esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Model Architecture
#### 3.4.3 Modified Cosine Similarity SoftMax Classifier Generator  
After having class prototypes, they are incorporated with the classifier generator \(c\) to produce the task classifier \(\hat{c}(S)\). Queried samples are embedded and input to the task classifier \(\hat{c}(S)\) where distances between them class prototypes and queried samples will be computed by distance function. Then the distances are input to SoftMax and output predicted results as probabilities. It is not suitable to directly use common L2 distance to measure the similarities between embeddings from embedding module and class prototypes from induction module due to different data magnitudes of different generators, mentioned in [10]. Cosine similarity is an ideal distance function to deal with magnitude problem because it only measures direction of vectors, which inherently normalizes vectors. Let \(s_{k,i}\) be the similarity computed between an embedded queried sample \(e_{k}^{Q}\) of input \(x_{k}^{Q}\) and \(i\)-th class prototype \(\hat{x}_{i}\), the probability that the queried sample belongs to \(i\)-th class \(c_{i}\) is:  
\[P(y_{k}^{Q}=c_{i}|x_{k}^{Q})=\frac{\exp(s_{k,i})}{\sum_{j=1}^{C}\exp(s_{k,j})}, \tag{4}\]  
where \(s_{k,i}\) is computed as:  
\[s_{k,i}=\frac{e_{k}^{Q}\cdot\hat{x}_{i}}{|e_{k}^{Q}||\hat{x}_{i}|}. \tag{5}\]  
However, pure cosine similarity can result in inadequate prediction when applied into SoftMax classifier. The value of cosine similarity ranges from \(-1\) to \(1\) where \(-1\) means \"not similar at all\" and \(1\) means \"most similar\". This indicates the highest probability can softmax classifier using cosine similarity produce is only \(e/(e+(N-1)e^{-1})\), far from the ground truth probability \(1\). As the number of class \(C\) increases, it will drop rapidly. To address this problem, we add a scale factor \(\alpha\) to all computed similarities as in [10,11] before applying similarities to softmax cdssifier. Thus, the predicting probability changes to:  
\[P(y_{k}^{Q}=c_{i}|x_{k}^{Q})=\frac{\exp(\alpha\cdot s_{k,i})}{\sum_{j=1}^{C} \exp(\alpha\cdot s_{k,j})}. \tag{6}\]As shown in Figure 4, the best predicted probability can be very close to \(1\) when the scale factor is about 4 for \(C=5\) and \(C=20\). Since it is unneccessary to make this scale factor changable, which is reported in [11], we fix \(\alpha\) to 10.  
The output of softmax classifier is a classification score vector with length of \(C\) in which each dimension refers the probability that queried sample belongs to the corrresponding class. It is widely acknowledged that cross entropy is a proper choice of loss function for classification problem using softmax classifier, so we use cross entropy loss for backpropagation:  
\[\mathcal{L}_{k}=-\log(P(y_{k}^{Q}|x_{k}^{Q})). \tag{7}\]  
For an episode, there are \(N\times C\) samples in \(Q\), so the total loss is the sum of negative log probabilities:  
\[\mathcal{L}=\sum_{k=1}^{N\times C}-\log(P(y_{k}^{Q}|x_{k}^{Q})). \tag{8}\]
# Intrusion Detection in IoT Using Deep Residual Networks with Attention Mechanisms
## 6 Model Evaluation and Discussions
### Hyperparameter Settings
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Performance Parameters**} & \multicolumn{8}{c}{**Batch Size**} \\ \cline{3-8}  & **16** & **32** & **64** & **128** & **256** & **512** & **1024** \\ \hline \multirow{4}{*}{LR = 0.01} & Accuracy & 0.9452 & 0.9476 & **0.9682** & 0.9360 & 0.9508 & 0.9156 & 0.9074 \\  & Precision & 0.9352 & 0.9383 & **0.9642** & 0.9269 & 0.9445 & 0.9022 & 0.8955 \\  & Recall & 0.9473 & 0.9487 & **0.9660** & 0.9340 & 0.9478 & 0.9193 & 0.9038 \\  & F1-score & 0.9406 & 0.9430 & **0.9651** & 0.9302 & 0.9461 & 0.9092 & 0.8993 \\  & Accuracy & 0.9870 & 0.9883 & 0.9931 & 0.9945 & 0.9943 & **0.9955** & 0.9908 \\  & Precision & 0.9862 & 0.9866 & 0.9920 & 0.9937 & 0.9931 & **0.9951** & 0.9888 \\  & Recall & 0.9852 & 0.9876 & 0.9929 & 0.9941 & 0.9943 & **0.9950** & 0.9911 \\  & F1-score & 0.9857 & 0.9871 & 0.9925 & 0.9939 & 0.9937 & **0.9950** & 0.9899 \\  & Accuracy & 0.9869 & **0.9924** & 0.9923 & 0.9759 & 0.9856 & 0.9552 & 0.9280 \\  & Precision & 0.9857 & **0.9918** & 0.9910 & 0.9719 & 0.9839 & 0.9505 & 0.9244 \\  & Recall & 0.9856 & **0.9915** & 0.9920 & 0.9753 & 0.9845 & 0.9511 & 0.9162 \\  & F1-score & 0.9856 & **0.9916** & 0.9915 & 0.9735 & 0.9842 & 0.9508 & 0.9201 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance variation at different parameters. Figure 6: Performance at different learning rates. Finally, we set the learning rate to 0.0001, and the other parameters were kept the same as those in the first two stages. The detailed performance comparison of these parameters is shown in Figure 6c. The best test accuracy is 99.24% when the batch size is 32. For batch sizes of 16, 64, and 256, the accuracy of the model is above 98%. For other batch sizes, the accuracy and other performance scores decreased, but the results were above 90%. The comparative experimental results of the above performance parameters show that the proposed model achieves the best results when the learning rate is 0.001, epochs are 100, and batch size is 512.
# Reverse Knowledge Distillation with Two Teachers for Industrial Defect Detection
## 2 Related Work
### Image Reconstruction and Recovery  
The idea of image restoration methods is to assume that the parameters of the model are only trained from normal samples. The model can only reconstruct the normal samples well, while the defective areas of the abnormal samples will produce large reconstruction errors. Such methods include autoencoder (AE) [8; 9], variational autoencoder (VAE) [16], and generative adversarial networks (GAN) [10; 11]. However, the image reconstruction-based approach has the disadvantage that the model may still reconstruct the unseen defects completely. So, another popular approach was introduced to store normal features by introducing feature memory [17; 18]. The researchers introduced memory to store the features of normal training samples, and in the inference phase, the sample features of the image to be tested are matched with the features in memory and reconstructed with them.  
Image recovery methods treat defects as noise and consider it as a denoising process. The core idea of such methods is to add defects to a normal image and then train a network model to recover it to the corresponding original image. One of the core issues of such methods is how to design defective images for construction. For example, Ref. [19] superimposed Gaussian noise on the original image. Ref. [20] used attribute cancellation. Ref. [21] took into account the positional relationships in the image, borrowed from puzzle restoration [22] to disrupt the order of image blocks in the original image, and trained the network to restore them. Other methods are to overlay a mask on the original image and train the network to restore what is covered by the mask. Ref. [23] added random rectangular masks to normal samples to simulate real defects. Ref. [24] randomly selected superpixels of the image as a mask for training the model.
## 5 Conclusions  
We demonstrated an effective homodyne detection system by employing a THz FET detector. The homodyne detection technique allows improving the SNR by up to 70 dB at the same frequency with a 100 ms integration time, which is more than 25 dB better than a direct detection system with the same components. The observed improvement in SNR using the homodyne technique with the TeraFET detector allows us to conclude that THz FET detectors can be successfully applied to continuous-wave THz spectrometry systems. Adding a second TeraFET detector to the same setup will allow us to simultaneously record transmission and reflection amplitudes as well as signal propagation delay times. Therefore, it opens the possibility to measure the complex dielectric properties of materials in the same way as with heterodyne-based vector network analyzers.  
Conceptualization, A.L.; methodology, A.L.; software, K.I. and D.B.B.; formal analysis, K.I.; investigation, K.I. and D.B.B.; data curation, K.I.; writing--original draft preparation, K.I.; writing--review and editing, D.B.B. and A.L.; supervision, A.L.; funding acquisition, A.L. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the \"International Research Agendas\" program of the Foundation for Polish Science co-financed by the EU under the European Regional Development Fund (No. MAB/2018/9) for CENTERA.  
Not applicable.  
The authors would like to thank the THz research group from the University of Frankfurt, led by [PERSON], for long-standing fruitful cooperation in developing THz detectors. The detector employed here was created within the scientific cooperation between the Frankfurt and Vilnius Universities' groups on a cost-sharing basis.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} THz & terahertz \\ FET & field-effect transistor \\ TeraFET & field-effect transistor with integrated antennas for THz detection \\ CMOS & complementary metal-oxide-semiconductor \\ Si & silicon \\ NEP & noise-equivalent power \\ SNR & signal-to-noise ratio \\ \end{tabular}
# Fuzzy Logic in Selection of Maritime Search and Rescue Units
## 2 Literature Overview (State of the Art)
For better detection and identification of vessels, data fusion principles from the Automated Identification System and Synthetic Aperture Radar have been developed and described in [14; 15]. Ships can be detected by satellites because they cause higher backscatter in images compared to the background. When combined with data from an identification system, it is possible to use it for safety or security purposes, but also to support the engagement of vessels in search and rescue operations.
# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 6 Future Works  
The field of XAI is constantly evolving, with new research and development efforts emerging at a rapid pace. Here are some key areas where the future work holds immense promise:  
Human-Centered Explainability: Moving beyond technical explanations, future XAI frameworks should prioritize explanations tailored to human comprehension. This may involve incorporating cognitive science principles and user feedback to design explanations that are not only accurate but also resonate with the intended audience.  
Explainable AI for Emerging Technologies: As AI ventures into new frontiers, like explainable reinforcement learning and interpretable deep fakes, the XAI frameworks need to adapt and evolve. Developing specialized tools for these domains will be crucial for ensuring the transparency and trust in these cutting-edge technologies.  
Explainable AI for Real-Time Applications: The current limitations of many XAI frameworks make them unsuitable for real-time applications. Research into more efficient algorithms and hardware-specific implementations will be essential to bridge this gap and enable the real-time explainability for time-sensitive scenarios. The development of XAI with ProcessGPT [103] can improve the human comprehension of and support for the business processes.  
Integration with Model Development Pipelines: Currently, XAI is often considered an afterthought in the ML workflow. The future advancements should integrate explainability considerations right from the model development stage. This would enable the proactive design of interpretable models and streamline the overall process.  
Standardization and Benchmarking: The lack of standardized evaluation metrics for XAI frameworks makes it difficult to compare their effectiveness. Establishing standardized benchmarks and fostering collaboration between researchers will be essential for objectively evaluating and improving the XAI techniques.  
By addressing these areas, the future of XAI holds the potential to transform our relationship with intelligent systems. We can move from a world of opaque algorithms to one where users can understand and trust the decisions made by AI, fostering the responsible development and deployment of these powerful technologies.
## 5 Method and Design
### Modular Approach
#### 5.2.1 Single CPU
The tInitJob transition requests access to the CPU and is only permitted to execute if the CPU resource is available; otherwise, it waits and retries later. The tRequeueInput and tJobDone transitions release the CPU resource, enabling the scheduling of another job on the CPU. Everything else in Figure 5 follows the same flow as the previous model. Regardless of the implemented scheduling algorithm, the overall model remains the same. Each algorithm utilizes the same modules, as depicted in Figure 5. The scheduling logic is embedded in pre-processor and post-processor files. The transition accountable for the scheduling decision is tInitJob, orchestrating job scheduling based on the implemented algorithm. Specific scheduling algorithms require additional parameters for decision-making, which are either included in the main simulation file, attached to the jobs, or both.
## 3 Results  
### Classification Performances  
We evaluated the accuracy and precision of the HC vs. RS classification task and classification errors \(EHC\) and \(ERS\) for healthy controls and respiratory symptoms, respectively. The performances obtained by means of the implemented machine and deep learning algorithms are shown in Figure 4. An overview of the classification performances is summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Learning Models** & **Accuracy** & **Precision** & **Error HC** & **Error RS** \\ \hline Random Forest & \(0.84\pm 0.04\) & \(0.84\pm 0.10\) & \(0.17\pm 0.10\) & \(0.16\pm 0.04\) \\ \hline MLP & \(0.85\pm 0.03\) & \(0.80\pm 0.08\) & \(0.20\pm 0.08\) & \(0.14\pm 0.03\) \\ \hline SVM & \(0.81\pm 0.03\) & \(0.79\pm 0.07\) & \(0.21\pm 0.07\) & \(0.19\pm 0.03\) \\ \hline DNN & \(0.82\pm 0.02\) & \(0.87\pm 0.02\) & \(0.13\pm 0.02\) & \(0.23\pm 0.02\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary table of the classification performance measures of the four implemented machine and deep learning models. Accuracy, precision and classification errors are reported with the respective standard deviations. According to a Kruskal–Wallis test, the differences within each column are statistically significant (\(p\)-value \(<\) 1%) MLP: multi-layer perceptron; SVM: support vector machine; DNN: deep neural network.  
According to a Kruskal-Wallis test [48], the four methodologies are significantly different despite these differences being quite comparable with the inherent uncertainties for each metric. We also made pairwise comparisons of their predictions (see Figure 5 and Figure A1 in the Appendix A).  
Overall, MLP had the best performance; thus, we only report its comparisons in the form of contingency tables. The remaining comparisons can be found in the Appendix A. It is worth noting that, in all three cases, the agreement between the classification models exceeds 76%.
# Investigation of an LED Strip Controller Based on a PWM Driver and a PIC Series Microcontroller
## 1 Introduction
KNX (Konnex) is a standardized international building automation and control system protocol used to control various homes and buildings [1; 2; 3; 21; 22; 23; 24]. KNX makes it possible to control not only lighting, but also other elements of building systems, such as heating, ventilation, etc. This makes it possible to create a comprehensive building management system. KNX GUIs can be limited when controlling LEDs, so additional hardware is required if more complex control effects are desired. Initial equipment and installation costs can be high, especially compared to other simpler light control systems. DALI (Digital Addressable Lighting Interface) is a standard lighting control communication protocol that is widely used for LED lighting systems. DALI allows individual control of each LED light or group of lights. It provides high flexibility, allowing it to create various lighting scenarios and atmospheres [2; 3; 24; 25; 26; 27; 28]. DALI uses a single wire, eliminating the need for complex wiring systems or additional control channels. This increases the ease of installation and reduces costs. DALI signal transmission can be limited by long cables or electromagnetic noise conditions [1; 2; 3; 4]. Controlling LEDs using microcontrollers offers many advantages, allowing efficient and flexible control of light parameters. Microcontrollers allow you to control each LED light source or a group of light sources individually. This enables the creation of dynamic and complex light patterns. Microcontrollers can support a wide range of light control functionalities, including PWM (pulse-width modulation) control strategies, temperature control, and other functions that allow the creation of a variety of light effects. The essential control aspects of LED strips and lighting systems are presented in Table 1.  
After analyzing the literature, it became clear that there are not many studies that analyzed the control of LED strips and lighting devices using a microcontroller and PWM. In addition, the studies discussed in the literature usually used the microcontroller itself to generate the PWM signal. This strategy of modulating and using the PWM signal has a disadvantage, because the percentage change resolution of the PWM signal is limited (at least every 20%). In this way, it is difficult to ensure the required amount of light flux is emitted by LED light sources (e.g., LED strips). The larger the number of light flux values, the more evenly it is possible to regulate the light flux emitted by LED lighting devices. In order to solve this problem and expand the limits of LED lighting device control possibilities, the goal of this study was to design and investigate an LED controller based on the concept of a PIC microcontroller and a separate PWM driver, thus expanding the amount of variable values of the light flux emitted by LED devices (e.g., LED strips).
## 5 Comparison Between the Shear Capacity Obtained from Tests and Codes
In summary, the shear capacity predicted by the specifications exceeds the test results by approximately 25% for the full-scaled specimens and by 10-25% for the scaled specimen. This discrepancy indicates that the Eurocode 4, AASHTO, JSCE, and GB codes do not fully account for the impact of cluster-distributed studs on the shear capacity in steel-concrete composite beams with straddle monorail. Consequently, the shear capacity of shear studs calculated using these codes tends to be higher than the experimentally obtained values. Further research is needed to refine the shear stud design formulas to better incorporate the influence of group effects. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Specimen** & **Stud Diameter/(mm)** & **Tests/(bN)** & **EC4/(bN)** & **AASHTO/(bN)** & **JSCE/(bN)** & **GB/(bN)** \\ \hline Z-40 & 19 & 86.0 & 108.9 & 105.1 & 106.8 & 107.3 \\ Z-32 & 19 & 88.0 & 108.9 & 105.1 & 106.8 & 107.3 \\ Z-16 & 19 & 90.5 & 108.9 & 105.1 & 106.8 & 107.3 \\ Z-13 & 10 & 23.7 & 30.2 & 29.1 & 29.6 & 25.7 \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of the shear capacity of one stud obtained from tests and codes.
## 3 Control Law Design and Stability Analysis
### Stability Analysis
According to (14), we have  
\[e_{1}=\mathbf{\mathcal{H}}s_{1} \tag{85}\]  
where \(\mathbf{\mathcal{H}}=\mathbf{\mathcal{L}}+\mathbf{\mathcal{B}}\), \(s_{1}=[y_{1}-y_{d},\cdots,y_{N}-y_{d}]^{T}\) represents the tracking error vector. Together with (84) and (85), we have  
\[|y_{i}-y_{d}|\leq\frac{\sqrt{2}}{\lambda_{\min}(\mathbf{\mathcal{H}}^{T}\mathbf{ \mathcal{H}})}\bigg{(}\frac{C_{0}}{(1-\lambda)C_{1}}\bigg{)}^{\frac{1}{2\mu}},\;\forall t\geq T_{r} \tag{86}\]  
where \(i=1,\cdots,N\) and \(\lambda_{\min}(\mathbf{\mathcal{H}}^{T}\mathbf{\mathcal{H}})\) is the minimum eigenvalue of \(\mathbf{\mathcal{H}}^{T}\mathbf{\mathcal{H}}\). By selecting appropriate \(C_{0}\) and \(C_{1}\), it can be ensured from (86) that the tracking errors \(|y_{i}-y_{d}|\) (\(i=1,\cdots,N\)) converge to a small neighborhood of zero in a finite time, namely, the asymptotic tracking control can be achieved. This completes the proof. \(\square\)  
**Remark 2**. : _Considering (86), we can adjust the values of \(C_{0}\) and \(C_{1}\) to change the size of tracking errors \(|y_{i}-y_{d}|\). In other words, by selecting the appropriate \(C_{0}\) and \(C_{1}\), the tracking errors can converge to a small neighborhood of zero._  
**Remark 3**. : _Observing (86) further, to achieve the desired tracking goal, we can decrease the value of \(C_{0}\) or increase the value of \(C_{1}\). For this purpose, we can decrease the values of \(\gamma_{i,m}\), \(\rho_{i,m}\), and \(\omega_{i,m-1}\) or increase the values of \(\Gamma_{i,m}\), \(\Upsilon_{i,m}\), \(\mathrm{N}_{i,k}\), and \(\tau_{i,k}\) to decrease \(C_{0}\); and we can also increase the values of \(c_{i,m}\), \(\gamma_{i,m}\), \(\rho_{i,m}\), and \(\omega_{i,k}\) or decrease the value of \(\tau_{i,k}\) to increase \(C_{1}\), where \(i=1,\cdots,N\), \(m=1,\cdots,n\), and \(k=1,\cdots,n-1\). However, the changes in \(\gamma_{i,m}\), \(\rho_{i,m}\), \(\omega_{i,k}\), and \(\tau_{i,k}\) will affect both \(C_{0}\) and \(C_{1}\). Therefore, when selecting appropriate design parameters, we should make a trade-off between the tracking performance and the amplitude of the control signal._
# Deep Unsupervised Embedding for Remote Sensing Image Retrieval Using Textual Cues
## 3 Experimental Results
### Experimental Setup  
We implemented the proposed method using Tensorflow-keras. We divided the TextRS dataset according to [32], while we randomly split the Merced dataset into: 80% is for training and 20% is for testing. We use a mini-batch size of 50 images for training the network. For optimization, we use Adam optimizer with its default parameters. In addition, we set the regularization parameters \(\lambda_{1}\)_and_\(\lambda_{2}\) controlling the contribution of the two losses to 1. We train the models for 50 iterations. For performance evaluation of the proposed method, we use the Recall@K (R@K) metric, which is widely used to match the scores of an image with a query sentence and vice versa. In the subsequent sections, we present the results in terms of R@K for different values of (K = 1, 5, and 10) calculated as follows:  
\[\text{R@k}=\text{(true positives @k)}/\text{((true positives @k)}+\text{(false negative @k))} \tag{7}\]  
All experiments are conducted on a workstation with an Intel Core i9 processor with a speed of 3.6 GHz, 64 GB of memory, and a GPU (with 11 GB GDDR5X memory).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of Images** & **Images from** & **No. of Sentences** & **Spatial Resolution** & **Image Size** \\ \hline
720 & NWPU & 3600 & 0.2 to 30 m & \(256\times 256\) \\ \hline
608 & PatternNet & 3040 & 0.062 to 4.7 m & \(256\times 256\) \\ \hline
480 & AID & 2400 & 0.5 to 8 m & \(600\times 600\) \\ \hline
336 & UC Merced & 1680 & 30 m & \(256\times 256\) \\ \hline Total: 2144 & Total: 10,720 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: TextRS Images Distribution.  
Figure 5: Example of images with five sentences in the following datasets: (**a**) TextRS and (**b**) Merced.