esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Simulation Environment  
We employ an OFDM system with a DFT size of \(M=2048\) and a CP length of \(M_{g}=144\). It operates in the 6 GHz frequency band, utilizing a transmission bandwidth of 40 MHz and a subcarrier spacing of \(\Delta_{f}=30\) kHz. As a consequence, the sampling frequency is equal to \(f_{s}=61.44\) MHz. In our simulations, we employ a 5G tapped delay line-B model for non-line-of-sight scenarios, as described in [30]. We consider two representative channel profiles: Very short delay (Vsd) spread and Very long delay (Vld) spread, each characterized by a different delay spread. Table 2 shows the values of the system and channel parameters used in the simulation. To account for the stability of commercial oscillators used in mobile applications, which is approximately \(\pm 20\) ppm, we choose the value of \(U\) to 4. For each realization, the ICFO is uniformly generated from the range \([-U,U]\). In the AMMSE scheme, we set the rank order of the reduced channel to be \(M_{r}=5\). To compute (31), we consider the scenario where \(M_{e}=2\)[27]. Unless otherwise stated, the Doppler frequency is equal to \(D_{f}=300\) Hz, which is equivalent to the UE speed of 54 km/h [31], \(\eta=0.1\), and \(U=4\).  
As an overall performance metric, we assess the probability of detection failure denoted by \(P_{f}=\text{Prob}\{(\hat{0},\hat{w})\
eq(\upsilon,w)\}\) of the presented NR-PSS synchronization schemes, or equivalently, the detection probability denoted by \(P_{d}=\text{Prob}\{(\hat{0},\hat{w})=(\upsilon,w)\}\). Also, we evaluate the probability of incorrectly detecting the ICFO and CIDS, each denoted by \(P_{f1}=\text{Prob}\{\hat{0}\
eq\upsilon\}\) and \(P_{f2}=\text{Prob}\{\hat{w}\
eq w\}\), respectively.
## 3 Proposed Neural-Network-Based Decoding Scheme
### Channel-Attention-Net-Based Encoder of Proposed Neural Network  
How to generate an appropriate descriptor of a sub-image using its internal feature is still a challenge. Theoretically, there are two aspects of CNN design that deserve special attention: one is better modeling spatial dependencies [30; 31], and the other is interdependencies between channels. For the task of extracting strong discriminative features, we enhance the network's sensitivity of channel-wise and plane-wise informative features by using the channel attention mechanism and residual mapping. As shown in Figure 7, the proposed encoder consists of two main architectures: residual unit and recalibration unit. These two architectures are shown in Figure 8. In basic blocks, the residual unit is responsible for fitting spatial residual distribution, and it benefits CNN to learn constructive solutions in backpropagation, resulting in a reduction of training error [32]. To transform lower-level extracted features to higher-level, we lay out two multichannel kernels in each residual unit, adopting a ReLU layer to model the nonlinear relationship between each layer of the feature map. A normal residual structure is not quite sensing the features in the third dimension ideally. Therefore, we design a recalibration unit to explicitly model channel interdependencies, using channel-wise global information to recalibrate filter responses. Utilizing the global layer and fully connected layer, we obtain the characterization of each channel of the input feature map. BatchNormalization layers constrain the output vector that is in the same distribution, which improves the smoothness of the optimization landscape in the process of iterating out the weight of each channel of input feature maps [33]. In the last stage of the encoder, the generation of a vector descriptor of the input sub-image is achieved by using average pooling. In details of the channel-attention-Net-based encoder, the size of the input image is \(7\times 10\), and the sizes of all convolution kernels are set to \(3\times 3\)  
Figure 6: Sketch map of the structure of the proposed CAE-LSTM-Net.
## 4 Conclusions  
We have demonstrated a record transmission distance of 30-km for 56 Gb/s PAM-4 signaling, with direct detection, employing a C-band EML without using any in-line dispersion compensation in the link. CD compensation is done at the receiver side by digital signal processing algorithms using a hybrid implementation of an FIR followed by an MLSE for signal equalization, making use of the benefits of both equalization schemes. First, the FIR filter acquires the channel response and partially equalizes the signal, performs linear feed forward signal equalization, and is used to increase the eye-opening of the received signal. However, only the FIR is not sufficient to completely remove the accumulated ISI after transmission; thus, the implementation of the MLSE at its output significantly enhances the performance, as it compensates for the bandwidth limitations and residual ISI and decodes the signal. Results showed that the hybrid FIR/MLSE scheme can enable transmission below the 7% HD-FEC threshold up to 20-km without the need for optical amplification in the link, and up to 30-km when an EDFA is employed. If a 20% overhead HD-FEC is used, error-free operation can be achieved with relaxed sensitivity requirements, as indicated by the results. In that case, lower complexity (i.e., a MLSE-16 states scheme) can also enable transmission up to 30-km. The proposed scheme would reduce the sensitivity requirements of short-reach links making use of the 1550-nm wavelength and avoid the deployment of in-line amplifiers due to minimum fiber attenuation in that region. At the same time, it minimizes the cost of the network, utilizing inexpensive small form-factor EMLs at the transmitter, and avoids making use of expensive components such as MZMs or DD-MZMs to enable the transmission. For an actual implementation, the DAC can be integrated together with transmitter and the receiver's DSP in a single chip, thus reducing the cost of a real implementation. Simplifications in the DSP implementations using MLSE variants that use less states to achieve the same performance would reduce the power consumption of the scheme [24]. As far as latency is concerned, all MLSE schemes experience similar value, as the data are processed symbol by symbol in the trellis; i.e., in one CMOS clock cycle, we process all branches together.  
F.K. conceived, designed, and performed the experiments, analyzed the data and wrote the paper; N.S. designed the digital signal processing algorithms. [PERSON] and Q.Z. revised the manuscript.  
The authors declare no conflict of interest.
## Methods
### Absorption and Beam Attenuation Coefficients  
The non-water absorption coefficient, \(a_{mu}(z,\lambda)=a(z,\lambda)-a_{w}(\lambda)\), and non-water beam attenuation coefficient, \(c_{mu}(z,\lambda)=c(z,\lambda)-c_{w}(\lambda)\), were measured at five stations in 2011 with an ac-9 instrument equipped with nine spectral bands; 412, 440, 488, 510, 532, 555, 650, 676, and 715 nm (WET Labs, Inc., Philomath, OR, USA). \(a(z,\lambda)\) and \(c(z,\lambda)\) are the total absorption and beam attenuation coefficients of seawater and \(a_{w}(\lambda)\) and \(c_{w}(\lambda)\) represent the pure seawater contributions to these coefficients, respectively. Note that \(a_{mu}(z,\lambda)\) is often written as a sum of \(a_{p}(z,\lambda)\) and \(a_{g}(z,\lambda)\) where the subscripts \(p\) and \(g\) represent the contributions associated with suspended particulate matter and CDOM, respectively. The coefficient \(c_{mu}(z,\lambda)\) can be written as a sum of \(a_{mu}(z,\lambda)\) and \(b_{p}(z,\lambda)\), where \(b_{p}(z,\lambda)\) represents the particulate component of the total scattering coefficient of seawater, \(b(z,\lambda)\).  
Vertical profiles of ac-9 data were obtained from the surface to \(\sim\)500 m depth. The first step of data processing included quality checking of downcast and upcast measurements and rejecting doubtful and excessively noisy data. Because data were often missing for the upcast profiles within the top \(\sim\)30-50 m layer, only the downcast data were used for further processing, which included temperature and salinity correction [85], subtraction of a baseline determined with Milli-Q water before the cruise, and binning to 0.5 m depth resolution. The \(a_{mu}(z,\lambda)\) values were additionally corrected for the scattering error by subtracting the values at 715 nm at all ac-9 wavelengths. After quality control, only two stations (indicated in Figure 1) provided ac-9 data with concurrent radiometric measurements described in Section 2.4.
# Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France
## 5 Conclusions  
As confirmed in this study, continuous (unclassified) predictors derived from satellite imagery contribute to build efficient SDM at large spatial scale [7]. However, because of temporal variability of remotely sensed predictors over the year, acquisition time periods of the data affect the models' performance. Here, we found that:  
* Compared to a combination of multiple NDVI time periods or to the components of DHI, performance of bird models is higher with single time period of NDVI variables.
* Models' performance is time and context-dependent. At the French scale, end summer (NDVI 14-Sep) and begin of autumn (NDVI 30-Sep) are in most cases, the best time periods to explain and predict woodland and farmland birds species richness.
* Best time period of NDVI is the one that better reflect dominant habitats in the bird survey plots.
* Habitat components at finer scale have to be combined with climate variables at macro scale to explain and predict patterns of habitat specialist species.  
These findings suggest that the most powerful approach is the simplest one. Only one image of appropriate time period is required for mapping bird species richness. This makes the use of unclassified remotely sensed data very operational for bird monitoring programs. Further work is needed to confirm the conclusions from multiple years and to verify the stability of the relationships. Predictions could also be improved using the new Sentinel-2 time series of higher spatial resolution.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] prepared the image data; [PERSON] and [PERSON] prepared the bird data; [PERSON], [PERSON] and [PERSON] performed the experiments and analyzed the results; [PERSON] and [PERSON] wrote the paper with feedback from [PERSON] and [PERSON].  
**Funding:** This research was partially funded by Institut National de la Recherche Agronomique (Payote research group) through the [PERSON]'s internship funding.  
**Acknowledgments:** We greatly thank to [PERSON] (MNHN) and [PERSON] (Terres Inovia) for allowing access to the French breeding bird data (STOC EPS program) and all the volunteer ornithologists who make the FBBS possible.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Improvement of Fast Simplified Successive-Cancellation Decoder for Polar Codes
## 3 Proposed Method
### Latency Analysis  
The number of time steps for polar decoding is referred to as the latency. As in [3], the calculations of \(\mathbf{\beta}_{v}\) and the hard decision \(\mathsf{h}(\mathbf{\alpha}_{v})\) can be performed instantaneously. For the Fast-SSC decoder discussed in [5], special hardware modules are required for Rep and SPC code decoding. The Rep code module mainly includes the adder tree structure and sign function. This adder tree can be implemented through combinational logical. The compare and select unit is the kernel of the SPC code. The short SPC code is decoded in one time step and the long SPC code requires \(2^{m-d_{v}}/P+c\) time steps, where \(c\geq 1\), because some steps are necessary to correct the most unreliable bit estimate and pipelining [5].  
We can ignore the computational cost of FC in the latency analysis, for the following reasons: (1) The main FC operation is matrix multiplication \(\hat{\mathbf{\beta}}_{v}\mathbf{G}_{m-d_{v}}\), and the generator matrix \(\mathbf{G}_{m-d_{v}}\) has a special structure; thus, it can be implemented using combinational logic; (2) It is generally considered that, if binary operation is not the main component of the all operation, the computational cost can be ignored; (3) If the R-other node satisfies the FC, the obtained \(\hat{\mathbf{\beta}}_{v}\) can be used as the bit estimate \(\mathbf{\beta}_{v}\) of the current node.  
Based on the above analysis, the decoding latency calculation in this paper is consistent with [10], for the sake of fair comparison of different low latency decoders. Here, the latency was determined under the constraint that only \(P\) processing elements(PEs) of a real value vector \(\mathbf{\alpha}_{v}\) can be calculated when evaluating Equations (1) and (2), and \(P\) was assumed to be a power of 2.  
1. For an R-0 node, the time step for calculation was ignored.
2. For an R-1, Rep, SPC, or R-other node, if its length was \(2^{m-d_{v}}\leq P\), the time step for calculation was one; otherwise, it was calculated as \(2^{m-d_{v}}/P\).
# Intelligent Space Object Detection Driven by Data from Space Objects
## 3 Space Object Detection Algorithm Model
### Elan  
A typical network design focuses on parameters, computation, and computational density. The ELAN module adopts the following design strategies to build an efficient network, applying control on the length of the longest and shortest gradient paths for effective learning and convergence in deeper networks, as shown in Figure 8a. In large-scale ELAN, a stable state can be reached, regardless of the length of gradient path and the number of stacked computation blocks. The ELAN module consists of two branches. Among them, one changes channel numbers through a \(1\times 1\) convolution, while the other adjusts channel numbers through a \(1\times 1\) convolution and then performs feature  
Figure 8: Conv refers to a convolutional module operation, composed of a convolutional layer, a normalization layer, and an SILU activation function. K represents the size of the convolutional kernel in the convolutional layer, and s represents the stride of the convolutional kernel during the convolution operation. Maxpool represents the maximum pooling operation, whereas Concat refers to the concatenation of the feature maps from upstream along the channel dimension. Rep consists of deformable convolution, a normalization layer, and a GELU activation function. (**a**) ELAN (**b**) ELAN-R (**c**) Architecture diagram of YOLOv7’s feature extraction network [36]. (**d**) Structure of SPPCSPC (spatial pyramid pooling convolutional spatial pyramid convolution).  
extraction with four 3 \(\times\) 3 convolutions to merge the four features for the final results of feature extraction. ELAN relies on group convolution to increase the cardinality of features, combining the features of different groups through shuffle and merge operations without any change in the paths of gradient propagation in the original architecture. This operation enhances the features learned by different feature maps, thus improving the utilization of parameters and computations.
## 3 Results  
Table 2 provides a summary of the main statistically significant differences identified in the analysis, both between test conditions (RBBT vs. VBBT) and between participant groups (healthy vs. SCI patients). These findings and their implications are elaborated in the subsequent subsections. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Kinematics–Cycle-Averaged Parameters**} \\ \hline \multicolumn{2}{c}{Differences Between Tests Within Each Sample} \\ \hline \hline \multirow{2}{*}{Trajectory Smoothness (SM)} & \multicolumn{1}{c}{H} & VBBT \textgreater{} RBBT \\ \cline{2-3}  & P & VBBT \textgreater{} RBBT \\ \hline \multirow{2}{*}{Trajectory Length (EM)} & H & VBBT \textgreater{} RBBT \\ \cline{2-3}  & P & VBBT \textgreater{} RBBT \\ \hline \multirow{2}{*}{Thumb Flexion (Median)} & H & RBBT \textgreater{} VBBT \\ \cline{2-3}  & P & VBBT \textgreater{} RBBT \\ \hline \multirow{2}{*}{Thumb Flexion (Range)} & H & VBBT \textgreater{} RBBT \\ \cline{2-3}  & P & VBBT \textgreater{} RBBT \\ \hline \multirow{2}{*}{MCP Flexion (Median)} & H & VBBT \textgreater{} RBBT \\ \cline{2-3}  & P & RBBT \textgreater{} VBBT \\ \hline \multirow{2}{*}{MCP Flexion (Range)} & H & VBBT \textgreater{} RBBT \\ \cline{2-3}  & P & VBBT \textgreater{} RBBT \\ \hline \multirow{2}{*}{PIP Flexion (Median)} & H & RBBT \textgreater{} VBBT \\ \cline{2-3}  & P & VBBT \textgreater{} RBBT \\ \hline \multirow{2}{*}{PIP Flexion (Range)} & H & VBBT \textgreater{} RBBT \\ \cline{2-3}  & P & VBBT \textgreater{} RBBT \\ \hline \multirow{2}{*}{Thumb Abduc. (Median)} & H & VBBT \textgreater{} RBBT \\ \cline{2-3}  & P & VBBT \textgreater{} RBBT \\ \hline \multirow{2}{*}{Thumb Abduc. (Range)} & H & RBBT \textgreater{} VBBT \\ \cline{2-3}  & P & - \\ \hline \multirow{2}{*}{Fingers Abduc. (Median)} & H & RBBT \textgreater{} VBBT \\ \cline{2-3}  & P & - \\ \hline \multirow{2}{*}{Fingers Abduc. (Range)} & H & VBBT \textgreater{} RBBT \\ \cline{2-3}  & P & VBBT \textgreater{} RBBT \\ \hline \multicolumn{3}{c}{Differences Between Samples Within Each Test} \\ \hline \multirow{2}{*}{Trajectory Smoothness (SM)} & \multicolumn{1}{c}{RBBT} & P \textgreater{} H \\ \cline{2-3}  & VBBT & P \textgreater{} H \\ \hline \multirow{2}{*}{Trajectory Length (EM)} & \multicolumn{1}{c}{RBBT} & P \textgreater{} H \\ \cline{2-3}  & VBBT & P \textgreater{} H \\ \hline PIP Flexion (Median) & RBBT & - \\ \hline \multirow{2}{*}{PIP Flexion (Range)} & VBBT & P \textgreater{} H \\ \hline PIP Flexion (Range) & RBBT & H \textgreater{} P \\ \hline \multirow{2}{*}{Finger Abduction (Range)} & VBBT & - \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of statistically significant differences in kinematic and electromyographic (EMG) parameters between test conditions (RBBT vs. VBBT) and participant groups (healthy vs. SCI). For brevity, H is used for healthy group and P for SCI patients’ group.
## 4 Wire Damage Model
### Parallel Wire-Wire  
Figure 5 shows the fitting curves for wire damage degree, \(D\), between the parallel wires under different contact loads and different fatigue loads. The corresponding equations of the fitting curve are shown in Table 4. The variation law of fatigue wire damage degree with fatigue cycle is the same as the wire-groove variation law. When the contact load, \(F_{n}\), is 60 N, 80 N, and 100 N, the maximum damage degree of the wire is 0.0107, 0.0184, and 0.0219, respectively. When the fatigue load peak, \(F_{max}\), is 1000 N, 1100 N, and 1200 N, the maximum damage degree of the wire is 0.0107, 0.0194, and 0.0265, respectively. These show that an increase in fatigue load and the increasing contact load both promote the damage degree of the wire. It is worth noting that under the same contact load, the damage degree of the fatigue wire in the contact between the wire and the groove is greater than that in the contact between wire-wire. However, the rule is reversed under the same fatigue load. The main reason is that the material of the friction pair is different.  
Figure 4: Fitting curves of damaged wire (pair: groove) damage degree under different contact loads (**a**) and different fatigue loads (**b**).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Parameters**} & \multicolumn{1}{c}{**Fitting Equation**} & **Goodness of Fit (R2)** \\ \hline \multirow{3}{*}{Contact load (N)} & 60 & \(D\) = 4.73 \(\times\) 10\({}^{14}\)\(N^{2}\) - 4.21 \(\times\) 10\({}^{-9}\)\(N\) + 0.0021 & 0.9987 \\  & 80 & \(D\) = 5.20 \(\times\) 10\({}^{-14}\)\(N^{2}\) + 5.98 \(\times\) 10\({}^{-9}\)\(N\) + 0.0024 & 0.9996 \\  & 100 & \(D\) = 5.76 \(\times\) 10\({}^{-14}\)\(N^{2}\) + 11.47 \(\times\) 10\({}^{-8}\)\(N\) + 0.0025 & 0.9990 \\ \hline \multirow{3}{*}{Fatigue load (N)} & 1000 & \(D\) = 4.73 \(\times\) 10\({}^{-14}\)\(N^{2}\) + 4.21 \(\times\) 10\({}^{-9}\)\(N\) + 0.0021 & 0.9987 \\  & 1100 & \(D\) = 8.64 \(\times\) 10\({}^{-14}\)\(N^{2}\) + 5.57 \(\times\) 10\({}^{-10}\)\(N\) + 0.0022 & 0.9994 \\ \cline{1-1}  & 1200 & \(D\) = 9.01 \(\times\) 10\({}^{-14}\)\(N^{2}\) + 1.39 \(\times\) 10\({}^{-9}\)\(N\) + 0.0025 & 0.9991 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fitting equation for damage degree of the fatigue wire (pair: groove).
## 1 Introduction
State-of-art text-independent approaches to obtain speaker embeddings from an utterance usually reduce temporal information by calculating the average across frames of the internal representations of the network. This approach may neglect the order of the phonetic information because, in the same phrase, the beginning of the sentence may be totally different from what is said at the end. An example of this is the case when the system asks the speaker to utter digits in some random order. In that case, a mean vector would fail to capture the combination of phrase and speaker. Therefore, one of the objectives of the paper is to show that it is important to keep this phrase information for the identification process, not just the information of who is speaking. In previous works, we have developed systems that need to store a model per user which were adapted from a universal background model. These systems were developed using two approaches: a statistical model based on Factor Analysis (FA) [20], and a model based on Neural Networks [21]. In both systems, the evaluation of the trial was a likelihood ratio. One of the drawbacks of this approach is the need to store a large amount of data per user and the speed of evaluation of trials since likelihood expressions were dependent on the frame length. To simplify this evaluation process, here we employ a cosine similarity as a metric. This metric is simple and allows us to evaluate the trials using a matrix structure, which reduces the time needed to make the whole evaluation. In this paper, we propose a new approach that includes alignment as a key component of the mechanism to obtain the vector representation from a deep neural network. Unlike previous works, we substitute the average of the internal representations across time that is used in other neural network architectures [4; 5] by an alignment mechanism to keep the temporal structure of each utterance. We show how the alignment can be applied in combination with a DNN acting as a front-end to create a supervector for each utterance. As we will show, the application of both sourcesof information in the process of defining the supervector provides better results in the experiments performed on RSR2015 [22] compared to previous approaches. This paper is organized as follows. In Section 2, we present our system and especially the alignment strategy developed. Section 3 presents the experimental data. Section 4 explains the results achieved. Conclusions are presented in Section 5.