esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Hierarchical Adaptive Deep Belief Network for Bearing Fault Diagnosis
## 2 Deep Belief Network and Its Improvement
### Deep Belief Networks  
Actually, efficiently simulating complex raw data using only one RBM model is difficult. Therefore, constructing a DBN model stacked with a plurality of RBM models is necessary. [PERSON] et al. [PERSON] (1992) put forward a fast learning algorithm for DBNs, which is essentially a stack of several RBM layers. DBN is a versatile deep learning model that can be used in unsupervised feature dimensionality reduction and supervised pattern classification. Whether supervised or unsupervised, DBN is essentially a series of feature learning processes to ensure improved feature representation. Similar to RBM, DBN is a probability stochastic model that aims to learn the joint probability distribution between the data from input and the hidden layer. Then, DBN correspondingly fits the data by adjusting the connection weight between every two adjacent layers of neuron nodes. The characteristic information is contained in the original input data.  
In Figure 2, a stacked DBN model consists of three RBM layers is shown. The multi-hidden DBN model construction can be summed up as two parts: unsupervised pretraining phase and supervised form of global fine-tuning phase. The first part is pretraining, which is also called initialization parameters. One of the most difficult problems in traditional neural network training is how to select the appropriate initialization parameters. Although random initialization is commonly used, once the initialization parameters are randomly given, the final training and test results are seriously affected. To solve this problem, a pretraining technique based on a greedy layer-by-layer unsupervised learning algorithm is proposed. The DBN model is decomposed into a series of RBM models. The hidden layer state of each low-level RBM model is fed to its higher-level RBM model. The lowest state of the entire depth model is the original input data. Each parameter RBM model is trained successively to provide improved initialization weights and offset values for the entire DBN model.  
The second part is global fine-tuning. To further enhance the diagnostic capabilities of the model, after the unsupervised pretraining of a series of RBM models is complete, the original training data are used as the tag data to supervise the entire deep structure. According to the maximum likelihood function, the back propagation algorithm [29] is applied to further optimize the entire deep model and fine-tune the parameters of each layer. The DBN model has achieved very good application results on some public datasets, thereby proving that a suitable training technique is more conducive to obtaining improved initialization parameter values and global optimal solutions than traditional random initialization.
# A Deep Neural Network Model for Speaker Identification
## 4 Conclusions  
In this article, we briefly introduced the research on the speaker identification task. Considering 2-D CNN has its advantage on feature extraction of a 2-D structure and voice spectrogram contains rich voiceprint information, we proposed a deep RNN model based on GRU combining a 2-D CNN layer for speaker identification. The proposed models integrate both advantages of feature extraction of 2-D CNN and temporal dependency of the GRU cell unit. In deep RNN networks based on the GRU cell unit, we further conducted cyclic memory learning on time series in the network to obtain hierarchical distinct features that represent each speaker in the embedding process. At the last layer, the softmax classifier layer in the network was used to learn and score the features to obtain the results of speaker identification.  
In comparison to the performance of these network models on the Aishell-1 speech dataset, the proposed deep GRU network model achieved the best performance in terms of model training time, recognition accuracy, and stability. However, the disadvantage of the proposed method is the time cost in the training phase, and the stability of the model under complex noise environments is worth further study. Thus, our future work will aim to improve the robustness of the model in general practical environments.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Name** & **Struct** & **Stride** & **Param\#** \\ \hline Input & 32 \(\times\) 40 \(\times\) 199 \(\times\) 3 & - & - \\
2-D Conv & 5 \(\times\) 5 kernel, 64 filter & 2 \(\times\) 2 & 4.8 k \\ Average pooling & 2 \(\times\) 2 pooling & 2 \(\times\) 2 & 0 \\ GRU & 1024 units & - & 5.1 m \\ GRU & 1024 units & - & 6.3 m \\ GRU & 1024 units & - & 6.3 m \\ average & - & - & 0 \\ Dense & 1024 \(\times\) 512 & - & 0.52 m \\ Length normalization & - & - & 0.2 m \\ Output (softmax) & 1024 \(\times\) 400 & - & 0 \\ Total & - & - & 18.42 m \\ \hline \hline \end{tabular}
\end{table}
Table 6: The details on the deep GRU network model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Name** & **Struct** & **Stride** & **Param\#** \\ \hline Input & 32 \(\times\) 40 \(\times\) 199 \(\times\) 3 & - & - \\
2-D Conv & 5 \(\times\) 5 kernel, 64 filter & 2 \(\times\) 2 & 4.8 k \\ Average pooling & 2 \(\times\) 2 pooling & 2 \(\times\) 2 & 0 \\ LSTM & 1024 units & - & 9.4 m \\ LSTM & 1024 units & - & 8.3 m \\ LSTM & 1024 units & - & 8.3 m \\ average & - & - & 0 \\ Dense & 1024 \(\times\) 512 & - & 0.52 m \\ Length normalization & - & - & 0.2 m \\ Output (softmax) & 1024 \(\times\) 400 & - & 0 \\ Total & - & - & 26.93 m \\ \hline \hline \end{tabular}
\end{table}
Table 5: The details on deep CNN + RNN-LSTM unit network model.
# Sugar Maple and Red Maple Face-Off: Which Produces More and Sweeter Sap?
## 4 Discussion
### Sugar Content and Physicochemical Characteristics of Sap
The slight decrease of the pH aligns with the increased concentration of organic acids, such as malic and succinic acid, as observed in other studies [31,42]. Potassium, sodium, calcium, and magnesium are the most abundant minerals present in maple sap [31]. Conductivity, the ability of a solution to conduct an electrical current, is associated with the presence of these mineral ions [14,31,42]. In our study, conductivity values remained almost constant throughout the season, with only a few points showing peaks at the beginning of the season. These could be related to a higher concentration of dissolved minerals in the sap. No significant difference in conductivity was observed between the two species, likely due to the fact that the trees are located on the same site and share similar climatic and environmental conditions. According to [PERSON] [31], concentrations of cations (Ca, Na, Mg, and K) increase toward the end of the season. The difference with our study is that they worked with mixed sap, while our results are based on measurements from individual saps, which could explain the divergence observed in the trends. Our results have significant implications in forest management, the maple syrup industry, and climate change adaptation strategies. Introducing red maple into sugarbushes could promote increased ecological diversity and enhance forest resilience to natural disturbances, particularly extreme climate events. For the maple syrup industry, red maple represents a strategic alternative in regions where sugar maple is less adapted or in which climate alterations could drive maladaptation. Due to its high adaptability to a wide range of environmental conditions, red maple could contribute to a more stable sap production.
## 2 Methods and Materials
### Correction Methods beyond Boag and Greening Limits  
In the previous two sections, we developed the online correction method from the analytic expressions of Boag and Greening for pulsed and continuous beams, respectively. We obtained simple closed-form expressions for the proposed correction method. However, in many experimental situations, the expressions presented above do not hold. For example, in gas-filled ionization chambers, it is customary to include the effects of initial recombination and recombination by diffusion in the overall collection efficiencies [20; 29], even though these contributions are smaller than general/volume recombination. Additionally, in gas chambers, charge multiplication may play a role too, and it should be considered when analyzing the response of the chamber [20]. The addition of these termswill lead to modifications of the methods presented above, but the methodology would remain the same.  
More interestingly, in situations where dose rates/doses per pulse are very large, volume recombination can be quite important, and the Boag/Greening analytic expressions will fail to describe the dependence of the collection efficiency on the dose rate/dose per pulse. Those expressions are only valid in the _near-saturation_ region, with \(f>0.9\). This may be the case in FFF radiotherapy, with doses per pulse reaching \(\simeq\) 2 mGy, and especially UHDR radiotherapy, with doses per pulse that can be several orders of magnitude higher. In these situations, the densities of charge carriers in the ionization medium are much larger, field screening effects become important, and the analytic description of the collection efficiency is no longer valid.  
However, in this situation, the dependence of the collection efficiency on the dose per pulse (ionization current) can be characterized experimentally,  
\[f(I)=g(\{a_{i}\},I) \tag{24}\]  
where \(\{a_{i}\}\) are a set of parameters that describe the dependence (they can arise from an interpolation of experimental data, or from a phenomenological function fitting the dose per pulse dependence, as in [31]). After the collection efficiency is accurately characterized (i.e., the function \(g\) is known, either analytically or numerically), the collection efficiency can be obtained following the procedure described in the previous section, by writing the equation as  
\[f(I)=g(\{a_{i}\},f(I)I_{col}) \tag{25}\]  
and solving for \(f(I)\).
# Development on Solid Polymer Electrolytes for Electrochemical Devices
## 3 Basic Solid Polymer Electrolytes
### Strategies to Improve Ionic Conductivity of SPEs
#### 3.2.7 SPEs Containing Other Kind of Additives  
It is an undeniable fact that ionic conductivity plays a crucial role in ensuring the performance of electrochemical cells, but the cationic transference number also influences the efficacy of devices in particular batteries. Enhancement in lithium-ion transference number has been observed in PEO-LiI SPEs upon the addition of calix [4] arene with values around 0.8 to 1 at elevated temperatures between 55 and 90 \({}^{\circ}\)C [189]. In most cases, the cationic transference number values are found to be below 0.4 without supramolecular additives in PEO-salt SPE systems as mentioned earlier. Based on ab initio computational work, [PERSON] [190] predicted that the cryptands, i.e., organic compounds having large 3D molecular structure, can trap anions, such as Cl\({}^{-}\), ClO\({}_{4}\)\({}^{-}\), BF\({}_{4}\)\({}^{-}\) and F\({}^{-}\), and form coordination complexes, thereby reducing their tendency to re-associate with Li\({}^{+}\) cation and consequently increasing the cationic transference number. However, the supramolecular compounds are unable to confine large PF\({}_{6}\)\({}^{-}\), TESI\({}^{-}\) or [(CF\({}_{3}\)SO\({}_{2}\))\({}_{2}\)N]\({}^{-}\) anions within a 3D network and cannot form complexes [190]. [PERSON] et al. [191] investigated the influence of calix[6]pyrrole on PEO-LiCF\({}_{3}\)SO\({}_{3}\) SPEs. Although the ambient conductivity was found to decrease with the addition of calix, the conductivity at 60 \({}^{\circ}\)C was higher than that without calix. Nevertheless, the \(t_{Li^{+}}\) of the calix-added SPE enhanced by 185% to 0.74 from 0.26 of the calix-free SPE at 60 \({}^{\circ}\)C [191]. Ambient conductivity decrement is said to be attributed to the increased rigidity of polymer network, whereas an improvement in lithium-ion transference number is due to increased amount of free ions from dissociated ion aggregates. The SPE has been employed in Li/MoO\({}_{3}\)S\({}_{y}\) cells that exhibited a power level of 2.1 mW cm\({}^{-2}\) at 90 \({}^{\circ}\)C and a discharge capacity of \(\sim\)24 \(\mu\)Ah on the 60 th cycle [191]. It is said that calix[6]arene can minimize the growth of lithium dendrites and subsequently improves cell performance [192]. A similar observation wherein an increased lithium-ion transference number (\(t_{Li^{+}}\) = 0.95 at 70 \({}^{\circ}\)C) was accompanied by conductivity decrement has been reported in PEO-LiBF\({}_{4}\)-calcix[6]pyrrole system [193]. In addition, the lithium diffusion-coefficient values were 2.0 \(\times\) 10\({}^{-7}\) cm\({}^{2}\) s\({}^{-1}\) and 2.5 \(\times\) 10\({}^{-7}\) cm\({}^{2}\) s\({}^{-1}\) for calix-free and calix-incorporated PEO-based electrolytes, respectively [193]. As aforementioned, the incorporation of supramolecular additives, such as calix-based compounds, is used to increase cationic transference number by trapping/immobilizing
###### Abstract  
In the present study, a bypass passive jet flow control method was proposed to mitigate unsteady wind loads and to manipulate the flow field around a single box girder of a bridge. With a geometric ratio of 1:125, the single box girder model was determined using the cross-section of the Great Belt East Bridge. During the experiments, one test model without control was adopted, while five different test models with different suction/jet configurations were employed to analyze the effects of the control method and to reveal the underlying mechanism of different control schemes. The incoming wind speed was fixed to 12 m/s and the wind attack angles were changed from \(-20^{\circ}\) to \(20^{\circ}\), resulting in a corresponding Reynolds number of \(\mathrm{Re}=0.28\times 10^{5}\)-\(0.74\times 10^{5}\) based on the different attack angles. A six-component force balance, a set of digital sensor array (DSA) pressure transducers, and a particle image velocimetry (PIV) system was used to measure the aerodynamic forces, pressure distributions, and flow fields around the test models to evaluate the control effectiveness of different control cases. Detailed flow structures are presented and discussed for two test cases when the angles of attack are \(+15^{\circ}\) and \(-20^{\circ}\). The effects of control on the aerodynamic forces were first investigated to determine and select the best one out of five control cases. The pressure distributions on the surface of the test model without control and the best control case were then compared to evaluate the control effectiveness of the pressure gradient and the fluctuating pressure coefficients. The flow fields around the test models demonstrate that the bypass passive jet flow control could decrease vortex strength, delay flow separation, and change recirculation region and size. The results of the aerodynamic forces, pressure distributions, and flow fields indicate that the bypass passive jet flow control method results in effective control.  
single box girder; bypass; passive jet flow control; wind loads; flow field measurement 1
## 4 Findings
### Technology  
The degree of technological maturity of the start-ups at the beginning and end of the programme was assessed according to the criteria shown in Appendix A for each of the following indicators: (1) degree of maturity of the technology employed by the start-up, (2) advantages of the proposed technology over other technologies proposed by competitors, (3) existence and degree of development of an architecture defining the structure, operation and interaction between the parts of the product/service, (4) consideration of standards for the development of the product/service, (5) degree of realisation of the product/service and availability of metrics for its verification, and (6) degree of consideration of cybersecurity aspects of the product/service from the design phase and existence of risk mitigation plans.  
In relation to the Technology category, Figure 2a,b shows that the most significant progress was achieved in the pre-COVID programmes, specifically in aspects related to the degree of product development (performance indicator, \(\Delta\) = 0.8), followed by addressing cybersecurity and resilience issues (\(\Delta\) = 0.6) and identifying the advantages of the technology supporting the products over the competition (\(\Delta\) = 0.6). In contrast, in the programmes delivered during the first two years of the pandemic, the progress achieved was not as high (between 0.2 and 0.5), despite the fact that start-ups started the programme with considerably higher levels of maturity in each of the categories considered. This may seem surprising at first glance but may be due to the fact that progression in the early stages is easier and/or faster, while the effort, experience and resources required to progress in the later stages are much greater. For example, less mature start-ups have to prioritise areas such as product development and core technology performance over standards development, cybersecurity and performance indicators, which are often left for later stages.
# Star Type Wireless Sensor Network for Future Distributed Structural Health Monitoring Applications
## 3 Results
### Field Work in Durham Cathedral Results  
Ringing up is when the ringer moves the bell from its resting, mouth-down position which can been seen in Figure 1 to its mouth-up position seen in Figure 2. The ringer does this by repeatedly pulling the rope to make the bell swing higher and higher until it rests in the mouth-up position. The measured angle during the ringing up of the largest bell of mass 1425 kg can be seen in Figure 9. Ringing down is the reverse of this process. After the bells in the Cathedral were rung down, they continued to oscillate with an amplitude of \(\sim\)5\({}^{\circ}\) for a period of time. The results of measuring this period of small oscillation are summarised in Table 6. The period of oscillation for each of the five lightest bells was measured by taking the average time for ten complete oscillations. This was done using the data obtained from the sensors. It should be noted that for all plots in this section, 0\({}^{\circ}\) and 360\({}^{\circ}\) corresponded to when the bell was in its mouth-up position.  
Figure 8: Oscillations recorded by two sensors attached to a pendulum.  
Figure 9: The ringing up of bell number ten of mass 1425 kg in Durham Cathedral.  
Based on the collected data set, it was possible to validate the assumption that the initial velocity given to the bell by the bell ringer at the start of its rotation was negligible. The initial velocity for all bells was observed to be negligible at the start of their rotation by observing the angular velocity against time curves calculated from the data set. The angular rotation of bell ten of mass 1425 kg and bell six of mass 563 kg for the first 800 ms of ringing can be seen in Figure 10. The initial angular velocity of both bells, calculated from the slope of the curves, at 360\({}^{\circ}\) was 14.45\({}^{\circ}\)/s for bell 10 and 7.95\({}^{\circ}\)/s for bell 6 which was negligible compared to the angular velocities of 224.10\({}^{\circ}\)/s and 147.92\({}^{\circ}\)/s at 310\({}^{\circ}\), respectively. Considering bell 10, which had a period of \(\tau\) = 2.08 s [6], plugging into the right hand side of Equation (4) yielded a value of 3.27 \(\times\) 10\({}^{-3}\) which was three orders of magnitude smaller than the maximum value of the left hand side of the equation.  
The rotational angle of bell ten, and the horizontal and vertical forces exerted on the bell due to its motion as a function of time, calculated using Equations (1) and (2), are shown in Figure 11. To the authors' knowledge, this is the first time that such plots could be produced based on the experimental data. Because each swing of the bell was slightly different, instead of averaging a set of swings, typical data of all swings of the bell were shown and they were observed to have the same shape as the data presented here.
## 4 Experimental Results
### Performance Metrics  
The standard parameters such as accuracy, precision, recall, f1-score, and training time were computed in this study in order to validate the proposed EFDPN model. These parameters determine the overall performance of the security framework, which is estimated using the following models:  
\[\text{Accuracy}=\frac{\text{T}_{+\text{ve}}+\text{T}_{-\text{ve}}}{\text{T}_ {+\text{ve}}+\text{T}_{-\text{ve}}+\text{F}_{+\text{ve}}+\text{F}_{-\text{ ve}}} \tag{22}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Attacking Classes** & **No of Samples** \\ \hline IS-1 & \\ Normal & 77,054 \\ DoS & 53,385 \\ Probe & 14,077 \\ R2L & 3749 \\ U2R & 252 \\ UNSW-NB 15 & \\ Normal & 2,218,761 \\ Generic & 215,481 \\ Exploits & 44,525 \\ Fuzzers & 24,246 \\ DoS & 16,353 \\ Reconnaissance & 13,987 \\ Analysis & 2677 \\ Backdoor & 2329 \\ Shellcode & 1511 \\ Worms & 174 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset details.  
\[\text{Precision}=\frac{\text{T}_{+\text{ve}}}{\text{T}_{+\text{ve}}+\text{F}_{+ \text{ve}}} \tag{23}\]  
\[\text{Recall or TPR}=\frac{\text{T}_{+\text{ve}}}{\text{T}_{+\text{ve}}+\text{F}_{ +\text{ve}}} \tag{24}\]  
\[\text{F1}-\text{score}=2\times\left(\frac{\text{Precision}\times\text{Recall} }{\text{Precision}+\text{Recall}}\right) \tag{25}\]  
\[\text{FPR}=\frac{\text{F}_{+\text{ve}}}{\text{F}_{+\text{ve}}+\text{T}_{-\text {ve}}} \tag{26}\]  
\[\text{FNR}=\frac{\text{F}_{-\text{ve}}}{\text{T}_{+\text{ve}}+\text{F}_{-\text {ve}}} \tag{27}\]  
where T\({}_{+\text{ve}}\) indicates a true positive, T\({}_{-\text{ve}}\) represents a true negative, F\({}_{+\text{ve}}\) is a false positive, and F\({}_{-\text{ve}}\) is a false negative. Figure 4 evaluates the performance of the proposed EEDPN model using IS-1 with and without the F\({}^{3}\)S mechanism.
# An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots
## 2 Live-Line Maintenance and Robot System
### Particularity of Live-Line Working Scene  
The Live-line Working scene has particularities, and the constraints caused by it need to be considered in the path planning. Affected by the cable layout, the position of the task target relative to the platform in different scenarios is quite different. The method of fixed path with end correction cannot meet the task requirements. Obstacles to robot motion planning include platforms, cables, and other robots. Affected by the size of the platform and the diversity of tools, the main obstacle in the movement of the manipulators is the platform itself, which can be regarded as a fixed obstacle, as shown in Figure 4. In different scenarios, the position of obstacles such as cables and crossarms relative to the platform is variable.  
The live-line working operation is a highly risk task, and planning results with strong randomness are not acceptable. In addition, the stable planning results make it easy for the operator to monitor the movement process and prevent accidents.  
Figure 4: Dense tool placement and small space limit robot movement.  
Figure 3: Autonomous live-line maintenance robot.  
The live-line working operation has special requirements for the robot's end posture. For example, it is necessary to prevent the unfixed parts from closing under gravity when moving the stripper. The drainage line will bend unpredictably during movement, which may lead to the cable winding to the robot body. Therefore, it is necessary to keep the robot body away from the cable when moving.  
In conclusion, the path planning in the live-line working scene has the following particularities:  
1. There are a lot of obstacles in the environment, and some of the obstacles are mobile;
2. The end posture of the robot is restricted during the movement of the robot;
3. Based on safety factors, the robot stays away from charged objects as much as possible during the movement.