esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design of the Platform
### Design of Machine Learning Models  
The core functionality of this platform is to diagnose diseases by analyzing CECT scans, and the platform relies on several types of ML models for diagnosing diseases. A set of six core ML models has been identified through a comparative analysis of representative standards for analyzing CECT scans.  
* Organ Segmentation Model for segmenting a target organ based on CECT slices.
* Lesion Segmentation Model for segmenting lesions based on CECT slices.
* Tumor Segmentation Model for segmenting tumors based on lesions.
* Image Feature Classifier for classifying various image features.
* Disease Type Classifier for classifying diseases from the identified tumors and their medical features.
* Disease Stage Classifier for classifying a stage for the identified diseases.  
A challenge in managing ML models for this platform is to handle the high variability of ML algorithms for various diseases and the multiple versions of an ML model trained with different training sets. Therefore, the platform should be designed to support the high configurability of training ML models with different algorithms and binding ML models on-the-fly.  
Our approach to meeting this requirement is to use ML management within the main program using the Bridge Design Pattern. This pattern facilitates system architecture by decoupling the abstraction part of the system from its implementation part. The design of ML management is shown in Figure 8.  
The class diagram depicted in the figure shows two abstract classes: the _Segmentation Model_ and the _CNN Segmentation Algorithm_. Each abstract class is specialized into multiple subclasses, which represent valid alternatives at their respective variation points. On the left side of the Bridge, there is a class hierarchy that includes various segmentation models tailored for specific segmentation targets such as Organ, Lesion, and Tumor. Conversely, the right side of the Bridge shows a class hierarchy for a range of CNN-based segmentation algorithms.  
This structure effectively demonstrates the decoupling of abstraction (segmentation models) from implementation (segmentation algorithms). This separation allows for the independent evolution of segmentation models and the underlying computational segmentation algorithms.
## 3 Results: Method Development, Implementation, and Testing of CADA Framework
### Method Development
#### 3.1.2 Case-Based Reasoning (CBR)  
Reusing existing knowledge and reasoning is an efficient way to enhance the design quality. A structured and context-rich knowledge base is essential [58; 59]. Our selected data layer, SN, covers this requirement. The core component of CBR is called case memory (CM), which is a database that stores all structured information and problem-solving relations. How the CM organizes its internal data can differ from domain to domain. [PERSON] and [PERSON] [58], early adopters of CBR, describe the Generalized Episode (GE) method. It is proven to apply to generalized and case-specific knowledge as it works in a simplified way of human reasoning and learning.  
Every GE has three components, namely norms, indices, and cases. Norms hold general information as the root node of an information cluster to characterize the following content. Indices build sub-nodes of a Norm in a more specific description (Figure 3). An index consists of two parts, the index name and index value, which can also be considered a search term and response (e.g., hair color = brown). These key-value pairs act as a condition to distinguish between different Cases. The Case Node keeps the information needed to form the problem solution statements. If different indices point to the same case, a new Layer is introduced as a new Norm holding its indices and cases [46; 58; 59].  
The goal of creating a database that can support future problems is widely known in problem-solving processes in production. An operator is asked to keep cause-effect descriptions as detailed as possible, with remarks on what best describes the details of the individual combination (Figure 4). This dataset can then be fed to train a Bayesian network that can predict actions for similar problems [60]. The same method could be applied to design problems, where problem-versus-solution statements train a model.  
Figure 3: Case memory working principle.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 6 Simulation--Basic Topologies
### Influence of Background Noise on Channel Capacity  
The aim of the investigation of the influence of white noise on channel capacity was to find the level of the white noise for particular conditions, e.g., laboratory condition (best-case), on-field good condition, and on-field very noisy condition (worst-case noise scenario).  
The range of measured background noise varies in literature [55; 56]. In a previous study [8], the value of \(-\)80 dBm/Hz was considered for simulation.  
Average spectral noise power density (PSD) for high voltage lines measured in a previous study [57] was around \(-\)105 dBm/Hz. For low voltage lines, the PSD is slightly lower and it is approximately \(-\)120 dBm/Hz.  
According to a previous study [58], for the underground high voltage lines, the average PSD is around \(-\)135 dBm/Hz and for the overhead high voltage line it is around 105 dBm/Hz. Low voltage line simulation in another study [54] was made with a noise floor of \(10^{-9}\) W. In another study [59], noise with PSD equal to \(-\)105 dBm/Hz, representing the average background noise spectral density, was considered. Noise with PSD equal to \(-\)130 dBm/Hz, representing the best-case noise scenario, was introduced in another study [60].  
Therefore, for the simulation the uniform average background noise PSD was considered as:  
* \(-\)90 dBm/Hz for ideal conditions (laboratory condition and on-field good condition).
* \(-\)70 dBm/Hz for on-field very noisy condition (worst-case noise scenario).  
To see the impact of the background noise on the throughput, the following scenario was considered: constant length between transmitter and receiver of 10 m, point-to-point connection without branches and cable NAYY150 SE.  
The throughput for \(-\)90 dBm/Hz for ideal conditions was 90.45 Mbps.  
The throughput for \(-\)70 dBm/Hz for on-field very noisy conditions was 43.09 Mbps.
## 4 Conclusions  
In this study, three different nanomaterials, iron oxide, aluminum oxide, and Oltu clay, were added to kaolin, and the permeability and heavy metal removal rates of kaolin samples passed through leachate were determined. On the basis of this study, the following results were obtained:  
1. The concentration of heavy metals in the leachate obtained from the solid waste landfill was determined to be Fe > Zn > Cu > Mn > Pb, from high to low.
2. Nano-sized iron oxide, aluminum oxide, and Oltu clay caused the clogging of the large-sized pores of the kaolin and reduced the leachate permeability of the kaolin. The permeability decreased with increasing amounts of all nanomaterials in kaolin. In terms of reducing the permeability of kaolin, Oltu clay with the smallest size and high surface area exhibited the best performance, followed by aluminum oxide and iron oxide, respectively. The permeabilities of 5% Oltu clay, 5% aluminum oxide, and 5% iron oxide added kaolin samples decreased by 96.89%, 81.26%, and 63.36%, respectively, compared to the permeability of kaolin without nanomaterial additives.
3. All of the kaolin samples containing nanomaterials exhibited a higher heavy metal removal rate over the 90-day measurement period than the undoped kaolin. The removal rate of heavy metals increased with increasing amounts of nanomaterials in the kaolin. While the highest removal rate of heavy metals (lead, copper, zinc, manganese, and iron) of kaolin over 90 days varied between 27 and 87%, for 5% Oltu-clay-added kaolin, this value varied between 41% and 93%. The highest heavy metal removal rates were obtained in kaolin + 5% Oltu clay sample, since Oltu clay has the highest surface area.  
This study is promising in terms of reducing the permeability of solid waste leachate with high pollutant content by adding nanomaterials to clayey soils used in landfills and preventing the risk of reaching groundwater by keeping heavy metals in the soil structure more. This study reveals that if the permeability of the kaolin clay used in landfills does not  
Figure 8: Lead removal rate of kaolin and kaolin/nanomaterial samples.  
meet the desired standards, the kaolin clay will mix with small amounts of nanomaterials (iron oxide, aluminum oxide, and Oltu clay), which will contribute to a significant reduction in permeability and increase the retention rate of heavy metals.  
Conceptualization, M.S.O.; methodology, M.S.O., S.A.; validation, M.S.O.; formal analysis, M.S.O., S.A.; investigation, S.A.; resources, M.S.O., S.A.; data curation, M.S.O., S.A.; writing--original draft preparation, M.S.O., S.A.; writing--review and editing, M.S.O., S.A.; visualization, M.S.O., S.A.; supervision, M.S.O.; project administration, M.S.O.; funding acquisition, M.S.O. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Research Fund of Yildiz Technical University, grant number FBA-2017-3158.  
Not applicable.  
The authors declare no conflict of interest.
# Tokens Shuffling Approach for Privacy, Security, and Reliability in IoHT under a Pandemic
## 3 Proposed Approach (TSA)  
The main idea of the TSA approach is to rely on the local storage of data in everyday situations. Additionally, it distributes the main service stages across multiple service providers. It enables the collaboration between users to protect their privacy and mislead the service provider. It deploys fog nodes to facilitate collaboration and boost anonymity. It uses encryption and blockchain to improve security and reliability. The proposed approach depends on several stages to preserve the privacy, security, and reliability of users' data while ensuring that it does not affect the quality of the leading service.
## 6 Analysis of the eLPRT Protocol
### Transmission when the Beacon is Lost  
The remaining results in this section take into account the occurrence of burst errors in the channel, through the use of the Gilbert-Elliot model, with the parameters presented in Section 4.  
Figure 6: Delivery ratio of eLPRT and GTS, with \(BER_{bad}=10^{-2}\) and without retransmissions.  
Figure 6 presents the delivery ratio as a function of the number of nodes in two cases. The first case, representing the normal functioning of the GTS scheme, considers that a node does not transmit its data packet when it misses the beacon at the beginning of the superframe. The second case uses the reallocation mechanism of the eLPRT protocol, enabling a node to transmit the data regardless of the reception of the beacon. In both cases, data packets are not retransmitted when affected by channel errors.  
Given the packet lengths considered in the evaluation scenario, in the bad state (\(BER_{bad}=10^{-2}\)), almost all (97.5%) data packets are corrupted by errors, while 76.5% of the beacons are affected. Since the delivery ratio of the eLPRT protocol depends only on the correct transmission of the data packet, and considering that the channel remains in the bad state 10% of the time, on average, the delivery ratio is slightly higher than 90% and independent from the number of nodes. The GTS scheme requires the correct transmission of both the beacon and the data packet, and therefore the delivery ratio is lower (around 84.5%).
# Incorporating External Knowledge Reasoning for Vision-and-Language Navigation with Assistant's Help
###### Abstract
In this paper, we propose to combine the power of Transformer and external knowledge to improve the agent's performance in VNLA. Firstly, inspired by [5], we design a memory-based Transformer with variable-length input as the policy module for VNLA, where the visual and linguistic clues constitute a scene memory token \(m_{t}\) at each time step \(t\). The token \(m_{t}\) is stored in the memory bank \(M\) ordered by the time sequence, which allows explicitly modeling history information, and the Transformer architecture naturally accommodates variable-length memory inputs to predict the next action. The information clues are effectively extracted from history tokens for the current decision-making of the agent through a learnable multi-layer attention. Secondly, we bring external commonsense knowledge from ConceptNet [6] into the VNLA task for comprehensive room- and object-entity reasoning from visual and textual information before decision-making. Thirdly, we apply a simulated human assistant in the environment, which can provide direct intervention assistance when requested, so as to realize \"Human-on-the-Loop\" indirectly. This is also the other form of external knowledge introduction. Last but not least, we take 360\({}^{\circ}\) panoramic RGB images of the agent in the horizontal direction as the visual input, which allows the agent to have a wider field of view for more precise reasoning and more accurate navigation. Specifically, we present Attention-based Knowledge-enabled Cross-modality Reasoning with Assistant's Help (AKCR-AH), a novel approach for incorporating commonsense knowledge from ConceptNet with simulated human assistance (knowledge) to help the agent find the target correctly. For the knowledge-enabled module, we perform room-entity and object-entity reasoning by learning the room-to-room correlations and internal-external parallel knowledge graph, respectively. We also consider the direct intervention of the simulated human assistant as a way of human knowledge involvement. For the attention-based module, we mainly design Multimodal Awareness and Internal-External Knowledge Fusion attention mechanisms, where the Multimodal Awareness attention mechanism having two branches to explicitly recognize rooms and objects from instruction and visual input separately, so as to bridge the cross-modal semantic gap between them. Integrating the above two will significantly improve the navigation performance of the agent and narrow the disparity between AI and humans in real life. Figure 1 illustrates a brief example of the AKCR-AH navigation process. We conducted experiments on the ASKNAV dataset [4] for the VNLA task, and the final results show that AKCR-AH outperforms previous baseline methods. Besides, extensive ablation studies verify the contribution and parameter selection of each sub-component of our method.
## 1 Introduction
Analyzing the modulation period of the spectral interference fringes, spectral interferometry calculates thickness, which has the advantage of being simple, nondestructive, and cost-effective [28; 29; 30]. Although its film-measurable thickness range is limited by the spectral bandwidth and resolution in principle, commercial spectrometers can be used to measure films in the thickness range of submicron to submillimeter. The thickness measurement accuracy of transparent films is influenced by system parameters and calculation methods. The major film thickness calculation methods developed using the interference spectrum include the extremum method [31], envelope method [32], full-spectrum fitting method [33], and frequency analysis [34]. The extremum method searches for adjacent spectral peaks corresponding to wavelengths and is easily affected by the accuracy of the curve fitting. The envelope method and the full-spectrum fitting method depend greatly on the reflectivity model, rendering them susceptible to non-ideal signals with noise and distortion of baselines in real-life measurements. The frequency analysis method calculates the film thickness by extracting the phase information from the high-frequency section of the spectral signal, thereby filtering out the influence of the light source and ambient light. This method relies less on the reflectivity model and can obtain thickness with sub-micro accuracy through a relatively cumbersome calculation process. Benefited by a simple configuration, spectral interferometry poses the potential for industrial thickness measurement of transparent films. However, limited by accuracy, speed, and a single-point measurement process, the existing technique is still incapable of large-area thickness measurement of transparent films during manufacturing. In this study, a multichannel spectral interference sensor was designed for measuring large-area and multi-layer thicknesses of transparent films. Linear 1-to-4 fan-out fiber optic bundles were used to connect multiple probes to the imaging spectrometer. The probes detected the spectral interference signal of transparent films at a vertical angle and could be arranged according to the actual location. The film thickness was calculated by fitting the peak of the power spectral density estimate for the spectral interference signal in the wavenumber domain. To verify the effectiveness and accuracy of the proposed method, two standard film samples of different thicknesses and varieties were employed for testing. The extremum method and the Fourier frequency transform method were compared with the proposed method. In addition, experiments on polyimide films demonstrated the real-time multilayer measurement and thickness imaging capabilities of the proposed method, which supported its potential application in industrial inspection.
# Security Framework for IoT Based Real-Time Health Applications
## 1 Introduction
### Health Care in IoT  
The healthcare industry has been transformed by the advent of IoT. Many wearable devices have been built and are being used in healthcare to monitor the patient's status in a real-time environment. In the domain of healthcare, sensors generate the data in bulk. It is quite hard to handle this type of data as it requires confidentiality, integrity, and authenticity of every single bit. Although these devices support health monitoring, the data that is being sensed and conveyed to the cloud require greater protection and security.  
Security is the primary concern of this era. From home privacy to health safety, everything demands security and protection. It is a crucial part of our lives. Indeed, people are more conscious about their personal data privacy, whether it is regarding money, vehicles, or health [17]. They do not want to discuss things that are not necessary to share with a third person. Confidentiality, integrity and availability are the primary requirementsof data security. Confidentiality ensures that the machine or network must be accessed by the authorized entity, whereas integrity ensures that only the legal and authorized user can modify the data with their permission. Furthermore, the availability requirement means that the data should be available for the legal entity at the time of need without any interruptions and limitations [18].
## 2 Methodology
### Corrosion Level Classification Network  
Based on the YOLOv10-vit network, the YOLOv10-vit-cls network is constructed for corrosion level classification. This network is responsible for classifying the corrosion levels. The steps for constructing the network are as follows:  
(1) On the basis of the constructed YOLOv10-vit, remove the Neck and Detect Head parts.  
(2) Remove the SPPF in the 11 th layer and the polarized self-attention (PSA) in the 12 th layer of the Backbone.  
(3) Add a Classification Head after the C2f in the 10 th layer of the Backbone.  
This results in the corrosion level classification network, YOLOv10-vit-cls, which is capable of determining the corrosion level. The modification in step 2 is mainly inspired by the Backbone structure in YOLOv8. A version of the network that retains the SPPF and PAS structures (YOLOv10n-SP-cls) is also considered, and comparisons are made in the later experimental section. In step 3, the proposed classification head structure is shown in Figure 4, where \(b\) represents the number of input feature maps, i.e., the hyperparameter batch size. The Classification Head converts the 2D feature maps extracted by the backbone  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Replacement** & **mAP50** & **FPS** & **Observations** \\
**Layer** & 0.605 & 13.34 &
\begin{tabular}{c} Significantly increased training cost due to \\ early feature map replacement. \\ Accuracy similar to 8 th layer but lower \\ FPS and higher computational cost. \\ Optimal balance between accuracy and \\ real-time performance. Smaller feature \\ maps reduced computational cost. \\ Limited spatial feature capture due to \\ small feature map sizes in deeper layers. \\ \end{tabular} \\
**Replacement Layer** & 0.693 & 23.36 &
\begin{tabular}{c} Optimal balance between accuracy and \\ real-time performance. Smaller feature \\ maps reduced computational cost. \\ Limited spatial feature capture due to \\ small feature map sizes in deeper layers. \\ \end{tabular} \\
**Replacement Layer** & 0.  
into 1D by pooling, flattening, and fully connecting operations. Finally, the Softmax function is used to obtain the maximum value in the 1D feature map, which corresponds to the corrosion level classification result.  
For training YOLOv10-vit-cls, a dataset was created using real-world images from actual field scenes. The images containing corrosion were categorized into four corrosion levels (Level A, Level B, Level C, and Level D) to build a classification dataset for water conservancy metal structure corrosion. This classification dataset does not require complex annotations; the images are simply categorized into the appropriate class based on their corrosion level. YOLOv10-vit-cls learns to recognize the respective corrosion levels from these classes. This approach is much easier than manually annotating a multi-class corrosion detection dataset, as it avoids the errors that can arise from human fatigue and subjective differences in annotations.