esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on High-Speed Vibration and Structure Optimization of Multiwire Sawing Machine
## 2 Materials and Methods  
### Principle Analysis of Vibration Characteristics  
A multi-wire sawing machine spindle mounted on a groove wheel with hundreds of diamonds wire saw is shown in Figure 1. The tension of many wire saws superimposed together will make the hollow structure of the slotted wheel deform, and the rotating parts in the spindle assembly are structurally complex. When the spindle is driven by the motor rotating at a high speed, it will cause the machine to self-excitedly vibrate, and the higher the speed is, the greater the excitation force generated is, and when the amplitude of vibration is greater the area above the main frame of the material platform will also vibrate, and the sawing stability will be reduced. This is one of the main reasons why the surface quality of the slices deteriorates when multi-wire sawing machines are run at higher line speeds.  
Figure 1: Image of the line network.  
Dynamic stiffness is the main indicator of machine tool vibration resistance; the dynamic stiffness of a machine tool can be expressed by using the following mathematical expression:  
\[K=\frac{F}{A} \tag{1}\]  
where \(K\) indicates the dynamic stiffness of the machine tool, \(F\) indicates that the machine tool is subject to the excitation force, and \(A\) indicates the size of the machine amplitude.  
For a multi-wire sawing machine at a certain alignment speed, the machine is subject to a certain maximum excitation force \(F\). In this state, if the dynamic stiffness \(K\) of the machine can be improved, the vibration amplitude \(A\) of the machine can be reduced accordingly, thus improving the operational stability of the multi-wire sawing machine and improving the surface quality of the slices.
## 3 Research Method
### Network Text Analysis (NTA)  
Network analysis is a research technique that focuses on seeking the relationship patterns between agents and their related data [53; 54]. Network text analysis (NTA) is a method that creates a model of nodes and links and quantitatively analyzes their phase structure as well as the process of diffusion and evolution [55]. Therefore, the network structure established through NTA is useful for interpreting certain social phenomena beyond the dictionary meaning of the relevant text [3]. According to [PERSON] and [PERSON] (2008), degree centrality analysis is the most used to understand the influence of entities and structures in a network [56]. The highest node has the strongest influence on spreading infectious diseases compared to the other nodes in the network [57]. NTA complexly connected the social network analysis technique with text analysis, and thus it simultaneously explores both knowledge and understanding of a given social phenomenon [9].  
Legal verdicts inevitably reflect social phenomena (e.g., nations' characteristics, ethics, social values); thus, this study adopted NTA for analyzing precedents. More specifically, this study conducted keyword-based network analysis for in-depth analysis of legal precedents by extracting specific keywords presented in privacy legal cases, which can be considered a text aggregate of socio-structural phenomena, using NetMiner 4.0. Specifically,a concept in the network consists of one or more related words, which are nodes in a social network analysis, while links represent the relationship between two concepts. In other words, two or more concepts simultaneously appearing in a single sentence indicates that the two have a close relationship, and the association of these linkages enables implementation of a semantic map [26]. That is, co-word analysis enables calculation of co-occurrence frequency of each word, as well as classification of the subject area in the relevant field, elicitation of keywords, and determination of correlation among subject areas.
## 3 Results
### Electrochemical Tests  
#### 3.2.1 Corrosion Potential  
The voltage at which the cathodic current density transitions to anodic when a metal is submerged in a solution, artificial sea water in this case, is called corrosion potential. The variation in corrosion potential over time provides a reliable indicator of the corrosion behavior, where oxidation and reduction reactions take place at equal rates on the samples surface [44].  
Figure 3 presents a clear tendency towards higher corrosion potential as the HEA volume percentage increases. The B4C.05 sample shows a remarkable drop close to 5 h of immersion, which indicates a surface activation during exposure to 3.5% NaCl solution; however, after 13 h of immersion its corrosion potential began to increase, inherent to the creation of a passive film on the outer layers of the composite, suggesting that it becomes thermodynamically stable under a marine environment. Sample B4C.1, at close to 8 h of immersion, shows another potential drop, followed by a slight tendency to a potential decrease. Sample B4C.3's corrosion potential first decreased but rapidly increased, just like sample B4C.2's corrosion potential; however, during exposure, B4C.3's corrosion potential shows a clear positive tendency.  
Figure 2: BSE-SEM images of B4C and B4C-doped composites: (**a**) monolithic B4C; (**b**) B4C.05; (**c**) B4C.1; (**d**) B4C.2; (**e**) B4C.3.
# Performance Analysis of a Cognitive RIS-NOMA in Wireless Sensor Network
## 4 Numerical Result and Discussion
Figure 6 plots system throughput versus \(\eta_{\mathcal{S}}\) in dB when varying the number of reflective elements \(N\). As in Figure 2, the outage probability of users is reduced when increasing \(\eta_{\mathcal{S}}\) and the number of reflective elements \(N\). That leads to an increase in system throughput, as shown in Figure 6. On the other hand, system throughput improved when increasing the value of \(\eta_{th}\), as shown in Figure 7. Furthermore, system throughput for RIS-NOMA outperformed RIS-OMA. In addition, RIS-NOMA and RIS-OMA converged to the throughput ceiling in the high \(\eta_{\mathcal{S}}\) regime.
# Peak Age of Information Optimization in Cell-Free Massive Random Access Networks
## 4 Optimization of Average Peak Age of Information
### Local PSO Algorithm Based on Neighboring Topology Information
This decentralized structure reduces signaling overhead and enhances the scalability of the algorithm. ```
0: Network parameters \(n^{S},g=\{1,2,\ldots G\}\), \(\lambda\); algorithm setting parameters \(K\), \(c_{1}\), \(c_{2}\), \(c_{2}\), \(\mathbf{v}_{min}\), \(\mathbf{v}_{max}\), \(\mathbf{q}_{min}\), \(\mathbf{q}_{max}\), \(\omega_{min}\), \(\omega_{max}\), \(I_{max}\). 0: Optimal channel access probability \(\mathbf{d}^{(I_{max})}=\left[q^{(1)},q^{(2)},\ldots,q^{(G)}\right]\)
1:for each particle \(k=1\) to \(k\)do
2: Initialize position \(\mathbf{q}_{k}^{(1)}\) and velocity \(\mathbf{v}_{k}^{(1)}\)
3:\(\mathbf{b}_{k}^{(1)}=\mathbf{q}_{k}^{(1)}\)
4:endfor
5: Initialize \(\mathbf{d}^{(1)}\) according to (13)
6: Set the initial iteration value \(i=1\)
7:for each iteration round \(i<I_{max}\)do
8:for each particle \(k=1\) to \(K\)do
9:for each element \(g=1\) to \(C\)do
10: Update velocity \(\mathbf{v}_{k,g}^{(i+1)}\) according to (14) and (17)
11: Constrain the velocity \(\mathbf{v}_{k,g}^{(i+1)}\) within the range \([\mathbf{v}_{min},\mathbf{v}_{max}]\)
12: Update position \(\mathbf{q}_{k,g}^{(i+1)}=\mathbf{v}_{k,g}^{(i+1)}+\mathbf{q}_{k,g}^{(i)}\)
13: Constrain the position \(\mathbf{q}_{k,g}^{(i+1)}\) within the range \([\mathbf{q}_{min},\mathbf{q}_{max}]\)
14: Calculate the PAoI according to (16)
15: Update \(\mathbf{b}_{k}^{(i+1)}\) and \(A_{k,g}^{(i+1)}\) according to (13) and (16)
16:endfor
17: Update \(\mathbf{d}^{(i+1)}\) according to (13)
18:\(i=i+1\)
19:endfor
20:endfor
```  
**Algorithm 2** PAoI Optimization Algorithm Based on Local PSO
## 4 Design of DDNB
### Node Constitution  
DDNB consists of 3 layers: Terminal layer, Service layer and Blockchain layer, as illustrated in Figure 1. Each layer consists of different type of nodes respectively.  
**Terminal Node (TN)** takes on a client-side user application role. It passes requests from users to Service Nodes (SN) to provide a specific service. Depending on how service providers, which consist of TNs and SNs, are designed, a number of logics can be implemented diversely.  
**Service Node (SN)** provides and processes actual services. Therefore, SN is where actual service is designed and implemented. SN can act like a server in traditional server-client architecture, and so is the scope of logics which can be implemented in SN is almost unlimited. Thus, various types of services can be provided, more than any smart contracts can do. SN performs a pre-defined function according to the request of TN. It is responsible for delivering the results of the execution to Blockchain Node (BN), and returning them to TN. In this process, no individual data are stored in SN. For this reason, unlike typical web applications, session and sensitive information are managed in TN, not in SN.  
**Blockchain Node (BN)** is the actual database for storing service related data. DDNB is designed to use permissioned blockchain so that BN can provide high transactions per second (TPS) and security while maintaining characteristics of blockchain. Also, by placing BNs behind VPN and firewall, it is possible to access control SNs to only those that are authorized. Therefore, service providers do not need to build their own nodes to participate in the blockchain. Instead, they only need to consider the consortium of the service networks. There can be multiple service networks consisting of individual domains to provide different services on a single blockchain network.
# Central Kurdish Text-to-Speech Synthesis with Novel End-to-End Transformer Training
## 2 TTS Literature Review
### One-Stage Systems  
One-stage text-to-speech (TTS) systems streamline the process of converting text into synthetic speech by employing a direct transformation model. These systems leverage end-to-end neural network architectures, such as sequence-to-sequence models with attention mechanisms, to map raw text directly to acoustic waveforms. In variational inference with adversarial learning for end-to-end text-to-speech (VITS) ([PERSON] et al., 2017), a duration predictor is introduced to improve the rhythm of the generated utterances. VITS was extended to allow the generation of diverse utterances for multi-language speakers using your-TTS. Although these models allow sampling from the input tokens, the quality of these generated utterances is still inferior to that obtained with single-speaker systems ([PERSON] et al., 2017).  
In ref. ([PERSON] et al., 2017), the authors introduced FastSpeech, a non-autoregressive version of transformer TTS ([PERSON] et al., 2017). They used the original model as a teacher and extracted the character durations from it. To generate all output frames, they trained a student model using a convolutional duration prediction.  
In 2021, [PERSON] et al. introduced Fast Speech 2 ([PERSON] et al., 2021), a non-autoregressive version of transformer TTS. The researchers used external durations to improve the training process and reduce the development costs. This approach assumes that the alignment model used for the language is of high quality.  
Recent advancements include models like VITS 2 ([PERSON] et al., 2017), which combines variational inference with normalizing flows and adversarial learning to directly generate high-fidelity speech waveforms from text. In ref. ([PERSON] et al., 2017), the authors proposed a framework for building controllable TTS systems that can generate speech with specific attributes. It combines a sequence-to-sequence TTS model with a conditional variational autoencoder (CVAE) to learn disentangled representations of speech attributes. The system enables flexible and controllable speech synthesis. The integration of large language models, such as LLaMA, into TTS systems, has been shown to enhance semantic understanding and generate more expressive speech ([PERSON] et al., 2017), highlighting the potential for semantic-aware TTS systems to further improve synthesis quality.  
Our proposed model lies in the category of one-stage systems, where no intermediate stages are needed to create mel-spectrograms and then to convert mel-spectrograms into waveforms, as our system regenerates waveforms directly from input text.
# An Evasion Attack against Stacked Capsule Autoencoder
## 4 Proposed Evasion Attack
### Identifying the Object Capsule Subset  
For image \(x\) and the SCAE model \(E\), let \(E(x)\) be the \(K\)-dimensional object-capsule presence that the SCAE outputs on \(x\) and let \(C(E(x))\) be the final classification result of \(x\) on the classifier \(C\). The goal of our attack is to find a perturbation \(p\) with the minimum \(\left\|p\right\|_{2}\) that makes the classifier \(C\) misclassify \(E(x+p)\). This problem is defined as follows:  
\[\begin{array}{rl}\text{Minimize}&\left\|p\right\|_{2}\\ s.t.&C(E(x+p))\
eq C(E(x))\\ &x+p\in\left[0,1\right]^{n}\end{array} \tag{1}\]  
Due to the sparsity loss used during training, different capsules are often associated with different classes. For an input image, only object capsules related to the label of the image will be activated and output with high presence, while the irrelevant object capsules remain inactive. This feature makes it possible to find the object capsule subset \(S\) related to the image. To misclassify a perturbed image \(x+p\), that is, \(C(E(x+p))\
eq C(E(x))\), we can lower the output of the object capsule subset \(S\) to reduce the contribution of this output to the original classification. In other words, we want the SCAE to mistakenly believe that \"the objects that belong to the real category do not appear in the image\", which is shown in Formula (2):  
\[\text{Minimize}\quad f(x+p)=\sum_{i\in S}E(x+p)_{i} \tag{2}\]  
For a well-trained SCAE model, the output object-capsule presence should be close to 1 or 0 (that is, the SCAE can accurately distinguish whether the objects represented by each capsule appear in the image). According to this feature, we compute the \(K\)-dimensional object-capsule presence \(E(x)\) for \(x\), calculate the average presence \(\overline{E(x)}=\frac{1}{K}\sum_{i=1}^{K}E(x)_{i}\), and finally obtain the activated object capsule subset \(S=\left\{i\middle|E(x)_{i}>\overline{E(x)}\right\}\).
## 8 Results and Discussion of Reviewed Papers
### UAVid  
The UAVid dataset has been extensively utilized as a benchmark by several researchers in the development of innovative methods for semantic segmentation in urban environments. [PERSON] et al. [97] introduced the Bilateral Awareness Network (BANet) and applied it to the UAVid dataset, achieving a notable mean Intersection-over-Union (mIoU) score of 64.6%. BANet's ability to accurately segment various classes within high-resolution urban scenes was demonstrated through both quantitative metrics and qualitative analysis, outperforming other state-of-the-art models like the MSD benchmark.  
Similarly, [PERSON] et al. [98] proposed the Attention Aggregation Feature Pyramid Network (A\({}^{2}\)-FPN) and reported significant improvements on the UAVid dataset. A\({}^{2}\)-FPN achieved the highest mIoU across five out of eight classes, surpassing BANet by 1% in overall performance. The model's effectiveness was particularly evident in its ability to correctly identify moving vehicles, a challenging task for many segmentation models.  
[PERSON] et al. [99] introduced UNetFormer, which further pushed the boundaries of semantic segmentation on the UAVid dataset. Achieving an impressive mIoU of 67.8%, UNetFormer outperformed several advanced networks, including ABCNet [114] and hybrid Transformer-based models like BANet and BoTNet [115]. UNetFormer demonstrated a strong ability to handle complex segmentation tasks, particularly in accurately identifying small objects like humans.  
Lastly, [PERSON] et al. [100] applied the Context Aggregation Network(CAN) to the UAVid dataset, achieving an mIoU score of 63.5% while maintaining a high processing speed of 15 frames per second (FPS). This model was noted for its ability to maintain consistency in both local and global scene semantics, making it a competitive choice for real-time applications in urban environments.
and carbon dioxide (CO\({}_{2}\)). As is shown in Figure 1, there is a strong CO\({}_{2}\) absorption band within the 3-5 \(\upmu\)m window at around 4.2-4.4 \(\upmu\)m, marked with a red line [4]. For high-precision radiometry, it has a noticeable effect. Therefore, it is desirable to correct the atmospheric transmission for target signature radiometry and recognition. The air force atmospheric laboratory has been working on global atmospheric research, and is one of the earliest scientific research institutes to carry out atmospheric study. Some scholars, such as [PERSON], conducted infrared radiometry on the sea surface [5], [PERSON] has developed software for atmospheric correction [6], and [PERSON] has studied the effect of atmosphere transmission on IR radiation features of targets and backgrounds [7]. When involving infrared radiometry, the effect of the atmospheric transmittance and the air path radiance cannot be disregarded.  
At present, the process of IR radiometry and the acquisition of atmospheric transmission are conducted simultaneously, so the radiance could be revised for each frame. In the conventional radiometric method, it is necessary to measure atmospheric parameters with suitable devices and calculate the atmospheric transmittance and air path radiance from these parameters by atmospheric radiation transport calculation software, such as MODTRAN (MODTRAN4.0, Ontar Corporation, _North Andover_, MA, USA, 2004) [8]. Even so, the error of the atmospheric transmittance at a high-pitch angles (usually more than 30\({}^{\circ}\)) obtained by software is about 10-20%; even worse, the data in the horizontal direction and low pitch-angle are unreliable. Further investigation of the method for atmospheric transmission is desirable to improve the precision of radiometry [9; 10; 11].  
In this article, to adjust the efficiency of IR radiometric calibration in the outfield, a model of high-speed calibration considering integration time is employed in Section 2. Following this, a radiometric method based on a constant reference to amend atmospheric transmission for high-precision radiometry is proposed in Section 3. Afterwards, calibration and radiometric experiments based on a MWIR system with a \(\Phi\)600 mm diameter are carried out to verify the theories described above in Sections 2 and 3. It is concluded, in Section 4, that our method yields high precision while allowing the user to perform radiometry without atmospheric radiative transfer calculation software. Additionally, it improves the flexibility and efficiency of radiometry.
## 3 Results, Analysis, and Simulations  
### Model Propagation Uma and Umi for 5G NR Radio Network Planning  
The number of sites needed in the area planning for the business/dense urban zone of the north zone of Quito city is first calculated by selecting adequate propagation models. As mentioned in the previous section, the selected propagation models are Uma and Umi, based on the link budget formulas for 5G planning from 3 GPP TS 38.901, considering a transmission antenna height of gnodeB of 25 m, a reception height from UE of 1.5 m, and an inter-site distance of 500 m [14] for a middle band and 30-100 m for a high band.
## 4 Results and Discussion  
### Data Acquisition and Annotation  
The study collected a total of 2735 images with resolutions ranging from 400 to 1280 pixels using camera equipment, which were then uniformly resized to 640 \(\times\) 640 pixels for training. This process expanded the dataset to 10,035 images of about 24,316 instances, including original images, which were augmented to 9000 images through techniques like rotation and flipping, along with 1035 images generated by CycleGAN. A typical image of a sewage discharge outlet is shown in Figure 9. Subsequently, the training set was augmented  
Figure 8: The process of enhanced dataset and model train by YOLOv5-BiFC and improved cycle GAN.  
using methods such as rotation, flipping, blurring, and low-light enhancement (using the EnlightenGAN algorithm). Without the low-light processing algorithm, it was challenging to ascertain the discharge conditions at night. The locations of sewage discharge outlets in the dataset were annotated using the LabelImg (version 1.8.1) software, and labels were assigned to indicate their pollution behaviors illustrated in Figure 10. All images, including those generated by the Cycle GAN, were manually annotated with labels. There are a total of 10 label categories: sewage brown, sewage orange, sewage green, sewage yellow, sewage orange, sewage white, sewage black, sewage pipe, sewage dam, and sewage drain. Among these, 1004 images of sewage discharge outlets were allocated to the test set, and another 1004 images were designated for the validation set, while the remaining data were maintained for model training in the training set. Figure 10 shows a portion of the labeled dataset.
# Intra-Reliability of a Wearable Near-Infrared Sensor for Monitoring the Intensity of Exercise
## 3 Results
Mean \(\pm\) SD values [with 95% CI] are presented for SmO\({}_{2}\) (%) in Test 1 (T1) and Test 2 (T2) at workloads from 50 to 250 W. Reliability indicators include intraclass correlation coefficient (ICC), coefficient of variation (CV), standard error of measurement (SEM), smallest worthwhile change (SWC), and minimal detectable change (MDC) [all with 95% CI].