esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
This paper studies the performance of a novel frameless slotted-ALOHA protocol when the maximum number of detectable collision users is best. According to the protocol, when packets conflict, signature codes are used to identify the set of transmitting users in the collision. Then, the sender transmits a replica of the conflicting packets according to the feedback mechanism proposed in Section 3.2, and then SIC technology is used to recover all the conflicting packets. Taking the number of backlogged users in each slot, \(N\), as the system state, a discrete-time Markov chain model with \(M+1\) states is established for the system. Based on theoretical analysis, the throughput trends, average number of backlogged users, average memory size and average successful transmission probability about the first transmission probability are simulated. The numerical results are consistent with the Monte-Carlo simulation results. Compared with the research in [11], this work is more in line with the actual communication scenario, and the results have more practical application value.  
**Author Contributions:** [PERSON] researched the literatures, conceived the study concepts, designed the algorithm, and took charge of the original draft preparation; [PERSON] improved the mathematical models, provided the systematic research and analysis methodology, edited the manuscript, and supervised the completion of the refinement of the paper; [PERSON] and [PERSON] completed some numerical simulations, and checked the formula deducing and the English grammar. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded in part by the National Natural Science Foundation of China under Grant 61663024, in part by the Hongliu First Class Discipline Development Project of Lanzhou University of Technology, and in part by the Excellent Postgraduate \"Innovation Star\" Project of Gansu Province.  
**Data Availability Statement:** The data supporting this article are from previously reported studies and datasets, which have been cited.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 13: Throughput vs. first transmission probability (\(K=3\) in our model vs. \(K=2\) in reference [11]).
# Miniature Coil for Wireless Power and Data Transfer through Aluminum +
## 2 Miniature Coil
### Coil Performance  
Figure 4 shows the circuit diagram used to measure the performance of the designed coil. For each operating frequency, from 1-10 kHz, the values of the parallel capacitors were changed to ensure the resonance condition and harvested power was measured. Figure 6 shows measured results for harvested power and power transfer efficiency versus frequency. The maximum harvested power using this configuration is 190 mW at 2 kHz with an input power of 10 W. At 2 kHz, the parallel capacitances to create resonance are \(C_{tx}=C_{rx}=24\) uF.  
Figure 4: Circuit used to measure harvested power for the designed coil.  
Figure 5: Harvested power and received voltage versus load for designed coil with 1 mm-thick aluminum plate in between. Operating frequency is 2 kHz. Input power is 8.5 W.  
Figure 7 shows the measured harvested power and power transfer efficiency versus input power at the optimal frequency of 2 kHz. The coil is designed to handle up to 35 W input power at the transmitting side so that the receiver coil can harvest up to 440 mW. The power transfer efficiency varies from 2.5% to a minimum of 1.2% at the highest input power of 35 W. The input power limit of 35 W is related to the maximum current and temperature ratings of 24 AWG wires from the manufacture datasheet [25; 26]. At 35 W input power, the measured temperature of the coil temperature using a thermocouple is 110 \({}^{\circ}\)C, which is below the rated operating temperature of 200 \({}^{\circ}\)C for 24 AWG wires specified by the manufacturer. The coil is separated 0.5 mm away from the metal plate at each side of the barrier.  
Table 2 shows a survey of similar works on energy transfer through aluminum and stainless steel. As seen from Table 2, coils used for through metal energy transfer are large and their volumes range from 21,205 mm\({}^{3}\) to 3,619,114 mm\({}^{3}\). The reported frequency of operation for through metal energy transfer is below 500 Hz. Our frequency is much higher, at 2 kHz, and our coil size of 15 mm \(\times\) 13 mm \(\times\) 6 mm and volume of 1170 mm\({}^{3}\) are the lowest. Our energy transfer per volume is 0.376 mW/mm\({}^{3}\) which is very high compared to other works.  
Figure 6: Measured harvested power versus frequency for designed coil with 1 mm-thick aluminum plate in between. Input power is 10 W.  
Figure 7: Measured harvested power and power transfer efficiency versus input power for the designed coil with 1 mm-thick aluminum plate in between. Operating frequency is 2 kHz.
# A Novel QoS-Aware ARQ Scheme for Multi-User Transmissions in IEEE802.11 ax WLANs
## 4 Performance Evaluation
### The Saturated Throughput  
First, we investigated the saturated throughput of each station under different BERs. The number of stations was 20, and the access category of streams was identical. Figure 12a,b depict the  
\begin{table}
\begin{tabular}{c c} \hline \hline Parameter & Value \\ \hline W & 64 \\ retry limit & 4 \\ maximum back-off stage & 2 \\ minimum contention window (CW) size & 8 \\ \(len_{MACheader}\) & 78 bytes \\ \(len_{payload}\) & 36 + 1472 bytes \\ \(\sigma(The slot\ time)\) & 9 \(\upmu\)s \\ \(T_{DIFS}\) & 43 \(\upmu\)s \\ \(T_{SIFS}\) & 16 \(\upmu\)s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for the WLAN system.  
Figure 11: The topology of the experimental network.  
throughput of each station against BERs for the downlink and the uplink, respectively. In both figures, the ASR-ARQ provides the lowest throughput because its aggregation efficiency and transmission efficiency are the lowest among the three. The ASR-ARQ only aggregates the failed packets in the retransmitted A-MPDUs so that the size of those A-MPDUs is relatively small and more padding bits should be added to align with other users. The BAW-ARQ is superior to the ASR-ARQ because the BAW-ARQ can aggregate the retransmitted and queued packet into an A-MPDU to lengthen the A-MPDU frame and increase the efficiency. The BP-ARQ outperforms the other two because the BP-ARQ fully utilizes the remaining duration to ensure that more packets are successfully transmitted. Moreover, the BP-ARQ also improves the blocking problem of the aggregation window so that more queued packets can be included in the next transmission.  
The performances in the downlink are better than those in the uplink because the AP needs extra overhead to gather the buffer information from scheduled stations to help allocate the duration in the uplink. Moreover, the buffer status report cannot reflect the blocking of the aggregation window. For the saturated condition, the buffer size in each station is much longer than the size of the aggregation window. When the AP allocates the duration based on the buffer status report, the remaining duration in the uplink is longer than that in the downlink.  
In the downlink, the BP-ARQ improves the throughput by up to 82% compared to the BAW-ARQ and 151% compared to the ASR-ARQ. In the uplink, the two values are expanded to 171% and 250%, respectively. The promotion in the uplink is greater than that in the downlink because the remaining duration is longer in the uplink. This fact degrades the transmission efficiency of the ASR-ARQ and the BAW-ARQ, while provides more space to enhance the sliding of the aggregation window in the BP-ARQ.  
In general, the BP-ARQ maintains the highest transmission efficiency and improves the throughput greatly under the error-prone channels.
# Drying Stress and Strain of Wood: A Review
## 1 Introduction
Furthermore, the drying stress occurs as a result of uneven shrinkage in wood drying, which is the combined effect of MC and temperature gradients, and shrinkage anisotropy of wood [24]. The drying stress is related to wood micro-structure and the transfer of water and heat during drying, which are the main causes of drying defects such as surface and internal checks. Studying the theory of drying stress and strain contributes to our understanding of the mechanism of defects during wood drying. Many researchers are committed to improving experimental methods [25; 26; 27] or establishing models [28; 29] to measure and predict drying stress and strain accurately, and to analyze the change rules of them, which have significance as scientific guidance for increasing drying rate, optimizing drying processes and improving drying quality. It is difficult to detect drying stress during drying; thus, people measure drying strain of wood at a specific stage to estimate the drying stress at that time. Drying strain detection includes the slicing method, acoustic emission method, etc. With the development of science and technology, digital image correlation (DIC) and near-infrared spectrometry (NIRS) have been gradually applied to the detection of drying stress and have obtained some breakthroughs, but there are still some limitations. It is a great way for researchers to study drying stress and strain, via establishing models to simulate the generation and development of drying stress and strain. At present, the theoretical model of wood rheology and numerical analysis method are used to simulate the drying stress. Furthermore, ANNs have been gradually applied to the field of wood science [11; 30; 31]. Benefiting from its characteristics of finding the optimal solution at a high speed, ANN is conducive to solving the complex and variable problems of stress and strain in wood drying. The study of drying stress is significant not only for the theory of wood science, but also for the actual process of wood processing. There have been few reports reviewing the research of wood drying stress in the past ten years. It is necessary to summarize the existing research results in order to promote the further development of drying stress research and application. Therefore, in this review, the relationship between drying stress and drying defects, the generation and development of mechanisms of drying stress and the composition of drying strain are presented firstly. Then, detection methods for stress and strain are introduced and compared, and the ANN simulation and prediction for drying stress and strain is summarized. Finally, the research emphases and development trend of drying stress in the future are put forward.
## 5 Experimental Results
### Validation of Additional Learning and Real-Time Recognition  
The next step involved conducting further experimentation with real-time Japanese sign language recognition using both ViT and CNN models. During the test, the researcher faced forward to the camera and executed sign language movements. The performed movements were eight types of '\(\&\)\(\mid\)\(\mid\)' to '\(\&\)\(\mid\)\(\mid\)', and '\(\&\)\(\mid\)\(\mid\)\(\mid\)', and '\(\&\)\(\mid\)\(\mid\)\(\mid\)'. As a result, the CNN model recognized the finger letters '\(\&\)\(\mid\)\(\mid\)\(\mid\)' to '\(\&\)\(\mid\)\(\mid\)\(\mid\)\(\mid\) with very high accuracy. The high recognition rate of these characters is because of their stationary expression with no accompanying finger movements. However, the three types of '\(\&\)\(\mid\)\(\mid\)\(\mid\)\(\mid\)\(\mid\)', '\(\&\)\(\mid\)\(\mid\)\(\mid\)\(\mid\)\(\mid\)', and '\(\&\)\(\mid\)\(\mid\)\(\mid\)\(\mid\)' were not recognized accurately due to the inclusion of finger movements. In particular, there was a strong tendency to recognize them as different characters at the beginning of the movement and at the end of the finger movement. The ViT model did not show significant differences compared to the CNN model in real-time recognition.  
Next, we explain the accuracy of recognizing words composed of a sequence of finger letters. The video we tested uses the _word video_ in Table 1, the words are \"\(\&\)\(\&\)\(\mid\)\(\mid\)\(\mid\)\(\mid\)\(\mid\)i-i-yamal\" and \"\(\&\)\(\mid\)\(\cap\)\(\&\)\(\mid\)\(\mid\)no-ri-mo-no\(\mid\)\(\mid\)\(\mid\)\". Both words consist of four letters. The CNN model correctly recognized '\(\&\)\(\mid\)\(\mid\)\(\mid\)\(\mid\)', '\(\&\)\(\mid\)\(\mid\)\(\mid\)\(\mid\)\(\mid\)', and '\(\&\)\(\mid\)\(\mid\)\(\mid\)\(\mid\)', with few false positives when transitioning to the next character. On the other hand, there was misrecognition in the characters  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **ViT with 4 Encoder Layers** & **CNN with 8 Convolutional Layers** \\ \hline Dataset 1 & 96.4\% & 96.7\% \\ Dataset 2 & 99.7\% & 99.3\% \\ Dataset 3 & 99.4\% & 99.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The recognition results compared to ViT and CNN.  
Figure 6: Comparison of recognition performance of ViT and CNN by dataset.  
'O|no|',',')|ri|', and 'b|mo|' which included actions. The characters 'O|no|',')|ri|', and 'b|mo|' were frequently misrecognized as 'X|e|' or 'X|ta|', 'L|si' or 'X|ru|', and 'X|na|' or 'X|ho|', respectively. As shown in Figure 7, the percentages of characters recognized as matching word characters for the number of frames in the video were 60.4% for \"X|i+y-aa|\" and 38.6% for \"O|n+y|no-ri-mo-no|\" in the CNN model, and 24.4% for \"X|i+y-aa|\" and 27.5% for \"O|n+y|no-ri-mo-no|\" in the ViT model, showing no difference in accuracy between the video without motion and the video with motion. Thus, ViT resulted in a small difference in accuracy between words with and without actions, while CNN resulted in a larger difference in accuracy between words with and without actions.
## 2 Materials and Methods
### Regression Models and Genetic Algorithm
#### 2.1.5 Stacking  
[PERSON] [68] introduced stacking as an ensemble learning technique that combines multiple base models (level 0) into a higher-level metamodel (level 1) to enhance generalization. The base models generate predictions, which serve as input for the metamodel. Cross-validation mitigates overfitting, and a holdout dataset improves the final model's robustness. While stacking typically outperforms the results of individual models, its success depends on the diversity and performance of the base models and requires increased computational resources.
## 4 Experimental Design
### Evaluation
#### 4.3.2 Equal Error Rate (EER)  
The experiment uses Equal Error Rate (EER) as a performance evaluation metric. EER represents the error rate when the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) are equal at a certain threshold. FAR refers to the ratio of cases where the correct speaker identity is incorrectly classified as an incorrect identity, relative to all cases predicted as negatives. FRR refers to the ratio of actual positive cases incorrectly classified as negatives, relative to all cases predicted as positives. The formulas for calculating FAR and FRR and their relationship are as follows, as shown in Equations (23) and (24):  
False Acceptance Rate (FAR):  
\[\text{FAR}=\frac{N_{\text{fa}}}{N_{\text{fa}}+N_{\text{tn}}}, \tag{23}\]  
False Rejection Rate (FRR):  
\[\text{FRR}=\frac{N_{\text{fr}}}{N_{\text{tp}}+N_{\text{fr}}}, \tag{24}\]  
where \(N_{\text{fa}}\) represents the number of times a non-target speaker is incorrectly identified as the target speaker, while \(N_{\text{tn}}\) represents the number of times a non-target speaker is correctly identified as a non-target speaker, \(N_{\text{fr}}\) refers to the number of times a target speaker is incorrectly identified as a non-target speaker, and \(N_{\text{tp}}\) represents the number of times the target speaker is correctly identified as the target speaker.  
The value when FAR and FRR are equal is the Equal Error Rate (EER). A smaller EER value indicates better speaker identification performance.
# Research and Analysis of MEMS Switches in Different Frequency Bands
## 3 The Research Status of MEMS Switches in Different Frequencies
### Research about Multiband MEMS Switches
The resonance frequency measured for the suspended bridge on the actuator presented an increase of around 18 GHz compared with the fixed bridge on the actuator. The tuning of the resonance frequency varied from 23 GHz to 30 GHz with different geometric parameters of the bridge.
## 5 Conclusions  
In this paper, we proposed an ELR-aided K-Best detector with its hardware implementation, that is capable of improving the performance of the LR-aided K-Best detectors for MIMO systems. Our proposed ELR-aided detectors are capable of outperforming their benchmark techniques, while requiring significantly lower complexity. Explicitly, the ELR-aided detectors are capable of attaining around 3.5 dB performance improvement at BER of \(10^{-5}\) compared to the K-Best detector when considering a MIMO system with 4 transmit and 4 receive antennas. A hardware implementation of the proposed ELR-aided K-Best detector has been developed and verified using 32 nm standard CMOS technology. Our cost analysis indicate that the performance gains are at the expense of an increase in power consumption and area overhead by 18% and 20%, respectively, compared to the conventional K-Best detector dispensing with LR.  
**Author Contributions:** [PERSON]-H. contributed to the decoder algorithm design, while [PERSON] and [PERSON] contributed to the hardware design.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Energy Consumption Planner  
The energy consumption planner has been developed to manage and optimize the energy consumption of the house, minimizing the cost of the required electrical energy. For this purpose, an optimization problem has been formulated.  
The objective function to be minimized is the cost of electricity over a period of time \(t_{p}\), so the objective function can be written as:  
\[\min_{C_{b},D_{b},C_{PV}}\sum_{t=t_{0}}^{t_{0}+t_{p}}\left(P_{g}^{t}\times \left(Cg^{t}+C_{g}^{t}-D_{g}^{t}\right)+P_{PV}^{t}\times C_{PV}^{t}\right) \tag{23}\]  
where \(P_{g}^{t}\) represents the price of electricity from the grid at time \(t\). \(Cg^{t}\) is the grid electricity consumption of the house, obtained by adding the predicted consumption of appliances, lighting, and HVAC, subtracting the part already covered by the self-consumption coming from the PV installation.  
Related to the batteries, \(C_{g}^{t}\) represents the charging power coming from the grid and \(D_{g}^{t}\) represents the discharging power by the batteries. \(P_{PV}^{t}\) is the price of charging the batteries with energy from the PV installation, and \(C_{PV}^{t}\) is the charging from photovoltaic surplus energy. If we assume that the cost of photovoltaic energy is 0, the objective function can be rewritten as follows:  
\[\min_{C_{b},D_{b},C_{PV}}\sum_{t=t_{0}}^{t_{0}+t_{p}}\left(P_{g}^{t}\times \left(Cg^{t}+C_{g}^{t}-D_{g}^{t}\right)\right) \tag{24}\]  
subject to the following constraints:  
\[(1-DOD)*STG\leq SOC^{t}\leq STG \tag{25}\] \[SOC^{t}=SOC^{t-1}+\alpha^{t}\cdot\left(C_{g}^{t}+C_{PV}^{t} \right)-\beta^{t}\cdot D_{g}^{t}\]  
\[\alpha^{t}+\beta^{t}\leq 1 \tag{26}\] \[0\leq C_{g}^{t}+C_{PV}^{t}\leq Pow_{C}\] (27) \[0\leq D_{g}^{t}\leq Pow_{D}\] (28) \[Cg^{t}+\alpha^{t}\cdot\left(C_{g}^{t}+C_{PV}^{t}\right)-\beta^{t} \ D_{g}^{t}\leq Pow_{PS} \tag{29}\]Constraint (25) refers to the technical limits of the batteries, where \(DOD\) is the depth of discharge of the batteries, \(STG\) is the capacity in kWh, and \(SOC^{t}\) in kWh is state of charge of the batteries at time \(t\). The model used does not take into account battery degradation.  
Constraint (26) ensures that the battery cannot charge and discharge simultaneously: For this reason, \(\alpha^{t}\) and \(\beta^{t}\) act as binary flags: when \(a^{t}=1\), charging occurs, and when \(\beta^{t}=1\), discharging occurs. Their sum being limited to 1 prevents overlapping operations, preserving the logical integrity of the system.  
Constraints (27) and (28) are also related to the technical limitations of the batteries, limiting the maximum charging \(Pow_{C}\) and discharging \(Pow_{D}\) power in kWh of the battery.  
Additionally, a constraint called \"peak shaving\" (29) is implemented to avoid power spikes, where \(Pow_{PS}\) in kWh represents the maximum power that should not be exceeded at any time. A graphical example of this function can be seen in Figure 2.
# Theoretical Derivation and Optimization Verification of BER for Indoor SWIPT Environments
## 5 Numerical Results  
### Environment Settings  
The target for the indoor SWIPT network is to provide wireless power transfer to receivers while transferring data to the desired receiver. We set the frequency range from 4 GHz to 5 GHz with a spacing of \(\Delta f=20\) MHz for the simulation in this study, using the regulations of the ultra-wideband indoor communication system developed by the Federal Communications Commission (FCC), R = 0.125 m, which corresponds to the adjacent distance between the antennas for the half wavelength of 4 GHz. The QoS measurement factor can be defined in two ways--data quality and energy efficiency. We simulated the proposed resource allocation schemes to evaluate the system performance. We investigated a realistic room environment, as shown in Figure 4. The concrete roomlength was 10 m, the width was 10 m, and the height was 3 m. We had two wooden bookcases and one working desk in the room, with heights of 2.5 m and 1 m, respectively. The Tx and Rx1, Rx2, Rx3, and Rx4 antennas were 0.5 m in height. The transmitter was a UWB dipole antenna circular array with 16 antennas. The transmitter Tx position was at 7 m, 6 m, and 0.5 m. The receiver antenna was one UWB dipole antenna. The receivers Rx1, Rx2, Rx3, and Rx4 positions were at 3 m, 9.5 m, and 0.5 m; 3 m, 7.5 m, and 0.5 m; 3 m, 5.5 m, and 0.5 m; and 3 m, 3.5 m, and 0.5 m, respectively. The frequency for the UWB was from 4 GHz to 5 GHz. The transmitted power for each transmitter antenna to the noise power ratio at the receiver side was 25 dB. Note that the signal to noise ratio (SNR) at the receiver is dependent on the distance and multipath. The angles from the transmitter to receivers were Rx1: 138.8\({}^{\circ}\), Rx2: 159.4\({}^{\circ}\), Rx3: 187.1\({}^{\circ}\), and Rx4: 212.0\({}^{\circ}\). Transmitter Tx transferred signals to all of the receivers. Receiver Rx1 split 50% of the signal as power, and used the other 50% signal to decode the data. Rx2 and Rx3 received all of the signals as harvested power. The maximum required BER for the signal at R1 was set as 10\({}^{-5}\).  
With the impulse and the frequency responses of the channel, we calculated the received power and BER, respectively. As a result of the omnidirectional characteristic of the array element, the range of the frequency of the channel was from 4 to 5 GHz. The objective function was discussed for maximum harvested power and was searched by evolution algorithms. In our research, the objective function was the inverse of the energy efficiency in Equation (14), and the number of generations was set to 600. In other words, when the generation reached 600, the algorithm will be terminated. The relative parameters for SADDE and APSO are as following: population size is 30 and crossover probability is 0.8. Note that the minimum objective function corresponds to the maximum harvested power.