the analysis, a permutation test was performed with 100 iterations. This test helps assess the statistical significance and robustness of the models. Following the statistical analysis, it was important to identify the characteristics or variables that were primarily responsible for the separation observed between the groups. The Variable Importance in the Projection (VIP) diagram and S-plot were used for this purpose. The VIP diagram provides a measure of the importance of each variable in the model, while the S-plot helps visualize the relationship between the variables and the differences between the groups. Furthermore, the \(t\)-test was utilized as the final step in distinguishing the features that significantly differed between the two populations. A feature was considered significant if its \(p\)-value was less than 0.05. The \(t\)-test is a statistical test used to compare means between two groups and determine if the differences observed are statistically significant [14; 15; 16].
# Deep Feature Learning with Manifold Embedding for Robust Image Retrieval
## 7 Conclusions  
In this paper, we propose to learn a robust feature embedding with the help of manifold relationships for image retrieval. The proposed approach applies affinity learning techniques to learn a more robust similarity, which can reflect the local geometry structure of the intrinsic data manifold. The learned similarity, in turn, is used to guide the selection process of training image pairs. As a result, fine-tuning the Siamese network with those selected image pairs transfers this learned manifold information into the newly learned feature embedding. With the newly learned feature embedding, the similarity between images is efficiently measured with easy-calculating Euclidean distance between the corresponding image features, while keeping the robust manifold relationships. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our proposed approach.  
**Author Contributions:** Conceptualization, X.C.; Methodology, X.C.; Validation, Y.L.; Writing Original Draft Preparation, X. C.; Writing Review & Editing, X.C. and Y.L.; Funding Acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Natural Science Foundation of Jiangsu Province under Grant No. BK20200725.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Network** & **Dims** & **UKBench** & **Holidays** \\ \hline SPoC [20] & V & 512 & 3.65 & 0.802 \\ Neural Codes [19] & FA & 4096 & 3.55 & 0.789 \\ NetVLAD [35] & FV & 256 & - & 0.821 \\ [PERSON] et al. [23] & FV & 512 & - & 0.825 \\ [PERSON] et al. [22] & FV & 512 & 3.78 & 0.864 \\ Ours & FV & 512 & 3.88 & 0.821 \\ Ours & FR & 2048 & 3.96 & 0.869 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison with state-of-the-art CNN-based image retrieval methods. The involved network models are marked with “A”, “V”, and “R” for AlexNet, VGGNet-16, and ResNet-50, respectively. Methods adopting a fine-tuned model are marked with “F”; otherwise, off-the-shelf models are implied. Retrieval accuracy is measured by the N-S Score for the UKBench dataset and mAP for the Holidays dataset. “Dims” denotes the dimensionality of image representation.
# Experimental PIV Radial Splitting Study on Expansive Soil during the Drying Process
## 3 Results and Discussion
### Discussion of the Intrinsic Mechanism of the Test Results  
To explore the intrinsic reasons for the lower tensile strength of the undisturbed specimens compared with the remolded samples under the radial splitting test, a three-dimensional plot of the variation in the tensile strength with the dry density and moisture content was plotted using Origin, as shown in Figure 6. Here, the x-axis represents the water content, the y-axis represents the dry density, and the z-axis represents the tensile strength. Note that when the x = 17.5 and y = 1.69 planes (the 17.5% water content plane and the 1.69 g/cm\({}^{3}\) dry density plane, respectively) were established, the tensile strength of the undisturbed specimens was significantly lower than that of the remolded specimens. Therefore, the structure of the undisturbed specimens was assumed to affect its strength.  
Comparing the images of the undisturbed and remolded specimens with \(w\) = 17.5%, as shown in Figure 7, it is obvious that the surface of the undisturbed specimen has depressions, whereas the surface of the remolded specimen is smoother. From this observation,  
Figure 6: Three-dimensional diagram of the tensile strength with respect to the dry density and water content.  
it can be assumed that the strength of the undisturbed specimen was affected by its own structural nature (the presence of internal fissures).  
According to the microscopic test results of ref. [36], during the drying process, the shrinkage degree of a specimen affects its strength. The cumulative mercury-intrusion-volume curve and the pore-size-distribution-density curve of undisturbed and remolded expansive soil were compared and analyzed. Because the undisturbed soil had a primary structure, there was a certain proportion of large pores or primary cracks between the aggregates (Figure 7a); after the remolded soil was crushed and sieved, the large pores between the aggregates disappeared and the pores in the aggregates gradually decreased, or even disappeared, during the drying process--mainly the inter-particle pores and small aggregates (Figure 7b). Therefore, the above radial splitting test results can be reasonably explained: because of their structural and fissure properties, the peak loads of undisturbed expansive soil specimens are lower than those of re-molded specimens and the crack characteristics of the remolded specimens are more regular than those of the undisturbed specimens under the same water-content conditions.
## 9 Discussion and Future Work  
This paper proposes improving the GN-AK protocol by using double-base scalar multiplication and point halving over binary elliptic curves. The implementations were performed on an 8-bit AVR and a 32-bit ARM Cortex-M4, and the improvements of the actual timings and energy use were measured to show the concrete benefits to be had by using point halvings instead of point doublings. This further motivates the adoption of efficient scalar multiplication strategies in lightweight protocols. The current method is implemented for binary Koblitz curves (NIST B-163), but in the future it could be implemented on other types of elliptic curves, such as prime field and Edwards curves.  
In terms of implementation-level impact, the lack of native support for point halving in existing cryptographic libraries represents a practical limitation. Future efforts will focus on designing modular, portable implementations of point halving routines and integrating them into mainstream ECC libraries to facilitate adoption.  
We also plan to investigate the deployment of the enhanced GN-AK protocol on embedded platforms equipped with hardware cryptographic support, such as ARM CryptoCell, Intel SGX, or RISC-V secure cores. These platforms offer promising avenues for reducing computational overhead via micro-architectural acceleration.  
From a security perspective, while our implementation employs constant-time routines and avoids data-dependent branching, formal side-channel resistance evaluation remains  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Protocol** & **Mult.** & **Invers.** & **Add./Half.** \\ \hline GN-AK (orig.) & 300+ & 15 & 80/0 \\ Enhanced GN-AK & 215 & 7 & 35/55 \\ HMQV & 240+ & 14 & 70/0 \\ ECKAS-DH1 & 280 & 12 & 65/0 \\ NAXOS & 250 & 10 & 50/0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance Comparison.  
necessary. Future work will include empirical validation using differential power analysis (DPA) and timing tests, as well as the application of countermeasures such as scalar blinding and randomized DBC traversal.  
Another important direction lies in the formal verification of the protocol's correctness and security guarantees. While this work includes security arguments and model-based reasoning, future versions will incorporate automated validation using formal methods such as BAN logic, ProVerif, and Tamarin to strengthen assurance.  
Long-term considerations include the adaptation of DBC principles in the context of post-quantum cryptography. Although ECC is likely to coexist with or be replaced by quantum-resistant primitives, performance optimization remains critical in hybrid deployments. Investigating whether similar acceleration methods can benefit lattice- or isogeny-based schemes is an open research question.
## 4 Establishment of the Finite Element Model  
### Establishment of the Copper Tube Blank Based on Ultrasonic Detection  
The wall thickness of the rolled copper tube varies irregularly, so wall thickness uniformity is proposed as an indicator to evaluate the axial wall thickness variation of the copper tube.  
In daily production, the uniformity \(W\) is commonly used to describe the unevenness of copper tubes [29]. According to [PERSON] [10], the uniformity can be described as follows:  
\[W=\frac{t_{max}-t_{min}}{t} \tag{8}\]  
where \(W\) is the uniformity, \(t_{max}\) and \(t_{min}\) are the maximum and minimum wall thicknesses before the copper tube is drawn, and \(t\) is the average wall thickness before drawing.  
This formula only reflects the uniformity between the maximum and minimum wall thicknesses at two points and does not adequately represent the overall situation of the copper tube. On this basis, the wall thickness uniformity formula is improved as follows:  
\[W=\frac{\sum_{i=1}^{n}|S_{i}-S|}{nS}\times 100\% \tag{9}\]  
where \(W\) is the wall thickness uniformity, \(S_{i}\) is the wall thickness at point iii, \(S\) is the average wall thickness, and \(n\) is the number of wall thickness data points.  
Combined with ultrasonic detection data, applying this formula can describe the wall thickness unevenness at each axial detection point of the copper tube after ultrasonic  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample Tubes**} & \multicolumn{4}{c}{**Ultrasonic Testing Data**} & \multicolumn{4}{c}{**Manually Detected Data**} \\ \cline{2-7}  & **Maximum Value** & **Minimum Value** & **Mean Value** & **Maximum Value** & **Minimum Value** & **Mean Value** \\ \hline Tubes 1 & 2.54 & 2.64 & 2.59 & 2.56 & 2.87 & 2.715 \\ Tubes 2 & 2.57 & 2.68 & 2.625 & 2.57 & 2.87 & 2.72 \\ Tubes 3 & 2.58 & 2.68 & 2.63 & 2.62 & 2.82 & 2.72 \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ Tubes 33 & 2.55 & 2.84 & 2.695 & 2.56 & 2.82 & 2.69 \\ Tubes 34 & 2.55 & 2.84 & 2.695 & 2.58 & 2.84 & 2.71 \\ Tubes 35 & 2.56 & 2.75 & 2.655 & 2.56 & 2.74 & 2.65 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of segmented detection data (mm).  
detection. The more uniform the axial wall thickness, the smaller the wall thickness uniformity \(W\), which better reflects the actual situation.  
A section of wall thickness data from the online ultrasonic detection is extracted, as shown in Table 4. The average values of wall thickness data from four different positions on the same cross-section were taken at 10 mm intervals to create a spline curve. Finally, a tube blank with a length of 50 mm, an outer diameter of 48.8 mm, a variable inner diameter, and a wall thickness uniformity of \(W=0.6\%\) was established.
# Wall-Climbing Mobile Robot for Inspecting DEMO Vacuum Vessel
## 3 Kinematic Analysis of WMR
### Trajectory Tracking Motion Control  
To realise the tracking control design of target trajectories for WMRs, we propose a trajectory-tracking motion control algorithm based on the tractrix principle in this paper. As shown in Figure 4, the axis of the WMR is \(L\) and the direction is vertically down along the Z-axis. One end of the rod, point B, moves translatable along the Y-axis, and the trajectory obtained by the other end, point A, is the tractrix [28,29]. From the trajectory formation condition, it is clear that the velocity vector at endpoint A always points along the connecting rod \(L\) toward point B. The equation of the trajectory of point A can be obtained by:  
\[\frac{dz}{dy}=\frac{-z}{\sqrt{L^{2}-z^{2}}} \tag{6}\]  
where \((y,z)\) is the coordinate of point A. The recursive operation of the tractrix can be used to solve the inverse kinematic problem of the WMR's trajectory tracking to achieve motion control of the WMR.  
As shown in Figure 4, the equation of the line along the rod length \(L_{AB}\) at the starting point \(A^{\prime}(y_{a},z_{a})\) of the connecting rod at the moment \(t_{i-1}\) can be calculated based on the tractrix principle.  
\[z=k_{AB}y+b_{AB} \tag{7}\]  
where \(k_{AB}=\frac{z_{b}-z_{a}}{y_{b}-y_{a}}\), \(b_{AB}=\frac{z_{a}y_{b}-z_{b}y_{a}}{y_{b}-y_{a}}\).  
At the next moment \(t_{i}\), point \(B^{\prime}(y_{b},z_{b})\) moves a small distance \(\Delta d\) in the direction of the Y-axis and by the rod length condition we know that \(A^{\prime}\) satisfies.  
\[L^{2}=\left(y-y_{b}\right)^{2}+\left(z-z_{b}\right)^{2} \tag{8}\]  
The orientation angle of the robot can be obtained as:  
\[\alpha=\text{atan}\bigg{(}\frac{z_{b}-z_{a}}{y_{b}-y_{a}}\bigg{)} \tag{9}\]  
The coordinates of the position of point \(A^{\prime}\) and the equation of the line passing point \(A^{\prime}\) at the next iteration \(t_{i+1}\) are obtained by combining Equations (7) and (8). The inverse kinematic solution and trajectory planning of the WMR can be accomplished by recursive operations on the above tractrix.  
The set-up WMR trajectory tracking curve is shown in Figure 5a. The feed rate is constant, and the trajectory-tracking motion control was conducted using the tractrix principle. The displacement curves of each wheel in the timing diagram of the moving process and the kinematic inverse solution are shown in Figures 5 and 6. The four position states in Figure 5 show that the WMR can track the target curve well and moves smoothly throughout the following process. The angle of rotation and angular velocity curve of the robot's drive wheels in Figure 6 shows that the angular velocity of the WMR is relatively smooth throughout the tracking process, thereby meeting the requirements of VV inspection.  
Figure 4: Trajectory-tracking principle.
## 1 Introduction  
Mobile ad hoc networks (MANET) [1] support or enhance next-generation networks for applications including but not limited to the Internet of Things (IoT) [2], Industrial IoT [3], massive IoT [4], fifth-generation (5G) cellular network [5, 6], and software-defined networks [7]. Almost all of the currently available routing protocols for ad hoc networks already support most of the mentioned network types, with or without modifications to better suit individual applications [8, 9, 10]. Apart from Media Access Layer (MAC) optimizations [11, 12], the type of routing protocol selected (proactive or reactive) affects the performance and reliability of the whole network and the ability to deliver or drop the packets for unknown networks successfully [13]. Several symmetric and asymmetric cryptography techniques provides security in MANETs[14]. BATMAN [15] (Better Approach ToMobile Ad Hoc Networking) was introduced with limited functionality, after which several updates were made to provide better flexibility and convenience for network engineers to set up and maintain mesh networks and adapted to wide array of networks such as IEEE 802.11s as shown in Figure 1.  
BATMAN is a distance-vector proactive routing protocol initially designed for self-configurable networks or wireless mesh networks with low overhead to support a wide array of ad hoc networks [16], e.g., IEEE 802.11s. Initially, BATMAN was a layer-3 routing protocol available with a daemon to be termed as _Batmand_ but soon, the development of a layer-2 mesh routing protocol was started to support seamless network configuration to layer 3 and _Batmand_ was abundant and is no longer developed or supported [17].  
An advanced version of BATMAN, namely BATMAN-adv, follows the development from algorithm version IV and incorporates the newly devised algorithm version V. BATMAN-adv is purely designed on layer-2, which utilizes a virtual network interface to communicate with the neighboring symmetric nodes. BATMAN-adv no longer uses a daemon to control BATMAN-related tasks and works as a kernel module of Linux to implement the routing protocol. BATMAN-adv transports all the information using raw Ethernet frames rather than sending UDP packets and handles all the data forwarded by BATMAN nodes until it reaches the destination. The unique behavior of the routing protocol emulates a local area network using a virtual Ethernet switch rather than a router. This enables all nodes to appear as flat topology on the link-local interface and remains unaffected by any routing changes happening on layer 3 of the OSI model. BATMAN uses a handy tool to manage all the routing and debugging functions offered by the protocol named \"battcl\" which can be invoked from the user space of the operating system.