esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Solder Fatigue  
This test was based upon the IPC-JEDEC J-STD-020D-01 prediction equations for solder fatigue reliability under thermo-mechanical and mechanical environments for all electronic parts (die attach, BGA, QFN, TSOP, chip resistor, through-hole, etc.). It was carried out for two types of solders, SAC305, which is lead-free; and PB90 SN10 which is lead-based. In each case, the tests were carried out for three reflow cycles, peak temperature = +260 \({}^{\circ}\)C.  
Figure 14: (**a**) The effects of displacement due to mechanical shock. (**b**) The effects of strain due to mechanical shock.  
Figure 15: Life prediction of PCB under mechanical shock.  
From the results shown in Figure 16a-d, all the of solders except PB90 SN10 performed to the expected requirements for the board. The solder was deemed to fail on two components, namely Schottky D161 and D261 diode packages on the bottom of the board. This is due to the high degree of thermal mechanical strain experienced by the joint in this region. Interestingly, looking at the original board, both of these components are in the vicinity of the greatest strain and displacement, under the heatsink. Hence, to ensure that the design meets the design lifetime criteria, ideally the components should be moved, or at least a different solder than PB90 SN10 should be used.
## 2 Description of the LMS-Based Algorithms
### Filtered-X LMS Algorithm Applied to a Feed-Forward Configuration  
Let us consider the physical system shown in Figure 2, composed of one reference signal \(x(n)\), two error signals \(e_{i}(n)\)\((i=1,2)\), and two signals \(y_{j}(n)\)\((j=1,2)\) generated by the control sources. The impulse responses of the primary paths \(p_{i}(n)\) describe the propagation of the reference signal from the source to the error microphones, where the noise disturbances \(d_{i}(n)\) are detected. The signals produced by the control sources are transmitted through the secondary paths \(s_{ji}(n)\) from the \(j\)-th source to the \(i\)-th microphone. They superimpose the existing noise disturbance \(d_{i}(n)\), generating the error signals measured at the microphones. The figure also shows the impulse responses associated with the feedback paths \(f_{j}(n)\) to indicate the physical possibility of reference signal corruption due to wave propagation from the control sources to the reference microphone. However, as depicted in Figure 2 and as required by the traditional FF_FXLMS algorithm, the output signals \(v_{j}(n)\) are not computed nor processed by the control unit.  
To correctly model the physical system and to guarantee stability, the FXLMS algorithm takes into account the presence of secondary paths inside the control unit. These impulse responses are estimated with the offline procedure reported in Section 2.1.1 and are saved as FIR filters \(s^{\prime}_{ji}(n)=[s^{\prime}_{ji,0}(n)\ s^{\prime}_{ji,1}(n)\ \ldots\ s^{\prime}_{ji,M-1}(n)]\), where \(M\) represents the length of secondary paths filters, assumed to be equal for all the filters. By means of these FIR filters, the (filtered) reference signals are computed as:  
Figure 1: Schematic representation of the feed-forward system.  
Figure 2: Schematic representation of FXLMS algorithm for a feed-forward configuration.  
\[x^{\prime}_{j\bar{i}}(n)=\sum_{m=0}^{M-1}s^{\prime}_{j\bar{i},m}(n)x(n-m). \tag{1}\]  
The algorithm implemented in the ANC system minimizes the cost function \(\xi(n)\), which represents the total amount of energy sensed by the error microphones:  
\[\xi(n)=\sum_{i=1}^{2}e_{i}^{2}(n). \tag{2}\]  
This can be achieved with an iterative procedure that modifies the adaptive filter coefficient vector \(w_{j}(n)=[w_{j,0}(n)\ w_{j,1}(n)\ \ldots\ w_{j,K-1}(n)]\) of length \(K\). In particular, the least mean squares approach translates the operation of cost function minimization in the updating expression of the adaptive coefficients shown in Equation (3):  
\[w_{j,k}(n+1)=w_{j,k}(n)-\mu\sum_{i=1}^{2}e_{i}(n)x^{\prime}_{j\bar{i}}(n-k)\ \ \ k=0,1,\ldots,K-1, \tag{3}\]  
where \(\mu\) is the step-size parameter, which regulates the convergence speed and determines the stability of the algorithm. Finally, the outputs of the algorithm that drive the two control sources are evaluated as:  
\[y_{j}(n)=\sum_{k=0}^{K-1}w_{j,k}(n)x(n-k). \tag{4}\]  
This procedure is repeated for any generic instant \(n\).
# Regular Wave Effects on the Hydrodynamic Performance of Fine-Mesh Nettings in Sampling Nets
## 2 Material and Methods  
### Hydrodynamic Experiment of Fine-Mesh Nettings in a Wave Tank  
#### 2.1.1 Netting Models and Hydrodynamic Experimental Setup  
Table 1 summarizes the structural characteristics of two fine-mesh nettings selected for hydrodynamic performance evaluation. Both nettings featured square mesh geometry and were fabricated from polyamide and Dyneema monofilaments. The average values of mesh bar length and twine diameter were derived from measurements of 10 individual mesh bars and 10 twine samples, respectively. The solidity ratio (\(S_{n}\)) was calculated using the expression \(S_{n}=d(2l-d)/l^{2}\), where \(l\) denotes the mesh bar length and \(d\) the twine diameter.  
Hydrodynamic experiments were carried out in a wave tank (length: 65 m, width: 1 m, and water depth: 0.7 m) located at the Rudong Testing Facility, managed by the Fisheries Engineering Institute of the Chinese Academy of Fishery Sciences (see Figure 1). The bottom surface was constructed of smooth cement and the sidewalls were made of glass. To ensure the nettings remained in a stable equilibrium position during testing without the influence of horizontal constraints, they were suspended using two vertical bridle lines, each 0.95 m long. Additionally, four symmetrically placed bridle lines, each 0.58 m in length, were connected to the netting and attached to tension load cells installed on either side. These load cells, affixed to a 10 mm diameter support rod, had a measurement capacity of 20 N and a precision of 1%. A fixed horizontal spacing of 0.75 m between the support rod and the net ensured minimal interference with the flow field. Wave force data were collected at a sampling frequency of 50 Hz over a duration of 60 s, with each test condition repeated three times to reduce experimental uncertainty. The wave-induced  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Netting No.** & **Materials** & **Netting Outline Area** & **Mesh Bar Length \(l\)** & **Twine Diameter \(d\)** & **Solidity Ratio \(S_{n}\)** \\  & & **(m)** & **(mm)** & **(mm)** & **Solidity Ratio \(S_{n}\)** \\ \hline Net 1 & PA & 0.5 \(\times\) 0.5 & 0.505 & 0.291 & 0.82 \\ Net 2 & Dyneema & 0.5 \(\times\) 0.5 & 1.950 & 0.360 & 0.34 \\ \hline \end{tabular}
\end{table}
Table 1: Structural parameters of fine-mesh nettings.  
force acting specifically on the netting was obtained by subtracting the baseline frame force from the total recorded load. Surface wave elevation at the netting location was monitored using a wave gauge (LYL-III, State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, China), and wave conditions were verified for consistency prior to data acquisition. Throughout the experiments, the water temperature and density were maintained at approximately 20.3 \({}^{\circ}\)C and 998 kg/m\({}^{3}\), respectively.
## 4 Model Proposal
### ABCDE Method: Goal, Actors, and User Stories  
The development of the blockchain-SSI model proposal was performed following the ABCDE method [33]. This method outlines eight steps for the development of blockchain software systems. The first three steps involve the definition of the requirements of the system, and hence the definition of the goal and the actors of the system, and the description of the requirements in terms of user stories. In the fourth step, the system is divided into on-chain and off-chain components, and in the last four steps, the design, the testing, the integration, and the release are carried out. In the following, some of the steps are described.  
The goal of the proposal is to present an SSI-blockchain-based model that allows for the management of all medical reports as VCs.  
The following user stories describe the functional requirements that the model proposal has to achieve.  
* The system administrator initializes the system and deploys the smart contracts. He publishes the identity of the issuers authorized to operate it.
* All stakeholders have their own digital identity.
* All stakeholders communicate with each other by identifying and authenticating themselves autonomously, without resorting to third parties.
* Medical reports are issued by doctors and/or delegation reports are issued by the patient/legal guardian.
* The patient keeps his/her medical reports and presents them as VPs when necessary.  
* IoT medical devices communicate data to the app in the patient's and/or in the doctor's device.
* Before considering the presented credentials valid, a verifier has to be verify the VC's claims and the VC's schema against the blockchain and IPFS, respectively.  
The actors are the doctor, the patient, the legal guardian, the IoT medical devices, the administrator, and the smart contracts.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 2 Research Methodology (CNN-LSTM-SAM-Attention Model)
### Spatial Attention Mechanism (SAM) Principles  
The Spatial Attention Mechanism (SAM) is an adaptive mechanism that improves the model's capacity to represent features at significant spatial locations by giving various features in the model varying weights to emphasize significant information and suppress unimportant ones. The following is the spatial attention mechanism's concept and implementation:  
1. The input feature map: Assume that the input feature map is F\(\in\)R\({}^{\ast}\)(H \(\times\) W \(\times\) C), where H is the height, W is the width, and C is the number of channels.
2. Pooling activities: To create a feature map \(F_{max}\) with a size of 1 \(\times\) 1 \(\times\) C, Global Max Pooling (GMP) is applied to the input feature map in the channel dimension.  
To create a new feature map \(F_{avg}\) with dimensions of \(1\times 1\times C\), the input feature map is subjected to Global Average Pooling (GAP) in the channel dimension.  
1. Fusion Pooling Result: To obtain a complete feature representation, fuse \(F_{max}\) and \(F_{avg}\) together. This can be achieved by concatenating (splicing) or performing an additive operation.
2. Attention weight computation: To create a spatial attention weight map \(A\in R^{H\times W}\), the fused features are run through a convolutional layer or another transformation function.
3. Applying Attention Weights: The computed attention weight map A is applied to the original feature map to weigh the features at each spatial location. This can be achieved by element-by-element multiplication, i.e., \(\overset{\sim}{F}=F\cdot A\), where \(\cdot\) denotes element-by-element multiplication.
4. Output weighted feature map: The weighted feature map \(\overset{\sim}{F}\) will be used as an input to the subsequent network modules for subsequent tasks such as classification, detection, etc.  
Specifically, given an input feature map \(F\in R^{H\times W\times C}\), the spatial attention mechanism generates an attention weight matrix \(M_{s}\in R^{H\times W}\) by a convolution operation indicating the importance of each spatial location. The computational process can be represented as  
\[M_{s}(F)=\sigma(Conv_{2}(F)) \tag{8}\]  
where \(Conv_{2}\) is a 2D convolution operation, \(\sigma\) is the sigmoid activation function, and \(M_{s}(F)\) is the generated attention weight matrix.  
Following the procedures mentioned above, the Spatial Attention Mechanism Pass (SAM) may draw attention to crucial regions and highlight the characteristics of significant spatial locations in the input feature maps, enhancing the model's prediction performance.
# A Heart Rate Variability-Based Paroxysmal Atrial Fibrillation Prediction System
## 2 Materials and Methods
### Signal Preprocessing  
To convert the HRV sequences from the afore-mentioned ECG signals, we downloaded RR intervals (in the unit of sec/beat) for each of those ECG signals from PhysioBank ATM at [[https://archive.physionet.org/cgi-bin/atm/ATM](https://archive.physionet.org/cgi-bin/atm/ATM)]([https://archive.physionet.org/cgi-bin/atm/ATM](https://archive.physionet.org/cgi-bin/atm/ATM)) (last accessed on 11 March 2021). Then, each HRV sequence (in the unit of beats/min) was converted from its corresponding RR intervals using 60/(RR intervals). According to PhysioBank, the locations of the R waves in an ECG signal were determined by a QRS detector that used a modified Pan and Tompkins algorithm [33; 34]. In the algorithm, a bandpass filter composed of cascaded low-pass and high-pass filters was applied to an ECG signal to reduce the influences of muscle noise, 60 Hz interference, baseline wander, and T-wave interference. The desirable passband was set to approximately 5-15 Hz in order to maximize the QRS energy [35; 36]. A testing result showed that the accuracies of the QRS detection algorithm were generally >95% on multiple ECG databases acquired from bedside monitors when the subjects were in a resting state [37]. No further filtering in the frequency domain was applied to the HRV sequences for two reasons. Firstly, an HRV sequence is an uneven sampling (i.e., RR intervals) from ECG. It is difficult to interpret a certain frequency band in the frequency transformation of HRV. Secondly, the HRV sequence has been shown to exhibit a continuous spectrum [6]. Without knowing the exact sources of the noise, any filtering in the frequency domain can introduce artifacts into the sequence. However, certain countermeasures did apply in the feature conversion stage to reduce the noise caused by inaccurate QRS detections, which will be covered in the next section.  
For the PAF Prediction Challenge Database, each 30 min HRV sequence was further divided into six 400-point subsequences, and overlapping was allowed if the sequence was less than 2400 points. As a result, 960 and 240 400-point HRV sequences were attained in the training and validation sets, respectively. The purpose of the division was not only to increase the dataset size, but also to reduce the data length for less computation and storage so that the constructed prediction system could be applicable to wearable devices. For the MIT-BIH Atrial Fibrillation Database, only episodes with 400 consecutive normal sinus beats (each beat is noted as \"N\" in the annotation file) and at least five consecutive normal sinus beats before and after the episodes were extracted from the two databases. Consequently, we acquired 3850 400-point HRV sequences from 18 healthy controls and 1035 400-point HRV sequences from 21 PAF patients (4 of the 25 patients in the database did not have a qualified episode). The detailed numbers of non-PAF and PAF HRV sequences in the training, validation, and testing datasets are listed in Table 1.
# Accurate Modeling, Operation Laws and Commutation Timing Matching for Concrete Pumping Systems
## 4 Conclusions  
The pumping system is the core of the concrete pump truck, which delivers high-pressure concrete to the pipeline through the coordinated action of the reciprocating linear motion of the main cylinder and the reciprocating motion of the swing cylinder. The key findings and recommendations are as follows:  
(1) This paper establishes an accurate pumping system simulation model. the accuracy is more than 90%, and the simulation model could simulate the actual pumping system. Utilizing this platform could reduce the cost and cycle time of pumping system development.  
(2) This study reveals the operation law and inherent characteristics of the pumping system: sequence, hysteresis, coupling and recirculation. There is unsynchronized commutation of the main cylinder and the swing cylinder in the operating process, and the swing cylinder always commutes when the main cylinder advances, which causes shocks and vibrations. The degree of matching of the commutation times determines the pumping efficiency and the life of wear parts.  
(3) An optimization scheme for the pumping commutation timing is proposed: reducing the pumping relief pressure of the main cylinder to reduce the swing load, shortening the swing time of the swing cylinder and moving the main cylinder changeover point towards the rear of the commutation interval to reduce erosion wear and the amount of concrete leakage. The solution is market-proven and significantly reduces wear on the cobbler plate and cutting ring and significantly increases pumping efficiency.  
Future research will be carried out in the following areas: further optimization of the simulation model to provide model accuracy and optimization of the structure of the pumping system using model simulation instead of experimentation; development of corresponding timing optimization schemes for different pumping systems; and industrial validation.  
Conceptualization, methodology, writing--review and editing, supervision, and funding acquisition, H.D.; software, writing--original draft, and formal analysis, C.W.; investigation, validation, data curation, and visualization, C.W. All authors have read and agreed to the published version of the manuscript.  
This work is supported by a Fundamental Research Funds for Central Universities (2019 XKQYMS37), and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.  
Not applicable.  
Date will be available on request.  
The authors declare no conflict of interest.
## 3 Experiment Results and Analysis
### Hysteresis Curve  
Figure 14 shows a comparison of the two sets of hysteresis curves. The hysteresis curve of FW-2 is fuller and encloses a larger area than FW-1. The bearing capacity of FW-2 is significantly greater than that of FW-1 at the same inter-story displacement angle, which indicates that FW-2 consumes more energy than FW-1 and absorbs seismic loads more effectively during earthquakes. The two sets of hysteresis curves behave asymmetrically, and the negative bearing capacity values are greater than the positive bearing capacity value. With the increase in loading displacement, FW-2 gradually approaches the inverse S-shape and \"pinche\", which indicates slippage in the upper part of the pendulums \(Z\)-panel connector and some residual deformation of the connection. The characteristic values on hysteresis curves are shown in Table 3.  
Figure 14: Hysteresis curves of FW-1 (**a**) and FW-2 (**b**).  
Figure 13: Crack pattern of specimen FW-2: (**a**) crack in the bottom of the plane; (**b**) vertical cracks are produced; (**c**) ALC panel corner shattering; (**d**) slippage of connectors was obvious.
# A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes
## 2 Trust Metric Model of Unmanned Aerial Vehicle Cluster Nodes Based on Hierarchical Blockchain
### A Dynamic Evaluation Method for Drone Node Trust Based on Task Perception
#### 2.2.1 Direct Trust Evaluation  
The calculation method of the direct trust degree is to evaluate the trust degree of the node by periodically observing the node behavior and combining the four trust evaluation factors of packet loss rate, data packet forwarding rate, trusted interaction degree, and detection packet reception rate. The definition of each impact factor is as follows.  
Packet loss rate (\(PLR\)) refers to the ratio of the total number of lost data packets to the total number of sent data packets.  
Packet forwarding rate (\(PFR\)) refers to the ratio of packets received by a node to those it forwards.  
Trustworthiness of interaction (\(TOI\)) refers to the degree of interaction between a node and other nodes. The number of interactions between a trusted node and an untrusted node can reflect the degree of trustworthiness of the node. When a node interacts with more trusted nodes and interacts less with untrusted nodes, the degree of trusted interaction will be lower. The weight of the trust evaluation factor can be calculated by the AHP and the Delphi method. Here, we use the Delphi method to calculate the weight of the trust factor of the direct trust degree. Then, after obtaining the actual data of the trust factor, the linear weighting method is used to calculate the direct trust value of the UAV node.
## 2 Materials and Methods
### Illumina RNA Sequencing  
The whole procedure was performed with our previously described method [20]. Sequencing of _L.__pubescens_ RNA was conducted using Set A and Set B of a TruSeq Stranded Total RNA LT Sample Prep Kit with Ribo-Zero Gold (Set A; Illumina, RS-122-2301 and Set B; Illumina, RS-122-2302, San Diego, CA, USA). Initially, ribosomal RNA (rRNA) was removed from total RNA using biotinylated and target-specific oligos and rRNA removal beads, and the purified RNA thus obtained was sheared into small fragments (100-400 bp) using divalent cations at 94 \({}^{\circ}\)C for 8 min. Oligo (dT)-dynahead selection was then performed to isolate poly (A) mRNA. First-strand cDNA was synthesized from the mRNA using random hexamer primers and reverse transcriptase, with the cleaved RNA fragments as templates. Prior to second-strand synthesis, the RNA templates were cleaned using RNase H and the second strand was synthesized by incorporating dUTP instead of dTTP to yield a double-stranded cDNA, which was subsequently 3\({}^{\circ}\)-adenylated and subjected to adapter ligation. The final step involved size selection of the resulting products, based on gel electrophoresis and polymerase chain reaction (PCR) amplification to generate the final cDNA library. The library was sequenced via paired-end (PE) flow cell Illuminausing a NextSeq 500 sequencer (Illumina, San Diego, CA, USA) to generate raw reads. The raw _L.__pulsecens_ RNA sequencing reads were preprocessed using both FastQC and CutAdapt adapter software [21] to generate high-quality cleaned reads by eliminating low-quality reads containing unknown base N, reads with more than 10% \(Q\) < 20 bases, and adapter sequences, given that low-quality and adapter sequences in the raw reads might cause errors during further de novo assembly. As a consequence of adapter and quality trimming, a total of 124,685,828 high-quality clean reads were prepared for the _L.__pulsecens_ transcriptome.