esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Dosimetric Characterization  
#### 3.2.1 Heating Rate Optimization  
The heating rate has a prominent effect on TL measurements, as stimulation energy significantly influences the trapped electron population [18]. Therefore, it is highly recommended to optimize the heating rate before further exploring the TL dosimetric properties for newly developed TL material. The heating rate affects the glow curve differently for first-order and second-order kinetics. The heating rate increment results in decreased glow curve intensity, shifting the maximum peak temperature to higher temperatures along  
Figure 3: Differential scanning calorimetry (DSC) thermogram of S35654.  
with broadening of the full width at half maximum (FWHM) of the glow curves, for the first-order kinetics. However, for second-order kinetics, the glow curve intensity increases with the increasing heating rate, and consequently, the maximum peak temperature shift towards higher temperatures [35]. Ideally, the heating rate significantly increases the peak intensity with a shift in the maximum peak temperature towards a higher peak temperature. However, in this study, the heating rate could be observed significantly shifting the maximum peak temperature towards a higher temperature without out affecting the peak intensity, as shown in Figure 4; this behavior could be attributed to the inhomogeneous sample size. The heating rate dependence of the TL response is shown in Figure 5, where each data point represents an averaged measurement value of three samples to minimize random errors. The heating rate was optimized between 1 \({}^{\circ}\)C s\({}^{-1}\) and 15 \({}^{\circ}\)C s\({}^{-1}\) due to the declining trend of TL response in the identified range. Although the maximum TL response could be observed with 1 \({}^{\circ}\)C s\({}^{-1}\), the optimum heating rate was identified as 7 \({}^{\circ}\)C s\({}^{-1}\), based on an economical and practical approach, which produces a significant TL response with minimal standard deviation and maximum peak temperature in the ideal range.  
Figure 4: Glow curves of S35654 obtained with different heating rates.  
Figure 5: Heating rate dependence of thermoluminescence (TL) from MgB\({}_{4}\)O\({}_{7}\):Zn glass.
## 3 Methods
### Overall Framework of the Model
1: #==== Branch1: Clean \(\rightarrow\) Hazy \(\rightarrow\) Cyclic Clean ====
2: Generated_Hazy, gt_beta = G\({}_{\text{H}}\)(Input_Dehazy)
3: Cyclic_Dehazy, pred_beta = G\({}_{\text{D}}\)(Generated_Hazy)
4: #==== Branch2: Hazy \(\rightarrow\) Dehazy \(\rightarrow\) Cyclic Hazy ====
5: Generated_Dehazy, gt_d = G\({}_{\text{D}}\)(Input_Hazy)
6: Cyclic_Hazy, pred_d = G\({}_{\text{H}}\)(Generated_Dehazy)
7: #==== Discriminators training ====
8: dis_real_clean = D\({}_{\text{D}}\)(Input_Dehazy)
9: dis_fake_clean = D\({}_{\text{D}}\)(Generated_Dehazy)
10: loss_dis_clean = adversarial_loss(dis_real_clean, True) + adversarial_loss(dis_fake_clean, False)
11: dis_real_hazy = D\({}_{\text{H}}\)(Input_Hazy)
12: dis_fake_hazy = D\({}_{\text{H}}\)(Generated_Hazy)
13: loss_dis_hazy = adversarial_loss(dis_real_hazy, True) + adversarial_loss(dis_fake_hazy, False)
14: total_dis_loss = (loss_dis_clean + loss_dis_hazy)/4
15: total_dis_loss.backward()
16: #==== Generators training ====
17: fake_clean_logits = D\({}_{\text{H}}\)(Generated_Dehazy)
18: fake_hazy_logits = D\({}_{\text{D}}\)(Generated_Hazy)
19: loss_gan = (adversarial_loss(fake_clean_logits, True) + adversarial_loss(fake_hazy_logits, True))/2
20: loss_cycle = L1(Input_Hazy, Cyclic_Hazy) + L1(Input_Dehazy, Cyclic_Dehazy)
21: loss_\(\beta\) = L2(pred_beta, gt_beta)
22: loss_d = L1(gt_d, pred_d)
23: total_gen_loss = \(\lambda\)_gen*loss_gan + \(\lambda\)_cycle*loss_cycle + \(\lambda\)_g*loss_\(\beta\) + \(\lambda\)_d*loss_d
24: total_gen_loss.backward()
```  
**Algorithm 1** FA-CycleGAN Network Training Process
# Determining Thresholds for Optimal Adaptive Discrete Cosine Transformation
## 4 Conclusions  
In this paper, we presented a novel image compression method based on adaptive discrete cosine transform (DCT) with two quadtree fragmentation thresholds. The superior compression ratio of the optimized method over non-adaptive DCT has been experimentally demonstrated. In particular, we found that the adaptive DCT is most effective when it produces larger fragments of solid color and the target quality is not high, expecting to trade details for compression. By adjusting the defined thresholds, the algorithm finds a sufficient balance between the level of image compression and the retained detail quality, as was measured by the MS-SSIM metric.  
The key relations were found between the compression ratio and the quality estimates of the compressed images, such as the amount of detail in the image. In the case of medium compression quality, the increase in the compression ratio reaches 15% on average, which we established by examining a dataset containing 600 images with road traffic scenes. When the image quality is low, the compression ratio of the adaptive DCT outperforms the corresponding value of the conventional DCT by 66%.  
Using an equivalent distribution metric for frame temporal correlation could allow extending the proposed adaptive algorithm to the time dimension, improving the compression of the video stream. The implementation of adaptive DCT to video compression is the goal of future research.  
Conceptualization, S.F.; data curation, A.S. and A.V.; formal analysis, A.K. and A.V.; funding acquisition, T.K.; investigation, A.K. and A.S.; methodology, S.F.; project administration, T.K.; software, A.K. and A.Z.; supervision, T.K. and S.F.; validation, A.S. and T.K.; visualization, A.V. and A.Z.; writing--original draft, A.K. and A.S.; writing--review and editing, A.V., A.Z. and T.K. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Russian Science Foundation (RSF), project 20-79-10334.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Quality** & \
\begin{tabular}{l} **MS-SSIM** \\ **(ADCT/DCT)** \\ \end{tabular} & **CR (ADCT/DCT)** &
\begin{tabular}{l} **PSNR,** \\ **(ADCT/DCT)** \\ \end{tabular} & **dB** \\ \hline High & 0.989/0.997 & 3.897/3.735 & 37.495/41.521 \\ \hline Medium & 0.955/0.987 & 9.519/8.803 & 31.913/34.188 \\ \hline Low & 0.887/0.936 & 37.186/27.635 & 27.045/27.787 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sunny highway photo, original ITDV = 14.02255.
# Systems Engineering: Availability and Reliability
## 3 Innovations in Maintenance Strategies
The importance order and the identification of key parameters are helpful to increase the operational efficiency of the inventory control and provide effective methods for improving inventory management. Lastly, a case study with a shortage and limited inventory capacity is used to demonstrate the proposed model. One of the most important contributions of this paper [6] is that based on the research of inventory systems, it was found that there was almost no literature on the reliability of an inventory system. Combining the concept of reliability with the inventory system, an inventory system reliability model was proposed. It could enrich the research in the field of inventory system reliability. The growing competitiveness of the market, coupled with the increase in automation driven by the advent of Industry 4.0, highlights the importance of maintenance within organizations. At the same time, the amount of data capable of being extracted from industrial systems has increased exponentially due to the proliferation of sensors, transmission devices and data storage via the Internet of Things. These data, when processed and analyzed, can provide valuable information and knowledge about the equipment, allowing a move towards predictive maintenance being considered one of the most innovative maintenance strategies. Maintenance is fundamental to a company's competitiveness since actions taken at this level have a direct impact on aspects such as cost and quality of products. Hence, equipment failures need to be identified and resolved. Artificial Intelligence tools, in particular Machine Learning, exhibit enormous potential in the analysis of large amounts of data, now readily available, thus aiming to improve the availability of systems, reduce maintenance costs and increase operational performance and support in decision making. This is why [PERSON] and [PERSON] [7] apply Machine Learning to a set of data made available online and the specifics of this implementation are analyzed, as well as the definition of methodologies, in order to provide information and tools to the maintenance area. Although the results obtained compare well with those presented so far in the literature, the biggest disadvantage in using the presented methodology lies in the definition of the features. If the selection of features is not the most correct, the results obtained can lead to wrong predictions. For future work, the application of feature learning concepts will be considered instead of feature engineering, which appears to be promising to improve the results obtained [7]. This section may be divided into subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.
# An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation
## 2 Algorithm Design
### Mask-R-Cnn  
Mask-R-CNN is a conceptually simple and flexible method for object instance segmentation. A region proposal network (RPN) is utilized for region of interest extraction [31]. While predicting the box offset and class for each region of interest, it outputs a binary mask. FCN (Fully Convolutional Network) can be applied to each region of interest (RoI) for the prediction of a segmentation mask, since the mask directly presents the correspondence between pixels by convolution [34; 35; 36]. The framework of Mask-R-CNN is given in Figure 3.  
\begin{table}
\begin{tabular}{l} \hline \hline
**Function** K-Means (input data, the number of center point \(K\)) \\  Get the Dim dimension and the number of input data \(N\) \\  Generate K Dim dimension points randomly \\ **While** K-Means algorithm does not converge \\ \(N\) points: \\  Calculate which category each point belongs to \\ \(K\) center points: \\  Find all the data points that belong to this category \\  Modify the coordinates to the coordinates of the center point \\ **End** \\  Output \\ **End** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The process of K-Means algorithm.  
Figure 3: Mask-R-CNN framework for instance segment.  
The specific procedure of the Mask-R-CNN is as follows.  
**Step 1**: input of the normalized captured image into the pre-trained network in order to get the corresponding feature map.  
**Step 2**: set a predetermined RoI for each point in this feature map to achieve multiple candidate RoIs.  
**Step 3**: feed the candidate RoI into the RPN network for binary classification (foreground or background) and Bounding Box regression; part of the candidate RoIs are then filtered out.  
**Step 4**: RoIAlign operation is performed on the remaining RoIs (that is, the pixel of the original image and the feature map is first corresponded, and then the feature map is corresponded to the fixed feature).  
**Step 5**: classify these RoIs, do Bounding Box Regression, and make MASK generation (FCN operation in each RoI);  
Ultimately, there are three branches generated, which contain the information to predict reg-layer, cls-layer and object mask. Then, the first two branches are used for bounding-box classification and regression in parallel. The third branch is used to output the binary mask of the objective feature [36; 37].
# Efficient Low-Complexity Turbo-Hadamard Code in UAV Anti-Jamming Communications
## 3 Low-Complexity Turbo-Hadamard Codes Based on Tail-Biting
### The Tail-Biting Turbo-Hadamard Decoder
The computational complexity of each iteration is shown in Table 1.  
We use LUTs to execute addition and multiplication operations (we treat division as multiplication) and use read-only memory (ROM) to execute exponent and logarithm operations. In the operating environment of Xilinx Vivado 2019.1, a 10-bit input takes up the resources of 10 LUTs, a 10-bit input multiplication takes up 116 LUTs, and a 10-bit exponent or logarithm takes up 0.5 Block RAMs (BRAMs). As a result, we can obtain the comparison of hardware resources occupied in our proposed algorithm and the conventional algorithm in Table 2.
## 3 Research Methodology
### The Metrics for Performance Evaluation
#### 3.3.1 Accuracy  
The accuracy, which indicates the overall correctness of the results or model, is the most common evaluation of a predictive model. It is usually calculated by considering four standard units, namely the following: \"True Positive (TP)\", \"False Positive (FP)\", \"True Negative (TN)\", and \"False Negative (FN)\". The number of TP instances indicates where the actual class is predicted and identified as the real class. The number of FP instances indicates the actual class that is not the actual class, and they are incorrectly classified as the actual class. The number of TN instances indicates where the actual class is not obtained, and the predicted class is not obtained as well. Finally, the number of FN instances indicates a situation where the actual class is obtained but they are wrongly identified as not supported. Then, the accuracy (ACC) of a predictive model is given by Equation (1), as follows:  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]
# Preliminary Field Results of a Dedicated 5G Positioning Network for Enhanced Hybrid Positioning +
## 2 Dedicated 5G Positioning Network
### Testbed Equipment  
The testbed equipment is based on commercial off-the-shelf (COTS) components while keeping a high degree of flexibility and modularity thanks to the use of SDR platforms and regular computers. Each node of the HOP-5G testbed shares the same high-level architecture, including both aerial and ground deployments. As shown in Figure 1, the nodes are based on four key components:  
* Computer: The HOP-5G software modules are installed and executed on a regular computer with Ubuntu 20.04 operating system. When installed within the UAV payload, an Intel NUC with a Thunderbolt3-to-10 GbE adapter is used to fulfil the size, weight, and power (SWaP) constraints. When fixed on ground, a workstation or tower computer with a 10 GbE adapter is preferred to support very high computational performance. In both cases, a Wi-Fi module is used to establish a remote connection with the operator.
* SDR: The USRP X300 is considered the preferred SDR to transmit and receive 5G signals. This SDR is built with an UBV daughterboard to operate at frequency range 1 (FR1), i.e., below 6 GHz, and with a GPS-disciplined oscillator (GPSDO) to keep a tight synchronization among testbed nodes.
* GNSS receiver: The COTS GNSS receivers are the u-blox F9P for ground-fixed nodes and the u-blox F9R for moving aerial or ground nodes.
* Antennas: The two additional antennas of the node are a 5G monopole omnidirectional antenna and a GNSS helical antenna. An L-band splitter is used to connect the GNSS antenna output with the SDR and the GNSS receiver.  
Thanks to the hardware and software flexibility and modularity, a node can be used as a BS or as UE with only software configuration updates. All the nodes are remotely controlled and monitored with a regular laptop, e.g., by exploiting a Wi-Fi connection.
## 4 Results and Discussion
### Effect of Indentation Speed  
The tests were performed at two different ram speeds: 2.5 mm/s and 16 mm/s. The effect of indentation speed can be seen in Figure 13 (Left) where the force was plotted against indentation depth for tests T1_10_6_C2_5 and T1_10_5_C2_16. Although the peak failure force was found to be on the same order of magnitude, a number of distinctions in the failure behaviour can be identified from the plot. For the slow tests, the force increases monotonically until the failure occurs with large pieces of ice being removed and the load dropping more than 90%. On the other hand, for fast tests, as the load increases it continues to produce local fractures with smaller ice pieces before a large load drop occurs and larger pieces of ice are removed. For higher rates, the existing flaws in the ice are subjected to a more rapidly increasing concentrated stress field (slower dissipative processes cannot  
Figure 11: Comparison of results for tests T1\(5\)3_C2_16 and T1\(5\)9_C2_16; (**Left**) nominal pressure and beam displacement at the centre plotted against time; (**Right**) average failure frequency plotted against ice temperature.  
Figure 12: Acceleration data from tests T1\(5\)3_C2_16 and T1\(5\)9_C2_16 with expanded view of the selected area shown on the right.  
relieve local intense stresses at a sufficiently fast rate, thus resulting in greater local stress accumulation), which results in a higher degree of local fracture. Also due to the release of stored energy upon rebound following local fracture events, the cumulative amount of energy stored in the beam is lower for fast tests, which in many cases, results in a lower load drop. This has important implications for the development of the damaged layer in the indentation contact zone. If a large amount of energy is stored in the beam during the loading period, the release of this energy upon failure can clear out most of the ice in the damaged layer through extrusion. However, if the energy is not sufficient, part of the damaged layer survives the rebound cycles and continues to grow triggered by the fracture event. Such behaviour can result in vibration within the damaged layer as has been observed in small-scale indentation tests [24]. The average failure frequency versus interaction speed is plotted in Figure 13 (Right). As expected, the average failure frequency was consistently found to be significantly higher for fast tests.
# Semantically Guided Enhanced Fusion for Intent Detection and Slot Filling
## 4 Experiments
### Discussion  
The SG framework demonstrates exceptional performance on the ATIS and SNIPS datasets, highlighting its pivotal role in advancing SLU systems. It surpasses traditional models in terms of accuracy for both IDSF tasks, thanks to its dynamic semantic processing capabilities.  
With the ATIS dataset, tailored for travel-related inquiries, the SG framework distinguishes between different user intents with finesse. For example, it adeptly discerns a general flight query from an explicit booking request, streamlining responses for users seeking flight information or making travel arrangements. This is crucial for enhancing the efficacy of online booking services.  
When applied to the SNIPS dataset, the framework's versatility becomes evident as it accurately processes a wide array of voice commands. For instance, a user's command to play music, like \"Play the latest album by Coldplay on Spotify\", is interpreted with precision, catering to specific requests and enhancing user interaction within smart home environments.  
The adaptability of the SG framework in these scenarios underscores its practical application and potential for integration into various forms of SLU technology. Recognizing the significance of context and dialect variations, future developments will focus on further refining the model's ability to handle the intricacies of natural language. This paves the way for SLU systems that are more intuitive and efficient in terms of user communication across diverse settings.
## 3 Results and Discussion
### Stage II
#### 3.2.5. Flowability  
Flowability was expressed as HR and, according to the definition, powders can be classified as medium flowing powders (1.1 \(<\) HR \(<\) 1.25), difficult flowing powders (1.25 \(<\) HR \(<\) 1.4) or very difficult flowing powders (HR \(>\) 1.4) [28]. Powders had HR varying from 1.11 \(\pm\) 0.06 to 1.67 \(\pm\) 0.14 (Figure 6). Powders of better flowability were obtained at IAT of 100 and 120 \({}^{\circ}\)C using all three levels of IAH, which underlines the importance of temperature in producing powders of satisfying flowability. Many factors may influence the flowability, such as moisture content, powders morphology or particle distribution, which are the obvious consequence of process parameters [29]. At ambient air humidity (9-10 g/m\({}^{3}\)), the best flowability was achieved at the highest IAT regardless of FR, which would appear that the IAT was the factor that led to the collapse of the structure of particles due to rapid water evaporation. Presumably, this phenomenon resulted in powders of more compact, irregular particles of lower porosity, achieving powders of lower HR. In many cases, the increased MC decreases HR; however, it was not observed in this study as variants of satisfying flowability had generally lower MC, which was confirmed by the PCA as no strong correlation was noted (Figure A2) [29; 30]. As aforementioned, the elevated temperature is probably responsible for better flowability of MD powders. This phenomenon was confirmed as well by the particles' morphology, as generally more particles of a larger size were observed at higher IAT (Figure 3). The evaporation rate would be more rapid, which in consequence enables the formation of the particle at the very beginning of the drying process, resulting in a generally larger particle size and better flowability.  
Figure 6: Flowability (HR) of MD powders spray dried at different inlet air temperature (80–120 \({}^{\circ}\)C), inlet air humidity (0.1–10 g/m\({}^{3}\)) and feed rate (0.16–0.83 mL/s) (Stage II); blue areas on the figure indicate the experiments where not-evaporated water was observed inside the drying chamber. The gradient of blue, orange and red color indicate the increasing inlet air temperature, while blue color demonstrates the lowest level, orange the middle level and red the highest level of inlet air humidity.
# Antimicrobial Peptides--Mechanisms of Action, Antimicrobial Effects and Clinical Applications
## 3 Antimicrobial Effects of AMPs
### Immunomodulatory Activity of AMPs
#### 3.4.3 Cathelicidins  
Cathelicidins are constitutively expressed at low levels in epithelial cells, skin, and mucosal surfaces and are released to a considerable extent in response to infections, especially by granulocytes and mononuclear phagocytes [114]. Cathelicidins in in vitro conditions have an apparent antimicrobial effect on parasites, fungi, bacteria, and enveloped viruses [115]. Cationic cathelicidins have different mechanisms of action on the cell, damaging the integrity of the negatively charged membrane, which results in cell death [116]. They can act on intracellular processes by promoting protein breakdown, weakening enzymes' role, and affecting RNA and DNA synthesis [117]. Numerous studies have observed that in the presence of glycosaminoglycans, salt, bacterial DNA, and mucin, i.e., under physiological conditions, the action of LL-37 is inhibited even at high peptide concentrations [88; 118]. Based on this, we can conclude that, in vivo conditions, most cathelicidins probably do not have a direct bactericidal effect. However, they still have great importance for preventing microbial infections due to their immunomodulatory effect [98; 100]. The ability of cathelicidins to attract many adaptive and innate immune cells to inflammatory sites by modulating the expression of chemokines and chemokine receptors should undoubtedly be highlighted [119]. Thus, cathelicidins can act on various immune cells as direct chemoattract. This effect is enhanced by their indirect impact on inducing the upregulation of chemokines and chemokine receptors on leukocytes [114; 119; 120].