esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion  
Several modifications and extensions can be made in the future to further develop this work. Additional measurements can be made to determine the torque and frictional forces applied when screwing the Herbert screw into the bone. Performing measurements on other types of screws would also be interesting. Herbert headless screws can vary in size or in the thread pitch; there are also screws with variable thread pitches [14].  
In this work, solely porcine bones were used for the experiments. Further study could focus on the modification of the experiment in the sense of using human cadaverous metatarsal or even other bones; this is, however, ethically more problematic and costly. Bones from other animals (e.g., bovine, calf, mutton, etc.), as well as artificial composite bones (e.g., products by Sawbones) or wooden bones, could be used for the experimental solution.  
Performing an experiment focusing on other ways of loading the screw (such as bending or fatigue testing) could also be interesting. The effect of bending or major deformations (true stress or logarithmic strain) or elastic/non-linear foundation could also be introduced in the experiments, as well as computational solutions. This is likely to lead to an improvement in accuracy, but the nonlinear solution will be much more complicated, and the advantage of the original simple linear solution will be lost.  
We can also build on our extensive experience in the field of interaction between external/internal fixators with bones [26; 27; 28] (finite element method, deterministic approaches, stochastic approaches, and experiments).  
Aside from that, we can use the finite elements method (FEM) for comparing the results. Using the Mimics software, X-rays (i.e., computed tomography) of the bones can be used for creating a CAD and finite element model of the fifth metatarsal and assigning material properties to its individual parts. Figure 18 shows a distribution of the elasticity modulus in the fifth metatarsal, ranging from 93.5 to 18548.3 MPa [29].  
Figure 17: Comparison of methods.  
According to [29], simulation revealed that the critical equivalent von Mises stress can be detected at the notch of the screw; see Figures 19 and 20.  
Figure 19: Critical von Mises stress in a headless screw in the 5 th metatarsal.  
Figure 20: Detail of critical stress in a headless screw in the 5 th metatarsal.  
The calculation using the FEM method gives results similar to the solutions presented in this paper; nevertheless, it is not the main focus of this publication, just complementary information. A deeper look at the FEM solution, which is more complex and more difficult to relate to stochastic approaches, will be a subject of future publications. However, the author team feels that it is appropriate to mention the FEM method here, as well as the fact that FEM calculations have confirmed the facts obtained from other computational approaches and experiments performed in this study.
# Gesture Recognition of Sign Language Alphabet Using a Magnetic Positioning System
## 2 Materials and Methods
### Machine Learning Algorithm
#### 2.3.4 Models  
We considered three different models that use different input data but share the same logic, which is illustrated in Figure 11. Each model is a SVM classifier with a Gaussian kernel, as described in Section 2.3.1, trained on 90% of the filtered dataset and tested on the remaining 10%. The models are all trained on landmark data extracted by MediaPipe from the images of the dataset described in Section 2.3.3. The models are then validated using data from the magnetic positioning system.  
Figure 10: Hand landmarks (red points) extracted by MediaPipe Hands and corresponding coordinates.  
The models differ in the reference system used and in the number of points considered for each frame:  
* Only the fingertips, i.e., the points corresponding to labels 4, 8, 12, 16, 20 of Figure 10, are used as input. The reference system used is the one chosen by MediaPipe: the origin of the \(xy\) plane is placed at the top-left corner, while the origin of the \(z\) axis is placed at the wrist. The \(x\) and \(y\) are normalized to [0, 1] by dividing by image width and height, respectively, while \(z\) uses approximately the same scale as \(x\)[51]. The model is validated by using raw data extracted from six nodes of the magnetic positioning system. The hand kinematic model is not used. The additional node is the one placed at the wrist as it was necessary to determine the origin of the \(z\) axis.
* The coordinates of the 21 hand landmarks given by MediaPipe are used as the input. The reference system used is the same as Model A, but this time, the model is validated by using data from the magnetic positioning system that have been processed using the kinematic hand model described in Section 2.2. This allowed us to reconstruct the position of 21 points within the hand, while using only seven transmitting nodes in the MPS.
* The same features chosen for Model B are used, but a reference coordinate system aligned to the hand is considered. Specifically, the \(x\) versor has the same direction of the vector that connect the endpoints of themiddle metacarpal bone, the \(z\) versor is orthogonal to the plane in which metacarpal bones of the index finger and middle finger lie, and the \(y\) versor is the cross product between the \(x\) and \(z\) versor. The main advantage is that this model is also capable of recognizing gestures when the hand is rotated at an arbitrary orientation.
## 3 Results
### Apical Reach
Indirect ultrasonic vibration in ultrasonic cavities resulted in 40% apical access in the MTA group, 20% in the CIS group, and no apical access in the Biodentine group. For all groups, the MTA group had the highest rate of apical access, while the CIS group had the lowest rate of apical access (Table 5). In both cavity preparation methods, bond strength values were significantly higher where apical access could not be achieved (Table 6). \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Cavity**} & \multirow{2}{*}{**Placement**} & \multicolumn{3}{c}{**The Apical Access**} & \multirow{2}{*}{\(p\)} \\ \cline{3-4} \cline{6-7}  & & & **Yes** & & **No** & & \\ \cline{3-4} \cline{6-7}  & & & **n** & **\%** & **n** & **\%** & \\ \hline \multirow{4}{*}{CIS} & \multirow{2}{*}{TC} & MC & 5 & 50.0 & 5 & 50.0 & \multirow{4}{*}{1.000} \\ \cline{3-4} \cline{6-7}  & & IUV & 5 & 50.0 & 5 & 50.0 & \multirow{4}{*}{1.000} \\ \cline{3-4} \cline{6-7}  & & MC & 2 & 20.0 & 8 & 80.0 & \multirow{4}{*}{1.000} \\ \cline{3-4} \cline{6-7}  & & IUV & 2 & 20.0 & 8 & 80.0 & \multirow{4}{*}{1.000} \\ \hline \multirow{4}{*}{MTA} & \multirow{2}{*}{TC} & MC & 10 & 100.0 & 0 & 0.0 & \multirow{4}{*}{-} \\ \cline{3-4} \cline{6-7}  & & IUV & 10 & 100.0 & 0 & 0.0 & \\ \cline{3-4} \cline{6-7}  & & IUV & 6 & 60.0 & 4 & 40.0 & \multirow{4}{*}{0.656} \\ \cline{3-4} \cline{6-7}  & & IUV & 4 & 40.0 & 6 & 60.0 & \\ \hline \multirow{4}{*}{BIO} & \multirow{2}{*}{TC} & MC & 10 & 100.0 & 0 & 0.0 & \multirow{4}{*}{0.211} \\ \cline{3-4} \cline{6-7}  & & IUV & 7 & 70.0 & 3 & 30.0 & \\ \cline{3-4} \cline{6-7}  & & MC & 0 & 0.0 & 10 & 100.0 & \\ \cline{3-4} \cline{6-7}  & & IUV & 0 & 0.0 & 10 & 100.0 & \\ \hline \hline \end{tabular}  
* CIS: Cam ionomer cement, MTA: Mineral trioxide aggregate, BIO: Biodentine, MC: Manual condensation, IUV: Indirect ultrasonic vibration, TC: Tungsten carbide cavity, U: Ultrasonic cavity.  
\end{table} 
Table 4: Apical access chi-square test according to placement method. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Placement**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Material**} & \multicolumn{3}{c}{**Apical Access**} & \multirow{2}{*}{\(p\)} \\ \cline{3-4} \cline{6-7}  & & & **Yes** & & **No** & & \\ \cline{3-4} \cline{6-7}  & & & **n** & **\%** & **n** & **\%** & \\ \hline \multirow{4}{*}{MC} & \multirow{2}{*}{TC} & CIS & 5 & 50.0 & 5 & 50.0 & \multirow{4}{*}{0.001 *} \\ \cline{3-4} \cline{6-7}  & & MTA & 10 & 100.0 & 0 & 0.0 & \\ \cline{3-4} \cline{6-7}  & & BIO & 10 & 100.0 & 0 & 0.0 & \\ \cline{3-4} \cline{6-7}  & & CIS & 2 & 20.0 & 8 & 80.0 & \\ \cline{3-4} \cline{6-7}  & & U & MTA & 6 & 60.0 & 4 & 40.0 & \\ \cline{3-4} \cline{6-7}  & & BIO & 0 & 0.0 & 10 & 100.0 & \\ \hline \hline \end{tabular}  
* CIS: Cam ionomer cement, MTA: Mineral trioxide aggregate, BIO: Biodentine, MC: Manual condensation, IUV: Indirect ultrasonic vibration, TC: Tungsten carbide cavity, U: Ultrasonic cavity.  
\end{table} 
Table 5: Apical access chi-square test according to materials.
# Metal Surface Defect Detection Using Modified YOLO
## 2 Related Work
### YOLOv3 Model  
YOLOv3 is a target detection model based on deep learning, provided by [PERSON] and [PERSON], which can achieve high reasoning speed on the premise of ensuring certain accuracy. Therefore, the YOLOv3 model has been selected for the experiments in this article.  
YOLOv3 essentially implements a deep convolutional network with a regression function. It predicts an objective score for each bounding box using logistic regression. As the first step of a deep convolutional network, image feature information is extracted. Compared with the two-stage detection algorithm in which the candidate box is used to extract features, the YOLOv3 algorithm directly conducts end-to-end training and reasoning for all areas of the entire image, which speeds up the operation speed and has a better distinguishing effect between foreground and background. In addition, this model fuses features of different scales, which leads to the fact that this algorithm is more conducive to identify small targets and suitable for defect detection. The second step of target detection is to classify the extracted features and divide the whole picture into several regions. If the center of the target to be detected falls within this region, the region will be classified by the classification model.  
Each network corresponds to a bounding box. Each bounding box consists of five predictions: x, y, w, h, and confidence. The (x, y) coordinates represent the upper left corner of the box relative to the bounds of the grid cell. The width and height are predicted relative to the whole image. Finally, the confidence prediction based on logistic regression will judge if there contains the target. A high confidence level suggests that there is a target in the bounding box, and then target classification will be done. The YOLOv3 network structure is shown in Figure 1.  
The YOLOv3 model has 53 convolutional layers and extracts target features at three convolutional layers using different scales. Furthermore, then, the features of these three scales are integrated to conduct target classification.
# A Comparative Analysis of Big Data Frameworks:
## 4 Big Data Processing Frameworks
### Samza
#### 4.4.1 Architecture  
Samza has a layered architecture that consists of three layers. Figure 8 depicts three layers of Samza architecture. A streaming layer which is responsible for providing replayable data source such as Apache Kafka, AWS Kinesis, or Azure EventHub. The execution layer which is responsible for scheduling and resource management, and processing layer which is responsible for data processing and flow management. The streaming layer and execution layer are pluggable components. Data streaming can be provided by any existing data sources. Similarly, cluster management and scheduling can be done by Apache YARN or Mesos. However, Samza has built-in support for Apache Kafka data streaming and Apache YARN for job execution. The execution model of a Samza Job is based on publish/subscribe task concept. It listens to a data stream from Kafka topic, processes the message when it arrives, and then sends its output to another stream. The data stream in Kafka consists of several partitions based on a key value pair. Samza tasks consume data streams and can run multiple tasks in parallel to consume all partitions of the stream in parallel. Samza tasks are executed in the YARN containers. YARN distributes containers on multiple nodes in the cluster and distributes tasks evenly among the containers. After execution the output can be sent to another stream for further processing. Unlike common stream processing systems, Samza is based on a decentralized model where there is no system level master to coordinate activities rather every job has a lightweight coordinator to manage it.  
Figure 8: The layered architecture of Samza.
# Efficient Edge Detection Method for Focused Images
## 1 Introduction  
Edge detection methods are very commonly used in many areas of image and video processing and computer vision. They are commonly used in object detection, classification, and recognition. This is because edges are the most important objects, from the Human Visual System point of view [1], that are present in images. There are many approaches to edge detection. Starting from the simple image filtering methods, which are extremely fast, through more advanced geometrical methods, which are used in shape representation, up to machine learning-based methods, especially deep convolutional neural networks, which need prior data training [2].  
The main problem of efficient edge detection follows from the fact that all edge detection methods work with a global assumption that the edge is defined as a step edge--a sharp change of image intensity. In fact, in the case of still images, we deal with edges of different levels of sharpness and smoothness. The most representative case is an image that is focused--it has a sharp foreground and smooth background. So, treating all edges in the same global way must be inefficient during edge detection of such an image. Thus, to overcome this problem, a method that works locally has to be applied, according to the known and very deep thought \"Think globally, act locally\". By working locally, such a method could adaptively adjust the definition of an edge depending on the local background. It means that in the sharp region, this method can detect sharp edges, while in smooth regions, it can detect smooth edges.  
In this paper, we address the problem of edge detection in focused images. In such a case, it is difficult to detect both kinds of edges simultaneously. The existing methods either do not detect smooth edges in the background or detect too much noise in the foreground. In this paper, we propose an approach that can detect all edges in focused images efficiently. The efficiency follows from the fact that the proposed method works locally and adaptively detects edges inside a sliding window. This method is based on the k-Means algorithmwell known in Machine Learning. The use of the k-Means algorithm locally gives a good balance between the speed of the pointwise methods and the efficiency of the CNN-based methods. To use the k-Means algorithm locally, we proposed the enhanced version of image filtering--we filter an image not pixel-by-pixel, but area-by-area. The proposed algorithm of edge detection was compared to the state-of-the-art methods taken from different classes of edge detection methods.  
This paper is organized in the following way. In Section 2, the related work is presented. In Section 3, the problem statement is posed. In Section 4, the new method is proposed. In Section 5, the experimental results of edge detection are presented. Finally, Section 6 concludes this paper.
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 3 Compliance Architecture
### Dynamic View and Information Flow
#### 3.4.2 Execution Phase  
In the execution phase the client calls the executeWorkflowQueue method of the extended execution entity. As soon as the request enters the execution pipeline, it is passed to the authorization handler where the verification of the signature takes place. For that, the handlerfetches the workflow descriptions of the queue's workflows from the execution entity and generates a WorkflowSignatureVerificationRequest also using the workflow parameters and the signature parsed from the signature header. The resulting request is sent to the CSP which then executes the following procedure in order to verify the workflow queue signature:  
1. Generate a Merkle tree from workflows contained in the verification request (see signing procedure).
2. Verify the root of the resulting hash tree against the signature received from authorization handler. Return true in case of success, false otherwise.  
This procedure ensures that each workflow instance in the queue and in addition also the order of workflows are verified. After the verification process has been finished, the CSP returns the result to the authorization handler. Again, the procedure above can be performed within the authorization handler itself in case a certificate-based public-key infrastructure is used.  
In case of a negative response the API returns an error code immediately without further processing the ExecuteWorkflowQueueRequest object. If the signature has been verified successfully, the request is passed to the execution controller which parses the request body and initiates the workflow queue execution at the underlying execution entity instance.
## 3 Results and Discussion
### Analysis of CIM's Potential for the Calculation of the Indicators
#### 3.2.41. The Indicator to Measure Access to Public Spaces  
This indicator promotes recommendations for assessing the availability and quality of existing or planned public spaces. This is developed by SBToolPT Urban, BREEAM C, and LEED ND, and addressed by ISO 37120, and the SDGs. Therefore, this indicator is recognized as a key social dimension of sustainability concern. The SBToolPT Urban Guide establishes three calculation parameters for this indicator. The first one is the Percentage of Open Public Spaces, which is determined by the ratio of the Area of Open Public Spaces and the Total Intervention Area. To be calculated using the model, the open areas must be identified and characterized, then a Dynamo routine is applied. The second parameter is the Availability of Public Spaces per Habitat Index, which is calculated by the ratio of the Area of Open Public Spaces and the Total number of inhabitants. Like the previous parameter, the model can calculate this one. It is necessary to add information regarding the total number of inhabitants to the Dynamo routine. The last parameter, the Quality of Public Spaces Index, is determined from a verification list. By using the model, the criteria from the list could be added and assessed.
## 6 Conclusions  
In summary, our study shows that with an appropriately adjusted cutoff threshold for class probability predictions, one can obtain operating points with both the precision and recall above 0.5 or with a true-positive rate of more than 0.9 and a false-positive rate of less than 0.2. The best performance was obtained using the gradient boosting and random forest algorithms. Using these algorithms, the best level of predictive performance with the area under the precision-recall curve is about 0.5 and the area under the ROC curve is about 0.92.  
The results presented in this article fill some gaps left by prior work and lay more solid grounds for future work. In particular, this extended study uses a broader set of state-of-the-art machine learning algorithms and more systematically evaluates the utility of different attribute subsets, corresponding to different properties of network paths and their components. For the best identified setups, the achieved level of the area under the precision-recall curve exceeds that reported previously [7]. Under severe class imbalance, this is the most reliable performance metric, and the obtained value of 0.5 indicates a more practically useful tradeoff between the capability to detect bad paths and the tendency to misdetect good paths as bad.  
Despite the progress our study represents, there remains space for further improvements in future work. Promising directions include examining the possibility of trained model transfer between different networks and enhancing network path descriptions by using additional attributes derived from physical channel properties.  
Encouraging results obtained with one-class classification algorithms [8; 45] suggest that it may be possible to combine binary and one-class classifiers into an even more successful hybrid model. Each of these future research lines would definitely benefit from more data, covering a wider range of network topologies and equipment and with a more comprehensive representation of unsuccessful path designs.  
Conceptualization, S.K., P.C. and S.S.; methodology, S.K. and P.C.; software, P.C. and P.P.; validation, S.K. and S.S.; formal analysis, S.K., P.C., and S.S.; investigation, S.K., P.C., and S.S.; data curation, S.K., P.C., and P.P.; writing--original draft preparation, S.K., P.C., and S.S.; writing--review and editing, S.K., P.C., and S.S.; visualization, S.K. and P.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Evaluation
### BER Analysis
#### 5.1.1 BER and Interference Patterns (Goal A)  
We set the transmit frequency to be 100 Hz and conducted the BER experiment under four signaling types:  
* **Type 1:** Only the transmit signal.
* **Type 2:** The transmit signal and ambient DC noise.
* **Type 3:** Transmit signal and Interference signal sending identical patterns in phase.
* **Type 4:** Transmit signal and Interference signal sending identical patterns at 180 deg out-of-phase.  
We report the BER from these experiments in Table 2. We can observe from the BER values from Case 1 that the error rates for the system are generally high when the desired signals and the interference are combined at the photodetector. We observe that the BER is practically low (for feasible data communication) when the pixels are selectively OPEN/CLOSE to allow only the desired signal, which has been achieved without major changes to the receiver. We can also observe from the BER values, the additive property of the receiver, where the BER is low when the interference signal is identical and of same phase (as the effective received signal amplitude is doubled) and high when the same interfering signal is out-of-phase.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Type 1**} & \multicolumn{2}{c}{**Type 2**} & \multicolumn{2}{c}{**Type 3**} & \multicolumn{2}{c}{**Type 4**} \\ \hline Case I & Case II & Case I & Case II & Case I & Case II & Case I & Case II \\ \hline \(5\times 10^{-4}\) & \(5\times 10^{-4}\) & \(4.9\times 10^{-1}\) & \(8\times 10^{-4}\) & \(2.2\times 10^{-4}\) & \(2.1\times 10^{-4}\) & \(4.9\times 10^{-1}\) & \(5.1\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bit error rate (BER) at 100 Hz signaling under different interference patterns. Case 1: All pixels OPEN, Case 2: Only desired signal pixel is OPEN.