esa-sceva/satcom-chunk-collection huggingface dataset:
# Adversarial Optimization-Based Knowledge Transfer
## 2 Related Works
### FSP-Based Knowledge Transfer Using Adversarial Network  
An adversarial network-based KTF technique using FSP-based knowledge distillation was proposed in [31] to improve the performance of the traditional adversarial network-based KTF using relaxed output distribution-based knowledge distillation [30]. For distilled knowledge, three flow-based items of source knowledge in the pre-trained DNN are extracted in the form of FSP matrices based on the input and output of the residual block in the ResNet structure. In [29], the FSP matrix was mathematically devised for flow-based knowledge distillation across two layers. Let \(F(x;W)\in\mathbb{R}^{h\times w\times m}\) and \(H(x;W)\in\mathbb{R}^{h\times w\times n}\) be two different feature maps with an input \(x\) and weights \(W\) in a ResNet. Then, the FSP matrix \(G^{W}(x;W)=(g^{W}_{ij})\in\mathbb{R}^{m\times n}\) between \(F\) and \(H\) is defined by  
\[g^{W}_{ij}=\frac{1}{h\times w}\langle F_{;,,,j},H_{;,,j}\rangle_{F} \tag{3}\]  
where \(F_{;,,j}\) and \(H_{;,,,i}\) denote \(i\)-th \(h\times w\) matrices of \(F\) and \(H\), respectively, and \(\langle\cdot,\cdot\rangle_{F}\) denotes the Frobenius inner product between two matrices of the same size.  
Next, for knowledge transfer, multiple flow-based source knowledge is transferred using the adversarial optimization procedure between the target ResNet and the three discriminators. The target ResNet was trained to build its FSP matrices to deceive the discriminators. Simultaneously, the discriminators were trained to distinguish FSP matrices created by the pre-trained ResNet from those created by the target ResNet. Therefore, the adversarial optimization-based KTF approach [31] was implemented in semi-supervised learning such that the target ResNet can (i) capture the distribution of the FSP-based source knowledge and (ii) simultaneously use a known dataset with true labels. According to the results of [31], the classification accuracy of the target ResNet trained using [31] is better than the existing adversarial optimization-based knowledge transfer method [30] because of the FSP-based rich source knowledge. In addition, a target ResNet using [31] can accurately capture better knowledge from the original pre-trained knowledge distribution than the \(l^{2}\)-norm-based knowledge transfer method using FSP-based distilled knowledge [29].
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 2 Literature Review
### The Analysis Stage
#### 2.2.2 Space Occupancy Classification-Based Technology  
Space occupancy classification is a core technology of automated parking systems. Occupancy analysis comprises primarily three methods: ultrasonic sensors, image analysis, and a combination of the two. Ultrasonic sensor-based methods [3,4] are the most common automated parking systems available on the market today. Multiple ultrasonic sensors mounted on the vehicle provide two-dimensional scanning and positioning of a parking space and nearby obstacles. After detecting relative positions of the space and obstacles, the system uses geometric parameters to plot a path for parking and displays a virtual track for this path. The system then controls the steering wheel to assist the driver with parking in a parking space, but the driver must manually shift gears and apply brakes as necessary.  
The image analysis-based method primarily targets and analyzes visible features within the parking space. [PERSON] et al. [11] determined whether a car was parked within a space by analyzing the ratio of edges in the parking space. The red area represents the region of interest for calculating edge ratios. Although a parking space characterized by a high ratio of edges could represent a space occupied by a car, basing occupancy classification on a single feature seems inadequate. For example, the features of roadside parking spaces are inevitably affected by noise, such as concrete surfaces, storm drain covers, or manhole covers, because of their locations. These environmental factors can greatly influence the performance of classification methods. [PERSON] and [PERSON] [8] proposed using a histogram of oriented gradient and fast Fourier transform magnitude to analyze parking spaces. A support vector machine was used to classify parking space occupancy. [PERSON] and [PERSON] [14,16] proposed a method to detect parking spaces by fusing the sensors of an ultrasonic-based automatic parking system and an AVM system. Firstly, distance information obtained from ultrasonic sensors is transformed and mapped to an AVM image. Next, each parking space is viewed as one cell of a grid. Finally, the posteriori probability of the grid is used to determine whether a parking space is occupied. For leaning-based space occupancy classification, [PERSON] et al. [20,21] proposed a decentralized solution for space occupancy classification using a deep CNN and smart cameras. This deep learning approach demonstrates high classification accuracy in the presence of light variation, partial occlusions, and shadows. In addition, [PERSON] et al. [22] proposed a Haar-AdaBoost cascade-based approach combined with a deep CNN to detect parked cars and reported an accuracy of greater than 95% for space occupancy classification.
###### Abstract  
To establish a collision model of wheat grains impacting a force plate with a piezoelectric sensor, and to investigate the influence of the elastic recovery coefficient on the sensor's detection accuracy during the collision process, this study employed object kinematic principles to construct a wheat elastic recovery coefficient measurement device. This device ascertains the elastic properties of wheat during collisions and determines the elastic recovery coefficient of the wheat collision model. The wheat variety Jinan No. 17 was selected for testing, and the effects of the contact material, grain drop height, material thickness, and grain moisture content on the collision recovery coefficient during the collision process were analyzed through single-factor and multi-factor experiments. The experimental results demonstrate that the collision recovery coefficient of wheat grains increases with the stiffness of the collision materials for different materials. The grain recovery coefficient of wheat exhibits a downward trend with increasing falling height and moisture content, while it tends to rise as the material thickness increases. Data analysis and comparison reveal that, given the determination of the collision material, the moisture content of wheat exerts the most significant effect on the elastic recovery coefficient, followed by material thickness, while the influence of falling height is less pronounced. The findings of this study can provide data support for simulation testing and product design of wheat combine harvester cleaning screen body mechanisms and wheat seeders.  
response surface method; collision model; elastic recovery coefficient; elastic characteristics; kinematics +
Footnote †: journal: Medical  
the discrete element parameters of wheat [2]. [PERSON] et al. measured maize seeds' recovery coefficient and established the contact parameters between maize seeds and seeders [3]. [PERSON] investigated and analyzed the recovery coefficient for seed cotton; [PERSON] et al. measured and analyzed the collision recovery coefficient for castor capsules [4]. [PERSON] determined the collision recovery coefficient for oil sunflower grains [5]. [PERSON] gauged the recovery coefficient of garlic seeds [6]. [PERSON] measured the recovery coefficient of mung beans [7].  
In this study, Ji'nan 17 wheat seed was selected, and based on the analysis of the principle of kinematics, a wheat seed falling impact test platform was designed. The effects of the contact material, grain fall height, material thickness, and grain water content on the collision recovery coefficient of wheat were investigated through single-factor and multi-factor experiments, providing a basic data reference for the design optimization of harvesting and seeding machinery structure and the simulation modeling of loss sensor [8,9].
# Mobile Charging Scheduling Approach for Wireless
## 3 System Model and Problem Formulation
### The Models of Energy Consumption and Charging  
The energy of the sensor is mainly consumed in information transmission and receiving. The sensor energy consumption rate is denoted as \(e_{sn_{i}}^{c},i=\{1,2,\ldots,n\}\), where \(i\) is the integral index of the sensors. We adopt the same energy consumption model as [5], which estimates the \(e_{sn_{i}}^{c}\) with the base station using the residual energy profile of the sensor. The initial remaining energy of sensors is \(E_{sn_{i}}^{0}\). Then, the energy of the MC is mainly consumed in communication and moving and charging the sensor. In particular, the energy consumed in communication can be ignored compared to the other parts. \(e_{MC}^{m}\) is the energy consumed to move a unit distance. \(p_{MC}^{c}\) and \(v_{MC}\) are the charging rate and moving speed of MC. \(E_{MC}^{max}\) is the MC energy capacity, and \(E_{MC}^{r}\) is the MC remaining energy. \(E_{MC}^{min}\) is the minimum remaining energy threshold for MC to return to the charging service station to replace the battery. When the remaining energy of MC reaches the threshold, the charging task should be terminated in advance.  
In this paper, we assume that the charging upper threshold of each sensor can be adjusted adaptively and ask the MC to charge the sensor via a one-to-one charging model with magnetic resonant coupling [16]. Therefore, when the MC performs the charging task, it mainly consists of three phases: (1) moving to the charging destination, (2) charging the sensor to its charging upper threshold, and (3) returning to the charging service station.  
Therefore, the moving time and the energy consumed between the two charging destinations are obtained by  
\[t_{MC}^{m}=\frac{d_{i,j}}{v_{MC}}, \tag{1}\]  
\[E_{MC}^{m}=d_{i,j}\cdot e_{MC}^{m}. \tag{2}\]  
Figure 1: Network model.  
The time to charge a sensor is determined by  
\[t_{MC}^{c}=\frac{\varsigma_{i}\cdot(E_{sn}^{max}-\widetilde{E}_{sn_{i}}^{r})}{p_{ MC}^{c}-\epsilon_{sn_{i}}}, \tag{3}\]  
where \(\varsigma_{i}\) represents the charging upper threshold of \(sn_{i}\), and \(\widetilde{E}_{sn_{i}}^{r}\) is the remaining energy before the charging action is performed. During one charging operation, the total energy consumed by MC is obtained by (4), which consists of moving to the sensor and charging.  
\[E_{MC}^{T}=d_{i,j}\cdot e_{MC}^{m}+\varsigma_{i}\cdot(E_{sn}^{max}-\widetilde {E}_{sn_{i}}^{r}). \tag{4}\]  
Furthermore, in one charging operation, the total energy consumed by sensor \(sn_{i}\) is  
\[E_{sn_{i}}^{T}=\epsilon_{sn_{i}}^{c}\cdot(t_{MC}^{m}+t_{MC}^{c}). \tag{5}\]
## 6 Conclusions  
This paper proposed a system that is composed of the ESS and the WF operated by the KE control. In addition, an optimal KE control was presented in order to consider the trade-off between the smoothing of WG output and the generating efficiency. Scenario simulations demonstrated that the proposed system could suppress the WF output fluctuation with a lower ESS rated power. Moreover,  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Conv.** & **Pro. (MA)** & **Pro. (Opt.)** \\ \hline Maximum 1-Min fluctuation of \(P_{\text{WF}}\) [\%] & 20.1 & 14.4 (\(-\)28.4\%) & 14.9 (\(-\)25.9\%) \\ Average of 1-Min fluctuation of \(P_{\text{WF}}\) [\%] & 9.62 & 6.62 (\(-\)31.2\%) & 7.57 (\(-\)21.3\%) \\ SD of \(P_{\text{WF}}\) [p.u.] & 0.0481 & 0.0379 (\(-\)21.2\%) & 0.0414 (\(-\)13.9\%) \\ Maximum 1-Min fluctuation of \(P_{\text{g}}\) [\%] & 8.22 & 8.18 (\(-\)0.48\%) & 8.49 (\(+\)3.28\%) \\ Average of 1-Min fluctuation of \(P_{\text{g}}\) [\%] & 3.46 & 3.36 (\(-\)2.89\%) & 3.84 (\(-\)11.0\%) \\ SD of \(P_{\text{g}}\) [p.u.] & 0.0300 & 0.0279 (\(-\)7\%) & 0.0296 (\(-\)1.33\%) \\ \(E_{\text{WF}}^{\text{loss}}\) [KWh/h] & – & 414.1 & 232.0 \\ \(E_{\text{ESS}}^{\text{loss}}\) [KWh/h] & 273.8 & 157.4 (\(-\)42.5\%) & 183.4 (\(-\)33.0\%) \\ Integral of WF output [MWh/h] & 41.61 & 41.20 (\(-\)0.98\%) & 41.38 (\(-\)0.553\%) \\ Battery life (Li-ion) [y] & 5.67 & 5.61 (\(-\)1.06\%) & 4.53 (\(-\)20.1\%) \\ Battery life (NaS) [y] & 15 & 15 & 15 \\ Cost (Li-ion) [100 million JPY] & 22.38 & 18.40 (\(-\)17.8\%) & 18.62 (\(-\)16.8\%) \\ Cost (NaS) [100 million JPY] & 24.0 & 19.0 (\(-\)20.8\%) & 16.9 (\(-\)29.6\%) \\ Profit (Li-ion) [100 million JPY] & 410.81 & 414.79 (\(+\)0.967\%) & 414.57 (\(+\)0.915\%) \\ Profit (NaS) [100 million JPY] & 409.24 & 414.20 (\(+\)1.21\%) & 416.32 (\(+\)1.73\%) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average of simulation results.  
Figure 19: Average profit.  
Figure 18: Average cost.  
the cost of the proposed system was investigated through the simulation results. Numerical simulations demonstrated that the proposed system could reduce the total cost of the system and increase the profit in the lifespan of WG. As a result, the proposed system with the optimal KE control increased the profit by approximately \(+0.967\%\) to \(+1.7\%\) as compared to the conventional system. The SD of output power is reduced by approximately \(1.33\%\) to \(1.7\%\).  
[PERSON] and [PERSON] developed the basic concept. [PERSON] performed the simulation analysis. [PERSON] the cost of the system. [PERSON] and [PERSON] developed an optimal kinetic energy control. [PERSON]-[PERSON], [PERSON] and [PERSON] revised this manuscript. [PERSON] wrote this manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Japan Society for the Promotion of Science under Grant G19K04452 and in part by Yashima Environment Technology Foundation of Japan.  
The authors declare no conflict of interest.
## 4 Discussion  
### Single-Stage AIC Method versus Two-Stage AIC Method  
Figure 10a-c compare the onset times determined by the single-stage AIC method for RT, RB, and PM cases. The onset times for RT and RB cases were well determined because the amplitude of the AE signals was large enough compared to the background noises. As a result, there was a clear valley with the minimum peak in the AIC curve, which estimated the first arrival of the AE signal well. Accordingly, this led to the average EDE of 2.65 cm for RT case and 1.08 cm for RB case.  
By contrast, the onset time in the PM case appears to be determined late compared to the actual onset time (Figure 10c). This caused a poor accuracy with the average EDE of 4.78 cm. It is due to the combination of the low SNR up to the point of \(-\)150 \(\upmu\)s and the abrupt increase in amplitude  
Figure 10: The onset times estimated using the single-stage AIC method for the hit sources on the raft top (**a**), under the bottom raft (**b**), and in the middle of the pile (**c**). The hit was located at Pile #14. Additionally, (**d**) the improved onset time estimated using the two-stage AIC method for the same hit in (**c**). The vertical (voltage) resolution in the 8-bit A/D converter was \(\sim\)7.81 mV for the acquired signals in (**a,b**), and \(\sim\)1.95 mV for the acquired signals in (**c,d**). The horizontal (time) interval was \(\sim\)60 ns.  
approximately at \(-50\) us, which flattened the AIC function near the minimum point (Figure 10c). In this study, we consistently found that the low SNR caused poor determination of the onset times by using the single-stage AIC picker, as the AE signal's travel distance from the source to sensor increased.  
Without any post-processing or filtering of signals, the onset time determination can be improved in the second-round AIC picker by using a narrower window that excludes the abrupt amplitude spikes [21]. Figure 10d shows the onset time determined by the two-stage AIC picker. Herein, the second round of the AIC used the window that included the minimum point of the 1 st round AIC function and excluded the point of an abrupt increase in amplitude. As an example, for the PM case in Zone 2, the two-stage AIC picker more accurately localized the AE source with a decreased average EDE from 6.72 cm to 4.81 cm.  
It appears that the single-stage AIC allows a sufficiently accurate estimation of the source location when a source is located at the raft top or raft bottom in the central zones. In such cases, the EDE was found to be less than 2.65 cm, equivalent to \(\sim\)6% of the side length of the model PRF. By contrast, when a source is located in the RT-, RB-, and PM-corner zones or the PM-central zones, the application of the two-stage AIC is recommended to increase the accuracy.
# An In-Network Cooperative Storage Schema Based on Neighbor Offloading in a Programmable Data Plane
## 5 Evaluation
### Performance Results
#### 5.2.1 Evaluation of the Selection Mechanism  
To evaluate our neighbor selection mechanism, we use the left part of the topology to compare our proposal with the ECMP mechanism under a constructed flow. We use three servers to simulate two 4 Gbps data flows and one 2 Gbps data flow. We limit the bandwidth between R4 and R5, and the bandwidth between R4 and R6, to 4 Gbps. In addition, the bandwidth between R4 and the core is limited to 2 Gbps. According to the ECMP mechanism, the paths R4 \(\rightarrow\) R5, and R4 \(\rightarrow\) R6 are equal cost paths.  
First, server one writes 4 Gbps data flows to R4 for 30 s. After 10 s, server two writes 4 Gbps data flows to R4 for 30 s. Then, server three writes 2 Gbps data flows to R4 for 30 s. We compared our proposed neighbor selection mechanism with ECMP and compared statistics for the throughput of the two mechanisms. Figure 7 shows the throughput of our selection mechanism and ECMP. The data flow generated by our mechanism is basically consistent with the expected data flow. However, the ECMP mechanism cannot achieve the expected data flow and has a long flow completion time. Our peak data traffic reached 10.43 Gbps and ECMP's peak data traffic was only 7.51 Gbps. In addition, during the period of 20 to 30 s of the whole experiment, the ECMP mechanism could not use more paths to offload the excess traffic. The network started to become congested, resulting in a drop in throughput. According to the ECMP mechanism, two paths can only be used for storing data and the peak throughput absorbed by R4 is about 8 Gbps. Our neighbor selection mechanism can discover three paths and split the data traffic, thereby improving the flow completion time.  
Figure 7: Results of the selection mechanism.
## 3 Method  
### Algorithm of Fall Detection System  
Figure 2(a),b show the flow charts of training and test modes of the proposed fall detection system, respectively. In both training and test modes, the data acquired from the edge device are converted into various parameters (see Section 3.2). In the training mode, the converted parameters are normalized, and then all parameters are learned using LSTM. Using the results learned by the LSTM, 3 types of falls and 4 types of ADLs are distinguished. In the test mode, the first possible fall is primarily determined through STM. The STM means that it is determined as a fall event when _SVM_ and \(\theta\) exceed the thresholds of _SVM_ (\(SVM_{th}\)) of 2.5g and \(\theta\) (\(\theta_{th}\)) of 46.42\({}^{\circ}\), respectively [17]. Then, the first determined fall data are just classified according to 3 types of falls and 4 types of ADLs using LSTM learned in training mode.  
The reason for using the STM for the test mode is to more efficiently control power problems in edge device environments with limited resources. Because power consumption  
Figure 3: Flow charts of the proposed fall detection system. (**a**) Training mode and (**b**) test mode.  
Figure 2: Photos performing 3 types of fall and their measured parameters, sum vector magnitude (_SVM_) and angle (Theta; \(\theta\)). (**a**) Falling forward, (**b**) Falling sideway, (**c**) Falling backward.  
of the edge device, including the Zigbee wireless communication module, is dominant at the transmission event, the transmission must be reduced. Because the edge devices are not suitable for applying deep neural network such as LSTM due to its limited computation resource, the converted parameters must be transmitted to the server. If the STM is used, the first determined fall data can just be transmitted to the server instead of transmitting all data. In general, fall-like situations that exceed the thresholds of \(SVM\) and \(\theta\) have a very low frequency, and thus better computational and power efficiency is expected.
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 2 State of the Art  
### Channel State Information  
Wi-Fi sensing is based on the propagation of radio waves through physical environments. As these signals travel from a Transmitter (Tx) to a Receiver (Rx), they encounter physical obstacles such as the air, walls, people and objects [10]. As the signal encounters these obstacles that cause reflection, refraction, diffraction and scattering, it splits into multiple paths that each arrive at the Rx at varying times (i.e., phase shift) with varying power (i.e., amplitude). This phenomenon is otherwise known as the multipath effect, and the overall multipath profile of a channel can be concisely characterised by a complex-valued metric known as Channel State Information (CSI) [1]. Due to the Orthogonal Frequency Division Multiplexing (OFDM) transmission scheme in 802.11n and subsequent Wi-Fi standards, the signal is modulated over a large domain of frequencies known as subcarriers [1]. CSI can be collected independently for each OFDM subcarrier, allowing us to analyse the propagation characteristics of the wireless environment as a function of frequency. In the same manner that varying wavelengths of visible light illuminate different objects, it has been shown that the CSI of different subcarriers can have utility for sensing different physical phenomena [11]. Tools such as Nexmon [12] sample CSI for all OFDM subcarriers on every incoming Wi-Fi packet. Hence, the data rate of CSI systems is bounded by the maximum number of packets they can propagate in the channel. CSI is then used to infer information about the channel.
# Finger-Gesture Recognition for Visible Light Communication Systems Using Machine Learning
## 6 Discussion
### Competing Systems and Cost  
Assuming that a VLC infrastructure was established, the additional cost for the GR subsystem would mainly be due to the software development time. The cost of a dedicated gesture system is worth consideration. In our work, we employed relatively expensive and bulky AWG and DSO. The off-line processing could be conducted in real-time using a low-power microprocessor, such as the MSP430 from Texas Instruments, which includes built-in signal converters. One competitor to the optical system is an accelerometer based design that could be positioned on the wrist by a strap or as part of a smartwatch. However, a wrist-based transmitter unit would also be needed for relaying the accelerometer data to a receiving unit for further processing. A VLC-based system is still preferable in a hospital environment or for the elderly who may not own a smartwatch or smartphone.