esa-sceva/satcom-chunk-collection huggingface dataset:
# Bearing Fault Diagnosis Based on Small Sample Learning of Maml-Triplet
## 3 Construction of the Dataset and Model
### Construction Method of the Maml Neural Network and Triplet Neural Network Model  
In the Maml-Triplet model, there are two parts: the maml neural network and the triplet neural network. The structure of the maml neural network and the triplet neural network is shown in the Figure 6. The triplet neural network is a part of the secondary maml neural network.  
As shown in Figure 6, the triplet neural network is composed of four convolution blocks and a full connection layer. The convolution block is composed of a convolution layer, pooling layer, and batch normalization layer. Among the four convolution blocks, only the convolution layer is different. The parameters of the first convolutional layer include 64 convolution kernels of size \(32\times 1\) with stride 2. The parameters of the second convolution layer include 64 convolution kernels of size \(16\times 1\) with stride 2. The parameters of the third convolution layer include 64 convolution kernels of size \(8\times 1\) with stride 2. Finally, the parameters of the fourth convolution layer include 64 convolution kernels of size \(3\times 1\) with stride 2. The pooling layer adopts the maximum pooling with a size of 2 and a step size of 2. The last layer is a fully connected layer with 256 nodes, which is used to transform signals into 256 dimensional eigenvectors.  
The maml neural network structure adds a full connection layer with Softmax activation function on the basis of the triplet neural network structure. The number of nodes is the total number of fault types included in each task during the training process of Maml. This paper sets the number of nodes as 3. The activation functions of the remaining layers are set to ReLU.  
The network structure of the comparison experiment Maml-CNN modifies the number of nodes in the last layer of the Maml neural network according to the number of fault types in the data set. The training process also extracts the weight of the Maml neural network and then uses the target dataset for training to obtain the final model.  
It should be noted here that, as shown in Figure 5, the initial weight of the triplet neural network is the neural network trained by the Maml neural network. In the subsequent training, the weight is updated at a lower learning rate.  
Figure 5: Processing process of bearing fault signal.
## 5 Experimental Data Analysis
### Analysis of Test and Simulation Results  
#### 5.3.1 Testing of Simulation Results  
Ten independent experiments were conducted on 70 types of acoustics, and the experimental results were the same. As shown in Table 3, among the 70 groups of acoustic signals to be measured, five groups of abnormal signals were wrongly judged as normal signals, three groups of normal signals were wrongly judged as abnormal signals, and the prediction results of the remaining 62 groups were correct, so the accuracy of the simulation experiment was 88.57%.
## 4 Performance Experiments
### Experiment Analysis  
As our experiments have shown, the proposed dual plane IoT system provides better performance than that of the previous single plane IoT system. The reason for the high performance of the proposed  
Figure 6: Comparison of throughputs.  
system can be explained in terms of packet IO performance and packet processing architecture. The DPDK solution applied in our system uses a polling mechanism to avoid time-consuming interrupts and system calls. This reduces the delay time of incoming packets stored in a receiving queue, and prevents packet losses caused by a full receiving queue. According to the comparison tests, the DPDK can improve system throughput by more than 200%. From an architectural point of view, the proposed dual plane architecture contains two planes and handles different types of messages in different planes. This helps to improve system performance by having data messages forwarded continuously, regardless of whether control messages are being processed. As shown in Figure 6, the dual plane architecture outperforms the single plane architecture even though both use the same DPDK solution, and the gap between the performance of the two systems becomes wider as the number of control messages is increased. As a result of these performance advantages, the proposed system shows a lower message loss ratio.  
To sum up, the proposed system can improve the network performance and shows high performance when receiving packets of a small size and a large number of control messages. This feature makes our system highly suitable for IoT environments, which usually involve the regular transmission of small-sized messages and frequent device attach and detach events.
## 3 Inter-Satellite Laser Communication Model Based on Simulink
### Mathematical Model Analysis of Signal Transmission
\(\omega_{\text{het}}\) is the angular heterodyne frequency for the main carrier-to-carrier beat note. \(\omega_{\text{sb1}}\) is the angular heterodyne frequency for the lower sideband-to-sideband beat note. \(\omega_{\text{sb2}}\) is the angular heterodyne frequency for the upper sideband-to-sideband beat note. \(\varphi\), \(\varphi_{\text{sb1}}\) and \(\varphi_{\text{sb2}}\) are the phases, which contain the science information. \(n(t^{\prime})\) is a Gaussian white noise. From a spectral perspective, the clock sidebands are located outside the main lobe of the main beat note frequency. Therefore, they do not affect the reception and modulation of the PRN code and can be neglected in the analysis of data transmission. Neglecting the sidebands, the beat note signal after processing and ADC conversion becomes:  
\[u_{\text{ADC}}(t^{\prime})=\sin\!\left(\omega_{\text{het}}t^{\prime}+\varphi +m_{\text{pm}}\sum_{n=-\infty}^{\infty}C_{n}p(t^{\prime}-nT_{c})\right)+n^{ \prime}(t^{\prime}) \tag{10}\]  
where \(n^{\prime}(t^{\prime})\) is also a Gaussian white noise.
## 3 Results and Discussion
### Morphology and Structural Analysis
From the structural configuration of the Na\({}_{2}\)Ti\({}_{7}\)O\({}_{15}\) in Figure 1c, the channel structure formed by sharing the corners and edges of octahedral TiO\({}_{6}\) was only along the \(b\) axis, and the Na ions were only located inside the channel. Most interestingly, the striped NTO NWs had an oriented structure along the (020) direction (\(b\) axis), as shown in Figure 2, which was the same as the channel direction of the Na ions in the NTO NWs. The channel direction of the Na ions was just along the NTO NW growth direction, which benefits the transport of Na ions. In contrast to the structure of the nanotubes [5], the stripe structure of the NTO NWs may be advantageous for Na ion diffusion, similar to the layer anode materials in SIBs [20]. The well-shaped and uniform NTO NWs presented a good channel structure along the nanowire axial direction and the characteristics of Na ions located in the channels. These special channel structures in the NTO NWs will greatly help the transport of Na ions for high-performance SIBs.
## 3 Results and Discussion
### Trends of NDVI  
#### 3.2.1 Trends of Areal Average NDVI  
At a national scale, a significant increasing trend of 0.0007 year\({}^{-1}\) (\(R^{2}=0.37,p<0.01\)) (Figure 5\(a\)) was observed in the mean growing season NDVI, indicating that the overall growing season NDVI has increased by 4.6% over the past 30 years. 1994 was a significant change point for the growing season NDVI. The increasing trend decreased by 62.5%, from 0.0008 year\({}^{-1}\) before 1994 to 0.0003 year\({}^{-1}\) after 1994, indicating that at a national scale, the increase of the growing season NDVI has slowed down since the mid of 1990s. This phenomenon was also reported in previous studies in North America [2], Eurasia [30] and East Asia [29]. These studies compared the change points in vegetation and climate factor series, and suggested that climate change is most likely to be responsible for the stalled or reversed trends of NDVI in mid-1990s. Although in this study, no significant abrupt change point corresponding to that in NDVI series was observed in either mean temperature or mean precipitation time series at a national scale, it is possible that these relations are regional specific hence cannot be revealed in terms of national average. Further relations between abrupt change points of climate factors and NDVI series will be discussed in Section 3.3.4. The overall trend of growing season mean NDVI (0.0007 year\({}^{-1}\)) was  
Figure 4: Map of growing season (**a**) temperature and (**b**) precipitation trends across China from 1982 to 2010.  
slightly higher than that of the whole year mean NDVI (0.0006 year\({}^{-1}\)) reported by [PERSON] _et al._[19]. This slight discrepancy implies that NDVI increased faster in the growing season than in the rest periods of a year, though the different areas that were masked out might also contribute to the difference.  
Among the three seasons (Figure 5b-d), NDVI increased at the highest rate of 0.0009 year\({}^{-1}\) (\(R^{2}=0.29,p<0.01\)) in spring, followed by that in autumn (0.0006 year\({}^{-1}\), \(R^{2}=0.22,p<0.01\)). While in summer, the trend was almost zero. Moreover, the increasing trend in spring reversed at 1996, changing from 0.0008 year\({}^{-1}\) to \(-\)0.0002 year\({}^{-1}\) while no abrupt change points were observed in summer and autumn. Therefore, despite the slight discrepancy between the two change points of NDVI in the growing season and spring, the stalled trend of NDVI in the growing season can be mainly explained by the reverse trend in spring, which partly overlaps with early growing season.
# Analysis of Controllability in Cyber-Physical Power Systems under a Novel Load-Capacity Model
## 4 Case Study and Discussion
### CPPS Controllability under Different Redistribution Strategies  
Network attacks can be divided into static and dynamic attacks. Static attacks refer to the attack sequence that has been determined when the system is established, while dynamic attacks dynamically adjust the attack sequence based on the real-time status and load of the system. Different from static attacks [34], this paper adopts dynamic attack strategies to make the attacks more targeted, and the network that survives this situation has higher stability. This section compares the load redistribution strategy introduced in this  
Figure 10: (**a–c**) The \(n_{d}\) variation curves of the CPPS under four different coupling strategies after random attack, degree attack, and betweenness attack, respectively. The CPPS is formed by coupling the Chinese 132-node system and the WS network.  
paper with the load redistribution strategies in other studies in the literature [35; 36], and it analyzes the impact of different load redistribution strategies on the network controllability under different attack strategies.  
Strategy I [35]: Distributing the failed load evenly among the neighboring nodes of the faulty node, based on the average number of neighboring nodes for each fault node.  
\[Q_{ij}=\frac{1}{N_{f(i)}} \tag{25}\]  
where \(N_{f(i)}\) indicates the number of neighbor nodes of node \(i\), and \(f(i)\) is the set of neighbor nodes of node \(i\).  
Strategy II [36]: Distributing the failed load evenly among the neighboring nodes of the faulty node based on the average number of neighboring nodes for each fault node.  
\[Q_{ij}=\frac{D_{j}}{\sum\limits_{n\in f(n)}D_{n}} \tag{26}\]  
where \(D_{n}\) indicates the degree of node \(n\).  
For degree attacks and betweenness attacks, the impact of various load redistribution strategies on system controllability was investigated in both scenarios.
# mPrivacy: A Privacy Policy Engine and Safeguard Mechanism in Mobile Devices
## 3 Problem Formulation
### System Model  
Figure 1 shows the system model of the mobile device. The system consists of the application layer, platform layer, and physical sensor layer. Users can install and interact with third-party applications at the application layer. The third-party application may come from an attacker. The platform layer is produced by the platform provider. The platform provides an operating system (OS) and services that enable third-party applications to access physical sensors (such as accelerometers, microphones, and gyroscopes) and logical sensors (such as address books, calendars, and text messages). Physical sensors and logical sensors may directly expose users' private information. For example, a third-party application can use GPS sensors to learn about the user's location or learn about the user's activities from schedules in the calendar. In particular, the information of the logical sensor is usually personal information. The attacker could use the network interface as a route of information from external sources. Access to the network interface can expose contextual information of the user. For example, the IP address or Wi-Fi environment can reveal the location of the user. The density of access points around the user can reveal what kind of areas the user is located in. For these reasons, we also consider the network interface as a physical sensor.  
The information that the application can use may come from the mobile device itself, or it may come from external resources, such as cloud servers, other mobile devices, ambient sensors, and internet of things (IoT) devices. Although information from external sources may not directly expose personal information, it can still expose users' contextual information, such as location, physical environment, activities, and interests. In this work, attacks related to external sources are out of scope.  
Recent mobile platforms have provided static (for example, Android, version \(\leqslant\) 5) or dynamic (for example, Android, version \(\geqslant\) 6) [31] permission control mechanisms. In Android 11, the latest Android version at the time of writing, there are some updates for privacy such as \"one-time permissions\" and \"permissions auto-reset\" [32]. Whether static or dynamic, the fundamental Android approach to privacy problems remains the same; it only tries to control the application's access to resources with a binary decision (allow or disallow). The decision comes only from the user. Therefore, from the information escalation attack's point of view, both mechanisms employed in Android are not effective. On the contrary, our approach overcomes the limitations of such privilege-based protections to defend against the more sophisticated attackers who focus on information rather than privilege. Therefore, we believe that the limitation of our experiments with old versions of Android does not affect the validity of our approach.
# Cartesian Aerial Manipulator with Compliant Arm
## 4 Estimation and Control
### Force-Torque Estimation with Compliant Arm  
The linear and angular deflection sensors integrated in the compliant arm allow the estimation of the force and torque exerted on the manipulator in the corresponding directions, as represented in Figure 7. On the one hand, the spring-lever transmission mechanism introduced in the servo joint allows the estimation and control of the torque from the deflection measurement [7,19]. If \(k_{3}\) and \(d_{3}\) denote the joint stiffness and damping, the torque delivered by the motor can be estimated as follows:  
\[\tau_{m,3}=k_{3}\Delta\theta_{3}+d_{3}\dot{\Delta}\theta_{3} \tag{18}\]  
This torque is spent to compensate three terms: gravity, output link inertia, and the torque due to external forces:  
\[\tau_{m,3}=J_{3}\ddot{q}_{3}+m_{3}gl_{3}\sin(q_{3})+F_{ext}\sin(\beta)L \tag{19}\]  
Here \(m_{3}\) and \(J_{3}\) are the mass and inertia of the output link, \(l_{3}\) is the distance from the rotation axis to the center of mass, and \(F_{ext}\) is the external force acting over the tool center point at distance \(L\) with an angle \(\beta\). The transmitted torque can be controlled through the servo position reference [19]:  
\[\theta_{3}^{ref}=\theta_{3}+\left(k_{P_{3}}\tau_{e,3}+k_{I_{3}}\int\tau_{e,3} dt\right) \tag{20}\]  
where \(\theta_{3}\) is the current servo position, \(\tau_{e,3}=\tau_{3}^{ref}-\tau_{m,3}\) is the torque error, and \(k_{P_{3}}\) and \(k_{I_{3}}\) are the gains in PI control. This controller will be applied in the experiment shown in Section 5.4. On the other hand, the force acting along the direction of the arm link is estimated from the elongation of the extension springs (see Figure 5), measured with a linear potentiometer:  
\[F_{4}=K_{4}\Delta l_{4} \tag{21}\]  
where \(K_{4}\) is the spring stiffness and \(\Delta l_{4}\) is the elongation. The experiment described in Section 5.3 (force monitoring in grabbing situations) exploits this estimation to detect over-loads and release the gripper in order to prevent crashes. The torque exerted by the compliant joint and the force along the link are supported by the thrust generated by the multirotor. These wrenches are depicted in Figure 7. However, note that the arm does not provide an estimation of the Y-axis wrenches.
# Effective RNA Regulation by Combination of Multiple Programmable RNA-Binding Proteins
## 2 Materials and Methods
### Luciferase Reporter Assay  
Here, \(1.5\times 10^{4}\) of NIH3T3 cells were seeded onto 96-well plates the day before transfection. Transfection was performed using Lipofectamine LTX (Thermo Fisher Scientific, Waltham, MA, USA). Transfection mixtures contained 100 ng TTP-PUF expression vector and 1 ng reporter vector. For control, pCMV-TTP(C147R)-GG-PUF (Addgene #100000051), in which the TTP gene but no PUF-encoding sequences were inserted, was used instead of TTP-PUF expression vectors. Then, 24 h after transfection, cells were lysed using Passive Lysis Buffer (Promega). Firefly luciferase and _Renilla_ luciferase activities were measured using the Dual-Luciferase Reporter Assay System (Promega) with measurements taken on a GloMax-Multi Detection System (Promega). Firefly luciferase activity (Fluc) was normalized to _Renilla_ luciferase activity (Rluc), and then relative luciferase activities (RLAs), compared to control samples, were calculated (Equation (1)). The degree of repression by a TTP-PUF relative to the control was indicated by the reciprocal of the relative luciferase activity (Equation (2)).  
\[\text{RLA }_{\text{(TTP-PUF)}}=[\text{Fluc }_{\text{(TTP-PUF)}}/\text{Rluc }_{\text{(TTP-PUF)}}]/[\text{Fluc }_{\text{(control)}}/\text{Rluc }_{\text{(control)}}] \tag{1}\]  
\[\text{Fold repression}=1/\text{RLA} \tag{2}\]
# Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects
###### Abstract  
Compared to traditional approaches, the spot scanning surface defect evaluation system (SS-SDES) has better performances on the detection of small defects and defect classification for optical surfaces. However, the existing system deviations will cause distortions and even a missing area in the defect image which is reconstructed from the acquired raw data based on the scanning trace, thus degrading the reliability of detection results. To solve these problems, a system calibration method is proposed with the parameterization of these deviations and the modeling of practical scanning trace. A constraint function, to characterize the straightness and scale errors in the image, is defined. Then an optimization is implemented to minimize it and hence to obtain the optimal estimate of the system deviations, which is subsequently used to adjust the system and reconstruct reliable defect images. Additionally, to further enhance the image quality, an image reconstruction method capable of suppressing signal noise through a weighted average strategy is proposed. Experiments show that with our methods, the system deviations are effectively corrected, and a complete and precise defect image with low distortions that are within 1.8 pixels is reconstructed. Therefore, the detection accuracy and reliability of the system can be improved.  
defect detection; calibration; image reconstruction; spot scanning; optical inspection +
Footnote †: journal: Applied Physics Letters
## 3 Background and Related Work
### Patient Admission Prediction  
Predicting which ED patients will require hospital admission is another area significantly transformed by DL. Early admission prediction models enable proactive bed allocation and have the potential to mitigate ED crowding. For instance, one study demonstrated that a DL-based model achieved an area under the receiver operating characteristic (AUROC) curve of 0.87-0.88 for predicting admissions, outperforming traditional risk scores [33]. Furthermore, incorporating triage text notes into these models has enhanced their prediction accuracy [27]. Such advancements empower ED staff to anticipate patient disposition more effectively and streamline care delivery.
## 4 Discussion
### Study Limitations  
While our work demonstrates the effectiveness of retrieval-based in-context learning for cross-lingual summarization, several limitations should be acknowledged that may affect the generalizability and applicability of our findings.  
**Language Selection and Geographic Bias.** Our evaluation is constrained by the selection of six low-resource languages that exhibit notable geographic and linguistic concentration. Four of the evaluated languages Gujarati, Marathi, Pashto, and Sinhala belong to the Indo-European family, while the remaining two Thai and Burmese) representTai-Kadai and Sino-Tibetan families respectively. This distribution creates potential biases in our assessment, as the predominance of Indo-European languages may overrepresent the effectiveness of cross-lingual transfer due to underlying structural similarities with English. Furthermore, our language selection primarily spans South and Southeast Asia, limiting the applicability of findings to languages from other regions such as Africa, indigenous languages of the Americas, or Pacific language families. Different linguistic regions may present unique challenges for cross-lingual transfer that are not captured in our current evaluation framework.  
**Methodological and Technical Constraints.** The quality of multilingual embeddings varies significantly across different script systems and languages, potentially affecting the accuracy of semantic similarity computations fundamental to our retrieval mechanism. Our reliance on BGE-M3 embeddings, while comprehensive, may introduce language-specific biases that influence retrieval effectiveness. Additionally, the computational overhead of three-shot configurations and beyond limits the practical scalability of our approach, particularly for resource-constrained deployment scenarios. The token count analysis reveals that extending beyond two-shot settings incurs substantial computational costs without corresponding performance improvements, constraining the potential benefits of additional examples.  
**Evaluation and Domain Limitations.** Our evaluation framework, while comprehensive, relies primarily on lexical overlap metrics (ROUGE-L) and contextual similarity measures (BERTScore) that may not fully capture the nuanced aspects of cross-lingual semantic preservation. The assessment is further limited to news article summarization, which may not reflect performance across other domains such as scientific literature, legal documents, or creative content. The quality of reference summaries in low-resource languages presents additional challenges for reliable evaluation, as human annotation resources are often limited for these languages. Moreover, our correlation analysis between example similarity and performance, while statistically significant, may not account for confounding factors related to specific linguistic phenomena or model-specific biases that could influence the observed relationships.