esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
A comprehensive comparison of the performances of different iron-based ores including hematite, ilmenite, limonite, magnetite and taconite was carried out by National Energy Technology Laboratory in US. Results illustrated the reactivities of ores were worse than that of pure Fe\({}_{2}\)O\({}_{3}\) due to the less active compositions content. However, the magnetite and limonite demonstrated better performances than hematite, ilmenite and taconite [36]. In Universitat Siegen, tests of two ilmenites in fixed bed reactor demonstrated they had high gas conversions, however the sintering of particles might be a substantial problem [37]. In China, the natural hematite and natural iron ore have been evaluated as oxygen carriers in CLC [27; 38]. The previous studies demonstrated that natural ores had relatively satisfying performances. However, they were high-grade minerals which had a high cost of the raw materials. The real situation of the iron minerals is that the low-grade lean iron ore takes much more inventory than the high-grade ones, thus the lean iron ore has more advantage in the price. If the lean iron ore is demonstrated to be capable for commercial applications, the operation cost of CLC can be decreased. But, the lean iron ore contains many other components which may affect the reduction of active component and make the reaction mechanism more complex. There were few literatures focusing on the properties of the lean iron ore. Previously, our group has designed and built a high-flux circulated fluidized bed (CFB) unit for chemical looping combustion [39]. In this unit, the riser was applied as the fuel reactor. Another 50 kW CLC unit in Southeast University also adopted fast fluidized bed as fuel reactor [40]. In these units, the contact time between fuel and oxygen carrier was short. And the OCs usually cannot be totally reduced. Thus, the previous studies on the OC performances may deviate from the real circumstance of OCs in practical operations. In this study, we conducted the experiment to investigate the performances of a type of Chinese lean iron ore as oxygen carrier. To be more practical, the effects of short-time and multicycle redox process on the performances of OC in CLC were investigated in this work. With the consideration of cost efficiency for future commercial application, a kind of Chinese lean iron ore was adopted as the oxygen carrier. The injection time of reduction gas was set to 60 s, 50 s and 40 s in a bench-scale fixed bed reactor to simulate the short residence times of oxygen carrier in reduction atmosphere in a dual circulated fluidized bed system. The effects of short reduction times on the reactivity were investigated. Multi-redox cycles were carried out to evaluate the stability of the reactivity. And the mechanisms of reactivity changes were analyzed by the characterizations of OCs using SEM, BET and XRD.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 1 Introduction
### Motivation
Despite the growing use of IoV in smart city applications, there is a lack of standardized datasets, consistent evaluation metrics, and focus on explainability across the studies. This highlights a critical gap in developing transparent, comparable, and generalizable solutions for real-world urban challenges. In this study, the analysis investigates the IoV and its significance for sustainable smart urban environments. It analyzes important factors, including the global marketplace trends and applications, and how vehicles relate to city infrastructure. The study emphasizes various IoV communication models, findings in current research, and developments in inter-vehicle communication. Additionally, it addresses open research challenges and security issues, and future directions in IoV research with a summary of key findings, highlighting the significance of IoV in shaping the future of urban mobility and sustainability. With the aim of developing a comprehensive understanding, this study has devised and investigated four specific research questions (R), outlined as follows. * R1: How do global market trends influence the development and adoption of IoV technologies, considering layered architectures, applications, and the levels of connected and autonomous vehicles, in the context of sustainable smart cities? * R2: What are the current advancements in IoV communication models, including wireless technologies and emerging technologies, and how do they contribute to the evolution of inter-vehicle communication research? * R3: What are the current limitations, challenges, and areas for further research in the implementation of IoV within smart city contexts, and how can these challenges be addressed to realize the full potential of interconnected transportation systems?
## 4 EBCRP: Energy-Balanced Cluster-Routing Protocol
### Clustering Sensor Nodes Based on Particle Swarm Optimization with Five Mutation Operators
#### 4.1.3 Cost Function in Sensor Node Clustering  
Each particle corresponds to a sensor node clustering solution in each generation. These solutions are expected to be searched towards the desired target (i.e., shorting the distance between sensor nodes and their centroids while balancing the number of sensor nodes between clusters). Therefore, the fitness of the clustering solutions need to be evaluated.  
In this study, two goals are expected. Firstly, it is hoped that the nearest sensor nodes are grouped into a cluster instead of any distance. The average distance between the sensor nodes and the centroids they belong to is calculated to achieve the goal, which is expected to have a small value. Secondly, balancing the number of sensor nodes between clusters is expected, which helps to balance energy consumption of sensor nodes so as to prolong the lifetime of WSNs. [PERSON]'s fairness index Jain (1998) is used to evaluate the balance index of the number of sensor nodes between clusters. The corresponding balance index can be defined as Equation (5):  
\[BI=\frac{(\sum_{i=1}^{k}n_{i})^{2}}{k(\sum_{i=1}^{k}n_{i}^{2})}, \tag{5}\]  
where \(BI\) is the balance index of the number of sensor nodes between clusters, \(n_{i}\) is the number of sensor nodes in the \(i\)-th cluster. The value of \(BI\) ranges in the interval of [0, 1]. When \(BI\) is close to 1, the more balanced the number of sensor nodes between clusters. Therefore, the optimization objective can be defined to maximize the Equation (6):  
\[max\;\;fit=\frac{w_{ad}}{Sum_{d}/(\sum_{i=1}^{k}n_{i})}+BI, \tag{6}\]  
where \(Sum_{d}\) represents the sum of the distances between all sensor nodes and their centroids, \(\sum_{i=1}^{k}n_{i}\) means the number of sensor nodes in all clusters, that is, \(\sum_{i=1}^{k}n_{i}=N\), \(Sum_{d}/N\) represents the average  
Figure 4: Structure of particles utilized in sensor node clustering optimization.  
distance, and \(w_{ad}\) is the weight of the average distance in fitness \(fit\) (\(w_{ad}=10\)). Since \(N\) is a positive integer, Equation (6) is equivalent to Equation (7).  
\[max\;\;fit=\frac{10}{Sum_{d}}+\frac{N}{k(\sum_{i=1}^{k}n_{i}^{2})}. \tag{7}\]
# Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter
## 2 Modeling and Parameter Identification
### The Second-Order RC Battery ECM  
According to the working mechanism of the lithium-ion battery, a model of the second-order RC ECM (2-RC ECM) was established, as shown in Figure 3. The model mainly includes an ideal voltage source, i.e., the OCV of the battery, an ohmic internal resistance, \(R_{0}\), and two RC networks. \(R_{1}\) and \(R_{2}\) are the polarization internal resistances, and \(C_{1}\) and \(C_{2}\) are polarization capacitors. Among them, the parallel circuit of \(R_{1}\) and \(C_{1}\) mainly simulate the short-time constant electrochemical polarization phenomenon in the electrode, and the parallel circuit of \(R_{2}\) and \(C_{2}\) mainly simulates the long-time constant concentration difference polarization phenomenon related to the diffusion process in the electrolyte and the porous electrode. The 2-RC ECM has a good balance between computational complexity and model accuracy. Therefore, we chose this model.  
Based on the 2-RC ECM, the state equation (Equation (4)) and measurement equation (Equation (5)) could be obtained using the Kirchhoff voltage and current laws.  
\[\left\{\begin{array}{c}\dot{U}_{1}=\frac{I_{L}}{C_{1}}-\frac{U_{1}}{R_{1}C_ {1}}\\ \dot{U}_{2}=\frac{I_{L}}{C_{2}}-\frac{I_{2}}{R_{2}C_{2}}\end{array}\right. \tag{4}\]  
\[U_{L}=U_{OCV}-I_{L}R_{0}-U_{1}-U_{2} \tag{5}\]  
According to the \(SoC\) definition and the ampere-hour integration method, the continuous-time state equation can be obtained.  
\[SoC(t)=SoC_{0}-\frac{\eta}{Q_{N}}\int_{0}^{t}I_{L}(t)dt \tag{6}\]  
Due to the need of Kalman filter, Equation (4) was solved and discretized, Equation (6) was discretized. The discrete-time state space representation (Equation (7)) and measurement equation (Equation (8)) were established.  
\[\left[\begin{array}{c}SoC(k+1)\\ U_{1}(k+1)\\ U_{2}(k+1)\end{array}\right]=\left(\begin{array}{cc}1&0&0\\ 0&e^{-\frac{Mt}{12}}&0\\ 0&0&e^{-\frac{Mt}{12}}\end{array}\right)\times\left[\begin{array}{c}SoC(k) \\ U_{1}(k)\\ U_{2}(k)\end{array}\right]+\left[\begin{array}{c}-\frac{\eta M}{Q_{N}}\\ R_{1}(1-e^{-\frac{Mt}{12}})\\ R_{2}(1-e^{-\frac{Mt}{2}})\end{array}\right]\times I_{L}(k)+\omega(k) \tag{7}\]  
\[U_{L}(k)=U_{OCV}(k)-I_{L}(k)\times R_{0}(k)-U_{1}(k)-U_{2}(k)+\
u(k) \tag{8}\]  
The circuit parameters include open circuit voltage \(U_{OCV}\), circuit current \(I_{L}\), battery load terminal voltage \(U_{L}\), internal resistance \(R_{0}\), polarization internal resistances \(R_{1}\) and \(R_{2}\), polarization capacitance \(C_{1}\) and \(C_{2}\), the initial SoC SoC\({}_{0}\), coulombic efficiency \(\eta\), the nominal capacity \(Q_{N}\), the system state input error \(\omega(k)\), the observation error \(\
u(k)\), and time constants, \(\tau_{1}\) and \(\tau_{2}\), where, \(\tau_{1}=R_{1}\)\(C_{1}\) and \(\tau_{2}=R_{2}\)\(C_{2}\). \(U_{1}\) is the voltage of \(R_{1}\), and \(U_{2}\) is the voltage of \(R_{2}\).  
Figure 3: The 2-RC ECM.
## 2 The Smart Angel Project
### Smart Angel and Urban Mobility
#### 2.5.2 The Monitoring System  
In addition to the games, a monitoring system has been developed to support those users who already have the ability to move independently in known areas of their own town. Even if they usually move without any problem, some specific issues needed to be addressed.  
In particular, unplanned situations, which may arise in everyday life, cause concern in the users. A strike of the public transport may cause a complete change in the \"going to work\" routine, missing a bus stop needs re-planning of one's walking route to go back on track, a road closed for street works may make an area inaccessible, _etc._ In all these cases, the monitoring system works in two ways. On the one hand, when the user leaves the area of town that he knows, the system signals the tutor that there is a possible problem and the tutor can then decide if intervention is needed. On the other hand, the system gives the user some immediate help by telling him where he is, suggesting alternative routes or simplyoffering him to ask for help to the tutor in charge. The use of standard maps is not possible for most people with intellectual disabilities since it requires a high level of abstraction, which can rarely be reached, therefore a specific system was needed by the target users.  
Furthermore, those people who, in the past, got lost for any reason may have fear of it happening again. Often just knowing that there is a system through which they get immediate support, so that they do not feel alone, is of basic importance: they can always ask the system if they are on the correct way, send a message to the tutor or get in touch directly with him.  
Finally, as previously reported, time management can be an issue. The monitoring system takes care of checking that the person is on time according to his appointments, and provides reminders when needed. Automatically a notice can be sent to the tutor when the user gets to his destination.  
Each end user has one or more safe areas, which are the areas in town that he knows well and in which he can move around with no problem. These areas are defined by the tutors who deeply know each person's abilities. The monitoring system alerts the tutor each time the user goes out of his safe areas, the tutor can then decide whether to intervene or not.  
The availability of a system on which to rely in case of need stimulates the user to increase his independence, explore new areas and try new activities. He feels supported and knows that in case of need help can be requested and will be given. Privacy issues and informed consent have been taken into account and the involved user associations are active in addressing these themes.
###### Abstract  
The integration of artificial intelligence (AI) with additive manufacturing (AM) is driving breakthroughs in personalized rehabilitation and physical therapy solutions, enabling precise customization to individual patient needs. This article presents the current state of knowledge and perspectives of using personalized solutions for rehabilitation and physiotherapy thanks to the introduction of AI to AM. Advanced AI algorithms analyze patient-specific data such as body scans, movement patterns, and medical history to design customized assistive devices, orthoses, and prosthetics. This synergy enables the rapid prototyping and production of highly optimized solutions, improving comfort, functionality, and therapeutic outcomes. Machine learning (ML) models further streamline the process by anticipating biomechanical needs and adapting designs based on feedback, providing iterative refinement. Cutting-edge techniques leverage generative design and topology optimization to create lightweight yet durable structures that are ideally suited to the patient's anatomy and rehabilitation goals. AI-based AM also facilitates the production of multi-material devices that combine flexibility, strength, and sensory capabilities, enabling improved monitoring and support during physical therapy. New perspectives include integrating smart sensors with printed devices, enabling real-time data collection and feedback loops for adaptive therapy. Additionally, these solutions are becoming increasingly accessible as AM technology lowers costs and improves, democratizing personalized healthcare. Future advances could lead to the widespread use of digital twins for the real-time simulation and customization of rehabilitation devices before production. AI-based virtual reality (VR) and augmented reality (AR) tools are also expected to combine with AM to provide immersive, patient-specific training environments along with physical aids. Collaborative platforms based on federated learning can enable healthcare providers and researchers to securely share AI insights, accelerating innovation. However, challenges such as regulatory approval, data security, and ensuring equity in access to these technologies must be addressed to fully realize their potential. One of the major gaps is the lack of large, diverse datasets to train AI models, which limits their ability to design solutions that span different demographics and conditions. Integration of AI-AM systems into personalized rehabilitation and physical therapy should focus on improving data collection and processing techniques.  
**Keywords:** 3D printing; additive manufacturing; artificial intelligence; clinical applications; machine learning; physiotherapy; rehabilitation
## 1 Introduction  
Artificial intelligence (AI) has initiated substantial transformations in the analysis of medical imaging. In gastroenterology, the scientific study of the health and diseases of the stomach, small intestine, colon, and rectum, AI-assisted colonoscopy has shown the potential to improve the diagnosis of colon cancer and reduce the incidence of colorectal cancer (CRC) [1]. The efficacy of artificial intelligence models is fundamentally dependent on the quantity and quality of training image data for colon cancer. A fundamental drawback in the advancement of successful models for medical imaging and image interpretation is the lack of sufficient training and testing data. The challenges of generating large datasets include privacy concerns, invasive methodologies, and the labor-intensive nature of professional data annotation [2].  
The absence of extensive, varied, and thoroughly annotated datasets hinders the training and development of sophisticated AI-assisted colonoscopy technologies [3, 4]. The inaccessibility of data in this context leads to the creation of severely defective AI models or algorithms with intrinsic bias when used for vulnerable populations [5]. The scarcity of ample high-quality training data undermines AI's efficacy in improving the identification and prevention of colorectal cancer, particularly among groups facing health care disparities. Like prior research, there has been an effort to utilize generative adversarial networks (GANs) for the synthesis of text-to-image mapping, among other methodologies [6, 7, 8]. Recent evaluations have emphasized the refinement of large language models based on vision, including Stable Diffusion (SD), Contrastive Language-Image Pre-training (CLIP), and DreamBooth + Low-Rank Adaptation (DB+LoRa), for the creation of medical images [9]. However, the application of these algorithms to generate a comprehensive array of high-quality colonoscopy images remains unexplored. The issue is the lack of a robust framework for generating synthetic colonoscopy images to enhance training data for more effective diagnostic algorithms.  
Researchers are exploring various approaches (GANs, VQGAN, CLIP, Bootstrapping Language-Image Pre-training (BLIP)) to address these issues by improving and diversifying medical imaging databases [10, 11, 12]. This study examines the use of artificial intelligence for text-to-image synthesis and intelligent data augmentation to improve AI models and promote demographic equity. Inception score (IS) and Frechet Inception Distance (FID) are utilized to evaluate synthetic images generated by fine-tuning the SD, CLIP and DB + LoRa models. In addition, advanced classification models such as BiT, ViT, FixResNeXt, EfficientNet, and DeiT are employed to train the generated images. Assessments of these models are based on standard metrics, including the F1 score, accuracy, precision, recall, and Area Under Curve (AUC), among others. All of this manifests itself as a structured plan to improve the standards of imaging and medical diagnosis.
## 2 Feature Extraction
### GLCM and Haralick Features
#### 2.2.1 Glcm  
The GLCM is a matrix that shows how often the different combinations of gray levels transpire in an image. It is widely used to extract features, especially in the research of liver diseases (e.g., see [46]).  
Figure 3: ROIs taken by MATLAB: The first row shows samples of HCC, and the second row shows samples of liver abscess.  
Figure 2: The cropping window.  
In other words, it is a way of presenting the relationship between two neighboring pixels. The whole procedure to extract the Haralick features is presented as Figure 4. The co-occurrence matrix can be calculated as in Equation (1)  
\[C_{d,\theta}(i,j)=\sum_{x=1}^{N}\sum_{y=1}^{M}\left\{\begin{array}{cc}1,\text{ if }I(x,y)=i,&I(x+\Delta x,y+\Delta y)=j\\ 0,&\text{otherwise}\end{array}\right. \tag{1}\]  
where \(C_{d,\theta}(i,j)\) is the number of occurrences of the pair of gray levels \(i\) and \(j\), with \(d,\theta\) the distance and angularity respectively; and \(I(x,y)\) is the intensity of a pixel in the \(x^{th}\) row and \(y^{th}\) column in the image. We can see that, with the different pairs \((d,\theta)\), the image could be explored in different directions and distances. In this research, we choose the four directions \(\theta=0,45^{\"},90^{\"},135^{\"}\) with \(d=1\) (illustrated in Figure 5). Thus, the pair \((d=1,\theta=0)\) is the nearest horizontal pixel. Moreover, there are also co-occurrence matrices for the vertical \(\left(\theta=90^{\"}\right)\) and diagonal axes \(\left(\theta=45^{\"},135^{\"}\right)\).  
For example, in Figure 6, we calculate the gray-level co-occurrence matrix of a 4 \(\times\) 4-pixel image. In this case, 3 and 1 are near each other 2 times in the image; therefore, the positions \((3,1)\) and \((1,3)\) are filled in with 2. Applying the same principle to all other pixels, we obtain the GLCM of the image.  
The ROI that we need to process is 32 \(\times\) 32 pixels with a 256-gray level. Therefore, we will have a 256 \(\times\) 256 matrix with a total of 65,536 cells; however, many cells are filled with zeros (because these combinations do not exist). This situation of many zero cells can lead to a poor approximation. The solution to this problem is that the number of gray levels is reduced, decreasing the number of zero cells, and the validity is improved considerably. In this research, the ROIs are scaled to 16  
Figure 4: GLCM process.  
Figure 5: Directions of gray-level co-occurrence matrix.  
gray-level images before computing GLCM. After that, Equation (1) is normalized to be converted into a probabilistic form by Equation (2). The procedure is shown in Figure 7.  
\[P(i,j)=\frac{C(i,j)}{\sum_{i=0}^{l-1}\sum_{j=0}^{l-1}C(i,j)}. \tag{2}\]  
Therefore, we obtain the GLCM as in Equation (3)  
\[G=\left[\begin{array}{cccc}P(0,0)&P(0,1)&\cdots&P(0,l-1)\\ P(1,0)&P(1,1)&\cdots&P(1,l-1)\\ \vdots&\vdots&\ddots&\vdots\\ P(l-1,0)&P(l-1,1)&\cdots&P(l-1,l-1)\end{array}\right]. \tag{3}\]  
From the co-occurrence matrix, the textural features proposed by [PERSON] are calculated.  
Figure 6. Extracting the gray-level co-occurrence matrix (GLCM) from an image.
### Anomalies  
There are three main types of anomalies distinguished in the literature: point anomalies, collective anomalies and contextual anomalies [1; 15; 16; 17; 18]. Point anomalies are individual data points \((t_{i},p_{i})\in\mathcal{T}\) that deviate significantly from all other instances, such as a fraudulent transaction among legal finance transactions [1]. Collective anomalies refer to whole subsequences \(\hat{S}_{a,b}\subset\mathcal{T}\) being anomalous, while the individual data points \((t_{i},p_{i})\in\hat{S}_{a,b}\) would not be considered a point anomaly [16]. For example, supraventricular premature beats in an electrocardiogram (ECG) are examples of collective anomalies. Contextual anomalies only appear anomalous depending on specific context variables. For instance, while an outside air-temperature measurement of 28 \({}^{\circ}\)C during August is considered normal in Panama, it would be anomalous in Antarctica. In this work, we extend this classification to 16 classes by dividing the class of collective anomalies into different subclasses, such as \"frequency change\" or \"time shift\", which are described in Section 2.2.2.