esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, a time-frequency feature extraction module based on the attention mechanism neural network is proposed, which combines the operation mechanism of the convolutional neural network, time-domain filtering and Fourier transform. The proposed method can extract the input time domain signal directly, which is an end-to-end training model. Classic deep learning methods search for features through neural networks such as black-box models, and it is difficult for researchers to analyze the inner workings and behavior of the models. The proposed method can output the frequency domain response of the neural network in real-time. It is convenient for researchers to understand the neural network learning process, which helps to strengthen the network model and improve its generalization. In addition, the feasibility of transferring non-underwater acoustic data as pre-trained data to underwater acoustic target recognition is verified. A series of classification experiments demonstrate the effectiveness of the proposed method, especially for tasks with a demand for model generalization ability.  
Conceptualization, D.L. and F.L.; methodology, D.L.; software, F.L. and D.L.; validation, T.S., D.Z. and L.C.; resources, T.S.; investigation, D.Z.; writing--original draft preparation, X.Y. and D.L.; writing--review and editing, X.Y. and L.C.; supervision, T.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data available in a publicly accessible repository. The data presented in this study are openly available in available at 10.1016/j.apacoust.2016.06.008 in ref [36].  
The authors declare no conflict of interest.
# Structure Approximation-Based Preconditioning for Solving Tempered Fractional Diffusion Equations
## 5 Numerical Experiments
### Test Problem 1  
Consider the coefficient  
\[d(x)=\frac{e^{5x}}{1+x}.\]  
An illustration of the corresponding analytical solution and the numerical solution is shown in Figure 1. We use GMRES with \(P_{DCS}^{-1}\), \(P_{SDTAS_{\tau}}^{-1}\), \(P_{CAI}^{-1}(l)\) and \(P_{TAI}^{-1}(l)\) preconditioners as well as GMRES without preconditioning to test. The results of the computations at discretization levels \(N=10^{8},10^{9},10^{10},10^{11},10^{12}\) are shown in Table 1. Here, \"IT\" denotes the average number of iterations per step, and \"CPU\" denotes the total computation time(s) of the algorithm. To facilitate the comparison of the performance of different preconditioning matrices, we visualized the results from Table 1, as shown in Figure 2. From Table 1 and Figure 2, we can see that the performance of \(P_{TAI}^{-1}(l)\) is significantly better than that of \(P_{CAI}^{-1}(l)\), \(P_{DCS}^{-1}\) and \(P_{SDTAS_{\tau}}^{-1}\). The number of iterations for each preconditioning method remains relatively stable as the discretization level increases, but the number of iterations for \(P_{TAI}^{-1}(l)\) is notably lower than that for the other three. Similarly, in terms of computation time, the computation time for \(P_{TAI}^{-1}(l)\) is much lower than that for the other three, and the growth in computation time for \(P_{TAI}^{-1}(l)\) is slower as the discretization level increases.  
To further illustrate the effectiveness of the proposed preconditioning matrices, we plot the eigenvalue distributions of the original coefficient matrix A, the preconditioner \(P_{DCS}^{-1}\), \(P_{SDTAS}^{-1}\), \(P_{CIA}^{-1}(l)\) and \(P_{TAI}^{-1}(l)\) when \(N=10^{8}\), as shown in Figure 3. It can be observed that the eigenvalue distribution of the coefficient matrix A without preconditioning is highly dispersed, while the eigenvalue distributions of the preconditioned matrices \(AP_{TAI}^{-1}(l)\) are concentrated around 1. Notably, the eigenvalue distribution of \(AP_{TAI}^{-1}(l)\) is more concentrated than those of the other preconditioners.  
We searched for the optimal number of interpolation points \(l\) in the proposed preconditioner \(P_{TAI}^{-1}(l)\). As shown in Table 2, we tested GMRES using \(P_{TAI}^{-1}(l)\) with the number of interpolation points \(l=4,6,8,10,12\). To determine the optimal number of interpolation points \(l\), we visualized the number of iterations, as shown in Figure 4. By analyzing Figure 4, we found that when the number of interpolation points \(l\geq 8\), the number of iterations tends to stabilize. With this observation, the number of interpolation points \(l=8\) was recommended for test problem 1.  
Figure 3: The eigenvalue distributions of the original coefficient matrix A, the preconditioner \(P_{DCS}^{-1}\), \(P_{SDTAS_{t}}^{-1}\), \(P_{CAI}^{-1}(l)\) and \(P_{TAI}^{-1}(l)\) (\(d=d_{1}(x)\), \(\lambda=1.5\), \(\beta=1.2\), \(\gamma_{1}=0.75\)).
## 4 Discussion
#### Limitations and Future Perspectives  
The present study suffers from some limitations. First, the lack of a PD control group and the small sample size limit the generalizability of the present study's results. Second, a specific corrective orthosis was used in this study; therefore, our results could not directly extend to other corrective corsets. Moreover, only people with postural abnormalities on the sagittal plane were enrolled; therefore, the evidence in the present study could not be generalized to other axial postural abnormalities on the other planes, such as Pisa syndrome. Additionally, patients were assessed only in a supervised laboratory-based setting. Future studies with a larger sample size, including patients with axial abnormalities beyond the sagittal plane, implementing other corrective corsets, and assessing posture parameters in free-living, unsupervised conditions could help overcome these limitations and further expand the findings of this study. Finally, future longitudinal studies are warranted to investigate whether the observed improvements in gait and balance parameters in order to reduce risk of falls and to obtain an improvement in functional mobility and independence, as well as to assess the long-term (beyond three months) effects and further postural changes over time.
## 3 Results and Discussion
### GFET Based Flexible Receiver  
Figure 5 shows the block diagram of a relatively simple graphene based radio frequency communication system. It consists of a graphite antenna which receives the RF signal. This is followed by an input match network for impedance matching between the antenna and graphene based frequency translation block (mixer or demodulator) that function as an RF signal down-converter. The low frequency output of the frequency translation block or mixer is connected to a graphene speaker through an output matching network. In the following subsections we describe the realization of graphene RF communication circuits.
# CEMB-YOLO: Efficient Detection Model of Maize Leaf Blight in Complex Field Environments
## 3 Cemlb-Yolo Network Model
### The Loss Function of CEMLB-Yolov5  
The regression loss in YOLOV5 adjusts the position of the predicted bounding box by calculating the intersection over the union ratio between the ground truth box and the predicted box, as demonstrated by Equation (19). The continued research on loss functions, GIOU [40], DIOU [41] and CIOU [42], have been proposed. CIOU regression loss converges faster than other alternative regression losses. This paper adopts CIOU as the model's regression loss, with its expression shown in Equation (20):  
\[Loss_{iou}=1-\frac{A\cap B}{A\cup B} \tag{19}\]  
\[Loss_{CIOU}=1-IOU+\frac{\rho^{2}(b,bs^{t})}{d^{2}}+av \tag{20}\]  
where \(A\) and \(B\) respectively, denote the area of the ground truth-bounding boxes and the predicted boxes, \(\rho(\cdot)\) indicates the Euclidean distance between the predicted and true box centroids and \(d\) represents the diagonal distance between the smallest closed regions. \(\alpha\) indicates the trade-off indicator. The value of \(v\) describes the similarity of the ground truth and bounding box shapes. \(\alpha,v\) can be expressed mathematically as follows:  
\[v=\frac{4}{\pi}(\arctan\frac{w^{gt}}{hs^{gt}}-\arctan\frac{w}{h})^{2} \tag{21}\]  
\[\alpha=\frac{v}{(1-IOU)+v} \tag{22}\]  
Generally, the distance between the centre of the predicted box and the true box increases as the size of an object increases. The larger the object, the more significant its contribution to the loss function, which can reduce the model's ability to detect smaller defects and result in false negatives. Therefore, in order to balance this difference, this paper takes the square root of the numerator when calculating CIOU. The enhanced expression of the CIOU function is as follows:  
\[Loss_{CIOU}=1-IOU+\frac{\sqrt{\rho^{2}(b,bs^{gt})}}{d^{2}}+av \tag{23}\]
# How to Isolate Non-Public Networks in B5G: A Review
## 1 Introduction
Then, the technologies to achieve isolation of the spectrum, isolation of the RAN and isolation of the CN for NPNs are summarized in the following three sections. Thus, the decisions about whether the spectrum, the RAN and the CN should be shared or not when deploying NPNs can be made according to the performance metrics of the isolation technologies. The following section describes open issues. Finally, conclusions are drawn.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 3 Proposed Scheme
### Hiding Module
#### 3.1.1 Scrambling  
The objective of this block consists in scrambling the secret content voice signal. To do so, the method proposed in [12] is used, in which a voice signal imitates a super-Gaussian noise signal with similar statistics. The result (scrambled signal), looks like a noise signal. From the adaptive process emerges a key, which allows mapping the scrambled signal positions with the positions of the original noise signal data. This key has the same length as the voice signal.  
Figure 1: Block diagram of the concealing module. Bits to Hold (BH), decimal (Dec), binary (Bin), less significant bits (LSB).  
The first sub-block consists on generating a super Gaussian signal (i.e., a signal with kurtosis higher than three, that looks like a Gaussian signal) with the same (or very similar) statistics and length of the speech signal.  
Firstly, a uniform signal is generated, according to:  
\[u=rand(L,1)-0.5 \tag{1}\]  
where \(u\) is a uniform signal generated in situ, from a random generator which depends on the seed. The length of the uniform signal is the same of that of the speech signal.  
With the above result, a super Gaussian signal, \(g\), is computed, according to (2) and (3).  
\[b=\frac{\sigma}{\sqrt{2}} \tag{2}\]  
\[g=\begin{cases}\mu-b*\log{(1-2*|\mu|)}&if\quad u\geq 0\\ \mu+b*\log{(1-2*|\mu|)}&if\quad u<0\end{cases} \tag{3}\]  
Values of \(\mu\) and \(\sigma\) are 0 and 0.2, respectively, which can be fixed in the system. They are selected according to several tests with the purpose of work with similar statistics of the speech signals. As results, the super-Gaussian signal has similar entropy than speech signals, and therefore is adequate to be imitated. It is worth noting that the super-Gaussian signal is changed every time for every value of the seed.
## 2 Bolt Tensile and Stress Wave Detection Test
### Bolt Tensile Process Nondestructive Testing System  
Figure 1 shows the pictures of the components of the test system, in which the bolt tensile and stress wave detection test system consists of a bolt tensile test bench, a stress wave detection device, and a bolt state monitoring device. The bolt tensile test bench was modified from the Teststar anchor testing machine, the stress wave detection device was SET-PWB-01 wireless bolt quality tester, and the bolt state monitoring device was designed according to the test requirements. The bolt tensile test bench can apply axial displacement to the bolt for tensile testing, and the displacement sensor and column sensor record the whole process displacement load data stored in the experiment console. The stress wave detection device is utilized to transmit the excitation signal into the bolt and record the waveform data of the stress wave propagation process for subsequent research. The bolt state monitoring device is used primarily to record the image data of the entire tensile test bolt deformation process.  
Figure 2 is a schematic of the test system. The test system for the bolt tensile experiment can record the displacement, load, image, and stress wave data of the whole process completely, which is important for the study of bolt deformation characteristics and stress wave propagation characteristics.  
Figure 1: Bolt tensile and stress wave detection test system.
## 4 Simulations and Discussion
### Straight-Line Trajectory Simulation
Therefore, the disturbance values observed by the disturbance observer can be used to replace the disturbances caused by wind, waves, and currents in the external environment, thus improving the robustness of the system. Figure 4a,b show the actual position variation curves and the actual velocity variation curves of the USV, respectively, in the three cases. As can be seen from the figures, the position and speed curves under the effect of disturbances have the same pattern as those without disturbances, which further indicates that the controller has an excellent anti-disturbance capability. Figure 4c,d show the position error variation curve and velocity error variation curve of the USV, respectively, in the three cases. The position and velocity error curves more clearly reflect the USV tracking reference trajectory. As shown in the figures,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value Used in Case 1** & **Value Used in Case 2** & **Value Used in Case 3** \\ \hline \(\beta_{1}\) & 0.5 & 1 & 1 \\ \(\beta_{2}\) & 0.3 & 0.5 & 0.5 \\ \(\varepsilon_{1}\) & 1.4 & 1.4 & 1.4 \\ \(\varepsilon_{2}\) & 1 & 1.5 & 0.5 \\ \(\phi\) & 0.01 & 0.01 & 0.01 \\ \(p_{1}\) & 3 & 3 & 3 \\ \(p_{2}\) & 3 & 3 & 3 \\ \(q_{1}\) & 5 & 5 & 5 \\ \(q_{2}\) & 5 & 5 there is a large error at the initial stage of navigation, but after a period of intervention both the position error and velocity error approach zero, which shows that the designed controller can effectively track the linear reference trajectory. Figure 5 shows the variation curves for the surge force and yaw moment. The output curve of the controller becomes a smooth curve, maintaining tracking with a short adjustment time. From the simulation results presented in Figures 2-5, it can be concluded that the control strategy proposed in this study performs well in overcoming external environmental disturbances and in tracking the linear reference trajectory.
# Profit-Efficient Elastic Allocation of Cloud Resources Using Two-Stage Adaptive Workload Prediction
## 3 System Architecture  
The cloud platform architecture is shown in Figure 2.  
The main components of a cloud platform are as follows:  
* Cloud resource pool--This component is composed of VMs that run in the physical server cluster. Based on virtual technologies, the computing resources of a VM (e.g., CPU, memory, disks, and networking) can be configured on demand. The resource pool also can be considered as an IaaS layer, which provides online computing resources.  
Task scheduling and networking interface--This component provides load balancing for the tasks that arrive from the Internet. Some studies, such as [31; 32; 33], have researched task scheduling optimization policies. Unlike these studies, we did not focus on task scheduling in the cloud center. Instead, we used a common load-balancing policy (i.e., round-robin; RR) as a task scheduling policy, meaning that the tasks are distributed evenly across the VMs.
* Cloud management platform--This component provides the resource management service in the cloud center. The admission control service and the resource monitoring service collect the resource pool running information (e.g., resource utilization of physical servers and VMs, the health status of physical servers, and hardware configurations). This collected information is stored in the database and used by the resource scheduling service to identify the suitable target physical server for launching the VM. The admission control component authenticates cloud center administrators and manages VM lifecycle operations, specifically handling requests for VM provisioning and de-allocation.  
As in [34], the cloud management platform with elastic resource management has the following external components:  
* Workload monitoring--This component provides the workload information collection service. In general, the workload monitoring component can periodically collect the task arrival workload information through the load-balancing interface.
* Workload prediction--Based on historical information, this component predicts the future workload.
* Resource determination--Based on a specific optimization objective (e.g., cost or profit optimization), this component determines the suitable number of VMs according to the predicted future workload. When the resource determination result is obtained, the component sends to the VM control a command to launch or release the corresponding number of VMs.  
Based on the architecture detailed above, this study examines key technologies for workload prediction and resource determination.  
Figure 2: The resource management architecture in cloud computing. IaaS, infrastructure-as-a-service. VMs, virtual machines.
# Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm
## 3 Materials and Methods
### Gait Phase Division  
A person's walking process is a rhythmic movement and a complete walking cycle consists of following the ground with one foot to landing again with the same heel [PERSON] et al. (2019). During the walking cycle, the legs support the body's weight during forward movement. [PERSON] et al. (2019) divided the gait phase into four phases, which comprises: (i) heel strike (HS); (ii) flat foot (FF); (iii) heel-off (HO); and (iv) swing phase (SW). This phase division is relatively detailed and makes the system more complex, thereby potentially lowering the recognition accuracy. [PERSON] et al. (2019) divided the gait cycle into four phases, namely, loading response (LR), flat-foot (FF), pre-swing (PS), and swing (SW) for the analysis, but there is a large error in the SW phase and the PS phase. Therefore, in order to improve the recognition accuracy, it is usually necessary to use fewer divisions. Most researchers tend to group this cycle into stance and swing phases [PERSON] et al. (2019). By referring to the above classification of gait, and ensuring the scientificity of the classification of gait, it is decided to divide the walking cycle into the stance and swing phases. Among them, the standing posture mainly includes HS, FF, and HO phases. The gait phase division is shown in Figure 3. The first 60% of the signal of a complete cycle belongs to the stance phase, and the next 40% of the signal belongs to the swing phase.  
Figure 3: Schematic diagram of the gait phase division. a_x represents the acceleration in the _x_-axis direction, a_y represents the acceleration in the _y_-axis direction, a_z represents the acceleration in the _z_-axis direction.  
Then we need to split the data. The data collected by the inertial sensor is a data stream distributed over time. It is not possible to directly extract and classify the features, and the data needs to be segmented. At present, the method of data segmentation is a multi-dimensional sliding window segmentation method [38]. The acceleration signal is divided into sections with different gait phase information by using a sliding window segmentation method. The sliding windows are divided into overlapping windows and separated windows. Overlapping windows usually minimize the influence of the transition process on the detection accuracy [39]. Therefore, a window with a 50% overlap is used in most studies [38]. The sliding window uses a 50% coverage period to evaluate the gait phase. Figure 4 shows a schematic diagram of the sliding window segmentation.
## 1 Introduction
These products can be categorized into three primary groups based on their method of estimation: (a) interpolation using field measurements and machine learning techniques [13]; (b) simulation through remotely sensed ET models, such as MODIS [14], and (c) reanalysis data and products assimilated using various land surface models [15]. This integration of advanced technologies and modeling approaches enhances our ability to understand and quantify ET dynamics across different spatial and temporal scales. A study regarding ET estimation was conducted in Puerto Rico. Geostationary Operational Environmental Satellite-Puerto Rico Water and Energy Balance (GOES-PRWEB) is a product with a 1 km image resolution that provides us with additional data on actual ET estimation [15]. GOES-PRWEB is also based on the Penman-Monteith (PM) equation. Moreover, it can be used to validate data provided by MOD16A2, which is also based on Penman-Monteith. The MOD16A2 product is an 8-day product, and GOES-PRWEB has daily, monthly, and annual data. Furthermore, additional calculations are necessary to be able to compare data sets. GOES-PRWEB's previous study suggested that ET values were underestimated [15]. Another study in Northeastern Brazil--a tropical region as well- used remote sensing technology to assess land degradation and drought impacts over terrestrial ecosystems based on water and carbon fluxes, i.e., MOD16A2 and MOD17A2 to estimate ET and gross primary production (GPP), respectively [16]. The authors reported changes resulting from human activity, which caused environmental degradation associated with drought impact. The study of the hydrological cycle using satellite imagery is an underdeveloped area of investigation in PR. Moreover, due to a lack of research regarding gross primary in PR are close to alluvial landforms and are classified as flood risk areas [18]. The results of a study that assessed agricultural activity in PR from 2012 to 2018, after the effects of major atmospheric events, showed that Hurricanes Irma and Maria (2017) destroyed many agricultural fields, which affected the agricultural sector, particularly small farms of local producers [19].
# Assessment of the Potential of High-Performance Buildings to Achieve Zero Energy: A Case Study
## 2 Building Information
### Actual Energy Performance  
The case building was completed in early 2017 and occupancy took place in April 2017. However, the mechanical system was still under commissioning until October. During the commissioning period, some equipment had operated 24/7. Therefore, we used the data after October 2017 in the study.  
Figure 4: Photovoltaic system layout.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Types** & **Power (Wp)** & **Quantities** & **Total Power (kWp)** \\ \hline
**Rain canopy** & & 192 & 91 & 17.47 \\
**Facade** & & 172 & 739 & 127.11 \\
**Blind** & & 24 & 336 & 8.06 \\
**Rooftop** & & 245 & 308 & 75.46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specification of the solar cell module.  
Figure 5 shows the monthly energy consumption for 12 months. The lighting energy consumption is steady for the whole year, ranging from 4000 kWh to 5000 kWh. The actual EUI is 23.9 kWh/(m\({}^{2}\) a), which is nearly half of the design goal of 42 kWh/(m\({}^{2}\) a). This is due to the less than expected occupancy level in the building.  
The case building was benchmarked against some other green buildings [24] located in the same climate in China as shown in Table 4. The actual EUI of the case building is only half of the 25 th percentile (52.6 kWh/(m\({}^{2}\) a)) of the other green office buildings.