esa-sceva/satcom-chunk-collection huggingface dataset:
# Overall Design of Satellite Networks for Internet Services with QoS Support
## 2 Proposed Design of Satellite Networks
### Design of Link Layer  
In the satellite network, the frequency resource should be efficiently used due to the limited resources. Thus, the fixed frequency band is not allocated to all UTs [11; 12; 25]. In the proposed design, all UTs initially execute the log-on process, and the DAMA with the MF-TDMA is used for the resource allocation to transfer data [29; 31]. In the log-on process, the UT sends the log-on request message to the NMS via the LOC. The LOC is the common channel shared by the slotted aloha scheme. If NMS admits the UT into the satellite network, the needed information such as the assigned RCC and the QoS policy is transmitted to the UT via the FCC. In the NCC, the information on the UT is registered. The FCC is the dedicated channel to the hub terminal with the NCC and the NMS. After the log-on process, the UT can request the resource allocation to the NCC via the RCC. The RCC is a dedicated channel to each UT. The information such as the status of the UT and the failure of the UT is also reported to the NCC and the NMS periodically. Based on this information, the NCC allocates the resource  
Figure 1: System model of the proposed design (link layer). NCC, network control center; NMC, network management center.  
by each channel environment of the UT, and the NMS monitors and manages UTs. In the resource allocation for the efficient frequency usage and the QoS support, a continuous resource assignment (CRA), a rate-based dynamic capacity (RBDC), and a volume-based dynamic capacity (VBDC) are used [28; 29; 30; 31]. In the VBDC, there are resource allocation delay including the propagation delay in the satellite link, the transmission of resource allocation request/response, the processing time for resource allocation in the NCC, and the generation of the MF-TDMA frame in UTs [28; 29; 30; 31]. The NCC calculates the resource allocation from request messages of UTs during the super-frame and transmits the response message to each UT in the next super-frame to use resources efficiently. Thus, in the proposed design, the delay in the resource allocation of the VBDC needs to be from three to four super-frames. The information on the resource allocation for each super-frame in the MF-TDMA is broadcast to all user terminals by a terminal burst time plan (TBTP) in the FCC.
## 4 NSGA-II -XGBoost Based Multi-Objective Back Analysis
### Computation Procedure for the XGBoost-NSGA-II-Based Back Analysis  
The algorithm for the XGBoost-NSGA-II-based back analysis uses the NSGA-II approach to calculate the optimal combination of geomechanical parameters in the deep soft rock tunnel engineering. XGBoost is adopted to surrogate this relationship instead of numerical models used in traditional back analysis methods. The back analysis procedure is shown in Figure 3 and explained below:_Step 1_ Collect the engineering information, such as geological conditions, field test and monitoring data, indoor experimental data, and in situ geostress, etc.  
_Step 2_ According to the information collected in Step 1, determine the range of mechanical parameters of surrounding rock that need to be recognized in the training set, and build their sample sets by the uniform test design method and a numerical model using finite element methods (FEM) or finite difference method, such as ABAQUS and ANSYS or FLAC3D.  
_Step 3_ Calculate the displacement values of the surrounding rock primary support system corresponding to each mechanical parameter sample.  
_Step 4_ Build the sample set for XGBoost. The mechanical parameter sets of the surrounding rock can be built. The displacement values of each sample set, calculated by using FEM or other numerical methods, are used to define the XGBoost model. Sample sets are composed of the geomechanical parameters of monitoring points and their corresponding displacement values.  
_Step 5_ Based on the sets of samples built in Step 4, the XGBoost model can be obtained by solving Equation (15).  
_Step 6_ Build the NSGA-II model based on Equations (16) and (17) to back-calculate the geomechanical parameters.
## 2 Principles and Methods
### Active Controller and Actuator Design  
#### 2.4.1 PI Controller Design  
The velocity \(\dot{x}\) and displacement \(x\) of the Optomechanical structure have a great influence on the displacement transfer function of the system, in which \(\dot{x}\) can reduce the peak value at the first natural frequency and \(x\) can reduce the transfer rate at the low frequency. Using these two quantities as feedback can play the role of active vibration control, and the PI controller is obtained.  
\[h_{c}=g_{I}x+g_{P}\dot{x} \tag{1}\]  
\(g_{P}\) is the velocity feedback gain and \(g_{I}\) is the displacement feedback gain. The expression of the controller is obtained by Laplace transform. \"\(s\)\" represents the virtual variable, which is the complex frequency domain parameter of the transfer function derived from the Laplace transform.  
\[H_{c}=(g_{I}+g_{P}s)X \tag{2}\]
## 4 Experimental Results  
In order to further verify the feasibility and effectiveness of the proposed model, two datasets, Brats 2019 and Brats 2021, were used to verify the model, and the innovative sensitive location coding module mentioned in the study was proven through ablation experiments. Both the Brats 2019 and Brats 2021 datasets were provided by previous brain-tumor segmentation competitions. The event is organized by international authorities, such as The Radiological Society of North America (RSNA) and the American Society of Neuroradiology (ASNR). Over the years, the event has become a prestigious competition in the field of brain-tumor segmentation, producing multi-scale 3D CNN, nnU-Net, Extending nn-UNet [19; 20; 21], and other advanced brain-tumor segmentation algorithms.
## 3 Results, Analysis, and Discussion
### Transient Flow Structures
#### 3.2.3 Flow Structures in Cutting Position: \(t^{*}=94\)  
The train exited the WT region and ran into the cutting region at \(t^{*}=94\). Figure 15 shows the pressure distribution around the train. In the cutting position, as shown in Figure 15a, the pressure in region B on the LWS was larger than that in region A of the WWS. Thus, the \(C_{Fy}\) values of three cars were negative. Furthermore, the pressure was negative in region C in the head car's WWS and region D in the tail car's LWS. Therefore, the absolute value of \(C_{Fy}\) gradually decreased along the train, and a similar pressure distribution phenomenon can be found in Figure 15b. For the pressure distributions at the top and bottom of the train shown in Figure 15b, it is evident that the head car's pressure difference was smaller. Thus, the \(C_{Fz}\) was near zero in the cutting position and gradually became positive along the train, especially for the tail car.  
Figure 14: Flow structures and speed contours around the train at different times: (**a**) \(t^{*}=75\) and (**b**) \(t^{*}=84\).  
Figure 13: Top view of the streamlines at a height of 0.5 h.  
Figure 16a displays the flow field around the train in the cutting region at \(t^{*}\) = 94. Without the impact of WT, vortex \(V1\) was absent, and only \(V2\)-\(V4\) appeared, similar to the flat ground position results. As shown in Figure 16b, vortex \(V2\) generated positive and negative alternating wind speeds in the WWS, and \(V3\)-\(V4\) mainly caused a negative wind speed value behind the tail car. In the WT region, a strong vortex and wind speed were generated by the right-angle structure. Figure 17 shows the top view of the flow structure with the train running far from the WT region. Vortices \(V10\) and \(V11\) occurred close to the WT region, similar to when the train operated on flat ground. On the LWS, uniform backflow occurred on the train side. On the WWS, as shown by the dashed, curved arrow, with the effect of vortex \(V11\) and the tail car, a part of airflow moved with the train at the tail of the train, but another portion of the airflow was compressed by the head car and ran opposite to the operation direction of the train. Influenced by these two parts of the airflow, unordered small vortices formed between them.
## 5 Use Cases  
The usefulness of the WSN-DPCM Toolset was evaluated within the project through creation, planning, simulation and deployment of two WSN-based demonstrator prototypes.  
An Outdoor Parking Demonstrator [2; 3] was implemented as a WSN installed in a parking lot which is able to detect free parking slots and to guide the drivers in real time to them.  
An Air Quality Demonstrator [4; 5] was realized by deploying a WSN near an industrial site close to a city to monitor in real-time atmospheric concentration of several dangerous gasses or particles: LPG, Natural Gas, Carbon Monoxide, Coal Gas, Liquefied Gas and Dust.
# A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities
## 6 Conclusions  
This paper is to propose a new mobility management by using a _follow-me cloud-cloudlet_ (FMCL) approach, which is a cooperation with cloud and cloudlet in Fog-RANs. All existing FMC results do not have the property of the VM migration between cloudlets, and all existing FME results do not have property of the service migration between data centers (DCs). As we can known, it is the first result to report a hybrid scheme by combining FMC and FME schemes into a fully new FMCL approach. The main contribution of the proposed mobility management using FMCL approach is to simultaneously keep the VM migration between cloudlets and service migration between DCs to significantly reduce the transmission latency in Fog-RANs. In addition, our mathematical analysis and simulation results shows that our proposed mobility scheme with FMCL approach outperforms existing FMC result in terms of the total transmission time, the average throughput, and the probability of packet loss, but with the more number of the control messages.  
Future work can be further considered the multicast problem using the new proposed FMCL approach in fog-computing-based RAN for smart cities.  
This work was supported by the National Science Council of China under Grant MOST-106-2221-E-305-007-MY3.  
[PERSON] and [PERSON] contributed equally to the main setup, test, measurements, write-up, and towards revising the paper.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Basic Physical Properties Test
#### 3.1.4 Carbonation Test  
Figure 9a presents the variations in the degree of carbonation as a function of carbonation duration for loess stabilized with 10% MgO alone. The data reveal that the carbonation process occurred rapidly within the first 3 h, reaching a plateau at approximately 6% after 10 h. This observation suggests that a minimum carbonation duration of 10 h is required to achieve effective stabilization of loess through MgO carbonation. Figure 9b examines the influence of CL on the carbonation efficiency of loess stabilized with 10% MgO. The results indicate that the addition of CL had no measurable effect on the degree of carbonation. This behavior can be attributed to the inherent properties of CL, which has a low calcium content (typically 4% to 6%) and a chemical structure where calcium ions are bound to sulfonic acid groups (-SO\({}_{3}\)H) through ionic bonds or electrostatic interactions. Consequently, these calcium ions are not available to participate in the carbonation reaction [43]. Additionally, the carbonation reaction of CL itself requires elevated temperatures exceeding 400 \({}^{\circ}\)C, well beyond the conditions used in this study. As a result, the carbonation process under these conditions was predominantly driven by MgO [44]. Approximately 6% of the dry soil mass was converted into stable magnesium carbonate during carbonation, effectively stabilizing the loess, while simultaneously reducing carbon emissions. To ensure complete carbonation and optimize both stabilization and environmental benefits, subsequent experiments adopted a carbonation duration of 24 h.
# Transpiler-Based Architecture Design Model for Back-End Layers in Software Development
## 5 Discussion
### About the Technical Aspects
#### 5.3.2 The Role of the Transpiler  
Transpiler technology commonly involves the creation of a specific programming language designed to transform code into other programming languages while adhering to their respective syntax. Consequently, the programming language introduced by the transpiler is considered a high-level language. For example, Haxe, a transpiler, introduces a general-purpose high-level language of the same name capable of translating code into multiple target languages, all of which are high-level themselves. However, it should be noted that the transpiler's language is not limited to high-level languages, and can be translated into low-level languages if supported by the transpiler.  
This research project does not propose a new transpiler; rather, it remains open to the use of any transpiler that meets the architectural criteria outlined in the article. Therefore, support for both high-level and low-level programming languages should be viewed as inherent features of the transpiler itself, rather than being specific to the proposed design model.  
While the general specification throughout the article may mention C#, Java, and PHP as examples owing to their widespread use, it is important to emphasize that the design model is not exclusively limited to these languages. The proposed design can accommodate various programming languages, both high-level and low-level, depending on the project requirements.  
Before a target language of the transpiler can be considered part of the proposed development scheme, whether high-level or low-level, a custom implementation must be developed, particularly focusing on the nest implementation. This entails creating the necessary architectural mechanisms to enable native compilation and execution frameworks that are specific to the target language.  
Additionally, the comparison between transpilers and interpreters, although not initially addressed in this article, presents an interesting discussion. Transpilers focus on translating code from a source programming language to a target language, with the resulting code compiled in the target language. In contrast, interpreters run code at runtime, providing dynamic and customizable execution, albeit with potential performance impacts. It is worth noting that there are examples of transpilable libraries that incorporate interpreter-like functionality and blend both concepts.
# Resource Allocation on Blockchain Enabled Mobile Edge Computing System
## 4 Simulation and Analysis
### Impacts of Arrival Rate  
The pattern of latency is measured by changing the arrival rate with the ratio of weight coefficients stable.  
As shown in Figure 7, it can be seen that the increase of \(\lambda\) affects group 6 and 7 greatly. When it reaches 24 s\({}^{-1}\), the \(t_{process}\) of group 6 and group 7 reaches 0.18 s and 0.26 s, respectively. However, other groups manage to resist the increase, maintaining the amplification within 70%. Furthermore, it can be concluded that the results obey the latency rule of \(M\)/G/s model raised by [PERSON] [38]. Within the results, we fit the equivalent removing rate of edge computing service system under this circumstance as \(\mu\approx 40\) s\({}^{-1}\). In the experiment, the average data size is 7 \(\times\) 5 Mb. If the policy is to lower the latency, the average data size distributed to each server is 35/4 Mb. Each bit needs a CPU Cycle of \((2+5+7)/3\approx 4.7\) Hz. The average capability of each server is 16.5 GHz. Thus, the average processing rate of each server can be worked out: \(\mu=\frac{16.5\times 10^{9}}{825\times 1024\times 8\times 4.7}=47.83\) s\({}^{-1}\). The estimation result and fitting result have the same order of magnitude.  
Figure 7: The relationship between \(t_{process}\) and arrival rate \(\lambda\).
# Probabilistic Ship Detection and Classification Using Deep Learning
## 6 Conclusions  
In this study, a probabilistic ship detection and classification system for video using deep learning was proposed. To train the detector and evaluate the proposed system, we collected thousands of ship images from a Google image search and built our own ship dataset. The probabilistic ship detection and classification system demonstrated better detection and classification performance compared to when only Faster R-CNN was used. The proposed method used IoU tracking to build a sequence of the bounding boxes and considered the confidence from the detector as a probability. The undetected ships were recovered by IoU tracking. Moreover, the probabilities of the detection accumulated over  
Figure 11: Captured images from Interval 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**AP (\%)**} \\ \cline{2-3}  & **Faster R-CNN** & **Faster R-CNN + IoU + Bayes** \\ \hline Aircraft carrier & 99.33 & 100.00 \\ Destroyer & 68.67 & 77.61 \\ Submarine & 98.00 & 100.00 \\ Container ship & 76.69 & 88.19 \\ Bulk carrier & 88.00 & 94.67 \\ Cruise ship & 96.33 & 96.97 \\ Tugboat & 98.67 & 100.00 \\ \hline mAP (\%) & 89.38 & 93.92 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of ship detection on test videos.  
time and the classes of the ships were determined by Bayesian fusion. In the experiments, the proposed method was tested with two sequences of images and showed considerable improvement in both detection and classification over prior methods.  
K.K., S.H. and E.K. designed the algorithm, and carried out the experiment, analyzed the result and wrote the paper; B.C. analyzed the data and gave helpful suggestion on this research.  
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology under Grant NRF-2016R1A2A2A05005301.  
The authors declare no conflict of interest.
###### Abstract  
This paper describes the development of a novel sorbent for selective extraction of endocrine disruptors (EDs) from aqueous media. The main goal was to obtain sufficient molecularly imprinted polymers (MIPs) for selective detection, preconcentration, and extraction of EDs such as bisphenol A (BPA) and progesterone (PG). Series of MIPs and their analogues, non-molecularly imprinted polymers (NIPs), were synthesised following a non-covalent imprinting strategy based on radical polymerisation. Sets of synthesis were performed in order to optimise variables of the polymerisation including solvent, cross-linker, and template ratio. The retention capacity of MIPs was determined using HPLC in the range of 33.3% to 96.6% and 32.5% to 96% for BPA and PG, respectively. The adsorption mechanism was studied by isothermal and kinetic assays. The kinetic analysis showed a high retention capacity within 15 min of contact. The polymer yield was obtained in the range of 30% to 100%. Additionally, there was no significant cross-reactivity observed upon testing MIPs with structural analogues and other endocrine disruptors instead of target molecules. The results also revealed the high importance of different concentrations of cross-linker and solvent during the polymerisation. Firstly, the pre-organisation of complementary functional groups, which were present in the polymerisation mixture, and secondly, selective cavity formation for target molecules.  
molecularly imprinted polymers; endocrine disruptors; selective extraction +
Footnote †: journal:  
0000-0001-5095-2233][PERSON]  
1 Departamento de Polimeros, Facultad de Ciencias Quimicas, Universidad de Concepcion, Edmundo Larenas #129, Concepcion 4070371, Chile; [EMAIL_ADDRESS] (C.C.); [EMAIL_ADDRESS] (B.R.)
# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
## 6 Experimental Results
### Segmented Global Path Planning Results
#### 6.3.3 Global Path Planning Efficiency
Simultaneously, the two environments' door areas were approximately the same size, while the topological area of the office was larger. It is inferred that the segmented global path planning method contributes more to navigation efficiency in the broad and complex scene. * In terms of path length, the path planned by our method was slightly longer than that of A* and Dijkstra, with an average of 6.42% and 6.25%. This is because the navigation trajectory by our method was forced to pass through the ANPs to improve the success rate and safety of domestic navigation, while the trajectory by A* or Dijkstra algorithm kept close to the obstacle for the shortest length.
## 4 Hardware Implementation
### Register Manager  
When using this controller to complete read or write operation, four parameters need to be provided: the data access starting position, data access length, data access direction (range/azimuth), and data access mode (read/write). In the proposed controller, the design is optimized by using the register to complete read or write operation of any length and direction starting anywhere by hooking up a 64-bit register. The specific definition of each bit of the register is as shown in Figure 15 and Table 3:  
Figure 14: The overall architecture of the proposed data controller.  
Figure 15: The arrangement of the register.
# Error Recovery in the Time-Triggered Paradigm with FTT-CAN
## 4 Error Recovery in FTT-CAN
### The Need for Multiple Replicas
This scenario is shown in the second timeline of Figure 5a. This case is a simple extension of the previous one, in which we consider the combined probability of both replicas being hit by faults. The probability of occurrence of this scenario (\(p_{1/2}\)) is given by Equation (9). It is immediate to see that the probability for the scenario \"one error/n replicas\" (\(p_{1/n\_replicas}\)), is given by Equation (10). The smallest number of replicas that makes Equation (10) lower than \(pe\) is sufficient to attain the desired global reliability level for this scenario. \[p_{1/1} =P_{\lambda}(1;\ LSW)\times P_{\lambda}(1;\ C_{MAX}) \tag{8}\] \[p_{1/2} =P_{\lambda}(1;\ LSW)\times P_{\lambda}(1;\ C_{MAX})^{2}\] (9) \[p_{1/n\_replicas} =P_{\lambda}(1;\ LSW)\times P_{\lambda}(1;\ C_{MAX})^{n\_replicas} \tag{10}\]  
Since the error model allows for the occurrence of multiple errors in one EC we will now consider the scenarios in which two TT messages scheduled for the same EC are affected by errors (Figure 5b). When considering first that a single replica per detected error is sent, if an error hits one of the replicas the recovery process fails. The probability of failure of the recovery process (\(p_{2/l}\)) is then simply obtained by adding the probabilities of both these combinations, each one with probability \(P_{\lambda}(2;\ LSW)\), probability of having two errors in the SW, times \(P_{\lambda}(1;\ C_{MAX})\), probability of one replica error, as expressed in Equation (11). Note that the scenario where both replicas are hit is also possible, but with much lower probability, so this contribution is not included in the equation. When considering now that two replicas per message are sent, a failure of the recovery process occurs only if both replicas  
Figure 5: Scenarios with 1 error (**a**) and with 2 errors (**b**) and retransmission with 1 to 3 replicas. of the same message are affected by errors, event with probability \(p_{2/2}\), expressed in Equation (12). Iterating the reasoning it is possible to obtain Equation (13), which allows for computing the probability (\(p_{2/n\_replicas}\)) of non-recovery for the \"two errors/n replicas\" scenario. \[p_{2/1}=2*P_{\lambda}(2;\ LSW)\times P_{\lambda}(1;\ C_{MAX}) \tag{11}\]  
\[p_{2/2}=2*P_{\lambda}(2;\ LSW)\times P_{\lambda}(1;\ C_{MAX})^{2} \tag{12}\]  
Figure 4:0  
The same methodology can be applied to obtain the generic expression for scenarios with arbitrary number of errors and replicas, stated by Equation (14). Figure 4:1