esa-sceva/satcom-chunk-collection huggingface dataset:
These techniques have been successful in achieving high power output and efficiency, but they often face limitations in maintaining a broad impedance space, particularly when aiming for multi-octave bandwidths.  
The Class-J mode, introduced by [PERSON] in 2009 [PERSON] (2009), was a significant breakthrough as it extended Class-B into a continuous mode, offering a broader impedance space for both fundamental and second harmonic frequencies. This feature provides more flexibility for power amplifier designs, enabling better performance in terms of efficiency and output power across a wider range of frequencies. However, while Class-J offered several advantages, the need to optimize both the fundamental and harmonic impedance spaces for high-performance designs remained a challenge. In response to this, the resistive-reactive Class-J (RRCJ) mode was developed [10,11], incorporating a resistive factor (\(\beta\)) into the voltage waveform. This modification extended the impedance space for the fundamental frequency to the left and for the second harmonic to the right on the Smith chart, creating an overlap that significantly improved efficiency and output power across broader frequency bands.  
Further advancing this approach, the extended resistive continuous class B/J (CCBJ) mode [12] introduced an additional factor (\(\gamma\)) that further refined the impedance space, allowing designers to achieve even higher efficiency while minimizing distortion. This mode proved especially effective in extending the bandwidth and improving the performance of GaN-based power amplifiers. However, according to load-pull results, a significant reduction in saturated power is observed within the expanded continuous Class-B/J impedance space. This behavior deviates from the ideal conditions predicted by theory. This discrepancy arises due to the fact the voltage swing limitation in actual PAs is neglected during the manipulation of the voltage waveform. As a result, despite these advancements, achieving a well-balanced design that ensures high power flatness, stable gain, and efficiency across the entire X-band remains a challenging task, especially when dealing with the complex impedance-matching requirements of modern communication and radar systems.  
In this context, the work presented in this paper leverages the benefits of the CCBJ mode while addressing its limitations for achieving good power flatness. Firstly, based on load-line theory, the impedance space of equal power contours is calculated and analyzed, and for the first time, the output power variation trend within the CCBJ theory impedance space is explored. Secondly, by combining the CCBJ impedance space with the calculated constant power contours, the target impedance space for designing high-efficiency PAs with minimum in-band power fluctuation is derived. Finally, a GaN MMIC power amplifier operating in the 8-12 GHz range is designed and fabricated. The proposed amplifier achieves high saturated power and efficiency and demonstrates significant advantages in in-band power flatness when compared to similar amplifiers reported in the literature.
# Data Structure and Management Protocol to Enhance Name Resolving in Named Data Networking
## 4 Experimental Setup and Results
### Time Performance
* _BCM Negative:_ This experiment focused on testing the best component matching mechanism using the GET Negative preset. This preset size was multiple times bigger than the original 100,000 preset. In the interest of clarity, we normalized the result to 100,000. As illustrated in Figure 6, the lookup performance of the proposed ECHT was much better than both component and character tries--approximately 76 times faster when the exact matching mechanism was used for the GET Positive case and 11.7 times faster for the Get Negative case. Moreover, when the BCM mechanism was used, ECHT was 65.3 to 71.63 times (98.47 to 98.6%) faster than component trie and character trie, respectively. For the case of BCM Positive and BCM Negative, ECHT was 97.06% faster than component trie  
Figure 5: Time performance of initial loading. and 98.61% faster than character trie. The main reason is that ECHT eliminates the need to decode/encode NDN names while parsing received NDN Interests or while creating NDN Data replies. In Component/Character tries algorithms, domain names have to be decoded to text before searching the trie, and then the results are encoded before sending them back to the caller.
###### Abstract
Various imaging methods have been developed based on different imaging calculations, such as phased array-based transmitting aperture focusing imaging [22; 23; 24], multi-wave focusing imaging [25], synthetic aperture imaging [26; 27], full-matrix focusing imaging [28], multi-frame full-rank imaging [29], and others. In the case of synthetic aperture array imaging, multiple frames of imaging can be generated by receiving the echo signals from different scatterers using different receiving apertures, after the sound waves emitted by various transmitting array elements scatter off the scatterer. In the multi-frame imaging of ultrasonic guided wave arc sparse arrays in plates, images from different frame orders can effectively characterize scattering information within the same imaging sound field region from multiple azimuthal perspectives. Multi-frame images of the same array often provide a better representation of scattering information compared to single-frame images, particularly for defects. However, further discussions are needed to address the challenges associated with the multi-frame imaging of the regular arrays and irregular arrays of ultrasonic guided waves under the condition of weld scattering. In the context of ultrasonic guided wave array imaging in plates, there exist various types of receiving apertures with the same number of array elements, known as receiving matrices. Three common examples of such receiving apertures with the same rank include left-rank receiving aperture, inner-rank receiving aperture, and right-rank receiving aperture. This paper focuses on studying the multi-frame imaging mechanism of the ultrasonic shear horizontal (SH) guided wave using a left-rank arc sparse array under the condition of weld scattering. By utilizing receiving apertures and receiving matrices as analysis tools, multi-frame images are employed to characterize sound field scattering information in welded structural plates from different azimuth angles. The findings of this research provide valuable insights for the further exploration and application of array imaging detection techniques in the inspection of welded structures.
## 6 Discussion
### Toolchain Development  
To bridge the gap between standardization, a need for flexible requirement adaptation, and practical application, the BIM2 BEM-Flow toolchain was developed. This innovative system integrates a web-based property management server (YAPS), a workflow management interface (PWM), and a Revit-integrated plugin (RWM). Key features of this toolchain include the following:  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Workflow** & \begin{tabular}{c} **Responsible** \\ **BIM** \\ \end{tabular} & **BIM Tool** & \begin{tabular}{c} **Responsible** \\ **SIM** \\ \end{tabular} & **SIM Tool** & **Use Case** & **Phase** & **Library** & **Default-Set** \\ \hline \begin{tabular}{l} Daylight \\ Simulation \\ \end{tabular} & [PERSON]. & Revit & [PERSON]. & DALEC & \begin{tabular}{l} Comparison \\ of Variants \\ \end{tabular} & \begin{tabular}{l} Early Design \\ \end{tabular} & \
\begin{tabular}{l} Project\_01 \\ \end{tabular} &
\begin{tabular}{l} PH Standard \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Workflow example.  
Mapping Parameters (server \(\rightarrow\) BIM \(\rightarrow\) BEM): The toolchain ensures that parameters are correctly mapped from the central database into BIM models and then translated for use in energy simulation tools, thereby streamlining the data exchange process.
* Connecting Default Value Libraries: By linking default value libraries directly to parameter libraries, the system provides validated, ready-to-use data that can be automatically assigned during the early design phase. This reduces manual input and minimizes errors.
* Defining Threshold Values for Certification/Verification: The integrated framework allows users to set and manage threshold values for simulation results. This is crucial for establishing certification criteria (e.g., Passive House standards) and for verifying the performance of the design against energy efficiency benchmarks.
* Data Import Capability and API Access: Recognizing that many organizations already manage data in formats such as Excel, the toolchain supports full data import functionality. Furthermore, an API facilitates access to and integration of the parameter data with external tools, enabling a seamless, automated workflow.  
This approach not only addresses the disconnect between BIM and BEM by creating a centralized, flexible, and adaptable management system, but it also overcomes the essential limitations of existing BEM tools, which are often perceived as complex and not user-friendly. By providing an intuitive interface and automating the mapping process, the toolchain helps to reassign responsibilities clearly, allowing property managers to oversee data accuracy and reducing the burden on architects.
# A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform
## 3 Materials and Methods
### The Deep Learning Model  
The proposed model aims to precisely classify and predict the DR stages by analyzing the image. The input image preprocessed data is used as features, while the label supplied to each image acts as the target variable. Multiple filters and layers extracted the image information and attributes throughout the feature extraction process. Following this stage, the images were categorized based on the desired target labels. The recommended model's architecture is presented in Figure 5.  
The input layer was modified by the input shape from the training data. The preprocessed image (converting RGB to grayscale and applying CLAHE and DWT) was fed into the CNN model. After applying the DWT method, the image resolution was halved.  
Convolutional layers have convolution operations to summarize and minimize the information features. The feature map output shows picture corners and edges. Later, further layers use this feature map to learn more input image features. To reduce computational expenses, the pooling layer reduces the convolved feature map size by lowering  
Figure 4: The level-1 decomposition of DWT. The input image size is 224 height by 224 width, which is then downsampled to half the resolution.  
the layer connections and separately operating on each feature map. The kernel size was 3, and the units of each convolutional layer were 16, 32, 64, and 128, respectively. The activation function employed was'relu'. Max pooling uses the most significant feature map element with a pool size of two. BatchNormalization in neural networks is a technique that normalizes the activation levels between layers. It helps to improve the efficiency and accuracy of model training by regularization. The Flatten layer reduces the dimensionality of the previous layer's pictures by flattening the last layer's results.  
The fully connected layers consisted of two dense layers. The first dense layer was 256 units, and the activation function was'relu'. Including a dropout layer set at a frequency rate of 0.5 mitigated the model's overfitting. These layers were then connected to the output dense layer, which had'softmax' activation and classified data into five different DR stages. The loss function was sparse categorical entropy, as the class labels were considered integers. The sparse categorical cross-entropy is a variant of the categorical cross-entropy loss and is used when the labels are provided as integers instead of one-hot encoded vectors (as shown in Equation (2)).  
\[\text{Loss}(\text{w})=-\frac{1}{N}\sum_{i=1}^{N}[y_{i}\text{log}(p_{i})+(1-y_{ i})\text{log}(1-p_{i})] \tag{2}\]  
where w refers to the model parameters, \(N\) is the number of samples, \(y_{i}\) is the actual labels, \(p_{i}\) is the predicted labels, and the labels are integers.
## 2 The application of Additive Manufacturing (AM) in industry 4.0
### Challenges, Obstacles, and Limitations  
While AM is cutting-edge technology and finding its way in various industries due to its numerous advantages, there are several barriers against its rapid growth. The major challenges are as follows:  
* **Imperfections:** Void formation between subsequent layers of materials negatively affect the mechanical performance of AM parts [61]. Parts produced using AM processes often reflect the stair-stepping effect, which is created by adding one layer on top of another and affects the surface quality and roughness. This nature of layer-wise production of components also results in parts with anisotropic mechanical properties microstructures [62]. In most of the AM processes, the surface finish of overhanging surfaces also, due to support removal, need to be post-processed.
* **Cost:** Not only are AM systems and materials expensive, but also a high cost in mass production is a major challenge for AM technology [63]. However, AM cost is reducing significantly compared to in the past. For example, the cost decreased by 51% from 2001 to 2011 for both machines and materials [28].
* **Production time:** AM technologies are more likely to be used in product customization rather than mass production, for which conventional methods are preferred [18].
* **Limitations of materials:** It is possible to use a wide variety of metals and polymers in AM technology [64; 65]. However, some interesting materials, such as magnesium and biodegradable polymers, need further research.
* **Size limitations:** AM systems are limited regarding the production of parts bigger than their build chamber. Even regardless of build chamber size restriction, an extended amount of time is required for the manufacturing of large-sized objects [30]. Nevertheless, a technology called Big Area Additive Manufacturing (BAAM), which was developed in recent years, has overcome this limitation by being able to create large-scale parts [66]. Most of the design and application constraints of small-scale AM still apply to BAAM as well [67].  
Research is being conducted to overcome the above-mentioned limitations. However, it is unlikely that AM technology will knock out traditional methods. Instead, they may be combined, and an integrated process could be developed to achieve the efficient production of complex products.
## 5 Conclusions  
Based on the consolidated untrained triaxial tests, the effects of fiber length, fiber content and cell pressure on the strength of basalt-fiber-reinforced loess were analyzed. A constitutive model for the reinforced soil was established to consider the effects of fiber content, fiber length and cell pressure, and an estimation method for the model parameters was proposed. The main conclusions can be drawn as follows:  
1. The stress-strain curve of the unreinforced soil exhibited strain-softening, while that of reinforced soil showed strain-hardening. The peak strength of the fiber-reinforced soil was significantly higher than that of the unreinforced soil and increased with the increase in fiber content.
2. The peak strength of fiber-reinforced soil gradually increased with the increase in fiber length, and the peak strength of 12 and 16 mm fiber lengths increased slightly than the 8 mm fiber length.
3. The peak strength of the fiber-reinforced soil increased with the increase in cell pressure. When the fiber length was 16 mm and the fiber content was 0.6%, the peak strength reached 158.01, 229.67, and 380.31 kPa under 25, 50, and 100 kPa cell pressure, respectively.
4. Soil particles had anchoring effect on fibers, and fibers had a bridging effect on soil particles. The anchoring effect and bridging effect improved the cohesion and friction of the reinforced soil.
5. The experimental results agreed well with the predicted results, indicating that the established model and the parameter estimation method are suitable for describing the constitutive relationship of basalt-fiber-reinforced loess.  
Conceptualization, H.K. and Q.M.; methodology, Q.M.; validation, H.K. and S.H.; formal analysis, H.K.; data curation, S.H.; writing--original draft preparation, H.K.; writing--review and editing, Q.M.; funding acquisition, Q.M. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Results comparison between measured and predicted results under 100 kPa cell pressure: (a) \(FL=4\) mm; (b) \(FL=8\) mm; (c) \(FL=12\) mm; (d) \(FL=16\) mm.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 52078194, the National Young Top-notch Talent of \"Ten Thousand Talents Program\", the Young Top-notch Talent Cultivation Program of Hubei Province, and the Innovation Demonstration Base of Ecological Environment Geotechnical and Ecological Restoration of Rivers and Lakes, grant number 2020 EJB004.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion  
### Experiment on PLA Cutting Parameter Optimization  
In the cutting parameter optimization experiment, three groups of cutting parameters were planned, including spindle speed, feed per tooth, and feed rate. An end mill with a diameter of 0.3 mm was used to perform path experiments with a fixed 0.03 mm depth of cut. Figure 9 presents the PLA cutting experiment results under the feed rate 600 mm/min, which revealed that a feed per tooth of 0.005 mm, corresponding to the spindle speed 6000 rpm, attained the minimum burr production (0.02 mm).  
Subsequently, the optimal feed per tooth (0.005 mm) was fixed and the varying spindle speeds used in Experiment 1 were adopted (6000, 10,000, 30,000, 60,000, and 80,000 rpm). Next, (1) was used to calculate the feed rates (\(F\)) corresponding to the varying spindle speeds for Experiment 2; Figure 10 presents the results. When the feed per tooth, spindle speed, and feed rate were 0.005 mm, 60,000 rpm, and 600 mm/min, respectively, the minimum burr production after cutting (0.012 mm) was attained. Experiment 3 employed the optimal spindle speed of 60,000 rpm from Experiment 2 and used varying feed per tooth of 0.05, 0.03, 0.01, 0.005, and 0.003 mm. Then, (1) was used to calculate the feed rates (\(F\)) corresponding to the varying feed per tooth; Figure 11 shows the results. Therefore, a spindle speed, feed per tooth, and feed rate of 60,000 rpm, 0.005 mm, and 600 mm/min, respectively, yield the minimum burr production after cutting (0.01 mm). In addition, the surface roughness measurement was performed on the milled sample with the best cutting parameters, and it was found that the average surface roughness (Ra) was 0.4 \(\upmu\)m. These optimal cutting parameters will be used in the following actual stent processing experiments.  
Figure 10: PLA cutting experiment (feed per tooth 0.005 mm).  
Figure 9: PLA cutting experiment (feed rate 600 mm/min).
###### Abstract  
The power electronic converter design is essential for the operation of the switched reluctance motor (_SRM_). Thus, a fault-tolerant power converter is fundamental to ensure high reliability and extend the drive operation. To achieve fault tolerance, fault detection and diagnosis methods are critical in order to identify, as soon as possible, the failure mode of the drive. To provide such capability, it is proposed in this paper a new fault-tolerant power converter scheme combined with a fault detection method regarding the most common power semiconductors failures in _SRM_ drives. The fast and reliable proposed diagnosis method is based on the entropy theory. Based on this theory, normalized indexes (diagnostic variables) are created, which are independent from the load and speed of the motor. Through this method, it is possible to identify the faulty leg, as well as the type of power semiconductor fault. To test and evaluate the proposed solution several laboratory experiments were carried out using a 2 kW four-phase 8 / 6 _SRM_.  
switched reluctance motor (_SRM_); motor drives; fault-tolerant; fault detection; fault diagnosis; entropy-based feature
# Backdoor Attacks on Deep Neural Networks via Transfer Learning from Natural Images
## 1 Introduction
The transferability of backdoors has been poorly evaluated for representative architectures such as residual networks (the ResNet series) [22] and densely connected convolutional networks (DenseNet series) [23]. In addition, the effectiveness of backdoors in DNN models fine-tuned from backdoored models may be limited. Moreover, several layers must be frozen during the transfer learning process to transfer backdoors to fine-tuned (or \"Student\") models [20]. In this study, we aimed to evaluate whether backdoors are effectively transferable from backdoored models to fine-tuned models via transfer learning in more realistic situations. Specifically, we set up backdoors in 10 representative DNN models pre-trained on the ImageNet dataset. We then performed transfer learning from the backdoored ImageNet models for four real-world applications, including pneumonia classification from chest X-ray images [3], emergency response monitoring from aerial images [24], facial recognition [25], and age classification from images of faces [26]. For each application, DNN models were obtained by fine-tuning backdoored ImageNet models; none of their trainable layers were frozen, and clean datasets were used. The effectiveness of the backdoors in the models fine-tuned from the backdoored ImageNet models was then evaluated. Moreover, we applied the commonly used \"neural cleanse\" [17] backdoor detection method to evaluate whether the backdoors set up in the fine-tuned models could be detected.
## 4 Discussion
### Advantages and Limitations  
In our study, the combined assignment method was utilized to measure the levels of the DE and GD, while the RBF kernel \(\varepsilon\)-SVR was employed to predict the GD levels. The combination assignment method takes into account indicator variability and correlation, addressing the limitations of the entropy weight method. The prediction model overcomes issues like overfitting and fuzzy nonlinear relationships. Previous research has focused more on mechanism testing, but this study verified the sustainable role of the DE in promoting GD by predicting future levels.  
While this study offers valuable insights into the relationship between the DE and GD, there are several limitations to acknowledge. First, due to data availability and the lack of a unified indicator standard, the selected indicators may not have fully captured the subject, leading to conservative estimates. Additionally, although the relationship between the overall DE and GD in China was examined, the spatial variability was not thoroughly investigated. Future research could address these limitations by improving the indicator selection, incorporating more micro-level data, and further exploring the spatial heterogeneity of DE and GD.