esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Robust Day-Ahead Dispatch Considering Primary Frequency Response of Wind Turbine
## Appendix A  
1. The speed and amplitude of the primary frequency regulation are in line with certain specifications. For the thermal power units, the parameters are as follows: * The dead band of the thermal power unit is controlled within \(\pm\)0.033 Hz; * The maximum load limit of the thermal power unit should not be less than 6% of the rated capacity of the unit; * Response includes: When the grid frequency change exceeds the unit's dead band of the PFR, the primary frequency regulation response time should be less than 3 s; the time to reach 50% of the targeted load should be no more than 6 s, and the time to reach 75% of the target load should be no more than 15 s. A complete response according to the crew response target should be done within 30 s.  
2. VSG-based independent active frequency control: When the fluctuation of the system frequency exceeds the range of the dead band (\(50\pm 0.03\) HZ) of the PFR and when the active output of the VSG is above 20% of \(P_{N}\), VSG should adjust the active output and participate in the active PFR of the power grid. The response should meet the following requirements:  
* the PFR time should be less than 3 s; and the time to reach 75% of the target load should be no more than 15 s. A complete response according to the crew response target should be done within 30 s;
* During the drop of the system frequency, the VSG should increase the active output. It should not be lower than the active output before the primary frequency regulation, and the maximum increase can be at least 10% of \(P_{N}\);
* During the rise of the system frequency, the VSG should cut the active output. It should not be higher than that from before the primary frequency regulation. The maximum reduction of the active output should be at least 10% of \(P_{N}\).
# Dual Image-Based CNN Ensemble Model for Waste Classification in Reverse Vending Machine
## 3 Materials and Methods
### The Proposed CNN Ensemble Model
Instead, when the probability of the argmax class shows the value above the decision threshold, we select that class as the final class. If it does not exceed the decision threshold, non-target classes are chosen. The decision threshold is an implicit classification metric for non-target objects when the prediction vector does not include the Non-target class. On the other hand, the decision threshold can act as the second verification when the prediction vector includes the Non-target class. For example, suppose that the final prediction vector is given as:  
\[(\text{PET, Can, Non-target})=(0.3815,0.2410,0.3775), \tag{1}\]  
the argmax class indicates that the object is likely to be PET even though the probability of the Non-target class is as high as the PET class. However, with the decision threshold of 0.5, this object is predicted as a non-target class in the end. The ensemble process of the top view and front view classifier can be expressed as:  
\[\mathbf{z}=(z_{1},\;z_{2},\;\cdots,\;z_{c}), \tag{2}\]  
\[z_{j}=\sum_{i=1}^{N}w_{j;i}\;P_{i}(x)+b_{j}\;,\quad j\in\{1,2,\cdots,C\}, \tag{3}\]  
where \(\mathbf{z}\) is the final vector and \(z_{j}\) is the \(j\)-th element of vector \(z\). \(w_{j;i}\) is the weight of SLP in our model, connecting \(i\)-th element of the input vector to \(j\)-th element of the output vector. \(b\) is the bias, and \(P(x)\) is the input vector, after concatenating the result vector of each classifier. \(N\) is the size of vector \(P(x)\) and \(C\) is the size of the final vector \(z\), respectively. Figure 7: Simple scheme of stacking ensemble learning procedure. The final vector \(z\) passes the softmax layer \(\sigma\), and we can get the class with the highest accuracy. The class with the highest probability \(H\) and its probability \(P_{f}\) are defined as:  
\[H=\operatorname*{argmax}\sigma(z), \tag{4}\]  
\[P_{f}=\max\sigma(z). \tag{5}\]  
Finally, we check if the predicted probability \(P_{f}\) is higher than the decision threshold. If it exceeds the threshold, the final result \(y\) is \(H\). Otherwise, it becomes \(N\), which denotes the non-target class. Equation (6) represents this process. \[y= \begin{cases}H&\text{if }\ P_{f}\geq\text{Decision Threshold}\\ N&\text{if }\ P_{f}<\text{Decision Threshold}.\end{cases}  \tag{6}\]  
The final framework of the proposed CNN ensemble model can be represented as Figure 8. First, the top view image of the input object is classified through MobileNetV2, and the front view image is classified through SqueezeNet. Then, the prediction vectors generated by each classifier are concatenated and classified into PET, Can, and non-target classes through SLP. The following section describes the specific experimental results and discussions on our path to find the proposed model.
## 3 MS Monitoring System for the Spillway Tunnel
### Construction of the MS Monitoring System  
Engineering Seismology Group (ESG), one of the top producers of MS monitoring systems worldwide, is the company that made the MS monitoring system used in this project. Its MS monitoring equipment is widely used because of its high applicability. The MS monitoring system for the spillway tunnel of the Jinchuan hydropower station was composed of hardware and software. The hardware part mainly consisted of (1) uniaxial acceleration sensors for signal pickup, (2) Paladin data acquisition substations for signal acquisition, and (3) a Hyperion data processing system for signal processing. The software part mainly consisted of (1) HNAS software for real-time signal acquisition and recording, (2) WaveVis software for waveform processing and analysis, and (3) Seisvis software for 3D visualization of MS information.  
With the geological conditions and site construction status of the spillway tunnel in the Jinchuan hydropower station taken into account, six acceleration sensors were installed on the side walls of three different sections, respectively, behind the tunnel face. These six sensors were placed at different altitudes to create a spatial mesh structure, and they had to move forward with the tunnel face as the project advanced to ensure that the vicinity of the tunnel face was in the effective range of the MS monitoring system. In particular, when the tunnel face advanced a certain distance, the two sensors farthest away from the tunnel face (for example, #1 and #2 in Figure 3) would be moved to be closest to it, and so on.  
Figure 4 shows the spatial topology of the MS monitoring system for the spillway tunnel project in the Jinchuan hydropower station. The six sensors could provide 24-h continuous monitoring of the micro-fractures near the tunnel face during the excavation of the spillway tunnel in real time. In particular, the sensors converted the received micro-fracture elastic wave signals into electrical signals that were transmitted to the Paladin data acquisition substation connected to the sensors through the cable. In the Paladin data acquisition substation, the electrical signals were converted into digital signals. Then, the converted digital signals were transmitted to the Hyperion signal processing host connected to the Paladin data acquisition substation via the optical fiber.  
Figure 4: Spatial topology of the MS monitoring system for the spillway tunnel project.  
Figure 3: Schematic of sensor deployment.
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
### Motivation, Problem Statement, and Main Contributions  
Ad hoc routing protocols, by design, rely on the fact that nodes cooperate in route discovery. For instance, the RFC of AODV says that the protocol is intended for usage in networks where all nodes can trust each other [14]. Nevertheless, it is not always the case. The reasons for the nodes' failure to cooperate can be different. However, the result is the same: the network performance degrades due to unexpected node behavior (i.e., the existence of untrusted nodes).  
This research aims to improve the functionality of ad hoc routing protocols to work more reliably in ad hoc networks with untrusted nodes. Incorporating trust in reactive ad hoc routing protocols can be solved by changing the protocol, which implies that the protocol algorithms and structures must be changed (e.g., in [4; 5; 6], authors introduced new message structures; in [3], the algorithm for updating the routing table was changed; in [4; 5; 15], the routing table structure was changed).  
The problem we will tackle is enabling trust-aware ad hoc routing without changing the routing protocol.  
The main idea of our solution is to enforce route discovery through trusted nodes from outside of the routing protocol. The utilized trust mechanism [11] uses a neural network to estimate the trust of nodes. These trust estimations are then used to influence the route discovery phase of a reactive ad hoc routing protocol to prefer more trusted routes. Untrusted nodes are penalized during the route discovery phase by delaying the Route Request messages of those nodes. Thus, the node trust values influence the routing decisions indirectly to improve the performance of reactive ad hoc routing protocols.  
A simulation proof-of-concept framework was built, and an extensive experimental evaluation of the proposed solution was conducted. As the ad hoc routing protocol, the AODV protocol was chosen, but the proposed method to enhance reactive ad hoc routing with trust can be used with any reactive ad hoc routing protocol. In addition, a study of how the proposed solution influences the routing protocol metrics was performed. Another problem is finding the best combination of parameters and creating recommendations on how to tune the method settings according to the particular use cases.  
To the best of our knowledge, all previous methods of enhancing ad hoc routing with trust used the trust values directly during route discovery [5; 6; 7; 9; 10; 15] or in routing decisions [4; 8; 16]. Our approach differs because the route discovery messages through the untrusted nodes are penalized by delays, but the routing algorithm and structures remain unchanged. The main contribution of this research is creating a method for enhancing reactive ad hoc routing protocols with trust that does not require changes to the routing protocol.
## 1 Introduction
Even though several studies have shown separate schemes to increase the EE and/or throughput (TP) of LSASs, few have shown combinations of schemes, and presented how much EE gain can be achieved by the schemes in the overall system. Based on the analysis in this paper, we believe more detailed work for the realization of high energy efficient BSs with LSASs is possible because this paper shows the necessary schemes and the maximum achievable energy efficiency gain as a reference. In our previous work [17], we presented an elegant power control scheme of future BS with LSAS. In the work, we proposed a future BS structure which includes several core components to improve EE. The structure is a kind of feedback system, and decision unit in the system gathers the related information and sends the information to the power control unit of the BS. Our current work is different from the previous work in several points. First, the main contribution of our previous work is proposing a new BS structure and related operation, while current work focuses on high level performance analysis of general LSAS; Second, we deals with various LSAS related obstacles and discussions which were not covered in our previous work, such as antenna size and form factor, channel estimation and reference signal (RS) overhead reduction issues; Third, there is little work to gather all the issues at once and show the expected performance gain from each perspective. In this work, we gathers all the related issues and combine all of them to show the expected performance gain. The rest of this paper is organized as follows. In Section 2, we show the system model, including power consumption model, and also present precoding and effective signal-to-interference-noise ratio (SINR) here. In Section 3, we present the performance metric to show EE gain. In Section 4, we overview each component in LSASs, and present necessary schemes and related performance. In Section 5, we gather the results from Section 4 and present overall performance gain and related discussion. Lastly, the concluding remarks are given in Section 6.
## 4 The Main Idea
### The Filtering Equation and Kernel Design  
We used Equation (2) to compute the pixel value at position \((m,n)\) as Follows:  
\[\hat{x}_{m,n}=-\operatorname{Im}\left[\frac{3}{4}\frac{1}{MN}\;\sqrt{\left( \sum_{m}\sum_{n}x_{m,n}h_{m,n}\right)^{2}-\frac{2}{3}\sigma_{x}^{2}MN[2 MNH-1] }-\epsilon\;\right], \tag{3}\]  
where \(x_{m,n}\) is the pixel within the window at position \((m,n)\), \(H=\ln(\sqrt{2\pi\sigma_{x}^{2}}\mathrm{e})\) for an image \(x\) with size \(M\times N\) and variance \(\sigma_{x}^{2}\). To filter, we propose to use the following well known kernel \(K=[h_{m,n}]\) for enhancing edges [18].  
\[K=\left[\begin{array}{ccc}-\frac{k}{8}&-\frac{k}{8}&-\frac{k}{8}\\ -\frac{k}{8}&k+1&-\frac{k}{8}\\ -\frac{k}{8}&-\frac{k}{8}&-\frac{k}{8}\end{array}\right]. \tag{4}\]  
Given the symmetry of this kernel, the convolution associated with the filtering process is equivalent to the correlation between the filter window and the kernel.  
Kernel Design  
By letting \(k=1\), we get the kernel \[K=\left[\begin{array}{ccc}-\frac{1}{8}&-\frac{1}{8}&-\frac{1}{8}\\ -\frac{1}{8}&2&-\frac{1}{8}\\ -\frac{1}{8}&-\frac{1}{8}&-\frac{1}{8}\\ \end{array}\right]. \tag{5}\]  
Above is a well-known kernel used for edge enhancement. Now, let us recall the following term of Equation (3):  
\[\sum_{m}\sum_{n}x_{m,n}h_{m,n}=x_{2,2}h_{2,2}-\sum_{m\
eq 2}\sum_{n\
eq 2}x_{m,n }h_{m,n}. \tag{6}\]  
This form of the equation illustrates how edge enhancement depends on the difference between the center and its surroundings. Furthermore, we started our dissertation by establishing that our premise is that we should rely only on the signal-matching capabilities of the filtering equation and its non-linear nature. As a consequence, we propose maximizing the sum in (6). The straightforward way to achieve this is to discard the minus sign so the second term adds instead of subtracting. In other words, we propose to use all values as positive, leading to the following kernel:  
\[K=\left[\begin{array}{ccc}1&1&1\\ \hline\mathfrak{S}&\mathfrak{S}&\mathfrak{S}\\ \mathfrak{S}&2&\mathfrak{S}\\ \mathfrak{S}&1&\mathfrak{S}&\mathfrak{S}\\ \end{array}\right]. \tag{7}\]  
To support the hypothesis that maximizing Equation (6) yields a better approach to edge detection, we present an example in Figure 2.
## 2 Materials and Methods
### Outcomes  
The Baiobit sensor was used as the primary assessment tool for measuring counter-movement jump (CMJ) performance in elite rhythmic gymnasts with low back pain [18; 19; 20]. This advanced sensor provides highly accurate and detailed data on several key performance metrics, making it an ideal tool for evaluating lower body power, strength, and jump mechanics.  
The Baiobit sensor is a portable device designed to measure and record the biomechanics of vertical jumps, such as the countermovement jump (CMJ), which is often used to assess explosive power in athletes [18; 19; 20]. The sensor is placed on the lower backor attached to the athlete's body to capture movement in three dimensions, providing real-time data on several key performance indicators analyzed in this trial as  
1. Maximal speed: the sensor measures the peak velocity achieved during the jump, giving insight into the athlete's explosive strength and ability to generate power quickly.
2. Maximal height: Baiobit accurately measures the highest point of the jump, which is indicative of the athlete's overall vertical leap and the efficiency of force production.
3. Maximal Force: the sensor records the maximum force exerted by the athlete during the push-off phase of the jump, which is a measure of lower limb strength and muscular power.
4. Concentric-Eccentric Exercise Performance: Baiobit distinguishes between the concentric (upward) and eccentric (downward) phases of the jump, assessing muscle function during both phases. This is important for understanding the balance between strength and flexibility in jump performance, which can impact recovery and injury prevention.
5. Flight time: the sensor records the time the athlete spends in the air during the jump, which reflects the efficiency of force application and how well the athlete can utilize stored elastic energy during the jump.
6. Contact time: the Baiobit sensor also measures the time the athlete spends in contact with the ground during the landing phase of the jump, providing insight into lower limb control and reaction time.
7. The Visual Analog Scale (VAS): it is used for countermovement jump (CMJ) performance measured with the Baiobit sensor, and it is employed to assess subjective perceptions related to jump execution, such as effort, fatigue, or pain during the movement. VAS consisted of a 10 cm horizontal line, where 0 represents \"no discomfort or effort\", and 10 indicates \"maximum discomfort or effort.\" [21; 22].  
For each participant, the Baiobit sensor was used to assess three repetitions of each jump, ensuring the collection of reliable and consistent data. The mean of the three jumps was recorded for each parameter, providing a clear measure of each athlete's maximal performance. These measurements were taken pre-intervention, post-intervention, and during a follow-up assessment to track changes in performance over the course of the study. These assessments were conducted consistently at the same time of day to control for potential diurnal variations in performance.
# Smart Antenna for Application in UAVs
## 4 Control System Development
### Microcontroller  
The microcontroller to be integrated in the circuit has as its main function the processing of the data related to the flight of the UAV and, according to these data, send the voltage values corresponding to the communication needs to reverse bias of the diodes that ensure the value of the necessary capacities to the deflection of the antenna lobe. For the performance of this function, there are numerous microcontrollers so that, as selection factors, the following parameters were considered: user interface; support communication Serial Peripheral Interface (SPI); C programming language; USB connection; and simple and economical.  
After analyzing all possibilities, the chosen microcontroller was Arduino Uno. In addition to complying with the requirements presented, it is a microcontroller that has been extensively tested and present in several simulators, which allows to test the expected result in a computational environment.
# A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method
###### Abstract  
To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper. A fast sample entropy calculation method based on a kd tree is adopted to improve the real-time performance of fault detection in this paper. In view of the non-linearity and non-stationarity of the vibration signals, the vibration signal of the rolling bearing is decomposed into several sub-signals containing fault information by using a wavelet packet. Then, the energy entropy values of the sub-signals decomposed by the wavelet packet are calculated to generate the feature vectors for describing different fault types and severity levels of rolling bearings. The multiclass relevance vector machine modeled by the feature vectors of different fault types and severity levels is used to realize fault type identification and a fault severity analysis of the bearings. The proposed fault diagnosis and severity analysis method is fully evaluated by experiments. The experimental results demonstrate that the fault detection method based on the sample entropy can effectively detect rolling bearing failure. The fault feature extraction method based on the wavelet packet energy entropy can effectively extract the fault features of vibration signals and a multiclass relevance vector machine can identify the fault type and severity by means of the fault features contained in these signals. Compared with some existing bearing rolling fault diagnosis methods, the proposed method is excellent for fault diagnosis and severity analysis and improves the fault identification rate reaching as high as 99.47%.  
rolling bearing; fault diagnosis; fault severity; sample entropy; wavelet packet energy entropy; multiclass relevance vector machine +
Footnote †: journal: Applied Soft. 2019, 9, 2356; doi:10.3390/app9112356
## 3 Modeling Details and Case Studies
In the first one, we use the classical Benders decomposition (BD) with a coarse relative-optimality-gap tolerance of 10% to get a, possibly, low-quality stability center (LQSC). To obtain the stability center of hopefully high quality, which we refer to as high-quality stability center (HQSC), we solve the expected-value problem directly with Gurobi 8.1.1 [33] with a relative-optimality-gap tolerance of 1%. The time limit for obtaining the initial stability centers LQSC and HQSC is set to 5 min. Additionally, the computing setting consists of seven machines of two types: 4 of them have 128 GB of RAM and two Xeon E5-2660 v3 processors with 10 cores clocking at 2.6 GHz; the other 3 machines have 32 GB of RAM and two Xeon X5690 processors with cores cores clocking at 3.47 GHz. All machines are in a LAN with 1-Gbps network interfaces. We test two machine combinations. In the first one, in Combination 1, there are four 20-core machines and one with 12 cores. In Combination 2, we replace one machine with 20 cores by 2 with 12 cores. Regardless of the combination, one 12-core machine is defined as the head node, where only the master is launched. Except for the master--for which Gurobi can take up to 10 cores--for all other processes, i.e., the workers, Gurobi is limited to computing on a single core. Our computing setting is composed of machines with different configurations. Naturally, solving the same component function in two distinct machines may result in different outputs--and different runtimes. Consequently, the path taken by the MP across iterations might change significantly between experiments on the same data. More specifically to asynchronous methods, the varying order of information arrival to the MP may also yield different convergence rates. Hence, to reduce the effect of these seemingly random behaviors, we conducted 5 experiments for each problem instance. Therefore, our testbed \(\mathcal{E}\) is defined as \(\mathcal{E}=\{\)40, 50, 70\(\}\times\{\)A, B, C\(\}\times\{\)L, M, H\(\}\times\{\)LQ-SC, HQ-SC\(\}\times\{\)Trial 1,..., Trial 5\(\}\times\{\)Combination 1, Combination 2\(\}\)--we have 54 problems and 540 experiments. In all instances in \(\mathcal{E}\), we divide \(\mathcal{S}\) into 16 subsets. Thus, following our previous definitions, w = 16 and any subset \(\mathcal{P}_{\text{j}}\) is such that \(|\mathcal{P}_{\text{j}}|=16\). Additionally, we set a relative-optimality-gap tolerance of 1% and a time limit of 30 min for all instances in \(\mathcal{E}\). Gurobi 8.1.1 is used to solve the MILP MP and the component functions (linear-programming problems) that form the subproblem. The inter-process communication is implemented with mpi4 py and Microsoft MPI v10.0.