esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamics Analysis of a Variable Stiffness Tuned Mass Damper Enhanced by an Inerter
## 1 Introduction
Compared with the traditional TMD, the TID has a smaller added mass and achieves a high performance in vibration reduction. [PERSON] et al. [40] studied a negative-stiffness TID to broaden the effective operating bandwidth and presented an optimal design for the negative-stiffness TID. [PERSON] et al. [41] proposed a novel nonlinear energy sink with an inerter. The inerter was used to replace the mass of the traditional nonlinear energy sink. These investigations revealed that compared with the mass block, the inerter not only has better performance in vibration reduction but also has less added mass. Although there have been diverse types of variable stiffness TMDs, the large additional mass is still the main constraint for engineering applications. In this work, a novel TMD is proposed with the stiffness adjusted by a PI controller and the mass block replaced by an inerter. It is expected to reduce the additional mass and enhance the vibration reduction performance at the same time. Furthermore, the proposed TMD exhibits a strong nonlinearity. In order to reveal the diverse nonlinear phenomena of the dynamic system, a harmonic balance method is adopted to obtain both stable and unstable frequency responses. The parameters are analyzed and optimized. The impulse response of the system is also investigated, and internal resonances are revealed through wavelet transform spectra. The remainder of this paper is organized as follows. In Section 2, an equivalent model of the primary structure with the VS-TMD-I is established. In Section 3, the harmonic balance method is applied to analyze the frequency responses. In Section 4, the performance of the vibration reduction and the dynamic behavior of the VS-TMD-I are analyzed. In Section 5, the conclusions are presented.
# Transitioning Broadcast to Cloud
## 5 Scale and Performance of Cloud-Based Media Delivery Systems
From a single job to almost 100 jobs can be created at each instance of time. This indicates that composition of work processed by this system includes not only constantly-running 24/7 channels, but also a good volume of special events (e.g., concerts, sports events, etc. ), triggering creations of new live delivery chains. Cloud-based architectures are highly suitable for handing variable workload of this kind. Next, in Figure 14 we show CDN bandwidth statistics reported for same set of users of cloud-based delivery platform. The graphs show the amounts of data delivered at CDN edge, as well as data pulled from origin servers, and the amounts of CDN midgress traffic. Here, we also observed significant variations. Around 12/20 8:00 to 11:00 (US eastern standard time) we saw a cascade of two significant spikes in traffic. The volume of data increased almost 10\(\times\) during this period. However, we also noticed, that the amount of origin traffic during the same period did not increase much. This illustrates that CDNs managed to absorb these spikes in traffic successfully. More generally, however, the amount of origin traffic may also fluctuate significantly. To support such a variable load, cloud-based delivery platforms provide means for auto-scaling and balancing of load on origin servers. Finally, in Figure 15, we also plot the numbers of concurrent players that are pulling live streaming content. It can be observed that this figure looks somewhat similar to CDN-reported statistics. Around 12/20 8:00 to 11:00 (US eastern standard time) we also saw a cascade of two spikes, where almost 1 M concurrent viewers joined. This explains spikes in CDN traffic noted earlier. Such spikes are pretty common for popular events, and the system is designed to handle them efficiently. Figure 14: CDN-reported bandwidth statistics. Figure 13: The numbers of new live streams created at each instance of time. Naturally, the above statistics capture only a small example set of use cases of today's existing cloud-based OTT delivery platforms. However, they show that such platforms are operational, mass deployed, and capable of handling volumes of transcoding and media delivery streams comparable to ones used in broadcast distribution systems. As also shown, the amounts of concurrent live events/streams, transcoders, transmuvers, origins, and other elements of delivery chan can be highly variable in practice. Cloud-based deployments are ideally suited for handling such variablity in resource requirements in cost-efficient manner. Transition of additional components of broadcasts workflows to cloud will lead to additional reduction in investments in hardware, will simplify management and maintenance, and will make overall design of such systems much more flexible, extensible, and future-proof.
# A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis
## 4 Experimental Verification and Analysis
### Comparison Methods  
This study validates the performance of the CDAN by comparing it with four advanced fault diagnosis methods. The four comparison methods are described as follows.  
Model 1: The fault diagnosis technology based on deep learning requires a large number of labeled datasets. However, considering different operating conditions, it is impractical to obtain labeled datasets for each operating condition. To address this issue, [PERSON] et al. [32] proposed a diagnostic method for unlabeled data by combining clustering and domain adaptation.  
Model 2: [PERSON] et al. [29] proposed a multi-linear mapping method for sample labels in order to learn more fault features from source domain samples and target domain samples to improve the accuracy of fault recognition.  
Model 3: [PERSON] et al. [18] proposed a new DA representation learning model by proposing a neural network. The model uses an extractor to map features from the source and target domains. In contrast, the model proposed in this paper sets different feature extractors for mapping source domain samples and target domain samples separately.  
Model 4: [PERSON] et al. [33] addressed the issue of decreased diagnostic accuracy due to distribution differences in bearing data under different operating conditions.  
All models were run on a computer equipped with an Intel Core i9-9900K processor, NVIDIA GeForce RTX 2080 Ti GPU, and 128 GB of RAM. The computing environment included Windows 10 operating system, Python 3.8, and PyTorch 1.11.0. All comparison models used SGD with momentum for backpropagation to update network parameters. The iteration period was set to 100, the learning rate to \(10^{-4}\) and the batch size to 100. Equal amounts of data from the laboratory and real working conditions were used in each batch. All models were trained until the loss function reached a relatively stable value.
# Detecting AI-Generated Images Using a Hybrid ResNet-SE Attention Model
## 4 Results and Discussion
### Qualitative Analysis of Classification Behavior
#### 4.5.2 Discussion of Implications and Limitations  
The experimental results obtained from the proposed SE-ResNet-50 model not only highlight its superior performance but also underline several broader implications and practical considerations for the real-world deployment of AI-generated image detection systems. Future investigations could address these limitations through enhanced optimization strategies, more robust data augmentation methods, or by exploring other lightweight attention mechanisms.  
Real-World Applicability and Societal Impact  
The increasing prevalence of AI-generated images in digital art, journalism, advertising, and social media introduces significant ethical and operational challenges, including misinformation, identity fraud, and digital forgery [1; 3]. The high accuracy (96.12%) and precision (97.04%) of the proposed hybrid model suggest it can serve as a reliable automated tool for detecting such synthetic content across various industries, particularly in content moderation, digital forensics, law enforcement, and media authentication.  
Importantly, the model demonstrates not just statistical excellence but operational robustness. Its ability to operate with moderate computational demands while retaining state-of-the-art accuracy positions it well for real-time or embedded applications, such as browser plugins, mobile applications, or edge AI devices. Moreover, its enhanced interpretability via attention weights allows stakeholders to better trust, audit, and explain decisions made by the system--an essential requirement in regulated environments like legal and governmental contexts.
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 2 Related Work
### Pseudo-Label in Video Temporal Grounding  
Pseudo-labels are extensively utilized in video temporal grounding tasks, fundamentally relying on model-generated predictions to form surrogate supervision signals. Such an approach bolsters performance in weakly or semi-supervised settings, while likewise demonstrating considerable viability in low-resource or unsupervised domains [27]. [PERSON] et al. proposed a pseudo-label generation method based on predictive confidence, where high-confidence time segments are filtered for training via a confidence threshold [28]. Nevertheless, this approach remains vulnerable to threshold selection, risking the exclusion of valuable segments or the inclusion of suboptimal ones [29]. An alternative tactic is to utilize the model's predicted temporal segments as pseudo-labels outright, for instance, leveraging initial predictions as pseudo supervision in weakly supervised video localization [30]. Yet the fidelity of such pseudo-labels is wholly determined by the initial model's predictive prowess, impeding effective mitigation of noisy segments [30]. While these methods may excel in particular scenarios, pseudo-label quality generally hinges on the accuracy of the underlying model predictions [31]. Inferior pseudo-labels can erode the efficacy of model training and inject noise into the learning procedure, thereby impeding potential gains in performance [32]. Moreover, existing methods lack explicit constraints on the pseudo-label generation range, making it difficult for the model to filter out irrelevant or low-confidence segments.
## 3 Human Activity Recognition Techniques
### AI-Driven HAR Algorithms  
Advancements in artificial intelligence (AI) have revolutionized HAR systems, enhancing their accuracy and adaptability. The ability to operate in real-time and adapt to diverse environments makes AI-driven HAR algorithms indispensable for a wide range of user-centric technologies. One of their primary strengths is the ability to learn complex patterns from high-dimensional data. Deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have significantly advanced HAR by automating the feature extraction process [65]. CNNs excel in capturing spatial features from data, such as video frames or sensor signals, while RNNs are well-suited for sequential data, such as time series signals from wearable devices. These models not only improve recognition accuracy but also adapt to a diverse set of activities without extensive manual intervention.  
In addition to supervised learning, unsupervised and semi-supervised learning methods are gaining traction in HAR. These approaches are particularly valuable for scenarios where labeled data are limited or difficult to obtain. Techniques like clustering and generative models can uncover hidden patterns in unlabeled data, enabling robust activity classification with minimal human input. Furthermore, advancements in transfer learning allow HAR systems to leverage pretrained models, reducing the computational cost and time required for deployment.
# Design and Experiment of a Moving Magnet Actuator Based Jetting Dispenser
## 5 Experimental Results and Discussion  
### Backpressure  
In the first experiment, the effect of the backpressure on the droplet size is indicated in Figure 10a, in which the droplet diameter steadily increases with increases in the applied backpressure. The average measured diameters of the jetted micro-droplets on the substrate are 1.36, 1.54, 1.68, 1.86, and 1.94 mm at backpressures of 0.1, 0.2, 0.3, 0.4, and 0.5 bar, respectively. When the backpressure is less than 0.1 bar, the high viscous fluid filling speed reduces, and thus the fluid cannot adequately fill the chamber under the high operating frequency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(P_{\text{gy}}\) (Bar) & \(z_{\text{m}}\) (mm) & \(F\) (Hz) & \(T\) (\({}^{\circ}\)C) \\ \hline Exp. 1 & 0.1–0.5 & 0.5 & 20 & 25 \\ Exp. 2 & 0.2 & 0.5–2.5 & 20 & 25 \\ Exp. 3 & 0.2 & 0.5 & 10–80 & 25 \\ Exp. 4 & 0.2 & 0.5 & 20 & 25–45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The setup parameters for the four experiments.  
Figure 9: Experimental test system. 1. MMA dispenser; 2. direct current (DC) fan; 3. Accelnet amplifier; 4. thermometer; and 5. oscilloscope.  
Furthermore, the low pressure also causes the fluid momentum to be too low to dispense the fluid out of the nozzle. As a result, the fluid accumulates around the outside of the nozzle head, and the surface tension prevents the fluid from falling. When the backpressure is larger than 0.5 bar, the fluid momentum becomes too high; the jetted droplet size is too big, and many small satellite dots are generated around the main dots, which reduces the dispensing quality. Some low viscous fluids, such as water and fuel oil, are jetted without applying backpressure because of the effect of the hydrostatic pressure on the self-filling ability of fluid in the chamber. Depending on the type of fluid, the fluid is not able to move the fluid from falling to falling.  
Figure 10: Experimental results for the droplet diameters. (**a**) Backpressure effect; (**b**) needle placement effect; (**c**) operating frequency effect; and (**d**) fluid temperature effect.  
materials, a reasonable amount of backpressure should remain during the operating process, which guarantees fluid adequacy in the chamber as well as jetting stability.
# An Improved RAIM Availability Assessment Method Based on the Characteristic Slope
## 3 PL-Based RAIM Availability Assessment
### Classic PL and Its Enhancement  
The classic PL, denoted as PL\({}_{c}\), is defined as the product of the maximum slope \(Slope_{max}\) and the minimum detectable bias \(\sqrt{\lambda_{a}}\) as follows:  
\[\text{PL}_{c}=Slope_{max}\sqrt{\lambda_{a}} \tag{8}\]  
where \(\lambda_{a}\) is the noncentral parameter of the fault detection test statistic _Ts_ under single-satellite fault mode [23].  
The enhanced PL, denoted as PL\({}_{e}\), has an additional term that protects against variation in the random error of the position solution on the basis of PL\({}_{c}\) as follows [10]:  
\[\text{PL}_{e}=Slope_{max}\sqrt{\lambda_{a}}+\alpha(P_{MD})\sigma \tag{9}\]  
where \(\alpha(P_{MD})=\Phi^{-1}(1-P_{MD})\) and \(\sigma\) is the standard deviation of the position error distribution.  
Slope reflects the relationship between the position error and the pseudorange residual. PL is the projection of the pseudorange residual on the position error as shown in Equations (8) and (9). According to \(\lambda=\left(\frac{\xi_{b}}{\sigma_{m}^{2}}\right)^{2}s_{mm}\) derived in Appendix B, the measurement bias \(\xi_{b}\) for a faulty satellite \(VS_{m}\), which causes _Ts_ to obey \(\chi^{2}(K-4,\lambda_{a})\), is \(\xi_{b}=\sigma_{m}^{2}\sqrt{\frac{\lambda_{a}}{s_{mm}}}\). Based on the position error derivation in Appendix A, this \(\xi_{b}\) makes the VPE under single-satellite fault mode obey:  
\[\text{VPE}\sim N\Big{(}Slope_{m}\sqrt{\lambda_{a}},a_{v}^{2}\Big{)} \tag{10}\]  
Contrasting Equation (8) with Equation (10), the classic vertical PL, denoted as VPL\({}_{c}\), is the expectation of VPE at \(\xi_{b}=\sigma_{m}^{2}\sqrt{\frac{\lambda_{a}}{s_{mm}}}\) with the maximum slope. Therefore,  
Figure 1: \(P(\text{HMI}|1F)\) for different satellites.  
\(P(|\text{VPE}|\geq\text{VPL}_{c}|1F)=0.5\) at \(\xi_{b}=\sigma_{m}^{2}\sqrt{\frac{\lambda_{a}}{s_{mn}}}\) in the worst case. Similarly, the enhanced vertical PL, denoted as \(\text{VPL}_{c}\), is the upper quantile of the \(P_{MD}\) of VPE distribution at \(\xi_{b}=\sigma_{m}^{2}\sqrt{\frac{\lambda_{a}}{s_{mn}}}\) with the maximum slope. Therefore, \(P(|\text{VPE}|\geq\text{VPL}_{e}|1F)=P_{MD}\) at \(\xi_{b}=\sigma_{m}^{2}\sqrt{\frac{\lambda_{a}}{s_{mn}}}\) in the worst case.
## 3 Experimental Study in Short Delay Regime
### Suppression of 1/f Noise  
In the measurement above, the delayed self-heterodyne signal exhibits a Lorentzian line shape, indicating that the frequency noise is dominated by white noise. In the experiment, 1/_f_ frequency noise arises in some devices probably induced by fabrication process [37]. As a result, the line shape transfers to Gaussian [38]. Nevertheless, it allows us to study the flicker noise characteristics in the proposed device structure. Gaussian linewidth and corresponding noise coefficient were extracted at different coupling currents, and the trend is shown in Figure 9. Flicker frequency noise is also suppressed in coupled DFB lasers and reduces with higher coupling strength. Since 1/_f_ frequency noise results from surface or interface recombination [37] and is supposed to be independent of output power [39], mutual injection locking provides a new method to suppress 1/_f_ noise.  
Figure 8: **(a)** Linewidth and output power of a 3 mm length device at different coupling currents. Inset: FP modes emerge at high currents; **(b)** delayed self-heterodyne signals at different coupling currents; **(c)** linewidth narrowing factor of mutually coupled DFB lasers with different device lengths; Inset: the change of heterodyne spectrum from free-running to mutually injection locked state.
## 2 Digital Workflow: From Intraoral Scan to the Printable Data  
### Data Acquisition  
A fundamental basis for the digital design of orthodontic appliances is gathering information regarding the intraoral situation including teeth, alveolar ridge, and soft tissues. This can be achieved using intraoral scanning or by employing digitized conventional plaster casts. Some studies found the intraoral scan to be more time-consuming than conventional methods [36; 37], whereas others did not observe any significant difference [38]. This might indicate that the practitioners' level of experience could have a major impact on time efficacy. In addition, several studies underlined a sufficient accuracy and precision of intraoral scans for orthodontic purposes [36; 37; 38; 39]. Further advantages of digital impressions compared to conventional ones include gains in comfort for the patient and a reduced amount of laboratory waste [7; 8; 37; 39].  
Data can be stored in various formats. Nonetheless, the Standard Tessellation Language (STL) format was shown to be the smallest common denominator for 3D data exchange between devices and software programs [8; 36].
# Measurement and Analysis of RSS Using Bluetooth Mesh Network for Localization Applications
## 3 Radio Channel Characteristics and Models
### Path Loss and Shadowing
In contrast, the location fingerprinting approach does not require path loss models, and it is also not affected by the shadowing effects; instead, RSS-based location fingerprinting approach depends on the correlation between RSS measurements and location coordinates. In this research, we employ Bluetooth mesh network to experimentally study the characteristics of RSS measurements in the context of channel modeling for localization applications.
## Materials and Methods
### Empirical Mode Decomposition (EMD)  
EMD, which has been proved to be an effective data preprocessing method, is usually applied to reduce the noise of a time series, which is non-stationary [25]. The main theory of EMD is that by local characteristic timescale filtering, the original series is decomposed into oscillatory modes (IMFs). The IMFs follow the rules as shown below: (1) The extremes and the zeros must have the same quantity or the difference of quantity must be within one in the whole time series; (2) The upper and lower bounds' mean must be equal to zero. Let the raw data be \(s(t)(t=1,2,\ldots,l)\). The main procedures of EMD are listed below:  
1. Set all local maxima and minima of time series.
2. Connect all local extreme to produce the upper bound \(e_{up}(t)\) and the lower bound \(e_{low}(t)\) by applying a cubic spline.
3. Compute the mean value from the upper and lower bounds \(m(t)=[e_{up}(t)+e_{low}(t)]/2\).
4. Compute the difference value between the raw data and the mean value \(h(t)=s(t)-m(t)\).
5. Inspect if \(h(t)\) fits characteristics of IMF. If yes, \(h(t)\) is defined as the \(i\)th IMF and the residual \(r(t)=s(t)-h(t)\) will replace \(s(t)\). If no, \(s(t)\) will be replaced by \(h(t)\).
6. Repeat the above-mentioned procedures. Stop when the value of the two successive siftings' standard deviation is lower than the threshold set earlier.  
By utilizing mentioned procedures, a group of IMFs is settled from the raw data, which is arranged based on the frequency from high to low.  
**Definition 1**.: _The raw data, which is used for decomposition and consists of \(n\) IMFs and one residual is obtained as:_  
\[s(t)=\sum_{i=1}^{n}c_{i}(t)+\mathrm{r}_{n}(t) \tag{1}\]  
_where \(n\) is the number of IMFs, \(\mathrm{r}_{n}(t)\) is the residuals representing a trend in \(s(t)(t=1,2,\ldots,l)\), and \(c_{i}(t)(i=1,2,\ldots,n)\) represents the IMFs. When describing the local characters of a raw time series, each IMF is independent._