esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design and Development of the Final Architecture Design
### Phase 4: Final Architecture Design
Several parameters are provided. EdgeNodeLabel indicates the location of the edge-node, where the service will be deployed. The label has been pre-configured (to an IP address) for each registered node in Rancher [18]. Additionally, external ports of WeightReceiver and Predictor-components, and internal service locations of the Predictor-component have to be provided. Steps 7-8: Rancher starts WeightReceiver based on the provided parameters. Additionally, the internal location of PredictorService-component is provided (InternalPredictorLocationService). The associated Docker image is downloaded from the Docker registry. Steps 9-10: A Predictor will be started based on the received parameters. The Predictor starts an internal HTTP server for serving prediction requests. Step 11: The PredictionService is initialised, and the public external port is provided as a parameter. Steps 12-14: The ML engineer starts model training from Rancher. Label of VM-node1 is provided as a parameter for indicating a node for deployment of the service. Also, the public location of the WeightReceiver-component is provided as a parameter. Finally, the Docker Image is downloaded from the Docker registry. Figure 6: UML sequence view illustrating model training, packaging, and transferring to the edge-node. Steps 15-17: Energy usage and building sensor data is read from internal files. Subsequently, the model is trained, and model weight file is packaged into a TAR-file. Steps 18-19: The TAR-file is transferred to the WeightReceiver-component (External-WeightFileService), and the files are extracted into a volume. Figure 7 presents model loading based on the received weight file. The view is a continuation of the earlier sequence view (Figure 6). The steps of the view are described as follows:  
Step 20: The WeightReceiver-component provides the location of the weight file via the internal control service of the Predictor (see Appendix A for REST API description). Step 21: The new Predictor provides the received parameter, and an internal port to be utilised for serving of prediction requests. Step 22: The model is prepared (including loading of weight files into the model). Step 23: An internal HTTP server is started for serving of prediction requests. Finally, 200 OK HTTP response is returned to the WeightReceiver. Step 24: The WeightReceiver will update the location of the new Predictor to the PredictionService, after it has received 200 OK response from the new Predictor. Steps 25-28: The old Predictor will be stopped. HTTP server and Keras/Tensorflow sessions are closed. However, an existing prediction request will be served before closing.
## 1 Introduction
The increasing demand for hops with new organoleptic characteristics and improved agronomic performance is generating a strong impetus for the development of new varieties. However, the continuous selection of existing breeding material and the use of traditional selection methods, mainly involving progeny from a single cross, impoverishes the genetic diversity of the starting material. Wild germplasm offers new genetic resources for breeding to overcome the limited genetic variation in modern hop breeding programs ([PERSON] et al., 2018), as they are the source of many important traits in modern hop cultivars, such as disease resistance and alpha acid content ([PERSON] et al., 2018). This study focused on assessing the genetic diversity and structure of wild hop populations in the northwestern part of Croatia, a region characterized by a hilly landscape and a large hydrographic network of small streams, tributaries of the Drava and Sava rivers. The water demand of the hop plant for the hop cone yield and the accumulation of secondary metabolites is quite high ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018). The riparian areas of northwestern Croatia thus represent an ideal ecosystem for hops and relatively large populations are found there. Therefore, high genetic diversity of these wild hop populations can be expected as it increases with a large effective population size and the decreasing effects of genetic drift according to the neutral theory ([PERSON] et al., 2018; [PERSON] et al., 2018). In contrast to neighboring countries (e.g., Austria, Slovenia), hop was not traditionally cultivated in northwestern Croatia. The only attempt of hop cultivation dates back to the period from 1999 to 2013 in Gregurovec (46.03 N, 16.45 E), but with limited success. Therefore, contamination of the sampled wild populations with cultivated material is highly unlikely. The sampled geographical area has the most intensive agricultural production in Croatia, consisting mainly of arable land (maize, wheat) and temperate deciduous forests. Wild hops as a ruderal species grows in disturbed habitats along field borders, roadsides, and forest edges. At the same time, the northwestern Croatia has the highest rate of urbanization, which poses the greatest threat to biodiversity as the natural habitats of wild hop may decline or even be irretrievably lost. Therefore, the specific objectives of this study were to determine: (1) genetic diversity within and among wild Croatian hop populations; (2) population differentiation and structure of wild hop populations; and (3) recent bottleneck events. The results obtained will be of utmost importance for future hop breeding programs as well as for the conservation of wild hop genetic resources.
# Space Node Topology Optimization Design Considering Anisotropy of Additive Manufacturing
## 2 Materials and Methods
### Effects of Anisotropy on Node Optimization  
Additive manufacturing is based on its stacked manufacturing method; thus, it can be regarded as a composite sheet, and the material elastic constant obtained from the tensile test is added as a material property. The tensile test results of different manufacturing directions can be approximately regarded as the properties of the direction. By changing the elastic constant, the printing direction of the model is defined in the algorithm. As shown in Figure 5, there will be different mechanical properties in different three directions in any layer which taken out from the printed part (small cube). It can be represented by the mechanical properties of flat, vertical, and side printed samples.  
Shear modulus is expressed as follows according to [35]:  
\[\mathrm{G}=\frac{\mathrm{E}}{2(1+\mathrm{v})},\mathrm{E}>0,\mathrm{G}>0, \tag{11}\]  
where E is the elastic modulus, \(\mathrm{v}\) is the Poisson's ratio, and G is the shear modulus.
## 2 Materials and Methods
### Model Training  
#### 2.2.1 Image Classification Task  
Two tasks were performed: a binary classification to classify normal and OCD images and a task to classify normal elbow-joint images into 4 classes. Of several pre-trained YOLOv8 models, the lightest model, YOLOv8n-cls (parameter size 2.7 M), was used. Transfer learning was performed on OCD data using YOLOv8n-cls pre-trained weight data. The software and computer used for training were as follows: Ultralytics YOLOv8.0.58, Python-3.9.13, PyTorch-1.13.1 GPU, and NVIDIA GeForce RTX 3050 laptop GPU. The training parameters were as follows: optimizer = SGD; initial learning ratio = 0.01; momentum = 0.937; epochs = 50. Of the total US image data, 60% was randomly assigned as training data, 20% as validation data, and the remaining 20% as test data. The performance of the trained model was evaluated using the test dataset, and accuracy, precision, recall, and F-measure were calculated from the confusion matrix. The area under the receiver-operating characteristic (ROC) curve (AUC) was also calculated to evaluate the accuracy of the model.
# Key Issues on Integrating 5G into Industrial Systems
## 5 Integration of 5G with Industrial Systems
### Key Technologies for Replacing Field Buses with 5G
#### 5.3.3 Improvement in Control Algorithms  
In traditional automatic control processes, control algorithms usually need to be executed in a strict periodical manner. When the execution time deviates from the preset cycle, it will inevitably affect the control quality. This situation is called \"jitter\". In the process of industrial automation, PID, as a classic control algorithm, is designed based on the strict periodicity of field bus communication, assuming zero jitters. The schematic diagram of the PID algorithm is shown in Figure 10.  
However, in wireless communication environments, jitters are inevitable due to various factors. Therefore, the application effect of the PID algorithm is no longer ideal, so corresponding adjustments and improvements need to be made to the PID algorithm according to the characteristics of wireless communication environments. Born in this context when formulating the WirelessHART standard, the PID+ algorithm is an improved version of the PID algorithm, designed to address the jitter issues. This algorithm is described in detail in a US patent titled \"Non periodic control communications in wireless and other process control systems\" ([PERSON], [PERSON], and [PERSON] et al.) [55]. It has been shown that the control effect of the PID+ algorithm is not inferior to PID control based on traditional wired field bus. The schematic diagram of PID+ algorithm is shown in Figure 11.  
In summary, when considering using 5G technology to replace field bus, it is also necessary to address jitter-related issues, so as to ensure the stability and effectiveness of control algorithms [56; 57].  
As shown in Figure 11, the positive feedback network may be modified to accommodate non-periodic measurement updates. Specifically, the filter used in this network can be modified to have the following behavior:  
1. Maintain the last calculated filter output until a new measurement is communicated;
2. When a new measurement is received, calculate the new filter output based on the last controller output and the elapsed time since a new measurement value was last communicated.  
To account for the process response, the filter output is calculated using formula (1) whenever a new measurement is received.  
\[\mathrm{F_{N}}=\mathrm{F_{N-1}}+(\mathrm{O_{N-1}}-\mathrm{F_{N-1}})*\left(1- \mathrm{e}^{\frac{\tau-\Delta\mathrm{T}}{\mathrm{Reset}}}\right) \tag{1}\]  
where  
\(\mathrm{F_{N}}=\mathrm{New}\) filter output  
\(\mathrm{F_{N-1}}=\mathrm{Filter}\) output last execution = Filter output after last new measurement  
\(\mathrm{O_{N-1}}=\mathrm{Controller}\) output last execution  
\(\Delta\mathrm{T}=\mathrm{Elapsed}\) time since a new value was communicated
# VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron
## 3 VGG-16 and Multilayer Perceptron
### Network Architecture
#### 3.2.2 Multilayer Perceptron  
The feature map obtained from the last max-pooling layer is flattened into a vector and fed to the multilayer perceptron. The multilayer perceptron is made up of two fully connected layers, two batch normalization layers, and a classifier layer. Both the fully connected layers consist of 512 neurons. The association among the obtained features and the class label is established in the fully connected layer. The leaky ReLU activation function is utilized in both the fully connected layers. Leaky ReLU is an improved version of the ReLU activation function, where the leaky ReLU function is able to represent the negative values along with the positive values. In doing so, the layer becomes more optimized and balanced, which speeds up the training process. The leaky ReLU activation function is computed as below:  
\[f(x)=\left\{\begin{array}{ll}x&x>0\\ \alpha x&x\leqslant 0\end{array}\right\} \tag{2}\]  
where \(x\) is to be multiplied with \(\alpha\) when the \(x\) is a negative value. As a consequence, the neurons in the negative regions are triggered and produce an output. Other than that, a dropout technique is used in the fully connected layers to avoid overfitting problems where several neurons are dropped randomly throughout the training. By doing so, the dropped neurons' weight is not revised and the contribution of the neurons is disregarded.  
Batch normalization layers are included after both the fully connected layers. The batch normalization is performed in mini batches, where subtraction and division of the mini batch's mean and standard deviation are carried out. The normalization ensures the training is executed in an efficient manner. As the gait recognition problem involves multiple classes, the Softmax function is used in the classifier layer. The Softmax function outputs the probability of each input pertaining to a particular class. The Softmax function is calculated as:  
\[S(y_{i})=\frac{\exp(y_{i})}{\sum_{j=1}^{n}\exp\left(y_{j}\right)} \tag{3}\]  
where \(y_{i}\) denotes the Softmax activation for class \(i\) and \(n\) is the number of classes.  
The Adam optimizer is employed during the training to accelerate the network convergence. In order to avoid over-training the network, an early stopping mechanism is applied in the model. The early-stopping mechanism plays a significant role in stopping the training process when the performance has ceased enhancing. Subsequently, the categorical cross entropy loss function is employed in the VGG16-MLP model as gait recognition involves multi-class classification. The categorical cross entropy loss function is calculated as:  
\[\text{loss}=-\sum_{i=1}^{n}\hat{y}_{i}\cdot\log y_{i} \tag{4}\]  
where \(\hat{y}_{i}\) is the true class label, \(y_{i}\) is the Softmax activation for class \(i\), and \(n\) is the number of classes. The layer-wise architecture of the proposed model is presented in Table 1.
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 1 Introduction
We used an experimental system with a 360-deg six-camera setup (shown in Figure 1) to test the performance of the proposed method. It is important to note that when the cameras are aligned, the overlapping field of view of each group of cameras must be greater than 120\({}^{\circ}\) to ensure the integrity of the reconstruction. The outline of this paper is as follows. Section 2 introduces the theoretical framework of the 360-deg panoramic full-field displacement measurement, including the measurement principles (Section 2.1), the intrinsic and extrinsic parameters calibration of the binocular camera pair (Sections 2.2 and 2.3), and the three-dimensional surface profile reconstruction and displacement measurement of the specimen (Section 2.4). The experiments and discussions are presented in Section 3. Finally, we draw conclusions in Section 4. Figure 1: 360-deg six-camera measurement system schematic. A circular array of three stereo-camera pairs surrounding the object to be measured and the field of view (FOV) of each stereo camera pair exceeds 120 degrees.
## 2 Materials and Methods
### Interpretation Accuracy of Claude AI Chatbot  
The interpretation accuracy of Claude AI chatbot was rigorously evaluated through a peer review process involving three independent medical reviewers with extensive experience in laboratory medicine [4; 8]. The core test sets were determined through a modified Delphi process involving 3 experts in laboratory medicine, including clinical chemists, pathologists, and primary care physicians.  
The review process utilized a standardized evaluation matrix developed through expert consensus. Three independent reviewers (senior laboratory physicians with >15 years' experience) who were not involved in system development evaluated all interpretations. A detailed scoring rubric assessed technical accuracy (40%), clarity of explanation (30%), appropriate contextualization (20%), and identification of clinically significant patterns (10%). Each reviewer independently assessed the chatbot's interpretations without knowledge of the others' evaluations, using a standardized scoring system to evaluate accuracy, completeness, and clinical relevance of the generated interpretations [12; 20].  
This structured validation process allowed us to quantify the reliability and consistency of the AI interpretations across different laboratory settings and instrumental platforms [2; 15]. The peer review process was particularly crucial in validating the chat bot's ability to maintain accuracy while translating technical data into patient-friendly explanations [6,7].  
Importantly, the reviewers also assessed the chatbot's performance in handling platform-specific variations in reference ranges and units of measurement [17,18]. This evaluation was essential given that different analytical platforms may produce slightly different reference intervals for the same analyte, requiring the AI system to contextualize results appropriately based on the specific methodology used [15,20].  
To ensure standardization of the review process, the medical reviewers used a detailed evaluation rubric that included specific criteria for assessing: accuracy of numerical value interpretation, appropriate contextualization of platform-specific reference ranges, correct identification of out-of-range values, proper handling of unit conversions when necessary, and consistency in interpretation across different analytical systems [8,12,16]  
The evaluation process was conducted over a three-month period, allowing sufficient time for thorough assessment of each report and its corresponding AI interpretation. This comprehensive validation approach helped establish the reliability and clinical utility of the AI system across different laboratory settings and analytical platforms [2,4,15].
## 3 Materials and Methods
### Feature Extraction and Reduction  
The feature extraction stage can be regarded as the most essential stage in the detection of ventricular arrhythmias. Within the methodology proposed, several discriminatory features from TDA were extracted. The temporal signal in each window was first transformed into Point Clouds using delay embedding. Then, topological representations were extracted: Persistence Diagram (PDR), Persistence Landscape (LR), and Power Weighted Silhouettes (SR) (Tables 1 and 2). Table 1 illustrates the data point clouds (Representation of Point Cloud - RPC) and the persistence diagram (Persistence Diagram Representation - PDR) for each class (_Normal_, _Other_, _VT_, and _VF_). As seen, those representations provide a clear difference among classes. Regarding _Normal_ class, the points in RPC have a focused distribution with respect to the rest of the arrhythmias where the points are scattered. Moreover, the point distribution differs between _VT_ and _VF_ as a very heterogeneous cloud is observed in _VF_, in contrast with _VT_. In the case of PDR, more points are located in a high birth-death ratio for _VF_, showing a clear difference with the rest of the rhythms. In Table 2, Persistence Diagrams (PDR) are compared with Persistence Landscapes Representation (LR) and Weighted Silhouettes Representation (SR). For each class, LR and SR show different shapes. From each representation, a number of parameters are calculated, which will become the input features to the classifier. Initially, 79 parameters are evaluated, combining the most commonly used features in the bibliography with the proposed topological features. However, a feature reduction stage was performed. This stage allows the removal of any potentially redundant features existing, as well as reducing the computational complexity of the data analysis. In addition, we combined these features with other usual time-domain features representing statistical characteristics, such as variance, skewness, and kurtosis. The feature selection was achieved using the Sequential Forward Selection (SFS) method, an iterative method that adds the best feature iteratively to the model until new additions do not improve the performance of the model. This method enables the selection of the most relevant features. Finally, a total of 27 features from all representations (time domain, RPC, PDR, LR, and SR) were selected amongst the 79 initial features. The extracted features are detailed in Table 3. This selection allows to improve the computational efficiency and reduce the generalization error of the model by removing irrelevant features or noise. In addition, another input feature set was obtained. In this case, the Calculate Gaussian KDE was applied to the Persistence Diagram, and then an image-like was obtained (Table 3). The obtained image will be used as a direct input to the classifier, in a similar form as in [47]. Table 4 shows the resulting Gaussian KDE from a PDR.
## Numerical Example  
### Example Description  
The model is applied to a real-world case that aims to design a feeder bus system for Nanjing City in China. The geographical space distribution of six bus depots (D1-D6), fifteen demand points(C1-C15) and one rail station(M), which are separately denoted as blue balloons, yellow balloons, and the purple balloon, as shown in Figure 4. The travel distance and time matrix are generated from the Open GIS tool. Using the demand collected from cell phone data exploration, the number of passengers, their preferred boarding time windows, and the subway schedule chosen in the demand points are shown in Table 2. Key input parameters of our model used in the case study are listed as follows:  
* Number of bus routes: 3;
* Route capacity: \(Q\) = 10 people;
* Maximum and minimum length of the vehicle route: \(D_{\min}\) = 3 km and \(D_{\max}\) = 25 km;
* Maximum travel time of the feeder bus route: \(T_{\max}\) = 25 min;
* Walking time for passengers from the drop-off point of the rail
## 3 Experimental Validation  
### Experimental Setup  
The experiment is conducted in a closed adiabatic system. The dimension of the system and the position of the levitation electromagnet module are exactly the same as illustrated in Figure 1. The experimental pressure in the system is maintained at 40 kPa, which corresponds to approximately 0.4 atmosphere pressure. The heating of the electromagnet is controlled by a constant electrical current of 25 A. The resistance of the primary magnet units and core-elevated magnet units is 0.755 \(\Omega\) while the resistance of the end magnet units is 0.574 \(\Omega\). The heating power of each type of magnet unit is shown in Table 1. The heating power of the magnet units is calculated according to [PERSON]'s law: \(P_{v}=I^{2}R/V\), where \(P_{v}\) is the heating power per unit volume, \(I\) and \(R\) are the rated current and internal resistance of the magnet unit, respectively, and \(V\) is the volume of the coil. This heating power value used in the numerical model and the experimental study is based on the actual operating condition of the EMUs. The initial environmental and device temperature in the experiment is 295 K. The stopping criteria for the experiment is a temperature rise of 100 K in the maximum temperature point of throughout the levitation electromagnet surface. All the temperature measurements are made by T type thermocouples. An uncertainty analysis of temperature measurements was conducted to determine the uncertainty of the measured temperature. The total uncertainty value is given as follows [42]:  
\[\frac{\delta T_{m}}{T_{m}}=\sqrt{\left(\frac{\delta T_{1}}{\Delta T_{l}} \right)^{2}+\left(\frac{\delta T_{2}}{\Delta T_{l}}\right)^{2}+\left(\frac{ \delta T_{3}}{\Delta T_{l}}\right)^{2}} \tag{13}\]  
where \(\delta T_{m}\) is the fluctuation of measured temperature, \(T_{m}\) is the average temperature of measuring point, and \(\delta T_{1}\) is caused by the accuracy of the T type thermocouples, which is \(\pm\)0.5 K. \(\delta T_{2}\) is caused by the accuracy of logger readings given by the manufacturer. The maximum contribution of the logger to the errors of T type thermocouples without compensation is \(\pm\)1.1 K. \(\delta T_{3}\) is caused by measurement error, which is \(\pm\)0.5 K according to experimental test. According to Equation (13), the combined uncertainty is 1.3%.  
Figure 2: Discretization of computational model: (**a**) Overview of the levitation electromagnet region mesh on the symmetric plane; (**b**) Detailed mesh view of primary magnet unit; (**c**) Surface mesh of end magnet unit and core-elevated magnet unit.