esa-sceva/satcom-chunk-collection huggingface dataset:
# IAACaaS: IoT Application-Scoped Access Control as a Service +
## 4 IoT Application-Scoped Access Control as a Service
### Sample Authorization Flow  
We now present a typical IoT use case to better illustrate the interactions between the modules we have presented in our model.  
Let us consider a scenario in which a _Device_ publishes the data it collects to a back-end _Service_. Our objective is to add an AC mechanism that allows restricting access to that application depending on:  
* the type of IoT device,
* the service resource that it is trying to access,* and the action that it is trying to perform with regard to the service resource.  
A _Service Admin_ is assumed to be registered in the _IdP_, with the needed rights to register new applications and manage policies in the _PAP_.
## 2 Study Area and Data
### Ground Survey and Sampling  
Field campaigns were carried out in Northeast China (Regions C, D, E, and F) from 2017 to 2022, and a series of ground survey points (Figure 1b-d) were collected, covering the main crop types (rice, maize, and soybean) in Northeast China (Figure 1e-h), as well as some other types of ground object such as wetlands, forests, etc. Among them, two ground surveys were carried out in Shuangcheng District (Region C) in early June and August 2017, and a large number of ground sample points were collected. These samples were used to produce an accurate rice distribution map in Shuangcheng District to verify the transfer accuracy of the rice classification model.  
The field campaigns in regions D, E, and F were conducted in areas where rice was concentratedly distributed. The main method was to conduct a ground survey along the main roads at regular intervals to collect sample points' photos and nearby crop distribution information. In region D, 401 ground survey points (blue dots in Figure 1b) were collected, most of which were located in region C; In region E, ground surveys were mainly carried out in the central area where rice was concentratedly distributed. Ground surveys were  
Figure 2: Number of available Sentinel-1 images between 1 April to 2 November in Northeast China in 2017 (**a**), 2018 (**b**), 2019 (**c**), and 2020 (**d**).  
carried out at a total of 26 locations at intervals of approximately 10-20 km (blue dots in Figure 1c); In region F, ground surveys were carried out at a total of 76 locations at intervals of approximately 5-10 km (blue dots in Figure 1d). These samples obtained through field surveys provide necessary support for rice mapping and accuracy assessment.  
Considering the uneven distribution and relatively small number of sample points collected, we used a random generation method to generate 1500 validation sample points in regions D, E and F to better evaluate classification accuracy. Visual interpretation was used to determine whether these sample points were rice based on their proximity to ground survey locations or routes. Photos obtained from ground surveys combined with high-resolution satellite images (Google Earth images) and SAR images were used to classify sample points located near ground survey locations or routes; for other sample points, high-resolution satellite images (Google Earth images) and time-series SAR images combined with Sentinel-2 optical images and existing rice mapping result [12] were employed. As a result, 217 rice validation sample points and 1283 non-rice validation sample points were obtained (Figure 1b-d).
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 4 Challenges That Influence the Design of In-Body Antennas
### Effect on Antenna Radiation Pattern  
The lossy medium present in a human body environment can cause broadening of the radiation pattern because of the reflection, refraction and scattering existing in or generated from the body tissues [78]. The radiation pattern of an in-body antenna would also be variable in the same medium if the mounting circumstances and in-body positions were different.
# A New Plasma Surface Alloying to Improve the Wear Resistance of the Metallic Card Clothing
## 2 Experimental Procedure
### Friction and Wear Test  
At room temperature (relative humidity \(<40\pm 5\%\)), the CET-I friction-wear tester with the Si\({}_{3}\)N\({}_{4}\) ball (hardness of 77 HRC (Rockwell hardness) and diameter of 3 mm) was utilized to conduct the dry wear sliding tests. For the friction tests, various sliding speeds (2 m/min, 420 g; 4 m/min, 420 g; 6 m/min, 420 g) were utilized, and the effects of different sliding speeds on the wear properties of metallic card clothing were studied. The wear test was described in the ASTM G133-05 standard [16]. During testing, the Si\({}_{3}\)N\({}_{4}\) ball produced reciprocating friction on the sample surfaces at various sliding speeds. The reciprocation length was 5 mm and the friction test duration was 10 min. After the friction test, the wear track profile was measured by the profilometer of the friction-wear tester. Three parallel experiments were conducted to improve the testing accuracy and the results were averaged. The schematic diagram of reciprocating friction (a), equipment drawing of CET-I friction-wear tester (b) and the schematic diagram of profilometer equipped by the friction-wear tester (c) are presented in Figure 3. The morphology of the wear track was analyzed through SEM. The weight loss comparison between the spring steel substrate and the alloy layers was inappropriate, because the densities were not comparable. In this paper, the wear volume was utilized for the wear rate calculations of the specimens. Additionally, the wear volume can be calculated through the wear track profile. The specific wear rate equation was as follows:  
\[K=V/PS \tag{1}\]  
where \(K\) is the specific wear rate (mm\({}^{3}\) N\({}^{-1}\) m\({}^{-1}\)), \(V\) is the wear volume (mm\({}^{3}\)), \(P\) is a load (N), and \(S\) is the total sliding distance (m) [17].
## 2 The New Smoothing Conjugate Gradient Method and its Global Convergence  
The new smoothing conjugate gradient direction is defined as  
\[d_{k}=\left\{ \begin{array}{ll}-\
abla\Psi_{\mu_{0}}(x_{0}),&k=0\\ -\
abla\Psi_{\mu_{k-1}}(x_{k})+\beta_{k}d_{k-1},&k\geq 1\end{array} \right. \tag{2}\]  
where \(\beta_{k}\) is a scalar. Here, we use \(\beta_{k}\) (see [24]) which is defined as  
\[\beta_{k}^{DY}=\frac{\|\
abla\Psi_{\mu_{k-1}}(x_{k})\|^{2}}{d_{k-1}^{T}y_{k-1}} \tag{3}\]  
where \(y_{k-1}=\
abla\Psi_{\mu_{k-1}}(x_{k})-\
abla\Psi_{\mu_{k-2}}(x_{k-1})\). When \(k=1\), we set \(\mu_{k-2}=\mu_{0}\). The line search is Armijo-type line search (see [25]), where \(\alpha_{k}=\eta^{m_{k}},0<\eta<1,m_{k}\) is the smallest nonnegative integer satisfies  
\[\Psi_{\mu_{k}}(x_{k}+\alpha_{k}d_{k})\leq\Psi_{\mu_{k}}(x_{k})+\delta\alpha_{ k}(\
abla\Psi_{\mu_{k-1}}(x_{k}))^{T}d_{k} \tag{4}\]  
\[(\
abla\Psi_{\mu_{k}}(x_{k}+\alpha_{k}d_{k}))^{T}d_{k+1}\leq-\sigma\|\
abla \Psi_{\  
In the following, we will give the analysis about the global convergence of Algorithm 1. (The Algorithm 2 is the algorithm framework of Algorithm 1.) Before doing this work, we need the following basic assumptions.  
**Assumption 1.**  
(i) For any \(x\in R^{n}\), \(0<\mu\leq\mu_{0}\), the level set \(L_{\mu}(c)=\{x\in R^{n}|\Psi_{\mu}(x)\leq c\}\) is bounded.  
(ii) \(\
abla\Psi_{\mu_{k}}(x_{k+1})\) is Lipschitz continuous, that is, there exists a constant \(L>0\) such that  
\[\|\
abla\Psi_{\mu_{k}}(x_{k+1})-\
abla\Psi_{\mu_{k-1}}(x_{k})\|\leq L\|x_{k+1} -x_{k}\|,\forall x_{k+1},x_{k}\in L_{\mu}(c)\]  
**Lemma 1. ** Suppose that \(\{d_{k}\}\) is an infinite sequence of directions generated by Algorithm 1, then  
\[-(\
abla\Psi_{\mu_{k-1}}(x_{k}))^{T}d_{k}\geq\bar{c}\|\
abla\Psi_{\mu_{k-1}}(x _{k})\|^{2},\forall k\geq 0,\bar{c}<\sigma\]  
**Proof** If \(k=0\), by Equation (2) and \(\bar{c}<1\), we can know that Equation (6) holds. If \(k>0\), by Equation (5) and \(\bar{c}<\sigma\), we can conclude that Equation (6) holds. **Lemma 2. ** Suppose that Assumption 1 holds. Then, there exists \(\alpha_{k}>0\) for every \(k\), and  
\[\alpha_{k}\geq\omega\frac{|(\
abla\Psi_{\mu_{k-1}}(x_{k}))^{T}d_{k}|}{\|d_{k} \|^{2}}\]  
with \(\omega\) is a positive constant. **Proof** From Step 0 of Algorithm 1, we know that \(d_{0}=-\
abla\Psi_{\mu_{0}}(x_{0})\), _i.e._, \(d_{0}\) is a descent direction. Suppose that \(d_{k}\) is satisfied  
\[(\
abla\Psi_{\mu_{k-1}}(x_{k}))^{T}d_{k}\leq-\sigma\|\
abla\Psi_{\mu_{k-1}}(x _{k})\|^{2}\leq 0\]for any \(\tilde{\alpha}_{k}\).
## Data and Methods
### Other Data
#### 2.2.4 AARI Ice Chart  
**Data source:** The weekly ice chart of the Russian Arctic and Antarctic Research Institute (AARI). The institute's ice charts are based on the automatic generalization of regional ice charts which are compiled based on the analyses of satellite (visible, infra-red, and radar) information and reports from coastal stations and ships [21]. The ice charts provide the ice concentrations of the Antarctic region which this study download for free the website: [[http://www.aari.ru/](http://www.aari.ru/)]([http://www.aari.ru/](http://www.aari.ru/)).
# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 2 Methods
### Training and Optimization
#### 2.3.2. ResNet34  
We utilize 80% of the dataset, which consists of a total of 12,177 images, to train the ResNet34 model, a widely recognized model in image classification, for detecting road abnormal events. Three approaches are employed to train the ResNet34.  
In the first approach, inspired by the linear probing [54], we freeze the pre-trained model and do not update its parameters; only the parameters of the final linear layer are adjusted. ResNet34 is a model that has been pre-trained on the ImageNet dataset. Given that the resolution of the training set data is suboptimal, our objective is to leverage the benefits of the pre-trained model's weights for image processing tasks. In this approach, only the affine layer is fine-tuned during training.  
In the second approach, after loading the model, we utilize the xavier_init() function to initialize all weights, followed by updating all weights throughout the training process.  
Figure 3: The urban road flooding VLM inference process.  
In the third approach, we load the pre-trained model, increase the learning rate (Ir) of the last layer by a factor of ten, initialize the weights of the last layer using xavier_init(), and then proceed to train the entire model.  
The results of the three approaches for the ResNet34 model are as follows (Table 6). It can be seen that the overall accuracy of the first approach is expected to be higher; therefore, it will be selected for subsequent comparisons.
# Quantum Coding via Quasi-Cyclic Block Matrix
## 5 Conclusions  
The coding method for massive data has important applications in the field of information transmission and big-data processing. With the rapid development of quantum  
Figure 5: The performance of required code length \(N\) with different parameters. Here, to reflect the relation of several parameters, it takes \(x=2,3,4\) so that 10-cycles, 14-cycles and 18-cycles are obtained with hypothesis \(\eta=\rho\). To satisfy the bound, the prime number \(n\) in the proposed method should also meet the condition of length \(N\) and cycles.  
computing, this class of coding construction has become one of the research fields in quantum information. In this paper, based on a class of circulant permutation matrices, we presented quantum quasi-cyclic CSS codes in terms of two long-length classical codes based on block matrices. Motivated by the advantages of convenient and low-complexity implementation of an iterative decoder for proposed code, a family of jacket block matrices were applied to quantum coding construction. The presented method can efficiently construct long-length quantum QC codes based on the proposed method of an iterative coding process. If the parameter is selected appropriately, the obtained quantum codes are number 4-cycles in their generators and have good performance for LDPC codes.  
Furthermore, we also analysed the marginal flip probability \(f_{m}\) with respect to our proposed codes; that a good performance was obtained for the bit error rate is shown in Section 4. Therefore, the obtained QC quantum codes that benefit from their semi-parallel architectures can potentially be applied to the future quantum information field in relation to massive data.  
Formal analysis, Investigation, Writing-original draft, Y.L.; Software, J.-Y.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Natural Science Foundation of Shanghai (No.19 ZR1420000).  
Not applicable.  
The data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
## 5 Implementation and Experiments  
### Proof-of-Concept Implementation  
We implemented a proof-of-concept system with its design. The development environment includes Python 3.1, Ubuntu operating system, MySQL for the database, and Django for the web framework.  
We implemented the eLearning system utilizing the ChatGPT 4.0 API (GPT-4-0613). The primary classes of the API employed in our implementation encompass _ChatGPT-Client_ for handling API authentication and session management, _ChatSession_ for managing chat sessions and conversation states, _Message_ for representing messages sent to and received from the model, _CompletionRequest_ for encapsulating parameters for generating text completions, and _FilteringOptions_ for specifying content filtering settings.  
All the functional components, data components, main control flow, Foundation Manager algorithm, and design for personalizing GPT prompts have been fully implemented as specified. The user interface for the main menu is shown in Figure 10.  
The user interface keeps displaying the current topics such as 'Unit 7 \(\rightarrow\) Topic 1 \(\rightarrow\) Inheritance and Subclassing' and it offers tabs for choosing an option among the four activities: _Foundation_, _Exercises_, _Submission_, and _Evaluation_.  
The system displays the foundational content of the current topic in the content frame. Upon completing the foundations, students proceed to choose the _Exercises_ tab, which will trigger the generation of exercise problems. Upon completing coding exercises, students can submit their solutions by using the _Submission_ Table. Students can view the results of the evaluation on their submissions by using the _Evaluation_ tab.  
The _PREV_ and _NEXT_ buttons allow students to freely navigate through units and topics within each unit. A click on the _NEXT_ button brings up the next topic in the unit and continues displaying the learning content. However, a click on the _NEXT_ button while studying the last topic in the unit will trigger a generation of exercise problems for the student.
## 2 Nonlinear Model Predictive Control
### Mathematical Model of Nonlinear System
#### 2.2.4 Constraints  
In practical applications, there will always be various constraints, including equality and inequality.  
The motor power for a real car steering system is limited, the rotation speed of the wheel corner cannot be arbitrarily large, and the wheel angle increment \(\mathbf{U}\) must be limited for real-world application. At the same time, there are physical limits on the wheel angle of the vehicle, so a wheel angle limit constraint must be added to the prediction model. In summary, the additional constraints are as follows:  
\[-\Delta\delta_{max}\leq\mathbf{U}\leq\Delta\delta_{max} \tag{6}\]  
\[-\delta_{max}-\delta_{0}\leq\mathbf{C}\mathbf{U}\leq\delta_{max}-\delta_{0} \tag{7}\]  
Figure 2: Vehicle kinematic model.  
where  
\[\mathbf{U}=\left[\begin{array}{c}\Delta\delta_{1}\\ \Delta\delta_{2}\\ \vdots\\ \Delta\delta_{n-1}\\ \Delta\delta_{n}\end{array}\right]\mathbf{C}=\left[\begin{array}{ccccc}1&0& \cdots&0&0\\ 1&1&\cdots&0&0\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ 1&1&\cdots&1&0\\ 1&1&\cdots&1&1\end{array}\right] \tag{8}\]  
\(\Delta\delta_{max}\) is the maximum wheel angle increment, and \(\delta_{max}\) is the maximum wheel angle.
## 3 Experiment Results and Analysis
### Experimental Results and Comparison Analysis
#### 3.3.2 Multi-Source Trajectory Data Set  
To further validate the performance of the proposed algorithm, we employed a more complex multi-source dataset. In order to accurately analyze the above evaluation indexes, The number of clusterings is randomly generated between 130 and 150, each algorithm was iterated 120 times in a single run to guarantee convergence. Table 8 is the comparison tables for the average Silhouette coefficient (SC), the average degree of separation (SP), and the average SSE value of 20 training sessions respectively.  
Figure 13: The SSE convergence curve of taxi GPS dataset from San Francisco (USA).  
All eight algorithms converge well within less than 120 iterations in the multi-source dataset. The K-Means++ and Random Swap Clustering algorithms are much less time consuming than the other algorithms, and both achieve very good clustering results. The analysis of the experimental data shows that the Random Swap Clustering algorithm outperforms the K-Means++ algorithm. The Random Swap Clustering algorithm also outperforms single-mode heuristics in many cases. With reasonable time consumption, the evaluation indexes of the proposed algorithm are significantly better than those of the compared algorithms. This may be due to the fact that the proposed hybrid heuristic algorithm has stronger non-homogeneous spatial search capability, but also brings more computing resources and time consumption. Overall, the clustering results of the proposed algorithms all outperformed the comparison algorithms in the terms of above evaluation indexes. The above suggests that the proposed hybrid heuristic algorithms are effective.
# Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review
## 1 Introduction
### Halftoning Technology  
Digital halftoning is a technique that simulates continuous-tone images by using dots of a limited number of colors and varying dots either in size or in spacing. According to the distribution of dots, it can be divided into amplitude-modulated (AM) screening and frequency-modulated (FM) screening as shown in Figure 2. The image hierarchy is represented by the size of the dots in AM screening whereas the dot distance is fixed in spacing and by the density of dots in FM screening [1]. Color halftone images refer to the overlapping halftone images generated from its each color channel. Figure 3 gives an illustration of color halftone image generated by RGB color image. If the image mode is CMYK (cyan, magenta, yellow, black), then C, M, Y, and K channel halftone processing is required instead of a R, G, and B channel image. Besides the AM and FM screening methods, a large amount of digital halftoning methods have been presented, such as ordered dithering, error diffusion, dot diffusion, iterative processing, and methods based on look-up table (LUT) [5].  
Figure 1: An illustration of halftoning and inverse halftoning, where the region of red box in each image is enlarged and overlapped on the left part of each image: (**a**) continuous-tone image, (**b**) halftone image, (**c**) restored image from (**b**).  
Figure 2: Sample graph showing continuous-tone image, amplitude-modulated (AM) screening image, and frequency-modulated (FM) screening image from left to right.  
For halftone images, there are two existing forms: one is in the form of an electronic document, and the other is in the form of a printed medium such as newspapers, books or magazines. For the halftone image in the form of electronic document, we call it a digital halftone image because it does not print on analog medium, whereas for the halftone image in the form of a printed medium, we call it a scanned halftone image because it needs to be scanned into the computer first when we want to reuse it. Figure 4 shows an example of these two types of halftone images. As shown in Figure 4, the scanned halftone image is more blurred and has more serious quality degradation than the digital halftone image.
## 5 Experiments in Actual Maritime Area
### Fault-Coping Algorithm Performance Test  
The performance of the fault-coping algorithm with the fault-coping algorithm was evaluated when unexpected changes in the current and RPM values of the developed USV occurred. The fault-coping algorithm successfully detected and assessed the fault, and the leader change flag was appropriately raised after the fault in the check threshold block was determined. The follower then assumed the role of the leader and continued the mission. Figure 13 shows a photograph of the actual fault situation that occurred during the test, and Figure 14 illustrates the path following results in the event of a fault.  
Figure 12: Distance error result of swarm-control performance test.  
Figure 13: Picture of actual fault scenario.  
These results are consistent with those of the swarm-control algorithm performance test. Initially, the follower USV, which was positioned behind the leader, successfully followed the leader at a constant distance from each waypoint. However, during the transition from waypoints 2 to 3, a fault occurred in the leader. Subsequently, the proposed fault-coping algorithm operated correctly, causing the leader to stop as the follower assumed the leader's role. Finally, the follower successfully completed the mission by following the previous leader's trajectory.  
The current consumption and RPM data for the leader during the fault occurrence are depicted in Figures 15 and 16, respectively.  
As was evident from an examination of the graphs, approximately 150 s (1500 intervals in the figure) after the commencement of the mission, the current value increased by approximately 1.5 times from the previous value. Simultaneously, the thruster steadily decreased. This indicates that obstruction of the thruster by a foreign object caused a gradual reduction in the rotational speed and an increase in the overall power consumption of the system.  
Subsequently, the fault-coping algorithm correctly identified the occurrence of a fault, leading to the cessation of the faulty USV system. Both the current and RPM values decreased to 0. Figure 17 illustrates the results, which confirm that the fault-coping algorithm accurately identifies the fault using current and RPM data, triggering the normal activation of the leader change flag.  
As observed in Figures 15 and 16, the leader change flag transitions from 0 to 1 simultaneously with the occurrence of the fault. These results verified that in a real operational environment, the proposed fault-coping algorithm would function accurately in unexpected fault scenarios during the mission.