esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
A two-stage method for weak fault feature extraction of a bearing by combining an adaptive morphological filter with a new frequency band selection strategy is introduced in the paper. Firstly, a new AMF is proposed that could adaptively determine the SEs of MF according to the analyzed signal itself. The new AMF used the preprocessing method to handle the original vibration of the faulty bearing for preliminary noise reduction. Subsequently, the introduced FBS is implemented on the de-noised signal to further eliminate the influence of interferences caused by a full frequency band envelope spectral. According to the simulation verification of the new proposed AMF and FBS and the effectiveness verification of the proposed two-stage method, the following conclusions could be drawn:  
1. The MHPO1 shown in Equation (13) is used as a morphological operator in the paper, which not only could de-noise the interferences preliminarily but could also enhance the transient features of the faulty bearing.
2. The proposed new AMF method could provide a new option for adaptive SEs design through simulation verification.
3. GI has the advantage of a higher reliability than the other advanced indexes such as the Hoyer measure, L2/L norm, and kurtosis index to reflect the cyclic transient features, which could be used as an index for FBS.
4. The two-stage rolling bearing weak fault feature extraction method combining an adaptive morphological filter with a new frequency band selection strategy could extract the periodic transient features of the faulty bearing much more effectively than only using one extraction method of the two proposed methods.  
Methodology, J.L.; Software, H.W.; Formal analysis, S.L.; Investigation, Q.D.; Resources, L.C. All authors have read and agreed to the published version of the manuscript.  
The research is supported by the Key Science and Technology Research Project of the Henan Province (approved grant: 232102221039).  
Not applicable.  
The authors declare no conflict of interest in preparing this article.
## 3 Mathematical Framework
### Lagrange Relaxation Approach  
The Lagrangian function can be written with respect to the non-negative multipliers of \(\lambda^{t}\) and \(\mu^{t}\) as:  
\[\mathcal{L}=F\big{(}S_{i}^{t},P_{i}^{t}\big{)}+\sum_{i=1}^{T}\lambda^{t}\Bigg{(} P_{net}^{t}-\sum_{i=1}^{N}S_{i}^{t}P_{i}^{t}\Bigg{)}+\sum_{t=1}^{T}\mu^{t} \Bigg{[}P_{net}^{t}+SR^{t}-\sum_{i=1}^{N}S_{i}^{t}\big{(}P_{i}^{t}+RU_{i}\big{)} \Bigg{]} \tag{17}\]  
To separately minimize the contribution of each generating unit, Equation (17) is rewritten in the following form:  
\[\mathcal{L}=\sum_{i=1}^{N}\sum_{t=1}^{T}\Big{\{}S_{i}^{t}\Big{[}F\big{(}S_{i}^{ t},P_{i}^{t}\big{)}+\Big{(}1-S_{i}^{t-1}\Big{)}FSU_{i}\Big{]}-\lambda^{t}S_{i}^{t}P_{i }^{t}-\mu^{t}S_{i}^{t}P_{i}^{t}\Big{\}}+\sum_{t=1}^{T}\big{[}\lambda^{t}P_{net }^{t}+\mu^{t}\big{(}P_{net}^{t}+SR^{t}\big{)}\big{]} \tag{18}\]  
At this stage, the complicating constraints can temporarily be ignored, and the first term of the expression can be minimized based on the problem:  
\[\underset{S_{i}^{t},P_{i}^{t}}{min}\dot{\mathcal{L}}=\sum_{i=1}^{N}min\sum_{t =1}^{T}\Big{\{}S_{i}^{t}\Big{[}F\big{(}S_{i}^{t},P_{i}^{t}\big{)}+\Big{(}1-S_{ i}^{t-1}\Big{)}FSU_{i}\Big{]}-\lambda^{t}S_{i}^{t}P_{i}^{t}-\mu^{t}S_{i}^{t}P_{i} ^{t}\Big{\}} \tag{19}\]  
This is subject to the constraints (10)-(15). By eliminating the coupling constraints, a guaranteed solution is expected if the constrained optimization task forms a relaxed problem that offers a lower bound to the original problem [32]. In the Lagrange relaxation method, the separability of the constraints is the underlying assumption that allows for the violation penalization towards optimality. Denoting with \(s^{k}\) the mismatches of the coupling constraints at iteration \(k\), the vector of Lagrange multipliers is updated considering the sub-gradient method as follows [9]:  
\[\Big{[}\lambda^{k+1}\;\mu^{k+1}\Big{]}=\Big{[}\lambda^{k}\;\mu^{k}\Big{]}+a^{ k}\frac{s^{k}}{||s^{k}||} \tag{20}\]  
\[a_{k}=\frac{1}{\alpha+\beta k} \tag{21}\]  
The terminating criterion is called a duality gap (\(\varepsilon\)) and shows the difference percentage between the primal (\(J\)) and the dual (\(q\)) problem, respectively, according to Equation (22).  
\[\varepsilon=\frac{J-q}{|J|}\leq error \tag{22}\]  
The repeating procedure stops when \(\varepsilon\) becomes lower than either a pre-specified tolerance (_error_) or a maximum number of defined iterations. Although the sub-gradient method is simple and computationally non-intensive, the oscillating behavior makes it difficult to design an appropriate stopping criterion. Typical solutions propose different step-size values for the over-constrained and under-constrained conditions at the expense of the exploration-space decrease and convergence time. In addition, unlike linear programming, integer linear programming cannot provide a strong duality theory. In this way, the optimal value of the dual Langrangian problem does not have to be the same as the optimal value of the original (primal) problem, which implies that the magnitude of the sub-optimality is controversial [33].
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 6 Approaches and Algorithms
### Global Spectrum Planning
#### 6.1.6 Planning Output  
The output module is responsible for storing the spectrum planning results in a database or file system, typically in structured formats such as tables or graphs. Additionally, the module displays the results through dashboards, reports, or other visualizations, providing real-time or periodic insights into the planned spectrum usage. The software architecture of the spectrum planning system is divided into four layers according to the idea of \"platform + application\", named as infrastructure layer, general service layer, service logic layer, and application service layer from bottom to top, as shown in Figure 9.  
* Infrastructure layer. This layer provides computing services, storage services, data transmission, and basic database resources for service and application, while providing physical resource management and scheduling functions.
* General service layer. It realizes the basic operations of spectrum data, such as collection, integration, conversion, and sharing. It also provides unified spectrum data to provide application-oriented general service support.
* Service logic layer. By integrating the service bus with the basic support provided by the general service layer, the system realizes spectrum planning and auxiliary decision-making functions, including radio wave propagation prediction and electromagnetic compatibility analysis.
* Application service layer. Based on the service demands of user terminals at various levels, the network system offers tailored data and computing services. It conducts spectrum planning and sets environmental parameters to ensure optimal performance. Additionally, the system provides a user-friendly interface (UI) for ease of operation and a simulation display interface for visualizing the processes.
## 4 Results and Discussions
### The Decision-Making Trial and Evaluation Laboratory-Based Analytic Network Process (DNAP) Analysis
This study supports the necessity of developing calibration and data adjustment methods to ensure accurate and reliable device performance when used in diverse environments. (4). Comfort  
\"Comfort\" remains a foundational aspect of wearable device design, particularly due to its direct impact on user experience. Early studies by [PERSON] et al. (1998) emphasized dynamic wearability as a design criterion [57], a view supported by ergonomics research [78; 79]. This study reaffirms that wearable devices must prioritize comfort in terms of materials, form factors, and attachment methods. Future research should extend to embedded devices to ensure their comfort in long-term use. (5). Appearance  
The analysis reveals that \"Appearance\" significantly influences user perception and purchasing behavior. Aesthetic design elements enhance the perceived value of wearable devices, making them both functional tools and expressions of personal identity [80; 81]. [PERSON] et al. (2021) demonstrated the application of fuzzy mathematics to optimize product aesthetics, a method that can guide future wearable device design. This study emphasizes that appearance is not merely a competitive advantage but a core driver of user engagement [82]. (6). Customization  
\"Customization\" is critical for aligning wearable devices with individual user needs, particularly in healthcare applications. [PERSON] and [PERSON] (2020) identified the simultaneous monitoring of diverse parameters and seamless connectivity as essential for future devices [83]. This study confirms that highly customizable solutions, adhering to the \"4U\" principle (unified, unique, ubiquitous, and unobtrusive), are pivotal for enhancing usability and meeting specific user and institutional requirements. (7). Intelligence  
\"Intelligence\" underpins the transformative potential of wearable devices in proactive healthcare. [PERSON] (2020) noted that smart wearable devices support the shift from reactive to preventive care by detecting and alerting users to abnormal health conditions [84]. This study emphasizes that the intelligence of wearable devices, particularly their capacity for real-time monitoring and early warning, is essential for addressing evolving healthcare challenges.
## 5 Measurement Uncertainty in Measuring Shielding Effectiveness  
Uncertainties in measurement arise in every measurement activity, stemming from imperfections in measuring instruments and devices. In the case of electromagnetic waves, errors can also be caused by the environment through which the electromagnetic wave propagates. To further utilize the results from measurements, it is necessary to establish  
Figure 6: Measurement of shielding effectiveness.  
this measurement uncertainty. This information is crucial in determining the approximate value of shielding effectiveness and the reflection of electromagnetic waves.  
The calculation of measurement uncertainty in this work was based on a document from the Slovak National Accreditation Service, which defines the following sources influencing measurement uncertainty:  
Relative amplitude accuracy of the spectrum analyzer.  
Amplitude stability of the signal source.  
Errors occurring during transmission.  
Errors occurring during reception.  
The relative amplitude accuracy of the spectrum analyzer was determined from the manual of the used spectrum analyzer. The value of relative amplitude accuracy is primarily determined for a selected frequency band. Similarly, the value of the amplitude stability of the signal source was determined from the manual of the signal generator.  
Errors occurring during transmission are largely caused by the effects of standing waves. Given that their influence cannot be excluded in the measurements described in this work, it is necessary to address them. When determining the measurement uncertainty during transmission, the standing wave ratio of 1.18:1 is considered. Based on the relationship described below, the reflection coefficient at the end of the cables is determined by [4]:  
\[R_{sys}=\frac{\left(1.18-1\right)}{\left(1.18+1\right)}=0.082 \tag{24}\]  
Then, the value of the error occurring during transmission is [4]:  
\[U_{sys}=20\log(1+\left(0.082\right)^{2})=0.06[\mathrm{dB}] \tag{25}\]  
Errors occurring during reception are defined in the same manner as those occurring during transmission. The same relationship applies; thus, the error arising during transmission is equal to the error incurred during reception. Table 1 displays a list of measurement uncertainty sources along with the corresponding value of the coverage factor K (coverage coefficient) and the determined or calculated value of the measurement uncertainty.  
Based on previous statements, it is possible to determine the total measurement uncertainty for the previously described workstation. The total measurement uncertainty is defined according to the following relationship [4]:  
\[U_{C}=\sqrt{\left(\frac{0.6}{\sqrt{3}}\right)^{2}+\left(\frac{0.02}{\sqrt{3}} \right)^{2}+\left(\frac{0.06}{\sqrt{2}}\right)^{2}+\left(\frac{0.06}{\sqrt{2} }\right)^{2}}=0.35[\mathrm{dB}] \tag{26}\]
## 3 Results and Discussion
### Band Gap Energy (Optical Properties)  
In all the characterizations above, there are optical data that strongly support the performance of the working electrodes tested. This is evidenced by the results concerning the energy band gap for the three surfaces (shown in Figure 8). Several previous studies [11; 14; 15] mentioned that the area of band gap energy for the TiO\({}_{2}\) layer is 3.2 eV, which, in the graph, shows the same value at 3.13 eV (Figure 8a). To enhance DSSC performance, a smaller band gap value is required in order to maximally absorb the photons, thus allowing for immersion in dye DN-F05 and Ag ion in the hybrid dye DN-F05. The results in Figure 8b show a decrease in the band gap energy value, indicating that the absorption of photons will be maximized, especially by hybridizing the Ag ion into the dye DN-F05.
# Signaling in the Tomato Immunity against _Fusarium oxysporum_
###### Abstract  
New strategies of control need to be developed with the aim of economic and environmental sustainability in plant and crop protection. Metabolomics is an excellent platform for both understanding the complex plant-pathogen interactions and unraveling new chemical control strategies. GC-MS-based metabolomics, along with a phytohormone analysis of a compatible and incompatible interaction between tomato plants and _Fusarium oxysporum_ f. sp. _locyersici_, revealed the specific volatile chemical composition and the plant signals associated with them. The susceptible tomato plants were characterized by the over-emission of methyl- and ethyl-salicylate as well as some fatty acid derivatives, along with an activation of salicylic acid and abscisic acid signaling. In contrast, terpenoids, benzenoids, and 2-ethylhexanoic acid were differentially emitted by plants undergoing an incompatible interaction, together with the activation of the jasmonic acid (JA) pathway. In accordance with this response, a higher expression of several genes participating in the biosynthesis of these volatiles, such as _MTS1_, _TomLoc_, _TomLoc_, and _AOS_, as well as _JAZ7_, a \(\mid\)A marker gene, was found to be induced by the fungus in these resistant plants. The characterized metabolome of the immune tomato plants could lead to the development of new resistance inducers against _Fusarium_ wilt treatment.  
Fusarium; tomato; natural products; volatiles; biotic interaction; metabolomics; GC-MS +
Footnote †: journal: _Microbial Biology_
# IG-Based Method for Voiceprint Universal Adversarial Perturbation Generation
## 6 Experimental Analysis
### Evaluation Indicators  
1. Attack Success Rate  
The attack success rate (ASR) is the likelihood of an adversarial perturbation added to an original sample causing the target classification model to make a classification error. ASR is calculated differently for the two distinct attacks, untargeted attack and target attack. If an adversarial sample with added perturbation is input into a target classification model during an untargeted attack and the model classifies the sample as any label other than the true label with high confidence, the attack is considered successful. The attack success rate under an untargeted attack is defined in Equation (8), where y_i represents the true label, and \(f(\cdot)\) is the voiceprint classification model.  
\[ASR=\frac{\sum_{i}f(x_{i})\
eq y_{i}}{\sum_{i}x_{i}} \tag{8}\]  
In a targeted attack, given the attack target label t_i, select any sample and its true label \(y_{i}\
eq t_{i}\). The target attack succeeds if, after input to the target model, it is classified with high confidence as the target label \(t_{i}\). The definition of the attack success rate under a targeted attack is presented in Equation (9).  
\[ASR=\frac{\sum_{i}f(x_{i})=y_{i}}{\sum_{i}x_{i}} \tag{9}\]
2. Mean Generation Time for Adversarial Samples  
The average generation time of the adversarial samples is calculated by dividing the overall generation time T of the antagonistic samples by the total number of antagonistic samples N, as expressed in Equation (10).  
\[t=\frac{T}{N} \tag{10}\]
3. Signal-to-Noise Ratio of the Counter Sample  
In the audio domain, the paradigm constraint and signal-to-noise ratio are used to measure the anti-perturbation performance in a comprehensive way, and the SNR is calculated as shown in Equation (11), where P(v) denotes the root mean square (RMS) of the anti-perturbation signal \(v\). The SNR of the anti-perturbation signal \(v\) is calculated as shown in Equation (11). All experiments in this paper take the average SNR of all adversarial samples as the evaluation index.  
\[SNR=10\log_{10}P(v) \tag{11}\]
4. Loudness of the Adversarial Sample  
A high signal-to-noise ratio implies a low level of added noise through the generic counterattack. Moreover, the Celsius decibel (dB) of the adversarial perturbation, which measures the loudness of the samples, serves as one of the performance metrics. It is calculated as depicted in Equation (12). This metric bears some similarity to the \(I_{\infty}\) paradigm in the image domain, where a lower value conveys that the adversarial samples are only slightly distorted compared to the genuine audio samples. Lately, researchers have em ployed this technique to gauge the effectiveness of adversarial attacks on voiceprint classification models.  
\[l_{dB_{x}}(v)=l_{dB}(v)-l_{dB}(x) \tag{12}\]  
where \(l_{dB}(x)\) is calculated as shown in Equation (13).  
\[l_{dB}(x)=max_{n}(20\log_{10}(x_{n})) \tag{13}\]
## 3 Results
### Comparison of Measurements with V/A-C and PSV  
The comparison of simulation measurements showing patient-ventilator interactions using different modes of ventilation is summarized in Table 2. During V/A-C mode, the results exhibit high standard deviations. This applies to \(\sigma_{\rm FLOW}\) for inhalation and exhalation peak flow as well as \(\sigma_{\rm PRESSURE}\) for inhalation peak pressure in both spontaneous breathing phases. The standard deviation for exhalation peak pressure can be neglected for both modes as the PEEP was set to zero for both modes and minor oscillations around zero for dynamic systems are expected. The high standard deviation outcome for V/A-C mode during spontaneous breathing is linked to double triggering, which can be seen in Figure 4 for every third breathing cycle. During simulated apnea in V/A-C mode, the standard deviation \(\sigma_{\rm FLOW}\) lies below 2.17% for inhalation and exhalation peak flow and \(\sigma_{\rm PRESSURE}\) lies below 4.42% for inhalation peak pressure. For the PSV mode, \(\sigma_{\rm FLOW}\) lies below 3.53% for inhalation and exhalation peak flow and \(\sigma_{\rm PRESSURE}\) lies below 0.41% for inhalation peak pressure when considering both phases.
## 4 Network Anomaly Forecasting
### The Structure of Network Anomaly Forecasting  
As shown in Figure 2, by leveraging the unique properties of LSTM as the prediction model, we can accurately predict future network indicators, which is critical for achieving the function of network anomaly forecasting.
## 2 Machinery Risk Assessment Methodology
### Basic Principles in Risk Assessment Procedure  
Despite the fact that it was mainly the machinery safety that initiated the development of a number of harmonized standards to formulate the algorithm of risk assessment and risk reduction, as a basis for the design and construction of the safest possible machinery, not even 30 years makes a sufficiently long enough period for the principles in question to be implemented properly in technical practice.  
The reason for this is obvious. Even with a basic knowledge of EN ISO 12100 [14; 24; 25; 26], the harmonized standards went through several alterations, from EN 292-1, 2, EN 1050, EN ISO 14121-1, to EN ISO 12100-1, 2), and there still happens to be incomprehension of the importance of the individual steps of risk assessment (Figure 1):  
**Risk analysis** starts with the classification of a system into elements (Step 1). The aim is to describe and identify the source of accidents as clearly as possible (Step 2), thereby highlighting what in the system is a hazard to a human, and provide a description of hazardous situations that may occur in a given activity (harm) and how (hazardous event), i.e., the _accident scenario_. Then, an estimation of probability and consequence follows, which requires the selection of a suitable methodology for the assignment of probability and consequence parameters (e.g., risk matrix, risk graph, fault tree analysis (FTA), event tree analysis (ETA) etc., and this methodology may exist in the form of qualitative, semi-quantitative or quantitative approaches [26; 27]. The next important step in the logical procedure is the expression of the relation of probability and consequence by a combination of their values, the so-called risk estimation (Step 3).  
**Risk evaluation** (Step 4)--is the step following risk analysis, which compares the estimated amount of risk (related to an identified hazard) with the'set' parameters of its acceptability (or tolerability).  
**Taking of measures--is related to the risk values which either exceed or reach the unacceptable level (Step 5). It may happen that even though the estimated risk level is within the area of acceptability, the manager (designer, producer) decides to take certain measures to reduce it further. Such an approach is marked as ALARP (As Low As Reasonably Practicable)--it represents a reduction of risk to the best reasonable level, i.e., effectively and efficiently [28; 29; 30]. In designing the machinery, this process goes through the so-called three steps (Step 6), where first, is the hazard source elimination (replacement of hazardous properties with safe alternatives), after is consideration of whether the acceptance of a particular measure is possible or is effective to ensure the protective measures are applied. The third and last step of risk reduction is the provision of information on the residual risk in the machinery use instructions.  
The whole process is considered as risk reduction but also risk control, and it requires repeated assessment in order to verify whether the proposed and implemented measures are effective.