esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### A Summary of I2 CG and I2 CL  
We collectively refer to I2 CG and I2 CL as the integration of communication and computation (I2C) strategy. The differences between I2 CG and I2 CL are as follows. I2 CG and I2 CL are, respectively, applicable to two communication-constrained scenarios: Scenario I and Scenario II. As stated previously, the communication conditions in Scenario II, where I2 CL clients are located, are more stringent. Second, while both methods utilize the local computing resources of communication-constrained clients for local updates, the sources of item model parameters for local updates vary. The item model of I2 CG is a global item model sourced from the server, whereas the item model of I2 CL is the one saved locally on the client after its previous training. Algorithms 1 and 2 delineate the comprehensive training procedure on the server and client sides, respectively. As illustrated in Algorithm 1, the server's objective is to select clients for participation in the ClientUpdate process and to aggregate the item model parameters obtained from these clients. Meanwhile, clients executing Algorithm 2 update their model parameters locally, and manage the downloading, uploading and saving of these parameters based on their client and strategy types. In this paper, we do not consider the combined use of I2 CG and I2 CL. FedRecI2C is capable of mitigating the limitations associated with other FL acceleration algorithms as introduced in the Introduction section. In I2 CG and I2 CL, the client  
Figure 2: The framework of FedRecI2C. upload channels are nearly entirely severed. This effectively circumvents the unreasonable presumption in Limitation 2, which posits that communication channels remain perpetually open. Owing to its more lenient communication requirements, FedRecI2C overcomes Limitation 1. Moreover, it enables the inclusion of clients that would otherwise be excluded by typical FL approaches in the training process. By ensuring the full engagement of these clients in training, FedRecI2C accelerates the rate of system convergence. Furthermore, our proposed method obviates the need to model client-to-client relationships. It preserves the original structures of client/item models, network topologies and parameter aggregation methods within existing FRSs. As a result, FedRecI2C exhibits enhanced compatibility with existing FRSs and is designed to be plug-and-play. ```
1:Initialize item and client model parameters, i.e., \(\delta_{i}^{0}\) and \(\theta_{u}^{0}\), respectively.
# Analyzing the Nonlinear Performance of Miniature Loudspeakers in Consideration of the Creep Effect
## 4 Conclusions  
This paper presents a nonlinear model for miniature loudspeakers in consideration of the creep effect. This model improves upon the traditional NL-NC model by conserving the creep effect caused by the loudspeaker suspension when evaluating the nonlinear performance. The creep effect is modeled as a combination of linear springs and viscous dashpots in reference to the theory of linear viscoelastic mechanics. The validity of the new NL-C model was confirmed by comparison with the experimental data. The displacements and THD values predicted by the NL-C model agreed well with the measurement values, especially in the low-frequency range. The results also confirmed that the creep effect plays a significant role in low-frequency displacement in miniature loudspeakers, with substantial low-frequency distortions generated by the enhancement of low-frequency displacements.  
The two key conclusions of this study are as follows. First, in comparison with the NL-NC model, the NL-C model is a more effective time domain method for the prediction of nonlinear displacement and THD in miniature loudspeakers, particularly within the low-frequency range. Second, the creep effect significantly enhances low-frequency displacement, which markedly increases harmonic distortions in the same frequency band. The NL-C model can be applied in future investigations of displacement and distortion compensation in miniature loudspeakers.  
Conceptualization, S.C. and J.H.; methodology, S.C.; software, S.C. and J.H.; validation, S.C. and F.S.; formal analysis, S.C. and F.S.; data curation, S.C.; writing--original draft preparation, S.C.; writing--review and editing, J.H., X.F. and Y.S; supervision, X.F. and Y.S.; project administration, X.F. and Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
Figure 7: Comparison of the predicted and measured displacement transfer functions for sample A under (**a**) linear and (**b**) nonlinear conditions.  
Funding: This work was supported by the National Natural Science Foundation of China (Grant No. 12374446).  
Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. Conflicts of Interest: The authors declare no conflict of interest.
# Robust U-Nets for Fetal R-Peak Identification in Electrocardiography
## 2 Methods
### Model Training  
In the training stage, processed segments of fetal ECG data and corresponding pulse train maps from the training data set are used to generate a model for features of the fetal ECG signal. The architecture of the proposed U-Net neural network used is illustrated in Figure 1. The scalp ECG segments are initially fed into the U-Net through the encoding blocks. The output of the 1D-convolutional (1D-CNN) layer is preserved before proceeding through the max-pooling layer. Subsequently, decoding blocks are employed to facilitate the upsampling process via 1D deconvolution (transpose) convolutional layers. The output of the 1D transpose CNN layer is connected through skip connections with the output of the 1D-CNN layer, which was saved during the encoding stage. Finally, the output of the last activation function is directed to the last convolutional layer to generate a predicted pulse train using a sigmoid activation function.  
Figure 2: An example of the raw fetal scalp ECG (direct FECG) signal (blue) and corresponding pulse train map that was generated from annotations for training (black). The red star represents the true R-peak annotation, corresponding to the center of each pulse.  
Both the encoder blocks and decoder blocks are composed of three 1D-CNN layers. The bottleneck block consists of two 1D-CNN layers. The filter parameters and kernel size in the convolutional layer in the encoding block were [16; 16; 32] and [9; 9; 6], respectively. Each convolutional layer is followed by a ReLU activation function. A max-pooling layer with a pool size of 2 is connected after each 1D-CNN layer to achieve dimensionality reduction. In the decoding block, the filter parameters and kernel sizes were [32; 16; 16] and [9; 9; 6] for the 1D-CNN layer and 1D-deconvolutional CNN layer, respectively. The stride of the deconvolution layer is set to 2.  
The model values were randomly initialized before training. Training and model parameters, including learning rate, neurons per layer, activation function, batch size, and number of epochs, were evaluated in terms of their effect on model learning and accuracy and selected accordingly. Binary cross entropy was selected as the loss function used by the model. The Adam optimizer was used to minimize the loss between the predicted and the original pulse train map. The learning rate was set to 0.001, with a batch size of 64. Each model was trained for 400 epochs. Five-fold cross-validation was performed using four records as the training set and the remaining record as the test set.
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
## 5 Multi-Identity Based Full Homomorphic Encryption Scheme
### Link-Mask Scheme
\]  
where \(\mathbf{e}_{\mathbf{x},\mathbf{t}}^{{}^{\prime}}=\mathbf{e}_{\mathbf{x},\mathbf{t}}\mathbf{M}^{-1}(\mathbf{L}_{ \mathbf{x},\mathbf{t}})\) has a norm \(\left\lVert\mathbf{e}_{\mathbf{x},\mathbf{t}}^{{}^{\prime}}\right\rVert_{\infty}\leq(m+1)\beta\). Now it suffices to show that \(\sum\limits_{\mathbf{x},\mathbf{t}}^{n,N}\mathbf{Y}[\mathbf{x},\mathbf{t}]\mathbf{L}_{\mathbf{x},\mathbf{t}}=\mathbf{A }^{\prime\mathsf{T}}\mathbf{Y}\). Note that \(\mathbf{L}_{\mathbf{x},\mathbf{t}}\) has \(\mathbf{x}\)-th column of \(\mathbf{A}^{\prime\mathsf{T}}\) on the \(\mathbf{t}\)-th column and 0 elsewhere. \[\sum\limits_{x=1}^{n}\sum\limits_{t=1}^{N}Y[x,t]L_{x,t}=\sum \limits_{x=1}^{n}\sum\limits_{t=1}^{N} \begin{bmatrix}0&\cdots&Y[x,t]{A^{\prime}}^ {T}[1,x]&\cdots&0\\ \vdots&\ddots&Y[x,t]{A^{\prime}}^{T}[2,x]&\cdots&0\\ \vdots&\vdots&\vdots&\cdots&\vdots\\ 0&\cdots&Y[x,t]{A^{\prime}}^{T}[n,x]&\cdots&0\end{bmatrix} \] \[=\sum\limits_{t=1}^{N} \begin{bmatrix}0&\cdots&\sum_{x=1}^{n}Y[x,t] {A^{\prime}}^{T}[1,x]&\cdots&0\\ \vdots&\ddots&\sum_{x=1}^{n}Y[x,t]{A^{\prime}}^{T}[2,x]&\cdots&0\\ \vdots&\vdots&\vdots&\cdots&\vdots\\ 0&\cdots&\sum_{x=1}^{n}Y[x,t]{A^{\prime}}^{T}[n,x]&\cdots&0\end{bmatrix} \] \[=\sum\limits_{t=1}^{N} \begin{bmatrix}0&\cdots&\sum_{x=1}^{n}Y[x,t] {A^{\prime}}^{T}[1,x]&\cdots&0\\ \vdots&\ddots&\sum_{x=1}^{n}Y[x,t]{A^{\prime}}^{T}[2,x]&\cdots&0\\ \vdots&\vdots&\vdots&\cdots&\vdots\\ 0&\cdots&\sum_{x=1}^{n}Y[x,t]{A^{\prime}}^{T}[n,x]&\cdots&0\end{bmatrix} \]  
where \({A^{\prime}}^{T_{\text{True}}}_{t}\) denotes the \(l\)-th row of \({A^{\prime}}^{\text{T}}\) and \({\gamma}^{\text{col}}_{l}\) denotes the \(l\)-th column of \(Y\). To sum up,  
\[s^{\text{T}}X=s^{\text{T}}\sum\limits_{x,t}^{n,N}Y[x,t]L_{x,t}+\sum\limits_{x, t}^{n,N}e^{{}^{\prime}}_{x,t}=s^{\text{T}}{A^{\prime}}^{\text{T}}Y+e\]  
where \(e=\sum\limits_{x,t}^{n,N}e^{{}^{\prime}}_{x,t}\) has norm \(\parallel e\parallel_{\infty}\leq n(m+1)^{2}\beta\). \(\square\)
# A Survey of Graphical Page Object Detection with Deep Neural Networks
## 3 Methodologies
### Deformable Convolutions  
Deformable convolutions differentiate the conventional convolutions by providing the leverage of deformable modules. The deformable module learns the sampling matrix with the location offsets. The offsets are learned according to the previous feature maps through additional convolution layers. This process makes the receptive field dynamic and enables the convolutional filters to adapt to different scales. While Figure 8 depicts the basic intuition behind the deformable convolutional networks, thorough information about the architecture is explained in [47]. Most of the mentioned methodologies have employed conventional convolutions in their object detection frameworks to solve page object detection in document images. Recently, instead of conventional convolutions, deformable convolutions [47] are investigated to detect tables, figures, and formulas. This section highlights those approaches.  
[PERSON] et al. [27] proposed an approach to detect tables that leverages deformable convolutions in their object detection framework. The authors argue that deformable convolutions are better suited for the problem of table detection. Because of their dynamic receptive field, tabular areas belonging to various scales and aspect ratios can be localized conveniently. The authors employed Faster R-CNN [11] by replacing a conventional Feature Pyramid Network (FPN) with a deformable FPN module. After extensive evaluations, the authors proved that deformable Faster R-CNN had outsmarted the conventional Faster R-CNN for the problem of table detection in document images.  
[PERSON] et al. [28] exploited a similar approach by employing a deformable FPN module to detect formulas and figures in document images. Instead of providing raw input images to their deformable Faster R-CNN model, the authors have proposed an image transformation method identical to [40]. With the combination of transformed image representation and deformable object detection architecture, the authors have produced state-of-the-art results for the figure and formula detection on the famous ICDAR-17 POD dataset [5]. Along with the novel approach, the writers have also corrected the ICDAR-17 POD dataset [5] and have made it publicly at available [[https://bit.ly/2](https://bit.ly/2) AUSlzI]([https://bit.ly/2](https://bit.ly/2) AUSlzI) (accessed on 25 April 2021).  
Figure 8: Architecture of 3 \(\times\) 3 Deformable Convolution. Image is obtained from [47].
# Random Access Performance of Distributed Sensors Attacked by Unknown Jammers
## 2 System Model
### System Description and RA Procedure
#### 2.1.1 RA Procedure  
For a TC, SN starts with preamble transmission (PT). In PT, SN randomly selects an access code in a set of available orthogonal signature codes and an access slot among available slots. HS usually broadcasts a list of available codes and slots for SNs' information. We assume that the preamble is 256 repetitions of the selected orthogonal spreading code of 16 chips long. It is noted that no user-specific information is carried by the preamble.  
If HS receives the preamble successfully, it sends AI on the downlink (from HS to SN) AI channel (AICH in Figure 1). Unless the SN receives the AI adequately, it retransmits the same preamble again at a higher power level after a random back-off delay. Preamble retransmission will continue up to an allowable maximum number or until the preamble request is successfully received [17; 19]. Parameter values used in PT (e.g., the transmission power range, the power control parameters and the maximum number of preamble retransmissions) are received through the higher layer control messages from HS.  
Figure 1: Mechanism of the RACH procedure.  
If PT is successful and AI is correctly received by SN, MSG transmission (MT) will start using the same (selected) signature code used for PT and the same power level used in the latest PT. MSG usually delivers the information data, as well as the control data, which include pilot bits and/or the frame information. A TC is completed after successful PT and MT.
## 5 Unit Cell Reflection Calibration Results  
To obtain the dynamic reflection phase calibration curve, all the MS columns were biased uniformly with the same DC voltage values (or capacitance values). The measurement and simulation results of the reflection phase calibration curves, which linked the varactor DC reverse bias or the \(C_{d}\) values to the reflection phase at the operating frequency for three working frequencies, are shown in Figure 12.  
The measurements of the dynamic reflection phase were in good agreement with the simulation results for the measured operating frequencies.  
The measurement and simulation results of the reflected magnitude, as a function of the varactor capacitance \(C_{d}\) and \(V\,dc\) values, respectively, for three operating frequencies, are shown in Figure 13.  
For higher-frequency operations, the calibration curve became moderated with fewer losses at the expense of decreasing the dynamic phase range, as shown in Figures 12 and 13. Furthermore, for 11 GHz, most of the dynamic capacitance range was acquired in lower absolute values of the DC reverse bias and for 13.5 GHz in higher absolute values. According to the varactor datasheet, about 75% of the total \(C_{d}\) range is enabled for the DC reverse bias range of 0 to 4 V, and the rest obtain for the 4 to 15 V [42]. Thus, additional moderating in  
Figure 12: Calibration of the reflection phase to the varactor reverse bias for 11 GHz, 12 GHz, and13.5 GHz from left to right, respectively: (**a**) simulation reflection phase calibration results and (**b**) measurement reflection phase calibration results.  
Figure 13: Reflection losses as a function of the varactor reverse bias for 11 GHz, 12 GHz, and 13.5 GHz from left to right, respectively: (**a**) reflection losses simulation results and (**b**) reflection losses measurement results.  
the calibration curves as a function of DC reverse bias will lead to higher steering angle resolution for a high operating frequency.  
Since the relation between the varactor reverse bias to the \(C_{d}\) value is not linear, the agreement between the simulation and measurement results in the context of losses is not shown directly from the results curves in Figure 13. Therefore, using post-processing, the measurements and simulations losses as a function of the reflection phase for all operating frequencies were calculated and are shown in Figure 14.  
The \(R_{S}+R_{P}\) values for each operating frequency were extracted by comparing the reflection's absorption intensity between the simulations and the measurements (see Figure 11). These \(R_{S}+R_{P}\) values showed a good agreement in the dynamic phase profile between the measurements and simulations at all operating frequencies.
# Effective PSCCH Searching for 5G-NR V2X Sidelink Communications
## 3 Proposed Effective PSCCH Searching
### Proposed Effective PSCCH Searching Based on Correlation
Available online: [https://portal.3 gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3213](https://portal.3 gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3213) (accessed on 17 November 2021). * (31) 3 GPP. TS 138.213: Physical layer procedures for control; v16.2.0. _Tech. Spec._**2020**. Available online: [https://portal.3 gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3215](https://portal.3 gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3215) (accessed on 17 November 2021). * (32) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems. _Sensors_**2020**, _20_, 575. [CrossRef] [PubMed]
## 5 Initial Alignment Error Compensation for Shearer's SINS
### Initial Alignment of SINS
\right)=\left( \begin{array}{c}\frac{-\delta\varphi_{x} ^{n}+C_{0}^{n}\Delta\varphi_{x}^{0}}{\delta\varphi_{0}}\\ \frac{\delta\varphi_{x}^{n}+C_{0}^{n}\Delta\varphi_{x}^{0}}{\delta\varphi_{0}} \\ \frac{\delta\varphi_{x}^{n}+\varphi_{0}\varphi_{y}\sin L-C_{0}^{n}\Delta\varphi _{x}^{0}}{\delta\varphi_{0}\cos L}\end{array} \right)=\left( \begin{array}{c} \frac{-\delta\varphi_{y}^{n}}{\delta\varphi_{0}}+\Delta\varphi_{x}\\ \frac{-\delta\varphi_{x}^{n}}{\delta\varphi_{0}}+\Delta\varphi_{y}\\ \frac{\delta\varphi_{x}\omega_{x}\sin L+\omega_{x}}{\omega_{x}\cos L}+\Delta \varphi_{x}\end{array} \right). \tag{14}\]  
The real-time estimation of \(\varphi_{x}\), \(\varphi_{y}\), and \(\varphi_{z}\) is completed by Equation (14), and the estimates are substituted into Equation (10) to yield an accurate initial attitude matrix, thus obtaining the accurate initial attitude angle of the shearer. For the shearer's SINS, the alignment time should be as short as possible. The system can complete alignment in a short time after start-up, reducing the response waiting time, which can effectively improve the shearer operating rate and facilitate the personnel and various devices at the working face and the process coordination among the personnel and devices. In the following section, the initial alignment error of the shearer will be suppressed based on its attitude sensing requirements and the onsite characteristics of the working face.
## 5 Simulation Results
### Second Case Study  
In this case, we supposed that the MG under study was connected to the upstream network until \(t=1\) s and then disconnected from it due to safety issues. Therefore, from \(t=1\) s onwards, it will work in islanding operation mode, and power distribution is conducted in accordance with the former situation, in 2-to-1 proportion. When the MG is connected to the upstream network, it is supposed that the power received from the upstream network is only reachable up to \(3+j\) 0.12 pu. In other words, reference current of the upstream network is restricted to the mentioned value, so that utilizing the downstream for satisfying load demands would be possible. This also helps with improving the efficiency of the DGs. After \(t=1\) s, the DGs of the micro grid distribute the load in a 2-to-1 proportion among themselves and this trend continues until \(t=3\) s, and even after an intense decrease of load, by \(1.2+j\) 0.6 pu at \(t=2\) s. At \(t=3\) s, the production unit number 2 leaves the circuit completely. Therefore, all load supply responsibility will be on the DG number 1.  
As it can be seen in Figure 13, after \(t=3\) s until the end of the simulation, the total amount of load is equal to active and reactive powers production of source number 1. Furthermore, in Figure 13b,c, the upstream network and active and reactive powers of scattered production sources are illustrated.  
Figure 12: main parameters in the first case study; (**a**) Network frequency, (**b**) the voltage of the DG\({}_{1}\) source, (**c**) and the voltage of the DG\({}_{2}\) source.  
Optimum power distribution among sources with certain proportions can be figured out by comparing Figure 13b with Figure 13c.  
In Figure 14a, the network frequency after the happenings mentioned previously is shown. It can be seen that the primary fluctuations dampen at most after 0.2 s and the steady state error becomes zero. Figure 14b,c show voltage diagrams of \(dq\) frame for DG indices 1 and 2.  
Figure 13: Second case study results: (**a**), total active power of load; (**b**), produced total active power share of sources; (**c**), total reactive power of load; (**d**); produced reactive power share of source.
# A Compact Low-Power LoRa IoT Sensor Node with Extended Dynamic Range for Channel Measurements
## 4 Measurements and Analysis
### Outdoor Received Power Measurements
#### 4.1.2 Received Power Measurements at RX2  
To verify the correct operation of these attenuators and the dynamic attenuation adjustment algorithm that controls them, similar measurements were performed by receiver RX2. As this receiver was located closer to the transmitter, in a building with older, conventional--not highly insulating--glass windows, it was expected that the attenuation settings would be altered immediately after receiving the first packets and would continue to change until the detector was no longer saturated. Figure 11 shows the actual startup behavior of the node, which indeed corresponds to this expected behavior. The slope in the graph corresponds to an increase in attenuation of 30 dB/h, which is equal to 1 dB every two minutes. This can of course be explained by legal restrictions only allowing the transmitter to send one packet every two minutes in each band. The slow response was deliberately conserved because we do not want the system to overreact to sudden changes caused by temporary effects such as potential interference. It should also be noted that the attenuation settings are only altered when receiving packets with the right ID tag, originating from our own transmitter. Consequently, potential strong signals from unknown LoRa sources do not influence the attenuation settings.  
The received power measurements gathered during this ten-day test are presented in Figure 12. Again, an averaging window of one hour is used. With standard deviations of 0.61 dB for the raw 434 MHz data and 0.64 dB for the raw 868 MHz data, these measurements show a lot less fluctuation than the ones made by receiver RX1. This could be attributed to the smaller distance to the transmitter and the resulting larger signal-to-interference ratio (SIR). Also in contrast to the data presented in Section 4.1, this time, the theoretically expected signal power difference of 6 dB between the 434 MHz band and the 868 MHz band is clearly visible. In conclusion, the packet loss for this second test is calculated, adding up to a mere 1.17% for the 434 MHz band and 1.6% for the 868 MHz band. This second packet loss value is a lot smaller than the packet loss measured for the 868 MHz band at RX1 thanks to the lower height of RX2 and the smaller number of nearby LoRa sources at RX2.  
Figure 11: Attenuator startup behavior at outdoor receiver RX2. The slow response is a result of legal restrictions, but also prevents the system from reacting to sudden, temporary effects.