esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
In parallel, an alternative approach has been embraced by 14 studies, wherein they directly utilise raw, unprocessed data as input for their classification models. This distinction highlights the variety of methodologies used within this research domain, where some researchers prioritise feature engineering, while others take advantage of deep learning and sophisticated machine learning techniques with raw data input [111]. Figure 8 illustrates the distribution of the utilisation of sleep datasets in studies dedicated to sleep stage classification through the utilisation of ML techniques. The most frequently used open-source datasets in sleep research are ISRUC-Sleep, Sleep-edf, Sleep-edfx, and MASS. These datasets stand out due to their availability and diversity. In contrast, the usage of the PhysioNet Challenge 2018 and the MIT-BIH, UCD, and SHHS datasets is comparatively low due to restrictive access and small size. However, when comparing the performances of machine learning models across studies, a significant challenge arises due to variations in datasets and sample sizes. These dataset differences, including data source, diversity, and size, introduce confounding factors that complicate direct model comparisons. A model trained on a small, specialised dataset may excel within that context but might not generalise to another dataset with distinct characteristics [112]. Therefore, it is essential to recognise that identifying the 'best' model is highly context dependent,  
Figure 7: The distribution of studies used feature extraction methods or raw data as input to machine learning. and meaningful comparisons necessitate careful consideration of the data and sample sizes underlying each model's evaluation. Figure 9 illustrates the ML models proposed in the literature to classify sleep stages. CNN-based architectures are the most popular models for classifying sleep stages. In our review, we found that 14 studies proposed models based on 1D-CNN, and 6 studies proposed models based on 2D-CNN. These 1D-CNN models have the advantage of being computationally less complex than 2D-CNN models. In addition, 2D-CNN-based models require input signals to be converted from 1D to 2D. This conversion process must be carefully handled to prevent the potential loss of important information [113]. Therefore, 1D-CNN models are well suited for real-time applications, such as home-based sleep stage classification.
## 3 Results and Discussion
### Test Result Analysis
#### 3.3.3 Effectiveness Analysis of Model Structure Optimization
In this paper, the semantic segmentation Unet++ multi-feature fusion model is used to replace the original FCN structure, maximize the relationship between each feature layer, and make full use of the image features of each layer so that the complex edges of citrus trees can be well-preserved. These experiments confirmed the feasibility of further improving the quality of crown mask segmentation by using the Unet ++ multi feature fusion strategy. To better verify the superiority of the segmentation performance of the MSEU R-CNN model, the changes to various evaluation indices during the training of different models are drawn as curves, including one for bbox AP50, and seg AP50 (Figure 10). It can be seen from the change trend of various indicators that the evaluation indicators of the MSEU R-CNN model are better than others in training, showing faster convergence and stronger robustness. Because the model proposed in this paper uses the information fusion method of the SE block to enhance its efficacy, suppress invalid features, and fully fuse the feature map information, its feature extractability is further improved so that the accuracy converges satisfactorily. Moreover, precision-recall curves graphs of various Mask R-CNN models are presented in Figure 11. The area under the curve (AUC) value of the MSEU R-CNN model is 0.9051, which is the largest value among those of the six models, indicating that the classifier works best. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & \begin{tabular}{c} **(Bbox)AP50** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **(Bbox)AR** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **(Seg)AP50** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **F1-Score** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **MioU50** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Run-Time** \\ **(s)** \\ \end{tabular} \\ \hline Model 1 & 93.2 & 70.6 & 91.3 & 65.6 & 64.6 & 0.17 \\ Model 2 & 77.9 & 57.3 & 76.7 & 52.0 & 61.0 & 0.18 \\ Model 3 & 80.7 & 57.8 & 75.5 & 54.1 & 56.6 & 0.21 \\ Model 4 & 80.5 & 61.4 & 77.4 & 57.1 & 71.0 & 0.23 \\ Model 5 & 93.8 & 72.9 & 93.6 & 68.4 & 72.9 & 0.19 \\ Model 6 & 96.6 & 73.0 & 96.2 & 69.4 & 74.2 & 0.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation results of different model on citrus crown segmentation. Figure 9: Segmentation effects of different models on Citrus tree-crown at different growth stages: (**a**) Original; (**b**) Label; (**c**) Model 1; (**d**) Model 2; (**e**) Model 3; (**f**) Model 4; (**g**) Model 5; (**h**) Model 6.
## 4 The Proposed Method for Predicting the Reactive Power Adjustment
### Reactive Power Optimization Method Based on Big Data
#### 4.3.2 Load Similarity Matching  
The big data RPO method is presented in detail as follows:  
_Step 1_: Establish the forecasting load augmented matrix of the day ahead, based on the forecasting load. According to the date of the day ahead, determine the load grouping properties \(\lambda\) and \(\mu\).  
\begin{table}
\begin{tabular}{c c c} \hline \(c(\lambda,\mu)\) & **Workday**, \(\mu=1\) & **Weekend**, \(\mu=1\) \\ \hline Spring, \(\lambda=1\) & \(t\in c(1,1)\) & \(t\in c(1,2)\) \\ Summer, \(\lambda=2\) & \(t\in c(2,1)\) & \(t\in c(2,2)\) \\ Autumn, \(\lambda=3\) & \(t\in c(3,1)\) & \(t\in c(3,2)\) \\ Winter, \(\lambda=4\) & \(t\in c(4,1)\) & \(t\in c(4,2)\) \\ \hline \end{tabular}
\end{table}
Table 1: Groups of load.  
Figure 3: Load grouping process.  
_Step_ 2: Based on grouping properties, select the group \(t\in c(\lambda,\mu)\) and establish the corresponding historical load augmented matrices \(A_{H}(t)\), where \(t\in c(\lambda,\mu)\).  
_Step_3: According to Equation (32), calculate the load similarity \(s(t)\) of historical load augmented matrix \(A_{H}(t)\) and forecasting load augmented matrix \(A_{F}\), when \(t\in c(\lambda,\mu)\).  
_Step_ 4: According to Equation (33), the best matching day \(t=t_{\max}\) can be found when the load similarity becomes the maximum.  
\[s(t_{\max})=\max[s(t)],\ t\in c(\lambda,\mu) \tag{33}\]  
_Step_ 5: Set the minimum load similarity margin \(s_{\min}\) based on experience.  
_Step_ 6: Compare the largest load similarity \(s(t_{\max})\) with the minimum load similarity margin \(s_{\min}\).  
_Step_ 7: If \(s(t_{\max})\geq s_{\min}\), the historical load of the day with date \(t=t_{\max}\) and forecasting load have high similarity. The reactive power control devices dispatching scheme can be obtained from the historical sequence of tap setting and sequence of compensation capacity.  
_Step_ 8: If \(s(t_{\max})\leq s_{\min}\), the historical load of the day with date \(t=t_{\max}\) and forecasting load have low similarity. The reactive power control devices dispatching scheme of the day ahead should be calculated with a fine adjustment method based on sensitivity analysis.  
_Step_ 9: Store the RPO data into database including the forecasting load and the reactive power control devices dispatching scheme.  
The flow diagram of the big data RPO method is shown in Figure 4.  
Figure 4: Fine adjustment method based on sensitivity analysis.
## 2 The Absolute Antenna Phase Calibration Platform at Wuhan University  
To precisely calibrate any test antenna, a calibration system has been strictly established on the rooftop of a laboratory building at Wuhan University, see Figure 2. This calibration platform is composed of two solid concrete pillars with a height of 1.6 m above the ground for the purpose of reducing multipath signals reflected from the ground. These two pillars lie along a south-north line separated by approximately 3 m. A very short baseline is necessary for high-precise antenna calibration to remove the satellite orbit errors, tropospheric or ionospheric propagation path delays and partial multipath effects in the differential model [2]. An arbitrary antenna is placed on one end of the baseline as a base station, and a flexible robot instrument with an antenna mounting point was set up on the other end. The accuracy of test antenna calibration certainly benefits from high quality phase observations. Therefore, both the test and the reference antenna are connected to a set of Trimble Net R9 geodetic GNSS receivers, respectively. It should be mentioned that in our procedure it is unnecessary to know the reference antenna PCO and PCV values.
## 4 Discussions  
### Discussion of Complexity  
In the above-described quantum proxy signature scheme, the complexity of the scheme attributes to the employed signing and verifying algorithms, which involve two encryption processes including the random sequence-based encryption algorithm and the QOTP-CNOT algorithm. In the former, a randomly produced binary sequence \(S\) with \(n\) bits is applied to encode the original message \(|\varphi\rangle_{M^{\prime}}\) with the same length into \(|\varphi\rangle_{M^{\prime\prime}}\). Its execution requires \(n\) unitary operations \(U_{i}\) (i.e., Pauli operator, \(I\), \(\sigma_{x}\), \(\sigma_{y}\), \(\sigma_{z}\)), which can be seen from Figure 3 and Table 2. In terms of key consumption, for encrypting an \(n\)-qubit message sequence, an \(n\)-length random binary sequence is enough due to the circular use, which differs from the QOTP encryption algorithm with \(2n\) bits required [48]. Therefore, our scheme saves the length of the keys and the corresponding storage space. In the language of mathematics and computer, the time complexity and the space complexity of the random sequence-based encryption algorithm both are proportional to \(n\). In the latter, i.e., the QOTP-CNOT algorithm, the secret key \(K_{AT}\) with \(n\) qubits produced by QKD system is needed and the encryption operations (i.e., \(\sigma_{x}\), \(\sigma_{z}\) and CNOT) are controlled by the key bits in \(K_{AT}\), i.e., \((K_{AT}^{i},K_{AT}^{i+1},K_{AT}^{i+2})\)\((i=1,2,\ldots,n)\), as shown in Equation (23). This algorithm is used to encrypt \(|\varphi\rangle_{M^{\prime\prime}}\) derived from the original message \(|\varphi\rangle_{M^{\prime}}\) with the random sequence \(S\) and then generate the quantum proxy signature state \(|S_{A}\rangle\). For encrypting an \(n\)-qubit message sequence \(|\varphi\rangle_{M^{\prime\prime}}\), the maximum number of the involved unitary operations is \(3n\) with the case of full \(1\) in \(K_{AT}\) according to the encryption rules in the QOTP-CNOT operation, which is linear with \(n\). Similarly, the secret key \(K_{AT}\) is also used circularly and hence the key length is reduced by three times, which improves the utility efficiency of the key when compared with the QOTP-based signature schemes [31; 33]. As a consequence, the time complexity and the space complexity of the proposed scheme are linear with \(n\).
## 2 System Model and Problem Formulation
### Problem Formulation  
In this paper, our objective is to enhance WSSR for the Bobs through the collective optimization of BF and TARCs, i.e., \(\mathbf{w}_{l}\) and \(\boldsymbol{\theta}_{l}\). Specifically, the optimization problem can be formulated as follows:  
\[\underset{\boldsymbol{\theta}_{l},\mathbf{w}_{l}}{\text{max}} \sum_{l}\varphi_{l}R_{l}^{SEC}\] (6a) s.t. \[\sum_{l}\lVert\mathbf{w}_{l}\rVert^{2}\leq P_{max}, \tag{6b}\] \[[\boldsymbol{\theta}_{l}]_{m}=\sqrt{\beta_{m}^{l}}e^{i\beta_{m}^ {l}},\sum_{l}\beta_{l}=1,\forall m\in\mathfrak{M},\] (6c) \[\theta_{m}^{l}\in[0,2\pi),\beta_{l}^{m}\in[0,1],\forall m\in \mathfrak{M},\] (6d) \[\text{SINR}_{Bob_{l}}\geq\Gamma_{min},\] (6e) \[\left|\theta_{m}^{T}-\theta_{m}^{R}\right|=\frac{\pi}{2}\text{ or }\frac{3\pi}{2},\forall m\in\mathfrak{M}. \tag{6f}\]  
where \(\varphi_{l}\in[0,1]\), \(\sum_{l}\varphi_{l}=1\) represent the weighting factor for the \(l\)-th Bob. Note that (6b) denotes the total power constraint for the BS with the maximum power \(P_{max}\), (6c)-(6d) denote the phase shift and amplitude constraints for the STAR-RIS, (6e) ensures the minimum QoS requirement of each legitimate user, which \(\Gamma_{min}\) denotes the minimum rate requirement of legitimate users. Constraint (6f) is only valid when the CP scheme is employed.
## 2 Materials and Methods
### Activity of Carboxymethyl Cellulose (CMCase) Production by Fungi  
Fungal spores of pure isolates strains illustrated in Section 2.2 at a concentration of \(2\times 10^{7}\) CFU/mL were added to the liquid fermentation medium. The liquid fermentation medium formulation consisted of 1.0% CMC-Na, 1.0% peptone, 0.5% beef extract, 0.2% KH\({}_{2}\)PO\({}_{4}\), 0.05% MgSO\({}_{4}\).7H\({}_{2}\)O, 0.2% (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) and 1 L of tap water. Four single variables were set for time, temperature, pH, and FeCl\({}_{3}\) concentration. Fungi were cultivated under single variable conditions as above.  
For measuring enzyme activity, 2 mL of fermentation brothwas centrifuged at 12,000 r/min, 4 \({}^{\circ}\)C for 5 min. Then 0.5 mL of the supernatant was incubated with 0.5 mL of 1% CMC-Na prepared with 0.05 mol/L citric acid buffer (pH 4.8) at 50 \({}^{\circ}\)C for 30 min. The reaction was terminated by addition of 3 mL of 3,5-dinitrosalicylic acid reagent. All of the assay tubes were placed in boiling water for 5 min. The absorbance of the contents in the tubes was measured at 530 nm. To the blank control group was added 0.5 mL of the fermentation supernatant after boiling to make sure it would not react with the reagents.  
The unit of CMCase (IU) was defined as the amount of enzyme required to produce 1 \(\mu\)mol of glucose per minute and per mL of crude enzyme solution under reaction conditions. The activity of CMCase calculation formula is X = (A \(\times\) V\({}_{\mathrm{T}}\)\(\times\) 1000)/(M \(\times\) V \(\times\) T). X is the CMCase activity (IU\(\cdot\)mL\({}^{-1}\)) in the sample; A is the glucose content (mg\(\cdot\)mL\({}^{-1}\)) calculated according to the standard curve; V\({}_{\mathrm{T}}\) is the total volume of the reaction solution (mL); M is the molar mass of glucose (180.2 g\(\cdot\)mol\({}^{-1}\)); V is the volume of the crude enzyme solution (mL) added in the reaction; T is the reaction time (min).  
The total amount of protein in the supernatant was measured by the Enhanced Lowry Assay Kit (Leagene, Beijing). The specific activity of CMCase is calculated and defined as the unit of enzyme activity per mg of protein (IU/mg) [19].
## 4 Experimental Review
### A Multipath Interferometry Analysis for Amplitude--Frequency Response  
The principal layout featuring real life photos of the experimental setup is presented in Figure 12. The setup comprised several key components, including a computer with dedicated software, a controller unit (Polytec, Irvine, CA, USA), a signal amplifier F10A (FLC Electronics, Partille, Sweden), a cantilever beam specimen clamped securely, and the 3D laser vibrometer camera system PSV QTec 3D (Polytec, Irvine, CA, USA).  
The experimental procedure involved applying a series of short bursts of sinusoidal base excitation across a frequency range of 0 Hz to 1000 Hz to the cantilever by means of the piezoelectric layer. The laser interferometry system measured the resulting eigen frequencies, capturing its dynamic response throughout the entire frequency spectrum.  
A total of 114 measurements were conducted over an area of 1000 mm\({}^{2}\) for each cantilever. The resulting data is presented in 3D models, accurately reflecting the eigen frequencies recorded at each specified data point along the cantilever during the experiment. Additionally, this clearly illustrates areas where changes in acceleration occur.  
The results of the experiment for the initial shape cantilever oscillating in its first eigen frequency are shown in Figure 13, and for the optimal shape in Figure 14. The graphs depict magnitudes in \(\upmu\)m/s in each axis, with blue color representing the Z axis (the main axis for amplitude measurement), red--Y axis, and green--X axis.  
Figure 11: Illustration of the manufactured cantilevers—top cantilever is optimal shape and bottom cantilever is uniform shape.  
Figure 14: Amplitude–frequency characteristic of the cantilever of an optimally shaped thickness.  
Figure 12: Photo of the experimental setup for amplitude-frequency characteristic.  
Figure 13: Amplitude–frequency characteristic of the cantilever of a constant thickness.  
The experiment yielded a fundamental frequency of 21.6 Hz for the initial cantilever shape and 20.6 Hz for the optimized shape. The difference of approximately 4.85% confirms that the optimized cantilever shape is sufficiently close to the initial design in terms of frequency characteristics. The Multipath Interferometry Analysis conducted using the Polytec 3D laser system effectively validated the eigen frequencies of the cantilevers developed through mathematical modeling, including both uniform and optimized shapes, across a wide frequency spectrum. The experimental setup facilitated the measurement of the eigen frequencies, revealing distinct resonance peaks for each design.
# Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices
## 4 Experimental Results and Analysis
### Average PSR Analysis
#### 4.4.2 Mobile EDs Scenario  
Figure 11 shows an analysis of the average \(PSR\) for a different number of mobile EDs. The mobility of the EDs has a high impact on the \(PSR\) because the mobility causes frequent changes in the topology, influencing the signal strength between an ED and a GW. As a result, the link budget used at the previous location after the ED movement would no longer be valid. Thus, these EDs are required to alter their SF due to the received signal strength variations. However, when the NS changes the SF and TP (in a typical ADR and ADR+), these parameters are no longer valid owing to ED mobility. As a result, a packet transmitted from these EDs is lost because of arriving under the sensitivity at the GW.  
Another reason for this massive packet loss in Figure 11 is due to the saturated receiver. Under this situation, a packet transmitted by the ED reaches the GW with adequate power; however, all of the available parallel reception paths are already busy in the reception of other incoming packets, resulting in packet loss. A GW can decode as many overlapping packets as the number of paths listening to that channel. In other words, when a packet arrives on a given channel, it will \"lock\" only one receive path. By contrast, other reception paths remain available to receive other incoming packets. Therefore, when a packet arrives at a channel, and there are no available receive paths, the packet is  
Figure 11: Average PSR of mobile EDs (uplink period = 24 packets/day, total simulation time = 4 days).  
Figure 10: Average PSR of static EDs (uplink period = 24 packets/day, total simulation time = 4 days).  
lost. Moreover, we adopt an assumption from [35] that if the GW is in a receive state and is asked by the NS to forward a DL packet to an ED, it will give up the incoming packet reception and transmit the DL packet. Both G-ADR and EMA-ADR (with and without I-SFA) manage a high \(PSR\) when compared to a typical ADR and ADR+. Furthermore, for a scenario similar to that in Figure 11, Table 7 shows the average \(PSR\) improvement for ADR+ and the proposed schemes when compared to typical ADR.
# An In-Vehicle Smartphone RTK/DR Positioning Method Combined with OSM Road Network
## 3 Results
### Effect of Heading Angle Correction  
In DR, \(\psi^{Base}\) represents the heading angle of the geodetic receiver; \(\psi^{DR}\) represents the heading angle obtained by using only the IMU data for attitude update; and \(\psi^{DR}_{r}\) represents the heading angle after map correction. This work uses \(\psi^{Base}\) as the actual value of the heading angle, so that the heading angle errors of the smartphones P30 and P40 are calculated when the heading angle correction is not performed. Due to a high amount of experimental data, Figure 7 shows the only results of the first group of tests. It can be found that the heading angle error updated by the angular velocity integration accumulates faster, and the noise levels of the two smartphones are quite different. At 600 s, P30 has a heading angle error higher than 120 degrees, while the P40 heading angle error also exceeds 30 degrees.  
This work also analyzes the corrected heading angle error, as shown in Figure 8. After the correction, the accuracy of the heading angle is significantly improved and the error distribution is more even.  
Figure 9 shows the horizontal trajectories of P30 and P40 dead reckoning during the first group of tests. The black line represents the geodetic GNSS receiver, while the red one  
Figure 8: Heading angle error after correction. (**a**) Smartphone Huawei P30; (**b**) Smartphone Huawei P40.  
Figure 7: Heading angle error before correction. (**a**) Smartphone Huawei P30; (**b**) Smartphone Huawei P40.  
represents pure DR, and the blue one represents DR_r after heading angle correction. It can be found that, as affected by the accumulation of heading angle errors, the trajectory of pure DR completely deviates from reality after a short period, losing the ability to navigate and determine positions. On the contrary, after using the map data to correct the heading angle, the trajectory of DR_r is more consistent with the benchmark.  
In order to further verify the effect of heading angle correction, this work determines the heading angle and horizontal error of two smartphones during four groups of tests, as shown in Table 1. The statistical results show that the heading angle errors of the two smartphones are high and irregular. Similarly, the RMS horizontal positioning errors are both higher than 40 m. After using the OSM road direction information to correct the heading angle, the error of this angle is limited to approximately 2 degrees and the horizontal positioning error is limited to approximately 5 m. Compared with the DR before correction, the smartphone heading angle and horizontal positioning accuracy are highly improved, and the results of each set of tests are also closer. The test results show that the heading angle correction method designed in this work is relatively stable, can weaken the error accumulation velocity of DR, and provides a good foundation for the RTK/DR positioning method.