esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Actions and Effects  
Table 3 shows the results obtained for each of the load cases and load combinations, according to the limit states identified as the most severe, in terms of maximum axial (N), shear (V), flexural (M), and torsional (T) stresses. Figure 7 identifies the critical sections, i.e., the locations where the maximum stresses occur. As shown, the worst scenario is the abnormal extreme condition. In this context, the maximum deflection obtained was 1.789 mm, the maximum axial stress was \(-\)86.05 kN, the maximum flexural stresses were \(-\)4.70 kN\(\cdot\)m and 12.81 kN\(\cdot\)m, the maximum negative and positive shear stresses were 36.43 kN, and the maximum negative and positive torsional stresses were 1.94 kN\(\cdot\)m.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Quantity** & **1 st Order Component** & **2 nd Order Component** \\ \hline Dispersion & \(\frac{g}{k}\tanh kd\) & \(\frac{g}{k}\tanh kd\) \\ Relationship, \(c^{2}\) & \(\frac{H}{2}\cos(kx-\omega t)\) & \(\frac{\pi H^{2}}{8L}\frac{\cosh kd}{\sinh^{2}kd}[2+cosh\ 2 kd]\cos 2(kx-\omega t)\) \\ Horizontal & \(\frac{\pi H}{T}\frac{\cosh ks}{\sinh kd}\cos(kx-\omega t)\) & \(\frac{3}{4c}\Big{(}\frac{\pi H}{T}\Big{)}^{2}\frac{\cosh 2 ks}{\sinh^{ 4}kd}\cos 2(kx-\omega t)\) \\ Vertical & \(\frac{\pi H}{T}\frac{\sinh ks}{\sinh kd}sin(kx-\omega t)\) & \(\frac{3}{4c}\Big{(}\frac{\pi H}{T}\Big{)}^{2}\frac{\sinh 2 ks}{\sinh^{ 4}kd}\sin 2(kx-\omega t)\) \\ velocity, \(v\) & \(\frac{\pi H}{T}\frac{\sinh ks}{\sinh kd}sin(kx-\omega t)\) & \(\frac{3}{4c}\Big{(}\frac{\pi H}{T}\Big{)}^{2}\frac{\sinh 2 ks}{\sinh^{ 4}kd}\sin 2(kx-\omega t)\) \\ Horizontal & \(\frac{2\pi^{2}H}{T^{2}}\frac{\cosh ks}{\sinh kd}sin(kx-\omega t)\) & \(\frac{3\pi}{2L}\Big{(}\frac{\pi H}{T}\Big{)}^{2}\frac{\cosh 2 ks}{\sinh^{ 4}kd}\sin 2(kx-\omega t)\) \\ Acceleration, \(\dot{v}\) & \(\frac{2\pi^{2}H}{T^{2}}\frac{\sinh ks}{\sinh kd}cos(kx-\omega t)\) & \(\frac{3\pi}{4L}\Big{(}\frac{\pi H}{T}\Big{)}^{2}\frac{\sinh 2 ks}{\sinh^{ 4}kd}\cos 2(kx-\omega t)\) \\ Dynamic & \(pg\frac{H}{2}\frac{\cosh ky}{\cosh kd}\cos\left[k(x-ct)\right]\) & \(\frac{3}{4}pg\frac{\pi H^{2}}{L}\frac{1}{\sinh 2 kd}\Big{[}\frac{\cosh 2 ks}{\sinh^{ 4}kd}-\frac{1}{3}\Big{]}\cos 2(kx-\omega t)\) \\ pressure, \(p\) & \(-\frac{1}{4}pg\frac{\pi H^{2}}{L}\frac{1}{\sinh kd}[\cosh 2 ks-1]\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: 1 st and 2 nd order components of stokes wave theory [62; 63].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Load Combination** & \(\mathbf{U_{r}}\) & \(\mathbf{N^{-}}\) & \(\mathbf{N^{+}}\) & \(\mathbf{V_{2,2}}\)\({}^{-}\) & \(\mathbf{V_{2,2}}\)\({}^{+}\) & \(\mathbf{V_{3,3}}\)\({}^{-}\) & \(\mathbf{V_{3,3}}\)\({}^{+}\) & \(\mathbf{M_{2,2}}\)\({}^{-}\) & \(\mathbf{M_{2,2}}\)\({}^{+
# Design Criteria for High-Gradient Radio-Frequency Linacs
## 5 Design Considerations for High-Gradient Accelerating Structures
### Future Work  
There are still many questions to be answered in the studies of high-gradient acceleration. Since the goal is a well-functioning particle accelerator, both fundamental physics questions and technological questions are abundant.  
One of the main physics questions is the nature of the breakdown trigger, which is not understood. We seem to know its statistical properties well, but it is not known what happens on the surface of the metal in the nanosecond before the start of the arc, and there are many hypotheses. It is possible that, if we understand the trigger, we can suppress it.  
Another physics question is why multicell structures behave differently than single-cell structures. Usually, the breakdown rate is higher in multicell structures. Here, again, there are many hypotheses, including interaction through gases, X-rays, dark currents, etc.  
Another important issue is the physics of conditioning. If we could understand what actually happens to the surface of metals during conditioning, we could speed up this process, or perhaps avoid it altogether.  
Technological issues include the selection of breakdown-resistant materials. Research is currently focused on hard metals, metals at cryogenic temperatures, and copper alloys. However, we have very limited data on clad structures or structures with multilayered metal surfaces.  
Another area of active research is the search for the optimal shape of the cavities. Since the shape is highly application-dependent, the parameter space is very large, so the search is difficult and expensive. Examples here are parallel-coupled structures or multisector accelerating structures.
## 5 Prototypical Validation and Case Study
### Variability4 TOSCA: An Extension of the TOSCA Standard
#### 5.1.1 Mapping EDMM to TOSCA  
Since the EDMM classes are part of VDMM, we first describe how they can be mapped to TOSCA. According to [PERSON] et al. [15; 1], EDMM can be mapped to TOSCA as follows: In TOSCA, the deployment of an application is described as a _Topology Template_. Such a Topology Template is the equivalent of our deployment model and consists of _Node Templates_ and _Relationship Templates_. These templates describe the application components along with their relations. Thereby, components of our deployment model correspond to Node Templates and relations to Relationship Templates. _Node Types_ and _RelationshipTypes_ semantically describe _Node Templates_ and _Relationship Templates_. Therefore, component types and relation types correspond to Node and Relationship Types. Node Templates and Types can be configured using _Properties_ that are equivalent to properties of components and their types. The same applies to Relationship Templates and Types. A more detailed description of TOSCA is given by [PERSON] et al. [16; 17].
## 1 Introduction
The impermeable integration is applied on the rotor surface and describes both the thickness and loading noise, but neglects the quadrupole noise. On the other hand, the permeable surface is placed in the volume and is able to capture all of the noise sources. However, even when a user elects to use the permeable surface method, there is no common approach for its topology and placement. The surface must be placed close enough such that the effects of numerical dissipation are minimized but far enough to avoid hydrodynamic waves contaminating the acoustic waves, leading to spurious signals [16]. Finally, it is still unknown which combination of CFD and CAA approaches are able to capture the broadband noise, which can be caused by turbulence ingestion, wake interactions, or self noise [17; 18]. Even the higher harmonic noise nominally associated with unsteady loading is seemingly sensitive to the methods. In general, researchers have shown contradictory results both experimentally and computationally, indicating that the aeroacoustic characterization is extremely sensitive to the methodology. This paper describes experimental and computational findings for a single small rotor and small dual rotors in hover. These rotor configurations are not new. However, the exact rotor geometry was available from the manufacturer, allowing computational grids to be developed that represent well the rotor used in the experiment. In addition, the CFD suite of codes that are used was validated well for traditional large rotorcraft and was only recently applied to small rotors. What is presented in this paper, therefore, adds to the database of small rotor results that must continue to grow in order to enable the community to determine best practices for small rotor simulations. To contextualize the current work, a summary of active research in the community is provided in the following background section, focusing on the three following topics: Section 2.1 highlights the significance of structural modeling and manufacturing discrepancies. Section 2.2 discusses the amplification of noise due to flow confinement. Section 2.3 compares results, using the impermeable and permeable surface, and also identifies the approaches to capturing broadband noise. Section 2.4 describes the efforts made to quantify aerodynamic interactions and their effects on noise. Section 3.1 provides a description of the rotor configuration. The experimental setup for acoustics measurements at Roma Tre University is described in Section 3.2. The computational methodology involving the CFD and CAA tools used, including the computational mesh, is defined in Section 3.3. Next, the performance results are presented and discussed in Section 4.1 and the acoustics results are discussed in Section 4.2. The paper concludes with a summary of significant findings and potential future work in Section 5.
# Passive Microwave Imagers, Their Applications, and Benefits: A Review
## 2 PMWI Applications and Impact Areas
### Impact Areas of PMWIs Within the NWS  
The NWS is one of the principal users of PMWI data. PMWI data allow the NWS to improve weather forecasts and precipitation estimates. The NWS uses conical-scanning PMWI data for precipitation estimation and other surface applications [111]. PMWI data, along with PMW sounder data and temperature records, allow the NWS to accurately forecast severe weather events, such as TCs and winter storms. Furthermore, these data are used for flood modeling and forecasting [111]. Specifically, the NWS National Water Center (NWC) is the principal user of the NESDIS flood products, which include Low Earth Orbit (LEO) satellite observations. Additionally, the NWS uses the Global Multisensor Automated Snow and Ice Mapping System (GMASI) as part of the global NWP model to accurately characterize snow and ice cover [111]. The GMASI algorithm inputs visible (VS), IR, and microwave spectral bands (such as Level 1 products from AMSR2, GMI, and Advanced Very High-Resolution Radiometer (AVHRR)) to generate daily snow and ice cover global maps [54; 111].  
As previously mentioned, one of the NWS applications of PMWI data is precipitation estimation. The NWS's Climate Prediction Center (CPC) utilizes Level 2 precipitation products from PMWIs (e.g., SSMIS, AMSR2, and GMI) to generate precipitation estimates,  
Figure 5: The blended soil moisture distribution on 11 January 2025, derived from the Soil Moisture Operational Product System (SMOPS) (source: figure created by authors).  
such as CMORPH2 [62]. The NWS produces a global precipitation product leveraging the algorithms of [PERSON], [PERSON] et al., and [PERSON] et al. for SSM/I, TMI, SSMIS, and GMI [55; 56; 62; 112]. Various applications underscore the importance of PMWI data in enhancing forecasting capabilities while also highlighting their potential societal impact. This impact varies across different applications of PMWI data, and further research is needed to fully quantify its extent.
## 2 Materials and Methods
### Procedures  
Participants completed one familiarization session and three randomized experimental sessions, each corresponding to a different BFR condition (0%, 40%, or 80% LOP). LOP was determined individually at rest in the supine position using a vascular Doppler probe (Occlusion Cuff\({}^{\circledR}\), Belfast, Ireland) placed over the tibial artery. The cuff was inflated gradually until the arterial pulse signal was no longer detected; this value was recorded as the LOP. Pneumatic cuffs were inflated manually during testing to either 40% or 80% of this individualized value using a mobile app control system.  
Each session began with a 5-min warm-up on a cycle ergometer. A functional electromechanical dynamometer (FEMD; MyoQuality\({}^{\circledR}\), Granada, Spain) was used for all testing procedures to ensure reproducibility and precise load control. Participants first  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Mean (SD)** \\ \hline Age (years) & 27.37 (10.02) \\ \hline Weight (kg) & 63.84 (12.45) \\ \hline Height (cm) & 164.23 (5.99) \\ \hline BMI (kg/m\({}^{2}\)) & 23.52 (3.75) \\ \hline LOP (mmHg) & 215.83 (26.94) \\ \hline
40\% LOP (mmHg) & 86.33 (10.77) \\ \hline
80\% LOP (mmHg) & 172.66 (21.55) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive Characteristics of the Sample.  
performed a maximal isometric knee extension test (IKE) to determine their maximum voluntary isometric force (MVIF). Then, three sets of dynamic seated knee extensions were completed, starting at 40% of the individual MVIF. Load increased automatically in 10% MVIF increments on each repetition until volitional failure. The progression was managed using the incremental resistance mode of the MyoQuality(r) electromechanical dynamometer, which allowed consistent and individualized loading based on each participant's pre-established MVIF. One-minute rest intervals were provided between sets.  
Immediately after cuff removal, participants performed an isometric mid-thigh pull (IMTP), followed by additional trials at 5, 10, and 15 min post-exercise. IMTP testing consisted of a 5-s maximal pull from a standardized position with hip and knee angles between 125\({}^{\circ}\) and 145\({}^{\circ}\). Consistent foot and hand positions were ensured using the FEMD setup. Figure 1 illustrates the session protocol.
# Regression of Likelihood Probability for Time-Varying MIMO Systems with One-Bit ADCs
###### Abstract  
This study proposes a regression-based approach for calculating the likelihood probability in time-varying multi-input multi-output (MIMO) systems using one-bit analog-to-digital converters. These time-varying MIMO systems often face performance challenges because of the difficulty in tracking changes in the likelihood probability. To address this challenge, the proposed method leverages channel statistics and decoded outputs to refine the likelihood. An optimization problem is then formulated to minimize the mean-squared error between the true and refined likelihood probabilities. A linear regression approach is derived to solve this problem, and a regularization technique is applied to further optimize the calculation. The simulation results indicate that the proposed method improves reliability by effectively tracking temporal variations in the likelihood probability and outperforms conventional methods in terms of performance.  
time-varying channels; quantized MIMO; likelihood probability; linear regression Article
## 5 Types of Phage-Based Biosensors
### Phage-Based Optical Biosensors
#### 5.1.2 Bioluminescence Sensors  
A bioluminescence sensor relies on the enzymatic (luciferase) cleavage of an organic compound, luciferin, which ultimately emits light in a living organism (especially in _Vibrio_ strains). The ATP bioluminescence tests are a fast, sensitive and uncomplicated approach to the detection of bacterial contamination. In this assay, the cytoplasmic ATP released from a lysed bacterial cell is measured by the luciferase bioluminescence reaction [14].  
Several studies have shown that different types of bioluminescence that have been obtained from different organisms can be integrated into the genome of bacteriophages for the quick and efficient detection of pathogens from food samples. For instance, the light-emitting features (luminescence values) of the NanoLuc luciferase (NLuc) reporter phage was designed by incorporating luciferase coding sequences derived from other organisms such as radiarians, bacteria, and crustaceans into the genes of the _Listeria_ phage A500 (A500:nluc \(\Delta\)LCR), and the signal was found to be 100-fold higher than those of the other reporters. Hence, the NLuc luciferase-based assay is sensitive and able to directly detect as low as 3 CFU/100 mL _L. monocytogenes_ in lettuce and milk samples, 72 h faster than culture-based approaches [14]. In a related study, a set of T7-based phages encoding an NLuc carbohydrate-binding module fusion protein (NLuc-CBM) were used for the detection of _E. coli_ in water with a detection limit of 1 CFU/100 mL in less than 10 h [20; 81].  
In a study by [PERSON] et al. [56], a reporter phage was designed to detect _E. coli_ O157:H7 in food samples. In this assay, the genome of the _E. coli_ phage, \(\Phi\)V10, was modified by incorporating a specific bioluminescent, Nluc, which is derived from _Oplophorus graciirosstris_ (deep-sea shrimp), coupled with the commercial luciferin (Nano-Glo\({}^{\circledR}\)). At a 1.76 \(\times\) 10\({}^{2}\) pfru/mL concentration of the reporter phage, the assay enabled the detection of 5 CFU of _E. coli_ O157:H7 grown in Luria-Bertani broth within 7 h. A comparable detection was obtained using \(\Phi\)V10 reporter phages in ground beef at 9.23 \(\times\) 10\({}^{3}\) pfu/mL within a 9 h turn-around time [56].  
[PERSON] and colleagues developed a bioluminescence sensor using an engineered reporter phage, SPC32H-CDABE, at a minimum detection limit of 20 CFU/mL of _Salmonella_ within 2 h, and the signals raised at a parallel rate to the concentration of contaminated bacteria found in milk, lettuce, and sliced pork [82]. The researchers proclaimed the sensor to be a promising diagnostic tool for the detection of _Salmonella_ contamination in food [82]. In another study, a substrate-independent luminescent phage-based biosensor was developed using the HK620 and HK97 bacteriophages for the detection of enteric bacteria such as _E. coli_ in water samples. The developed bioluminescence was specific and allowed the detection of 10\({}^{4}\) bacteria/mL in 1.5 h post-infection without the need for enrichment or a concentration step [83].
# Dynamic Analysis Method for Fault Propagation Behaviour of Machining Centres
## 4 Case Application of a Machining Centre
### Implementation
Given that \(In(v_{NC},v_{B})=0.1400>In(v_{NC},v_{F})=0.1396\), the critical node is \(B\). Similarly, for the fault absorption layer system component node, \(In(v_{B},v_{F})>In(v_{B},v_{T})\), thus the feed system \(F\) is the critical component of this layer. Therefore at 1500 h, the critical nodes are D, NC, B, and F, and the path \begin{table}
\begin{tabular}{c c} \hline
**Paths** & \(P_{E}\left(v_{i}v_{j}\right)\) \\ \hline D\(\rightarrow\)NC\(\rightarrow\)F & 0.004799 \\ D\(\rightarrow\)NC\(\rightarrow\)B\(\rightarrow\)F & 0.000342 \\ D\(\rightarrow\)NC\(\rightarrow\)B\(\rightarrow\)T & 0.00038 \\ D\(\rightarrow\)B\(\rightarrow\)F & 0.004219 \\ D\(\rightarrow\)B\(\rightarrow\)T & 0.004178 \\ D\(\rightarrow\)W\(\rightarrow\)F & 0.004714 \\ Q\(\rightarrow\)T & 0.042622 \\ Q\(\rightarrow\)B\(\rightarrow\)F & 0.003326 \\ Q\(\rightarrow\)B\(\rightarrow\)T & 0.003293 \\ H\(\rightarrow\)T & 0.063015 \\ H\(\rightarrow\)B\(\rightarrow\)T & 0.004647 \\ H\(\rightarrow\)B\(\rightarrow\)F & 0.004692 \\ R\(\rightarrow\)T & 0.052492 \\ R\(\rightarrow\)F & 0.053345 \\ R\(\rightarrow\)B\(\rightarrow\)T & 0.003963 \\ R\(\rightarrow\)B\(\rightarrow\)F & 0.004001 \\ K\(\rightarrow\)F & 0.056592 \\ \hline \end{tabular}
\end{table} 
Table 10: The fault propagation probability value of each path in the fault propagation model of a machining centre at 1500 h.  
Figure 12: Hierarchy structure model of fault propagation intensity in a machining centre. composed of critical nodes is the critical fault propagation path. At 1500 h, the most likely propagation path of the fault is D\(\rightarrow\)NC\(\rightarrow\)B\(\rightarrow\)F.  
Similarly, the fault propagation intensity value of each directed edge of a machining centre at any time can be determined; in turn, the critical fault nodes and paths of a machining centre at any time can be obtained. This paper arbitrarily chooses the running time of 200 h and 5000 h as examples and draws the comparison diagram of the fault propagation intensity value of each directed edge as shown in Figure 13.  
In reference to Figure 13, the fault propagation intensity values of each directed edge are varied at different times and the critical fault propagation paths of the machining centre at different times are distinct. The critical fault propagation path of 200 h is D\(\rightarrow\)NC\(\rightarrow\)B\(\rightarrow\)T, at 1500 h the critical fault propagation path is D\(\rightarrow\)NC\(\rightarrow\)B\(\rightarrow\)F, and at 5000 h the critical fault propagation path is D\(\rightarrow\)NC\(\rightarrow\)B\(\rightarrow\)T.
## 4 Key Agreement, Distribution, and Security Bootstrapping
### Key Agreement Protocols  
Asymmetric (public-key) cryptographic algorithms are often the basis for key agreement protocols, yet other techniques that do not involve the adoption of asymmetric cryptography have been proposed. A polynomial-based key pre-distribution protocol has been defined [59] and applied to Wireless SensorNetworks in [60]. A possible alternative key agreement protocol is SPINS [61], which is a security architecture specifically designed for sensor networks. In SPINS, each sensor node shares a secret key with a base station, which is used as a trusted third-party to set up a new key, with no need of public-key cryptography. The authors of [62] present three efficient random key pre-distribution schemes for solving the security bootstrapping problem in resource-constrained sensor networks, each of which represents a different tradeoff in the design space of random key protocols.
# Data-Driven Damage Classification Using Guided Waves in Pipe Structures
## 6 Conclusions  
Due to the complicated mathematical-physical model involved in wave propagations and interaction between guided wave and damages, it is difficult to identify the damage type through the conventional guided wave signal processing methods. In order to solve this problem, we proposed a data-driven method for the damage classification of pipeline structures using reflected guided wave signals. This method can distinguish damage types through the time-domain guided wave signals. The ability of the guided wave method is improved for SHM. The specific conclusions are as follows:  
(1) For guided wave signal processing, the ANN framework with two hidden layers has higher classification efficiency and accuracy than the framework with only one layer. The results show that the number of hidden layers seriously affects the recognition accuracy. The neural network with two hidden layers can converge only after 13 iterations, reaching 94.6% classification accuracy. It is more effective for processing guided wave signals obtained from pipes.  
(2) Using ReLU as the hidden layer activation function has a better classification effect. The recognition effects of two activation functions, tanh and ReLU, were compared on a  
Figure 11: Training and test confusion matrices of neural network with ReLU hidden layers: (**a**) training confusion matrix; (**b**) test confusion matrix.  
Figure 12: Test results under the effect of noise: (**a**) SNR = 30 dB; and (**b**) SNR = 20 dB.  
training set containing 420 sample data and a test set containing 100 sample data. The training process of the network with two ReLU hidden layers converges faster and is more accurate for damage type recognition.  
(3) By adding Gaussian noise with the SNR of 30 dB and 20 dB to the original signals, the robustness to noise of the proposed method was investigated. The corresponding recognition accuracy was 88% and 82%, respectively. Therefore, guided wave signals with low SNR may lead to misjudgment, and filtering is necessary for the damage classification.  
Investigation, software, formal analysis, writing - original draft, X.Z.; supervision, writing-review & editing, funding acquisition, W.Z.; conceptualization, project administration, H.L.; resources, Y.Z. All authors have read and agreed to the published version of the manuscript.  
The study was supported by the National Science Foundation of China (Grant Nos. 51978217, 51975581).  
Not applicable.  
Data Availability StatementData available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.