esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Geographically Weighted Regression (GWR)  
Geographically Weighted Regression (GWR) provides a local model for fitting a regression equation to every feature in the dataset. GWR incorporates the independent variables falling within the bandwidth identified based on the chosen Kernel type, which was the k-neighbours in this case (identical to the one used for the LOF model as explained later). GWR is a supervised machine learning method that allows the fitted regression model to vary spatially considering adjacency or local variations. A major parameter for the GWR is the Kernel function, which is used to set the distance between a point and its closest neighbour. In this study, the Gaussian Kernel function was used for its known robustness where the bandwidth corresponds to the standard deviation of the function. To run the GWR, a 70:30 data splitting was performed between training and testing. This was carried out using the sklearn.model_selection.train_test_split function in Python Version 3.11.
## 1 Introduction
### Similar Methods and Constrains on Applicability  
In general, categories in root-cause finding are hard to be defined, but if one borrows the terminology from Intrusion Detection Alarms [25], they can claim that there are three major categories: anomaly detection, correlation, and clustering. Specific examples of all three in decision making are given below, in the next paragraph. The alternative classifications of the Decision Support Systems that are given below permit this categorization; the first classification [26] regards: (i) Industry specific packages, (ii) Statistical or numerical algorithms, (iii) Workflow Applications, (iv) Enterprise systems, (v) Intelligence, (vi) Design, (vii) Choice, and (viii) Review. Using another criterion, the second classification [27] concerns: (a) communications driven, (b) data driven, (c) document driven, (d) knowledge driven, and (e) model driven.  
There are several general purpose methods that are relevant and are mentioned here to achieve root-cause finding. Root Cause Analysis is a quite good set of techniques, however, it remains on the descriptive empirical strategy level [28; 29]. Moreover, scorecards are quite descriptive and empirical, while the Analytical Hierarchical Process (AHP) requires criteria definition [30]. Finally, Factor Analysis, requires a specific kind of manipulation/modelling due to its stochastic character [31].  
Regarding specific applications in manufacturing-related decision making, usually finding that the root-cause has to be addressed through identifying a defect in the production. Defects can refer to either product unwanted characteristics, or resources' unwanted behaviour. Methods that have beenpreviously used--regardless of the application--are Case Based Reasoning [32], pattern recognition [33; 34], Analysis of Variance (ANOVA) [35], neural networks [36], Hypothesis testing [37], Time Series [38], and many others. However, none of these methods is quick enough to give the results from a deterministic point of view and without using previous measurements for training. Additionally, they are quite focused in application terms, which means that they cannot be used without re-calibration to a different set of KPIs. On the other hand, traditional Statistical Process Control (SPC) does not offer solutions without context, meaning the combination of the application and method [31].
# Compensating Uncertainties in Force Sensing for Robotic-Assisted Palpation
## 2 Robotic palpation with Force Based Approach
### Force Based Robotic palpation  
The cancerous nodule in the soft tissue can be palpated by a surgeon during the open surgery, mainly because the tumor is stiffer than the normal surrounding tissue. Researchers in [33] have conducted indentation tests of human and porcine abdominal organs in vivo and ex vivo, and proved that healthy tissues demonstrate similar compliance, but a noticeable difference can be observed for the abnormal tissue surrounded by healthy tissue. When the robot is controlled to palpate the tissue with a force sensor attached to the end, the difference of the force response can be observed when the robot palpates different parts of the tissue at the same indentation depth. It can further be concluded that the one with a large force response is more likely to be tumor. A brief schematic of robotic palpation with a force sensor and probe is illustrated in Figure 1. The force sensor is used to measure the interaction force between the probe and the soft tissue. During the palpation, the probe will be moved to contact the soft tissue, and the real-time force response will be measured by the sensor to describe the applied force during the palpation. When the indentation depths are different, the simple force response is not sufficient enough to explain the location of a tumor, and it is therefore necessary to quantitatively evaluate the mechanical property of the soft tissue for detailed analysis.
# Iterative Decoding of LDPC-Based Product Codes and FPGA-Based Performance Evaluation
## 2 Residual Error Characteristics of Multiple Consecutive LDPC Codes
### The Product Codes with LDPC Codes  
In this paper, we consider a type of product codes using very high rate block code \(C_{1}(n_{1},k_{1})\) and LDPC code \(C_{2}(n_{2},k_{2})\)[23]. Different from traditional product codes, which usually use short codes as component codes, the inner and outer codes of this type of product codes are designed with longer code lengths, and the rate of the outer code is very high with limited error correction capability. This type of product codes can be decoded with low complexity.  
The structure of the product code is illustrated in Figure 1. First, the information bits are arranged into \(k_{1}\) rows and \(k_{2}\) columns. Second, the \(k_{1}\) bits of each column are encoded into an outer codeword, and \(R_{2}\) outer codewords form the matrix of \(n_{1}\) rows and \(k_{2}\) columns. Third, \(k_{2}\) bits in each row are encoded into an LDPC codeword, and all the \(n_{1}\) LDPC codewords form the final data block with the dimension of \(n_{1}\times n_{2}\). Therefore, the code rate of the product code is \(R=R_{1}R_{2}=(k_{1}k_{2})/(n_{1}n_{2})\), where \(R_{1}\) is the code rate of the outer code, namely \(R_{1}=k_{1}/n_{1}\) close to 1, and \(R_{2}\) is the code rate of the LDPC code, namely \(R_{2}=k_{2}/n_{2}\). In this paper, we choose Hamming codes, which can only correct a single error, in order to achieve the highest code rate for outer codes. Therefore, the overall code rate \(R\) of the product code is very close to the code rate \(R_{2}\) of the LDPC code.
## 3 Experimental Results and Analysis
### Experimental Analysis
#### 3.3.3. Backbone Network  
In order to select a suitable backbone network, we performed ablation experiments using Resnet50, EfficientNetB0, and EfficientNetB1. We set the number of translation transformers and orientation transformers to 6, respectively.  
As shown in Table 5, the two EfficientNet variants achieve better performance than the ResNet50 backbone. Compared with EfficientNetB0 and EfficientNetB1, the results are roughly the same, and EfficientNetB0, with fewer parameters, is finally selected.
## 4 Lightweight and Efficient Convolutional Neural Network (LE-CNN)  
LE-CNN is a model that was built on the foundation of the CNN due to its ability to extract local and global features from the input images, such as edges, colors, shapes, and other features that are important to solving the target problem and that can happen through the convolutional layers [18]. Through the pooling layers, the different relationships between the image objects and the surrounding background can be learned and handled [19]. Also, the CNN model has the ability to deal with complex relationships between image features through the activation functions [20].  
The LE-CNN was trained and tested using the ArTS dataset with Arabic and English traffic signs. The proposed model, as shown in Figure 3, consists of 9 layers, three of which are convolutional, three are dropout layers, two are pooling layers, and one is a flatten layer.  
* Input Layer: Initial information entered into the model, where the pre-processed image is fed into the model. The input image is a three-channel image with input shape (32.32.3) that was captured by a camera on the road.
* Convolutional Layers: The core of the CNN model. It uses learnable filters such as (64 & 32) and small kernel sizes (such as \(5\times 5\) & \(3\times 3\)) to extract features like edges, lines, or shapes. These features are the basics for detecting and identifying the shapes and the symbols of the traffic signs. The output of a convolutional layer \(Z\) can be calculated as follows: \[Z=f\Big{(}\sum_{i=1}^{n}(X*W_{i})+b\Big{)}\] (1) where \(*\) refers to the convolution operation, \(n\) is the number of filters, \(f\) is the activation function used (ReLU), \(X\) is the input image, and \(W\) is the filter.
* Max Pooling Layers: Layers that are responsible for down-sampling the features maps by taking the maximum value for every small grid to reduce size and computational cost.
* Drop Out Layer: This layer is responsible for deactivating a certain percentage, such as 0.25, 0.25, and 0.5 (p), of neurons during training to prevent overfitting.
* Flatten Layer: This layer reshapes the output that comes from the previous layer from multi-dimensional into a one-dimensional vector to be then used with the dense layer.
* Output Layer: Typically, the ultimate layer is utilized to classify activity signs by anticipating the likelihood of them having a place in each conceivable activity sign lesson utilizing the softmax work.  
Figure 2: Samples of the traffic signs with different augmentation techniques.
# Beyond Static Estimates: Dynamic Simulation of Fire-Evacuation Interaction in Historical Districts
## 4 Discussions
### Bridging Dynamic Interactions Between Evacuation and Building Environment Changes in Disasters
A key concern is that large numbers of tourists, unfamiliar with the layout of the district, tend to cluster along major arterial roads [16], leading to significant congestion, while the densely packed secondary roads within the district remain underutilized. Additionally, this imbalance is further exacerbated by potential psychological and social behavioral factors. For example, crowd density and panic levels have been shown to reduce evacuation efficiency in panic dynamics models [55], while stronger herd behavior has similarly been linked to lower evacuation efficiency in cruise ship emergencies [56]. To address these challenges, we propose implementing a \"coordinated evacuation\" strategy tailored to historical districts. This strategy should prioritize the identification and guidance of alternative evacuation routes to promote balanced load distribution. Given the spatial constraints that preclude road widening, the focus should shift to optimizing the use of existing infrastructure. Appropriate signage and dynamic path guidance systems can help alleviate potential traffic pressure. Due to the high road network density in historical districts, evacuation flows can theoretically be distributed across multiple routes. Therefore, rather than relying solely on physical expansion, we advocate for multi-route guidance as a viable alternative. This approach can help mitigate bottlenecks while accommodating varying evacuation demands. Moreover, future research should incorporate psychological effects, information dissemination mechanisms, and route accessibility to construct a more comprehensive evacuation framework specific to historical districts. It is also necessary to examine variations in evacuation patterns between day and night to capture temporal shifts in demand [60].
# An Approach to Designing Critical Railway Voice Communication
## 9 Conclusions  
Critical voice communications in railways are required between train drivers, controllers, shunting teams, and other railway staff members. These types of communications must be authorized based on the user's functional identity, context, and location and assured with a guaranteed quality of service. Arbitration needs to be applied for simultaneous communications competing for the same user's attention. A user involved in a call should be able to invite another user to join the ongoing call. Multiuser communications also require talker control. These functions can be implemented as reusable service bricks, which can be configured with application-specific data.  
This paper presents an approach to designing FRMCS critical voice communication applications that follows the principles of the REST architectural style. Common functionalities required by FRMCS applications, such as FRMCS data management and policy management, are identified and designed as services. Functions, such as role management and presence, assured voice communications, invite-a-user messaging, and multiuser talker control, are also designed as RESTful services. The relevant resources for each service are defined and their unique identifiers are organized in a tree structure. Use cases illustrate the use of the synthesized service APIs in critical voice applications. The common application logic is modeled using call-state models, which present the application and network views. The models are formally described and it is mathematically proven that they exhibit equivalent behavior. The service API's performance is studied through emulation to evaluate the induced latency. The average latency is about 2 milliseconds, which is acceptable for mission-critical services.  
The specification of the synthesized services requires data model definition, which takes the form of a REST API and defines the necessary information exchanged in communications. In future work, we plan to refine the data types and structures used in the APIs of the synthesized services.  
The synthesized APIs are easy to implement as they rely on HTTP and provide a uniform interface. Because REST APIs are stateless at the server side, each request can be processed independently. Resource data can be represented in different formats but the use of the JSON format makes the data more compact. Additionally, REST decouples the client from the server, providing another level of flexibility.  
**Author Contributions:** [PERSON] contributed to the software and methodology. [PERSON] contributed to the conceptualization, writing--original draft preparation, and updates. [PERSON] contributed to the formal analysis and verification. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Bulgarian National Science Fund grant number KP-06-H37/33.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Voter Authentication Using Enhanced ResNet50 for Facial Recognition
## 2 Related Works  
Many studies have investigated anti-cheating mechanisms in online voting systems to combat voter fraud. For example, [PERSON] et al. proposed a system that combines face detection and OTP-based authentication to prevent cheating kumar2017 secure. Similarly, [PERSON] et al. presented a smart voting system that uses facial recognition technology to ensure secure and accurate voting processes [20]. [PERSON] et al. explored the use of facial recognition and machine learning algorithms to detect and prevent cheating in online voting systems [21]. Facial recognition techniques have also been studied in depth. Traditional methods rely on manual extraction of facial features using techniques such as Local Binary Patterns (LBP) and Histogram of Oriented Gradient (HOG) for classification [22]. However, these approaches are difficult to implement for large datasets. Artificial neural networks have recently been used to automatically extract features from facial images using substantial amounts of data. For example, [PERSON] et al. used multitask cascaded convolutional networks (MTCNNs) to extract features from facial images. Deep convolutional neural networks (CNNs) have achieved State-of-the-Art performance in a variety of image recognition tasks. ResNet50, a variant of the ResNet family (Residual Network), has been widely used for facial recognition. [PERSON] et al. employed a deep learning approach using transfer learning with ResNet50, fine-tuning, data augmentation, and a softmax classifier for face and gender recognition, achieving a classification accuracy of 99.33% for face recognition and 93% for gender classification [19]. [PERSON] et al. used an 8-layer AlexNet to manually filter a dataset of 2.6 million images, ultimately producing 982,803 usable images [17]. [PERSON] et al. improved the performance of ResNet50 for facial recognition by incorporating ArcFace loss, achieving an impressive 99.53% accuracy in the LFW dataset [23]. [PERSON] et al. used a 22-layer GoogleNet, incorporating a triple loss function to extract facial image features [18]. However, their proposed vote security system has limitations in handling cases of identical voters or false positives. To address this, we propose a new voter authentication system that leverages the CutMix algorithm for data augmentation, convolutional neural networks (CNNs), and ArcFace loss to minimize false positives in facial recognition. Our approach aims to improve the efficiency of the system in solving the problems associated with multiple voting. To improve the efficiency of the model, we incorporated an existing dataset (LFW) and our image approach performed an election simulation which gave us better accuracy. To validate our system, we performed an election simulation with a limited number of images captured by our voting system over four months (July-November 2021). This complete dataset will be used at three polling stations--P1, P2 and P3.
# Smart Clothing Framework for Health Monitoring Applications
## 3 Proposed Framework for Smart Clothing (Leggings)
### Conducting Polymers  
Conducting polymer materials are used for producing smart clothes due to their great output power, high reliability, low-cost characteristics and adaptability in terms of structural design and material choices [135; 136]. [PERSON] et al. [18] presented a strategy to design and fabricate fibre/fabric-based triboelectric nanogenerators (TENGs) that were knitted from conductive silver-plated nylon threads using a commercially available knitting method. A power-generating polyethylene terephthalate (PET) fabric is developed in [137].  
Conductive fabric was used as an electrode, attaching onto the back of a different fabric, which can be cut into desired sizes. Then, the fabric was integrated with the garment for harvesting the mechanical energy from human movement.
# Direct Mobile Coaching as a Paradigm for the Creation of Mobile Feedback Systems
## 7 Conclusions  
In this paper, we presented the model Direct Mobile Coaching as an extension of Mobile Coaching. We discussed the concepts and elements of DMC and presented several example feedback systems and their characteristics. The model provides researchers and practitioners in the sport science domain with a tool that allows them to model most of the types of modern feedback systems. A system modelled using DMC will consist of a mobile unit (e.g., a smartphone), sensors connected to this unit and server components (backend and frontend). Concurrent feedback can be provided using one or both of the respective components: a \"controller\", which runs directly on the device, or an \"AI module\" running on the server. It is also possible to model communications between controllers and an AI module, which allows multi-agent systems (e.g., athletes competing in real-time against each other) to be created with DMC. Furthermore, terminal feedback can be provided through one or more \"post-processing modules\". Data recorded during an activity are sent to the backend server, where it is stored in a (relational) database. This also enables the provisioning of various charts and reports via a web-interface/frontend.  
Furthermore, we showed that various types of systems in the field of sport science can be modelled using Direct Mobile Coaching and provided examples where DMC and PEGASOS have been used as part of scientific studies. From this, we conclude that DMC is a valuable concept for researchers and practitioners in the domain of feedback systems in sports.  
One limitation of DMC is its emphasis on sensor-based systems. This concerns, for example, feedback systems where data originates from a (proprietary) third-party platform, i.e., where data are not collected via the mobile client but, for example, originates from a third-party platform that does not offer access to live data. Due to these technical restrictions of such platforms, DMC cannot be used without modifications to model such systems when concurrent feedback is desired.  
Future work will include applications of DMC as part of scientific investigations of feedback in sports. Furthermore, the training of sport scientists in modelling feedback systems using DMC could be conducted. Investigations into the effectiveness and outcomes of teaching model-based abstraction using DMC to sport science students could provide insights into gaps in existing sport science curricula (c.f. [83]). Additionally, an investigation of PEGASOS, the framework implementing DMC should be conducted.  
Conceptualisation, M.D., S.O. and M.S.; software, M.D.; writing--original draft preparation, M.D.; writing--review and editing, M.D., S.O., M.S. and A.B. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Hochschuljubilaumsstiftung der Stadt Wien, grant number H-327152/2018.  
Open Access Funding by the University of Vienna. The authors declare no conflict of interest.