esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design
### System Overview  
ICN's main principle is to separate the identity and location, and the location can use different location information, such as the IP address, private address, etc. The existing ICN solutions can be divided into two categories according to the content discovery mechanism. One type is routing-by-name ICN solutions represented by CCN/NDN. The other is the lookup-by-name ICN solutions represented by MobilityFirst.  
As shown in Figure 1, the system of ILDM is composed of multicast sources, multicast receivers, routers, and NRS. The ICN scheme in our system is similar to MobilityFirst, and also uses the lookup-by-name content discovery mechanism. We completely separated the identifier (or name) from the locator and used NRS to dynamically bind the identifier and the locator. We used Entity-ID (EID) as the identifier and Network Address (NA) as the locator. We regarded the elements in the network as entities, including devices, contents, services, etc. We assigned each entity a flat and globally unique EID, which was supported for self-certifying. To be compatible with current IP-based networks, we used IP (IPv4 or IPv6) addresses as NAs. The EID could be mapped to one or multiple NAs by NRS. NRS can use existing NRS solutions, which can be distributed or centralized.  
In our approach, multicast was regarded as a type of service, and each multicast service was assigned with a multicast service EID (MSEID), which could uniquely identify the multicast service and be used for the transmission of multicast packets in the network. Compared with the multicast IP address, the globally unique MSEID has a larger namespace, which enables more multicast services. Meanwhile, MSEID also eliminates the need for complicated multicast IP address management and allocation mechanism, such MASC [8]. For each multicast service, the corresponding MTNs that forward the multicast flow can also provide the multicast service to users. The NRS maintains the mapping between the MSEIDs and the NAs of a set of MTNs, thereby providing the ability to select appropriate MTN for each receiver to join the multicast. Multicast receivers can easily join or leave a multicast service based on the MSEID without caring about the locations of the multicast sources.  
The ICN router [21] maintains both IP routing information and name forwarding information. Routers can forward ordinary IP packets based on the IP address and forward ICN packets based on the EID. Each router maintains the multicast name forwarding table (MNFT) for forwarding ICN multicast packets. MNFT consists of a set of multicast name forwarding entries (MNFEs), each of which contains an in-interface, MSEID, and out-interface list. When a router receives a multicast packet, it finds the matching MNFE based on the ingress interface of the packet and the MSEID carried in the packet. If there is a matching MNFE, the multicast packet is cloned for each out-interface of the MNFE, and these duplicates are forwarded out from the corresponding out-interface, respectively. If there is no matching MNFE, the multicast packet is just discarded.
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 3 Methodology
### PDR Positioning
#### 3.3.2 Step Length Estimation  
The step length estimation model can be classified into two categories: static model and dynamic model. The static model considers that the user's step length is fixed throughout the process and is only related to the user's individual characteristics, such as height. However, using a fixed step length will degrade the positioning accuracy since the step length is also affected by other factors, such as velocity, acceleration magnitude, the environment, etc. The dynamic model allows the step length to be different at different moments for the individual user. In this paper, we adopt a nonlinear step estimation model based on statistics proposed in [51]. The step length \(l\) is given by  
\[l=\tau\sqrt[4]{a_{\mathrm{max}}-a_{\mathrm{min}}}, \tag{7}\]  
where \(\tau\) is a scale factor that can be obtained using offline training, \(a_{\mathrm{max}}\) and \(a_{\mathrm{min}}\) are the maximum and minimum values of the synthetic acceleration in the step detection process.
## 2 Conceptual Reliability-Based Decision Support Framework  
Social PPPs refer to public infrastructure or services procured via PPP concession agreements. More broadly, social infrastructure is defined as \"community facilities, properties, services that help individuals, families, groups and communities meet their social needs and maximize their potential for development and enhance community wellbeing\" [22]. Unlike economic PPPs, the social PPPs, such as schools, hospitals and prisons, are procured using more performance-based service specifications [23], while requiring less capital expenditure. They tend to be complex and have dynamic service delivery requirements throughout their life cycle, particularly in terms of ongoing involvement with the community [23; 24]. Social PPPs are generally in the form of availability-based payments whereby government retains demand risk and pays for the service delivered based on the facility being available to the specified standard [25]. Research has started to explore approaches to evaluate PPP performance. [PERSON] et al. [26] sought to establish a life cycle and stakeholder-oriented performance framework based on a series of relevant indictors. Specifically, [PERSON], et al. [27] explored the relationship between building space with the performance outcomes of a public school. Similarly targeted at PPP schools, [PERSON] et al. [28] proposed an enhanced framework for better assessing operational performance in consideration of international PPP audits and practices. [PERSON] et al. [29] developed a building information modeling-based performance management system for PPPs. Nevertheless, there remains a paucity of research that give guidance on how to comprehensively measure the performance of social PPPs even though it is pivotal for achieving value for money for government throughout their life cycle [30] and the specific value added during the operation and maintenance phase [14].  
The proposed RDSF has three core concepts being: the assessment of service delivered by KPIs, the measurement of end-user perceived service quality and reliability analysis in consideration of uncertainty project context. The representation of this framework is shown in Figure 2. This model explicitly considers an assessment of the delivered performance against the contracted services (detailed in agreement output specifications) and the perceived standard of services delivered through the eyes of the end-user. The model considers aspects of key services, such as general service specification, building maintenance and utility/waste/security/cleaning/\(\dots\) management services for government and space, operation and maintenance for the end-user.
# Siamese Tracking with Adaptive Template-Updating Strategy
## 2 Related Work
### Template Update  
In real-world environments, many recent algorithms have achieved great success in visual-object tracking [24; 25; 26; 27; 28; 29; 30; 31]. These algorithms were able to solve the problems of several types of appearance changes in the target, for instance, pose variations, occlusion, and illumination changes. However, if these changes are very extreme, most of the methods may fail to track the target. An example of false tracking caused by vehicle turning is shown in Figure 1. Test images are from the DARPA dataset's video sequence \"egtest03\".  
Figure 1: Example of false tracking due to target deformation: (**a–c**) are images to show tracking results for three frames in DARPA dataset, green boxes, ground truth; red, tracking results.  
In order to track targets with severe appearance changes, some methods [5; 32; 33] update the target template over time to improve tracking accuracy. The most common template-updating strategies either update every single frame, or update with a fixed number of frame intervals. These template-updating tracking algorithms assume that the current updated template can describe the target better than any other templates in previous frames. However, this assumption sometimes may face the following problem. Typically, the template is updated based on tracking results according to the current frame. While trackers are not able to precisely track the target, tracking results include errors that force the template to be incorrectly updated. As a result, the updated template is not able to accurately describe the current target and eventually causes the tracker to fail.  
Obviously, the aforementioned simple template-update methods are not able to satisfy our needs in tracking applications. For one case, when a tracking object is partially shielded by other disturbing objects, such as a tank shielded by a tree, traditional bounding-box template updating treats part of the tree over the tank as a tracking object and introduces interference. To avoid background clutter and template pollution, two template-updating strategies are required. First, instead of simply updating every fixed frame, we need to let the tracker know the most appropriate opportunity to update the template. This strategy can not only reduce template-pollution risks, but also reduce computational complexity of frequent useless template updating. Another beneficial strategy is to reserve backup templates; once the current template is not able to match the target, the backup ones may play a significant role. This strategy can effectively improve the robustness of tracking algorithms.
## 4 Results and Discussion  
This section will analyze the convergence time and positioning accuracy of the stations in different MS periods and at low, middle, and high latitudes. The PPP performance in terms of convergence time in the horizontal and vertical components was evaluated at 68% and 95% levels. For SF PPP solutions, when the positioning accuracy was better than 0.5 m (95%) and 0.3 m (68%) in the horizontal and vertical components, respectively, and was maintained at all the epochs, the solution was considered to be convergence [10]. We analyzed the effects of SF GPS convergence time on TIC1 and TIC2 schemes. We performed statistical analysis of the convergence times during the MS and NMS periods in two constraint schemes.
## 2 Materials and Methods
### YOLOv8 Model  
The YOLOv8 model is an improvement based on YOLOv5. Released by Ultralytics in 2023, the YOLOv8 model boasts five variants, YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x, each variant's number of parameters being directly correlated with its training duration. The intricate model architecture of YOLOv8n, illustrated in Figure 2, can be neatly summarized into four components: Input, Backbone, Neck, and Predict.  
In comparison to YOLOv5, YOLOv8 incorporates the innovative C2F (Cross-Stage Partial Network Bottleneck with 2 Convolutions) within its Backbone layer, which harmoniously integrates ELAN and C3 [17]. This refinement contributes to YOLOv8's enhanced accuracy and quicker performance compared to its predecessor. Additionally, YOLOv8 employs SPPF (Spatial Pyramid Pooling-Fast) as its spatial pyramid pooling module. SPPF efficiently performs pooling operations across feature maps of varying scales without altering their dimensions, thus augmenting overall accuracy [18].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configure** & **Parameter** \\ \hline CPU & 12 th Gen Intel(R) Core(TM) i5-12490F @ 3.00 GHz \\ (Intel, Santa Clara, CA, USA) & (Intel, Santa Clara, CA, USA) \\ GPU & NVIDIA GeForce RTX 4060 (Nvidia, Santa Clara, CA, USA) \\ accelerated environment & CUDA12.1 \\ operating system & Windows 11 64-bit system \\ Pytorch & 2.1.0 \\ compiler language & Python3.11 \\ initial learning rate & 0.01 \\ momentum & 0.937 \\ weight\_decay & 0.0005 \\ batch & 24 \\ epochs & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment.  
Figure 1: The four categories of the dataset. (**a**) Safe, (**b**) un-safe, (**c**) no helmet, and (**d**) no jacket.  
Exhibiting remarkable versatility, YOLOv8 can seamlessly operate on diverse hardware platforms, including CPUs and GPUs, and offers a smooth transition between its multiple versions, demonstrating unparalleled flexibility.
# DeAPIR: Efficient and Enhanced Dental Arch-Guided Panoramic Image Reconstruction from CBCT Data
## 2 Proposed Methodology
### Teeth Localization  
The section on teeth localization plays a critical role in achieving accurate dental arch estimation and detection. Inaccurate CBCT slices can result in the generation of a false dental arch, ultimately leading to an incorrect panoramic image at the final stage. To address this issue, a meticulous process is employed to identify the dental teeth slices within the input CBCT. This process utilizes a horizontal moving average threshold applied to the MIP view of the CBCT. The complete dental slice detection procedure is illustrated in Figure 2. The MIP is derived from the CBCT data by considering only the maximum intensity value at each \((x,y)\) position, as defined by the following equation.  
\[\chi(x,y)=\max_{i=1}^{K}\left(I^{ct}(x,y,z_{i})\right) \tag{1}\]  
\[\mu(y)=\frac{1}{N}\sum_{x=1}^{N}\chi(x,y) \tag{2}\]  
\[\lambda(n)=\frac{1}{w}\sum_{k=n-\frac{w-1}{2}}^{n+\frac{w-1}{2}}\mu(k) \tag{3}\]  
\[\tilde{\lambda}(n)=\frac{\lambda(n)-\min(\lambda)}{\max(\lambda)-\min( \lambda)} \tag{4}\]  
\[\xi(n)=\begin{cases}1&\text{if }\tilde{\lambda}(n)>\tau\\ 0&\text{otherwise}\end{cases} \tag{5}\]  
In Equation (1), \(\chi\) represents the front MIP image derived from the input CBCT volume, denoted as \(I^{ct}\). The coordinates \(x\), \(y\), and \(z\) are defined within the context of \(I^{ct}\), where the index \(z\) varies from 1 to \(K\) in the input CBCT. Figure 2A illustrates the distribution of \(\chi\) concerning each location defined by \(x\) and \(y\). In Equation (2), \(\mu\) corresponds to the mean values obtained from the MIP \(\chi\) with respect to each index \(y\). Figure 2B provides a visual representation of the \(\mu\) values as a function of \(y\). Equation (3) introduces \(\lambda(n)\), which represents the moving average of the mean values \(\mu\) obtained from Equation (2). The symbol \(w\) denotes the window size used for the moving average procedure. Figure 2C displays the \(\lambda\) values in relation to the index \(n\). Additionally, \(\tilde{\lambda}(n)\) represents the normalized moving average vector \(\lambda(n)\). Figure 2D illustrates the \(\tilde{\lambda}\) values as a function of \(n\). Figure 2E provides insight into the \(\xi\) values, where \(\xi\) equals 1 for those CBCT slices containing the dental teeth section as given in Figure 2. Subsequently, the teeth section undergoes further analysis for dental arch detection.  
Figure 1: DeAPIR Framework: Transforming 3D CBCT into 2D Dental Panoramic Image—**(A)** Teeth Localization **(B)** Arch Detection **(C)** Arch Extension **(D)** MIP Generation and Stitching **(E)** Image Enhancement.
## 5 Experiments
### Experiment 3: Distribution and Recovery Time of Malicious Connections
Second, the resource utilization rate increases with higher splitting factors, as evidenced by faster \begin{table}
\begin{tabular}{c c c} \hline \hline
**Partitioning Factor** & **Average Connections Saved (\%)** & **Average Time Elapsed (m)** \\ \hline Epoch 1 & 0 & 1.01 \\ Epoch 2 & 50 & 1.59 \\ Epoch 3 & 50 & 1.93 \\ Epoch 4 & 62.50 & 2.20 \\ Epoch 5 & 68.75 & 2.44 \\ Epoch 6 & 71.87 & 2.72 \\ Epoch 7 & 74.21 & 3.13 \\ Epoch 8 & 75.38 & 3.79 \\ Epoch 9 & 77.72 & 4.95 \\ Epoch 10 & 79.96 & 6.98 \\ Epoch 11 & 82.75 & 10.56 \\ Epoch 12 & 85.65 & 16.89 \\ Epoch 13 & 88.73 & 26.93 \\ Epoch 14 & 90 & 42.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average connections saved and time elapsed per epoch for partitioning factor 2. Figure 9: Percentage of connections saved to time elapsed. The graph indicates the time required for partial to full recovery of the system. In this case, we consider a pollution factor of 10% and splitting factors of 2 and 10. Note that more than 80% of benign connections are saved within roughly 10 and 4 min, respectively. node accumulation. Despite this, our scheme demonstrates relative resilience, with higher splitting factors enabling quicker recovery times, albeit at the cost of increased resource utilization. As mentioned in Section 5.2, refined schemes may better balance the time and resources.
## 1 Introduction
By establishing a robust methodology for calculating the aging coefficient, this study not only advances the quantification of insulation aging but also develops a comprehensive aging condition index and a technique for estimating the remaining lifetime. Ultimately, the findings enhance capabilities for reliability assessment, maintenance planning, and long-term asset management of power cables for efficient and sustainable power transmission systems.
## 3 Fundamentals of RF Energy Harvesting
### RF Energy Sources and Availability
The disparity in RF power levels between urban and rural regions underscores the need for adaptive and efficient energy-harvesting techniques tailored to different environments [52; 53]. Figure 7: Ambient RF power levels [50].
# Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging
## 2 Materials and Methods
### Materials
#### 2.1.3 DICOM and CTP  
Along with HL7 FHIR, DICOM is another medical imaging data standard that contributes to the establishment of increased interoperability. In the case of general medical imaging, only information regarding the image itself is included such as Bitmap, Tagged Image File Format (TIFF), JPEG, and the bit count of each pixel. However, DICOM medical imaging also includes the name of the patient, data imaging date, medical equipment used for imaging, and the imaging specialist, among other information [14].  
The clinical trial processor (CTP) of the Medical Imaging Resource Center (MIRC) used de-identifying modules for medical imaging. According to a study conducted by [PERSON], [PERSON], [PERSON], the modules showed the highest performance rate in different environments among all freely accessible DICOM de-identifying modules [15]. CTP's de-identifier tool follows DICOM PS 3.15 Annex E's Attribute Confidentiality Profile. The section includes a total of seven profiles comprising a \"Basic Profile\" and six other profiles that can be applied to different situations [16].  
As is the case with HAPI FHIR, CTP was operated on Windows 10 and used \"jdk1.8.0_211\" and \"jre1.8.0_211.\"  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layout** & **Resource** \\ \hline Clinical & Observation, ImagingStudy, Procedure \\ Identification & Patient, Practitioner, Organization \\ Workflow & Schedule, Task, Encounter \\ Financial & Coverage, Invoice, Claim \\ Conformance & OperationDefinition, ImplementationGuide \\ Infrastructure & Questionnaire, MessageHeader, Parameters \\ \hline \hline \end{tabular}
\end{table}
Table 1: Resources for Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) Layouts.
# The Lightweight Deep Learning Model in Sunflower Disease Identification: A Comparative Study
## 3 Results
Without transfer learning, training a model from scratch on our limited data would likely lead to overfitting and poor performance. To adapt to the four-class classification of sunflower disease images, we performed the following steps for model adjustment:  
* First, instantiate the model. * Modify the parameters of the classification layer of the instantiated model to match the four-class sunflower disease image classification task. * Read the pre-trained weights provided by PyTorch official for the models on ImageNet, and also read the existing model weights. * Screen out the parameters whose keys and shapes match between the pre-trained model and the new classification layer setup. * Update the model weights using the selected parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Class** & **Precision** & **Recall** & **F1 Score** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{SqueezeNet} & Downy mildew & 0.715 & 0.723 & 0.719 & 72.34 \\  & Fresh leaf & 0.957 & 0.874 & 0.914 & 87.38 \\  & Gray mold & 0.940 & 0.987 & 0.963 & 98.73 \\  & Leaf scars & 0.771 & 0.802 & 0.786 & 80.20 \\ \hline \multirow{4}{*}{ShuffleNetV2} & Downy mildew & 0.734 & 0.617 & 0.67 & 60.70 \\  & Fresh leaf & 0.882 & 0.942 & 0.911 & 94.17 \\  & Gray mold & 0.925 & 0.937 & 0.931 & 93.67 \\  & Leaf scars & 0.714 & 0.693 & 0.703 & 69.31 \\ \hline \multirow{4}{*}{MnasNet-A1} & Downy mildew & 0.785 & 0.777 & 0.781 & 77.66 \\  & Fresh leaf & 0.910 & 0.981 & 0.944 & 98.06 \\  & Gray mold & 1.0 & 0.987 & 0.993 & 98.73 \\  & Leaf scars & 0.863 & 0.812 & 0.837 & 81.19 \\ \hline \multirow{4}{*}{MobileNetV3-Small} & Downy mildew & 0.736 & 0.681 & 0.707 & 68.09 \\  & Fresh leaf & 0.867 & 0.951 & 0.907 & 95.15 \\ \cline{1-1}  & Gray mold & 0.929 & 1.0 & 0.963 & 100 \\ \cline{1-1}  & Leaf scars & 0.826 & 0.752 & 0.787 & 75.25 \\ \hline \multirow{4}{*}{EfficientNetV2-Small} & Downy mildew & 0.874 & 0.809 & 0.840 & 80.85 \\  & Fresh leaf & 0.962 & 0.971 & 0.966 & 97.09 \\ \cline{1-1}  & Gray mold & 0.950 & 0.962 & 0.956 & 96.20 \\ \cline{1-1}  & Leaf scars & 0.854 & 0.871 & 0.862 & 87.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The precision, recall, F1 score, and accuracy of each class of five models. * Load the updated model weights back into the model. Then, conduct the model training process with our sunflower disease image dataset. The overall performance of five models in test dataset before and after incorporating transfer learning is given in Table 8. The average precision, recall, F1 score, and accuracy in Table 8 are the arithmetic mean of the precision, recall, F1 score, and accuracy obtained for each class of images in the test set. According to the results given in Table 8, after incorporating transfer learning, EfficientNetV2-Small achieved the highest test accuracy of 99.20% among the five models. MnasNet-A1 achieved the lowest classification accuracy of 94.96%. Compared with the accuracy before incorporating transfer learning, the accuracies of these five models were improved by 14.2%, 20%, 7.2%, 15.2%, and 10%.