esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Traditional hearing aids, such as behind-the-ear (BTE), in-the-ear (ITE), and completely implantable (CIC) hearing aids, amplify sound and transmit it to the eardrum via a hearing aid receiver. Digital hearing aids can compensate for hearing loss based on individual hearing data, but these still encounter problems. First, it is difficult to compensate for hearing loss due to the frequency characteristic limitations of the receiver. Patients with sensorineural hearing loss have significant hearing loss in the frequency range above 3 kHz, so a receiver with excellent high-frequency characteristics is required [1, 2, 3, 4]. However, it is difficult for conventional receivers driven by small electromagnets to achieve sufficient sound pressure in the high-frequency range because they utilize the resonance characteristics of the vibration system [5, 6, 7]. Second, the sound from the receiver reaches the eardrum at different times due to reflections and resonances in the ear canal, resulting in loss of speech recognition. Third, due to ventilation problems, if the ventilation hole is enlarged, then the feeling of ear blockage is reduced, but the sound pressure in the ear canal decreases and sound leaking from the ear canal may cause acoustic feedback [8].  
[PERSON] et al. devised a light-driven contact hearing aid that can be installed in the ear canal without surgery and can provide the same effect as a middle-ear implant, with a uniform sound pressure of 110 dB SPL (sound pressure level) in the range 100 Hz to 10 kHz [9,10]. This method modulates sound into light and sends it to a solar cell and signal processing module installed near the tympanic membrane; then, a micro actuator in contact with the tympanic membrane vibrates to transmit acoustic vibration. However, the solar cell and signal processing unit requires surgery.  
Direct transmission of sound to the center of the tympanic membrane using ultrasound is advantageous because it does not require a solar cell or a micro actuator. Recently, [PERSON] et al. reported the results of an experiment using a parametric ultrasound array that can transmit sound of approximately 68 dB SPL to a virtual tympanic membrane at a distance of 2.5 cm. This compact ultrasonic device consists of 12 planar PZT (lead zirconate titanate) actuators and is designed to fit into the ear canal. However, the authors did not analyze the frequency characteristics to obtain the acoustic gain at high frequencies [11].  
In this paper, we propose a single-radiator ultrasonic hearing aid receiver (serving as the output emitter) that increases power efficiency by increasing the ultrasonic radiation area and lowering the radiation frequency in the ear canal. We implemented an ultrasonic receiver using multilayer PZT devices for low-voltage driving and measured the directivity and frequency response by driving a SRAM (square root amplitude modulation) parametric ultrasonic modulator. The results revealed the potential benefits of an ultrasonic hearing aid.
## 6 Conclusions  
In this paper, a modified electromagnetic band gap for suppressing the MC between two rectangular patch UWB MIMO antennas is presented. The proposed MEBG was applied to minimize the MC between two adjacent MIMO antenna arrays. The antennas are kept apart at a distance of 44 mm. Two MEBGs were applied symmetrically behind the antenna and a relatively high isolation around \(-23\) dB (more than a 10 dB improvement over the reference antenna) was achieved across the whole operating bandwidth of the MIMO UWB antenna. The antenna performance with and without the MEBG decoupling structure was also examined. Simulation results of reflection coefficient S\({}_{11}\), mutual coupling, radiation features, and diversity performance based on the ECC were equally examined. The MEBG structure introduced significantly reduces the MC effect without degrading the antenna performance. With the introduction of the MEBG decoupling structure, a comparatively low ECC value less than 0.09 was achieved with a wide bandwidth. The antenna results were verified in CST simulation tools and the results are similar to HFSSS simulation within the UWB bandwidth. The results show that the antenna exhibits better diversity performance and is a good choice for UWB applications.  
[PERSON] conceived the original idea of the paper and was in charge of the manuscript draft while [PERSON] and [PERSON] helped with some improvements in the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Performance Evaluation
### Performance Evaluation
This can be contrasted with the residual energy and void nodes considered in RRSS. Secondly, we compare energy consumption. Simulation results show that both DBR or VBF consume more energy than RRSS, as in Figure 13b. VBF and DBR are multicast routing. The best next-hop is determined according to the RSSH as well as RSSB in RRSS, and the best next-hop node selected is the nearest to the RSS vector. Thirdly, end-to-end delay with different sink nodes is compared in Figure 13c. Simulation results show that the delay of RRSS is shorter than that of DBR in the network with either five sink nodes or one sink node, which results from the RSS vector reflecting the propagation distance. The shorter the propagation distance, the shorter the end-to-end delay.
## 3 Results and Discussion
### Influence of Centrifugation on Particle Size Distributions
Data for mean particle-size distributions as a function of suspension composition extracted from laser diffraction and dynamic laser scattering experiments are shown in Figure 4.  
It can be readily seen that, except for the pure AgNP suspension, mean particle size matches that of FLG (2-4 um) for all un-centrifuged suspensions. However, after centrifugation mean particle size does not only reduce, but also shows some dependence on suspension composition; at low FLG content it is close to that of pure AgNPs, which dominate in weight particle-size distribution. Then, around 50 wt.% of FLG content overall size matches that of FLG. At higher FLG concentrations (over 75 wt.%), mean particle size drops steadily. Although further research is needed, this effect may be partially explained by both a significant drop in liquid viscosity as the concentration of water in water/EGsystem increases, and increased average weight of AgNP-decorated FLG as compared to pure FLG (see data for 100 wt.% FLG content). Figure 3: Particle-size distributions of the as-obtained (**a**,**c**,**f**) and centrifuged (30 min). (**b**,**d**,**e**) suspensions. (**a**,**b**) AgNPs, (**c**,**d**) FLG, (**e**,**f**) hybrid (75 wt.% FLG).
## 4 Fabrication Process
This clears the way for the subsequent stages that resemble piezoelectric resonator fabrication. The ZnO layer is subsequently etched by diluted hydrochloric acid to pattern the via-holes, which are carefully engineered to expose the bottom electrode without compromising the integrity of the piezoelectric material. The top electrode is then deposited and patterned by the lift-off method, finalizing the electrical contacts necessary for device operation under desired excitation signals. To ensure that each functional block of the resonator operates independently without strong interference, a deep-reactive ion-etching (DRIE) is employed, as seen in Figure 4. This step is critical, as it not only defines the resonator body but also establishes clear isolation between ports and the ground, thus maintaining the resonator's performance under various operational conditions, as illustrated in Figure 3e. The final fabrication step of the hybrid resonator is to release the resonator structure, which is achieved through a process of backside wafer etching, as shown in Figure 3f. This critical phase involves selectively etching away the underlying layers from the back of the wafer, effectively suspending the resonator structure on the front side of the substrate. This suspension is essential for ensuring that the resonator is completely rereleased, minimizing any potential substrate coupling and feedthrough interactions that could negatively impact the device's performance. The precision and repeatability of the rear side release etching process are crucial as they not only enable the resonator to perform optimally under its lateral extensional mode but also safeguard its structural integrity. The fabrication exploits the intricate and precise nature of foundry-compatible manufacturing of hybrid MEMS resonators. Each step, from the first photolithography to the final rear side dry-etching release, plays a pivotal role in achieving the desired performance characteristics and high yield, enabling these resonators to perform effectively in advanced RF front-end applications. Figure 4 presents microscope image of the fabricated hybrid resonator.
## 1 Introduction
This system addresses traditional data acquisition blind spots on vertical cliffs and above residential areas, significantly improving the efficiency of rapid surveys for hazardous rocks on steep slopes. It provides reliable parameters to guide protective engineering measures, including the placement and strength of rockfall barriers. As a result, it offers scientific and dependable support for long-term safety management and disaster mitigation of transportation, energy, and urban infrastructure in Yunnan and similar mountainous gorge regions, demonstrating significant practical engineering value.
# Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points
## 3 Error Analysis Model and Influencing Factors of Positioning Algorithm
### Influencing Factors of Positioning Algorithm Accuracy
In fact, aerial remote sensing images are the projection of a three-dimensional region on a two-dimensional plane. A single aerial image cannot directly provide the height information of the object itself, and most existing examples of research neglect this issue. A building target positioning algorithm based on target detection and base point selection is proposed in paper [97], and the related errors are analyzed. Figure 15 illustrates the simulation results of a traditional algorithm with building heights from 10 m to 100 m. In specific, the \(X\)-axis in the figure is the height of building target, and the \(Y\)-axis represents the average positioning error. It can be clearly found that the target height and imaging angle are directly corresponding to the positioning error. For instance, the positioning error (the spatial distance error) reaching 63 m will be generated under the condition of a 65\({}^{\circ}\) imaging angle when applying the traditional positioning algorithm with only a 10 m height of the building. On the other hand, under the same imaging angle and random error conditions, the positioning error is about 39 m if the target is on the ground. Moreover, when the tilt angle reaches 70 degrees, the error value will increase by nearly 90 m, and the positioning error will be as high as 126 m when the target height is 80 m.  
However, although the algorithm can reduce the impact of the target height on the positioning results to a certain extent, the accuracy of the algorithm still needs to be improved because the detection algorithm can only provide a rectangular detection box and the detection rate is not 100%. Figure 14: Schematic diagram of the positioning error caused by image distortion. Similarly, the impact of the atmosphere on the imaging environment will also significantly lead to the deviation between the imaging visual axis and the ideal visual axis. There is no perfect work to analyze the impact of the atmosphere on positioning, so it is necessary to establish an appropriate atmospheric analysis model to reduce its impact. With the introduction of various algorithms in the field of artificial intelligence into the target location process [98; 99; 100; 101; 102; 103; 104; 105; 106], the corresponding error factors also need to be dealt with in error analysis and simulation test. In addition, the positioning process using the target detection algorithm should focus on the detection accuracy and the coincidence rate between the detection box and the real label. The use of a 3D reconstruction algorithm should focus on the significant impact caused by the load body position and the LOS control deviation. For remote sensing images involving image super division and registration, the real projected pixel position of the target must be considered. The main potential error factors that have not been considered or analyzed are shown in Table 3.
# A Deep Neural Network Model for Speaker Identification
## 2 Materials and Methods
### Background
#### 2.1.2 Long Short-Term Memory (LSTM)  
The common drawback of a traditional RNN model is that, as the time step increases the network fails to derive context from the time steps of previous states much further behind. Such phenomenon is known as long-term dependency. Due to the deep layers of a network and recurrent behavior of a traditional RNN, exploding and vanishing gradient problems are also encountered quite often. Moreover, to address this problem, the LSTM model is introduced by deploying memory cells with several gates in a hidden layer [41]. Figure 2 illustrates the block of a hidden layer with an LSTM unit.  
An LSTM network computes a mapping from an input sequence \(x=(x_{1},x_{2},...,x_{T})\) to an output sequence \(y=(y_{1},y_{2},..,y_{T})\) by calculating the network unit activations using the following equations iteratively from \(t=1\) to \(T\):  
\[i_{t}=\sigma(W_{ix}x_{t}+W_{im}m_{t-1}+W_{ic}c_{t-1}+b_{i}) \tag{3}\]  
\[f_{t}=\sigma(W_{fx}x_{t}+W_{fm}m_{t-1}+W_{fc}c_{t-1}+b_{f}) \tag{4}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot g(W_{cx}x_{t}+W_{cm}m_{t-1}+b_{c}) \tag{5}\]  
\[o_{t}=\sigma(W_{ox}x_{t}+W_{om}m_{t-1}+W_{oc}c_{t}+b_{o}) \tag{6}\]  
Figure 1: Conventional recurrent neural network (RNN) model architecture. The input is a time series, \(x_{t}\) represents the input information at time \(t\). The middle box represents the hidden state. \(y_{t}\) represents the output at time \(t\).  
\[m_{t}=o_{t}\odot h(c_{t}) \tag{7}\]  
\[y_{t}=\phi(W_{ym}m_{t}+b_{y}). \tag{8}\]  
Here, the \(W\) terms denote weight matrices (e.g., \(W_{ix}\) is the matrix of weight from the input gate to the input), \(W_{ic}\), \(W_{fc}\), \(W_{oc}\) are diagonal weight matrices for peephole connections, the \(b\) terms denote bias vectors (\(b_{i}\) is the input gate bias vector), \(\sigma\) is the logistic sigmoid function, and \(i\), \(f\), \(o\), and \(c\) are respectively the input gate, forget gate, output gate, and cell activation vectors, all of which are the same size as the cell output activation vector \(m\), \(\odot\) is the element-wise product of the vectors, \(g\) and \(h\) are the cell input and cell output activation functions, generally and in our experiment \(tanh\), and \(\phi\) is the network output activation function, Leaky ReLU in our experiments.
# Reliable Augmentation and Precise Identification of EPG Waveforms Based on Multi-Criteria DCGAN
## 2 Materials and Methods  
### Experimental Data Collection  
Adult Asian citrus psyllids tested in the experiment were collected from the wild in Gutian County (Fujian Province) and transferred to a net house at Fujian Agriculture and Forestry University for rearing. They were fed on tender shoots of the virus-free citrus cultivar Jiu Li Xiang (Murraya). The newly sprouted summer shoots with a length of approximately 8-10 cm and containing more than six new leaves were selected. The cut ends of the shoots were wrapped with cling film and inserted into water for moisturizing [18].  
For data collection (as shown in Figure 1), 2-3 cm copper wires were soldered to copper pins, and 5 cm gold wires were fixed to the other end of the copper wires using water-soluble silver glue. The psyllid was gently held at the wing tip with tweezers, and a drop of water-soluble silver glue was applied to its back. Under a stereomicroscope, one end of the free gold wire was inserted into the silver glue on the back of the psyllid and left for 1 min to ensure a tight bond, avoiding contact between the gold wire and other body parts of the psyllid. The copper pin was inserted into a micromanipulator, which was adjusted to allow the psyllid to feed freely on the leaf. Finally, the plant electrode was inserted into the nutrient pot, the Faraday cage was closed to shield external electromagnetic interference, and the EPG recording system was activated to record EPG data [4].  
Figure 1: Schematic diagram of EPG data collection.  
A Giga-8 DC-EPG system was used to record the EPG signals. (Wageningen, The Netherlands). The experiment was conducted in a climate-controlled chamber with light conditions (temperature at 25 \(\pm\) 1 \({}^{\circ}\)C, relative humidity at 60 \(\pm\) 5%) within the Faraday cage. Each experiment lasted for 8 to 20 h. A total of 300 h of psyllid EPG waveform data were collected, excluding weak signals and waveforms where the gold wire had detached [19].
## 1 Introduction
### Mechanisms of Abnormal Impacts of Extreme Meteorological Factors on Power Grid Equipment
#### 3.1.1 Mechanism of Abnormal Effects of Extreme Meteorological Factors on Wind Power Output  
The high wind speeds during tropical cyclones cause significant fluctuations in turbine output power [25]. Assuming that the wind farm \(i\) consists of \(N\) identical turbines, and based on Equations (1) and (2), the wind speed perceived by the turbine at time period \(j\) is \(v_{i,j}\), the output power \(P_{\text{w}i,j}\) can be expressed as follows:  
\[P_{\text{w}i,j}=\begin{cases}\frac{1}{2}Np\
u_{ij}^{3}sc_{p},&v_{\text{in}}\leq v _{i,j}<v_{\text{rate}}\\ NP_{\text{w}}^{\text{rate}},&v_{\text{rate}}\leq v_{ij}<v_{\text{out}}\\ 0,&v_{i,j}\geq v_{\text{out}}\end{cases} \tag{3}\]  
In the equation, \(s\) represents the swept area; \(P_{\text{w}}^{\text{rate}}\) denotes the rated power of the wind turbine; \(v_{\text{in}}\) indicates the cut-in wind speed; \(v_{\text{rate}}\) represents the rated wind speed; and \(v_{\text{out}}\) signifies the cut-out wind speed. The air density \(\rho\) can be expressed as follows:  
\[\rho=\frac{P_{\text{a}}}{RT_{\text{e}}} \tag{4}\]  
In the equation, \(P_{\text{a}}\) represents the atmospheric pressure; \(R\) denotes the atmospheric constant; and \(T_{\text{e}}\) indicates the temperature.  
The power coefficient \(c_{p}\) can be expressed as follows:  
\[c_{p}=0.5176\times(116\frac{1}{\lambda^{*}}-0.4\beta-5)e^{-\frac{21}{\lambda^{* }}}+0.0068\lambda \tag{5}\]  
In the equation, \(\lambda\) and \(\lambda^{*}\) represent the tip speed ratio and its optimal value, respectively, and \(\beta\) denotes the pitch angle. The tip speed ratio and its optimal value can be expressed as follows:  
\[\lambda=\frac{\omega r}{v_{i,j}} \tag{6}\]  
\[\frac{1}{\lambda^{*}}=\frac{1}{\lambda+0.08\beta}-\frac{0.035}{\beta^{3}+1} \tag{7}\]  
In the equation, \(\omega\) is the rotational angular velocity of the wind turbine, and \(r\) denotes the blade radius of the wind turbine. From Equations (1)-(7), it can be observed that the wind power \(P_{\text{w}i,j}\) varies between 0 and \(NP_{\text{w}}^{\text{rate}}\) as \(v_{i,j}\) changes.
# Optimized Scheduling of IoT Devices in Healthcare Facilities: Balancing Cost and Quality of Care
## 3 Materials and Methods  
### Research Problem  
In the context of our study, a healthcare facility accommodates patients for defined periods, and IoT devices must be allocated to patients throughout their stay to optimize both operational costs and quality of care.  
Each patient requires a specific class of IoT device tailored to monitor particular parameters. The allocation of these devices incurs costs related to their operational expenses, which vary depending on the type of device used. In addition, the devices' quality and characteristics--such as weight, size, and battery life--directly influence patient quality of care. These factors affect the ease with which patients can wear and use the devices, thereby impacting their overall experience. Consequently, it is essential--particularly when considering the patient's priority level--to utilize devices that maximize the patient experience. However, it is important to recognize that cost minimization and quality of care are often competing objectives.  
The overarching goal is to effectively schedule the allocation of available IoT devices to patients over time in a manner that minimizes operational costs while simultaneously maximizing patient well-being.  
The mathematical formulation of the scheduling problem is described as follows.  
A list of patients is provided, with each patient assigned a priority level by healthcare professionals based on medical assessments, urgency, and institutional protocols. Additionally, each patient specifies their stay duration in the facility, including start and end times, as well as the required device class.  
A set of IoT devices is available, each characterized by a specific quality level that impacts the comfort and well-being of the wearer. Each device is also associated with an hourly operational cost, a limited operational lifespan (battery duration), and a predefined availability window (start and end times) during which it can be utilized. Additionally, the device's class parameter specifies the subset of patients for whom it is suitable, while the setup time parameter represents the time required to transfer the device from one patient to another.  
The constraints are of various types. First, the device-patient allocation is valid only if it falls within the availability window of both the device and the patient. Second, the allocation is valid if the device class matches the patient class. If the device is reassigned to another patient, a setup activity must occur between the two allocations. Within the time window that identifies the patient's stay in the facility, the patient must always be assigned to a device. Therefore, if a device needs to be replaced because it has reached its operational lifespan, a new device must be immediately allocated to the patient. Finally, the total periods during which a device is allocated to multiple patients must not exceed the device's operational lifespan.