esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Approach and Considerations  
Under the environment of MATLAB/Simulink, the PID controller based on a GA was used to simulate the DTC control of a DFIM, including ISE, IAE, and ITAE, separately and in a weighted combination, with the aim of finding optimal gains that meet the problem requirements. GA parameters (VarPmin, VarPmax, VarImin, VarImax, VarDmin, VarDmax, Pop, and niter) must be initialized to very large values at initialization (VarDmax = VarImax = VarPmax = 100, VarDmin = = VarImin = VarPmin- 100, Pop = 100, and niter = 100) to increase the possibility of optimal values for KP, Ki, and KD. However, in this case, the system's convergence is attained only after a certainconsiderable amount of time, possibly reaching a few days, and from the optimal value, the range of change of these parameters  
Figure 4: Flowchart of the GA.  
may be reduced to a value near the optimal value, because the system would converge to the optimal valuesand then return from these values optimally, reducing the number of iterations, because the system converged toward its optimumtargets.As a result, the system can swiftly converge on the ideal answer. The GA-generated PID controller's parameter values fell within the fluctuation range shown in Table 2. The settings in Tables A1 and A2 in the Appendix A, were used to configure the system, and the system was then tested for reference speed and torque. On a 0.8 kW machine, the simulation results of the two techniques, standard DTC and GA-DTC, were examined and set up as follows:  
* Sampling time: fs = 0.0001 s.
* Hysteresis bands: \(\Delta\)Yr = \(\pm\)0.001 Wb, \(\Delta\)Ys = \(\pm\)0.001 Wb, and \(\Delta\)Tem = \(\pm\)0.01 Nm.
* Nominal load (TL = 5 Nm) at t = 0.5s.
## Materials and Methods
### Microscopic Evaluation Using Cryo-Scanning Electron Microscopy (Cryo-SEM)  
Two tissue specimens (6 mm diameter) were obtained from the equatorial area (Figure 2) of the second and third fleshy scales (scales 2 and 3, Figure 1) of the same onion bulb, either untreated or PEF-treated, using a cork borer. One specimen was evaluated at the abaxial (outer waxy, concave shape) surface of the onion epidermis, and the other specimen was observed at the adaxial (inner, convex shape) surface, with the tunic layer removed. The specimens were rinsed using a sodium phosphate buffer (0.01 M NaH\({}_{2}\)PO\({}_{4}\), pH 7.0) to remove surface cytosol and to prevent the remnant cytosol from forming a crystal sheet on the specimen surface during sample preparation and freezing with liquid nitrogen that could interfere with the subsequent surface morphology imaging. Each specimen was glued onto an aluminum disc using a Tissue-Tek optimum cutting temperature compound (Sakura Finetek Inc., Torrance, CA, USA), plunged into liquid nitrogen, and stored in liquid nitrogen until viewing. The specimen was transferred to a Gatan Alto 2500 cryo-preparation chamber (Gatan Inc., Pleasanton, CA, USA) using a brass cryo-holder. The specimen was left to stand in the chamber to allow ice crystals on the surface of the specimen to sublime at -85 \({}^{\circ}\)C under a vacuum condition.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**PEF Treatment**} & **Electric Field Strength (kV/cm)** & **Pulse Width (μs)** & **Pulse Frequency (Hz)** & **Pulse Number** & **Specific Energy Input (kJ/kg)** \\ \hline PEF1 & 0.6 & 20 & 50 & 540 & 6 \\ PEF2 & 0.6 & 20 & 50 & 5400 & 60 \\ PEF3 & 1.2 & 20 & 50 & 120 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of three different Pulsed electric fields (PEF) treatments.  
After that, the surface morphology of the specimen was viewed and captured in a JOEL JSM-6700F field emission scanning electron microscope (JOEL Ltd., Tokyo, Japan) at 100\(\times\) magnification and a low accelerating voltage of 3 or 5 kV.
## 6 Analysis of Experiment Results
### Combined Methods for Data Merging  
Table 15 demonstrates the performance of algorithms on a dataset that has been combined, on subsets of 250 samples, with average values from 50 experiments each. The accuracy percentage computation time with merged and converted values may be lower than with those that did not undergo this, they are still not very far off. As it seems, the approach is usable. Interestingly, the Complement Naive Bayes classifier shows better results than during tests on unmerged datasets.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Number of Steps N** & **Predicted Values** & **Actual Values** & **Computation Time** \\ \hline
10 & 361.79, 2.57, 0.51 & 365.25, 2.54, 0.45 & 14,794.72 \\ \hline
20 & 377.86, 2.56, 0.43 & 365.25, 2.54, 0.45 & 27,955.9273 \\ \hline
30 & 380.79, 2.53, 0.47 & 365.25, 2.54, 0.45 & 40,537.82 \\ \hline
40 & 373.96, 2.60, 0.47 & 365.25, 2.54, 0.45 & 57,885.87 \\ \hline
50 & 370.09, 2.55, 0.45 & 365.25, 2.54, 0.45 & 67,754.48 \\ \hline
100 & 368.08, 2.58, 0.46 & 365.25, 2.54, 0.45 & 143,455.42 \\ \hline
250 & 373.50, 2.55, 0.46 & 365.25, 2.54, 0.45 & 328,075.21 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Evaluation of similarity-based conversion approach and effect of the experiment number N, with 250 samples (k = 250) per experiment.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Number of Similar Samples** & **Predicted Values** & **Actual Values** \\ \hline
50 & 523.12 & 365.25 \\ \hline
100 & 494.48 & 365.25 \\ \hline
200 & 498.29 & 365.25 \\ \hline
250 & 542.89 & 365.25 \\ \hline
300 & 532.68 & 365.25 \\ \hline
500 & 471.80 & 365.25 \\ \hline
1000 & 509.07 & 365.25 \\ \hline
2500 & 503.09 & 365.25 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Results for experimental unit conversion approach with two common traits matched from previous methods and 100 steps depending on the number of similar samples.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 3 Smart Meter Technological Solutions in Europe and Projects for Their Implementation
### Sweden  
Sweden is the second European country that has completed the smart meter roll-out beside Italy. According to [62] the techniques for data transmission from the smart meter to the energy provider can vary. The most common one is power line communications combined either with radio (40%) or GSM/GPRS (13%) or as a stand-alone technology (27%). Wireless solutions are also chosen, like radio (13%) or radio combined with GSM/GPRS (7%). Vattenfall has completed the smart meter roll-out in 3 phases. The first one (Actaris project) aimed at deploying 110,000 smart meters (June 2003-June 2006). During the second phase (Iskraemeco project) 150,000 smart meters have been applied (July 2004-September 2006). The final phase was the realisation of the Telvent/Echelon project (June 2006-June 2008), in which 600,000 smart meters have been installed [63]. During the third phase the smart meters are used along with IP connected data concentrators. Communications are realised through PLC GSM and GPRS [64]. It should be noted at this point, that some of the smart meters provided by Echelon support the IEC 62056-21 and the ISO/IEC 14908 standard [65; 66].  
Vattenfall has also participated in several research work activities, like the Smart Grid Gotland project, which targets at integrating large quantities of renewable energy sources in the grid by the use of modern technology [67]. E.on Sverige has installed so far 1 million smart meters in the country. The main technologies used for smart meter data transmission has been power line communications and GPRS [68]. Another energy provider, Malarenergi, has deployed around 100,000 smart meters which are functional since 2009. The data transmission from the smart meter to the data concentrator can be realised with power line communications or with radio technologies. For the second telecommunication link GPRS is used. The company has mainly chosen radio solutions between meters (75%) and secondarily power line communications (25%) with usage mostly in zones where radio technologies cannot be used [69]. In accordance to the country's regulations for the smart meter roll-out, another energy provider, Fortum, has deployed 860,000 smart meters [70]. Svenska Kraftnat has participated in the smart grid project \"Stockholm Royal Seaport\", which indicates a sustainable city with a smart grid as the enabler [71]. Finally, Eskilstuna Energy and Environment has been appointed leader of \"Planning for Energy Efficient Cities\" (PLEEC), which is an FP7 project funded by the European Commission and which targets at making European cities more energy efficient [72].  
Even though in the aforementioned countries there are several pilot projects for smart meters that have been carried out, this is not the case for all EU countries. In many countries the roll-out or even the pilot efforts have just started or are planned to take place in the future.
# DCS-ST for Classification of Breast Cancer Histopathology Images with Limited Annotations
## 2 Related Works
### Transformers in Medical Image Analysis  
Transformer models, originally developed for natural language processing, have been adapted for computer vision with significant success. The Vision Transformer (ViT) [28] pioneered this transition by segmenting images into patches and treating them as sequences, achieving competitive performance on benchmark datasets like ImageNet. However, ViT's global self-attention mechanism can be computationally intensive for high-resolution images, such as those in medical imaging.  
The Swin Transformer [5], a hierarchical Transformer with a shifted window mechanism, addresses this limitation by efficiently capturing local and global features, which makes it well suited for high-resolution medical images. In medical image analysis, the Swin Transformer has been applied to tasks such as segmentation. [PERSON] et al. [29] achieve state-of-the-art results by leveraging their hierarchical structure to model spatial relationships in medical images.  
For breast cancer histopathology image classification, Transformer-based models are gaining increasing attention. Recent works such as RDTNet [30], a residual deformable attention-based Transformer network, and the discrete wavelet-enhanced Transformermodel proposed by [PERSON] et al. [31], have demonstrated promising performance. Furthermore, [PERSON] et al. [32] introduced a token vision transformer that accelerates and improves the classification of histopathology images. However, these methods often assume access to large labeled datasets, which is not feasible in many real-world medical scenarios.  
Our work builds on the Swin Transformer by introducing dynamic window prediction and cross-scale attention, specifically designed to handle limited annotations in breast cancer histopathology image classification across datasets such as BreakHis, ICIAR2018 BACH Challenge and Mini-DDSM.
# Review on Quality Control Methods in Metal Additive Manufacturing
## 4 Experimental Quality Monitoring Methods in Metal AM and Welding
### Experimental Quality Monitoring Methods in Metal AM
The proposed method showed more accuracy and robustness over a wider concentration range than did previous methods. [PERSON] [80] demonstrated the feasibility of using acoustic signals for porosity prediction. [PERSON] [81] also focused on acoustic signals for metal AM monitoring and suggested the possibility that acoustic signals can be used to recognize the thermal properties of the product, such as conductivity, heat capacity, and coefficient of thermal expansion. Based on these existing studies, [PERSON] et al. [82] and [PERSON] and [PERSON] [83] noted the possibility of real-time monitoring systems using acoustic signatures for metal AM. Other experimental studies have focused on the implementation of multisensor monitoring systems [64; 67; 68; 84; 85; 86]. [PERSON] et al. [64] proposed a multisensor monitoring concept with lower monitoring errors to identify the melt pool for real-time monitoring. The thermal image, laser wavelength, and melt pool depth were collected simultaneously. [PERSON] et al. [86] proposed a multisensor monitoring system that includes optical emissions and image sensors to identify plume characteristics. The relationship between the plasma emission of the plume and the plume area was quantified using the proposed monitoring system. The relationship showed that strong plume plasma emissions resulted in an increased plume area. Additionally, the authors demonstrated that the plasma emission of the plume can be used to evaluate the buildup quality of the DED process. [PERSON] et al. [68] used two distinctive thermal and image data of the melt pool to predict the internal porosity in a metal AM product with high accuracy. The authors noted that the infrared image of the melt pool can have some associations with porosity, and thus can be used for porosity prediction. The thermal data collected by pyrometer was trained using CNN and the images collected by infrared camera were trained using long-term recurrent convolutional networks. The authors fused two models, which showed better prediction accuracy than the individual models.
# IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring
## 2 Related Work
### Monitoring Station  
The monitoring station is controlled by the authorities who are responsible for managing and controlling the traffic or transportation. For instance, this station should be accessible to traffic officers so that they can monitor vehicles' speeds and identify violators. The station has a communication module that connects to the cloud, which is often accomplished over the internet. Some local computations are managed by the local servers, such as issuing fines when exceeding the number of permitted passengers, speed violation fines, and seatbelt-related fines. The local server also responds to other inquiries made by traffic authorities.  
The traffic monitoring station's software subsystem includes the user interface. The user interface is an important part of the system because it enables users to view the data of the road and edges (vehicles), carry out administrative functions, and issue fines for vehicles that violate the rules. For example, the station receives a notification that a vehicle has committed a violation, and consequently the station confirms the fine. This subsystem allows users to make queries and generates reports about roads and vehicles. Moreover, authorized users can view and update the map settings, user accounts, and system parameters on the cloud. The MySQL local database is also a part of this subsystem, and this enables the local server to efficiently handle the majority of the computations on this subsystem.
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 2 Related Work
Notably, by quantifying the significance of individual features and calculating their dominance rate, the authors provided valuable insights into how the classifier operates for various Internet services, shedding light on its underlying mechanisms. [PERSON] et al. [28] tackled the challenge of traffic classification within SDN environments by introducing an innovative approach that leverages a variational autoencoder (VAE). Their objective was to effectively categorize different classes of Internet services and ensure the quality of service. To achieve this, the VAE was trained using six statistical features, enabling the extraction of latent feature distributions for flows within each service class. The classification of query traffic was accomplished by comparing its latent feature distribution with the previously learned distributions. The experimental results demonstrated the efficacy of this method, with an average accuracy of 89.00%, surpassing the performance of conventional statistics-based and machine learning-based approaches. In the literature review, the authors propose traffic classification models with DL techniques, which were analyzed based on accuracy, number of input features, and number of classes identified in the model output. Although the models demonstrate good accuracy, problems such as multiclass imbalance, training time, computational cost, and availability of the training dataset persist. In addition, with the exponential growth of applications and the constant change in network attack signatures, another challenge arises in network traffic classification, known as concept drift. Variations in the environment and data distribution occur due to temporal location fluctuations. Identifying all applications and all network attacks is a complex and impractical task. Therefore, the present research proposes developing a DL-based classifier that identifies applications and the most common network attacks into classes based on specific traffic flow characteristics. With this proposal, different applications and attacks can be grouped into classes. This classification of application and attack traffic flows is more efficient than classifying them individually, facilitating assigning QoS policies or restrictions to the network traffic.
## 4 Discussion of Test Results
### ITBF Test Results  
#### 4.2.1 ECC-Steel Composite Specim Fatigue Cracking Test  
The SCB fatigue tests could evaluate the fatigue cracking resistance of ECC under different assumed stress ratios. However, when ECC is applied as OSBD pavement material, it works together with steel decks. Therefore, in order to measure the fatigue cracking resistance of ECC under actual OSBD pavement working conditions, a fatigue cracking test for ECC-steel composite specimens needed to be carried out.  
In order to overcome the disadvantages of existing OSBD pavement material composite beam fatigue tests, an improved three-point composite beam fatigue (ITBF) test was developed according to the actual working conditions of OSBD pavement [22]. Before testing, stress or strain fields in the specimen were calculated using the finite element method (FEM) and set to be consistent with the mechanical response of actual OSBD pavement by adjusting the test loading. UTM-25 was usually applied as loading equipment in this test, and the plane size of the specimens was 440 mm \(\times\) 150 mm. During the loading process, two loading plates were used to simulate the wheel loads and the center support was adopted to simulate the OSBD stiffner. The mechanical analysis results show that this test could simulate the negative bending moment on the top surface of decks pavement under actual wheel load. The load transmission path and the stress distribution in this test are shown in Figure 11, which were highly consistent with the actual stress conditions of OSBD pavement. The specimen in the ITBF test was composed of a 25 mm thickness ECC layer and a 6 mm thickness steel plate. Epoxy resin binder was used as the tack coat between ECC and steel.  
Figure 10: Logarithmic relationship between stress ratio and fatigue life.
# Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study
## 4 Case Study Results
### Misfit Adjustment for Web Mapping  
The least squared approximation method for 2D conformal transformation was used in the estimation of four transformational parameters to align GNSS data with GE web imagery. For all sites, the scale factor was unity, and the rotational value was near zero (with average values of \(s\): 0.9999 and \(\theta\): 0.0273). A simple mean shift vector is, therefore, recommended to translate GNSS data for display on GE web imagery. For example, for the FNQ-2 orchard (with row-to-row spacing of 8 m), a shift of \(-0.499\) m east and 2.051 m north was applied for the alignment of GNSS data acquired on 21 August 2022 from a moving vehicle with 2021 GE web imagery (Figure 6).  
The web mapping error could, therefore, be empirically accommodated through the following workflow (Figure 7):  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Imagery** & **RMSE\({}_{\mathbf{x}}\)** & **RSME\({}_{\mathbf{y}}\)** & **RMSE\({}_{\mathbf{xy}}\)** \\  & **(m)** & **(m)** & **(m)** \\ \hline Bing & 3.018 & 3.199 & 4.398 \\ Google & 1.155 & 3.086 & 3.295 \\ ESRI & 2.588 & 0.823 & 2.716 \\ QLD & 1.985 & 1.386 & 2.421 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average RMSE across the four sites for four web imagery providers.  
Figure 6: Display of GNSS data from a moving vehicle on FNQ-2 on 21 August 2022 on 2021 GE web imagery. The red points are raw GNSS WGS84 (GDA2020 epoch) data collected at 10 Hz. The yellow points are adjusted using a misfit shift vector of \(-0.499\) m east and 2.051 m north, estimated from GCPs on the farm.  
1. Establishment 1. Identify probable checkpoints in web imagery; 2. Extract coordinates of these points using line segment intersection points; 3. Acquire GCP location data using WGS84 (GDA2020 epoch) datum, GDA2020, or ATRF2014, or an equivalent national system; 4. Estimate the misfit vector(s) from web imagery with respect to CPs (s1 = misfit + xdt) and plate model (s2 = xdt), where x is the magnitude of plate movement over the time interval (dt) between coordinate epoch and 2020.000 (of CP or MV) data; 5. Curate the misfit vector for each farm, each web imagery provider, and each date of the image. 2. Implementation 1. Collect and curate field data with the metadata of the datum and the date of acquisition; 2. For web map display, convert data to WGS84 (with GDA2020 epoch); 3. Apply the misfit vector to all field-collected data 'on-the-fly' before display; 4. Undertake client-side transformation WGS84 to Web Mercator datum by the web mapping application.  
However, this approach is invalidated when different imageries are present for one farm at a given time from a web map provider, as shown in the example in Figure 8. In this example (right panel), the misfit of the position of end trees of each tree row is between 2019 and 2022 GE web imageries.  
Figure 7: Workflow for the misfit adjustment for web mapping.
## 4 Protocols Protecting Terminal Devices in Zero-Trust Networks
### Example of Use Case in Zero-Trust Networks  
The protocol presented in Section 4.1 is applied to protect autonomous vehicles operating in a zero-trust network with poor signal quality [56; 57; 58; 59; 60]. For example, the vehicle contains a set of encrypted files with the instructions and software revisions required to react to certain circumstances. The set of keys required to decrypt these files should not be stored in the vehicle for security reasons; therefore, a server transmits them through the open network as requested. The latencies of the suggested protocols must be small for near real-time operations. The use of standardized cryptographic algorithms is preferred.